Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic detection of human interactions from RGB-D data for social activity classification

We present a system for temporal detection of social interactions. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications, it is important to be able to move to more realistic data. For this reason, the proposed approach temporally detects intervals where individual or social activity is occurring. Recognition of human activities is a key feature for analysing the human behaviour. In particular, recognition of social activities is useful to trigger human-robot interactions or to detect situations of potential danger. Based on that, this research has three goals: (1) define a new set of descriptors, which are able to characterise human interactions; (2) develop a computational model to segment temporal intervals with social interaction or individual behaviour; (3) provide a public dataset with RGB-D data with continuous stream of individual activities and social interactions. Results show that the proposed approach attained relevant performance with temporal segmentation of social activities.
Fine particulate air pollution associated with increased risk of hospital admissions for hypertension in a tropical city, Kaohsiung, Taiwan

ABSTRACT The aim of this study was to assess whether a correlation exists between fine particles (PM2.5) levels and number of hospital admissions for hypertension in Kaohsiung, Taiwan. Hospital admission frequency and ambient air pollution data were obtained for Kaohsiung for 2009–2013. A time-stratified case-crossover method was used to estimate relative risk for hospital admissions, controlling for weather, day of the week, seasonality, and long-term time trends. Odds ratios and 95% confidence intervals were calculated for a 10 µg/m3 increment of PM2.5 for lags from days 0 to 6. Data showed no significant associations between PM2.5 levels and number of hypertension-related hospital admissions on warm days (>25°C). However, on cool days (<25°C), a significant positive association was found with frequency of hypertension admissions in the single-pollutant model (without adjusting for other pollutants) with a 10 µg/m3 rise in PM2.5 on day of admission (lag 0) associated with a 12% increase in number of admissions for hypertension. In the two-pollutant model, the association of PM2.5 with rate of hypertension hospitalizations remained significant after including SO2 or O3 on lag day 0. Data demonstrate that an association between short-term exposure to PM2.5 and elevated risk of hypertension-related hospital admissions may exist in Kaohsiung, Taiwan, a tropical city.
Maternal obesity reprograms offspring's executive brain centers in a sex‐specific manner?

This editorial highlights an article by McKee and colleagues in the current issue of Journal of Neurochemistry, in which the authors report epigenetic changes linked to one‐carbon metabolism in prefrontal cortex (PFC) of murine offspring from dams fed high‐fat diet to mimic maternal obesity. The group found that high‐fat diet feeding in utero increases weight gain in offspring and dynamically alters DNA methylation in the PFC of male but not female brains. These epigenetic marks were associated with a shift in brain one‐carbon metabolism (folate and methionine) intermediates and were normalized by early‐life methyl‐donor supplementation in a sex‐specific manner.
GIST: A Gibbs sampler to identify intracellular signal transduction pathways

Identification of intracellular signal transduction pathways plays an important role in understanding the mechanisms of how cells respond to external stimuli. The availability of high throughput microarray expression data and accumulating knowledge of protein-protein interactions have provided us with useful information to infer condition-specific signal transduction pathways. We propose a novel method called Gibbs sampler to Infer Signal Transduction pathways (GIST) to search dys-regulated pathways from large-scale protein-protein interaction networks. GIST incorporates different knowledge sources to extract paths that are highly associated with biological phenotypes or clinical information. One of the most attractive features of GIST is that the algorithm will not only provide the single optimal path according to the defined cost function but also reveal multiple suboptimal paths as alternative solutions, which can be utilized to study the pathway crosstalk. As a proof-of-concept, we test our GIST algorithm on yeast PPI networks and the identified MAPK signaling pathways are well supported by existing biological knowledge. We also apply the GIST algorithm onto a breast cancer patient dataset to show its feasibility of identifying potential pathways for further biological validation.
Identification of microorganisms in upper endoscopy of patients with different gastrointestinal diseases

The microbiota of the human stomach and its composition remain largely unknown endoscopic biopsy samples show a diverse community of many phylotypes which was identified, our study is carried on 21 patients submitted to upper gastrointestinal endoscopic examination the majority of microorganisms were Gram +ve cocci 52% (Kocuria kristina, Staph hyicus and Staph sciuri, staph intermiduis). Gram –ve bacilli were 5% and Giardia were 10% but 33% of cases have no growth. The relation between microorganisms in upper gastrointestinal tract endoscopy and its relation with the endoscopic finding showed that microorganisms increased with erosive gastritis and gastropathy, direct relation between bacterial growth in gastric wash and proton pump inhibitor drug intake with higher frequency of increased bacterial growth with proton pump inhibitor drug intake, but there is inverse relation between Helicobacter growth with proton pump intake (PPI) intake as there is higher growth with non PPI intake and this may result from patchy bacterial colonization through the stomach and altered distribution because of gastritis with atrophy. So multiple gastric biopsies were recommended from different sites. Correspondence to: Khadiga Ahmed Ismail, Laboratory Medicine Department, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia, E-mail: khadigaahmed68@yahoo.com
Resistance to Bacitracin in Bacillus subtilis: Unexpected Requirement of the BceAB ABC Transporter in the Control of Expression of Its Own Structural Genes

ABSTRACT The Bacillus subtilis BceAB ABC transporter involved in a defense mechanism against bacitracin is composed of a membrane-spanning domain and a nucleotide-binding domain. Induction of the structural bceAB genes requires the BceR response regulator and the BceS histidine kinase of a signal transduction system. However, despite the presence of such a transduction system and of bacitracin, no transcription from an unaltered bceA promoter is observed in cells lacking the BceAB transporter. Expression in trans of the BceAB transporter in these bceAB cells restores the transcription from the bceA promoter. Cells possessing a mutated nucleotide-binding domain of the transporter are also no longer able to trigger transcription from the bceA promoter in the presence of bacitracin, although the mutated ABC transporter is still bound to the membrane. In these cells, expression of the bceA promoter can no longer be detected, indicating that the ABC transporter not only must be present in the cell membrane, but also must be expressed in a native form for the induction of the bceAB genes. Several hypotheses are discussed to explain the simultaneous need for bacitracin, a native signal transduction system, and an active BceAB ABC transporter to trigger transcription from the bceA promoter.
How to lessen the effects of user resistance on the adoption of an e-learning environment: screenshot annotation on Flickr

ABSTRACT The study makes use of the photo-hosting site, namely Flickr, for students to upload screenshots to demonstrate computer software problems and troubleshooting software. By creating non-text stickers and text-based annotations above the screenshots, students are able to help one another to diagnose and solve problems with greater certainty. In addition to applying the technology acceptance model, three factors from the user resistance perspective, namely, user resistance, peer opinion, and self-efficacy for change, were chosen as the basis for the research model. Based on data collected from 399 students, the results indicated that students’ resistance had a negative and direct effect on their behavioral intention. Both peer opinions and students’ self-efficacy for change had negative and direct effects on their resistance, which positively and indirectly influenced behavioral intention. User resistance could be significantly improved by promoting positive peer opinions or enhancing students’ self-efficacy for change.
Cell adhesion and fluid flow jointly initiate genotype spatial distribution in biofilms

Biofilms are microbial collectives that occupy a diverse array of surfaces. The function and evolution of biofilms are strongly influenced by the spatial arrangement of different strains and species within them, but how spatiotemporal distributions of different genotypes in biofilm populations originate is still underexplored. Here, we study the origins of biofilm genetic structure by combining model development, numerical simulations, and microfluidic experiments using the human pathogen Vibrio cholerae. Using spatial correlation functions to quantify the differences between emergent cell lineage segregation patterns, we find that strong adhesion often, but not always, maximizes the size of clonal cell clusters on flat surfaces. Counterintuitively, our model predicts that, under some conditions, investing in adhesion can reduce rather than increase clonal group size. Our results emphasize that a complex interaction of fluid flow and cell adhesiveness can underlie emergent patterns of biofilm genetic structure. This structure, in turn, has an outsize influence on how biofilm-dwelling populations function and evolve. Author summary Biofilms are bacterial groups, often attached to surfaces, in which a broad variety of cooperative and competitive interactions typically occur. The spatial organization of different strains and species within biofilm communities strongly influences their global functioning, but little is known about how such structure arises. Combining experiments on V. cholerae and simulations of a cellular automaton, we show that the complex interaction between bacterial traits (cell adhesion) and environmental factors (fluid flow intensity) strongly influences the early origins of biofilm spatial structure. In most cases, we found that highly-adhesive strains form larger clusters than the weakly-adhesive ones. Against intuition, however, we also found the opposite outcome: weakly-adhesive tend to form larger clusters than the highly adhesive ones when flows are weak or the population density of colonizing cells is high.
Ultracold collisional magnetic field gradiometer

Tomasz Wasak, Krzysztof Jachymski, Tommaso Calarco, and Antonio Negretti 1 Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland 2 Institute for Theoretical Physics III & Center for Integrated Quantum Science and Technologies (IQST), University of Stuttgart, Pfaffenwaldring 57, 70550 Stuttgart, Germany 3 Institute for Complex Quantum Systems & Center for Integrated Quantum Science and Technologies (IQST), Universität Ulm, 89069 Ulm, Germany 4 Zentrum für Optische Quantentechnologien and The Hamburg Centre for Ultrafast Imaging, Universität Hamburg, Luruper Chaussee 149, D-22761 Hamburg, Germany (Dated: March 9, 2018)
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving

Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural networks (BWNs) are the extreme case which quantizes the float-point into just 1 bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet- and MobileNet-based binary weight YOLOv2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.
Operation of Photobioreactor on Refinery Gases for CO2 Sequestration and Fatty Acid Production Using Algae

India over 80% of its energy needs are met through fossil fuels. India experienced dramatic growth in fossil fuel fed CO2 emissions averaging to 5.7% per year and becoming world third largest fossil fuel emitting country. Microalgae are capable of capturing green house gas- CO2 and converting to fatty acid. CO2 sequesting algae identified as Chlorococcum sp was isolated from environment for fatty acid production. Flue gas from refinery with 10-15 % CO2 has been studied for CO2 sequestration in photobioreactor. The biomass and lipid productivity for Chlorococcum sp. with 0.1% of CO2 from flue gas were 139 mg/l/d and 81.2 mg/l/d respectively in photobioreactor.
Optimal Power Scheduling for Uplink Transmissions in SIC-Based Industrial Wireless Networks With Guaranteed Real-Time Performance

The <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-SIC (successive interference cancellation) technology can support at most <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> parallel transmissions, and thus fast media access can be provided, which is vital for real-time industrial wireless networks. However, it suffers from high power consumption because high interference caused by parallel transmissions has to be overcome. In this paper, given the real-time performance requirements, we consider a network supporting <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-SIC, and study how to minimize aggregate power consumptions of user equipments for uplink transmissions by jointly addressing power allocation and user scheduling. We show that the problem is solvable in polynomial time in the case of continuous transmit powers by an algorithm with complexity of <inline-formula><tex-math notation="LaTeX">$O(n\text{log}(n))$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of user equipments. In the case of discrete transmit powers, the problem is shown to be polynomially solvable for 2-SIC, and we also propose an approximation algorithm for <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-SIC where <inline-formula><tex-math notation="LaTeX">$k>2$</tex-math></inline-formula>. Experimental evaluations reveal that the real-time performances have tremendous impacts on both the aggregate power consumption and the maximum of the transmit powers of user equipments. Besides, the usability of SIC in low-power applications is also shown by experimental evaluations.
Variation in infectivity and aggressiveness in space and time in wild host–pathogen systems: causes and consequences

Variation in host resistance and in the ability of pathogens to infect and grow (i.e. pathogenicity) is important as it provides the raw material for antagonistic (co)evolution and therefore underlies risks of disease spread, disease evolution and host shifts. Moreover, the distribution of this variation in space and time may inform us about the mode of coevolutionary selection (arms race vs. fluctuating selection dynamics) and the relative roles of G × G interactions, gene flow, selection and genetic drift in shaping coevolutionary processes. Although variation in host resistance has recently been reviewed, little is known about overall patterns in the frequency and scale of variation in pathogenicity, particularly in natural systems. Using 48 studies from 30 distinct host–pathogen systems, this review demonstrates that variation in pathogenicity is ubiquitous across multiple spatial and temporal scales. Quantitative analysis of a subset of extensively studied plant–pathogen systems shows that the magnitude of within‐population variation in pathogenicity is large relative to among‐population variation and that the distribution of pathogenicity partly mirrors the distribution of host resistance. At least part of the variation in pathogenicity found at a given spatial scale is adaptive, as evidenced by studies that have examined local adaptation at scales ranging from single hosts through metapopulations to entire continents and – to a lesser extent – by comparisons of pathogenicity with neutral genetic variation. Together, these results support coevolutionary selection through fluctuating selection dynamics. We end by outlining several promising directions for future research.
AVE0991, a nonpeptide analogue of Ang-(1-7), attenuates aging-related neuroinflammation

During the aging process, chronic neuroinflammation induced by microglia is detrimental for the brain and contributes to the etiology of several aging-related neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. As a newly identified axis of renin-angiotensin system, ACE2/Ang-(1-7)/MAS1 axis plays a crucial role in modulating inflammatory responses under various pathological conditions. However, its relationship with aging-related neuroinflammation is less studied so far. In this study, by using SAMP8 mice, an animal model of accelerated aging, we revealed that the neuroinflammation in the aged brain might be attributed to a decreased level of Ang-(1-7). More importantly, we provided evidence that AVE0991, a nonpeptide analogue of Ang-(1-7), attenuated the aging-related neuroinflammation via suppression of microglial-mediated inflammatory response through a MAS1 receptor-dependent manner. Meanwhile, this protective effect might be ascribed to the M2 activation of microglia induced by AVE0991. Taken together, these findings reveal the association of Ang-(1-7) with the inflammatory response in the aged brain and uncover the potential of its nonpeptide analogue AVE0991 in attenuation of aging-related neuroinflammation.
Autism spectrum disorder: Consensus guidelines on assessment, treatment and research from the British Association for Psychopharmacology

An expert review of the aetiology, assessment, and treatment of autism spectrum disorder, and recommendations for diagnosis, management and service provision was coordinated by the British Association for Psychopharmacology, and evidence graded. The aetiology of autism spectrum disorder involves genetic and environmental contributions, and implicates a number of brain systems, in particular the gamma-aminobutyric acid, serotonergic and glutamatergic systems. The presentation of autism spectrum disorder varies widely and co-occurring health problems (in particular epilepsy, sleep disorders, anxiety, depression, attention deficit/hyperactivity disorder and irritability) are common. We did not recommend the routine use of any pharmacological treatment for the core symptoms of autism spectrum disorder. In children, melatonin may be useful to treat sleep problems, dopamine blockers for irritability, and methylphenidate, atomoxetine and guanfacine for attention deficit/hyperactivity disorder. The evidence for use of medication in adults is limited and recommendations are largely based on extrapolations from studies in children and patients without autism spectrum disorder. We discuss the conditions for considering and evaluating a trial of medication treatment, when non-pharmacological interventions should be considered, and make recommendations on service delivery. Finally, we identify key gaps and limitations in the current evidence base and make recommendations for future research and the design of clinical trials.
Fank1 and Jazf1 promote multiciliated cell differentiation in the mouse airway epithelium

ABSTRACT The airways are lined by secretory and multiciliated cells which function together to remove particles and debris from the respiratory tract. The transcriptome of multiciliated cells has been extensively studied, but the function of many of the genes identified is unknown. We have established an assay to test the ability of over-expressed transcripts to promote multiciliated cell differentiation in mouse embryonic tracheal explants. Overexpression data indicated that Fibronectin type 3 and ankyrin repeat domains 1 (Fank1) and JAZF zinc finger 1 (Jazf1) promoted multiciliated cell differentiation alone, and cooperatively with the canonical multiciliated cell transcription factor Foxj1. Moreover, knock-down of Fank1 or Jazf1 in adult mouse airway epithelial cultures demonstrated that these factors are both required for ciliated cell differentiation in vitro. This analysis identifies Fank1 and Jazf1 as novel regulators of multiciliated cell differentiation. Moreover, we show that they are likely to function downstream of IL6 signalling and upstream of Foxj1 activity in the process of ciliated cell differentiation. In addition, our in vitro explant assay provides a convenient method for preliminary investigation of over-expression phenotypes in the developing mouse airways. This article has an associated First Person interview with the first author of the paper. Summary: Transcription factors controlling ciliated cell differentiation are still being determined. Here, we show that Fank1 and Jazf1 are necessary and sufficient for ciliated cell differentiation in the mouse airways.
Multigeneration Product Diffusion in the Presence of Strategic Consumers

Frequent new product releases pose significant challenges for firms as they manage successive generations of product diffusion. We develop an analytical model to study the effect of different purchase options by strategic consumers on a firm’s profit and the firm’s strategies for the timing and pricing of its successive generations of product diffusion. We show that consumers’ strategic behavior, although adversely affecting the sales of the first-generation product, positively influences the sales of the second-generation product through an initial “seeding” effect. The influence of strategic consumers on profit and sales depends largely on the discount-to-price ratio of the first generation relative to the performance improvement in the second generation. When the relative discount is small, the seeding effect on the second-generation product dominates. When the relative discount is large, the “cannibalization” effect on the first-generation product dominates. We further demonstrate that the optimal ent.