INTRODUCTION
============

Pollen beetles (*Meligethes aeneus* F.) are a major target of spring-applied insecticides in oilseed rape (OSR) (*Brassica napus* L.) in Europe.[@b1]--[@b3] They cause feeding damage to flower buds, resulting in bud abscission and loss of yield.[@b4]--[@b7] Improving the sustainability of pollen beetle management has become an increasingly urgent issue in the light of evidence for overuse of insecticides in their control and the threat posed by widespread insecticide resistance.[@b8]--[@b13] There is a need for detailed studies on pollen beetle biology and behaviour to underpin the development of robust and practical decision support tools to improve targeting of control methods and stewardship of insecticides.[@b1]

Temperature strongly influences the activity and behaviour of pests and their interaction with host plants, and it is a key input to many decision support systems (DSSs) for pest management.[@b8],[@b14]--[@b18] DSSs generally address two different processes, risk prediction and risk assessment, providing guidance firstly as to when a crop is likely to be at risk and secondly on how the severity of risk can be assessed (and hence whether control is needed). Until recently, risk prediction advice for pollen beetles in Europe has relied upon the use of simple flight temperature thresholds. For example, current UK advice states that adult pollen beetles migrate at temperatures over 15 °C, and that oilseed rape crops are at risk from pollen beetle damage when they are at the 'green-yellow bud stage' (BBCH growth stage 51--59).[@b19],[@b20] This flight threshold accords with the generally accepted temperature threshold for mass migration of pollen beetles in Europe.[@b21]--[@b25]

A simple temperature threshold is unlikely to define good migration conditions sufficiently for it to be a wholly reliable risk prediction tool for pollen beetles. Current knowledge of flight--temperature relationships are derived from field studies, and flight has been reported at temperatures significantly below 15 °C.[@b26]--[@b29] For example, Ferguson *et al.*[@b29] found pollen beetle flight within a plot of OSR at 12 °C, while Láska and Kocourek[@b27] reported flight at 10.2 °C and mass flight at 12.3 °C. A more sophisticated understanding of pollen beetle flight--temperature relationships is needed. Some authors assert the importance of accumulated periods of temperatures above a lower threshold for enabling mass flight.[@b28],[@b30] Recently the DSS 'proPlant expert' (<http://www.proplantexpert.com>) has become widely used throughout Europe for pest management in OSR crops.[@b31]--[@b34] This web-based DSS uses phenological models driven by weather data (air temperature, rainfall, sunshine and wind speed) automatically downloaded from local meteorological stations. It provides local three-day forecasts of pollen beetle migration risk and indicates days when crop monitoring is needed. An analysis of the relationship between pollen beetle flight and temperature under controlled laboratory conditions could provide valuable data for refining such models.

Once a potential risk has been identified using the forecasting element of the DSS, the level of risk must be assessed, usually by comparing the abundance of the pest with an established economic threshold. This implies the existence of a simple relationship between pest numbers and yield loss, yet recommended pollen beetle control thresholds vary markedly between European countries.[@b2] This suggests that local conditions may influence the ability of plants to withstand injury. It has been proposed that more robust economic thresholds could be developed if they were moderated by field-specific information such as crop cultivar, crop density, crop vigour and soil conditions, as well as pest numbers.[@b1],[@b35],[@b36] However, there is a potential conflict between the need for accuracy of risk assessment, the need to inspire confidence in the DSS and the ease of its employment. There is good evidence that many growers do not use existing pollen beetle control thresholds because, as with many decision support tools, they are perceived to be onerous, the labour costs of employing them being significant in the context of the low cost of control chemicals and the potential to tank-mix them with fungicide applications.[@b8],[@b9],[@b36]

There may be potential to use weather data to improve risk assessment without the need for extra sampling input by the user. The impact of pollen beetles on the yield of a plant is likely to depend not only on their abundance but also on the balance between pollen beetle activity, the rate of plant development and the ability of the plant to compensate for damage.[@b17],[@b37]--[@b39] These processes are probably differentially influenced by temperature. The present authors propose that model-based DSSs could be extended to model the weather-dependent relationship between pest numbers monitored on the crop and damage in WOSR. This could enable the DSS not only to forecast immigration risk but also the degree of damage risk associated with any sampled abundance of beetles in the context of expected weather.

The objective of this study is better characterisation of the influence of temperature on pollen beetle activity. Using a novel bioassay conducted under controlled laboratory conditions, an examination is made of evidence for a flight temperature threshold in pollen beetles, and for the first time the influence of temperature on feeding and oviposition rates on oilseed rape buds is characterised.

MATERIALS AND METHODS
=====================

Pollen beetles
--------------

Overwintered-generation pollen beetles were collected by sweep net from a crop of flowering winter OSR on Rothamsted Farm, Hertfordshire, United Kingdom, between 21 May and 5 June 2012. Emergence traps set out on the crop indicated that new-generation pollen beetles had not emerged by 5 June. The beetles collected were maintained until use in groups of up to 500 in ventilated plastic boxes (174 × 116 × 60 mm) with cut flowering racemes of glasshouse-grown spring OSR (cv. Heros) at 8 °C with a 16/8 h L/D cycle. All experiments and observations took place in June and July 2012.

Controlled environment conditions
---------------------------------

All experiments and observations were made in insect cages in four matched controlled environment (CE) cabinets with opaque white walls (Conviron Adaptis A1000, <http://www.conviron.com>). Constant light levels were maintained throughout, the insect cages receiving 260--270 µmol m^−2^ s^−1^ of PAR light (400--700 nm). Temperatures were set to achieve the desired temperature treatments (6, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20 or 23 °C) within flight bioassay cages. Temperatures in all cages were recorded at 1 min intervals throughout experiments using type K thermocouple sensors and Testo 177-T4 temperature dataloggers (<http://www.testo.co.uk>). This allowed all analyses to be related to the actual temperatures experienced by the pollen beetles.

Relationship between temperature and flight
-------------------------------------------

### Flight bioassay

In preparation for each test, 50 pollen beetles of mixed sex were placed into a glass 'release vial', 73 × 22 mm internal diameter, enclosed with a screw cap and chilled to 0 °C to render them inactive. Immediately prior to its introduction to the flight cage, the release vial was packed in ice inside an open-topped plastic vial, 60 × 37 mm internal diameter, forming an ice jacket. The top of the release vial protruded ca 20 mm above the ice jacket, to which it was sealed using Parafilm® M sealing film, to prevent beetles from falling into the ice during the experiment ([Fig. 1](#fig01){ref-type="fig"}).

![Insect flight cage ('Bugdorm-1', <http://www.megaview.com.tw>) with the front removed to demonstrate the experimental set-up for the flight bioassay. (A) The central release point, consisting of the release vial containing ca 50 pollen beetles at the start of the run, the ice jacket around the release vial and the central water trap to prevent beetle escape by walking. (S) Four 'sink' water traps to reduce the chance that pollen beetles that escaped the central release point would return to the central water trap.](ps0071-0459-f1){#fig01}

Each test was conducted in a well-ventilated translucent polypropylene insect flight cage (300 × 300 × 300 mm; 'Bugdorm-1', <http://www.megaview.com.tw>) on an open shelf in the middle of one of the four CE cabinets. The temperature probe (see above) was positioned in the middle of the cage. On the floor of each insect cage there were five water traps, each consisting of a glass petri dish base (95 mm diameter) filled with a 2.5% solution of detergent (Teepol) in tap water. One trap was placed in each corner of the cage, set against a yellow background to improve its attractiveness to pollen beetles, and the fifth was placed in the centre ([Fig. 1](#fig01){ref-type="fig"}).

On introduction into the flight cage, the release vial and ice jacket were placed in the middle of the central water trap, and the screw cap of the release vial was removed to allow beetles to exit. The release vial, ice jacket and central water trap will hereafter be referred to as the 'release point'. As the ice melted, the vial warmed, so that the beetles experienced the temperature set within the cage and their activity level responded accordingly. The purpose of the central water trap was to prevent escape of beetles from the release point except by flight. The other traps served as sinks for those that had escaped the release point, to reduce the chance of their return to the release point. At 20 h after the introduction of beetles into the cage, the number of beetles that had escaped the release point was counted and was taken to be evidence of the propensity to fly at the treatment temperature.

Tests were run in two experiments from 4 to 19 June and from 9 to 24 July 2012, respectively. In the first experiment, eight temperatures, in increments of 2 °C from 6 to 20 °C, were tested in four CE cabinets (one flight cage per cabinet), using a balanced incomplete block design. Four temperatures were simultaneously tested in each of 14 twenty-hour runs (incomplete blocks), and each temperature was replicated 7 times. Following a preliminary examination of the data from the first experiment, additional test temperatures were chosen for a second flight experiment to increase confidence in the temperature--response model. To enrich data in the temperature range critical to spring immigration, temperatures of 9, 11 and 13 °C were chosen. To these were added 23 °C, to extend the range of temperatures upwards, and two temperatures already tested, 12 and 18 °C, to link the two datasets. These six temperatures were tested in three CE cabinets, again using a balanced incomplete block design, three temperatures being tested in each of ten runs and each temperature being replicated 5 times.

### Video observations

To provide evidence that the escape of pollen beetles from the release point in the flight bioassay was indeed indicative of flight, video observations were undertaken at each of the temperatures used in the bioassay, using the same experimental cage and pollen beetle release procedures ([Fig. 1](#fig01){ref-type="fig"}). One temperature was tested at a time in a single CE cabinet. At each temperature, the behaviour of one set of 50 beetles was closely observed by video for 7 h. The camera (Sony DCR-SX33E 'Handycam') was enclosed within the cabinet, its lens protruding through the entrance sleeve to the flight cage and focused on the top of the release vial, giving an in-focus field of view of ca 45 × 80 mm wide. Recordings were made at 25 frames s^−1^. For each temperature, the video footage was observed to record the method of movement used by each beetle as it left the field of view on escape from the release vial. Each beetle escape was allocated to one of two movement categories: 'closed wings' or 'open wings'. The closed wing category included beetles that walked from the top of the release vial down the outside of the vial and out of view (see supplementary material videoclip S1 <http://youtu.be/m1Jjs2WzLVc>), as well as those that dropped from view without opening their elytra (supplementary material videoclip S2 <http://youtu.be/3-jdVCk9woo>). The open wing category included any beetle that departed the release vial in the air, with its elytra open and its wings spread (supplementary material videoclips S3 <http://youtu.be/bb-mprRI7xA> and S4 <http://youtu.be/8eGbYQzAHJ4>, showing upward and downward trajectory flights, respectively), and this was taken as evidence of a propensity to fly. Any beetles that could not be seen well enough to determine whether their elytra were open were excluded from observations. This was usually because they were out of the narrow plane of focus or were obscured by other beetles when they made their movement. Temperatures were tested in randomised order, and all temperatures were observed once during the experiment. All video observations were made from 12 to 31 July 2012.

Relationship between temperature and bud damage: feeding and oviposition bioassay
---------------------------------------------------------------------------------

The influence of temperature on the feeding and oviposition of female pollen beetles on buds was tested in a replicated bioassay. Gravid female beetles were selected for use in the bioassay using a protocol similar to that of Hopkins and Ekbom:[@b40] 20 unsexed beetles were enclosed singly at room conditions for a minimum of 12 h (including overnight) on cut racemes of glasshouse-grown spring OSR with five or more flower buds of optimum size for oviposition (2--3 mm long).[@b5],[@b40],[@b41] The buds were examined for oviposition damage, and the four beetles that had oviposited into the most buds (minimum two buds) were selected for use in the feeding and oviposition bioassay the following day.

Tests were conducted in small cylindrical cages, 45 × 32 mm inside diameter, concurrently with experiment 1 of the flight bioassay (4--19 June 2012). One small cylindrical cage was placed alongside the flight cage in each of the four CE cabinets used for the flight bioassay (see above). Each small cage was made from a 60 mL transparent polystyrene vial (Sterilin Ltd, Newport, Gwent, UK), with the bottom removed and replaced with a plug of Oasis® Floral Foam (Smithers-Oasis UK Ltd, Washington, Tyne and Wear, UK) that was fully hydrated and wrapped in plastic film. The temperature probe (see above) was inserted through the top of the cage which was closed with a polyethylene screw cap into which a ventilation hole (30 mm diameter) was cut and covered in nylon mesh. Each cage contained a cut raceme of glasshouse-grown spring OSR (cv. Heros) with its stem inserted into the floral foam and onto which a single female beetle was introduced. Racemes presented were selected to be as uniform as possible, with a minimum of ten buds of optimum size for oviposition (2--3 mm long), together with the terminal rosette of smaller developing flower buds, but no larger buds. This provided females with opportunities for oviposition and feeding well in excess of daily bud usage reported by other authors (under glasshouse conditions: 1.2 buds per female for oviposition and 1.7 buds for feeding;[@b5] in the field: ca three buds per female for oviposition[@b41]). After 20 h at the test temperature in the CE cabinet, the beetle was removed from the cage and all buds 0.5 mm or longer were examined to count the number with feeding damage, the number with eggs and the total number of eggs laid. Preliminary observations indicated that temperatures in the small cages were about 0.5 °C higher than in the flight cages. Thus, the temperatures recorded in the small cages during the feeding and oviposition bioassay ranged from ca 6.5 °C to ca 20.5 °C, in increments of ca 2 °C.

Owing to a temperature recording failure, replication at target temperatures 12, 14, 16 and 20 °C in experiment 1 of the flight bioassay and replication in the bud damage bioassay were reduced from seven to six cages per target temperature.

Statistical analysis
--------------------

### Flight bioassay and video observations

The numbers (out of ca 50) of beetles escaping per cage in the flight bioassay were regressed on the maximum temperature per cage during the experiment (logged base 10) using non-linear logistic regression (GLM with binomial error and logit link). Data from the two flight bioassay experiments were analysed together. A series of nested models maximally allowing 'immunity' (i.e. allowing for a proportion of beetles that would not fly at any temperature), and allowing all intercept and slope parameters to differ between experiments, were fitted and compared; from these, the best-fit parsimonious model was chosen. For the video observations, the binary response (wings open or closed) of each insect was analysed by logistic regression in relation to the temperature recorded in the flight cage at the time of escape (logged base 10). For both the flight bioassay and the video observations, the temperatures at which 10, 25 or 50% of beetles (allowing for immunity) showed evidence of a propensity to fly ('flight temperature thresholds' denoted by FT~10~, FT~25~ and FT~50~, respectively) were estimated from the chosen model.

### Feeding and oviposition bioassay

Counts from each cage of the bud damage bioassay were analysed using linear mixed models and restricted maximum likelihood to describe their relationship with the mean temperatures recorded within the cages during each run. The data were analysed either as untransformed counts or, if necessary, as counts transformed to a natural log scale \[ln (*y* + 1)\] to stabilise the variance. Straight-line regression relationships were fitted (fixed model), and the blocking structure was accounted for within the random model. All analyses were performed using Genstat (<http://www.vsni.co.uk>/).

RESULTS
=======

Temperatures in cages
---------------------

For both bioassays and in the video observations, temperature series with well-spaced intervals were achieved in cages (Table [1](#tbl1){ref-type="table"}). However, in each case, temperatures departed somewhat from the target temperatures, and so actual recorded temperatures were used for analyses. In the flight bioassays, mean cage temperatures were within 0.2 °C of the target, and mean maxima were within 0.3 °C of the mean (Table [1](#tbl1){ref-type="table"}). In the video observations, the mean temperature at the time of beetles\' escape from the release point was usually slightly higher than the target temperature (Table [1](#tbl1){ref-type="table"}). This probably, at least in part, reflects a tendency to move more at warmer times in the thermostatically controlled cycling of temperature around the set point. Consistent with this, in preliminary analysis of the flight temperature bioassay, a model using the maximum cage temperature gave a slightly better fit to the data than one using the mean cage temperature, and therefore maximum temperature was used for the full analysis. Mean temperatures in the cages of the bud damage bioassay were 0.4--0.7 °C higher than in cages of the flight bioassay run at the same time (Table [1](#tbl1){ref-type="table"}).

######

Mean temperatures recorded in flight bioassay cages, video observations and bud damage bioassay. Standard errors of means (SEM) are given in parentheses

       Flight bioassay   Video observations   Bud damage bioassay
  ---- ----------------- -------------------- --------------------- --- ------------- ---- ------------- ---
  6    1                 6.2 (0.05)           6.5 (0.04)            7   5.9 (0.03)    24   6.7 (0.09)    7
  8    1                 8.2 (0.06)           8.4 (0.07)            7   8.4 (0.04)    20   8.6 (0.13)    7
  9    2                 9.2 (0.09)           9.5 (0.09)            5   9.7 (0.05)    40
  10   1                 10.0 (0.07)          10.2 (0.07)           7   10.5 (0.04)   41   10.6 (0.09)   7
  11   2                 10.9 (0.11)          11.2 (0.13)           5   11.3 (0.02)   42
  12   1                 12.2 (0.06)          12.5 (0.06)           6   12.4 (0.02)   48   12.7 (0.11)   6
  12   2                 12.1 (0.11)          12.3 (0.12)           5
  13   2                 12.9 (0.14)          13.2 (0.12)           5   13.2 (0.02)   46
  14   1                 14.2 (0.07)          14.5 (0.08)           6   14.4 (0.02)   46   14.7 (0.13)   6
  16   1                 16.1 (0.09)          16.4 (0.11)           6   16.0 (0.04)   48   16.7 (0.14)   6
  18   1                 18.2 (0.04)          18.4 (0.04)           7   18.1 (0.03)   40   18.7 (0.07)   7
  18   2                 18.0 (0.11)          18.3 (0.07)           5
  20   1                 19.9 (0.05)          20.2 (0.05)           6   20.2 (0.02)   46   20.4 (0.11)   6
  23   2                 23.0 (0.08)          23.3 (0.06)           5   23.3 (0.02)   45

Relationship between temperature and flight
-------------------------------------------

### Flight bioassay

The relationship between the proportion of beetles escaping and the temperature in the two experiments was best described by separate lines for each experiment, but with a common slope ([Fig. 2](#fig02){ref-type="fig"}). The chosen model for the data included five terms: two separate 'immunity' parameters (i.e. estimates of the proportion of beetles that would not escape whatever the temperature), two *y*-axis intercepts (logit proportion of beetles) and a single regression coefficient common to both experiments (slope) (Table [2](#tbl2){ref-type="table"}). In experiment 1, 10% of 'non-immune' beetles showed evidence of a propensity to fly at 12 °C (FT~10~), rising to 50% at 15.5 °C (FT~50~) ([Fig. 2](#fig02){ref-type="fig"}). In experiment 2, started 35 days later than experiment 1, values of FT~10~, FT~25~ and FT~50~ were found to be about half a degree higher than in experiment 1, although their 95% confidence limits overlapped with the values from experiment 1 ([Fig. 2](#fig02){ref-type="fig"}). The model also suggested that in experiment 2 about 37% of beetles were 'immune', i.e. would not escape at any temperature, compared with 15% in experiment 1 (Table [2](#tbl2){ref-type="table"}).

![Fitted temperature--response models for flight bioassay experiments 1 and 2, indicating estimated flight threshold values FT~10~ \[experiment 1, 12.0 °C, 95% confidence limits (CL) 11.5, 12.4; experiment 2, 12.5 °C, 95% CL 11.8, 13.2), FT~25~ (experiment 1, 13.6 °C, 95% CL 13.1, 14.2; experiment 2, 14.2 °C, 95% CL 13.4, 15.1) and FT~50~ (experiment 1, 15.5 °C, 95% CL 14.8, 16.4; experiment 2, 16.2 °C, 95% CL 15.2, 17.5). All axes and FT estimates are back-transformed from the log~10~ scale to the natural scale. Escape from the release point was taken as evidence of a propensity to fly. Filled circles represent the observed data, i.e. the proportion of pollen beetles in each cage that escaped the release point, plotted against the maximum temperature recorded in the cage during the run (*N* = 56 runs for experiment 1 and 30 runs for experiment 2).](ps0071-0459-f2){#fig02}

######

Parameter estimates from the final non-linear logistic regression model fitted to the proportion of pollen beetles escaping from the release point in the flight bioassay and the proportion of beetles opening their wings in the video observations. SEM given in parentheses

                                            Flight bioassay
  ----------------------------------------- ----------------- -------------- --------------
  Proportion of beetles 'immune'            0.15 (0.052)      0.37 (0.047)   0.20 (0.046)
  Intercept (logit proportion of beetles)   −23.2 (1.82)      −23.5 (1.83)   −29.2 (5.63)
  Slope (log~10~ temperature)               19.5 (1.64)       19.5 (1.64)    26.1 (5.15)

### Video observations

The fitted temperature--response model for the proportion of beetles opening their wings in video observations showed a similar sigmoidal form to that from the flight bioassay ([Fig. 3](#fig03){ref-type="fig"}, Table [2](#tbl2){ref-type="table"}). Estimates of the three flight temperature threshold values FT~10~, FT~25~ and FT~50~, based on video observations, were 1.6--2.0 °C lower than in experiment 2 of the flight bioassay, which was conducted concurrently, and there was a lower level of 'immunity' ([Figs 2](#fig02){ref-type="fig"} and [3](#fig03){ref-type="fig"}, Table [2](#tbl2){ref-type="table"}).

![Fitted temperature--response model for video observations of flight activity, indicating estimated pollen beetle flight threshold values FT~10~ (10.9 °C, 95% CL 9.9, 11.5), FT~25~ (12.0 °C, 95% CL 11.4, 12.6) and FT~50~ (13.2 °C, 95% CL 12.7, 14.2). Both axes and all FT estimates are back-transformed from the log~10~ scale to the natural scale. Wing opening is taken to be indicative of a propensity to fly. Filled circles represent the observed data for each of the *N* = 486 pollen beetles that escaped the release point, plotted against the cage temperature recorded at the moment of escape. For each beetle, the observed data are of binary form, representing the wing status (open or closed).](ps0071-0459-f3){#fig03}

Relationship between temperature and bud damage
-----------------------------------------------

Both the numbers of buds damaged and the numbers of eggs laid by beetles showed strong positive relationships with cage temperature ([Fig. 4](#fig04){ref-type="fig"}). The total number of damaged buds increased with temperature ([Fig. 4](#fig04){ref-type="fig"}A), the majority of buds being injured by feeding ([Fig. 4](#fig04){ref-type="fig"}B). Three-quarters of all buds that were fed upon were 'small buds' (length ≥ 0.5 mm, \< 2 mm), increasing numbers of which were fed upon at higher temperatures ([Fig. 4](#fig04){ref-type="fig"}C). All eggs were laid into buds 2--3 mm long, and separate feeding damage lesions were not observed in buds with eggs. Buds with eggs contained a mean of 1.7 eggs, range 1--4. Both the number of buds with eggs and the total number of eggs laid increased with temperature ([Figs 4](#fig04){ref-type="fig"}D and E), the oviposition rate rising to about four eggs per beetle per 20 h at 20 °C ([Fig. 4](#fig04){ref-type="fig"}E). The maximum number of buds 2--3 mm long receiving any damage in any run was 7, and the maximum receiving eggs was 6, confirming that the minimum of ten buds in this size range that were offered to each beetle represented an excess to requirement. Similarly, the number of small buds available always exceeded the number damaged (mean 21% with feeding damage, range 0--80%). The total number of small buds presented, which had not been counted before each test, did not vary significantly with test temperature ([Fig. 4](#fig04){ref-type="fig"}F), suggesting that the temperature relationships in [Figs 4](#fig04){ref-type="fig"}A to E were not influenced by the number of small buds presented.

![Relationships of temperature with pollen beetle bud damage and egg laying on oilseed rape racemes. Regression lines are fitted by linear mixed model analysis. Curves in (A), (D) and (E) represent values back-transformed from the natural log scale. (A) Total number of damaged buds of all sizes (slope 0.42, SE 0.070; intercept −1.64, SE 1.073; *F*~1,41.7~ = 36.8, *P* \< 0.001). (B) Total number of buds of all sizes with feeding damage (slope 0.25, SE 0.047; intercept −0.68, SE 0.698; *F*~1,42.9~ = 27.8, *P* \< 0.001). (C) Number of small buds (length ≥ 0.5 mm, \< 2 mm) with feeding damage (slope 0.20, SE 0.046; intercept −0.66, SE 0.660; *F*~1,50.0~ = 18.1; *P* \< 0.001). (D) Number of buds with eggs (slope 0.17, SE 0.037; intercept −1.03, SE 0.564; *F*~1,41.8~ = 21.3, *P* \< 0.001). (E) Total number of eggs laid (slope 0.33, SE 0.052; intercept −2.23, SE 0.846; *F*~1,40.5~ = 38.6, *P* \< 0.001). (F) Total number of small buds available (slope −0.08, SE 0.054; intercept 10.42, SE 0.773; *F*~1,45.6~ = 2.1, *P* = 0.16, no significant relationship).](ps0071-0459-f4){#fig04}

DISCUSSION AND CONCLUSIONS
==========================

Strong behavioural responses to temperature in pollen beetle flight, feeding and egg-laying activity have been demonstrated and characterised. These temperature relationships have important implications for risk assessment and pest management.

Relationship between temperature and flight
-------------------------------------------

Flight bioassay data and video observations provided compelling evidence that the propensity to fly follows a sigmoid temperature--response curve in the 6--23 °C temperature range tested. When analysing samples of field populations of flying insects, it has become customary to allow for variable population size by plotting the data for each temperature as the proportion of samples in which flying insects were caught.[@b15] This produces similar sigmoid temperature--response curves for many species.[@b16],[@b42]--[@b44] The assumption that beetle escapes in the flight bioassay were indicative of wing-powered movement was strongly supported by the similarity of the temperature response of wing opening in the video observations. However, firm evidence of the extent of powered flight proved elusive. In a preliminary analysis of the video data, the proportion of beetles that took an upward trajectory as they escaped the release point (e.g. supplementary material video clip S3 <http://youtu.be/bb-mprRI7xA>) was recorded as incontrovertible evidence of powered flight. This proportion only exceeded 50% at 20 and 23 °C, temperatures well above those where there is good evidence for mass migration in the field,[@b21],[@b22],[@b24] suggesting that the tendency to engage in powered flight would be greatly underestimated by the number flying with an upward trajectory. Video observations using a camera with greater resolution and shutter speed and with a wider field of view would allow wing movement to be analysed directly.

The temperature range over which evidence was found for flight in pollen beetles under laboratory conditions agreed well with field observations. It was estimated that FT~10~, the 10% flight temperature threshold, fell in the range 10.9--12.5 °C, approximating reports in the literature of 10.2--13.5 °C for the lower limits of field-observed flight.[@b22],[@b26]--[@b29] Present estimates of 15.5 and 16.2 °C for FT~50~ in the flight temperature bioassay agree with advice that pollen beetle migration is likely at temperatures around 15 °C or above.[@b19],[@b20],[@b22]--[@b25] This close match between field and laboratory observations of pollen beetle flight activity confirms that temperature (as distinct from factors that are correlated with temperature in the field, such as day length) is indeed a key factor limiting movement from overwintering sites to OSR crops in spring.[@b21],[@b24],[@b27],[@b28],[@b32],[@b33] The physiology of the pollen beetle may be adapted to limit energetically expensive flight activity until host plants at the preferred growth stage are available.

There was clear evidence that beetles showed less tendency to fly in the second of the two flight bioassays. The proportion of pollen beetles not expected to fly at any temperature was higher, and the flight temperature thresholds were also marginally higher. Although other factors cannot be ruled out, this is consistent with an effect of ageing, adults of this univoltine beetle having eclosed in July the previous year.[@b45],[@b46] It is important for modelling of migration risk that the slope of the relationship with temperature remained consistent between the two experiments and the intercepts were very close (Table [2](#tbl2){ref-type="table"}). This suggests that the main difference between the experiments was in the size of the population of insects that would fly, and that the underlying relationship with temperature remained the same. This hypothesis should be tested by examining the flight--temperature response of beetles at different times through the year, both before and after overwintering, as their physiological condition changes with age. The responses of pollen beetles from the time of their emergence at overwintering sites until their migration to crops is of particular relevance to risk modelling for OSR.

Many authors have sought flight temperature thresholds as part of their studies of the influence of meteorological conditions on the migration of pest species.[@b14]--[@b16],[@b47] Now that sophisticated and near-real-time risk modelling is possible,[@b31]--[@b33] it is appropriate to reassess the value of attempting to define flight temperature thresholds. A model of the relationship between temperature and migration is considerably more informative than an arbitrary threshold and could be interpreted in the light of the influence on migration risk of other meteorological variables and of population size.[@b9] Nevertheless, the simple concept of the flight threshold is likely to remain useful to those planning pest monitoring and management, especially if without access to a DSS model, provided its limitations are understood.

Relationship between temperature and bud damage
-----------------------------------------------

Strong positive relationships between temperature (6--20 °C) and the rates of pollen beetle feeding and oviposition on flower buds of oilseed rape have been demonstrated. More buds were damaged by feeding than by egg laying, consistent with other studies,[@b5] and the relative importance of feeding damage is accentuated if males and non-egg-laying females (excluded from this study) are considered. This is noteworthy because adult feeding damage to a bud is usually more severe than that caused by larvae and is more likely to lead to bud abscission and yield loss.[@b45] All eggs were laid in buds 2--3 mm long, as reported by other authors, whereas most feeding damage was in smaller buds, indicating a partial separation of resource allocation between the generations.[@b5],[@b40],[@b41] The number of eggs laid per female (rising to an average of about 4 eggs per 20 h at 20 °C) was in keeping with other reported estimates ranging from 1.1--2.5 eggs female^−1^ day^−1^ in the glasshouse to 3.4--6.6 eggs female^−1^ day^−1^ in the field.[@b5],[@b40],[@b41]

Implications for integrated pest management of pollen beetles
-------------------------------------------------------------

In this study, the first to examine pollen beetle flight--temperature relationships under controlled laboratory conditions, the authors have estimated parameters that could be used to refine the accuracy of weather-based phenological models that use local weather data to provide locally relevant forecasts of pollen beetle migration risk, such as 'proPlant expert'.[@b31]--[@b33] It is proposed that this analysis of the relationship between temperature and bud damage could help to extend a weather-based risk model to forecast not only the risk of pollen beetle migration into OSR but also the severity of damage risk when the beetles are on the crop.

Any damage risk model for pollen beetles in OSR should take into account the influence of weather both on beetle activity and on plant growth and compensation.[@b4] Such a model has already been developed for the effect of pollen beetles on yield in spring OSR in Denmark.[@b17] This model confirmed that, when growing conditions were better (more pre-immigration precipitation, higher post-immigration temperature), plants were better able to compensate for pollen beetle damage, and the negative impact on yield was reduced. Plant growth stage should also be a key input to damage risk models, as early beetle immigration is likely to result in continuing damage to buds over a longer period, whereas it is generally believed that damage becomes negligible once the crop is in flower.[@b20],[@b45] Ultimately, a risk model driven simply by automatically downloaded local weather data and weather forecasts and by plant growth stage could not only predict immigration risk but could also provide some information on the degree of damage risk and offer a control threshold moderated to local conditions.

Future work should seek to quantify the effects of weather variables and growth stage on the ability of OSR to compensate for pollen beetle damage and should expand on the present studies to include a cross-section of newly migrated individuals in spring, including males. For the foreseeable future, counts of pollen beetles on plants are likely to remain an essential input to damage risk models, and a key function of a good locally tailored DSS will be to enable sampling effort to be minimised and implemented at the appropriate time.

This work was funded under the Defra Sustainable Arable LINK programme (Project LK09108) by the UK Department for Environment, Food and Rural Affairs, the UK Health and Safety Executive (Chemicals Regulation Directorate), the HGCA division of the UK Agriculture and Horticulture Development Board, VSN International and Bayer CropScience. LN received funding from a Lawes Agricultural Trust Summer Bursary. The authors thank Andreas Johnen of proPlant GmbH for helpful discussions, and Julian Franklin and Ian Pearman at Rothamsted for assistance with CE cabinets. Rothamsted Research is a national institute of bioscience strategically funded by the UK Biotechnology and Biological Sciences Research Council (BBSRC).


This is the most fucked up, shitty line-up for a festival I've ever seen!
Even non-metal fests are better.
Everyday I'm scared to open MS 'cause of the Graspop announcements!
Ok, it's not easy to make it work for everyone but this is just hilarious.
LOOK AT IT!

This is the most fucked up, shitty line-up for a festival I've ever seen!
Even non-metal fests are better.
Everyday I'm scared to open MS 'cause of the Graspop announcements!
Ok, it's not easy to make it work for everyone but this is just hilarious.
LOOK AT IT!

Then why are you attending the fest?

As far as I can get it, this year, Graspop's promoters have decided to put up a more "mainstreamish" line-up. That's it.

This is the most fucked up, shitty line-up for a festival I've ever seen!
Even non-metal fests are better.
Everyday I'm scared to open MS 'cause of the Graspop announcements!
Ok, it's not easy to make it work for everyone but this is just hilarious.
LOOK AT IT!

Then why are you attending the fest?

As far as I can get it, this year, Graspop's promoters have decided to put up a more "mainstreamish" line-up. That's it.

It's so difficult to stay away from a big metalfest if it's only 10km away from your frontdoor

And that Graspop decides to go more mainsteam is one thing, but there are a lot of "extreme metallers"
who supported Graspop all these years and they are left out now.


BM #74 Week 2 Day 2 -For day 2 check out this mango based pudding which is a delight to have. I felt that the pudding was on the lighter side but if the whipped cream is increased then it might make it richer. But we enjoyed this version as well.Ingredients -Mango Pulp 3 cups (I used store bought)Powdered Sugar 1/4 cup (or as needed)Agar Agar Flakes 4 tsp Water 1/2 cupWhipped Cream 1/2 cup (I Used Cool Whip brand)

Method Of Preparation -

Chop the Agar flakes as finely as possible.In a pan boil about 1/2 cup of water and after it comes to a rolling boil, add the Agar flakes and mix until the flakes are dissolved. It took me about 3-4 minutes of active stirring.Remove from heat and pour in the mango puree and powdered sugar. Mix well until the sugar is dissolved.After the mixture is sufficiently cool, gently fold in the whipped cream until the color and texture of the mixture looks uniform.Transfer into individual serving bowls of your choice, cover with cling wrap and refrigerate for 2-3 hours to set.Unmould, garnish and serve chilled.


Johann Friedrich Franz Burgmüller was born in 1806, Regensburg
Germany. He was the son of Johann August Franz Burgmüller (German
organist and conductor 1766-1824) and elder brother of Norbert
Burgmüller (German composer and pianist 1810-1836). Friedrich
studied with Ludwig Spohr (1784-1859) and Moritz Hauptmann
(1792-1868). After years of studies with Spohr and Hauptmann,
Friedrich settled in Paris (until his death in 1874), and he
adopted Parisian music. Friedrich Burgmüller wrote numerous
pieces of salon music for piano and published several albums of
piano studies that have become standard works.

Burgmüller's piano music is mainly intended for juvenile
performers, and his opus 68, 76, 97, 100, and 105 etudes are
widely used for piano teaching today. Etudes in opus 100, Twenty-Five
Easy and Progressive Studies for Pianoforte, are used for
piano lessons especially for juvenile learners with small hands
because of the following reasons:

·There are no
octave fingerings

·The widest
range (interval) is the seventh

·Each piece
has an unique title, and the pieces are pleasant (joyful)

Zen-On Publishers' Burgmüller Op. 100 Twenty-Five Etuden
(published in Japan) has been used for piano lessons by Yamaha
method. After completing a two-year primary course (age 4-6) and
a two-year secondary course (age 6-8) of basic musicianship,
Yamaha students (age 8-10) usually start taking individual
lessons with this etude book.

This is a mild (frank) practicing piece. At the beginning
levels (elementary to early intermediate levels), it is difficult
to use the fourth finger, but there is not difficult
fourth-finger usage with this piece. Practice with rhythm (an
accent on the first beat and third beat), and pay attention to
expression marks; try to be expressive.

Note:

Allegro moderato - Allegro means fast, and it usually gives more specific
meanings with other terms.

This piece is very fast. Like a foliage (arabesque design),
try to express brilliant sound movements. It is also basic
practice for scales; playing 1,2,3,4, and 5 of the right hand
(also left hand) with fast speed are difficult at the beginning
levels because it usually comes up unevenly. Try to get rid of
unevenness, listen to your sound carefully, and articulate
clearly.

Try to make the melody sing because sound movements with
"feeling" are necessary among the well-balanced melody
lines of this tune. Follow exact fingerings as indicated; this
piece is good for fingering practice. Do not use the same finger
for the repeated notes in the melody; using a different finger
makes a better balanced melody and phrase. Do not hurry to play
grace notes, take a time before the beat, and put an accent on
the beat.

The first six measures are formed like an introduction;
imagine that the left hand is "calling everybody" and
the right hand is "everybody starts gathering." Then
the party begins in the seventh measure; the left hand talks and
the right hand responses, or the right hand talks and the left
hand responses. There are many conversations and discussions, and
the party continues until it gets back to the first atmosphere
(in m. 27, motive of m. 7). Be aware that the third-interval
scales (sequence in m. 10 and other) are very difficult. Practice
to play two notes (sound) simultaneously and the same strength
(intensity). For scales with the second and fourth fingers,
settle the fingers and try to play by movements of hand (arm),
but do not tense fingers too much.

Note:

Allegro no troppo- Allegro, but not too fast.

No. 5 Innocence in F major

This is a descending-scale-practice piece (see
"Arabesque" as ascending scale practice). This piece
also contains an ascending scale, so practice to play lovely
(pretty) ascending and descending scales.

Note:

Moderate - medium speed.

grazioso - graceful.

No. 6 - Progres (Progress) in C
major

This is a piece to practice both hand scales and staccato
evenly. After the ninth measure, there are unusual accents with
slurs. It is very important to practice those unusual accents
correctly. Play staccato with light feelings like hopping.

Note:

Allegro - fast, cheerful.

D.C. al Fine - return to the beginning (D.C.) and end at Fine.

No. 7 - Le Courant limpide (The Limpid
Stream) in G major

Well-balanced sounds (even-sounds) are quite necessary to
express "the limpid stream." The first notes of
triplets play melody lines, accompanying by other two notes (of
triplet); therefore, make (put) a little accent on the first note
of each triplet, and play other two notes (rests of triplet)
softer and evenly. The left hand plays melody and the right hand
accompanies in mm.9-16.

Note:

Allegro vivace - very fast and lively.

mormorendo - like whispering.

No. 8 - La Gracieuse (Grace) in F
major

This is a piece to practice embellishment (grace notes). There
is a variety of fingerings; however, it is very important to play
embellishing notes evenly and clearly. Notice the locations of
accent marks (on the eighth notes or on the doted-quarter notes)
and practice those accents correctly.

There are octave fingerings (g1-g2) in this piece, but they
are not played at the same time, so that there is no problem for
children with small hands. Leap fingers to play if fingers can
not reach octave notes, but make sure to practice to play (land
to) the correct key. Here many hunting-whistle sounds approach,
horses start cantering, hunting dogs also start cantering, the
whistles sound again louder, and everything (hunting band)
gradually goes away.

Note:

un poco agitate - little agitated.

dolente - sadly.

rallent - gradually growing sloer.

No. 10 - Tendre Fleur (Tender Blossom)
in D major

This is a very important piece to practice to play flowing
melody, well-balanced left hand and right hand dialog, and minute
(delicate) fingerings.

Note:

delicate - gracefully, elegantly.

No. 11 - La Bergeronnette (The
Wagtail) in C major

Bird-singing-like sounds (played by broken chords) cheerfully
come up part by part. Try to play lightly like bird flying with
the attention to dynamics.

This is a very agitated piece, expressing sadness of a
farewell to close (intimate) friends. Triplets are very tiny and
vertiginous, but practice to play evenly with clear articulation.
The tune becomes C major in the middle part, expressing gentle
consolation; then the tune returns to A minor, expressing sadness
again. At the codetta, the tune starts falling away,
expressing resignation. Play the final cadence resolutely,
expressing a period (full stop).

Note:

Allegro molt agitato- fast and very
agitatedly.

expresivo - expressively.

No. 13 - Consolationin C major

This is a piece to practice to make clear distinction of
melody-line notes and accompanying notes; in the first five
measures, make (put) an accent on the whole notes of the hand
(play them as a melody line), and play the eighth notes (right
hand) as accompaniments (make sure to articulate clearly). In the
sixth measure, melody moves closely (the eighth notes); then from
the third beat in the seventh measure, "G" starts
accompanying. Remember these points throughout the piece, try to
make melody sung, and pay attention to balance of the sound.

Note:

dolce lusingando - softly and gently.

No. 14 - La Styriennein G major

This piece is a waltz. Understand well the waltz rhythm, One
(strong) -- two (weak) -- three (weak). The tempo is fast;
practice slowly, but try to catch up the tempo indicated later.
Make clear distinction of legato and staccato.

Note:

mouvement de valse - waltz speed.

deciso - decisive, bold.

No. 15 - Balladein C minor

Ballade is a story tune. The right hand plays
accompaniments and the left hand plays the melody in the
beginning part and ending part. In the middle part (C major), the
right hand plays the melody and the left hand plays
accompaniments, so make sure to play the melody louder (right
hand, not left hand). Try not only to make the melody sing but
also to make melody recite.

Note:

Allegro con brio - fast and sprightly (lively).

mesterioso- mysteriousl.

No. 16 - Douce Plainte (Tender
Grieving) in G minor

This is a sad tune in minor to practice P (piano-soft
sound) beautifully. Practice an accent among weak sounds, and
make the rhythm clear and even.

No. 17 - La Babilarde (The
Chatterbox) in F major

In the first six measures, the right hand plays legato
and the left hand plays staccato with a big leap.
Practice to play surely. Make the left hand's rhythm clear mm.
7-14, and practice (left hand) to play the same note (key) with
different fingers (the first and second) evenly and smoothly
mm.15-18; don't you think that the tune is chattering? Practice
to play the right hand's third-interval melody evenly and 3-2-1
(fingering) on the same note from m. 33; it might be awkward at
first, but be patient and practice slowly.

No. 18 - InquietudeinE
minor

Make a strong accent on the left hand's chords and make the
right hand's P (piano-soft) sounds clear and even. The
left hand and right hand should be linked each other, so practice
"not to scatter them."

No. 19 - Ave Mariain A major

Melody is both polyphonic and monophonic, played by both hands
(sometimes by LH and sometimes by RH). Practice with the
attention to these points and with gentle singing aspect.

Note:

religioso - religiously.

No. 20 - La tarentelle (Tarantella) in
D minor

La tarentelle has a unique rhythm. Platy lightly with
two beats in one measure. Pay attention to grace notes in the
middle part. Make an accent on the down beat (the first beat) and
practice slowly.

Note:

Allegro vivo - fast and lively.

No. 21 - L' lfarmonie des Anges
(Harmony of the Angels) in G major

This is a piece to practice a wide range (compass) and broken
chords. The most important thing is to understand the distinction
between an accent on only the first beat in one measure and an
accent on each beat. The first and second measures are examples
of "first beat accent" and the third and fourth
measures are examples of "each beat accent." Link the
right hand and left hand evenly; do not let listeners notice
(sense) the "left hand and right hand change."

Note:

armonioso - harmonically.

piu lento - quite gently (slowly).

No. 22 - Barcarolle (The Chantey) in
A-flat major

This is a typical sailor's-song-rhythm piece, 6/8. Do not play
too strongly (roughly) the left hand's rhythm from m. 12, and
play the melody rollingly with rhythm like a boating.

Note:

Andantino quasi
Allegretto -
faster than Andante like Allegretto.

No. 23 - Le Retour (The Return) in
E-flat major

This is a piece to practice chords. Avoid scattered chords;
practice to play the notes (for the chords) simultaneously.
Melody lines are top voices (right hand), so make them come up.

Note:

Molto agitato quasi
Presto - very
agitatedly like Presto (very fast).

simile - similar.

cresc. assai - enough crescendo.

No. 24 - L' Hirondelle (The Swallow)
in G major

This is a piece to practice the left hand crossing over the
right hand. Notes played by the left hand are melody, and the
right hand plays accompaniments. Practice to play melody lines
and accompaniments clearly.

This is a piece for all-round (synthetic) practice (review for
what you have learned so far). Express an elegant but unsteady
horse walk with various rhythms and scales. Remember everything
you have learned, adapt your technique, and make the piece as a
brilliant finale for your study with this etude book.


Introduction
============

Candiduria, defined as various Candida species found in the urine, is becoming an increasingly serious problem in hospitalized patients. Antibiotics, corticosteroids, immunosuppressive agents, and urinary catheters, all of which are used extensively in clinical practice, are regarded as possible risk factors ([@ref-9]). The presence of Candida in urine does not always signify a fungal urinary tract infection (UTI) and may derive from colonization or sample contamination ([@ref-14]). Nevertheless, candiduria could be considered a valuable clue to the diagnosis and treatment of fungal UTIs, especially in high-risk hospitalized patients. Numerous studies in experimental animals and clinical observations have indicated that the kidney consistently acts as the main fungus-bearing organ during candida infection ([@ref-4]; [@ref-32]; [@ref-34]). In severely ill patients, candiduria has been considered to be significantly associated with disseminated candidiasis ([@ref-8]; [@ref-12]; [@ref-3]). In surgical intensive care unit (ICU) settings, candiduria has been proved to be an independent factor associated with mortality ([@ref-22]).

For most clinical laboratories, urine culture is the first-line technique to determine candiduria. This conventional manual assay is time consuming, labor intensive and unsuitable for large-scale screening and investigation purposes. For a long time, flow cytometry was regarded as a useful tool for yeast characterization ([@ref-13]). By utilizing fluorescent staining and flow cytometric technology, the Sysmex UF-1000i system measures each detected formed element, including fungi in urine, simply and efficiently. Several studies ([@ref-11]; [@ref-21]; [@ref-23]) have supported the reliability of an automatic urine analysis system for bacteria and yeast-like cell (YLC) detection.

Previous epidemiological studies of candiduria ([@ref-1]; [@ref-18]; [@ref-26]) have been based on culture assay and limited to small study populations. Hence, further data collected by automated systems from a large target population are necessary for more comprehensive understanding of candiduria in hospitalized patients. The aim of this study was to collect epidemiological data on candiduria with the Sysmex UF-1000i urine flow cytometry analysis system and to assess the incidence of candiduria in hospitalized patients. The risk factors and outcomes of ICU candiduria patients were also investigated in this study.

Materials & Methods
===================

Study population
----------------

This study was conducted between September 1, 2016, and August 31, 2017, in Bethune International Peace Hospital of PLA and was approved by the local research ethics committee (approval number: 2016-KY-034). We studied 45,371 urine samples collected from 31,648 hospitalized patients, including 17,840 males and 13,808 females. The enrolled patients were aged 1 day to 106 years old with a median age of 56 years. Samples from infants younger than 3 years accounted for 8.86% of the total number of samples. Clinical data, including age, gender, primary diagnosis, risk factors (e.g., presence of catheter, antibiotics, parenteral nutrition, immunosuppressive treatment, renal failure, abdominal surgery, diabetes and malignancy) and prognosis, were obtained from medical records. For ICU patients, Acute Physiology and Chronic Health Evaluation (APACHE) II scores were used to evaluate ICU admission severity.

Patients were instructed on how to collect mid-stream urine before sample collection. For babies younger than 1 year, a sterile urine collection bag was applied according to the manufacturer's instructions. The samples were collected in the morning and were stored in a disposable sterile bottle with a screw lid. All samples were analyzed within 3 h after collection.

All urine samples received were examined with a Sysmex UF-1000i analysis system and the count of yeast-like cells (YLCs) was used as a candiduria marker ([@ref-11]). For patients who underwent repeated urine examinations, the maximum YLC count value was recorded.

Microbiology analysis
---------------------

To determine the performance of the Sysmex UF-1000i in detecting candiduria, a cross-sectional study was designed and conducted. A total of 203 YLC-positive and 127 negative samples were randomly chosen and subjected to microbiological analysis. Briefly, 0.01 ml of each urine sample was spread on a CHROMagar plate with a calibrated loop, and the culture plates were incubated aerobically at 35 °C for 48 h. The Candida species were identified based on growth color and colony shape and were further confirmed by the API 20C AUX Yeast Identification System (BioMerieux, Craponne, France). The determined level for quantitative cultures to be considered significant was 10^3^ CFU/ml (10 CFU/plate).

Statistical analysis
--------------------

The LOG YLC counts results of the culture-positive/negative groups are given as mean ± standard deviation (SD). The candiduria rate according to age groups, genders and wards were statistically compared using Fisher's exact test. Risk factors for ICU patients were assessing by logistic regression. Statistical analysis of YLC counts were performed with the Mann--Whitney test. A *P* value \< 0.05 was considered significant. Statistical analysis was performed using software GraphPad Prism 7.0 and SPSS 17.0. The characteristics of YLC for determining candiduria, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated.

Results
=======

YLC detection capacity of the Sysmex UF1000i and cutoff value determination
---------------------------------------------------------------------------

By microbiological culture assay, we isolated 132 strains of Candida species from 119 (119/203) YLC-positive samples and eight strains from eight (8/127) YLC-negative samples. [Table 1](#table-1){ref-type="table"} displays the prevalence of the 140 isolated Candida species strains, including 47 strains of *Candida glabrata*, 44 strains of *Candida tropicalis*, 34 strains of *Candida albicans*, six strains of *Candida krusei*, four strains of *Candida parapsilosis* and five strains of other species.

10.7717/peerj.6935/table-1

###### The prevalence of candida species in the urine investigated by culture.

![](peerj-07-6935-g002)

  Candida species                                    No. (%) indentified[^a^](#table-1fn1){ref-type="fn"}
  -------------------------------------------------- ------------------------------------------------------
  *C. glabrata*                                      47 (33.6)
  *C. tropicalis*                                    44 (31.4)
  *C. albicans*                                      34 (24.3)
  *C. krusei*                                        6 (4.29)
  *C. parapsilosis*                                  4 (2.86)
  Other *C. spp*.[^b^](#table-1fn2){ref-type="fn"}   5 (3.57)

**Notes.**

13 patients had \>1 candida species isolated at baseline.

Includes *Candida guilliermondii* (2), *Candida lusitaniae* (1), *Saccharomyces cerevisiae* (1) and *Candida pseudotropicalis* (1).

[Figure 1A](#fig-1){ref-type="fig"} shows the YLC counts obtained from the YLC-positive samples. Compared to that of the culture-negative group, the YLC count (LOG n) of the culture-positive group was significantly higher (2.259 ± 0.067 VS 1.524 ± 0.052, *P* \< 0.001). The receiver operating characteristic curve (ROC) was utilized to evaluate the predictive ability of the Sysmex UF1000i YLC count to predict candiduria. The sensitivity and specificity were calculated based on a determined cutoff value of the ROC curve. As [Fig. 1B](#fig-1){ref-type="fig"} shows, the area under the curve (AUC) was 0.789 with a 95% confidence interval of 0.728--0.851. The Youden index (sensitivity + specificity − 1) was generated, and 84.6 YLCs/µL was determined as the best cutoff value. The sensitivity, specificity, PPV and NPV of the YLC counts to determine candiduria were 61.7%, 84.1%, 88.6% and 66.3%, respectively.

![Yeastlike cells (YLC) performance for screening candiduria.\
(A) For YLC positive samples, YLC count (LOG n) of culture positive samples is significantly higher than culture negative samples (*P* \< 0.001). The error bars represent the standard deviations; (B) The receiver operating characteristic curve (ROC) was utilized to determine the cutoff YLC value for candiduria identifying. The area under the curve (AUC) was 0.789 and the cutoff value was determined as 84.6 YLCs/µL.](peerj-07-6935-g001){#fig-1}

Characteristics of patients with candiduria
-------------------------------------------

For the investigated samples, 684 of 45,371 (1.51%) were determined to be positive, and 509 of 31,648 (1.61%) patients were considered candiduria-positive with a YLC cutoff value of 84.6 YLCs/µL.

[Table 2](#table-2){ref-type="table"} shows the age and gender distribution of the candiduria patients. The enrolled patients were divided into five age groups, and per-age maximum candiduria determination was displayed as follows: \<1 group (3.41%), followed by the 60 + (2.51%) and 18∼59 groups (1.06%). The 1∼6 (0.20%) and 7∼17 groups (0.54%) showed very low candiduria rates. Of the 509 positive patients, 235 were male, and 274 were female, and the candiduria occurrence rate was statistically higher in female patients than in male patients (*P* \< 0.001).

10.7717/peerj.6935/table-2

###### Age and gender of 509 patients with candiduria.

![](peerj-07-6935-g003)

  Characteristic      Candiduria (*n* = 509)   Non-candiduria (*n* = 31,139)
  ------------------- ------------------------ -------------------------------
  Age, years, n (%)
  \<1                 49 (3.41)                1,387
  1∼6                 5 (0.20)                 2,553
  7∼17                7 (0.54)                 1,292
  18∼59               157 (1.06)               14,604
  60∼                 291 (2.51)               11,303
  Gender, n (%)
  Male                235 (1.32)               17,605
  Female              274 (1.98)               13,534

Underlying disease was recognized as the main factor for candiduria. [Table 3](#table-3){ref-type="table"} exhibits the ward distribution of the candiduria patients. Generally, candiduria rates in patients from internal medicine wards were more than two times higher than those of patients in the surgical wards (1.98% vs 0.94%, *P* \< 0.001). The ICU (22.89%), nephrology (7.06%), geriatrics (5.66%), NICU (3.66%), neurosurgery (2.94%), hematology (2.78%), oncology (2.60%) and respiratory (2.54%) wards were the leading wards for candiduria, with occurrence rates were significantly higher than the average rate (1.61%).

10.7717/peerj.6935/table-3

###### Number of candiduria patients from different medical wards.

![](peerj-07-6935-g004)

  Ward[^a^](#table-3fn1){ref-type="fn"}   Candiduria, %   Non-candiduria   *P* value [^b^](#table-3fn2){ref-type="fn"}
  --------------------------------------- --------------- ---------------- ---------------------------------------------
  Internal medicine                       411 (1.98)      20,802           0.02[^\*^](#table-3fn3){ref-type="fn"}
  CCU                                     18 (1.38)       1,284            0.57
  Gynaecology                             10 (0.60)       1,654            0.0015[^\*^](#table-3fn3){ref-type="fn"}
  Infectious disease                      9 (1.74)        508              0.78
  Hepatology                              5 (0.62)        800              0.03[^\*^](#table-3fn3){ref-type="fn"}
  Pediatrics                              9 (0.33)        2,729            \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  NICU                                    42 (3.66)       1,104            \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Respiratory                             47 (2.54)       1,804            0.0016[^\*^](#table-3fn3){ref-type="fn"}
  Emergency                               2 (2.22)        88               0.66
  Geriatrics                              65 (5.66)       1,083            \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Endocrinology                           19 (1.58)       1,180            1.00
  Neurology                               12 (0.72)       1,656            0.0052[^\*^](#table-3fn3){ref-type="fn"}
  Nephrology                              68 (7.06)       895              \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Gastroenterology                        22 (1.68)       1,291            0.79
  Cardiovascular                          7 (0.30)        2,303            \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Hematology                              11 (2.78)       384              0.06
  Rheumatology                            8 (1.22)        649              0.46
  Oncology                                19 (2.60)       712              0.03[^\*^](#table-3fn3){ref-type="fn"}
  ICU                                     38 (22.89)      128              \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Surgery wards                           98 (0.94)       10,337           \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Hepatobiliary                           4 (0.65)        615              0.07
  ENT                                     11 (0.39)       2,793            \<0.0001[^\*^](#table-3fn3){ref-type="fn"}
  Orthopaedics                            13 (0.84)       1,530            0.02[^\*^](#table-3fn3){ref-type="fn"}
  Urology                                 17 (1.74)       961              0.70
  General surgery                         16 (0.87)       1,833            0.015[^\*^](#table-3fn3){ref-type="fn"}
  Neurosurgery                            19 (2.94)       627              0.0066[^\*^](#table-3fn3){ref-type="fn"}
  Vascular surgery                        7 (1.09)        637              0.32
  Cardiothoracic                          1 (0.26)        391              0.04[^\*^](#table-3fn3){ref-type="fn"}
  Ophthalmology                           4 (0.55)        726              0.02[^\*^](#table-3fn3){ref-type="fn"}
  Obstetrics                              6 (0.77)        774              0.07

**Notes.**

CCU, cardiac care unit; NICU, neonatal intensive care unit; ICU, intensive care unit; ENT, ear, nose and throat.

Compared with overall candiduria incidence rate (509/31648, 1.61%); *P* value \< 0.05 was considered significant.

*P* \< 0.05.

Risk factors and outcome of ICU candiduria patients
---------------------------------------------------

Subjects in this study included 166 patients from the ICU of our hospital, among whom 38 were identified as having candiduria (38/166, 22.89%). Previous reports showed high candiduria rates in ICU patients, and we further investigated the risk factors and outcomes of the ICU candiduria patients.

Among the several risk factors investigated, the presence of a catheter was found to be statistically significant by multi-factor logistic regression analysis (*P* \< 0.05, [Table 4](#table-4){ref-type="table"}). Other factors, including age \>60 years, antibiotic and immunosuppressive therapy, parenteral nutrition, abdominal surgery, diabetes, malignancy, renal failure and APACHE II scores were not found to be statistically significant.

10.7717/peerj.6935/table-4

###### Risk factors and outcome for ICU candiduria patients.

![](peerj-07-6935-g005)

  Characteristic for ICU patients                 Candiduria (n, %)   Non-candiduria (n, %)   Single-factor logistic regression analysis   Multi-factor logistic regression analysis
  ----------------------------------------------- ------------------- ----------------------- -------------------------------------------- ------------------------------------------- ----------------------------------------- -----------------------
  ***Risk factors***
  APACHE II score (median, interquartile range)   17 (12--23)         13 (9--16)              0.001[^\*^](#table-4fn1){ref-type="fn"}      1.109 (1.047--1.175)                        0.774                                     0.932 (0.576--1.507)
  \>60 years                                      28 (73.68)          90 (70.31)              0.561                                        1.273 (0.564--2.870)                        0.911                                     1.051 (0.437--2.532)
  Presence of catheter                            22 (57.89)          41 (32.03)              0.004[^\*^](#table-4fn1){ref-type="fn"}      3.025 (1.437--6.369)                        0.006[^\*^](#table-4fn1){ref-type="fn"}   3.161 (1.385--7.212)
  Antibiotics                                     33 (86.84)          109 (85.16)             0.795                                        1.150 (0.399--3.318)                        0.633                                     0.749 (0.228--2.456)
  Parenteral nutrition                            23 (60.53)          69 (53.91)              0.665                                        1.176 (0.566--2.444)                        0.965                                     0.981 (0.417--2.308)
  Immunosuppressive treatment                     2 (5.26)            3 (2.34)                0.368                                        2.315 (0.372--14.390)                       0.883                                     1.192 (0.115--12.377)
  Abdominal surgery                               4 (10.52)           7 (5.47)                0.377                                        1.765 (0.501--6.216)                        0.366                                     1.902 (0.472--7.664)
  Renal failure                                   19 (50.0)           38 (29.69)              0.057                                        2.054 (0.980--4.303)                        0.077                                     2.203 (0.917--5.291)
  Diabetes                                        14 (36.84)          35 (27.34)              0.262                                        1.550 (0.721--3.332)                        0.473                                     1.372 (0.579--3.251)
  Malignancy                                      6 (15.79)           12 (9.38)               0.269                                        1.812 (0.631--5.207)                        0.098                                     3.348 (0.801--13.996)
  ***Outcome***
  Dead in hospital                                13 (34.21)          13 (10.16)              0.001[^\*^](#table-4fn1){ref-type="fn"}      4.600 (1.904--11.113)                       --                                        --
  Discharge                                       25 (65.79)          115 (89.84)             --                                           --                                          --                                        --

**Notes.**

*P* value \< 0.05.

A total of 26 ICU investigated patients died in hospital (13 were identified with candiduria and 13 did not). The death rate was significantly higher among candiduria patients than among those without candiduria (13/38 VS 13/128). For the 38 ICU candiduria subjects, eight received antifungal therapy, and two of them died; 30 did not receive antifungal treatment, and 11 of them died. There was no significant difference between the death rates of patients who received antifungal therapy and of those who did not (2/8 VS 11/30). One candiduria patient was clinically diagnosed as having candidemia with a positive blood culture of *Candida albicans*. This patient died within 15 days despite aggressive antifungal therapy.

Discussion
==========

The Sysmex UF-1000i is a fully automatic fluorescence cytometric urine analyzer capable of detecting various urine particles, including bacteria (BACT), red cells (RBCs), white cells (WBCs), yeast-like cells (YLCs), epithelial cells, casts, crystals, spermatozoa, small round cells and mucus ([@ref-6]). Several studies ([@ref-11]; [@ref-21]; [@ref-23]) have shown that the Sysmex UF-1000i analyzer can determine UTIs and candiduria in a simple manner with considerable quantification. Our data indicated that the sensitivity, specificity, PPV, and NPV were 61.7%, 84.1%, 88.6% and 66.3%, respectively, using a cutoff value of 84.6 YLCs/µL. According to a report from [@ref-11], the sensitivity, specificity, PPV and NPV of urine YLC counts for determining candiduria were 87.3%, 97.0%, 9.3% and 99.9%, respectively, with a cutoff value of ≥50 YLCs/µL. Compared to the present study, their study used a relatively low cutoff value and enrolled all YLC-negative subjects into the calculation, leading to a very low PPV. When determining the cutoff value, we only included the YLC-positive subjects in the calculation because the main question of the present study was whether positive YLC indicated candiduria.

The urinary tract is one of the most frequent sites for candida colonization, and the presence of candida in urine should not be ignored because candida colonization is a risk factor for the progress of invasive candidiasis (IC) ([@ref-20]; [@ref-7]). In our study, the general incidence of candiduria was 1.61% in hospitalized patients. Actually, according to limited data available, the candiduria incidence rates were varied from 0.77% to over 20% ([@ref-18]; [@ref-5]). Our result was comparable to a clinic-based retrospective and prospective study using culture assay conducted by [@ref-5]. Compared to study data regarding candiduria epidemiology in community settings, by which the candiduria rates were reported respectively as low as 0.4% ([@ref-11]) and 0.14% ([@ref-5]), our investigation result is much higher in hospitalized patients. This result indicates that the incidence of candiduria varied according to subject groups. *C. glabrata* and *C. tropicalis* were found more often than was *C. albicans*, agreeing with the trend of non-albicans Candida prevalence worldwide. Especially in the urinary tract, non-albicans Candida species were reported better-adapted to the environment ([@ref-30]). *C. glabrata* ([@ref-28]) and *C. tropicalis* ([@ref-29]) were identified as the most prevalent urinary tract Candida species in various studies.

Candiduria occurrence rates varied according to age groups. The incidence was 2.51% in patients older than 60 years and 3.41% in patients younger than 1 year, significantly higher than the overall incidence rate (1.61%). Older age is a classical risk factor for candiduria ([@ref-10]). In healthy babies, the urinary tract is generally considered to be clean and without candida colonization, while in NICU patients, particularly premature infants, candiduria and candidemia were not rare ([@ref-16]; [@ref-33]). Gender is another factor that may affect the candiduria incidence ([@ref-15]). In the present study, the candiduria incidence was significantly higher in females than in males. A possible explanation to this finding may be the common colonization of Candida in the urogenital tract or Candida vulvovaginitis in females ([@ref-5]; [@ref-19]).

To explore the correlation between underlying diseases and candiduria in hospitalized patients, the candiduria incidences in patients who were admitted to various wards were investigated. As expected, ICU patients showed the highest candiduria incidence (22.89%). Patients from nephrology (7.06%), geriatrics (5.66%), NICU (3.66%), neurosurgery (2.94%), hematology (2.78%) and oncology (2.60%) wards also showed considerably high candiduria rates. This result might imply that stays in the ICU, major surgery, very old/young age, diabetes, renal disease, malignancy and immunosuppressive therapy, in addition to gender, could be considered to be major risk factors for candiduria. For patients undergoing neurosurgery, whether a urinary catheter was used in the peri-operative period is very important for the development of candiduria ([@ref-19]). In the present study, all 274 patients undergoing head operations had a record of urinary catheter usage that might be a major cause of the high prevalence of candiduria in the neurosurgery ward.

In ICU patients, urinary catheterization appeared to be statistically significant risk factor. The presence of a catheter in the urinary tract was always an important factor associated with candiduria and various UTIs, as confirmed by many studies ([@ref-25]; [@ref-24]; [@ref-31]; [@ref-17]). Previous antibiotic therapy has been considered to be another important risk factor, playing a crucial role in the pathogenesis of candiduria ([@ref-35]). Excessive antibiotic use is widely recognized in the ICU patients we investigated (142 of 166 patients had a record of antibiotic therapy). The high prevalence of antibiotic use in ICU patients makes it difficult to clarify the role of antibiotic therapy in the prevalence of candiduria or Candida colonization in our report.

The total mortality rate for ICU candiduria patients was 34.21%, approximately three times higher than that of the non-candiduria patients. This figure is very close to the mortality rate reported by [@ref-2]. Antifungal treatment had no effect on the mortality of ICU candiduria patients in our investigation. It appears likely that candiduria is a "red flag" or marker for seriously ill patients who need particular attention to their indwelling devices or underlying disease but has no direct contribution to the high mortality. Although the connection between candiduria and candidemia has been reported by several groups ([@ref-27]), only one ICU candiduria patient was finally diagnosed as having candidemia by microbiological culture in this study.

The findings of this study have to be seen in light of some limitations. First, the epidemiological data are obtained from the Sysmex UF-1000i YLC parameter. The performance of YLC for candiduria screening is relatively low, which might bias the results of our overall and subgroup analyses. However, retrospectively investigating candiduria with YLC could greatly reduce the selection bias caused by target population, for the reason that urine culture is warranted only when the patient exhibits urinary tract infection symptoms in clinical practice, while urinalysis is performed as a routine examination on each patient. Second, the sample size for ICU patients is not big enough. Third, as a retrospective study, we only conduct follow-up evaluation for the outcome of ICU patients.

Conclusions
===========

To our knowledge, this is the first epidemiological study of candiduria with a large-scale investigated population conducted using the Sysmex UF-1000i urine analyzer. The incidence rate of candiduria in hospitalized patients was approximately 1.61% and varied according to age, gender and the wards where they were isolated. Presence of a catheter was the only independent risk factor in ICU patients. Antifungal therapy had no effect on the mortality of ICU candiduria patients, implying that candiduria was not directly associated with mortality.

Supplemental Information
========================

10.7717/peerj.6935/supp-1

###### Dataset of the present study

This dataset including 4 files named A, B, C and D. (A) indicates the urine YLC counts in patients admitted in internal wards; (B) indicates the urine YLC counts in patients admitted in surgery wards; (C) indicates the performance of YLCs in screening candiduria, based on which we draw the ROC curve and determined the YLC cut-off value. (D) indicates risk factors and outcome of ICU patients with/without candiduria, N means No, Y means Yes.

######

Click here for additional data file.

We thank all the physicians and nurses who have contributed to the research.

Additional Information and Declarations
=======================================

The authors declare there are no competing interests.

[Zhengxin He](#author-1){ref-type="contrib"} conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

[Yanli Liu](#author-2){ref-type="contrib"} and [Tingting Wang](#author-3){ref-type="contrib"} performed the experiments, approved the final draft.

[Yan Cheng](#author-4){ref-type="contrib"} analyzed the data, approved the final draft.

[Jing Chen](#author-5){ref-type="contrib"} analyzed the data, contributed reagents/materials/analysis tools, approved the final draft.

[Fukun Wang](#author-6){ref-type="contrib"} conceived and designed the experiments, approved the final draft.

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

Ethical approval was obtained from the Ethics committee board of Bethune International Peace Hospital of PLA hospital of people of liberation army (IRB approval number: 2016-KY-034) and the need for individual patient consent was waived.

The following information was supplied regarding data availability:

The raw data are available in a [Supplementary File](#supplemental-information){ref-type="supplementary-material"}.


Cell surface NADH oxidases (ECTO-NOX proteins) with roles in cancer, cellular time-keeping, growth, aging and neurodegenerative diseases.
ECTO-NOX (because of their cell surface location) proteins comprise a family of NAD(P)H oxidases of plants and animals that exhibit both oxidative and protein disulfide isomerase-like activities. The two biochemical activities, hydroquinone [NAD(P)H] oxidation and protein disulfide--thiol interchange alternate, a property unprecedented in the biochemical literature. A tumor-associated ECTO-NOX (tNOX) is cancer-specific and drug-responsive. The constitutive ECTO-NOX (CNOX) is ubiquitous and refractory to drugs. The physiological substrate for the oxidative activity appears to be hydroquinones of the plasma membrane such as reduced coenzyme Q10. ECTO-NOX proteins are growth-related and drive cell enlargement. Also indicated are roles in aging and in neurodegenerative diseases. The regular pattern of oscillations appears to be related to alpha-helix-beta-structure transitions and serves biochemical core oscillator of the cellular biological clock. Period length is independent of temperature (temperature compensated) and synchrony is achieved through entrainment.


Test performance outpaces state

Test performance outpaces state

Posted on 10/19/2016

FOR IMMEDIATE RELEASEWednesday, October 19, 2016

District students’ performance on new testsoutpaces state; other exam results are mixed

BEAUFORT – District students outperformed their peers across South Carolina in five of six grade levels tested in the first statewide administration of new assessments in English Language Arts and mathematics, according to data released by the South Carolina Department of Education.

“It’s encouraging to see our students outperforming their peers in nearly all of the new SC Ready tests,” said Superintendent Jeff Moss. “The next step will be to analyze these initial results and develop plans to keep improving classroom instruction.”

The Education Department also released 2016 results for statewide science and social studies assessments as well as high school end-of-course exams. Beaufort County students’ performances on those exams were mixed, with some scores higher than last year and some lower.

“There’s a lot of hard work ahead, for educators and for students alike,” Moss said. “Our goals remain the same – to move forward, keep making progress and ultimately see our students perform at the highest levels.”

SC READY results for 2016

SC READY assessments in English Language Arts and mathematics are designed to measure progress toward South Carolina’s new, more rigorous academic standards and higher expectations. Superintendent of Education Molly Spearman said that adopting rigorous standards and accompanying assessments would require an adjustment by students, parents and teachers as educators and students transition to a more challenging academic environment.

Beaufort County students outperformed the state in five of the six grades tested in both mathematics and English Language Arts. District students’ scores – more than 9,500 students were tested – were higher in grades 3-7 but lower for eighth-graders.

In SC READY English language arts:

Grade 3 – 44.5 percent met or exceeded state standards compared to 43.7 statewide. An additional 35.2 percent of district students “approached” meeting state standards.

Grade 4 – 47.4 percent met or exceeded state standards compared to 43.4 statewide. An additional 29 percent of district students “approached” meeting state standards.

Grade 5 – 45.8 percent met or exceeded state standards compared to 41.2 statewide. An additional 34.2 percent of district students “approached” meeting state standards.

Grade 6 – 43.2 percent met or exceeded state standards compared to 41 statewide. An additional 37.8 percent of district students “approached” meeting state standards.

Grade 7 – 42.2 percent met or exceeded state standards compared to 40.7 statewide. An additional 35.8 percent of district students “approached” meeting state standards.

Grade 8 – 44.2 percent met or exceeded state standards compared to 44.7 statewide. An additional 32 percent of district students “approached” meeting state standards.

In SC READY mathematics:

Grade 3 – 57.9 percent met or exceeded state standards compared to 53.7 statewide. An additional 23.2 percent of district students “approached” meeting state standards.

Grade 4 – 50.5 percent met or exceeded state standards compared to 46.7 statewide. An additional 29.5 percent of district students “approached” meeting state standards.

Grade 5 – 54.5 percent met or exceeded state standards compared to 44.3 statewide. An additional 29 percent of district students “approached” meeting state standards.

Grade 6 – 41.6 percent met or exceeded state standards compared to 39.5 statewide. An additional 32.7 percent of district students “approached” meeting state standards.

Grade 7 – 41 percent met or exceeded state standards compared to 34.7 statewide. An additional 35.9 percent of district students “approached” meeting state standards.

Grade 8 – 30.8 percent met or exceeded state standards compared to 32.4 statewide. An additional 39.7 percent of district students “approached” meeting state standards.

SC PASS results for 2016

South Carolina students in grades 4-8 have taken SC PASS assessments in science and social studies since 2009. More than 7,800 students were tested.

In 2016, Beaufort County students’ performance on SC PASS science exams improved in three of the five grades tested:

Grade 4 – 64.5 percent met or exceeded state benchmarks in 2016 compared to 68.6 last year (65 percent statewide).

Grade 5 – 68.3 percent met or exceeded state benchmarks in 2016 compared to 63.8 last year (65.7 statewide).

Grade 6 – 61.3 percent met or exceeded state benchmarks in 2016 compared to 61.5 last year (62.1 percent statewide).

Grade 7 – 72.1 percent met or exceeded state benchmarks in 2016 compared to 65.5 last year (70.6 statewide).

Grade 8 – 62.9 percent met or exceeded state benchmarks in 2016 compared to 60.3 last year (66.2 percent statewide).

Looking at five-year trend data for SC PASS science, Beaufort County scores decreased in four of five grades tested while state scores decreased in all five grades.

Beaufort County students’ performance on SC PASS social studies exams improved in two of the five grade levels tested and improved relative to statewide averages in four of five grade levels tested:

Grade 4 – 80.9 percent met or exceeded state benchmarks in 2016 compared to 84.7 last year (81.3 percent statewide).

Grade 5 – 71.9 percent met or exceeded state benchmarks in 2016 compared to 68.3 last year (71.5 statewide).

Grade 6 – 75.1 percent met or exceeded state benchmarks in 2016 compared to 76.4 last year (76.3 percent statewide).

Grade 7 – 70.4 percent met or exceeded state benchmarks in 2016 compared to 63.8 last year (68.4 statewide).

Grade 8 – 65.1 percent met or exceeded state benchmarks in 2016 compared to 71.7 last year (69.5 percent statewide).

Looking at five-year trend data for SC PASS social studies, Beaufort County scores improved in four of five grades tested while statewide scores improved in two of five grades tested.

End-of-course exams

South Carolina students’ scores on end-of-course exams count for 20 percent of final grades in Algebra 1, English 1, Biology and U.S. History and Constitution. In 2016, Beaufort County students – more than 4,800 tested – improved their percentages of passing grades in two courses in 2016 while passing rates declined in two. Beaufort County scores improved and were higher than state averages on two exams and lower on two:

Algebra 1 – The percentage of students passing decreased from 81.0 percent in 2015 to 79.3 in 2016 (state passing percentage was 81.9).

Biology – The percentage passing decreased from 81.0 in 2015 to 79.8 in 2016 (state passing percentage was 75.7).

English 1 – The percentage passing improved from 73.7 percent in 2015 to 77.9 in 2016 (state passing percentage was 78.6).

U.S. History and Constitution – The percentage passing improved from 71.5 in 2015 to 73.3 in 2016 (state passing percentage was 71.0).

Looking at five-year trend data, district students have improved on two of the four tests:

Algebra 1 – The percentage of students passing decreased from 80.7 in 2012 to 79.3 in 2016.

Biology – The percentage passing decreased from 83.0 percent in 2012 to 79.8 in 2016.

English 1 – The percentage passing improved from 74.9 percent in 2012 to 77.9 in 2016.

U.S. History and Constitution – The percentage passing improved from 47.9 in 2012 to 73.3 in 2016.

The biggest school-level improvements were:

Algebra I: Whale Branch Early College High’s passing rates improved from 44.7 percent in 2012 to 69.7 percent in 2016.


Fires are raging across Queensland and New South Wales – 80 across Queensland alone. As is the case with the Amazon fires, for many the strong urge is to hide from the news.

It’s not for lack of caring.

It’s because you don’t even need to have survived fire seasons to remember how this awful story ends – just a childhood memory of Bambi or Smokey Bear. The forest paradise is destroyed, the world made more ugly in its ruin. The animals are left homeless, scarred or dead. Watching the news at a distance is to feel as powerless to intervene and save the innocent creatures as we did back then. We’re just weeping children stuck in their chairs while on the other side of the cinema screen – or the world – that which is most precious is annihilated.

'Like a hell': Sunshine Coast locals see suburban street become a firestorm Read more

Amid the growing global climate anxiety, there’s an intimate, personal distress induced by these blazes. Other desperate omens – the disappearing glaciers in Iceland, the bioluminescent coastlines in India, melting lakes in the Alps – register as disturbing but more abstract terrors. One explanation of why forest catastrophes are uniquely terrifying perhaps also explains the powerful impact of the Bambi story, even on children who have never seen a living deer. In his book Children’s Dreaming and the Development of Consciousness, psychologist David Foulkes suggests animals are recruited as avatars of the emerging self in children’s dreams; they feature heavily within them. “Animals carry human concerns,” Foulkes writes, “and readily become objects of identification.”

So it’s not just on a metaphoric or rational level that we know what’s happening to the creatures terrorised, scorched and destroyed in the flames is also happening to us. We grasp it psychologically. No wonder the instinct is to run away, hoping for a hero, a cosmic deus ex machina or someone’s clever mum to save us, especially given governments from Brazil to Australia and the US seem so uninterested.

The horrifying science is that deforestation itself drives more forests to burn

Thinking about child psychology, the way children problem-solve and a panic-inspired, imperative emotional need to rescue every Bambi, everywhere, provoked me to the sudden question: Mum, can we regrow all the burning forests in our backyards at home?

Amid much intimation that the Amazon fires are a convenient event for a Brazilian government intent on land clearing, the vocal point was made that Australia also faces a land-clearing crisis. Irrespective of what damage is wreaked by fires, more than three million hectares of local woodland are facing destruction through deliberate means by 2030. This means that three-quarters of already threatened species are likely to lose what remains of their habitat. The UN reports that a third of greenhouse gases are attributable to land clearing for farming. But governments, like that of New South Wales – a state currently on fire – have responded by weakening land-clearing laws and gifting amnesties to illegal land clearers.

The horrifying science is that deforestation itself drives more forests to burn. And this is why both habitat protection and active reforestation and revegetation has to be prioritised by governments – at all levels – in the pressing climate fight. The battlefront is not only to save the forests we have left, but to properly revegetate the spaces humanity has already taken over – for while wildlife areas perish, cities are also warming. Plant life sequests carbon, and retains habitat and biodiversity, but trees also make the most significant contribution to urban cooling – a necessity for liveability as the climate heats.

In Australia there are some efforts to do this at a local government level, but in the absence of a prioritised national strategy, it’s piecemeal. A recent study reported in the Conversation found 54 out of 139 of communities studied “suffered statistically significant” losses of green space within the preceding decade – because it’s not only the Amazon that’s disappearing. In the empty space of any policy framework, we’re building over the Australian backyard – tragically, maddeningly. The backyards and other green spaces of our communities are the one place where individuals can, in fact, make some material contribution to fixing the collective problem.

More trees are the answer to cool down our cities | Marco Amati and Lauren Rickards Read more

It’s not the present Morrison government but Bob Hawke’s legacy, Landcare, showing leadership in replanting a denuded Australia. The networked national organisation of community-based groups does mobilise volunteers to assist land rehabilitation. It does plant trees. In the areas where their pilot scheme, Gardens for Wildlife, is running, they will even come to your home and advise how to turn what land you have into a working contribution to carbon capture and habitat extension.

The most dangerous thing about this pilot scheme is the hope it inspires. Imagine if it was part of a coordinated national strategy, funded on a level the climate crisis should oblige. Imagine instead of volunteers, governments employed people in the business of doing the manual work of neighbourhood climate action. Gosh, it might even start to look like those “jobs of the future”, secure and local, so often discussed yet so rarely delivered.

It’s precisely because ideas are dangerous that pursuing these schemes – wherever we can – is so important. Running towards what frightens us instead of away is how we used to distinguish our adulthood from our childhood.

The times are upon us to rebuild our living planet, street to street, place to place, land to land. Not just for the sake of a burning Queensland or NSW or the Amazon. Or just for Bambi. But because to have any chance of surviving the terrible fire, even Bambi has to grow up.

• Van Badham is a Guardian Australia columnist


Q:

Access Shared Preferences from Different Activity (Android)

When you establish a shared preference such as below...
public static final String PREFS_HI = "MyPrefsFile";

Can you access it from other activities just like you would normally do?
SharedPreferences settings = getSharedPreferences(PREFS_HI, 0);

Or is there something unique that you must do to access the preferences?

A:

I would store your shared preferences name (What you are calling PREFS_HI) in the resources xml file (strings.xml). Then you can just use getSharedPreferences(getResources().getString(R.string.sharedPrefs)). Your solution works as well, though. (And the performance may be slightly better in your version.)

A:

Figured this out myself. I just used global variables and stored sharedpreferences inside there. When another activity wanted to access the preferences, it would simple access a global variable.


Distribution of the flocculation protein, flop, at the cell surface during yeast growth: the availability of flop determines the flocculation level.
The yeast FLO genes encode cell surface proteins which are expected to play a major role in the control of flocculation. We have assessed the availability of the Flo proteins at the cell surface during the growth of two flocculent strains, ABXL-1D (FLO1) and STX347-1D (FLO5) using immunological approaches, enzyme-linked immunosorbent assays and immunofluorescence. Our data show that they are not permanently present at the cell surface but that their amount increases during growth. With both strains the flocculation level is tightly correlated to the amount of Flop antigen detected, suggesting that it is the availability of the Flo proteins at the cell surface which determines the flocculation level. Our data are consistent with the idea that the Flo proteins correspond to the flocculation lectins. The differences of flocculation pattern among strains could originate from variations in the regulation of the expression of the FLO genes. Monitoring of the distribution of the Flo proteins during cellular development revealed that they are incorporated essentially in the cell wall of growing buds. Incorporation of the Flo proteins in the cell wall displays a highly polarized aspect, at the bud tip and at the mother-daughter neck junction, which can persist in mature cells. Such a localization could be relevant to constraints of the cell wall incorporation of the mannoproteins. Depending on the regulation of Flop expression and on the incorporation of the proteins in the cell wall, a yeast population can be highly heterogeneous in Flo protein equipment.


Allergic contact dermatitis to underwear elastic. Chemically transformed by laundry bleach.
Six subjects, allergic to the elastic in their undergarments, were patch test-negative to the rubber components and new elastic from the suppliers. However, washing the rubber with sodium hypochlorite gave the subjects allergic responses due to the effect of bleach on the rubber accelerator, zinc dibenzyldithiocarbamate (ZDC). To identify the allergen, ZDC was reacted with the sodium hypochlorite, and the resultant gum was extracted with diethyl ether. Eight compounds were identified in the reaction mixture by gas chromatography-mass spectrometry. The individual components were tested on volunteers after sensitization to the reaction mixture was produced in 14 of 25 volunteers. One component, N,N-dibenzylcarbamyl chloride produced an allergic response in each sensitized volunteer.