Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning Implications Related to Sterilization-Sensitive Science Investigations Associated with Mars Sample Return (MSR).

The NASA/ESA Mars Sample Return (MSR) Campaign seeks to establish whether life on Mars existed where and when environmental conditions allowed. Laboratory measurements on the returned samples are useful if what is measured is evidence of phenomena on Mars rather than of the effects of sterilization conditions. This report establishes that there are categories of measurements that can be fruitful despite sample sterilization and other categories that cannot. Sterilization kills living microorganisms and inactivates complex biological structures by breaking chemical bonds. Sterilization has similar effects on chemical bonds in non-biological compounds, including abiotic or pre-biotic reduced carbon compounds, hydrous minerals, and hydrous amorphous solids. We considered the sterilization effects of applying dry heat under two specific temperature-time regimes and the effects of γ-irradiation. Many measurements of volatile-rich materials are sterilization sensitive-they will be compromised by either dehydration or radiolysis upon sterilization. Dry-heat sterilization and γ-irradiation differ somewhat in their effects but affect the same chemical elements. Sterilization-sensitive measurements include the abundances and oxidation-reduction (redox) states of redox-sensitive elements, and isotope abundances and ratios of most of them. All organic molecules, and most minerals and naturally occurring amorphous materials that formed under habitable conditions, contain at least one redox-sensitive element. Thus, sterilization-sensitive evidence about ancient life on Mars and its relationship to its ancient environment will be severely compromised if the samples collected by Mars 2020 rover Perseverance cannot be analyzed in an unsterilized condition. To ensure that sterilization-sensitive measurements can be made even on samples deemed unsafe for unsterilized release from containment, contingency instruments in addition to those required for curation, time-sensitive science, and the Sample Safety Assessment Protocol would need to be added to the Sample Receiving Facility (SRF). Targeted investigations using analogs of MSR Campaign-relevant returned-sample types should be undertaken to fill knowledge gaps about sterilization effects on important scientific measurements, especially if the sterilization regimens eventually chosen are different from those considered in this report.
Antioxidant status in hypertensive disorders of pregnancy

ABSTRACT Objective Pregnancy can be associated with maternal hypertension leading to possible complications in pregnancy outcome. Antioxidant status may be proned to changes during pregnancy with hypertension. The aim of our study was to estimate antioxidant status through high-risk pregnancies. Methods Seventy-nine pregnant women with high-risk for preeclampsia development were included and 46 of them developed some hypertensive disorder in pregnancy. Superoxide-dismutase (SOD) and paraoxonase 1 (PON1) activities and relative proportion of PON1 activiity on different HDL subclasses were determined in 1st, 2nd, and 3rd trimester and prior to delivery. Results SOD activity was significantly lower in 2nd and 3rd trimesters when compared to 1st trimester (P˂0.001) whereas PON1 activity was significantly higher in 3rd than in 1st trimester (P˂0.05) in group of hypertensive women. This group had significantly higher SOD and PON1 activities and relative proportion of PON1 on HDL3c subclasses in the 1st trimester, significantly increased PON1 in the 3rd trimester and prior to delivery and significantly higher PON1 activity on HDL3c subclasses (P˂0.05) than nonhypertensive group. In 1st trimester and prior to delivery, total PON1 activity and relative proportion of PON1 on HDL3c subclasses exhibited significant ability to mark out hypertension in pregnancy (P˂0.05). Conclusions SOD activity decreased whereas total PON1 activity increased during pregnancy with hypertension. Pregnant women with hypertension had higher activities of PON1 and SOD and relative proportion of PON1 on HDL3c subclasses than nonhypertensive ones. PON1 activity and relative proportion of PON1 on HDL3c subclasses exhibited significant association with hypertension in pregnancy.
The impact of psychosocial defeat stress on the bed nucleus of the stria terminalis transcriptome in adult male mice

The bed nucleus of the stria terminalis (BNST) is a focal point for the convergence of inputs from canonical stress‐sensitive structures to fine‐tune the response to stress. However, its role in mediating phenotypes of stress resilience or susceptibility is yet to be fully defined. In this study, we carried out unbiased RNA‐sequencing to analyse the BNST transcriptomes of adult male mice, which were classified as resilient or susceptible following a 10‐day chronic psychosocial defeat stress paradigm. Pairwise comparisons revealed 20 differentially expressed genes in resilience (6) and susceptible (14) mice compared with controls. An in silico validation of our data against an earlier study revealed significant concordance in gene expression profiles associated with resilience to chronic stress. Enrichment analysis revealed that resilience is linked to functions including retinoic acid hydrolase activity, phospholipase inhibitor and tumour necrosis factor (TNF)‐receptor activities, whereas susceptibility is linked to alterations in amino acid transporter activity. We also identified differential usage of 134 exons across 103 genes associated with resilience and susceptibility; enrichment analysis for genes with differential exon usage in resilient mice was linked to functions including adrenergic receptor binding mice and oxysterol binding in susceptible mice. Our findings highlight the important and underappreciated role of the BNST in stress resilience and susceptibility and reveal research avenues for follow‐up investigations.
Population size impacts host–pathogen coevolution

Ongoing host–pathogen interactions are characterized by rapid coevolutionary changes forcing species to continuously adapt to each other. The interacting species are often defined by finite population sizes. In theory, finite population size limits genetic diversity and compromises the efficiency of selection owing to genetic drift, in turn constraining any rapid coevolutionary responses. To date, however, experimental evidence for such constraints is scarce. The aim of our study was to assess to what extent population size influences the dynamics of host–pathogen coevolution. We used Caenorhabditus elegans and its pathogen Bacillus thuringiensis as a model for experimental coevolution in small and large host populations, as well as in host populations which were periodically forced through a bottleneck. By carefully controlling host population size for 23 host generations, we found that host adaptation was constrained in small populations and to a lesser extent in the bottlenecked populations. As a result, coevolution in large and small populations gave rise to different selection dynamics and produced different patterns of host–pathogen genotype-by-genotype interactions. Our results demonstrate a major influence of host population size on the ability of the antagonists to co-adapt to each other, thereby shaping the dynamics of antagonistic coevolution.
An MD-2-related lipid-recognition protein is required for insect reproduction and integument development

The myeloid differentiation factor 2 (MD-2)-related lipid-recognition protein is involved in immune responses through recognizing bacteria lipopolysaccharide in mammals, arthropods and plants. However, the physiological roles of MD-2 in other biological processes are largely unknown. Here, we identified three homologue MD-2 genes (NlML1, NlML2 and NlML3) by searching the genome and transcriptome databases of the brown planthopper Nilaparvata lugens, a hemipteran insect species. Temporospatial analysis showed that the NlML1 gene was highly expressed in the fat body but much less so in the other tissues, while the NlML2 and NlML3 genes were highly expressed in the testis or digestive tract. RNA interference-mediated depletion of the NlML1 gene significantly downregulated the transcription of numerous integument protein genes. The NlML1 knockdown led to moulting failure and mortality at the nymph–adult transition phase, impaired egg laying and hatching, and reduced 20-hydroxyecdysone (20E) production in the nymphs. 20E could rescue the deficient moulting phenotypes derived from dsNlML1 RNAi. These novel findings indicate that NlML1 is required for nymphal moulting and female reproductive success as it plays an important role in regulating 20E synthesis, lipid and chitin metabolisms in N. lugens, thus contributing to our understanding of developmental and reproductive mechanisms in insects.
State and Prospects of Providing Mining and Smelting Plants of Ukraine with High-Quality Domestic Refractory Products

Refractory products are widely applied almost in all branches of industry, however metallurgy is still one of the primary refractory consumers (to 80 % from total production). The primary refractory producers in Ukraine are integrated into association Ukrogneupor that includes JSC "Zaporozhogneupor", JSC “Chasovoyarsk Refractory Integrated Works”, JSC “Velikoanadolsk Refractory Integrated Works”, JSC “Krasnogorovsk Refractory Integrated Works”, JSC “Krasnoarmeysk Dinas Plant”, JSC “Panteleymonovsk Refractory Plant”, JSC “Kondratevsk Refractory Plant”, JSC “Khristogorovsk Refractory Block and Concrete Plant”, JSC “Kalderis Ukraine”. These plants make approximately 99 % of all refractory products in Ukraine. The association includes also JSC “Ukrainian Research Institute of Refractories named after A. S. Berezhnoy” and Donetsk State Institute of Scientific Research and Project Works and Engineering Services in Refractory Industry. Refractory plants as well as the whole metallurgical and mining complex were operating under conditions of the world economic crisis in 2009. The volume of refractory output was 360.1 thousand tons in 2009 which was lower by 42.5 % in 2008. Refractory plants of Ukraine produce almost the whole range of refractory materials necessary for mining and smelting plants. Refractory products made in Ukraine are at the world analogues level. Import of refractory products has been fluctuating within 10-15 % from their total production for last years. Traditional import of refractory products is periclase, periclase-chromite and periclasecarbonaceous goods mainly produced in China. Aluminosilicate and dinas refractories are imported in small amounts for last years. However, there are no objective reasons for refractory import. In this case, price policy, relationships between owners of iron & steel and refractory plants and other factors are important. Data about domestic refractory production for the basic processes of metallurgical production are presented below.
Deep Learning to Improve COVID-19 Detection: Using CNN-Based Transfer Learning from Chest X-Ray Images

Abstract- Novel coronavirus (COVID-19) is a contagious disease that has already caused millions of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. Artificial intelligence techniques regarding image classification can assist in the early diagnosis of the disease. In this context, deep learning algorithms can be used to improve radiologists’ COVID diagnosis. Specifically, these algorithms have recently demonstrated their potential for screening and detecting COVID-19 in CT and x-ray images. In this paper was explored the effectiveness of Convolutional Neural Networks with transfer learning in the rapid and reliable detection of COVID-19-induced and pneumonia based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied using dataset involved three classes of chest X-ray images: 415 images with confirmed Covid-19, 1,666 normal cases and 166 pneumonia. Those were collected from the available X-ray images on public medical repositories Github and Kaggle. The study revealed the superiority of Model DenseNet121 over Vgg16 model where the model performed best in terms of overall scores and based-class scores, where DenseNet121 pretrained model achieved overall accuracy of 94% and by VGG16 pretrained model only 89% is achieved. According to the results, deep learning with X-ray imaging is conducive for the physicians to make a diagnosis of this infectious disease and DenseNet121 can be used effectively for detecting COVID-19 from chest radiology images.
Analysis of the users’ emotional state in social networks

In the era of digitalization, everyone who has access to the Internet uses social networks. Social networks make it possible to maintain contact with classmates, acquaintances, expand the circle of acquaintances in real-time, and are also a source of various kinds of information. In addition, social networks can become a source for analyzing user behavior, emotional state, especially children. This article proposes a method for analyzing and classifying information to determine the emotional state of network users.
Effect Types of Encoder on Performance Optical Communication Systems at 10 Gb/s SMF link

Abstract- By the expansion of high-speed, long-distance optical fibre communication systems, linear factors develop one of the important factors restrictive high-speed, long-distance optical fibre transmission. The effect of variation in system encoder of bit error ratio (BER) and quality (Q) value. The power penalty suffered by the system SMF operating at 1550 nm, with NRZ and RZ encoders. The Q-factor was used to evaluate the performance of the system at a BER= 10-13 which corresponds to a Q factor of 7. It is found that NRZ has the highest Q-factor and lowest BER values comparative with RZ encoder. It also type of encoder affected the on jitter in optical communication systems.
Finite Element Analysis of Fluid-Filled Cell Response to External Loading

ABSTRACT A parenchyma cell is modelled as a thin-walled, fluid-filled sphere. The fmite element technique is used to analyze the response of an aggregate of cells under uniaxial compressive loading. The results are compared to earlier results in which the cell was assumed to be a cylinder compressed transversely. These two models provide bounds on the influence of cell turgidity on tissue strength, and on the likelihood of rupture due to compression. For parameter values typical of potato parenchyma, it is found that increasing wall modulus tends to increase tissue stiffness, but its effect on tissue strength depends on the initial turgor pressure. Increasing the initial turgor pressure causes the tissue's apparent freshness to increase but also increases the susceptibility of the tissue to bruising.
So you’ve graduated college and need to test apps: What barriers might you face?

With the increasing complexity and business surrounding mobile applications (apps), there is a growing demand from the industry for qualified test professionals. That’s why several universities have included app testing content in their curriculum. However, it is observed that novice app testers, despite being able to enter the market, may face barriers when performing activities related to testing. These barriers impact the tester’s experience and the quality of software testing. Although there are works that explore the barriers that newcomers face to participate/contribute in open-source communities or participate in projects that are already in progress, none of them address them from the perspective of app testing. Thus, this study advances in answering the following research question ”What are the barriers that novice testers face in the planning, execution, and analysis stages of the testing process? ”. Through an opinion survey with 30 novice testers, six themes and 36 categories of barriers were synthesized, such as lack of knowledge about software measurement and about tools and technologies used in the test. The identification of these barriers can support professors in adapting and prioritizing content on Software Testing, especially in undergraduate courses. Furthermore, such contributions provide indications of the need for research on strategies to mitigate barriers.
Design High Gain Integrated Antenna Based on Micromachining Feeding Techniques for 94 GHz Applications

Abstract- This paper presents the use of micromachining techniques to enhance the performance of rectangular microstrip antenna. The patch antenna is printed on GaAs substrate that has high dielectric constant (_r=12.9 ). These antennas are designed for MMICs application at 94GHz band The firstly technique is the back cavity patch antenna with inset tapered microstrip line has demonstrated a significantly improved radiation performance and bandwidth than the conventional microsrtip antenna placed on dielectric material type of Galluim Arsenide. The second design elevated patch antenna is consist of microstrip feed line type of Coplanar Waveguide (CPW) directly connected with feeding post, supporting four posts and elevated antenna. The elevated antenna gives the lowest possible dielectric substrate (Air) compare with another techniques. The elevated patch integrated fed by CPW have been used to reduce surface waves and improve antenna performance, thus will increase the radiation efficiency and show the gain is (9.9dB) with the front to back ratio of (13.6) and bandwidth is about 900MHz.
Chart classification: an empirical comparative study of different learning models

Charts are powerful tools for visualizing and comparing data. Representation of information through charts grows with time due to its easy and aesthetically attractive structure. With the increase in the number of documents with various chart types, chart classification has become an important task for downstream applications such as chart data recovery, chart replenishment, etc. Though there have been various studies reported in the literature on chart classification using different classification methods, three of the important concerns are small dataset size, a small number of chart types, and inconsistencies in the performance reported in different studies. Motivated by the above concerns, this paper curates a large dataset of real chart images (110k samples) with a large number of chart types (24 charts types) and evaluates 21 different machine learning models. To the best of our knowledge, this is the largest (in sample size and chart types) real chart dataset reported in the literature to date. We further study - (i) the effect of dataset size on the classification model, (ii) the nature of chart noises and their influences on classification performance, and (iii) confusing chart pairs leading to misclassification.
Evaluation of the Knowledge of Medical and Health Informatics Students for Bioinformatics and Biomedical Research

New technologies that emerge at the interface of computational and biomedical science could drive new advances in global health. Bioinformatics is one of these new technologies that can be defined as a concept that deals with all applications of information technology in the field of molecular biology. This paper aims to evaluate the knowledge of medical students and health informatics students about bioinformatics and their willingness to donate biospecimens for the bio-research and to recognize factors associated with biospecimen donation. A cross-sectional study was conducted among senior students of medicine, dentistry, and health informatics at the Libyan International Medical University, Libya. The data were obtained using a self-distributed questionnaire to evaluate the respondents' general knowledge about bioinformatics, the factors influencing their willingness to donate biospecimens, and their general attitudes towards biomedical research. The results showed a lack of bioinformatics knowledge. However, there was a strong willingness to donate biospecimens to facilitate the process of researches and medical studies. Proper education is needed to increase the understanding of medical students in bioinformatics activities.
Comparison of P&O and PSO Algorithms using Matlab/Simulink

Abstract- In this work, Matlab/Simulink is used to compare Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) algorithms in terms of their convergence and efficiency. PSO and P&O are methods of maximum power point tracking (MPPT) used to optimize the energy extraction in photovoltaic (PV) power system. Convergence speed, efficiency and energy losses of both methods are considered in the paper. Simulink model of both methods is implemented at temperature of 25o and radiation of 1000 watt/m2. We concluded that the P&O is the faster to obtain the MPP, Nonetheless; it generates significant energy losses due to its constant oscillation around the MPP. On the other hand, it is observed that the PSO algorithm is slower to obtain the MPP, yet it presents less energy losses in comparison with the P&O algorithm. Finally, only the PSO algorithm can guarantee the convergence to the maximum power point.
Coarse-to-fine 3D clothed human reconstruction using peeled semantic segmentation context

3D reconstruction of human body model from a monocular image is an under-constrained and challenging yet desired research problem in computer vision. Recently proposed multi-layered shape representation called PeeledHuman attempted a sparse non-parametric 2D representation that can handle severe self-occlusion. However, the key limitation of their PeeledHuman model is that the predicted depth maps of self-occluded parts are sparse and noisy, and hence after back-projection lead to distorted body parts and sometimes with discontinuity between them. In this work, proposed to introduce Peeled Segmentation map representation in a coarse-to-fine refinement framework which consist of a cascade three networks namely, PeelGAN, PSegGAN and RefGAN. At first, we use original PeeledHuman as baseline model to predict initial coarse estimation of peeled depth maps from input RGB image. These peeled maps are subsequently fed as input along with monocular RGB image to our novel PSegGAN which predict Peeled Segmentation maps in a generative fashion. Finally, we feed these peeled segmentation maps as additional context along with monocular input image to our RefGAN which predicts the refined peeled RBG and Depth maps. This also provides an additional output of 3D semantic segmentation of the reconstructed shape. We perform thorough empirical evaluation over four publicly available datasets to demonstrate superiority of our model.
A Comparative Analysis of Frequency Bands in EEG Based Emotion Recognition System

Computer Science department, Baniwalid University, Baniwalid, Libya, kf.aljribi@gmail.com Emotion recognition from brain signals has been interesting research area in computer science to help enhancing human computer interactions. In this paper emotion recognition system has been designed and applied on two different datasets of brain signals. First is a 15-subject from online provided AMIGOS dataset and the second is 25-subject dataset collected especially for this work to investigate the best frequency bands for emotion recognition. The recognition system has been developed by using Mel Frequency Coefficient Components to extract features from signals and the Multi-Layer Perceptron neural network for classification. ElectroEncephaloGrahpy signals have been analyzed in alpha, beta, gamma, and total (8-45Hz) bands as well as in high-gamma (50-70Hz) band for the second dataset. Results from both datasets have shown that emotion recognition accuracy differs from one subject to another and emotions can be better recognized from gamma band rather than from low frequency bands.