Miracle-dz/UniFAR-FTUs huggingface dataset:
Proceedings of the SeqBio 2015 workshop: String algorithms for bioinformatics

The advent of high-throughput experimental methods have revolutionized the functional genomics ﬁeld, now allowing for proﬁling transcription factor binding at the level of the genome. The widely-adopted ChIP-seq technique, and the high-resolution ChIP-exo approach, still necessitate computational analyses to precisely identify sequences responsible for recruitment of transcription factors to individual loci. We will present the RSAT suite ( http://rsat.eu ) and ExoProﬁler ( http://github.com/ComputationalSystemsBiology/ExoProfiler ) tools to discover motifs and identify regulatory elements from ChIP-seq and ChIP-exo data. Bio
Performance Analysis of Industrial Cooperative Communication System in Generalized Fading Environment

This paper considers the performance analysis of M-ary phase shift keying industrial cooperative relaying system in Nakagami-m multipath fading channel with Gamma shadowing, which is known as the Generalized-K composite fading channel. Since the paper deals with the industrial environment, the communication channel is also affected by the additive Middleton's Class-A impulsive noise. The bit error rate is used as a performance measure and the closed-form average bit error rate expression was derived. The influence of different fading and noise parameters on the system performance is investigated. The obtained results may be used for the improvement of the present and future industrial wireless communication systems.
Phytochemical Analysis and Biological Activity of Convolvulus althaeoides and Convolvulus stachydifolius var. villosus

The high percentage of water content in aerial parts of Convolvulus althaeoides and Convolvulus stachydifolius var. villosus observed in leaf which can be affected the metabolic processes in plant. The percentage of total carbohydrates and total lipid of Convolvulus althaeoides and Convolvulus stachydifolius var. villosus plants reached its maximum values in leaf decreasing gradually in flower to reach the lowest percentage in stem. Investigation of the free sugars showed that, galactose have maximum values in flower, galactose and glucose in leaf, glucose and galactose in stem in both plant respectively. The highest amount of total nitrogen and protein content was recorded in winter. The percentage of total nitrogen of Convolvulus althaeoides and Convolvulus stachydifolius var. villosus plants reached its maximum values in flower decreasing gradually in leaf to reach the lowest percentage stem; amino acid analyzer showed that, plants contains 15 free amino acids, respectively. Successive extraction showed that, methanol extracts have maximum values in flower, water extract have maximum values in leaf and stem respectively. Antimicrobial studies by used 6 bacterial strain and 5 fungal strains showed that, Convolvulus althaeoides and Convolvulus stachydifolius var. villosus plants can be used as antimicrobial agents, no Anti-virulence activity in both plant under investigation. [Ahmed FA, Sharaf AMA, Sofy MR and Elhaw MH. Phytochemical Analysis and Biological Activity of Convolvulus althaeoides and Convolvulus stachydifolius var. villosus. Rep Opinion 2014;6(7):36-44]. (ISSN: 1553-9873). http://www.sciencepub. net/report. 6
MIMO Monopole Antenna Design with Improved Isolation for 5G WiFi Applications

In this manuscript, a compact multiple-input-multiple output (MIMO) monopole antenna for 5G WiFi applications is proposed. The antenna is designed using a cheap FR-4 substrate with properties of ε=4.3 and δ=0.025. The proposed design has microstrip-fed circular ring radiators and a T-shaped defected ground plane (DGS). By inserting the T-shaped DGS, the mutual coupling characteristic of the antenna has been reduced and good isolation is obtained. The antenna operates in the range of 5 to 6 GHz with a reflection coefficient<-30 dB and mutual coupling<-20 dB which covers the required frequency bandwidth of 5G WiFi systems. The antenna has overall dimension of 30×20×0.8 mm 3 . The simulated results in terms of different antenna specifications have been presented and discussed.
Findlay, J. and Hermannsson, K. (2019) Social origin and the financial feasibility of going to university: the role of wage penalties and availability

The evidence on why students from lower social origin are persistently underrepresented in higher education (HE) suggests social, educational and economic factors all play a role. We concentrate on the influence of monetary costs/benefits and how these are influenced by social origin. In particular, we consider the effect of a class-based wage penalty in the labour market and, using evidence from a large-scale survey of Scottish students, we show how the greater financial constraints facing working class students affects the incentive to participate in higher education. Using a simple model of human capital investment, the low rate of working-class participation in HE is shown to be consistent with rational behaviour, i.e. weighing the monetary costs and benefits, participating in HE is a less attractive investment proposition for some students. We conduct simulations which suggest this could be mitigated by a generous income-contingent support.
Current Advances in Pathogen- esis in Obesity: Impact of Hy-

Obesity has assumed epidemic proportions worldwide. Still though some therapies are approved by Food and Drug Administration (FDA) like Qsymia (topiramate and phentermine), contrive (naltrexone and bupropion), lorcaserin, all are not available in all countries, besides the use of GLP1 receptor agonist liraglutide for obesity as well besides diabetic control. Whatever one has to offer causes very little weight loss besides maintenance of weight loss despite lifestyle interventions is a herculean task as shown by Diabetes Prevention Program (DPP) and Look AHEAD studies. The only effective therapy remains bariatric surgery which cannot be offered to all with its cost, complications. Hence more in-depth knowledge is needed regarding pathogenesis of obesity. Here we review further how high fat diet effects gut flora through effects of TLR4 signaling with help of intermediates like Myeloid Differentiation factor (MyD88) and TRIF. Besides the role of hypothalamic inflammation occurring as a primary event in High Fat Diet (HFD) has been emphasized and how hypothalamic glioses affects with neuronal injury occurring as a primary event and ultimate damage of Proopiomelanocortin (POMC) neurons which changes ratio of orexigenic Agrp (Agouti Related Peptide) to POMC neurons ratio in favor of weight gain. If one can intervene at hypothalamic inflammation level newer therapies can be
DETERMINANTS OF WORK INCOME OF FAMILY WORK UNITS OF SWISS DAIRY FARMS

The paper analyses the determinants of annual income of a family work unit (FWU) for Swiss dairy farms based on data from the Farm Accountancy Data Network (FADN). Two regression types, namely a random-effects model and a quantile regression focusing on deciles, are applied. Organic and full-time farming, large farm size, high milk yield per cow and a diversification in either arable crops or agriculturerelated activities are identified as attributes of a financially successful farm. In addition, the quantile regression reveals that the impacts of milk yield and concentrate inputs on annual income per FWU vary clearly among deciles, indicating that a high milk yield contributes most to the financial success of the best performing farms. The comparison between the two models shows that quantile regression is an appropriate analysis tool in the presence of substantial heterogeneity as is the case for Swiss dairy farms.
Talker intelligibility and listening effort: the role of speaking rate

Listening to speech in noise induces increased cognitive effort and fatigue. Effort is mediated by listener characteristics such as nativeness or hearing status. Moreover, noise-related issues can be exacerbated by talker differences. Nevertheless, past research has so far not related talker differences to cognitive effort. Based on an anechoically recorded corpus of sentences spoken by sixteen Southern British English speakers, we conducted listening experiments in combination with pupillometry. We presented temporally or spectrally distorted speech (using time-compression and noise-vocoding, respectively). Furthermore, undistorted speech was presented in quiet and embedded in speech-shaped masking noise. Intelligibility measures were obtained based on keywords recalled correctly. Listening effort was measured by tracking pupil size over time. Our results show that peak pupil dilation increased for both distorted and masked speech. Peak pupil dilation also increased for less intelligible talkers in the temporally degraded condition. The results indicate a link between speaking rate differences and listening effort and will be discussed in the context of models of listening effort.
Detecting Cultural Influences on Social Cognition: The South African-Adapted NEmo Test Battery

The psychological construct of social cognition comprises several distinct forms of mental processing that are essential for healthy interpersonal relations. Two separate and hierarchically inferior constructs, emotion recognition and theory of mind (ToM), are central to social cognition and have been the subject of intense neuroscientific study. Although these constructs are universal, numerous studies have shown that cultural-linguistic influences might affect expression of social cognitive abilities in these domains. Given the importance of intact emotion recognition and ToM for adaptive functioning, it is imperative for local research to describe possible cultural and linguistic influences on their expression and to ensure that tools used to assess them are contextually appropriate. In this study, I evaluated a South African-adapted version of the NEmo battery, a newly developed battery by the Swiss Epilepsy Centre. The tasks on the NEmo test battery were translated from their original German into English, and South African faces and voices replaced the original Swiss faces and voices as stimuli for the emotion recognition tasks. South African university students (N = 40; age M = 22.14 ± 3.84 years) completed the NEmo test battery and other standardized cognitive tests. English-speakers and Afrikaans-speakers performed significantly better on tasks assessing ToM than Xhosa-speakers. This suggests that culture influences the expression of this construct. Future research needs to ensure these tasks are adapted to Xhosa to ensure that social cognition is reliably measured in this language group.
Using Drosophila to characterize the acute and longterm moleucular and physiological response in the adult nervous system following traumatic brain injury

OF THE THESIS Using Drosophila to Characterize the Acute and Long-Term Molecular and Physiological Responses in the Adult Nervous System Following Mild Traumatic Brain Injury by Ayeh Barekat Master of Science in Microbiology San Diego State University, 2016 Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. In addition, there has been a growing appreciation that even repetitive, milder forms of TBI (mTBI) can have long-term deleterious consequences to neural tissues. Hampering our understanding of genetic and environmental factors that influence the cellular and molecular responses to injury has been the limited availability of effective genetic model systems that could be used to identify the key genes and pathways that modulate both the acute and longterm responses to TBI. Using the abundance of genetic tools available in Drosophila, this thesis provides evidence that key molecular pathways that respond to TBI mammalian models, exhibit similar responses and alteration profiles. These identified molecular mechanisms include inflammatory response autophagy alterations, oxidative stress, tau phosphorylation, and changes in neuronal organization and morphology. In addition, using this novel injury paradigm, we demonstrate the powerful utility of Drosophila system to conduct tissue-specific analysis of adult Drosophila head transcriptome following traumatic brain injury. Combined with recent advances in RNA sequencing technologies, our Drosophila model of brain injury can facilitate identification of genes and pathways that are involved in the development and progression of secondary injury mechanisms associated with neurological disorders and development of neurodegeneration following traumatic brain injury.
Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. This article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concentrations based on atmospheric variables. In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks. Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model. Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model. In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.
Haplotype Analysis of Candidate Genes Involved in Inflammation and Oxidative Stress and the Susceptibility to Preeclampsia

Unbalanced inflammatory reactions and oxidative stress are inseparably interconnected, and both may play crucial roles in the pathophysiological mechanisms of preeclampsia (PE). In the published previous studies, we have genotyped for SNPs that related to inflammation (rs2227485, rs153109, rs17855750, rs2027432, rs2275913, rs763780, rs4819554, and rs13015714) and oxidative stress (rs1695, rs4680, rs1800566, rs4807542, rs713041, rs7579, rs230813, rs1004467, rs3824755, and rs9932581) to investigate whether these polymorphisms were associated with susceptibility to PE in a Chinese Han population. In this present study, we collected these data of experimental and clinical from above studies for haplotype analysis of inflammation-related SNPs in 631 PE patients and 720 normal pregnancy and oxidative stress-related SNPs in 342 PE patients and 457 normal pregnancies for susceptibility to PE. The data of genotype distribution and allele frequency comparisons after correction for multiple comparisons (P/8 or P/10) showed 2 among the 8 candidate inflammation-related SNPs have significant differences (rs2027432 genotype χ2 = 407.377, p < 0.001, p < 0.00625). Moreover, the minor alleles of rs2027432 T (minor allele χ2 = 450.923, p < 0.001, p < 0.00625; OR = 21.439, 95%CI = 15.181‐30.278) and rs4819554 G (minor allele χ2 = 163.465, p < 0.001, p < 0.00625; OR = 5.814, 95%CI = 4.380‐7.719) were confirmed as risk allele of PE, respectively. Our analysis revealed rs2027432 (TT) of NLRP3 and rs4819554 (GG) of IL-17RA are risk factors for PE. However, no significant difference was found at the oxidative stress-related SNPs. In the candidate loci for oxidative stress, we also identified 3 SNP matches (rs4807542 and rs713041, rs230813 and rs75799, rs1004467 and rs3824755) that had high linkage disequilibrium (LD) with each other and were selected as a block (r2 = 0.98, r2 = 0.97, r2 = 0.97, r2 > 0.9), and the GT and GC haplotypes of rs4807542 and rs713041 in GPX4 showed significant differences between the PE and control groups (χ2 = 5.143, p = 0.0233, p < 0.05; χ2 = 6.373, p = 0.0116, p < 0.05). So, we inferred that polymorphisms of NLRP3 rs2027432 and IL-17RA rs4819554, which are related to inflammation, and the rs713041 variant of GPX4, which is related to oxidative stress, were associated with susceptibility to PE. The GT and GC haplotypes of rs4807542 and rs713041 in GPX4 may increase the risk of PE in the Chinese Han population.
EMOTION REGULATION: PREDICTING SMART-SHAMING TENDENCY ON SOCIAL MEDIA COMMUNICATION

Smart-shaming has been nurtured by the false notion that “my ignorance is just as good as a discrete social phenomenon and required an aspect of self-regulation. The initial purpose of this study was explanatory and designed to measure whether smart-shaming is an emotional response and an endpoint of cognitive inconsistency. This notion was best explained in the light of Festinger (1957) theory of Cognitive Dissonance. This study examined the causes, motivations and behaviours of individuals on social media. The underlying forces as to how people process their cognitive functions of reappraisal and expression suppression is the focal point of this study. This paper was divided into two parts. A total of 406 respondents with the age range of 18-43 years old with social networking programs participated in this study. In study 1, there were two hundred fifty-six (256) participants answered Emotion Regulation Questionnaire (ERQ) and Online Smart Shaming Scale (OSS). In study 2, there were sixty (60) selected participants out of one hundred fifty (150) social media users. This part used nine (9) vignette stories that were based from the Online Smart Shaming Scale. In study 1, researchers’ used correlational design for data gathering procedure and linear regression for data analysis and found that a low level of emotion regulation promotes smart-shaming tendency. Specifically, low cognitive reappraisal and expression suppression promotes smart-shaming tendency. In study 2, T-test was utilized to determine the difference of high and low Cognitive Reappraisal and Expression Suppression scores for Smartshaming tendency. In study 2, T-test was utilized to determine the difference of high and low Cognitive Reappraisal and Expression Suppression study to reveal that there is a significant difference (p>0.05) between the two scores. In relation with the initial hypothesis, there is a negative relationship between emotion regulation and smart-shaming tendency. Likewise, cognitive processing and emotion regulation are intertwined constructs.
" A Class of Algorithms for Fast Digital Image Registration

4,258,264 3/1981 Kotera et al. . 250/484 4,276,473 6/1981 Kato et al. . 250/327.1 4,315,318 2/1982 Kato et al. . 364/515 4,356,398 10/1982 Komaki et al. . 250/327.2 4,387,428 6/1983 Ishida et al. . 364/414 4,636,953 1/1987 Kageyama . 364/413.23 4,685,146 8/1987 Fenster et al. . 382/288 4,710,875 12/1987 Nakajima et al. . 364/414
Effectively and Efficiency Consideration for Spatial Database

spaces are very useful in spatial database, and other applications that deal with it. Especially when want to found object that are similar to other object. This condition does not handle the relative position that found in some tree, such that, R- Tree, R+-Tree, and R*-Tree. Instead of handling distances between objects, using Euclidean Distance to compute the similarity between them, and retrieve the sets of object from database based on Euclidean distance that make this situation is happen and occurs. This paper proposed methods for similarity search that make the general assumption in the similarity can be done, by focusing on the effectively and efficiency in metric space for spatial database. This paper introduces how to improve the effectively and efficiency of the spatial database. It provided to extract some knowledge from spatial database has been presented. The main goal is to know how the search occurs, by using some assumption and ensures that there is similarity between a given queries Q and a set of object that found in the database.
METAL ALLOY FOR HYDROGEN SEPARATION AND PURFICATION

This is a continuation-in-part of application. Ser. No. 264,017, filed Mar. 11, 1963, now abandoned. The present invention relates to the separation and purification of hydrogen gas from multicomponent gas mixtures containing hydrogen and other gases including particularly hydrogen sulfide and other sulfurous constitu ents in accordance with gaseous diffusion techniques and, more particularly, to a novel hydrogen separation method which comprehends the diffusion of hydrogen through a poison-resistant permeation barrier made from an alloy of palladium and gold. It has long been known that certain selected com ponent gases comprised in multicomponent gas mixtures can be separated out of the mixtures by directing respec tive streams of the mixtures against barriers which are permeable to a particular selected component gas but im permeable to other gases and collecting the respective selected component gases at the barrier sides opposite the sides against which the streams are directed. Such proc esses are performed most effectively at elevated temper atures, It is known that palladium is permeable to hydro gen and impermeable to other gases. Illustrative of the fundamental technique of hydrogen separation through palladium diffusion barriers is the teaching found in United States Patent No. 1,174,631 to Snelling. Thus, by directing a multicomponent gas stream including hydro gen against a thin sheet or film of palladium maintained at an elevated temperature, the hydrogen can be selec tively separated and collected in a high purity state at the downstream side of the barrier. High purity hydrogen in large quantities is required in many industrial chemical production processes and, pres ently, in national defense work. Procurement of high vol ume hydrogen supplies of adequate purity at reasonable cost has long been a matter of concern to industry. While not conclusively proven to date, the existing theory re specting the transfer of hydrogen by diffusion through these hydrogen-specific palladium barriers is that the molecular hydrogen, in whatever gas mixture applied to a palladium barrier surface, dissociates into atomic hydro gen upon contacting the palladium and diffuses through interstices of the palladium metal lattice structure. Experi mental work indicates that the rate of hydrogen transfer through the permeation barriers take place generally in accordance with the relationship: 0.
Deep Active Inference for Autonomous Robot Navigation

Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is an approximate Bayesian inference engine, building an internal generative model to drive agents towards minimal surprise. Although this theory has shown interesting results with grounding in cognitive neuroscience, its application remains limited to simulations with small, predefined sensor and state spaces. In this paper, we leverage recent advances in deep learning to build more complex generative models that can work without a predefined states space. State representations are learned end-to-end from real-world, high-dimensional sensory data such as camera frames. We also show that these generative models can be used to engage in active inference. To the best of our knowledge this is the first application of deep active inference for a real-world robot navigation task.