Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic ancestry and population differences in levels of inflammatory cytokines in women: Role for evolutionary selection and environmental factors

Selection pressure due to exposure to infectious pathogens endemic to Africa may explain distinct genetic variations in immune response genes. However, the impact of those genetic variations on human immunity remains understudied, especially within the context of modern lifestyles and living environments, which are drastically different from early humans in sub Saharan Africa. There are few data on population differences in constitutional immune environment, where genetic ancestry and environment are likely two primary sources of variation. In a study integrating genetic, molecular and epidemiologic data, we examined population differences in plasma levels of 14 cytokines involved in innate and adaptive immunity, including those implicated in chronic inflammation, and possible contributing factors to such differences, in 914 AA and 855 EA women. We observed significant differences in 7 cytokines, including higher plasma levels of CCL2, CCL11, IL4 and IL10 in EAs and higher levels of IL1RA and IFNα2 in AAs. Analyses of a wide range of demographic and lifestyle factors showed significant impact, with age, education level, obesity, smoking, and alcohol intake, accounting for some, but not all, observed population differences for the cytokines examined. Levels of two pro-inflammatory chemokines, CCL2 and CCL11, were strongly associated with percent of African ancestry among AAs. Through admixture mapping, the signal was pinpointed to local ancestry at 1q23, with fine-mapping analysis refined to the Duffy-null allele of rs2814778. In AA women, this variant was a major determinant of systemic levels of CCL2 (p = 1.1e-58) and CCL11 (p = 2.2e-110), accounting for 19% and 40% of the phenotypic variance, respectively. Our data reveal strong ancestral footprints in inflammatory chemokine regulation. The Duffy-null allele may indicate a loss of the buffering function for chemokine levels. The substantial immune differences by ancestry may have broad implications to health disparities between AA and EA populations.
Attentional biases to threat in social anxiety disorder: time to focus our attention elsewhere?

ABSTRACT Background: Cognitive models propose that attentional biases to threat contribute to the maintenance of social anxiety disorder (SAD). However, the specific characteristics of such biases are still object to debate. Objectives: The current study aimed to disentangle effects of trait and state social anxiety on attention allocation towards social stimuli. Methods: Participants with SAD (n = 67) and healthy controls (n = 62) completed three visual search tasks while their eye movements were recorded. Half of the participants in each group were randomly assigned to a state anxiety induction. Results: Contrary to our predictions, neither trait nor state social anxiety was associated with a facilitated attention to or a delayed disengagement from threat. However, participants with SAD did show reduced fixation durations for threatening stimuli, indicating an avoidance of threat. Induction of state anxiety led to an increased distractibility by threat. Conclusions: We suggest that attention allocation in SAD is characterized by an avoidant rather than a vigilant attentional bias. Accordingly, our results contradict previous results that associate SAD with facilitated attention to threat and existing approaches to modify attentional biases, that aim to decrease attention towards threatening stimuli.
Length frequency and morphometric analysis of the non-indigenous red-rimmed melania (Melanoides tuberculata) populations in Slovakia

The red-rimmed melania Melanoides tuberculata (O. F. Müller) has been used in the aquarist trade and biocontrol programs, leading to its emergence outside its native range. Two populations of the red-rimmed melania occur in Slovakia. We investigated the morphometric features of mentioned populations and made a comparison. These two populations differ significantly in investigated features. Mean shell length of red-rimmed melania individuals from the Opatovce nad Nitrou was 14.7 mm, while the mean shell length of specimens from Piešťany was 24.4 mm (p < 10−6). Mean shell width, aperture length, and aperture width for specimens from the Opatovce nad Nitrou was 4.4 mm, 4.1 mm, and 2.3 mm, respectively. Mean shell width, aperture length, and aperture width of Piešťany specimens was 7.5 mm (p < 10−6), 7.1 mm (p < 10−6), and 3.8 mm (p < 10−6), respectively. The maximum shell length of specimens from the Opatovce nad Nitrou site was 22–23 mm, while shell length of specimens from Piešťany reached 32–33 mm. Regression analysis of the shell length and shell width, aperture length and aperture width, indicate equivalent relationship of the shell patterns for the two populations. Population structure analysis provided first inside of their population status, identifying a proliferating population at the Opatovce nad Nitrou site, while only adult individuals were observed at the site in Piešťany. The red-rimmed malania is a good candidate for possible biological invasions in thermal waters of temperate zone.
Measurement of thermal and electrical contact resistance between conductive materials

Recently, the heat generation density of electrical devices has increased along with miniaturization of electrical devices, and thermal management of electronics is important for reliability of electronics. The contact thermal resistance is one of the important parameter for thermal design. In this study, the contact thermal resistance is measured using steady state method. Then, contact electric resistance is measured using the four-terminal method. We investigate the pressure and surface roughness dependency of contact thermal resistance and contact electric resistance. Further the relationship between the contact thermal resistance and the contact electrical resistance is discussed using the theory of Wiedemann-Franz law.
In silico approaches to discover the functional impact of non-synonymous single nucleotide polymorphisms in selective sweep regions of the Landrace genome

Objective The aim of this study was to discover the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) that were found in selective sweep regions of the Landrace genome Methods Whole-genome re-sequencing data were obtained from 40 pigs, including 14 Landrace, 16 Yorkshire, and 10 wild boars, which were generated with the Illumina HiSeq 2000 platform. The nsSNPs in the selective sweep regions of the Landrace genome were identified, and the impacts of these variations on protein function were predicted to reveal their potential association with traits of the Landrace breed, such as reproductive capacity. Results Total of 53,998 nsSNPs in the mapped regions of pigs were identified, and among them, 345 nsSNPs were found in the selective sweep regions of the Landrace genome which were reported previously. The genes featuring these nsSNPs fell into various functional categories, such as reproductive capacity or growth and development during the perinatal period. The impacts of amino acid sequence changes by nsSNPs on protein function were predicted using two in silico SNP prediction algorithms, i.e., sorting intolerant from tolerant and polymorphism phenotyping v2, to reveal their potential roles in biological processes that might be associated with the reproductive capacity of the Landrace breed. Conclusion The findings elucidated the domestication history of the Landrace breed and illustrated how Landrace domestication led to patterns of genetic variation related to superior reproductive capacity. Our novel findings will help understand the process of Landrace domestication at the genome level and provide SNPs that are informative for breeding.
RoboCup@Home: Summarizing achievements in over eleven years of competition

Scientific competitions are important in robotics because they foster knowledge exchange and allow teams to test their research in unstandardized scenarios and compare result. In the field of service robotics its role becomes crucial. Competitions like RoboCup@Home bring robots to people, a fundamental step to integrate them into society. In this paper we summarize and discuss the differences between the achievements claimed by teams in their team description papers, and the results observed during the competition1 from a qualitative perspective. We conclude with a set of important challenges to be conquered first in order to take robots to people's homes. We believe that competitions are also an excellent opportunity to collect data of direct and unbiased interactions for further research.
Establishment of genetically encoded biosensors for cytosolic boric acid in plant cells

Boron (B) is an essential micronutrient for plants. To maintain B concentration in tissues at appropriate levels, plants use boric acid channels belonging to the NIP subfamily of aquaporins and BOR borate exporters. To regulate B transport, these transporters exhibit different cell-type specific expression, polar localization, and B-dependent post-transcriptional regulation. Here, we describe the development of genetically encoded biosensors for cytosolic boric acid to visualize the spatial distribution and temporal dynamics of B in plant tissues. The biosensors were designed based on the function of the NIP5;1 5'-untranslated region (UTR), which promotes mRNA degradation in response to an elevated cytosolic boric acid concentration. The signal intensities of the biosensor coupled with Venus fluorescent protein and a nuclear localization signal (uNIP5;1-Venus) showed negative correlation with intracellular B concentrations in cultured tobacco BY-2 cells. When expressed in Arabidopsis thaliana, uNIP5;1-Venus enabled the quantification of B distribution in roots at single-cell resolution. In mature roots, cytosolic B levels in stele were maintained under low B supply, while those in epidermal, cortical, and endodermal cells were influenced by external B concentrations. Another biosensor coupled with a luciferase protein fused to a destabilization PEST sequence (uNIP5;1-Luc) was used to visualize changes in cytosolic boric acid concentrations. Thus, uNIP5;1-Venus/Luc enables visualization of B transport in various plant cells/tissues.
pWebDAV: A Multi-Tier Storage System

Experiments using the Large Hadron Collider (LHC) currently generate tens of petabytes of reduced data per year, observational and simulation data in the climate domain is expected to reach eXabytes by 2021, and light source experiments are expected to generate hundreds of terabytes per day. At such extreme scale, the substantial amount of concurrency can cause critical contention issue of the I/O system. This study introduces pWebDAV as a heterogeneous, multi-tier storage system. pWebDAV proposes a dynamically coordinated I/O architecture offering overall data flow solutions (remote-local access). The fundamental idea is to implement, for each data transfer, I/O policies on the fly. pWebDAV controls all I/O nodes, participating in the data transfer, directly regardless of the tier. pWebDAV approach can fully utilize the provided I/O & network resources and is able to minimize disk and network contention. The focus in this study is the Metadata node scalability performance.
Body Joints Selection Convolutional Neural Networks for Skeletal Action Recognition

Action recognition from a skeletal sequence is a hot topic in computer vision. The recent representation learning approaches have achieved outstanding performance but still have some issues to be solved. One of them is the automatic selection of discriminative joints for different actions. Recent attempts on this problem suggested that RNNs are very effective to learn to selectively focus on discriminative joints. Considering that the convolutional neural network is more suitable for modeling the spatial dependency, we design a joints selection convolutional neural network for skeleton based action recognition in this paper, which enables the network itself to choose discriminative joints of skeleton for different actions at different time steps. Our method achieves state-of-the-art performance on three benchmark datasets. And through the extensive experimental analyses, the effectiveness of the proposed model has been demonstrated.
The Fermi surfaces of β-(BEDT-TTF)2X

We present de Haas-van Alphen (dHvA) measurements of the isostructural quasi-two-dimensional (quasi-2D) organic superconductors β-(BEDT-TTF) 2 IBr 2 and β H -(BEDT-TTF) 2 I 3 with superconducting transition temperatures of T c ≃ 2.5 K and T c ≃ 7 K, respectively. For both salts almost the same dHvA frequency and a beating behavior of the dHvA signal are found which reflect the similarity of the Fermi surface (FS) topology in both materials. The FS can be well described by a slightly corrugated quasi-2D cylinder. The corresponding overlap integral t⊥ between the highly conducting organic layers could be determined with high precision. t⊥ is larger for β H -(BEDT-TTF) 2 I 3 although this salt has the larger interlayer distance. The effective cyclotron masses are very similar in both materials (m c 4 m e ). On the other hand, the observed spin-splitting zeros suggest a rather large difference and large absolute values of the conduction electron g-factor which seems to reflect the importance of electron-electron interactions in the β-phase salts.
A Hybrid Parallel Implementation for the Maximum Flow Problem

The maximum flow problem is a classical combinatorial problem with many applications. In this work a hybrid parallel algorithm using both multi-core and many-core technologies for computing the maximum flow in a network is presented. The proposed implementation is applicable in OpenMP/CUDA-enabled computing environment. To improve the performance two strategies were implemented: an adaptive approach where the algorithm alternate GPU/CPU processing according to the number of active nodes and implementations of the global relabeling and gap relabeling heuristics on multi-core approach. When compared against the best sequential implementation, the speedups range from 2.36 to 5.38 in several kinds of graph. Results show that the proposed algorithm is faster than previous parallel implementations on CPU/GPUs for all kinds of tested graphs.
Maximum Throughput of TS/PS Scheme in an AF Relaying Network With Non-Linear Energy Harvester

The design of energy harvesting (EH) scheme is one of important issues in energy-constrained relaying networks. Since most of the existing EH schemes assume a linear EH model, which cannot capture the properties of EH circuits, the EH schemes dedicated for a practical EH model should be redesigned. Based on the time switching (TS) and power splitting (PS) receiver architectures, we study two EH schemes, namely, PS scheme and TS scheme, in an amplify-and-forward (AF) energy-constrained relaying network under a non-linear EH model. Two problems are formulated to maximize the achievable throughput based on the instantaneous channel state information by optimizing the PS ratio and TS ratio, respectively. The solutions for both EH schemes are obtained. Numerical results are presented to show that the designed EH schemes based on the non-linear EH model are able to achieve a higher throughput than those based on the existing linear model. It is found that employing a non-linear energy harvester instead of the linear one into AF relaying network, and the PS scheme still outperforms the TS scheme in general.
Assessing indices for predicting potential nitrogen mineralization in soils under different management systems.

A reliable laboratory index ofN availability would be useful for making N recommendations, but no single approach has received broad acceptance across a wide range of soils. We compared several indices over a range of soil conditions to test the possibility of combining indices for predicting potentially mineralizable N (N 0 ). Soils (0-5 and 5-15 cm) from nine tillage studies across the southern USA were used in the evaluations. Long-term incubation data were fit to a first-order exponential equation to determine N 0 , k (mineralization rate), and N 0 * (N 0 estimated with a fixed k equal to 0.054 wk -1 ). Out of 13 indices, five [total C (TC), total N (TN), N mineralized by hot KCI (Hot_N), anaerobic N (Ana_N), and N mineralized in 24 d (Nmin_24)] were strongly correlated to N 0 (r > 0.85) and had linear regressions with r 2 > 0.60. None of the indices were good predictors ofk. Correlations between indices and N 0 * improved compared with N 0 , ranging from r = 0.90 to 0.95. Total N and Hush of CO 2 determined after 3 d (Fl_CO2) produced the best multiple regression for predicting N 0 (R 2 = 0.85) while the best combination for predicting N 0 * (R 2 = 0.94) included TN, Fl_CO2 Cold_N, and NaOH_N. Combining indices appears promising for predicting potentially mineralizable N, and because TN and Fl_CO2 are rapid and simple, this approach could be easily adopted by soil testing laboratories.
Modular software architecture for human-robot interaction applied to the InterBot mobile robot

This paper describes a new modular software architecture designed to allow collaborative Human-Robot Interaction (HRI). The proposed HRI system is applied to the InterBot (Interactive mobile roBot), which integrates two Human-Machine Interface (HMI) devices: an on-board portable device and a remote station. The software architecture is mainly developed using the Robot Operating System (ROS) framework, and is designed to be modular and versatile. The proposed software modules provide a reliable structure for users to interact with and directly control the InterBot, e.g. allowing a human to be guided during a tour, or for a human to order the InterBot to perform tasks such as delivering messages or items between laboratories. The remote station acts as the supervisor, being in charge of planning the robot tasks sequentially, while displaying the robot information in a perceivable way. A series of tests and experiments were carried out in order to assess the performance of the InterBot platform in different test scenarios and under three Human-Robot control modes: teleoperation, autonomous and shared control. Presented real-world tests show the functional flexibility of the proposed architecture and outline future directions of development and research. The results also show that the InterBot software architecture is robust to the occurrence of network delays.
Adiposity and Serum Selenium in U.S. Adults

Requirements for selenium and other antioxidant nutrients are increased in pro-oxidant and pro-inflammatory conditions such as excess adiposity. Data concerning the association of excess general and central adiposity with circulating selenium concentrations, however, are limited. We examined the cross-sectional associations of body mass index (BMI), percent body fat (%BF), and waist circumference (WC) with serum selenium concentrations in 6440 men and 6849 women aged ≥20 years who participated in the U.S. Third National Health and Nutrition Examination Survey. In multivariable analyses, the average difference (95% confidence interval (CI)) in serum selenium comparing the highest with the lowest quartiles of BMI was -4.0 (-5.5, -1.6) ng/mL in both men and women. These inverse associations were evident after further adjustment for WC. For %BF, the average differences (95% CI) in serum selenium between the highest and the lowest quartiles of %BF were -1.7 (-4.2, 0.7) ng/mL in men and -4.5 (-7.0, -1.9) ng/mL in women. The inverse association in women persisted after adjusting for WC. For WC, the average differences (95% CI) in serum selenium between the highest and the lowest quartiles were -1.9 (-3.8, -0.1) ng/mL in men and -3.9 (-5.8, -2.0) ng/mL in women. After further adjustment for BMI, the inverse association became positive in men and null in women. Our findings suggest that general and central adiposity have different associations with serum selenium levels and that these associations may depend on gender.
Design of CMOS-memristor Circuits for LSTM architecture

Long Short-Term memory (LSTM) architecture is a well-known approach for building recurrent neural networks (RNN) useful in sequential processing of data in application to natural language processing. The near-sensor hardware implementation of LSTM is challenged due to large parallelism and complexity. We propose a $0.18 \mu \mathrm {m}$ CMOS, GST memristor LSTM hardware architecture for near-sensor processing. The proposed system is validated in a forecasting problem based on Keras model.
Soft commutated soft-two-switch DC/DC converter

In this paper, an improved structure of an isolated DC/DC converter is proposed. The presented structure achieves soft commutation and soft switching conditions. Soft commutation is gained using a passive-clamp circuit at low voltage side of the transformer. Also, a switch at high voltage side creating a freewheeling path during the commutation time interval to speed up the commutation is used. Soft switching is achieved using parasitic capacitors of the switches and diodes and also magnetic and leakage inductances of the transformer and there is no need to any auxiliary circuit or extra components. The theoretical analyses are verified according to the PSCAD simulation results.
iOOBN: A Bayesian Network Modelling Tool Using Object Oriented Bayesian Networks with Inheritance

The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.
An Investigation of the Impact of Guessing on Coefficient α and Reliability

Guessing is known to influence the test reliability of multiple-choice tests. Although there are many studies that have examined the impact of guessing, they used rather restrictive assumptions (e.g., parallel test assumptions, homogeneous inter-item correlations, homogeneous item difficulty, and homogeneous guessing levels across items) to evaluate the relation between guessing and test reliability. Based on the item response theory (IRT) framework, this study investigated the extent of the impact of guessing on reliability under more realistic conditions where item difficulty, item discrimination, and guessing levels actually vary across items with three different test lengths (TL). By accommodating multiple item characteristics simultaneously, this study also focused on examining interaction effects between guessing and other variables entered in the simulation to be more realistic. The simulation of the more realistic conditions and calculations of reliability and classical test theory (CTT) item statistics were facilitated by expressing CTT item statistics, coefficient α, and reliability in terms of IRT model parameters. In addition to the general negative impact of guessing on reliability, results showed interaction effects between TL and guessing and between guessing and test difficulty.