Search

Aggregation of therapeutic bispecific antibodies negatively affects the yield, shelf-life, efficacy and safety of these products. Pairs of stable Chinese hamster ovary (CHO) cell lines produced two difficult-to-express bispecific antibodies with different levels of aggregated product (10-75% aggregate) in a miniaturised bioreactor system. Here, transcriptome analysis was used to interpret the biological causes for the aggregation and to identify strategies to improve product yield and quality. Differential expression- and gene set analysis revealed upregulated proteasomal degradation, unfolded protein response and autophagy processes to be correlated with reduced protein aggregation. Fourteen candidate genes with the potential to reduce aggregation were co-expressed in the stable clones for validation. Of these, HSP90B1, DDIT3, AKT1S1, and ATG16L1, were found to significantly lower aggregation in the stable producers and two (HSP90B1 and DNAJC3) increased titres of the anti-HER2 monoclonal antibody trastuzumab by 50% during transient expression. It is suggested that this approach could be of general use for defining aggregation bottlenecks in CHO cells.

The growth of social networks is ever-increasing. Many available scientific publications evidence the interest of researchers in this area. Within a time span of eight years from 2011 to 2018, approximately 2600, 230, 150, and 110 scientific articles were published from the USA, Iran, Saudi Arabia, and Turkey, respectively around this area of research. To comprehensively survey all the sub-fields and interests within this research area, the present paper proposes a novel density-based method for finding topic descriptors from academic articles. By employing a robust to noise fuzzy clustering algorithm, the terms are clustered, and by utilizing a modified Parzen window, k topic descriptors from each cluster are extracted. Besides, an optimization problem has been designed to detect the similarity between word pairs. By conducting the experiments, the research priorities for four countries within this time span have been found. Moreover, the closeness of the research in developing countries to the developed country have been measured. The experimental results show that for four years, the research topics in Turkey were close to the research topics in the USA on average, and the research topics in Saudi Arabia were close to the USA topics during the past two years. Additionally, the experimental comparison of the proposed method with two clustering baselines indicates the superiority of the proposed method in terms of precision, recall, and accuracy.

Predictive and prognostic biomarkers to guide 2019 novel coronavirus disease (COVID-19) are critically evolving. Dysregulated immune responses are the pivotal cause of severity mainly mediated by neutrophil activation. Thus, we evaluated the association of calprotectin, neutrophil secretory protein, and other mediators of inflammation with the severity and outcomes of COVID-19.This two-center prospective study focused on PCR-proven COVID-19 patients (n = 76) with different clinical presentations and SARS-CoV-2 negative control subjects (n = 24). Serum calprotectin (SC) was compared with IL-6 and other laboratory parameters.Median levels of SC were significantly higher in COVID-19 patients in comparison to the control group (3760 vs. 2100 ng/ml, p 0.0001). Elevated SC was significantly respective of disease severity (3760 ng/ml in mild up to 5700 ng/ml in severe cases, p 0.0001). Moreover, the significant positive and negative correlations of SC with disease severity and oxygenation status indicated disease progression and respiratory worsening, respectively. It was found that SC was high in severe patients during hospitalization and significantly declined to normal after recovery. The logistic analysis identified the independent predictive power of SC for respiratory status or clinical severity. Indeed, SC behaved as a better discriminator for both outcomes, as it exhibited the largest area under the curve (receiver operating curve analysis), with the highest specificity and sensitivity when the predictive value of inflammatory biomarkers was compared.Calprotectin can be used as a reliable prognostic tool to predict the poor clinical outcomes of COVID-19 patients.

Fabricating graphite electrode corrected with nanofiber by electrospinning as a considerable procedure for utilization in the fluid materials, milk, and syrup for detection of T2 mycotoxin is a significant technique. The modern biosensor was fabricated at normal degrees of room and utilized via buffer Britton–Robinson (B‐R) in pH = 5 to refine the chemico‐mechanical specifications. The electrochemical manner of the modified surface was surveyed using the scanning electron microscopy (SEM), cyclic voltammetry (CV), square wave voltammetry (SQWV), electrochemical impedance spectroscopy (EIS), and differential pulse voltammetry (DPV). The corrected electrode displayed a linear reply to T2 toxin in two distinct concentration ranges of 30–100 nM with correlation coefficients of 0.99. The greatest signals in the square wave spectrums for the B‐R buffer created on the uttermost signals of the obtained streams were pH = 5 and 0.5 M of KNO3 for T2 toxin. The modified electrode has a big signal, broad dynamic concentration and high sensitivity and selectivity., Mycotoxins produced by fungal and microbial growth from food and agriculture products. T2 Mycotoxin is a naturally occurring mold byproduct of Fusarium spp. Electrospinning has considerable application to detect several T2 toxin.

Today, growing evidence indicates that patients with type 2 diabetes (T2D) are at a higher risk of developing Alzheimer's disease (AD). Indeed, AD as one of the main causes of dementia in people aged more than 65 years can be aggravated by insulin resistance (IR) and other metabolic risk factors related to T2D which are also linked to the function of the brain. Remarkably, a new term called "type 3 diabetes" has been suggested for those people who are diagnosed with AD while also showing the symptoms of IR and T2D. In this regard, the role of genetic and epigenetic changes associated with AD has been confirmed by many studies. On the other hand, it should be noted that the insulin signaling pathway is highly regulated by various mechanisms, including epigenetic factors. Among these, the role of noncoding RNAs (ncRNAs), including microRNAs and long noncoding RNAs has been comprehensively studied with respect to the pathology of AD and the most well-known underlying mechanisms. Nevertheless, the number of studies exploring the association between ncRNAs and the downstream targets of the insulin signaling pathway in the development of AD has notably increased in recent years. With this in view, the present study aimed to review the interplay between different ncRNAs and the insulin signaling pathway targets in the pathogenesis of AD to find a new approach in the field of combining biomarkers or therapeutic targets for this disease.

Background and Objective: In psychology and in the field of social cognition, avoidant behaviors can be influenced by the extent to which that people feel vulnerable to infectious diseases. The present study aimed to examine the factor structure and psychometric properties of the Persian version of Perceived Vulnerability to Disease Questionnaire (P-PVDQ). Materials and Methods: This is a cross-sectional-analytic study. 500 students from Tehran University were recruited through convenience sampling from six different faculties and the participants completed Perceived Vulnerability to Disease Questionnaire, Disgust Scale-Revised and Health Anxiety Questionnaire (the two latter ones used for convergent validity). The process of translation and back translation was conducted for PVDQ. After data collection, exploratory and confirmatory factor analysis, reliability coefficients and convergent validity were conducted by SPSS18 and AMOS20. Results: Exploratory factor analysis showed two factors: “Perceived Infectability” and “Germ Aversion”. Moreover, this two-factor model has good fit indices in Confirmatory factor analysis including 1.68 for CMIN/DF and 0.05 for RMSEA. This questionnaire showed enough convergent validity and had enough cronebach’s alpha (α=0.83) and test-retest reliability coefficients. Conclusion: Except item 13 which has loaded on the different factor in the Persian version, compared to the original questionnaire, the Persian version of Perceived Vulnerability to Disease Questionnaire is consistent with the English version and has enough psychometric properties and can be applied in research which needs to evaluate self-reported perceived vulnerability to disease.

One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class) is available for learning. Well-known methods such as SVDD and convex hull have tried to find the enclosed boundary around target class, but their high complexity makes them unsuitable for large data sets and also online tasks. This paper presents a novel online one-class classifier adapted to the streaming data. Considering time complexity, an incremental convex–concave hull classification method, called ICCHC, is proposed which can significantly reduce the computational time and expand the target class boundary. Also, it can be adapted to the gradual concept drift. Evaluations have been conducted on seventeen real-world data sets by hold-out validation. Also, noise analysis has been carried out. The results of the experiments have been compared with the state-of-the-art methods, which show the superiority of ICCHC regarding the accuracy, precision, and recall metrics.

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a highly pathogenic and transmissible virus. Infection caused by SARS-CoV-2 known as Coronavirus disease 2019 (COVID-19) can be severe, especially among high risk populations affected of underlying medical conditions. COVID-19 is characterized by the severe acute respiratory syndrome, a hyper inflammatory syndrome, vascular injury, microangiopathy and thrombosis. Antiviral drugs and immune modulating methods has been evaluated. So far, a particular therapeutic option has not been approved for COVID-19 and a variety of treatments have been studied for COVID-19 including, current treatment such as oxygen therapy, corticosteroids, antiviral agents until targeted therapy and vaccines which are diverse in each patient and have various outcomes. According to the findings of different in vitro and in vivo studies, some novel approach such as gene editing, cell based therapy, and immunotherapy may have significant potential in the treatment of COVID-19. Based on these findings, this paper aims to review the different strategies of treatment against COVID-19 and provide a summary from traditional and newer methods in curing COVID-19.

To investigate the efficacy of short-term treatment with ciprofloxacin in alteration of gut microbiota pattern and reduction of seizure frequency in adult patients with drug-resistant epilepsy.In a prospective study, we investigated the effect of a 5-day course of treatment with ciprofloxacin on gut microbiota pattern and seizure frequency of 23 adults with drug-resistant epilepsy. Fecal samples were collected before and after treatment and were analyzed for microbial load and species. Changes in seizure frequency were registered for 12 weeks. Responders were defined as patients who experienced ≥50 % seizure reduction in comparison to baseline. Outcome measures were specified as alteration in fecal microbial burden in days 5-7 and responder rate in 4th and 12th weeks.The mean baseline frequency of seizures was5.6 ±7.7 per week. All patients were on polytherapy with a mean of 3 ± 1.2 anti-seizure medications. Microbial analysis showed a considerable increase in Bacteroidetes/Firmicutes ratio after treatment. Seizure frequency significantly decreased at the end of first week and the therapeutic effect continued to week 12 (P0.001). The responder rate at 4th and 12th weeks were 69.6 % and 73.9 % respectively with a more prominent response in patients with symptomatic generalized epilepsy (P:0.06).Alteration of abnormal gut microbiota pattern by methods such as short-course antibiotic therapy, prescription of probiotics and fecal microbiota transplant might be effective in treatment of drug-resistant epilepsy.

In data analysis, incomplete data commonly occurs and can have significant effects on the conclusions that can be drawn from the data. Incomplete data cause another problem, so-called uncertainty which leads to producing unreliable results. Hence, developing effective techniques to impute these missing values is crucial. Missing or incomplete data and noise are two common sources of uncertainty. In this paper, an effective method for imputing missing values is introduced which is robust to uncertainties that are arising from incompleteness and noise. A kernel-based method for removing the noise is designed. Using the belief function theory, the class of incomplete data is determined. Finally, every missing dimension is imputed considering the mean value of the same dimension of the members belonging to the determined class. The performance has been evaluated on real-world data sets from UCI repository. The results of the experiments have been compared with state-of-the-art methods, which show the superiority of the proposed method regarding classification accuracy.

Synthesis of biometal nanoparticles (BM-NPs) in a green-synthetic approach is the favorite research in the scope nanobiotechnology. Based on the literature, numerous microorganisms have been introduced for fabrication of BM-NPs. But, to our knowledge, no comprehensive review is available to classify the species of any microorganism genera of producing BM-NPs. Hence, the present approach systematically reviewed the biogenic preparation and pharmaceutical potential of BM-NPs synthesized and stabilized by exploiting Penicillium species. Records identified through databases involving Cochrane, Web of Science, PubMed, ProQuest, Scopus, Embase, Science Direct, and Google Scholar. In this study, 238 papers were identified following the initial search of databases, whereas 179 studies were excluded and eventually 59 papers were included in the study. Remarkably, 25 Penicillium species were introduced for biosynthesis of BM-NPs. Notably, silver NPs with 70.76% of the studies were the first predominant which produced by exploiting Penicillium species. Significantly, among the studies that explored applications of NPs, most of them investigated antimicrobial efficacy of BM-NPs. Moreover, a hefty 91.22% studies reported spherical NPs, while only 8.77% refers to other morphologies. Furthermore, pharmaceutical potentials of BM-NPs were discussed in details.

In this paper, a lumped mathematical model is introduced to study static and dynamic performances of rectangular aerostatic pads endowed of multiple inherent orifice type restrictors. The comparison of the results both with those of distributed parameters model and experimental data are discussed. Finally, a theoretical sensitivity analysis of the geometrical parameters on the pads behavior is carried out.

Integrated Pest Management (IPM) is often described as a knowledge-intensive approach to invertebrate pest management, requiring information on the biology, ecology and phenology of a pest combined with an understanding of the interactions between crop growth and pests and between pests and their natural enemies. We conducted a systematic quantitative literature review to summarise what is known about pest and natural enemy species common to Australian grain production systems, based on 1513 published and unpublished research studies. Drawing on this information, we address three issues: what are the knowledge gaps in relation to grain pests and their natural enemies, do these knowledge gaps limit the development of an IPM package for grain growers in Australia and what further ecological or biological information might growers require to enhance the use of IPM approaches for managing pests? The main gaps identified include a lack of understanding around specific factors that lead to pest outbreaks or factors that could be useful for predicting when and where pest outbreaks will occur in the future. Monitoring techniques for many pests are not well developed, and therefore, it is difficult to link the density recorded in a field with crop damage and yield loss and to develop economic thresholds that can be linked with intervention decisions. For most natural enemies, the impact in terms of reduction in pest numbers has not been quantified, with very few studies including both pests and natural enemies together. There is large variability in the level of control provided by natural enemies between years and regions, and the factors leading to this variability are not well understood. Finally, the lack of taxonomic resolution for individual species within groups is identified as a critical knowledge gap. We suggest that a more comprehensive fundamental knowledge base is required across the invertebrate community in grain systems aimed at reducing insect pest outbreaks, combined with a greater depth of understanding in monitoring strategies for pests that contribute to pesticide-use decisions.

This paper presents current-mode multi-input m:3 (4 ≤ m ≤ 7) counters based on carbon nanotube FET (CNTFET). Two different approaches are considered for these designs. In the first approach...

Physical unclonable function (PUF) can provide a unique chip fingerprint, which is tough for attackers to hack. This paper presents a novel energy-efficient arbiter PUF (APUF) using 45nm CMOS technology with excellent uniqueness. In every individual PUF cell, the device mismatch and racing in terms of delay between two different paths map a challenge into a response. There are also some process variation sensitive selecting modules, which form the competing paths. At the end of PUF circuit, an arbiter produces the output response of the PUF. The properties of randomness, uniqueness, reliability, energy efficiency, noise immunity, and area of the given APUF are evaluated, and demonstrate very promising results.

The proteins secreted by human tissues and blood cells, the secretome, are important both for the basic understanding of human biology and for identification of potential targets for future diagnosis and therapy. Here, a high-throughput mammalian cell factory is presented that was established to create a resource of recombinant full-length proteins covering the majority of those annotated as ‘secreted’ in humans. The full-length DNA sequences of each of the predicted secreted proteins were generated by gene synthesis, the constructs were transfected into Chinese hamster ovary (CHO) cells and the recombinant proteins were produced, purified and analyzed. Almost 1,300 proteins were successfully generated and proteins predicted to be secreted into the blood were produced with a success rate of 65%, while the success rates for the other categories of secreted proteins were somewhat lower giving an overall one-pass success rate of ca. 58%. The proteins were used to generate targeted proteomics assays and several of the proteins were shown to be active in a phenotypic assay involving pancreatic β-cell dedifferentiation. Many of the proteins that failed during production in CHO cells could be rescued in human embryonic kidney (HEK 293) cells suggesting that a cell factory of human origin can be an attractive alternative for production in mammalian cells. In conclusion, a high-throughput protein production and purification system has been successfully established to create a unique resource of the human secretome.

Adder core respecting to its various applications in VLSI circuits andsystems is considered as the most critical building block in microprocessors,digital signal processors and arithmetic operations. Novel designs of a lowpower and complexity Current Mode 1-bit Full Adder cell based onCNTFET technology has been presented in this paper. Three major partsconstruct their structures; 1) the first part that converts current to voltage; 2)threshold detectors (TD); and 3) parallel paths to convey the output currentsflow. Adjusting threshold voltages which are significant factor for settingthreshold detectors switching point has been achieved by means of CNTFETtechnology. It would bring significant improvements in adjusting thresholdvoltages, regarding to its unique characterizations. Simple design, lesstransistor counts and static power dissipation and better performancecomparing previous designs could be considered as some advantages of thenovel designs.

Friction and wear are tribological phenomena that have produced significant economic damage and environmental issues throughout our history. Different kinds of lubricants and coatings have been employed in the attempt of reducing friction losses. Because of their remarkable performance, gas lubricated systems are characterized by almost zero friction and wear. For this reason, they can be utilized in applications where rolling and fluid bearings cannot, e.g., metrology, medical equipment and tool machine. This paper reviews the evolution of gas lubrication through history. The topography of the subject is described from its origins to how it was shaped over the centuries, trying to preserve the logical cause and effect relationship of the events. For the sake of consistency, the review considers only the key past contributions which are strictly related to gas lubrication.

Background and objectives Anxiety is the most common response to myocardial infarction (MI). The objective of the study is to compare the effect of eye movement desensitization reprocessing (EMDR) and cognitive behavioral therapy (CBT) on anxiety in patients with myocardial infarction. Methods In this clinical trial, 90 eligible patients suffering from MI were selected using the convenience sampling method and randomly assigned to two intervention groups and one control group. The first intervention group attended eight 1-h EMDR sessions in a tranquil environment in a hospital. The second intervention group attended ten 90-min CBT sessions under an identical condition. The control group, on which no intervention was made, merely received prescribed medication by a physician (Oxazepam 10 mg) at 10:00 P.M. on a nightly basis. The mean anxiety was measured for all three groups before and after intervention through the Beck Anxiety Inventory (BAI). Data were analyzed using SPSS v.20, and such statistical tests and the chi-squared, analysis of variance (ANOVA), and Tukey's Honestly Significant Difference (HSD) post hoc test. P values are significant at P Results Results pointed to a significant statistical difference between the intervention and control groups in terms of the mean anxiety score differences (P Conclusion Both EMDR and CBT reduced anxiety levels in patients with MI, with the former being the more effective one.

Novelty detection is the identification of new observation that a machine learning system is not aware. Detecting novel instances is one of the interesting topics in recent studies. The problem of the current methods is their high run-time, so often make them unusable for large data sets. This paper presents the proposed method concerning this problem. Focusing on the task of one-class classification, the labeled data are mapped into two hypersphere regions for target and non-target objects. This mapping process is considered as a nonlinear programming. The problem is solved by employing the filled function for finding global minimizer. The global minimizer is considered as a boundary which is fit the target class. In the end, a one-class classifier to detect target class members is obtained. To present the power of the proposed method, several experiments have been conducted based on 10-fold cross-validation over real-world data sets from UCI repository. Experimental results show that the proposed method is superior than the state-of-the-art competing methods regarding applied evaluation metrics.

Recommender systems aim to support users in decision-making through the knowledge extracted from historical ratings. However, many of these ratings may be noisy and/or missing, causing degradation in the quality of the recommendations. Considering these issues, this paper presents a new one-class classifier to predict ratings in recommendation systems. The proposed method estimates the shared informative neighbors of each user by a probability fuzzy rough set method. Since the fuzzy rough set theory is sensitive to noisy samples, the quarter-sphere SVM classifier is designed to reduce the impact of noise on the results. The proposed classifier can satisfactorily determine a boundary around the target class while it reduces the acceptance probability of the outliers and non-target class(es). The theoretical interpretations are provided to prove the statistical stability of the proposed method. Also, noise analysis has been carried out. Through extensive experiments on several real-world data sets, it is confirmed that the proposed method outperforms the other six methods in terms of accuracy, recall, precision, and computational time.

This article presents the relationship of machining parameters containing pulse-on time (Ton), pulse-off time (Toff), peak current (IP) and servo voltage (SV) on surface integrity characteristics, including white layer thickness (WLT), heat-affected zone (HAZ) and surface crack density (SCD) and also on material removal rate (MRR), after wire electric discharge machining of Ti–6Al–4V. Taguchi’s method was utilized to design the experiments, and response surface methodology (RSM) was employed for developing the empirical models. Results indicated that Ton and IP played a significant role on surface integrity characteristics. In addition, the microstructure of selected machined samples was analysed using a field emission scanning electron microscope (FESEM) and energy-dispersive X-ray (EDX) analysis. Accuracy of models was examined using residual analysis and confirmation runs. Finally, multi-response optimization of process parameters was obtained using desirability approach. Results can be used to improve the quality of the machined workpiece significantly to fulfil the requirements of the various industries. The novelty of this research is mainly investigation and multi-response optimization of all the surface integrity characteristics at the same time.

Employing inexact arithmetic circuits in error-resilient applications results in reduction of hardware-level metrics such as power consumption, delay and occupied area. These criteria are very important in portable applications because they are battery limited. Full Adder cell is as a building block of many arithmetic circuits. Therefore, it can influence the performance of the entire digital system. This paper presents a novel low-power and high-speed design of one-bit inexact full adder cell based on 32-nm (CNFET) technology for error resilient applications. This design technique can be utilized in various applications particularly in image processing. The presented design employs capacitive threshold logic (CTL) approach which significantly reduces the number of transistors. The peak signal-to-noise ratio (PSNR) is considered to evaluate accuracy of circuits at application level. Then extensive simulations regarding various power supplies, temperatures and loads at transistor level are performed to measure power consumption and propagation delay criteria. Moreover, some new metrics are introduced to trade-off between application and hardware level parameters. Comprehensive simulations demonstrate the supremacy of the proposed cell than others.

Flight initiation is fundamental to insect dispersal. Insights into the meteorological and environmental drivers of flight initiation, and their relative importance, can therefore help determine the conditions under which mass dispersal events may occur. In relation to insect pestmanagement, the ability to anticipate such events would allow us to predict mass colonisation of crops and better manage outbreaks of an insect pest. This study insects aims to better predict flight initiation of Rutherglen bug, Nysius vinitor, and thus crop outbreaks originating from over-winter weed hosts. We examined the influence of temperature in combination with sex and the availability of food and water, under controlled environment conditions inside a flight chamber. Most of the variation in flight initiation was explained by temperature. We found that warm temperatures are required for flight initiation: the lower flight threshold was determined as 21 degrees C, the optimum temperature for flight as 25 degrees C and the upper flight threshold as 28 degrees C, fitting a quadratic distribution. However, additional data shows that such thresholds are not limiting: when N. vinitor is under extreme heat stress (35-40 degrees C) for a short period, they have a very high propensity to take-off. The rate of flight initiation was fastest within the first 5 h in both males and females for all tested temperatures. A lack of food and water resulted in a faster take-off rate of males, but had no effect on female flight initiation. Application of our analysis to temperature data in New South Wales (NSW) and Queensland (Qld), Australia, indicates that flight initiation in N. vinitor is likely to be highly seasonal, with very little flight activity expected in winter in eastern Australia. We found a longer suitable flight period and a greater number of suitable flight days per month in Qld compared to NSW. Such estimates could be used to inform regional pest arrival forecasts, integrated pest management decisions and the development of dispersal simulation models for this pest.

Predictably regulating protein expression levels to improve recombinant protein production has become an important tool, but is still rarely applied to engineer mammalian cells. We therefore sought to set-up an easy-to-implement toolbox to facilitate fast and reliable regulation of protein expression in mammalian cells by introducing defined RNA hairpins, termed ‘regulation elements (RgE)’, in the 5′-untranslated region (UTR) to impact translation efficiency. RgEs varying in thermodynamic stability, GC-content and position were added to the 5′-UTR of a fluorescent reporter gene. Predictable translation dosage over two orders of magnitude in mammalian cell lines of hamster and human origin was confirmed by flow cytometry. Tuning heavy chain expression of an IgG with the RgEs to various levels eventually resulted in up to 3.5-fold increased titers and fewer IgG aggregates and fragments in CHO cells. Co-expression of a therapeutic Arylsulfatase-A with RgE-tuned levels of the required helper factor SUMF1 demonstrated that the maximum specific sulfatase activity was already attained at lower SUMF1 expression levels, while specific production rates steadily decreased with increasing helper expression. In summary, we show that defined 5′-UTR RNA-structures represent a valid tool to systematically tune protein expression levels in mammalian cells and eventually help to optimize recombinant protein expression.

Instance reduction from class-balanced data has been investigated in much research. However, there is a lack of studies on class-imbalanced data. Learning from imbalanced data lately has attracted a lot of attention due to the practical applications. In the case of two-class imbalanced data, the instances from one class, majority class, are more numerous than the instances from the other class, which is a minority class. The present paper aims to introduce a new instance reduction method that preserves between-class distributions in the balanced data and handles minority class instance reduction in two-class imbalanced data, efficiently. The proposed method solves the instance reduction issue from an unconstrained multi-objective optimization problem aspect. Accordingly, a new combined weighted optimizer is designed. By employing the chaotic krill herd evolutionary algorithm, both the minority and majority class spaces with the accelerated convergence are explored. Through this method, the original data set is purged of those instances that decrease accuracy, and Gmean. The performance has been evaluated on both imbalanced and balanced data sets collected from the UCI repository by the 10-fold cross-validation method. Evaluations show that the proposed method outperforms state-of-the-art methods in terms of classification accuracy, Gmean, reduction rates, and computational time.

Physical unclonable functions (PUFs) are new hardware security primitives proposed for protecting resource-constrained devices. This paper presents two PUFs in voltage-and current-modes. The new designs are based on carbon nanotube field effect transistors (CNTFETs). Sensitivity to strong process variation is considered as a demerit of this emerging nanoscale device. However, this deficiency can be the source of constructing unique PUF instances. The proposed circuits are simulated and tested by Synopsys HSPICE using a standard 32nm CNTFET technology. The properties of randomness, uniqueness, reliability, energy efficiency, and area of the implemented CNTFET-based PUFs are evaluated showing very promising results.

Introduction Acetaminophen (APAP) as an analgesic and antipyretic drug can result to liver damages while using more than 4 g/day. Therefore, APAP toxicity causes the liver to dysfunction. This study aims to investigate the hepatoprotective and antioxidant activity of hydroalcoholic extract of watercress (WC) in APAP-induced hepatotoxicity in rats. Materials and methods Randomly, twenty-four Wistar rats were divided into four groups of six each. Groups named as control, APAP, APAP + WC and APAP + S for group 1, 2, 3, and 4, respectively. Group 1 received distilled water 1 ml/kg for 7 days. In group 2, 3, and 4, rats pretreated by receiving distilled water (1 ml/kg), WC extract (500 mg/kg), silymarin extract (mg/kg) for 7 days, respectively. Of note, to induce acute hepatotoxicity in groups 2, 3, and 4, rats posttreated by orally intoxicated with single dose of APAP (2 g/kg) on the sixth day. The animals were sacrificed on the seventh day. Alanine amino transferase (ALT), aspartate amino transferase (AST), ferric reducing ability of plasma (FRAP), protein carbonyl (PCO), total thiol (T-SH), glutathione peroxidase (GPx), superoxide dismutase (SOD) and catalase (CAT) activities were measured in plasma. It should be noted that the chemical composition of WC extract was identified by GC-MS analysis. Results The results have shown that there was a significant increase in AST, ALT, FRAP and PCO content in APAP group in comparison to control. Also, there was a significant reduction in T-SH levels and GPx activity in APAP group compared to control. However, administration of WC extract and silymarin not only causes a significant decrease in AST activity, but they markedly increased T-SH content and GPx activity compared to APAP group. GC-MS analysis showed the major compositions were found to be benzenepropanenitrile (48.30 %), Phytol (10.10 %), α-cadinene (9.50%) and linolenic acid (8.0). Conclusions It is concluded that the WC extract reduces APAP-induced toxicity through its hepatoprotective and antioxidant activity in rats.