Miracle-dz/UniFAR-FTUs huggingface dataset:
Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners. Abbreviations ANOVA: ANalysis Of Variance; AUC: Area Under the Curve; Best Interface: BI; CAPRI: Critical Assessment of Prediction of Interactions; CC-D: Complete Cross-Docking; DNA: DesoxyriboNucleic Acid; FDR: False Discovery Rate; FRIres(type): Fraction of each Residue type in the Interface; FP: False Positives; GI: Global Interface; HCMD: Help Cure Muscular Dystrophy; JET: Joint Evolutionary Tree; MAXDo: Molecular Association via Cross Docking; NAI: Nucleic Acid Interface; NPV: Negative Predicted Value; PDB: Protein Data Bank; PIP: Protein Interface Propensity; PiQSi: Protein Quaternary Structure investigation; PPIs: Protein-Protein Interactions; PPV: Positive Predicted Value; Prec. : Precision; PrimI: Primary Interface; RNA: RiboNucleic Acid; ROC: Receiver Operating Characteristic; SecI: Secondary Interface; Sen.: Sensitivity; Spe. : Specificity; TN: True Negatives; TP: True Positives; WCG: World Community Grid.
Artificial Intelligence for Wireless Connectivity and Security of Cellular-Connected UAVs

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.
Novel Mutations in pncA Gene of Pyrazinamide Resistant Clinical Isolates of Mycobacterium tuberculosis

In clinical isolates of Mycobacterium tuberculosis (MTB), resistance to pyrazinamide occurs by mutations in any positions of the pncA gene (NC_000962.3) especially in nucleotides 359 and 374. In this study we examined the pncA gene sequence in clinical isolates of MTB. Genomic DNA of 33 clinical isolates of MTB was extracted by the Chelex100 method. The polymerase chain reactions (PCR) were performed using specific primers for amplification of 744 bp amplicon comprising the coding sequences (CDS) of the pncA gene. PCR products were sequenced by an automated sequencing Bioscience system. Additionally, semi Nested-allele specific (sNASP) and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods were carried out for verification of probable mutations in nucleotides 359 and 374. Sequencing results showed that from 33 MTB clinical isolates, nine pyrazinamide-resistant isolates have mutations. Furthermore, no mutation was detected in 24 susceptible strains in the entire 561 bp of the pncA gene. Moreover, new mutations of G→A at position 3 of the pncA gene were identified in some of the resistant isolates. Results showed that the sNASP method could detect mutations in nucleotide 359 and 374 of the pncA gene, but the PCR-RFLP method by the SacII enzyme could not detect these mutations. In conclusion, the identification of new mutations in the pncA gene confirmed the probable occurrence of mutations in any nucleotides of the pncA gene sequence in resistant isolates of MTB.
Genome-wide association study identiﬁes nine novel loci for 2D:4D ﬁnger ratio, a putative retrospective biomarker of testosterone exposure in utero

The ratio of the length of the index ﬁnger to that of the ring ﬁnger (2D:4D) is sexually dimorphic and is commonly used as a non-invasive biomarker of prenatal androgen exposure. Most association studies of 2D:4D ratio with a diverse range of sex-speciﬁc traits have typically involved small sample sizes and have been difﬁcult to replicate, raising questions around the utility and precise meaning of the measure. In the largest genome-wide association meta-analysis of 2D:4D ratio to date ( N ¼ 15 661, with replication N ¼ 75 821), we identiﬁed 11 loci (9 novel) explaining 3.8% of the variance in mean 2D:4D ratio. We also found weak evidence for association ( b ¼ 0.06; P ¼ 0.02) between 2D:4D ratio and sensitivity to testosterone [length of the CAG microsatellite repeat in the androgen receptor ( AR ) gene] in females only. Furthermore, genetic variants associated with (adult) testosterone levels and/or sex hormone-binding globulin were not associated with 2D:4D ratio in our sample. Although we were unable to ﬁnd strong evidence from our genetic study to support the hypothesis that 2D:4D ratio is a direct biomarker of prenatal exposure to androgens in healthy individuals, our ﬁndings do not explicitly exclude this possibility, and pathways involving testosterone may become apparent as the size of the discovery sample increases further. Our ﬁndings provide new insight into the underlying biology shaping 2D:4D variation in the general population.
Enhancing Performance and Bit Rates in a Brain–Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs

A brain–computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class (N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class (N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class (N = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5–4 Hz), θ: (4–8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 324 bits/min with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of 10–25 bits/min. In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.
Immunological markers after long-term treatment interruption in chronically HIV-1 infected patients with CD4 cell count above 400 × 106 cells/l

Objective:To analyse immunological markers associated with CD4+ lymphocyte T-cell count (CD4+) evolution during 12-month follow-up after treatment discontinuation. Method:Prospective observational study of chronically HIV-1 infected patients with CD4+ above 400 × 106 cells/l. Results:CD4+ changes took place in two phases: an initial rapid decrease in the first month (−142 × 106 cells/l on average), followed by a slow decline (−17 × 106 cells/l on average) The second slope of CD4+ decline was not correlated with the first and only baseline plasma HIV RNA was associated with it. The decline in CD4+ during the first month was steeper in patients with higher CD4+ and weaker plasma HIV RNA baseline levels. Moreover, the decline was less pronounced (P < 10−4) in patients with CD4+ nadir above 350 × 106 cells/l (−65 × 106 cells/l per month) in comparison with those below 350 × 106 cells/l (−200 × 106 cells/l per month). A high number of dendritic cells (DCs) whatever the type was associated with high CD4+ at the time of treatment interruption and its steeper decline over the first month. Moreover, the myeloid DC level was stable whereas the lymphoid DC count, which tended to decrease in association with decrease in CD4+, was negatively correlated with the HIV RNA load slope. Conclusions:The results support the use of the CD4+ nadir to predict the CD4+ dynamic after treatment interruption and consideration of the CD4+ count after 1-month of interruption merely reflects the 12-month level of CD4+. Although DCs seem to be associated with the CD4+ dynamic, the benefit of monitoring them has still to be defined.
Unmetabolized folic acid and total folate concentrations in breast milk are unaffected by low-dose folate supplements.

BACKGROUND Many lactating women in North America are exposed to high synthetic folic acid intakes because of food fortification and vitamin supplement use. Few data exist on the potential long-term effect of high folic acid intakes on milk folate concentrations, whereas no data are available on the effect of supplemental [6S]-5-methyltetrahydrofolate ([6S]-5-methylTHF). OBJECTIVE The aim of the present study was to investigate the effect of 3 treatments (placebo, folic acid, and [6S]-5-methylTHF) on milk folate and folate-binding protein (FBP) concentrations and to determine whether unmetabolized folic acid is present in milk. DESIGN In this 16-wk randomized, placebo-controlled intervention, 69 lactating women were randomly assigned to receive [6S]-5-methylTHF (416 microg/d, 906 nmol/d) or a placebo, or were assigned to receive folic acid (400 microg/d, 906 nmol/d) within 1 wk postpartum. Total milk folate, FBP, and unmetabolized folic acid concentrations were measured at 16 wk. RESULTS Unmetabolized folic acid was detected in 96% of milk samples tested representing approximately 8% of total milk folate concentrations. Total milk folate, FBP, and the proportion of unmetabolized milk folic acid did not differ between treatments; however, FBP concentrations were significantly lower than those published before mandatory folic acid fortification of the food supply. CONCLUSION Maternal intake of synthetic folic acid leads to the appearance of unmetabolized folic acid in milk and, seemingly, a down-regulation of milk FBP synthesis. The impact of these changes on the bioavailability of folate in infants requires further exploration.
Network Analysis in the Legal Domain: A complex model for European Union legal sources

Legislators, designers of legal information systems, as well as citizens face often problems due to the interdependence of the laws and the growing number of references needed to interpret them. Quantifying this complexity is not an easy task. In this paper, we introduce the "Legislation Network" as a novel approach to address related problems. We have collected an extensive data set of a more than 60-year old legislation corpus, as published in the Official Journal of the European Union, and we further analysed it as a complex network, thus gaining insight into its topological structure. Among other issues, we have performed a temporal analysis of the evolution of the Legislation Network, as well as a robust resilience test to assess its vulnerability under specific cases that may lead to possible breakdowns. Results are quite promising, showing that our approach can lead towards an enhanced explanation in respect to the structure and evolution of legislation properties.
Modeling black bear population dynamics in a human-dominated stochastic environment

Many large carnivore populations exist in human-influenced stochastic environments where availability of natural food sources vary annually and anthropogenic food sources can supplement energetic demands, but at a potential demographic cost due to human–wildlife conflict and subsequent conflict management. Understanding how these competing factors influence a population is complex and difficult to study, but here we demonstrate the utility of using a stochastic projection matrix model and perturbation analysis to gain insight into this problem. We modeled a black bear population subjected to stochastic failures of fruiting and masting species, but with access to garbage in urban environments. We parameratized our model with data from a 6-year study on black bears in Aspen, Colorado and data synthesized from other research studies. Using computer simulation, we investigated the effect that different levels of conflict-bear removal can have on a bear population by comparing a “reference” scenario where bears did not benefit from human food sources or experience conflict-bear removals with two urban scenarios where bears had varying access to human foods, but conflict bears were removed. We used perturbation analyses to evaluate consequences for changing population vital rates and to estimate the impact each vital rate change had on population growth. Simulations were used to identify how much variation in each vital rate influenced variation in the population growth rate. We identified the survival rate of breeding adult females during good natural food years as having the highest elasticity value. We found that the benefit of increased cub production from available human food sources during natural food failure years was quickly negated if management of conflict bears through removal reduced adult female survival. Increasing the frequency of years when natural food production fails resulted in disproportionate impacts from available urban food and conflict-bear removals, where population growth rates in a High Removal scenario declined 1.5 times faster than in the reference scenario. Our findings suggest that for regions where changing climates will increase the frequency of natural food failures, managers may need to utilize non-lethal practices in managing conflict bears and municipalities will need to secure human food sources to reduce the need for conflict-bear removals and potential population declines. © 2014 Elsevier B.V. All rights reserved.
Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking

In this paper, we are interested in estimating the angle of arrival (AoA) of all the signal paths arriving at a receiver array using only the corresponding received signal magnitude measurements (or, equivalently, the received power measurements). Typical AoA estimation techniques require phase information, which is not available in some WiFi/Bluetooth receivers, and is further challenging to properly measure in a synthetic antenna array due to synchronization issues. In this paper, we then show that AoA estimation is possible with only the received signal magnitude measurements. More specifically, we first propose a framework, based on the spatial correlation of the received signal magnitude, to estimate the AoA of signal paths from fixed signal sources (both active transmitters and passive objects). Next, we extend our AoA estimation framework to a dual setting, and further utilize a particle filter, to show how a moving target (both active transmitters and passive robots/humans) can be tracked, based on only the received signal magnitude measurements of a small number of fixed receivers. We extensively validate our proposed framework with several experiments (total of 22), in both closed and open areas. More specifically, we first utilize a robot to emulate an antenna array, and estimate the AoA of active transmitters, as well as passive objects using only the received WiFi signal magnitude measurements. We next validate our tracking framework by using only three off-the-shelf WiFi devices as receivers, to track an active transmitter, a passive robot that writes the letters of IPSN on its path, and a walking human. Overall, our results show that AoA can be estimated, with a high accuracy, with only the received signal magnitude measurements, and can be utilized for high quality angular localization and tracking.
Predicting Online Islamophobic Behavior after #ParisAttacks

The Paris terrorist attacks occurred on November 13, 2015, prompting a massive response on social media including Twitter, with millions of posted tweets in the first few hours after the attacks. Most of the tweets were condemning the attacks and showing support to Parisians. One of the trending debates related to the attacks concerned possible association between terrorism and Islam, and Muslims in general. This created a global discussion between those attacking and those defending Islam and Muslims. In this paper, we use this incident to examine the effect of online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event. In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We quantitatively analyzed users' network interactions and historical tweets to predict their attitudes towards Islam and Muslim. We provide a description of the quantitative results based on the content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users' stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims with 82\% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms.
Association of MTHFR C677T gene polymorphism with metabolic syndrome in a Chinese population: a case–control study

Abstract Objective To investigate the association of the MTHFR C677T gene polymorphism with metabolic syndrome (MetS) in people in Hubei Province, China. Methods A case–control study was conducted with 651 subjects with MetS (MetS group) and 727 healthy controls (control group) at Renmin Hospital of Wuhan University between January and December 2016. The MTHFR C677T genotype was detected by the gene chip technique and clinical data were collected. Results Body mass index, waist circumference, the waist-hip-ratio, systolic and diastolic blood pressure, fasting blood glucose, fasting insulin, triglyceride, total cholesterol, low-density lipoprotein-cholesterol, and homocysteine levels, and the homeostasis model assessment of insulin resistance were higher in the MetS group than in controls. The risk of MetS was higher for the TT genotype and T allele carriers than for the CC genotype and C allele carriers. With MetS, the TT genotype increased the risk of elevated blood pressure, fasting glucose levels, and triglyceride levels. Patients with MetS and the TT genotype showed more severe abdominal obesity, dyslipidaemia, insulin resistance, elevated blood pressure, elevated fasting glucose levels, and hyperhomocysteinaemia compared with those with the CC genotype. Conclusions In this population, MTHFR C677T gene polymorphism may be a risk factor for MetS.
Photoelectrocatalytic reduction of carbon dioxide to methanol at cuprous oxide foam cathode

In order to increase the reduction rate of CO2 to methanol, the photoelectrocatalytic reduction of CO2 at Cu2O foam electrodes is proposed. The Cu2O foam electrodes are fabricated by electrodeposition of Cu2O coatings on copper foam substrates. The effect of bath pH and deposition time on the morphology and structure is investigated. The Cu2O foam electrodes deposited at bath pH 10 for 20 min exhibit higher intensity of (111) diffraction peak. The photoelectrocatalytic performance of Cu2O foam electrodes for CO2 reduction to methanol depends largely on exposed Cu2O{111} facets. At the applied potential of −1.5 V (vs. saturated calomel electrode), the optimum methanol concentration and the faradaic efficiency of methanol formation are obtained within 1.5 h, and they are 1.41 mM and 29.1%, respectively. The formation rate of methanol achieves 23.5 µmol cm−2 h−1 within 1.5 h.