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**11**

*Egypt*

**Ultrastructure Alterations in the**

**Induced By Gamma Irradiation**

*Atomic Energy Authority, Abo-Zaabal, Cairo,*

**Red Palm Weevil Antennal Sensilla**

*Biological Applications Department, Nuclear Research Center,*

Eman A. Mahmoud, Hussein F. Mohamed\* and Samira E.M. El-Naggar

Palm beetles (Coleoptera: Curculionidae) are harmful pests for cultivated palm – oil, date or coconut palms (Bedford, 1980, Giblin-Davis *et al,* 1996). The red palm weevil (RPW) *Rhynchophorous ferrugineus* (Oliv.) is the most important pest of the date palm in the Middle-East (Abraham & Pillai*,* 1998). The insect was discovered in Saudi Arabia in the mid-1980s (Gush, 1997 & Faleiro *et al*., 1999). Since then it has spread over most of the date palm areas (Bokhari & Abuzuhira 1992 & Vidyasagar *et al.*, 2000), due to the transfer of infested

It was introduced accidentally into the Gulf states from Pakistan in 1985, from whence it spread throughout the Arabian Peninsula. It was first reported in Egypt in 1992. It infested the governorates Ismailia and Sharkia, an area with an estimated one million palm trees (Salama & Abdel Aziz, 2001). lthough they have been known as major pests for a long time, efficient and acceptable methods of controlling them are still lacking in many cases. In addition, the selection of plants for oviposition is determined by the physical nature of their surface and by chemical factors which are detected after contact (Fenomore, 1980, Chadha & Roome, 1980). As such, the identification of olfactory and contact chemoreceptors in insects has received the attention of many workers (Albert,

Antennae in insect are organs of taste, smell and stimulation (Wiggles worth, 1972). Other authors (Stubbs, 1980, Nakamuta, 1985 & Obata, 1986) have suggested a possible role of sensory stimuli (olfactory, gustatory or mechanical) in prey, or mate detection. The antennae also play kinetic roles and normally keep the nervous system in a state of tone in which it responds to stimuli of all kinds. Antennae of insects vary greatly in length, overall size, size of the individual segments, segmentation, setation and other aspects with the structures being closely related to their function ( Srivastava & Omkar , 2003). Notably, some authors studied the ultrastructurally effects of gamma rays on the features of normal antennal sensilla of *R. Ferrugineus* as Oland *et al*., (1988); El-kholy & Mikhaiel (2008) and El- Akhdar &

**1. Introduction**

offshoots and palm trees.

Afia, 2009.

1980, Bland, 1984 & Salama *et al*., 1987).

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**1. Introduction**

### **1.1. Snake venom serine proteases**

Serine proteases have been isolated from the venoms of viperidae snakes [1, 2] and affect several physiological processes such as the coagulation cascade. These enzymes are called snake venom serine proteases (SVSPs), they are multi-functional proteins with a catalytic triad formed by HDS amino acids [3].

The SVSPs resembles at least in part thrombin, a multifunctional protease that plays a key role in coagulation. Therefore these enzymes are denominated snake venom thrombin-like en‐ zymes (SVTLEs), and are widely distributed in the venoms of several genera [4,5]. While thrombin is able to cleave both fibrinopeptide A (FPA) and fibrinopeptide B (FPB) from fibrino‐ gen leading the formation of fibrin and activating factor XIII, some actions of SVTLEs usually cleave FPA alone and only a few cleave FPB. Thus, without cleavage of both FPA and FPB they are unable to activate factor XIII producing fibrin monomers that are not cross-linked, leading to clots markedly susceptible to digestion by plasmin and are rapidly removed from circulation by either reticuloendothelial phagocitosis and/or normal fibrinolysis. This process causes a breakdown in the fibrinolytic system and effective removal of fibrinogen from the plasma [6].


**Author details**

Cyndy Garcia-Weyandt Kalamazoo College, Kalamazoo, Michigan, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*2.7. CNN Training in an Imbalanced-Class Scenario*

For each CNN model in our study, the number of model parameters was much larger than the available training sample size, which might cause overfitting and numerical instability. In addition, there was a high ratio of class imbalance (positive:negative = 1:20), posing another challenge to training predictive models. Different training techniques were adopted to address these problems. Batch normalization was added after each layer to stabilize gradient updates and reduce the dependence on initialization [31]. The neural network weights were initialized from the glorot uniform distribution: U(-sqrt(6/(# of input weights+# of output weights)), sqrt(6/(# of input weights+# of output weights))) or He's normal distribution in ResNet for all layers [32,33]. The parameters were estimated through the Adam optimizer [34]. The learning rate and batch size were tuned across multiple grid values (Table S2). We used the weight decay and dropout for regularization [35], and more details about our final training parameters for all main CNNs are in Table 1. Another regularization technique is the early stopping that was determined by the validation data on chromosomes 8 and 9; the training was

stopped if no improvement of the validation F1 score was observed over 10 epochs. Final evaluation on the test data used the model with the highest validation F1 score across all the training epochs before early stopping.

In order to address the problem of highly imbalanced classes in the training data, we used a weighted objective function—a binary cross entropy for this imbalanced dataset. In the training data, the weight for positive or negative pairs was given by the ratio of a half of the sample size over the number of positive or negative pairs. We also attempted to achieve a balanced training sample by augmenting the training data through oversampling positive pairs (i.e., the minority class) or down-sampling negative pairs in generating batched training data for neural networks, but the test performance was not significantly better than using a weighted objective function (with a *p* value of 0.4097 from the paired *t*-test; Table S4).


**1. Introduction**

Arteriovenous fistula (AVF) represents the gold standard vascular access (VA) for haemodialysis (HD). Over time, AVFs may develop dysfunction and lower blood flow due to a series of biological changes that can lead to the formation of a stenosis and subsequent thrombosis. This event has a severe impact on the clinical status of dialysis patients; in the best scenario, endovascular and surgical interventions can restore a satisfactory AVF flow; if not, a central venous catheter (CVC) needs to be placed for interim dialysis access.

Considering the strong negative impact of AVF failure on patient survival, morbidity and quality of life, recent guidelines focused on potential strategies for AVF preservation.

**Citation:** Peralta, R.; Garbelli, M.; Bellocchio, F.; Ponce, P.; Stuard, S.; Lodigiani, M.; Fazendeiro Matos, J.; Ribeiro, R.; Nikam, M.; Botler, M.; et al. Development and Validation of a Machine Learning Model Predicting Arteriovenous Fistula Failure in a Large Network of Dialysis Clinics. *Int. J. Environ. Res. Public Health* **2021**, *18*, 12355. https:// doi.org/10.3390/ijerph182312355

Academic Editor: Tim Hulsen

Received: 15 October 2021 Accepted: 22 November 2021 Published: 24 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The National Kidney Foundation's (NKF) (KDOQI) Guidelines [1], recommend AVF periodical physical examination (PE), or ultrasound evaluation as primary monitoring methods to detect access dysfunction. However, there is no evidence on the advantages to routine AVF surveillance by measuring intra access blood flow (Qa) [1,2] to improve access patency; nevertheless, its assessment should be considered [3,4].

The controversy concerning the best surveillance strategy to ascertain and evaluate venous stenoses has not yet been solved [5]. The rationale for surveillance is based on the hypothesis that progressive stenosis can be accurately detected by reduced Qa and increased venous pressure (VP) before VA thrombosis occurs [4,6].

Even though both Qa surveillance and ultrasound examination, coupled with preemptive correction of hemodynamically significantly reduces the risk of thrombosis and access loss [7–12], false positive tests would lead to unnecessary intervention procedures [13] which may ultimately promote further neointimal hyperplasia [14]. No current surveillance method is without pitfalls. Major concerns for Qa surveillance relate to low reproducibility in clinical practice which corresponds to a minimal detectable change as large as 25%, questionable cost-effectiveness as the sole surveillance strategy [15] and suboptimal inter-rater agreement across different measurement techniques [16]. Furthermore, the accuracy in identifying stenosis with Qa varies according to patient characteristics and location [15,17]. On the other hand, ultrasound examination requires significant operator training and skill, may not be readily available in all clinical contexts and may not yield conclusive indications for interventions [18,19]. Structured physical examination has been proposed as a convenient alternative monitoring method. The assessment of PE accuracy in detecting and locating AVF stenosis has shown mixed results; whereas few studies have shown acceptable accuracy in either the diagnosis of outflow and of inflow stenosis [20,21] compared with angiography; few others [22,23] reached opposite conclusions. In addition, a metaanalysis of randomized control trial (RCT) studies showed that blood flow measurement was superior in predicting outcomes [24–26]. Furthermore, PE is operator-dependent [27], and has limited long-term prediction power thus explaining why, in a large majority of the cases, many patients may need more frequent surveillance when assuming a rapid AVF deterioration. Taken together, the impact of PE alone on actual prevention of thrombosis is limited [28].

An excellent surveillance method should be quick, easy, accurate, non-invasive, nonoperator-dependent and cost-effective. It is clear, that none of the existing methods can fulfil such expectations alone and a one-fits-all approach is not be able to adequately capture the diversity of AVF functional trajectories between and within patients.

In principle, an automatic triage system based on routinely recorded data requiring no additional effort by healthcare professionals may be used to personalize surveillance strategies based on expected risk stratification.

To this end, we sought to develop and validate a risk model based on the machine learning methods predicting the occurrence of AVF failure within three months.

### **2. Materials and Methods**

#### *2.1. General Description of the Arteriovenous Fistula Failure Model (AVF-FM)*

The AVF Failure Model (AVF-FM) aims at predicting the occurrence of a composite AVF failure endpoint (see, Endpoint Definition below) within three months based on routinely recorded clinical information readily available in health information systems for dialysis patients.

The model is based on the XGBoost algorithm, an iterative method where, at each iteration, a new sub-model is added to correct the prediction error of the previous iteration. Each sub-model is an ensemble of decision trees. A decision tree can be roughly described as a flowchart-like structure in which each internal node represents a "discrimination test" on a given attribute (e.g., any clinical parameter or demographic characteristics); each branch of the decision tree represents the result of the discrimination test (i.e., passed

or not), and each leaf node represent the probability of the outcome. This probability represents the prevalence of events occurring in each leaf in the training set.

The iterative process ends in accordance with a pre-specified stopping rule (e.g., maximum number of iterations or minimal acceptable average prediction error). The structure of the model is computed as a function optimization process combining the minimization of both training error and model complexity.

We selected XGBoost since it is characterized by a good prediction accuracy in a broad variety of problems coupled with short computational time. Furthermore, SHapley Additive exPlanations (SHAP) analysis [29] enables intuitive model interpretation through an accurate and efficient estimation of the contribution of each input variable to the risk.

### *2.2. AVF-FM Training*

The AVF-FM was derived using the information collected in the European Clinical Database (EuCliD®, Fresenius Medical Care, Deutschland GmbH, Wendel, Germany), a large, multinational, database including in-centre dialysis patients [30].

We enrolled all HD/HDF adult patients in Italy, Spain, and Portugal with at least five treatments performed using AVF as vascular access, in the period January 2015–October 2019 and at least three months of follow-up. Furthermore, we considered only AVFs with more than three months of maturation. The unit of analysis for model development and testing was the patient-quarter. The final dataset included all eligible patient quarters (January, April, July and October) for each year. The ascertainment period for feature computation is represented in Figure 1. To ensure sufficient data completeness, we excluded patients with less than 90 days of ascertainment period before the index date for computation.

**Figure 1.** Study Design: the diagram represents the ascertainment period design for different groups of variables.


**4.2 Chemical flooding in L field**

L oil field is located in Bohai Bay, characterized by huge thickness, high permeability, severe heterogeneity, high crude oil density (0.947 g/cm3 ), and medium oil viscosity (7.2–19.4 cp). L oil field was put into production in January of 2005 and started to inject water in September of 2005. Based on well understanding of the mechanism and effect of the early polymer flooding, single-well polymer

### **Table 1.**

*Comparison of the three offshore chemical flooding fields with Daqing Field.*

**Figure 6.** *Field production curves in S field.*

**Figure 7.**

*Water cut curves under water flooding, chemical flooding, and actual situation.*

injection pilot test was carried out since 2006 when the water cut in the pattern was lower than 10% [22–24]. After that, another five water injectors were converted to polymer injectors from 2007 to 2009. For the early-stage polymer flooding, the characteristics of the responses on producers were different from the case in which polymer flooding was conducted during high water cut stage. The water production of the producers continued to rise up after polymer flooding, but the simulation research showed that the water cut increasing rate was lower than the rate during water flooding. Of course, water cut drop was observed in some wells (**Figure 7**). By December 2014, the total incremental oil by polymer flooding was about 754,650 m3 , and the stage oil recovery was enhanced by 3.0%. The polymer flooding is still effective, and more incremental oil will be obtained later.


**2. Material and methods**

**1. Introduction**

44 Water Stress in Plants

of lives and properties throughout Europe [4].

usage was restricted [8–11].

Rapidly increasing population in the world, limitless industrialization process, poor urbaniza‐ tion activities, regional wars, pesticides which are used to increase crops, and unconscious use of fertilization and chemicals such as detergents have started to contaminate the environ‐ ment, which results in damage for living beings as a result of extensive air, water, and soil pollutions. The use of fossil fuels has been on the increase since the industrial revolution. With the addition ofrapid deforestation,these factors have yielded a serious situation nearly beyond prevention [1, 2].Throughout history,the amount of CO2 concentration in the airdidnot exceed 320 ppm. However, current concentration is above 385 ppm and it keeps on increasing [3]. This situation has led to a concern and long debates in relation to the effects of global warming [1]. It is inevitable that climate change manifests its effects all around the world due to global warming. The increase in temperature and changing precipitation are expected to increase water problems, which are already felt in certain regions. It is estimated that there will be changes in the frequency and severity of droughts and floods, which may lead to serious loss

Drought is a phenomenon resulting from certain variables such as precipitation, temperature, humidity, evaporation, and transpiration. Basically, drought refers to water deficit resulting from below‐average emergence of natural water assets, which are used by various systems, in some regions for a particular time period [5]. When drought is mentioned, precipitation and water deficit come to one's mind first. It can be defined as having higher levels of water loss

Twenty‐eight percentage of usable territories on the world are affected by drought [5]. Due to greenhouse gas accumulation in the atmosphere, a climate change will take place in the upcoming years, which is likely to leave us with arid and sub‐arid territories. These, in turn, will add to the water problems in urban areas, and there will be an increase in the demand for water for both agricultural and drinking purposes. Hence, in addition to the expansion of arid and sub‐arid territories, there will be an increase in summer drought period and severity accompanied by desertification, salinization, and erosion processes [7]. Undoubtedly, one of

The drought increasing day by day and the thirstiness as a result of this make their destructive effects felt in green fields as it is in all parts of our lives. Almost all of the green fields constituted with the approach of classical landscaping design, which requires great amounts of water especially in our metropolitan cities, were damaged greatly in a few months in which water

While the purpose was healing the environment‐ambience quality in the applications of landscaping architecture and repairing the damaged environment conditions in previous years, the wise usage of water for the worries depending on the climate change and herbal applications resistant to drought have come to the fore recently [12–16]. The plants used in landscaping fields are desired to be resistant to drought, and this becomes the most important criterion, which affects the choice of plants in some regions even [17–21]. It is of great impor‐

due to evaporation than the water supplied by precipitation in a certain region [6].

the most vulnerable territories to drought is green and outdoor spaces in cities.

In the study, a total of 10 species have been used which are *Cupressus sempervirens* L., *Ailanthus altissima* (Mill.) Swingle, *Pyracantha coccinea* Roem, *Thuja orientalis*, *Pinus sylvestris* L., *Sophora japonica*, *Cedrus libani* A. Rich., *Acer pseudoplatanus* L., *Pinus brutia* Ten., and *Pinus nigra* Arnold. ssp. *pallasiana* (Lamb.) Holmboe and which are frequently used in landscaping applications. The fruits and cones of above‐mentioned species were gathered in October in the parks and gardens in Kastamonu, and seeds were attained after cleaning the cones and fruit fleshes. The seeds are except the ones of *Pinus nigra* and *Pinus brutia* which have been subject to stratification for a period of 8 weeks, and then, germination trials have been conducted in -2, -4, -6, and -8 Bar water stress, and in addition to these, control group has been used. The water potential of the germination substrates (0, -2, -4, -6 and -8 Bar) was determined using PEG‐6000 solution and prepared as described by Michel and Kaufman [24, 25].

Germination tests were performed in 11‐cm‐diameter glass petri dishes on two layers of filter paper saturated with water solutions. Fifty pieces of seeds have been placed in each petri dish in a way that the seeds will not have a touch with each other, and the study has been conducted with 4 repetitions. In this way, a total of 9000 seeds consisting of 50 pieces of seeds, 4 repetitions, 5 applications, and 9 species have been used in the study.

The seeds have been subject to germination with the period of 35 days in 25 ± 1.0°C constant– temperature in germination cabinet, and the filter papers have been renewed in each 3 days.

At the end of 35‐day period, non‐germinated seeds were cut and checked. Unfilled seeds were excluded from the evaluation, and germination percentage (GP) was calculated by propor‐ tioning the number of germinated seeds to healthy ones.

Germination percentage results were subjected to factorial variance analysis. The differences between species and the degrees of significance of such differences were revealed. In order to eliminate the misleading effects of the germination capability differences between the species included in the experiment in checking operations (0 Bar) on the analysis results, the values of these operations were proportioned to 100, and cumulative germination percentages (CGP) were calculated. Thus, the differences between the species were tried to be demonstrated more rationally. The data to be subjected to variance analysis were entered as they were propor‐ tioned to 100.

SPSS 17.0 statistics program was used to evaluate the data obtained through experiments. When statistically significant differences (P < 0.05) were found through analyses of variance, Duncan's test was performed to form homogeneous groups. Duncan's test showed that the operations were in the same or different categories in terms of the measured characteristics [26]. Multivariate analysis of variance (ANOVA) employs two or more factors and attempts to reveal significant differences between the mean scores of many groups according to these factors. In other words, multivariate analysis of variance is used to test the difference between the mean scores of k‐dependent groups [27]. In order to interpret study results with more ease, Excel program was used to create graphs.