Q:

How to continue a URL for a new view with already selected primary key (pk) in Django / Django Rest Framework

From my index view I list the available "clubs" in my database, when a user select a club it re-directs them to the club home page. I have included a player registration page but I have no idea how I can render this page based on what club the user selected previously in the index. So what I would like to do is output the url like so: http://127.0.0.1:8000/account/club_home/3/player_registration/ "3" being the primary key of the selected club.
urls.py:
url(r'^club_home/$', views.club_home, name='club_home'),
url(r'^club_home/(?P<pk>\d+)/$', views.club_home, name='club_home_with_pk'),
url(r'^club_home/player_registration/$', views.RegisterPlayer.as_view(), name='player_register'),

views.py:
class Index(TemplateView):
template_name = 'index.html'

def get(self, request):
    users = User.objects.all()
    clubs = ClubInfo.objects.all()

    args = {'users': users,
            'clubs': clubs}
    return render(request, self.template_name, args)

def club_home(request, pk=None):
if pk:
    club = ClubInfo.objects.filter(pk=pk)
    user = request.user
else:
    club = ClubInfo.objects.filter(user=request.user)
    user = request.user
args = {'club': club,
        'user': user
        }
return render(request, 'club_home_page.html', args)

class RegisterPlayer(APIView):
    renderer_classes = [TemplateHTMLRenderer]
    template_name = 'player_registration.html'

    def get(self, request):
        serializer = PlayerRegistrationSerializer()
        return Response({'serializer': serializer})

   def post(self, request):
        serializer = PlayerRegistrationSerializer(data=request.data)
        if serializer.is_valid():
            serializer.save()
            return Response({'serializer': serializer})

model:
from django.db import models
from django.contrib.auth.models import User

class ClubInfo(models.Model):

user = models.OneToOneField(User, on_delete=models.CASCADE)
club_name = models.CharField(max_length=50, default='')
club_logo = models.ImageField(upload_to='profile_pics', blank=True)
club_address1 = models.CharField(max_length=30)
club_address2 = models.CharField(max_length=30, default='')
club_address3 = models.CharField(max_length=30, default='')
club_town = models.CharField(max_length=30)
club_county = models.CharField(max_length=30)
club_country = models.CharField(max_length=30)
# slug = models.SlugField(max_length=150, unique=True)

def __str__(self):
    return self.club_name

class PlayerRegistration(models.Model):

club_name = models.ForeignKey(ClubInfo, on_delete=models.CASCADE)
first_name = models.CharField(max_length=20)
last_name = models.CharField(max_length=20)
dob = models.DateField(max_length=8)
email = models.EmailField(max_length=50)
phone = models.CharField(max_length=12)
mobile = models.CharField(max_length=15)
emergency_contact_name = models.CharField(max_length=40)
emergency_contact_mobile = models.CharField(max_length=15)
address1 = models.CharField(max_length=30)
address2 = models.CharField(max_length=30, default='')
address3 = models.CharField(max_length=30, default='')
town = models.CharField(max_length=30)
county = models.CharField(max_length=30)
country = models.CharField(max_length=30)

def __str__(self):
    return "%s %s" % (self.first_name, self.last_name)

serialiser:
 class UserSerializer(serializers.HyperlinkedModelSerializer):
 class Meta:
    model = User
    fields = ('url', 'username', 'email', 'groups')

class PlayerRegistrationSerializer(serializers.ModelSerializer):

class Meta:
    model = PlayerRegistration
    fields = '__all__'
    labels = {
        'dob': ('D.O.B'),
    }
    widgets = {
        'dob': DateInput(attrs={'type': 'date'})
    }

A:

I accomplished this by creating a session key for the primary key. So when the user selects the club a session is created for this pk and i can then use it throughout the project:
# set a session variable
self.request.session['key'] = 'value'

# get a session variable
self.request.session.get('key', None)


We can convert Unicode text in Shree-Lipi 7.0 for Kannada Language by following steps.

1. Select Unicode text and copy it.2. Paste the text in WordPad or Notepad. Again copy it from WordPad or Notepad.3. Open Exchange Utility from Shree-Lipi group.4. Select Source as 'Clipboard'.5. Select Data Format as 'Text'.6. Select Language as 'Kannada'.7. Select Input Font Format as 'Unicode'.8. Select Output Font Format as 'Shree-Lipi 7.0'.9. Click on 'Open Converted File'.10. Click on 'Convert' button.11. The converted file will get open in notepad. You can select the text and apply "SHREE-KAN7-xxxx" font of "Shree-Lipi 7.0" layout to the text file or copy converted text to the respective application and then apply "SHREE-KAN7-xxxx" font of "Shree-Lipi 7.0" layout to that copied text.

Please note that it is not possible to convert English data along with language data. It has to be replaced manually. After conversion you have to re-layout the job.


The San Francisco 49ers return to practice following their first day off of training camp. The 49ers put in four practices Friday through Monday, and then had Tuesday off. It was good timing as injuries were starting to pile up, particularly in the secondary. The 49ers get back to practice today at 10:15 a.m. PT.

Practice will be followed by defensive coordinator Robert Saleh’s first press conference of training camp, and then player availability. The team will continue live-streaming the first part of practice through one-on-one drills. Once the Facebook Live stream is available, I’ll add it up top (expected around 10:35 a.m.). They will also stream the press conferences on their website, and you can also watch it on Periscope and the team’s app.

The 49ers beat writers will also have updates from the media sessions. I’ve embedded our beat writer Twitter list so you can follow along. Jennifer Chan and 49ers Fan Girl Tracy Sandler will be at the facility. They will also provide some analysis of things on Facebook Live after the practice and press conferences. You’ll be able to check it out on the NN Facebook page.


Product Description

The stereo rhythm delay has 16 taps, each with its own resonant 2nd-order filter and granular pitch shifter. The filters have independently adjustable resonance and cutoff frequency, and can be set to either lowpass, bandpass, highpass or bypass operation. The filters are also capable of self-oscillation, enabling modal synthesis techniques. The granular pitch shifters have a range of 15 semitones up or 16 semitones down. Each tap can repeat at a rate value set in tap#’s in order to create regenerating echo effects. The feedback signal has a lowpass/highpass tone control and its own pitch shifter. The tap delay times can range from 0.1 millisecond up to a maximum of 20 seconds. The overall delay times can be set with the rotary encoder, the tap tempo button, or synced to an external clock.

The stereo comb resonator section has 64 time-delayed taps which are summed together. This produces a deep comb filter or resonating effect. At large comb sizes (long time delays) a multiple echo effect is produced. The density of the resonator can be varied from 2 to 64 taps. The tap with the longest delay time is fed back to the input. High feedback levels, with short comb sizes, produce “Karplus-Strong” types of plucked string sounds. The relative timing of the 64 taps are set according to one of 16 different preset patterns, which provide different resonator timbres or echo rhythms. The comb size can be set either with the rotary encoder, an external clock or through a transposable 1v/oct input voltage.

The Rainmaker module has extensive voltage control capabilities. Direct voltage control is available for the delay feedback level and tone, the global pitch shift for all delay taps, the comb resonator size, and comb feedback level. There are two assignable modulation inputs, which can be assigned to different parameters in each preset. A trigger input with assignable functionality can trigger built-in types of noise bursts, freeze the delay buffer, reverse the delay, randomize parameters, mute taps, and much more.

96KHz, 24-bit (32-bit internal) signal processing

Most parameters are controllable by external control voltages, either directly or through the two auxiliary modulation inputs (MODA, MODB)


From an early age we learn to live in a world with numbers: on classroom doors, street signs, price tags, our phones, on our work activities. Typically we learn how numbers and quantities relate to each other (e.g., smaller, larger) from an early age; and most of us received some formal training in math, starting from grade school. Grade school training in mathematics coincides with protracted development of the pre-frontal cortex (e.g., [@bib0245]). The pre-frontal cortex is a key brain region, concordant across mathematical-cognition studies in healthy adults ([@bib0035], for meta-analyses). Much progress has been made in understanding brain correlates of mathematical cognition; however, despite the increase in the studies examining children's mathematical problem solving (i.e., quantity discrimination and mathematical operations), a neuropsychological model for children is still not available. We have compiled data from functional magnetic resonance imaging (fMRI) studies and report concordant findings on brain correlates of typically developing children when solving math tasks with and without formal calculations (i.e., operations).

Behavioral protocols with children can be designed using math tasks such as printing or naming numbers, counting and sorting objects ([@bib0005], [@bib0360]). Neuroimaging tasks, however, are largely restricted to the visual domain, because they need to adhere to constraints/limitations of the imaging methodology ([@bib0330], [@bib0025], [@bib0030]). For fMRI studies, task protocols must be as time limited as possible. For instance, stimulus presentation should be brief, a few seconds; longer intervals are harder to control for irrelevant intruding processes (e.g., mind wandering). Manual responses are preferable to verbal responses, being less likely to produce head motion that compromise image quality. Moreover, calculation tasks are typically simple, often 1- or 2- digit operations, so that participants can provide a response within a limited time frame. Most fMRI studies that examine brain correlates of mathematical cognition, either in children or adults, follow these basic task characteristics.

The majority of fMRI studies in the literature investigated mathematical cognition in adults (e.g., [@bib0405], [@bib0230]), and the parietal lobes received the most attention in early studies of mental arithmetic. Indeed, parietal brain regions, such as bilateral intraparietal sulci, left angular gyrus, and bilateral superior parietal cortices, play distinct roles in number processing ([@bib0170]). Although the parietal cortex is fundamental to process mathematical problems, other regions are involved as well ([@bib0015], [@bib0280], [@bib0645], [@bib0650]). Coordinate-based meta-analyses of 53 adult fMRI studies show areas, such as cingulate gyri, insula and the prefrontal cortex that are concordantly active in tasks of numbers and calculation ([@bib0035]).

*Number tasks* are those that contain numbers (e.g., viewing different digits) and quantities (e.g., viewing small or large arrays of dots), but require no formal calculation (e.g., multiplication). They have in common some sort of semantic judgement on numbers or quantities based on stipulated rules. *Calculation tasks* require as well operation rules, such as addition, subtraction and multiplication, applied to numbers. Distinct and common brain areas are active in adults during number and calculation tasks ([@bib0035]). Specifically, they elicit brain responses within regions such as inferior parietal lobule and inferior frontal cortex; however, calculations also recruit prefrontal areas, particularly in middle and superior frontal gyri ([@bib0035]).

The first fMRI study with children was published 16 years ago, by [@bib0220], testing children with or without a velocardiofacial syndrome, on an arithmetic task. fMRI studies of children working on mathematical problems are gradually increasing. As in adult studies, tasks administered to children divide into those investigating numerical processes (e.g., [@bib0015], [@bib0010], [@bib0085], [@bib0320]) and those studying mathematical operations (e.g., [@bib0060], [@bib0140], [@bib0190], [@bib0410]).

Studies that examine numerical processes typically ask children to select the larger number in a set of numbers (e.g., [@bib0015]). The numbers in the set can differ by either small differences/distance (i.e., 1, 2 and 3) or large (i.e., 5, 6, and 7) ones. When the difference is small, children show activity in the superior parietal lobe, medial and inferior frontal gyri, the insula, and subcortical regions − mostly in the right hemisphere ([@bib0015]). A subsequent study by the same researchers shows that when number differences are large, children activate the left hemisphere's dorsolateral prefrontal cortex, inferior frontal gyrus, and intraparietal sulcus ([@bib0010]). Other studies compared brain responses to numbers versus responses to shapes ([@bib0085]); or examined numerical processes versus a control task ([@bib0295]).

Procedural differences in mathematical operations often lead to activity in different cortical regions ([@bib0305], [@bib0545]). Studies examining various operations in the same children are important: [@bib0545] examined brain responses to subtraction and multiplication, and [@bib0305] examined three mathematical operations, addition, subtraction and multiplication. They found several common brain regions associated with them all. In the prefrontal cortex, for instance, addition and subtraction recruit the left middle frontal cortex, whereas multiplication recruits left middle and inferior frontal cortices ([@bib0305]); further, unlike addition and multiplication, subtraction elicited activity in the right intraparietal sulcus. More fMRI studies are needed that examine multiple mathematical operations in the same children.

A meta-analysis by [@bib0275] reports concordance across seven fMRI studies, which tested children with either number or calculation tasks, in right inferior and middle frontal gyri, left superior frontal gyrus and left middle occipital gyrus. This meta-analysis supports the view that prefrontal regions play an important role in mathematical cognition ([@bib0560], [@bib0020], [@bib0035]). [@bib0275] did not detect extensive involvement of parietal cortex, which is critical in mathematical cognition, possibly because of variability in the original studies' methodology and the low number of foci. Another meta-analysis examined 19 fMRI studies that included children ([@bib0300]). Perhaps due to the low number of studies, the authors ([@bib0300]) chose to include studies with fixed effects analyses (e.g. [@bib0290], [@bib0105]), coordinates from contrasts with variable performance (i.e., interaction of brain activity of high and low performers, [@bib0335]), and variable age (i.e., coordinates resulting from a conjunction analysis between children and adults, [@bib0265]); they also included age ranges spanning over participants older than 18 years (i.e., 8.53--19.03 years, [@bib0560]). Although such approach increases the number of studies and coordinates in the meta-analyses, it obscures the reliability of results.

Targeted meta-analyses were recently performed to identify brain correlates of number processing and notation (i.e., symbolic vs non-symbolic) in adults ([@bib0595]) and children ([@bib0310]). These studies suggest a network of parietal and frontal areas that underlie symbolic and non-symbolic processes. Based mainly on adult data theoretical models of mathematical cognition (e.g., [@bib0160], [@bib0035]) may not be suitable for accounting for developmental data ([@bib0030]). Also, it is challenging to identify developmental theories of cognition that make clear neural predictions on mathematical processes. We used a domain general cognitive theory of development for hypotheses building. The Theory of Constructive Operators ([@bib0505], [@bib0470], [@bib0030]) outlines brain correlates associated with schemes and operation types to predict performance. The theory of constructive operators would predict that brain responses to number and calculations tasks are not material-driven, but process-driven and vary with the trade-off between participants' mental-attentional capacity and the mental demand of the task. Specifically, this trade-off predicts that the right hemisphere is involved in processing of automatized schemes, whereas the left hemisphere is involved in processing problems that involve the child's mental-attentional capacity, and are not automatized yet (details on this account is given in the discussion). Thus, we anticipated that number tasks should favour right frontal and parietal regions, whereas calculation tasks within the child's mental-attentional capacity will recruit additional frontal and parietal regions in the left hemisphere. In the current meta-analyses we explore brain areas involved in mathematical cognition of children younger than 14 years, and provide normative fMRI atlases in standard stereotaxic space for number and calculation tasks.

1. Methods {#sec0005}
==========

1.1. Literature search and article selection criteria {#sec0010}
-----------------------------------------------------

The literature was searched, in June 2017, by means of web-of-science (<http://www.isiknowledge.com>), using the terms fMRI and arithmetic and children; fMRI and calculations and children; fMRI and math and children; and fMRI and numerical and children. We have also added five papers using manual search. [Fig. 1](#fig0005){ref-type="fig"} shows the number of articles from this search, and the process we followed to identify eligible articles. Specifically, after eliminating duplicates, the articles were subjected to a series of selection criteria. For inclusion articles needed to: (a) be written in English, (b) have used fMRI and tasks involving numbers and mathematical operations; (c) have healthy children participants as the main or control group; (d) have reported whole-brain, within-group results using random-effects analysis; (e) have reported stereotaxic coordinates in Talairach or Montreal Neurological Institute (MNI) coordinates. Forty-three articles survived these criteria. To maintain data independence, we eliminated articles that reported contrast with analyses involving other age groups (e.g., conjunction between children and adults) and/or other tasks (e.g., conjunction between working memory and arithmetic problem solving). We also eliminated articles that included participants over 18 years in the children group (i.e., age range 8.53--19.03 years. [@bib0560]; age range 7.7--21 years, [@bib0315]; mean age 17 years 11.5 months, [@bib0550]), and one article for including the same experiments (i.e., contrasts) using same participants in different publications ([@bib0395], [@bib0400]). These controls resulted in 32 acceptable articles testing children 14 years or younger.Fig. 1PRISMA flowchart for identification and eligibility of articles (template by [@bib0415]). n = number of papers.Fig. 1

Contrast coordinates were selected based on whether the experimental paradigm was related either to number tasks or calculation tasks. *Number tasks* were defined as those involved in numerical processing without having prescribed operations (e.g., number-distance effects, numerical comparisons). In the number tasks meta-analyses we included data from 18 independent subject-groups that included 30 experiments (i.e., contrasts) using symbolic (i.e., Arabic) stimuli ([@bib0015], [@bib0065], [@bib0085], [@bib0180], [@bib0295], [@bib0345]), non-symbolic (e.g., dots) stimuli ([@bib0010], [@bib0070], [@bib0075], [@bib0260], [@bib0350], [@bib0365], [@bib0400], [@bib0635]), or a combination of both ([@bib0225], [@bib0445]). *Calculation tasks* were defined as tasks that involved prescribed mathematical operations − addition, subtraction or multiplication. Included among calculation tasks were 42 experiments from 18 independent subject groups that used symbolic stimuli, with three exceptions: one paper using non-symbolic stimuli ([@bib0060]) and two others having a combination of symbolic and non-symbolic stimuli ([@bib0340], [@bib0535]). Insufficient experiments (less than 17--20 experiments; [@bib0215]) did not allow us to examine operations separately. Similarly, due to insufficient data of symbolic and non-symbolic experiments in children under 14 years, we could not investigate the hypothesis of whether numbers have abstract or multiple representations (e.g., [@bib0120]). Three experimenters (MA, MPL and MS) performed contrast selection separately, and final decisions were taken in agreement. [Table 1](#tbl0005){ref-type="table"} shows article information, participant demographics, and contrast selection for each category.Table 1Descriptive information of articles and contrasts used in the meta-analyses.Table 1AuthorYearSample (N)FHandAgeNumber tasksFociAnsari200512n/rn/r10.4 (9.2--11.11)Distance effect (small \> large)8Ansari200693R10.4 (9.11--11.11)Distance effect (small \> large)3Berteletti20142011n/r11.5 SD = 1.7 (8.6--13.1)Numerosity Task1Berteletti20153922n/r11:4, SD = 1:6 (8:5--13:7)Numerical Processing Large vs Small4Bugden20121714R105.5 m SD 6.09 m (95--116 m)Distance effect ratio5Cantlon200685n/r4.75 (4.25--4.95)Number \> shape7Demir-Lira20163320n/r10.9 ± 1.5 (8--13.8)Spatial localizer (dot arrays) at Time 14Emerson201224n/rn/r8.24 SD = 2.26 (4.32--11.86)Number network5Gullick2013166R10y;8 m (9;11--11;9)Fifth graders: Positive \> Negative numbers11Fifth graders: Negative \> Positive numbers1Fifth graders: Positive distance effect9Fifth graders: Negative distance effect19Fifth graders: Positive \> Negative distance effect5Fifth graders: Negative \> Positive distance effect14155R12y;8 m (11;9--13.5)Seventh graders: Negative \> Positive numbers1Seventh graders: Positive distance effect12Seventh graders: Negative distance effect9Seventh graders: Positive \> Negative distance effect9Seventh graders: Negative \> Positive distance effect5Kaufmann2008124R8.6 SD = 1.2Nonsymbolic numerical processing2Kersey20173522n/r5.45 (3.6--6.99)Conjunction:Adaptation and numerical deviant effect5Kucian2011157n/r10.6 SD 1.5Non-Symbolic Numerical distance effect14Kucian201116n/rn/r9.5 SD 1.1Order vs. control task11Libertus2009157n/r8.8 (8y 11 days--9y 1 mon)Digits \> (Letters and Faces)3Meintjes201018n/rR(8--12)PJ \> control task17Park20142112R5.55 (4.82--6.59)All Number Tasks16Numerical \> Nonnumerical7Symbol \> Dots9Close \> Far2Vogel20151913n/r10.2 SD 2.55 (6--14)Parametric modulation for number adaptation5AuthorYearSample (N)FHandAgeCalculation TasksfociAshkenazi20121711n/r8.12 SD = 6.59monsComplex \> Simple Addition23Bertelletti20153922n/r11:4, SD = 1:6 (8:5--13:7)Subtraction large vs small6[@bib0095]20162514R8.8 SD 0.7(7.7--10.7)Subtraction − Control14Davis20092714n/r8.1 (7.1--9.4)Single-digit exact calculation5Double-digit exact calculation13Single Digit Approximation18Double Digit Approximation8Davis20092412n/r8.2 (8y1m--9y1m)Exact calculation task2Approximate calculation task.7[@bib0150]20111812R11.77 (10.08--12.92)Small \> large11Large \> small8Addition \> subtraction1Subtraction \> addition15Du201319n/rn/r10.62 SD 0.31 (10.12--11.26)AP addition v EX addition (non trained group)17[@bib0285]2015158n/r(7.5--9.6)Arithmetic problem solving before tutoring3Kawashima200484R11.6 SD = 1.6 (9--14)Addition6Subtraction6Multiplication6Krinzinger2011209n/r108 SD = 21 m (69--105 m)Symbolic exact addition12Non-symbolic exact addition12Meintjes2010b18n/rR(8--12)Exact addition \> control task25Metcalf20137440R7.8 (7--9)Complex \> Control6[@bib0420]20112413R9.6 SD = 0.9 (7.8--11.4)16n/rR3-Operand (Simple) Addition or Subtraction216n/rR4-Operand (Complex) Addition or Subtraction38n/rR3-Operand (Simple) Addition or Subtraction58n/rR4-Operand (Complex) Addition or Subtraction6Peters201622105L10.73 SD = 0.87 (9--12)Subtraction: Digits − Fixation6Subtraction: Words − Fixation10Subtraction: Dots − Fixation8Subtraction: Words − Digits2Subtraction: Dots − Digits10Subtraction: Dots − Words10Subtraction: Digits − Dots6Subtraction: Words − Dots5Prado20143421n/r11.54 (8.47--13.56)Smaller subtraction2Smaller subtraction \> smaller multiplication3Larger \> smaller problems2Larger \> smaller multiplication,4Smaller \> larger (multiplication and subtraction combined).4[@bib0555]20142813n/r(7--11)Addition \> Control collapsing across Time-1 and Time-217[@bib0565]20119039R2nd graders 7.67 SD = 0.4; 3rd graders 8.67 SD = 0.4Addition: Complex \> Simple5Addition: Simple \> Complex5[^1]

1.2. Meta-analyses {#sec0015}
------------------

Activation Likelihood Estimate (ALE) is a coordinate-based meta-analytic method ([@bib0615], [@bib0205], [@bib0210]). Foci from experiments (i.e., contrast coordinates) from selected articles are used to generate 3D maps indicating the likelihood of activation within a given voxel of a template brain ([@bib0205]). Data are compared against a random spatial distribution (i.e., noise) to identify the likelihood of significant clusters. A whole-brain statistical map of ALE scores is generated that estimate the probability of the significance of a brain region being active during a given cognitive function.

ALE meta-analyses were computed using GingerALE 2.3.6 (<http://brainmap.org/ale/>). First, MNI coordinates were transformed into Talairach space using the best-fit MNI-to-Talairach transformation ([@bib0355]). Each meta-analysis (i.e., number and calculation tasks) contains foci from all possible relevant experiments as the analyses algorithm minimizes within-group effects and provides increase power ([@bib0620], [@bib0215]). We report brain areas that survived an uncorrected voxel-level threshold of p \< 0.001 with a cluster-level correction at p \< 0.05 for multiple comparisons ([@bib0210], [@bib0215]). Contrast analyses are performed on images corrected for multiple comparisons with cluster level p = 0.05 and uncorrected p = 0.001, thus the threshold for contrast number tasks vs calculation tasks is set to p = 0.01 uncorrected, with 5000 permutations and minimum volume 50 mm^3^ (e.g., [@bib0595]). For displaying results from ALE maps we employed AFNI ([@bib0125]).

For comparison we performed meta-analyses of fMRI data that examine number and calculation tasks with adults (methods and result presented in supplementary material).

2. Results {#sec0020}
==========

Mean age and/or age range of children in the meta-analyses for all experiments are shown on [Table 1](#tbl0005){ref-type="table"}. The average age of children included in the meta-analyses was calculated to be 9.34 ± 2.18 years for number tasks and 9.58 ± 1.4 years for calculation tasks. Of the articles reporting gender, 44% and 52% of participants were female for number and calculation tasks, respectively. About 75% of the all articles reported handedness and tested right-handed participants with the exception of five left-handed participants in the sample for calculation tasks.

2.1. ALE maps {#sec0025}
-------------

### 2.1.1. Number tasks {#sec0030}

In children, number tasks are associated with significant ALE values in the inferior parietal lobule Brodmann area (BA 40) extending to parts of the inferior parietal sulcus, claustrum and insula (BA 13) in the right hemisphere ([Table 2](#tbl0010){ref-type="table"}, [Fig. 2](#fig0010){ref-type="fig"}).Table 2Concordant areas for processing number and calculation tasks in children.Table 2Volume mm^3^ALE ValuexyzBrain regionChildren: Number tasks117360.01938−4854Right Inferior Parietal Lobule BA 400.01838−4642Right Inferior Parietal Lobule BA 400.01234−3448Right Postcentral Gyrus BA 3210720.01830188Right Claustrum0.01536160Right Insula BA 13

Children: Calculation tasks142960.02901050Left Superior Frontal Gyrus BA 60.02422044Right Medial Frontal Gyrus BA 80.02041042Right Cingulate Gyrus BA 32231280.04432186Right Insula BA 13328480.030−30−6238Left Precuneus BA 190.021−42−4842Left Inferior Parietal Lobule BA 40[a](#tblfn0005){ref-type="table-fn"}0.018−28−7242Left Precuneus BA 19413680.034−30166Left Claustrum512400.026−46436Left Precentral Gyrus BA 6[b](#tblfn0010){ref-type="table-fn"}69200.0222−7048Right Precuneus BA 7

Children: Number tasks \> Calculation tasks

No suprathreshold clusters

Children: Calculation tasks \> Number tasks19843.5402840Right Cingulate Gyrus BA 323.156−31344.5Left Medial Frontal Gyrus BA 63.03621246Right Medial Frontal Gyrus BA 625282.9483−7348Right Precuneus BA 731602.489322410Right Insula BA 1341042.512−30126Left Claustrum2.447−32166Left Insula BA 13

Children: Calculation tasks AND Number tasks19680.01830188Right Claustrum0.01536160Right Insula BA 13[^2][^3][^4]Fig. 23D rendered ALE activation maps superimposed on an anatomical brain. All regions survived cluster level correction p = 0.05 for multiple comparison control at an uncorrected p = 0.001. All coordinates are listed in [Table 2](#tbl0010){ref-type="table"}.Fig. 2

### 2.1.2. Calculation tasks {#sec0035}

When solving mathematical operations children activate a diverse set of areas ([Table 2](#tbl0010){ref-type="table"}; [Fig. 2](#fig0010){ref-type="fig"}). The largest cluster is centered on the left superior frontal gyrus (BA 6) and extends to the right medial frontal gyrus (BA 8) and cingulate gyrus (BA 32). The second largest cluster with the highest likelihood for being detected is found in the right insula (BA 13). Concordance in parietal areas is observed bilaterally; left precuneus (BA 19) and inferior parietal lobule (BA 40) including parts of the inferior parietal sulcus and angular gyrus BA 39, and right precuneus BA 7. The left claustrum has the second highest likelihood of being detected. A cluster centered on the left precentral gyrus (BA 6) encompasses also grey matter in the inferior and middle frontal gyri (BA 9).

### 2.1.3. Contrast: number tasks vs calculation tasks {#sec0040}

In children, compared to calculation tasks, number tasks show no suprathresholed clusters. Compared to number tasks, calculation tasks show increase concordance in the right cingulate gyrus (BA 32), bilateral medial frontal gyrus (BA 6), right precuneus (BA 7), bilateral insula and left claustrum, ([Table 2](#tbl0010){ref-type="table"}). Areas that show concordance in conjunction between number and calculation tasks are the claustrum and insula (BA 13) in the right hemisphere ([Table 2](#tbl0010){ref-type="table"}).

3. Discussion {#sec0045}
=============

Brain activity related to mathematical cognition in typically developing children was examined using coordinate-based ALE meta-analyses. Children activate a varied set of areas in established parietal and frontal regions when solving problems with numbers and operations. We highlight the role of insula and claustrum in mathematical operations because these regions have not been previously emphasized in this sort of cognition. To facilitate discussion, we organized findings by cortical regions, and for each we indicate characteristics of activity (e.g., its extent or magnitude) and the categories (i.e., number and calculation tasks), which elicit the activity. We build on previous models ([@bib0160], [@bib0035]) to offer a topographical model and a theoretical interpretation of mathematical cognition in children.

Number and calculation tasks elicit responses in the parietal lobes in children. This is a critical region linked to mathematical problem solving ([@bib0170]). Specifically, the first and most concordant cluster for number tasks is in the right inferior parietal lobule (BA 40), where complex (concrete or abstract) effort-demanding (non-automatized) objects are found (e.g., [@bib0515], [@bib0470]). The left inferior parietal lobule (BA 40) is activated during calculation tasks − sites for "active" complex objects that can receive transformations/operations (e.g., [@bib0470]). The critical role of the inferior parietal lobule in mathematical cognition has been discussed extensively in adults ([@bib0165], [@bib0170], for reviews) and children ([@bib0085], [@bib0145]). The current findings are consistent with previous reports; and highlight a hemispheric dominance for number tasks in the right hemisphere, in contrast to calculation tasks driven by the left hemisphere.

When children solve mathematical operations, areas with the highest likelihood of being detected appear in the right insular cortex for calculation tasks. The left claustrum also shows one of the highest values of significant concordance for calculation tasks. The insula and claustrum are also concordant in a smaller cluster for number tasks. The insula is a structure that connects the temporal and frontal lobes deep within the lateral fissure; the claustrum borders the insula medially. Initially, the insula became known for its involvement in affective processes (for meta-analyses, see [@bib0255], [@bib0195]). Its role in the interaction of cognition, emotion and interoception has also been discussed ([@bib0630]). Such interpretations of insular role are consistent with the hypothesis that, together with the anterior cingulate, insula may be involved in intrinsically motivated behaviours ([@bib0600], [@bib0625], [@bib0495], [@bib0030]). The insula, together with subcordical structures such as the basal ganglia, have been implicated in studies that involve learning and training ([@bib0100]). The role of the claustrum is often bundled with the functions of the insula; however, this is a distinct region anatomically ([@bib0385]), and in terms of structural connectivity ([@bib0440]). Recent reviews suggest the claustrum to function as a cross-modal integrator to create conscious percepts ([@bib0130], [@bib0250], for reviews). Thus, we propose that for children solving mathematical operations, high activation in the insula may express their *intrinsic motivation* in the task, with reference to learning and training. For the claustrum, we propose that it helps integrate motivated top-down and bottom-up processes. Importantly, neither the insula nor the claustrum have been implicated in models of mathematical cognition, although the current findings suggest that these areas are critical.

Another critical region for calculation tasks is the cingulate gyrus (BA 32), which extends medially from superior frontal (BA 6) and medial frontal (BA 8) gyri. The cingulate gyrus is also a key region for mental-attention and working memory, since it may be where affective intentions convert into cognitive goals ([@bib0045], [@bib0495]). Significant clusters of concordant activity in the dorsal cingulate gyrus (BA 32) are observed for calculation tasks in children. The cingulate gyri have been related to various higher order cognitive activities. Progressively more evidence highlights its role as a coordinator of activity in multiple attentional systems ([@bib0540]), multimodal functions ([@bib0580]), and as coordinating activity based on task complexity ([@bib0610]). We suggest that in children cingulate gyri integrate affective motives and available information to generate solutions involving specific cognitive goals.

Calculation tasks also elicit concordance in a cluster in the left precentral gyrus (BA 6) that extends to inferior and middle frontal gyri (BA 9). The middle frontal gyri, often referred to as dorsolateral prefrontal cortex, are associated with executive-driven mental attention and working memory ([@bib0045], [@bib0110], [@bib0115]). Compared to inferior frontal cortices, which are associated with simpler cognitive actions, the dorsolateral prefrontal cortex implication in coordinated cognitive control of complex processes is important, and it was identified as such early on ([@bib0575], [@bib0110], [@bib0115]). Studies of mathematical cognition do associate activity in middle frontal gyri with procedural complexity and working memory processes ([@bib0175], [@bib0230], [@bib0325], [@bib0590], [@bib0650]). This hypothesis is supported by adult data, which show that middle frontal gyri are implicated in calculation tasks but not in number tasks ([@bib0035]). We have replicated the latter finding with adult meta-analyses of number and calculation tasks (Table S2).

3.1. Topographical model of mental-arithmetic in children {#sec0050}
---------------------------------------------------------

There is a close agreement on brain locations implicated in children and adults during mathematical problem solving (Table S2--S3; [@bib0035]), and predictions by the triple-code model ([Fig. 3](#fig0015){ref-type="fig"}). The only unaccounted brain region by either the triple-code model ([@bib0160]) and the adult meta-analyses is the claustrum, which was concordant for children. A critical difference we observe is that children implicate the right insula (BA 13) more extensively than adults in calculation tasks, whereas adults implicate more prefrontal areas (BA 44, 46, 10; Table S3). We provide a theoretical framework that may explain these findings in the next section.Fig. 3Mapping results on children meta-analyses (in red), on triple-code model (green), and adult meta-analyses (orange). We illustrate in green the schematized cortical locations of the triple-code model proposed by [@bib0155], [@bib0160]: (1) Inferior parietal cortex: quantity representation, (2) Temporal cortex: visual-computational number symbols, (3) Articulatory loop, (4) Verbal system, (5) Basal ganglia: arithmetic facts, (6) Thalamus: arithmetic facts, and (7) Prefrontal cortex: strategy choice and planning. In orange are additional schematic locations of areas concordant among adult studies, as demonstrated by meta-analyses ([@bib0035]): (a) Superior frontal BA 10: formulates complex goals, sub-goal creation, (b) Middle frontal BA 46: in more or less misleading situations it monitors more than a few items, (c) Inferior frontal BA 9: monitor simple rules or a few items, (d) Precentral gyrus: eye movements, (e) Insula: interoceptive motivation of goal-directed and default-mode processes, (f) Cingulate gyrus: converts affective goals into cognitive goals to be implemented, (g) Right angular gyrus: visual-spatial fact retrieval (i.e., spatial-temporal schemes with non-verbalizable configural relations), and (h) Cerebellum: goal directed, visual motor sequencing. Sub-cortical regions specific to meta-analyses of number or calculation tasks were not depicted. Here we added the (i) right basal ganglia: coordination of top-down and bottom-up operative/motor processes. In red are schematic locations of areas concordant among children studies, as demonstrated by the current meta-analyses. (j) Claustrum: integration of motivated top-down and bottom-up processes.Fig. 3

3.2. Developmental theory: the theory of constructive operators (TCO) {#sec0055}
---------------------------------------------------------------------

The Theory of Constructive Operators (TCO) is a general, constructivist theory of development and learning ([@bib0515], [@bib0520], [@bib0530], [@bib0450], [@bib0470], [@bib0475], [@bib0480], [@bib0490]). It provides a mathematical model for predicting developmental stages ([@bib0505], [@bib0485], [@bib0495], [@bib0040]). The TCO is framed in terms of organismic *operators* (content-free brain utilities, i.e., regulations or general controls), *schemes* (contextual information carriers − cell assemblies or networks), and, finally, organizing *principles* ([@bib0500], [@bib0505], [@bib0515], [@bib0455], [@bib0470], [@bib0475]; [Fig. 3](#fig0015){ref-type="fig"}). Operators are general-purpose and content free resources, which can apply on schemes in any domain. [Table 3](#tbl0015){ref-type="table"} provides a short description and basic brain location of operators in the TCO. [Fig. 4](#fig0020){ref-type="fig"} suggests how operators interact with schemes to elicit performance. Psychologically, schemes are self-propelling information-bearing units (cell assemblies or networks), which can be classified into three groups: executive, operative (action blue-prints), and figurative (object or feature representations). Thus, schemes carry information that can appear under three distinct categories: figurative, operative, and executive (Pascual-Pascual-Leone and Baillargeon, 1994; [Fig. 3](#fig0015){ref-type="fig"}): (a) *figurative schemes* are perceptions and representations of concepts or objects (e.g., in the case of numbers: symbolic 3 or ★★★); (b) *operative schemes* correspond to actions and specific procedures, applicable to objects or concepts (e.g., in the case of math, the act of multiplying 3 × 3); and (c) *executive schemes,* a subdivision of operative schemes, that carry general contextualized procedures (e.g., in the case of math, to plan the order of steps for solving 3 + 3/(3 × 3)). The currently activated and dominant set of compatible executive schemes can function as an operator (which we call *E* for executive). *E* can regulate or control the current functioning of other operators (e.g., *M-* or *I-*operator; [Table 3](#tbl0015){ref-type="table"}) as they apply to modify schemes in their here-and-now dynamism (e.g., [@bib0455], [@bib0470], [@bib0475]).Table 3Description of Operators and their Corresponding Brain Regions in their Likely Evolutionary Order (after [@bib0470]; [@bib0470]).Table 3OperatorDescriptionBrain Region*A*Set of **affective** processes that intervene in motivation and attentive arousal.Limbic Lobes*C*Both the process of **content** learning and the schemes derived from associative content.Primary & secondary association areas*F*The **field** operator together with the Schemes' Overdetermination Principle (*SOP*, see below) act as the brain\'s *binding mechanism* bringing closure to mental representations in a neo-Gestaltist manner.All Areas*LC*The process of automatized **logical-structural** learning derived from **content** learning through over-practice.Right Hemisphere*T***Temporarily** and effortlessly collates *sequences* of schemes, thus facilitating coordination of temporally-structured invariants that constitute distal objects.Occipito-temporal*S*Facilitates emergence of **spatial** schemes by coordinating *relations of coexistence* among activated schemes effortlessly within the situation.Occipito-parietalBSocial '**Being**' Schemes − related to personal, self/other referential schemes.Default-mode areas*I*The attentional **interrupt**: It produces the central *active inhibition* of unwanted schemes, which were activated by the situation or the mind.Prefrontal*M*Effortful **mental-**attentional *activation* of simple or complex (functional-structure) schemes.Prefrontal*LM***Logical-structural** learning caused by effortful use of **mental**-attentional capacity.Left hemisphere tertiary areas*E***Executive,** i.e., dominant *set of activated and compatible executive schemes* in the person's repertoire that are useful for the task at hand.PrefrontalFig. 4A simplified illustration of the Theory of Constructive Operators (TCO): Operators (in green; operator definitions are listed in [Table 3](#tbl0015){ref-type="table"}), schemes (in blue), and the principle of schematic over-determination of performance − or SOP (in red).Fig. 4

A principle of Schematic Overdetermination of Performance (*SOP −* [@bib0455], [@bib0460], [@bib0470], [@bib0475], expresses both the spreading of activation in the brain, and its *final common path* of neuronal resolution into a performance − which is a generalized version of Sherrington's original idea ([@bib0585], [@bib0390]). This *SOP* determines which set of compatible and dominant schemes eventually will apply to generate an outcome. Mutually incompatible schemes compete, and those more strongly activated eventually apply.

3.3. Integrating neuroimaging data and the TCO {#sec0060}
----------------------------------------------

Our findings have important theoretical and practical implications. Practically they provide coordinates in stereotaxic space that future studies may benefit from. Theoretically, they offer support for developmental theories that follow process-specific rather than content-specific approaches ([@bib0505], [@bib0515], [@bib0475], [@bib0480], [@bib0430]). To clarify this issue we distinguish processes across four main dimensions of variation: (a) operative versus figurative processing, (b) complexity levels of processing, (c) left- versus right-hemisphere processing, and (d) motivation and self-control.

### 3.3.1. Operative versus figurative {#sec0065}

We suggest that operative processes and executive schemes are expressed in frontal and prefrontal areas, respectively, whereas representations and figurative/object schemes are expressed in parietal, temporal and occipital areas ([@bib0515], [@bib0470]). This is in agreement with early neurologists who viewed the brain as separating distinct, different modes of processing (e.g., [@bib0370]). It is important to explain why more operative processes and executive schemes are needed when solving calculations, expressed by additional brain activity in frontal and prefrontal areas. A clear distinction between the operative processes implicated in calculation tasks versus number tasks is found in adults (Table S2--S3), consistent with past meta-analyses ([@bib0035]). Operative processes are also important in children's math tasks, as expressed by the involvement of prefrontal and sub-lobar regions, including insular cortex, during these tasks.

### 3.3.2. Complexity levels in processing {#sec0070}

Association areas may in some sense express level of *processing* required by a task. Consider that both *operative* (motor, efferent associative) and *figurative* (sensorial, afferent associative) schemes can be organized in context-sensitive *heterarchical* ways (i.e., functionally-flexible hierarchies). These heterarchies were first described by classic neuropsychologists (e.g., [@bib0375], [@bib0200]) as functionally nested sequences of *primary* areas (afferent or efferent − e.g., BA 4, and BA 1, 2, 3 and BA 17), *secondary* areas (coordination of local unimodal processes − e.g., BA 6, and BA 5, and BA 18), *tertiary* areas (coordination of *regional multimodal* areas − e.g., BA 8 and BA 7, BA 19), and finally *general multimodal* areas, also called *high tertiary or quaternary* (which are functionally generic, applicable across domains, and integrating the totality of regional multimodal processing − e.g., BA 9, BA 47, BA 46, BA 10, BA 39, BA 40). Distinguishing levels of processing complexity of a task is key for predicting and interpreting mathematical performance. In our data we find, for instance, that number tasks in children implicate secondary and tertiary association areas, whereas calculation tasks elicit activity in more tertiary areas.

### 3.3.3. Left hemisphere versus right hemisphere processing {#sec0075}

Traditionally semantic-pragmatic differences between left and right hemispheres, have been verbal-analytical versus visuospatial-global processing, respectively ([@bib0240]). However, this is no longer tenable as a main distinction between left versus right hemispheres − because we find that both hemispheres activate with both content types (i.e., verbal and visual-spatial). Within a constructivist-developmental viewpoint, we claim that propensities fostered by left hemisphere are best characterized as *analytical mental-attentional processing* (or novel, effortful working-memory) − often in *demanding* problem-solving situations; whereas right hemisphere fosters propensities of *overlearned or automatized processing* that engage in either very easy or very difficult tasks ([@bib0050], [@bib0515], [@bib0475]). [Table 4](#tbl0020){ref-type="table"} shows circumstances that give rise to right or left hemisphere dominance by considering the novelty of task, mental-demand (*Md*) of the task and mental-attentional capacity (*Mc*) of the individual ([@bib0450], [@bib0475]). A trade-off between *Md/Mc* can be used to predict hemispheric dominance such that when *Md \<\< Mc* processing would favour the right hemisphere, when *Md *≤ *Mc* processing would favour the left hemisphere and when *Md \>\> Mc* processing would favour the right hemisphere. This distinction is novel with respect to the function of right hemisphere, which would be implicated in two different sorts of instances: (a) In more or less automatized (very easy) processes, or (b) when the needed mental-attentional resources are above and beyond what the individual has available (i.e., above his/her mental-attentional capacity; [@bib0510]). In the latter case, when a task's mental attentional demand is too high for the left hemisphere to cope with alone, the right hemisphere is mobilized in search of potentially useful overlearned or automatized schemes. For instance, in the current data ([Table 2](#tbl0010){ref-type="table"}) we see that the largest cluster elicited in children when solving number tasks is in the right parietal cortex (since figurative schemes for number tasks are easier). In contrast, calculation tasks elicit activity mainly in the left parietal cortex (calculation involves harder − relational − figurative schemes to be coordinated using the child's mental attentional capacity). Similarly, activity for the simple number tasks is observed in the right hemisphere, whereas calculation tasks implicate the left frontal cortex more extensively. It would be interesting for future studies to examine brain correlates of incorrect trials, to identify whether right hemisphere is a main contributor to attempts to solve trials that are too difficult for the children to solve.Table 4Model of Right-Left-Right hemispheric dominance.Table 4Mc/Md trade offFamiliarity/NoveltyHemisphere − strategyFactors of transformation − HemisphereMd \<\< McHigh Familiarity**Right** − associative heuristic to cope fast with easy taskwith experience remains familiar −processing at RightMd ≤ McNovel**Left** − mental attentional heuristics for problem solvingwith experience becomes familiar, overlearned − transfer to RightMd \>\> McHigh novelty**Right** − associative heuristic to cope with a too-difficulty taskwith maturation Mc increases, and with experience task becomes less novel-- transfer to Left[^5]

### 3.3.4. Motivation and self-control {#sec0080}

Motivation is the process whereby *affective* tendencies are expressed in conscious or unconscious affective goals (motives); which are then converted into conscious or unconscious cognitive goals ([@bib0465], [@bib0030], [@bib0055]). *Affective goals* are dispositions to pursue desirable vital/life outcomes or consequences, whereas *cognitive goals* are explicit intentions to do what is believed congruent with one's affective goals. Motivation crosses borders between affective and cognitive goals, and is critical for successfully doing difficult mental tasks. We propose that these affectively-driven *endogenous* cognitive goals rely critically on the insular cortex.

The insula is a structure that connects temporal and frontal lobes deep within the lateral (Sylvian) sulcus; it is active in many diverse task situations, including cognition, interoception, perception and emotion ([@bib0090], [@bib0630], for meta analyses). It is also implicated in cognitive processes such as inhibitory control ([@bib0080]), and speech and language processing ([@bib0435]). In a study examining math anxiety, data showed that anticipating an upcoming math-task elicited increased activity in the posterior insular cortex ([@bib0380]). Together with sub-cortical structures like the basal ganglia, the insula is involved in learning and training tasks ([@bib0100], [@bib0605], [@bib0235]). It has been shown to participate in the affective − aversive − feeling of difficult effort ([@bib0135], [@bib0425], [@bib0640]). The insula integrates interoceptive affect feedback and is related to the orbitofrontal cortex − involved in exteroceptive feedback ([@bib0270]). Regarding interoceptive feedback, Damasio writes: "... relative to the visceral and internal milieu, the insula is the equivalent of the primary visual or auditory cortices" ([@bib0135]). The insula is proposed to have a generic role in problem solving ([@bib0035]), and mathematics may serve as a domain for targeted testing of the understanding of interceptive feeling of effort when comparing children and adults.

Self-control is related to motivation, in the sense that a motivated action requires self-control to be carried out, particularly in complex situations. When task problem-solving appears complicated and misleading, self-consciousness may become particularly necessary (e.g., [@bib0135], [@bib0525]). The operative aspect of this self (i.e., the functional organization that William James would call I-self) is possibly in medial prefrontal cortex and anterior cingulate cortex; this is the operative aspect of self-control found in the *medial frontal cortex* ([@bib0050], [@bib0570], [@bib0495]). The figurative/representational self-processes (the me-self of William James) would be located (see [@bib0135]), in the *posteromedial cortex*, i.e., the combination of posterior cingulate cortex, retrosplenial cortex, and precuneus.

The active coordination between motivation and self-control would elicit activation in the anterior cingulate and insular cortices. Both anterior cingulate and insular cortices are prevalent clusters in children's mathematical problem-solving. In adults, the cingulate and insula cortices activate bilaterally in number and calculation tasks (Table S2). Importantly, these activations in adults are listed further down in the ALE-obtained coordinate list (Table S2 cluster 3 and 4 for number tasks and cluster 5 and 7 for calculation tasks), possibly because adults' prior practice has already transformed into cognitive goals and cognitive circuits/networks the original affective goals (motives) *and* interoceptive feelings of effort, within the math domain. In contrast, the cingulate gyrus and the insula occupy in children respectively the first and second larger clusters of activity for calculation tasks; moreover the insula has the highest likelihood of being detected during calculations in children. In our view this finding is important, suggesting that intrinsic (interoceptive) motivation and self-control in children are critical to perform difficult calculations − but much less so during number tasks, which are more automatized.

3.4. Limitations and considerations for future studies {#sec0085}
------------------------------------------------------

The current meta-analyses focus on functional correlates of brain regions in children relating to number and calculation tasks. We report data representing concordance across these types of task. There are limitations associated with the meta-analysis method, and with choices we had to make, due to the methodology in the original studies. Shortcomings of the ALE method have been previously discussed (e.g., [@bib0185], [@bib0050]). However, ALE meta-analysis remains superior to traditional review approaches, because it permits investigating over-arching patterns of activity across studies. An advantage is the automated computational steps that permit quantification of locations of common activation across studies. In terms of article selection, we chose a conservative approach; we eliminated many articles that appeared to use same participants (see method section), and articles that used children in large age ranges. Eliminating these studies may have lowered strength of concordance detected, but the conclusions drawn from such conservative approach are likely to be more representative of brain responses in school age children, which was our main concern. Further, because of a lack of studies, we have not specifically examined brain responses to mathematical operations separately (i.e., only addition, only multiplication). Methodological choices in some studies lacked developmental theoretical grounding; for instance, since we know from behavioural research that performance on mathematical abilities improves with age, studies should specify a theoretical reason for averaging data over large age ranges. Similar behavioural performance on a task does not imply that the same set of brain regions support the particular performance across age groups ([@bib0030]). For future studies, in addition to demographics such as gender and age range of participants (missing in some articles), it is critical to report within-group contrast coordinates for healthy children − even when this is not the main focus of the paper. Such practices would be beneficial for future meta-analyses on the topic.

4. Conclusion {#sec0090}
=============

These meta-analyses investigate brain activity in children that underlies processing of number and calculation tasks. These are the first meta-analyses in children younger than 14 years distinguishing between number and calculation tasks. Based on these results we sketched a neuropsychological developmental model of mathematical cognition in stereotaxic-space. We find that mathematical performance in children emerges from known core-regions associated with number processing, such as parietal and frontal areas; but it also emerges from regions not previously recognized in a mental-arithmetic network, such as the insula, the claustrum, and the cingulate gyrus. The insula, in particular, may play a critical role in children's mathematical calculation, because children need strong intrinsic motivation and affective goals to cause their effort in attention and complex processing. Future behavioural and neuroimaging work on children's mathematical cognition should benefit from a refined topographical atlas of mathematical processes in healthy children.

Conflict of interest {#sec0095}
====================

None.

Appendix A. Supplementary data {#sec0105}
==============================

The following is Supplementary data to this article:

Support is gratefully acknowledged from the Natural Sciences and Engineering Research Council of Canada to M.A. and J.P-L, and from the Russian Science Foundation (\# 17-18-01047) to M.A.

Supplementary data associated with this article can be found, in the online version, at [http://dx.doi.org/10.1016/j.dcn.2017.08.002](10.1016/j.dcn.2017.08.002){#intr0015}.

[^1]: N = number of participants; F = females; Hand = handedness of participants; n/r = not reported; R = right handed; L = left handed Age = mean age, SD = standard deviation, and or age range in parenthesis.

[^2]: Note: Single-study clusters survived a voxel-level threshold of uncorrected p \< 0.001 with a cluster-level threshold for multiple comparisons at p \< 0.05 ([@bib0215]). Contrast threshold was set to p = 0.01, 5000 permutations, \>50 mm^3^. Coordinates (x, y, z) are reported in Talairach convention; L, Left; R, Right; BA, Brodmann area; ALE, Activation likelihood estimate.

[^3]: Encompasses gray matter within angular gyrus BA 39.

[^4]: Encompasses gray matter in the inferior and middle frontal gyri (BA 9).

[^5]: *Note:* Mc = mental-attentional capacity of the individual; Md = mental-attentional demand of the task.


By BosNewsLife Asia Service

KATHMANDU,NEPAL (BosNewsLife)– Native Christian missionaries in Nepal say they will continue evangelism despite unrest over a newly constitution that enshrined a long-time ban on proselytizing.

The new constitution signed by Nepal’s president on September 20 outlaws “any act to convert another person from one religion to another or any act or behavior to undermine or jeopardize the religion of another,” with violations punishable by prison and/or fines.

The ban fails to allow choosing one’s faith to be seen as a matter of individual rights as required by international treaties that Nepal has signed and ratified, said advocacy group Christian Solidarity Worldwide (CSW).

Yet, Christians were relieved that framers ultimately did away with a reported concession to Hindu groups to ban all religious conversions, said Christian Aid Mission (CAM), which supports native missionaries.

The government instead approved a constitution defining the state as secular and neutral toward all religions, “over rancorous objections and violence that included attacks on three church buildings”, CAM told BosNewsLife.

PART OF TRANSITION

Nepal’s new constitution is part of the transition the country began in 2008 from the world’s only Hindu monarchy to a secular, multiparty, constitutional republic. Hindu groups opposed these developments and a mission leader based in Nepal warned of a possible backlash.

“There could be some reaction,” he said in remarks obtained by BosNewsLife, “though not so much in the majority-Nepalese-speaking areas. But in other areas where there’s not been much gospel work, there could be some reaction. We’ll just see how things go.”

Ethnic demands, along with violence that killed more than 40 people, overshadowed the religious concerns in the constitutional framing process, he said.

Despite the turmoil, Christianity has flourished in recent years, the mission leader added. Originating during a harsher period in the 1980s and ’90s, he said his “ministry” learned from the outset how to “proclaim [Jesus] Christ” is Lord in discreet ways.

Bible correspondence courses were primary.

PEN-PALS FOR LOVE

“It started with my mom copying down 100 names and addresses from the back of a magazine of youths looking for pen-pals – basically, they were looking for love,” said the ministry director, whose name is withheld for security reasons. “My mom started writing letters to them, and she would say, ‘If any of your friends would like to read my letters or would like to read more about

Jesus, then please send me their addresses.'”

People sent addresses of their friends and relatives, and the current director’s parents officially began the ministry in 1992. When students began completing the courses two years later, however, those who had earned certificates sent them back to the ministry leaders.

“Some were torn to pieces,” the director recalled. “They didn’t accept the certificates. Basically, the problem was they wanted more of the Gospel. That was their way of protesting.”

Some of the students showed up at the ministry office demanding more knowledge, while others waited two days at its Post Office box to accost the leaders and request more instruction, he said.

NEW FAITH DISCOVERED

“It was new; so many had never even heard the name of Christ before,” the director added.

“They wanted to know who these people were that were sending all these gospel materials. Except for one person, all of them were college graduates. Some were in the police and military, some were judges and professors. That’s when the ministry of discipleship training started. We realized it’s about time we start a face-to-face.”

For the first on-site training session, the ministry sent out 50 invitations; 265 people showed up.

“We thought 10 might show up,” the director explained. “It was a seven-day class, and at end of the training, more than 100 took baptism.”

Since then, more than 500,000 students from Nepal’s 75 districts have enrolled in the Bible correspondence courses, with 50,000 completing them, he said. The Bible correspondence courses are tailored to reach Hindus (75 percent of the population), Buddhists (16 percent), animists and communists, CAM told BosNewsLife. After taking the Bible courses, more than 10,000 students have participated in the “ministry’s discipleship training process”, according to missionaries.

75-DAY LEADERSHIP TRAINING

“With a philosophy of ministry geared toward quality rather than quantity, the ministry then provides a 75-day leadership training for potential Gospel workers, as well as pastoral training,” said CAM which supports the project.

Taking place at different church halls every three months, the pastoral instruction involves nine, week-long sessions over two years. “The ministry is training two groups of 25 pastors each, at a cost of $15,000 to $20,000 per year for each group,” the mission director said.

“When there was persecution, professing the name of Christ was very costly, but since the government became secular there’s freedom,” he explained. However freedom has also brought “a lot of false teaching coming in, so it’s imperative that they know what true teaching is,” the director said.

“With Jehovah’s Witnesses, Mormons, prosperity gospel and others coming in, that problem is increasing, and the people that they are shepherding need to get fed the truth of the Scripture.”

As unreached peoples are a high priority, he said his “ministry’s strategy is to reach out first to those who know both Nepalese and a tribal language.” These new, bilingual Christians would then reach their own people in their own local language.

MORE TRAINING NEEDED

“Ideally we would like to be training 100 people – four groups,” he said. “But we need teachers able to do that, so we’re working to get them, and of course we need finances to get that going. The teachers are paid, and we use local pastors. We do have pastors with just Biblical degrees, but ideally we’re looking for guys who have a master’s of divinity and are pastoring local believers.”

Five years ago the Operation World handbook estimated the Christian population of Nepal at 2.85 percent, or 850,801 people, most of them evangelicals; that figure is now estimated at about 1 million.

With 309 unreached people groups, Operation World estimated 55 percent of Nepal’s people had never heard the Gospel.

After a period of reported persecution in the 1990s and more freedom since a transition to a secular democracy began in 2008, “the ministry that has quietly proclaimed Christ as Lord claims to have planted hundreds of churches,” CAM said. It is currently helping 66 churches, 13 less developed fellowships and 14 incipient cell groups.

“The harvest is plentiful, the workers are few,” the director added. “We need the people capable of taking the work forward.”

(With writing by Amie Cotton. BosNewsLife’s MISSION WATCH is a regular look at missionaries working in the two-thirds world and other difficult areas and related developments).

Why people are ready to suffer for Christ? Want to know more what they believe? Click here

(BosNewsLife (2004-2015) is the first truly independent news agency covering persecuted Christians. It has been ‘Breaking the News for Compassionate Professionals’ since May 2004. Please help BosNewsLife to remain a voice of the voiceless. Click here for a subscription).

Visit BosNewsLife’s Link Partners:

http://www.thejerusalemgiftshop.com/ Visit them at these strategic times and support believers in Israel.

http://www.religiousfreedomcoalition.org Raising its voice for religious freedom. Every day.

http://christmasforrefugees.org/ A real Christmas for Syrian and Iraqi children refugees.


Articles Posted in the " Kids Films " Category

Dance theatre is not a dirty word. Even if you normally find the idea of a film that contains almost nothing but dance to be the purest form of hell, the sheer quality of the dancing on display in Pina – and the immaculate way in which it’s presented – make it difficult to resist.

The muscle babes of Die Hard and G-Unit come in Setup, a painfully predictable action thriller. With plenty of guns, a bald mob boss or two, a handful of heavily clichéd Russians, a thirst for vengeance and the odd sexy lying bitch, there really isn’t anything original to this diamond heist gone wrong. Except for Fiddy Cent in a role where he takes no prisoners. Oh wait…

Kingdom of Gladiators tells the story of a king who makes a pact with a demon, and then has a gladiator tournament, and these two things are somehow connected. Steer clear if you can, this tits-and-hits action film offers a very poor show of both the former and the latter.

What is it with horribly titled British films lately? First Swinging With the Finkels, now this? C’mon people, just name your film after a dinosaur, there’s loads of them left. It’d be much easier to sell a dark comedy set in a Scottish brothel (apparently they’re legal up there, fancy that!) if it was called Tyrannosaurus Sex. Just sayin. Luckily though, Spanking in Paradise is infinitely better than its dreadful title, a witty, surprisingly restrained and thoroughly Scottish gem.

When China Met Africa is a indepth documentary about China’s economic foray into Africa in 2006. Described as a “rare, grass-roots view into one of the most important economic challenges of our age” by The Times, it’s just as exciting as it sounds.

Natalie Portman’s shiny new Academy Award is going to reflect an awful lot of spotlights on her over the next few years. This should be fine, of course, but it’s not ideal when the project currently in the post-Oscar glare is a distinctly average drama which she made before Black Swan and which has only just crept onto DVD. The only impossibility in Love and Other Impossible Pursuits is enjoying it.

“Blood Out” – Mexican Mafia Motto: To blood out is a requirement to leave a gang. To join, “blood in”, you must kill someone. The only way to leave, “blood out”, is to be killed yourself.
For a film that opened with crack, tits and hip-hop Blood Out was more than a little disappointing.

The fly on the wall documentary American Harmony delves into the obsessive world of modern barbershop quartets; where fast-food managers moonlight as minor celebrities and grown men serenade french fries (well, it is America).

Upon its release in 1972, On Any Sunday, a low-budget documentary about the lives of motorcycle racers, immediately generated interest (perhaps largely due to the participation of Steve McQueen) and was nominated for the Academy Award for Best Documentary. Now due to be re-released on DVD on the 12th of September, it’s our job to see how the documentary stands up against the test of time. And also Steve McQueen is in it. Did we mention Steve McQueen is in it?


Contrary to many assertions, defeating the self-proclaimed Islamic State may not be particularly complex in the parts of Iraq, Syria, Libya and other countries where the group has sought to hold terrain. If local armed forces get organized and receive air support, they will defeat the Islamic State on the battlefield, and in doing so they will tarnish the group’s reputation for success and limit its recruitment potential. But there is one thing standing in the way of this victory: the lack of unity and motivation of its opponents.

In the absence of a major international ground force deployment, the pace of the war will continue to be driven by local actors — meaning the fight will run on their timeline rather than ours. While defeating the Islamic State may be Washington’s top concern, it is not the over-riding priority of most local actors arrayed against the group on the ground. The reality is that there is no cohesive team of allies fighting against the Islamic State and this war is only one of a number of wars being fought or prepared for across the region. In many cases actors are fighting the Islamic State purely to better position themselves for these other conflicts.

The United States and the international coalition fighting the group in the Middle East has sought to keep the war against the Islamic State as simple as possible by stressing points of agreement between allies and avoiding the issue of nation-building. Coalition military planners has have been told to avoid considering the broader regional impact and interconnections of the war and have instead been blinkered. They are like carthorses only allowed to see the road ahead.

Useful as this goal might be for building a coalition of outsiders, the local players in Iraq, Syria, and other theaters where the Islamic State is flourishing (such as Libya, Afghanistan, and Yemen) have always allowed themselves to take in the full strategic landscape of the region. All of our allies and rivals have far more complex goals than degrading and defeating the Islamic State. For them, the current battle is really a game of positioning for the truly decisive action that will begin as soon as the Islamic State is defeated.

The first priority of most actors is consolidating their control on the ground. The Kurds in Syria and Iraq are staking out their long-term territorial claims. Iranian-backed groups like Badr are carving out principalities in Iraqi areas like Diyala and Tuz Khurmatu. Abu Mahdi al-Muhadis, the most senior Iranian proxy in Iraq and a U.S.-designated terrorist involved in the deaths of U.S. and British troops, is seeking to quickly build the Popular Mobilization Units (PMU) into a new permanent institution akin to a ministry, complete with budgets and infrastructure, in order to stave off the risk of demobilization after the Islamic State is gone. His ambition is no less than to grow a new parallel army equivalent to and subservient to Iran’s Revolutionary Guard Corps, boosting Iran’s efforts to take over Iraq’s political and religious leadership.

The non-coalition players are also strengthening their relations with new external supporters. The Assad regime in Syria is integrating with the Russian military machine. Armed Syrian Kurdish groups are learning how to work with the U.S.-led coalition while keeping options open with Assad and the Russians. Syrian Sunni groups are tightening military ties to Saudi Arabia, Qatar, and Turkey. Iranian-backed groups in Iraq continue to deepen their ties with Russia and Iran. The Iraqi Kurds are welcoming the international coalition’s presence in the Kurdish region and recently hosted new Turkish ground forces, spurring fierce rhetorical pushback from Baghdad.

Local actors’ preparations for the next war — or, likely, wars — helps explain the slow progress of the battle against the Islamic State so far. Iraqi Kurdish leaders are open about the coming clash with Shia militias and other Baghdad-backed forces along the disputed boundary with federal Iraq. The Baghdad Operations Command continues to hold around half of the offensive-capable Iraqi military units in reserve in the capital despite the declining risk of an Islamic State attack on Baghdad. Why? To offset the risk posed by the Shia militias. The Kurds in Syria are readying for a future war against Turkey to preserve their de facto statelet along the Turkish–Syrian border. All these actors will use the weapons provided or captured during today’s war against the Islamic State to fight tomorrow’s wars against each other.

This is hardly unprecedented. When Stalin halted his forces outside Warsaw in August 1944 and let the Nazis kill off the Polish resistance he was thinking forward to the next war: the occupation of Eastern Europe and the Cold War. On the other side, Nazi leaders continually hoped that a separate peace might be made with some Allied nations until the very end of the war. And as the war was ending both sides, West and East, were scrambling for terrain, technology and control of key local leaders with the coming Cold War in mind.

The Cold War analogy may be appropriate to today’s situation in the Middle East. Perhaps the clearest trend in recent years has been the tidying up of the sides in a potential Shia–Sunni conflict and the threading together of multiple conflicts into a single tapestry.

On one side is the “Axis of Resistance” — actors like Iran, Lebanese Hezbollah and Iranian proxies in Iraq like Badr, Asaib Ahl al-Haq and Abu Mahdi al-Muhandis’ Kataib Hezbollah. Russia has seemingly bet on that camp. This axis is aligned against the Sunni world writ large and determined to exclude the U.S. military from the region. Fighting the Islamic State is a means to an end for these groups: cover for operations against other Sunni groups and a means to build legitimacy in domestic politics. Defeating the Islamic State is not strictly necessary as long as the group stays in Sunni areas.

In the other corner is a less cohesive but strengthening alliance that comprises Saudi Arabia, Turkey, Qatar, and the UAE. This group is focused on fighting the Shia Axis of Resistance first and foremost, and, in Turkey’s case, constraining the growth of Kurdish power in Syria and Turkey. This Sunni alliance views the Islamic State and other jihadist elements as a less serious threat than the Shia, and even views the Salafi militants as a tool in some circumstances, such as in Yemen where al-Qaeda in the Arabian Peninsula fights a parallel war against the Iranian-backed Houthis and alongside the Saudi and UAE-led Arab alliance.

Where is the U.S.-led coalition in all of this? It is the odd man out, unaligned with — and determined not to see — the increasingly tidy line-up of local sectarian alliances. Though Washington may seek to play the role of the balancer between these camps, the U.S. government is faced with impossible choices between traditional Sunni allies and the up-and-coming Shia actors who are critical players in the war against the Islamic State.

How can Washington seek to earn and keep the trust of Shia in Iraq while at the same time working closely with traditional allies like Turkey and Saudi Arabia, the former which seems to be working to break up Iraq and support Salafi extremists in Syria, and the latter which is seen as indiscriminately bombing Shia Zaydi civilians in Yemen through a brutal air campaign, and suppressing Shia communities across the Gulf?

Alternately, can Washington maintain access and influence within the broader Sunni Arab world if it begins to cooperate, tacitly or explicitly, with Iranian and Russian actors in Syria, when these states are keeping the Assad regime in power and when Iranian-backed militias are ghettoizing Sunni populations in Iraq and trying to undermine the elected government?

Under such circumstances Washington’s focus on the Islamic State is somewhat understandable: The group presents a real threat to the U.S. homeland and citizens abroad, and it is the one issue on which all parties can rhetorically agree on and the least thorny of the region’s challenges. But the Islamic State will not last forever as a fig leaf to cover the yawning cracks that are opening up in the regional order. The United States needs to start preparing to deter or fight the next wars now because our opponents are already starting to act. This means clearly defining Washington’s long-term interests and fighting the war against the Islamic State in a manner that supports these long-term objectives.

In Syria, Washington’s baseline strategic interest is in the survival of a Syrian state that has basic legitimacy and which is no longer a safe haven for terrorist groups and an exporter of refugees. This can only be achieved through resolution to the civil war that is acceptable to all regional actors while denying a clear-cut win to any of them. The first step to negotiating such an outcome may be to stabilize the military balance in Syria in a manner that prevents the Assad regime, the Islamic State, and al-Qaeda from overrunning the Sunni opposition in northwestern Syria. At the same time, the United States should build its influence over the Syrian–Kurdish PYD, help the Syrian Democratic Forces liberate Raqqa, and defeat the Islamic State in eastern Syria. Against a backdrop of mounting military pressure, the Axis of Resistance actors may be more willing to negotiate Assad’s departure. The United States might complement this effort with a parallel campaign to compel the Gulf states to support a sustainable ceasefire and peace deal in the Yemeni civil war.

In Iraq, the solution is arguably simpler, and Washington is already broadly on the right track. The United States needs to continue providing the strongest backing for Prime Minister Haider al-Abadi, a moderate and a reformer, and for the Iraqi Army, still the most respected national institution in Iraq and the key potential balancer to the Shia militias. Washington must not abandon Baghdad and attempt short-cut solutions that undermine Abadi and play into the hands of the militias, such as directly arming the Iraqi Sunnis and Kurds. Washington can still help Iraq to put in place a strong military chief of staff under which command is increasingly unified. The United States can support Iraq in its quest to receive IMF and World Bank funding to weather the storm of its economic crisis. And the U.S. military can ramp up its airstrikes to visibly help Abadi to deliver further battlefield victories in Ramadi and Mosul.

If fighting the Islamic State were the only U.S. objective and interest in the Middle East then the current strategy could just about suffice. But we need to be mindful of the many wars within the war against the Islamic State that are already beginning. The regional states are keeping their eye on the ball: They have a balanced view of the geostrategic long game, not just the near-term issue of the Islamic State. To have any hope of deterring, limiting, or winning the coming conflicts in the Middle East, the United States needs to widen its view beyond the Islamic State in the manner that regional states are already doing.

Michael Knights is the Lafer Fellow at the Washington Institute for Near East Policy. He has worked in every Iraqi province and most of the hundred districts, including periods spent embedded with the Iraqi Security Forces, the Peshmerga, and most recently with Combined Joint Task Force Operation Inherent Resolve.


Please ensure the proper authorization for release of information is attached to the request.

Additional Types of Verifications

Additional types of verification requests or requests for other individuals associated with EVMS (i.e. medical students, allied health professionals, or faculty) should be directed to the following individuals:


mon divisor of c and 36.
18
Suppose -22 = 2*l - 4*l. What is the greatest common factor of l and 275?
11
Suppose -4*z + 15 = -25. Suppose -8 = -2*f + z. What is the highest common divisor of 1 and f?
1
Suppose -4*b + 5*q - 61 = 0, 2*b = -3*b + q - 50. Let g be 12*(b/2)/(-3). Let f = -8 + g. Calculate the highest common factor of f and 40.
10
Suppose 2*g - 47 = -2*k - 9, 5*k = -2*g + 80. Calculate the highest common divisor of k and 98.
14
Suppose -364 = -11*z + 285. What is the greatest common factor of z and 118?
59
Let y = 7 + 2. Suppose c = 5*j - 215, -4*j = 2*c + 84 - 242. Let r = j - -57. Calculate the highest common factor of y and r.
9
Let n be -5*(0 - 56/20). Calculate the greatest common factor of 35 and n.
7
Suppose a = -2 + 8. Suppose -2*t + 54 - a = 0. What is the greatest common divisor of 16 and t?
8
Let s be -1 - 4/((-4)/3). Suppose -99 = n - s*n. What is the greatest common divisor of n and 9?
9
Let u be (17/(-2))/((-6)/(-24)). Suppose -w = 2*w + 210. Let m = u - w. Calculate the greatest common factor of m and 12.
12
Let y = -21 - -45. Let s = y + -20. Let h = -43 + 79. What is the greatest common divisor of h and s?
4
Let r = -4 - -6. Suppose 0*u + 48 = r*u - 3*g, 3*g - 102 = -3*u. Calculate the greatest common factor of u and 20.
10
Let d = 356 + 20. Let z be 6 - ((3 - 2) + 0). Suppose 64 + d = z*g. Calculate the highest common divisor of g and 11.
11
Let l = -10 + 23. Let v = -5 + l. Let c be v/(2 + 16/(-10)). Calculate the greatest common factor of c and 8.
4
Suppose 0*a = 4*a. Let u(c) be the first derivative of -c**2/2 + 85*c + 4. Let s be u(a). What is the greatest common factor of s and 34?
17
Let i be ((-216)/(-28))/(27/252). Let c be 4/(2 - (-32)/(-18)). Calculate the greatest common divisor of i and c.
18
Let z be ((-18)/(-8))/((-4)/240*-3). Calculate the greatest common factor of 9 and z.
9
Let i = 50 + -21. Suppose l - i - 71 = 0. Calculate the greatest common divisor of 20 and l.
20
Suppose 2*d + r = -1, 5*d - 4*r + 32 = d. Let x be 0 + (d - (0 - 1)). Let w be ((-12)/x)/(1/7). Calculate the highest common factor of 63 and w.
21
Suppose -5*o + 162 + 88 = 0. Calculate the highest common divisor of 10 and o.
10
Let r = 0 - -11. Suppose -2*l - 5*q = 11, q + q = 2*l + 18. Let g = r + l. Calculate the highest common divisor of g and 27.
3
Suppose 7*c - 180 = 3*c. What is the greatest common divisor of c and 5?
5
Let p(m) = m**2 - 14*m + 11. Let i be p(14). What is the greatest common divisor of 33 and i?
11
Let u(o) = -2*o + 6. Let k be u(-6). Let l = 2 - 0. Suppose 4*q - 3*q = l. Calculate the highest common factor of k and q.
2
Let p = 9 + -6. Suppose -2*g - 4 = 0, -4*x - 5*g = -p*g - 68. Suppose -3*u + 37 = -t, -4*u + 3 = 3*t - 42. What is the highest common divisor of x and u?
6
Let c be 12/9 + 2/(-6). Let z(b) = 4*b**2 + 1. Let s be z(c). Let m = 14 + 21. Calculate the greatest common divisor of s and m.
5
Let x be 2*104 + (-2)/1. Let t = -129 + x. Let h = 30 + -23. What is the highest common divisor of h and t?
7
Let a be (-4)/(-2) + 1/(-1). What is the highest common factor of 7 and a?
1
Suppose 4*p = -g - 114, -5*g + 3*p = -0*g + 593. Let z = -48 - g. Let q be 2/(-3) - (-44)/3. Calculate the highest common factor of z and q.
14
Let d be ((-2 - 0) + -38)*-1. Calculate the greatest common factor of d and 30.
10
Let p be 0 - (1 - (-153)/1). Let i = 346 + p. Suppose 7*m - i = 3*m. Calculate the greatest common divisor of 12 and m.
12
Let r(j) = -j**3 - 3*j**2 - 3*j + 5. Let y be r(-4). What is the greatest common divisor of 11 and y?
11
Let u = 0 - 1. Let x(s) = 97*s + 2. Let r(y) = -49*y - 1. Let i(n) = 5*r(n) + 2*x(n). Let p be i(u). Calculate the highest common divisor of 20 and p.
10
Let z(q) = q**3 + 3*q**2 - 3*q - 2. Let x = 4 + -7. Let h be z(x). Let g = -10 - -17. Calculate the greatest common divisor of g and h.
7
Let j(u) = u**3 - 8*u**2 + 3*u - 9. Let x be j(8). Calculate the highest common divisor of 90 and x.
15
Let o be (-4*6 - 4)*-2. What is the highest common divisor of 7 and o?
7
Suppose 0 = -3*f + 6*n - n + 48, 2*n = 3*f - 57. What is the highest common factor of 7 and f?
7
Let g(j) = j**2 - 6*j + 8. Let h be g(11). Suppose -2*p = -3*p + h. Calculate the highest common factor of p and 7.
7
Let m be 3/(6/16*2). Let n(l) = l + 4. Let u be n(m). Let r be 41 - (3 - (0 + 2)). Calculate the highest common divisor of r and u.
8
Suppose 4*d - 2*w = 0, 3*w + 2 = 5*d + w. Let y be (-225)/(-10) + d/(-4). Calculate the greatest common factor of y and 110.
22
Let z be (648/3)/6 + 0 + 2. What is the highest common divisor of 266 and z?
38
Let n = -5 + 8. What is the greatest common divisor of 12 and n?
3
Suppose 19 = 4*m - 45. Let d(y) = -y**2 + 9*y - 4. Let i be d(4). Calculate the highest common factor of m and i.
16
Let q be ((-10)/(-8))/((-3)/(-12)). Suppose f - q*f + 52 = 0. Calculate the greatest common divisor of f and 52.
13
Suppose -4*m = -m - 63. Calculate the greatest common factor of 168 and m.
21
Let y = 282 + -166. What is the highest common factor of 29 and y?
29
Let b(o) = -2*o. Let j be b(3). Let w be ((-56)/10)/(j/15). Let h be (0 - 0/(-1)) + 7. What is the highest common divisor of w and h?
7
Let g(q) = q**3 - 7*q**2 + 8*q - 2. Let x be g(6). Let m be (-1)/(-2) + (-18)/(-4). What is the greatest common divisor of x and m?
5
Suppose -4*v - 2*g = -582, v - 56 = -5*g + 67. Let z = 648 - 432. Let d = z - v. Calculate the greatest common factor of d and 17.
17
Suppose 6*t = 2*t + 204. Let i(f) = -f**2 + 10*f + 8. Let u(s) = s**2 - s + 9. Let l be u(0). Let r be i(l). Calculate the greatest common factor of t and r.
17
Let x be (-5 + 53)/(6/8). Calculate the highest common factor of 8 and x.
8
Suppose -u - m + 43 = 0, 0 = 4*m - 4. Let s = 61 - u. Suppose w - 2*p - 68 = -7, -2*p = -3*w + 175. Calculate the highest common factor of s and w.
19
Suppose 0 = -6*c + c + 495. What is the greatest common divisor of c and 11?
11
Suppose -3*o + 36 = l, 2*o + 172 = 5*l - l. What is the highest common factor of 14 and l?
14
Suppose -p + 3 = -4*r + r, -5*r = -4*p + 33. Calculate the highest common factor of 108 and p.
12
Suppose -3*t + 2*t = -73. Let r = -52 + t. Calculate the greatest common factor of 42 and r.
21
Suppose 7 = 4*b - 9. Suppose 5*q = 0, -3*c + 5*q = -4*c + 32. What is the greatest common factor of c and b?
4
Let p be 6/(-15) - 72/(-5). What is the highest common divisor of 7 and p?
7
Suppose -2*t - 5*b + 64 = 0, -125 = -4*t - t + 5*b. Calculate the highest common divisor of 18 and t.
9
Suppose 3*o + 26 = 23. Let k be 1*(2 - 1 - -14). Let f = o + k. What is the highest common factor of f and 35?
7
Let n(m) = -m + 3. Let q be n(-6). Calculate the highest common divisor of 18 and q.
9
Suppose 0 = -3*z - 3, -j - 4*z = -34 - 17. What is the highest common divisor of j and 10?
5
Let x be 6/15 - (-8)/5. Let j(f) = -f + 2*f**2 + 0 - f**x + 4*f - 3. Let b be j(2). What is the highest common factor of b and 63?
7
Let l(s) = s**3 - 6*s**2 + 13. Let z be l(6). Suppose 16*y - z*y - 378 = 0. Suppose 14 = g - 0*g. What is the highest common factor of g and y?
14
Let i(o) = -10*o - 16. Let s be i(-3). Calculate the highest common factor of 21 and s.
7
Let h = -1 + 3. Suppose h*z + 34 = -m, -4*z + m - 4*m = 70. Let y be (-4)/z - 31/(-4). Calculate the highest common factor of 20 and y.
4
Let n be 88 - 9/3 - -3. What is the greatest common factor of 8 and n?
8
Let w be -2 + -1 + 2 + 0. Let u = w - -3. Suppose -2 = -u*x, 4*p + 3*x - 39 = p. Calculate the highest common factor of 60 and p.
12
Let p = 3 - 3. Suppose p = -3*t - 0*t. Suppose t = q - 4 + 1. Calculate the greatest common factor of 9 and q.
3
Let p(t) = t - 3. Let u be p(5). Suppose u*k + 944 = 4*i, -206 = -i + 3*k + 40. What is the greatest common factor of 26 and i?
26
Suppose 2*v - 3 = 3. Suppose 0*r - 84 = v*r. Let y = r - -70. What is the highest common divisor of 6 and y?
6
Let l(y) = 8*y + 3. Let r be l(1). Calculate the highest common divisor of r and 11.
11
Let q = 2 + 3. Suppose q*c - 96 = c. What is the greatest common factor of c and 36?
12
Let c be 126/24 + 2/(-8). Suppose 3*q = 80 - c. Suppose -64 + q = -3*t. What is the highest common divisor of t and 13?
13
Let l = -48 + 70. Suppose -2*u + 9 = u. Suppose -2*s + u*s = 55. What is the


Hyundai Motorsport star Thierry Neuville thanked his home fans with a peerless drive to victory at Ypres Rally Masters last week.

The Belgian dominated the Friday-Saturday event, which ran alongside the main Ypres Rally, winning every stage.

The i20 Coupe WRC driver finished 8m39s ahead of second-placed Paul Liataer’s Ford Escort Cosworth. Had Neuville been driving in the main Ypres field, he would have taken more than three minutes out of the fastest R5 cars on the 12 stages the Rally Masters crews contested.

Neuville said: “The main goal last weekend in Ypres was to have some fun and to show the Belgian spectators the World Rally Car, as most of them don’t have this opportunity.

“We have lot of support in Belgium from all over the country and this was a small gift we could give our supporters to thank them.”

As well as entertaining the thousands of fans who turned out to enjoy their local hero in the sunshine, Neuville used the event to tune himself and his i20 into asphalt trim ahead of next month’s ADAC Rallye Deutschland.

Neuville added: “We also took the opportunity to try things on the car, some settings and new things ahead of the upcoming Tarmac rallies in the World Rally Championship.

“Driving the Hyundai i20 Coupe WRC in Ypres was a joy, the sense of speed was incredible on these roads – I had a lot of fun and I hope the many spectators who were watching the stages enjoyed it as well.”

Neuville wasn’t the only Hyundai Motorsport driver competing at the weekend. Sébastien Loeb tackled the Rallye Terre du Haut-Var, making his competitive debut in an i20 R5 on the French gravel rally. Predictably, Loeb was fastest by far and only missed out on the win after dropping time with a broken ball joint on the second stage.

Hyundai’s latest recruit Craig Breen won the main Ypres Rally field, leading the field from the fourth stage until the finish in an accomplished debut aboard a Volkswagen Polo GTI R5.

VIDEO

More News


Methicillin-resistant Staphylococcus aureus (MRSA) is associated with low within-herd prevalence of intra-mammary infections in dairy cows: Genotyping of isolates.
Staphylococcus aureus is one of the most common mastitis-causing pathogens worldwide. In the last decade, livestock-associated methicillin-resistant S. aureus (LA-MRSA) infections have been described in several species, included the bovines. Hence, this paper investigates the diffusion of MRSA within Italian dairy herds; the strains were further characterized using a DNA microarray, which detects 330 different sequences, including the methicillin-resistance genes mecA and mecC and SCCmec typing. The analysis of overall patterns allows the assignment to Clonal Complexes (CC). Overall 163 S. aureus isolates, collected from quarter milk samples in 61 herds, were tested. MRSA strains were further processed using spa typing. Fifteen strains (9.2%), isolated in 9 herds (14.75%), carried mecA, but none harboured mecC. MRSA detection was significantly associated (P<0.011) with a within-herd prevalence of S. aureus intra-mammary infections (IMI) ≤5%. Ten MRSA strains were assigned to CC398, the remaining ones to CC97 (n=2), CC1 (n=2) or CC8 (n=1). In 3 herds, MRSA and MSSA co-existed: CC97-MRSA with CC398-MSSA, CC1-MRSA with CC8-MSSA and CC398-MRSA with CC126-MSSA. The results of spa typing showed an overall similar profile of the strains belonging to the same CC: t127-CC1, t1730-CC97, t899 in 8 out of 10 CC398. In the remaining 2 isolates a new spa type, t14644, was identified. The single CC8 was a t3092. The SCCmec cassettes were classified as type IV, type V or type IV/V composite. All or most strains harboured the genes encoding the β-lactamase operon and the tetracycline resistance. Streptogramin resistance gene was related to CC398. Enterotoxin and leukocidin genes were carried only by CC1, CC8 and CC97-MRSA. The persistence of MRSA clones characterized by broader host range, in epidemiologically unrelated areas and in dairy herds with low prevalence of S. aureus IMI, might enhance the risk for adaptation to human species.


Search our news, events & opinions

Retail ‘Armageddon’ and lease renewals – to let or not to let?

It is difficult not to notice the changing climate of the retail sector, with shop closures and empty high streets regularly making the news headlines. The style of lease negotiations between retail tenants and landlords is changing as a result, with tenants driving harder bargains, particularly when it comes to rent.

What’s changing and why?

It used to be the case that the lease renewal process was a predictable game. Tenants would propose certain terms for the new lease, landlords would counter-propose others, and the result of the agreed renewal lease would be a compromise somewhere between the two.

Now, each party is calling the other’s bluff much more than before. The squeeze on the retail sector means tenants cannot afford high rents or to be tied into long lease terms – however, landlords are pushing for the opposite to provide them with certainty of rental income. It is now a real prospect that if the lease terms proposed by the tenant are not accepted, that the tenant will simply close the shop and walk away altogether. No landlord wants retail units to be rented at less than market value, but an entirely vacant unit is much worse. The competing interests of landlords and tenants is nothing new – however, the high street is seeing unprecedented levels of shop closures and the impact of this is filtering into all areas of the retail sector.

With many shops feeling the effects of a squeeze on profit margins, the decision to renew a lease (regardless of terms) or consider closure is increasingly difficult for retail tenants. This can mean that tenants drag their feet during lease renewal negotiations in order to access trading levels and, particularly within shopping centres, changing levels of footfall to decide whether to close shop if it is not profitable enough – all to the frustration of the landlord.

Lease renewal claims

Negotiations can take longer than before, or even reach a stalemate. As lease renewals increasingly cannot be negotiated, settled, and completed between landlord and tenant (and their respective agents, if any), more lease renewal claims are being protected by way of issued proceedings. This is resulting in further delays while claims are listed for directions or hearings – which are subject to the Court timetable.

For either tenants or landlords approaching a lease renewal, it is clear that the negotiations will not be as formulaic as before. Retail ‘Armageddon’ is changing the balance of negotiation power between landlord and tenant, and anyone involved in lease renewals must be prepared for harder talks going forward. What is clear is that further thought is needed as to how to approach lease renewals in the retail sector, and both tenants and landlords should give consideration to their aims well in advance of any deadline.

For any queries on your lease renewal, please contact with our specialist Retail sector team on:


Q:

Why doesnt a default value affect the table in sqlalchemy?

Im trying to update my tables and add new column, to this column should be a default Integer value, I tried to use default and also default_server as recommend here: Why isn't sqlalchemy's default column value working
 but the value of this field stay null, It is possible to set Integer default value?
col = Column(field_name, data_type, default=0)
column_name = column.compile(dialect=engine.dialect)
column_type = column.type.compile(engine.dialect)
engine.execute('ALTER TABLE %s ADD COLUMN %s %s' % (table_name, column_name, column_type))

A:

The problem is that your manual ALTER TABLE statement does not add the default and leaves the column nullable; having server_default on the Column object has no effect. If you want a default, add it:
engine.execute('ALTER TABLE %s ADD COLUMN %s %s DEFAULT 0' % (table_name, column_name, column_type))

I'd highly recommend using alembic for database migrations.