Miracle-dz/UniFAR-FTUs huggingface dataset:
HIV-1 Envelope Protein gp120 Promotes Proliferation and the Activation of Glycolysis in Glioma Cell

Patients infected with human immunodeficiency virus (HIV) are more prone to developing cancers, including glioblastomas (GBMs). The median survival for HIV positive GBM patients is significantly shorter than for those who are uninfected, despite the fact that they receive the same treatments. The nature of the GBM–HIV association remains poorly understood. In this study, we analyzed the effect of the HIV envelope glycoprotein gp120 on GBM cell proliferation. Specifically, we performed cell cycle, western blot, protein synthesis and metabolomics analysis as well as ATP production and oxygen consumption assays to evaluate proliferation and metabolic pathways in primary human glioma cell line, U87, A172 cells and in the HIVgp120tg/GL261 mouse model. Glioma cells treated with gp120 (100 ng/mL for 7–10 days) showed higher proliferation rates and upregulation in the expression of enolase 2, hexokinase and glyceraldehyde-3-phosphate dehydrogenase when compared to untreated cells. Furthermore, we detected an increase in the activity of pyruvate kinase and a higher glycolytic index in gp120 treated cells. Gp120 treated GBM cells also showed heightened lipid and protein synthesis. Overall, we demonstrate that in glioma cells, the HIV envelope glycoprotein promotes proliferation and activation of glycolysis resulting in increased protein and lipid synthesis.
Sensorimotor integration in Caenorhabditis elegans: a reappraisal towards dynamic and distributed computations

The nematode Caenorhabditis elegans is a tractable model system to study locomotion, sensory navigation and decision-making. In its natural habitat, it is thought to navigate complex multisensory environments in order to find food and mating partners, while avoiding threats like predators or toxic environments. While research in past decades has shed much light on the functions and mechanisms of selected sensory neurons, we are just at the brink of understanding how sensory information is integrated by interneuron circuits for action selection in the worm. Recent technological advances have enabled whole-brain Ca2+ imaging and Ca2+ imaging of neuronal activity in freely moving worms. A common principle emerging across multiple studies is that most interneuron activities are tightly coupled to the worm's instantaneous behaviour; notably, these observations encompass neurons receiving direct sensory neuron inputs. The new findings suggest that in the C. elegans brain, sensory and motor representations are integrated already at the uppermost sensory processing layers. Moreover, these results challenge a perhaps more intuitive view of sequential feed-forward sensory pathways that converge onto premotor interneurons and motor neurons. We propose that sensorimotor integration occurs rather in a distributed dynamical fashion. In this perspective article, we will explore this view, discuss the challenges and implications of these discoveries on the interpretation and design of neural activity experiments, and discuss possible functions. Furthermore, we will discuss the broader context of similar findings in fruit flies and rodents, which suggest generalizable principles that can be learnt from this amenable nematode model organism. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
Rapid diagnosis of Zika virus through saliva and urine by Loop-mediated isothermal amplification (LAMP)

ABSTRACT Background: Zika virus (ZIKV) is a single-stranded RNA virus and member of the Flaviviridae family. Recent studies have reported that saliva can be an important alternative to detect ZIKV. Saliva requires less processing than blood greatly simplifying the assay. Loop-mediated Isothermal Amplification (LAMP) is a rapid assay that detects nucleic acids, including ZIKV RNA. Aim: The aim of this study was to evaluate the efficacy of saliva and urine to diagnose ZIKV infection in subjects during the acute phase, through ZIKV RNA detection by LAMP. Method: A total of 131 samples (68 saliva and 63 urine samples) from 69 subjects in the acute phase of ZIKV infection, and confirmed positive for ZIKV by blood analysis through real time-PCR, were collected and analyzed by Reverse Transcriptase Loop-mediated Isothermal Amplification (RT-LAMP). Results: From the 68 saliva samples, 45 (66.2%) were positive for ZIKV with an average time to positivity (Tp) of 13.5 min, and from the 63 urine samples, 25 (39.7%) were positive with the average Tp of 15.8 min. Saliva detected more samples (p = 0.0042) and had faster Tp (p = 0.0176) as compared with urine. Conclusion: Saliva proved to be a feasible alternative to diagnose ZIKV infection during the acute phase by LAMP.
Group Deviation Detection Methods

Pointwise anomaly detection and change detection focus on the study of individual data instances; however, an emerging area of research involves groups or collections of observations. From applications of high-energy particle physics to health care collusion, group deviation detection techniques result in novel research discoveries, mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights. In particular, static group anomaly detection is the process of identifying groups that are not consistent with regular group patterns, while dynamic group change detection assesses significant differences in the state of a group over a period of time. Since both group anomaly detection and group change detection share fundamental ideas, this survey article provides a clearer and deeper understanding of group deviation detection research in static and dynamic situations.
Coherence-Aware Neural Topic Modeling

Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.
Big boys don't cry: male secondary school students' attitudes to depression

Abstract Objectives: In an earlier qualitative study we explored the attitudes of young men aged 15-19 (Group A) to mental health and, in particular, to engaging with the various mental health services available. We found that the participants perceived stigma in connection with mental ill health and they displayed particularly strong negative attitudes in relation to both doctors and medication. The investigation was then repeated with students who had been given a short (less than two-hour) programme called ‘Beat the Blues’ (BTB) about mental health (Group B) in order to assess the effect of that exposure by comparing the attitudes of the two groups of students. This present phase of the analysis is a quantitative examination of the written responses by both Groups A and B to an administered questionnaire. Methods: A total of 42 young men took part in eight focus groups held in boys-only Dublin secondary schools, described in Burke et al. A questionnaire, administered to each participant, examined the students' attitudes to depression and mental illness. The results were analysed by computer using SPSS to search for any trends and any contrasts between groups A and B and among the different socio-economic groups (SEGs) within the sample. Results: Almost no statistically significant differences were found between groups A and B. However, some differences were found among the SEGs. In particular, very significant differences (p < 0.01 in each case), were found in attitudes towards depression, with increasing support for statements such as “People with depression just need to snap out of it”, “Drinking alcohol can help cure depression” and “Depression is only an excuse for laziness” found among the lower SEGs. A very high percentage of students indicated their desire to talk to someone in times of personal stress; this was almost always their best friend or their mother. However, most students said they would be uncomfortable if a friend raised such a topic. Conclusion: The main conclusion – that a single exposure to a positive programme about depression produces little or no effect – is hardly unexpected. Nonetheless, there are indications of a great willingness among older secondary students to learn about and discuss mental health issues. Furthermore, the highly negative attitudes among students from the lowest socio-economic group in this study would seem to indicate that the greatest need for education about mental health lies with working-class adolescents. Hence, it is recommended that a programme of multiple interventions be introduced into the senior cycle of secondary education.
Serum Lipid Profile and Its Association with Diabetes and Prediabetes in a Rural Bangladeshi Population

Dyslipidemia is commonly associated with diabetes (T2DM). This has been demonstrated for the Caucasian population, but few data are available for Asian Indians. The paper aims to investigate serum lipids (separately or in combination) and their association with glucose intolerance status (T2DM and prediabetes) in a rural Bangladeshi population. A sample of 2293 adults (≥20 years) were included in a community based cross-sectional survey in 2009. Anthropometric measures, blood pressure, blood glucose (fasting and 2-h oral glucose tolerance test) and fasting serum lipids (total cholesterol, T-Chol; triglycerides, Tg; low density lipoprotein cholesterol, LDL-C and high density lipoprotein cholesterol, HDL-C) were registered. Analysis of covariance (ANCOVA) and regression analysis were performed. High Tg levels were seen in 26% to 64% of the participants, depending on glucose tolerance status. Low HDL-C levels were seen in all groups (>90%). Significant linear trends were observed for high T-Chol, high Tg and low HDL-C with increasing glucose intolerance (p for trend <0.001). T2DM was significantly associated with high T-Chol (Odds ratio (OR): 2.43, p < 0.001), high Tg (OR: 3.91, p < 0.001) and low HDL-C (OR: 2.17, p = 0.044). Prediabetes showed a significant association with high Tg (OR: 1.96, p < 0.001) and low HDL-C (OR: 2.93, p = 0.011). Participants with combined high Tg and low HDL-C levels had a 12.75-fold higher OR for T2DM and 4.89 OR for prediabetes. In Asian Indian populations an assessment of serum lipids is warranted not only for T2DM patients, but also for those with prediabetes.
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference

This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. Secondly, we adopt factorization layers for efficient and expressive compression of alignment vectors into scalar features, which are then used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving competitive performance on all. A lightweight parameterization of our model also enjoys a 3 times reduction in parameter size compared to the existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining competitive performance. Additionally, visual analysis shows that our propagated features are highly interpretable.
Mixed-ADC/DAC Multipair Massive MIMO Relaying Systems: Performance Analysis and Power Optimization

High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2–3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.
Risk Preference: A View from Psychology.

Psychology offers conceptual and analytic tools that can advance the discussion on the nature of risk preference and its measurement in the behavioral sciences. We discuss the revealed and stated preference measurement traditions, which have coexisted in both psychology and economics in the study of risk preferences, and explore issues of temporal stability, convergent validity, and predictive validity with regard to measurement of risk preferences. As for temporal stability, does risk preference as a psychological trait show a degree of stability over time that approximates what has been established for other major traits, such as intelligence, or, alternatively, are they more similar in stability to transitory psychological states, such as emotional states? Convergent validity refers to the degree to which different measures of a psychological construct capture a common underlying characteristic or trait. Do measures of risk preference all capture a unitary psychological trait that is indicative of risky behavior across various domains, or do they capture various traits that independently contribute to risky behavior in specific areas of life, such as financial, health, and recreational domains? Predictive validity refers to the extent to which a psychological trait has power in forecasting behavior. Intelligence and major personality traits have been shown to predict important life outcomes, such as academic and professional achievement, which suggests there could be studies of the short- and long-term outcomes of risk preference—something lacking in current psychological (and economic) research. We discuss the current empirical knowledge on risk preferences in light of these considerations.
The role of surgery in refractory epilepsy secondary to polymicrogyria in the pediatric population

Polymicrogyria (PMG) is a common malformation of cortical development. Many patients with PMG will have medically refractory epilepsy but the role of epilepsy surgery is unclear. The objective of this study was to assess the efficacy of surgical resection/disconnection in achieving seizure control in pediatric patients with PMG.
Resveratrol and Depression in Animal Models: A Systematic Review of the Biological Mechanisms

Depression is currently treated by pharmacotherapies that can elicit debilitating side effects for patients. Novel treatment options with limited side effects are currently being researched. Resveratrol is a polyphenol and phytoalexin found in the skins of grapes, red wine, Japanese knotweed, and peanuts. It has been studied extensively for its antioxidant and anti-inflammatory properties. Resveratrol has also gained attention for its neuroprotective properties. The aim of the review was to examine the mechanisms by which resveratrol reduces depressive behaviors in animal models. In total, 22 studies met the established criteria for final review. Behavioral aspects of depression were investigated using validated measures such as the forced swimming test, tail suspension test, sucrose preference test, and open field test. While many physical measures were taken, three main biological mechanisms were explored: Regulation of the hypothalamic–pituitary–adrenal axis; decreased inflammation; and increased Brain-Derived Neurotrophic Factor and neurogenesis. Based on these findings, resveratrol may be deemed an effective treatment for depression in animal models at doses between 10–80 mg/kg/day, although higher doses had the most significant effects. Future studies should examine the effects of resveratrol on depression in humans to determine the eligibility of resveratrol as a natural antidepressant with less severe side effects.
Prevalence, Risk Factors, and Genotypes of Toxoplasma gondii in Food Animals and Humans (2000–2017) From China

Toxoplasma gondii as a food-borne pathogen, the infection of it in food animals has relation with human toxoplasmosis, but the trends and epidemiological features of T. gondii infections in food animals are rarely studied in China. The aimed of this study was to assess the epidemiology and risks of T. gondii in sheep, goats, swines, chickens, yaks, cattle and humans from 2000 to 2017 and to explore prevention and control strategies. The overall seroprevalence of T. gondii infections in food animals is 23.7% (39,194/165,417, 95%CI, 23.49–23.90%), which is significantly higher than that in humans (8.2%, 95%CI, 8.06–8.39%, 8,502/103,383) (P < 0.0001). Compared the prevalence of T. gondii infections in animals and humans sampled from 2000 to 2010, it was significantly increased in the period 2011 to 2017 (P < 0.0001). Compared the food animals from non-Yangtze River, animals from regions of the Yangtze River have high seroprevalence rates for T. gondii (P < 0.0001). Furthermore, samples from the western to eastern regions of the Yellow River showed an increase in seroprevalence for T. gondii (P < 0.0001). It was speculated that T. gondii oocysts may be transmitted by water and annual precipitation possible help the oocyst spread and retain accessible for potential hosts. Effective prevention and control strategies are including water filtration or water boiling, inactivating oocysts from feline’s feces, monitoring birds and rodents. Chinese 1 (ToxoDB#9) is the predominant genotype in food animals from China.
Open Set Adversarial Examples

Adversarial examples in recent works target at closed set recognition systems, in which the training and testing classes are identical. In real-world scenarios, however, the testing classes may have limited, if any, overlap with the training classes, a problem named open set recognition. To our knowledge, the community does not have a specific design of adversarial examples targeting at this practical setting. Arguably, the new setting compromises traditional closed set attack methods in two aspects. First, closed set attack methods are based on classification and target at classification as well, but the open set problem suggests a different task, \emph{i.e.,} retrieval. It is undesirable that the generation mechanism of closed set recognition is different from the aim of open set recognition. Second, given that the query image is usually of an unseen class, predicting its category from the training classes is not reasonable, which leads to an inferior adversarial gradient. In this work, we view open set recognition as a retrieval task and propose a new approach, Opposite-Direction Feature Attack (ODFA), to generate adversarial examples / queries. When using an attacked example as query, we aim that the true matches be ranked as low as possible. In addressing the two limitations of closed set attack methods, ODFA directly works on the features for retrieval. The idea is to push away the feature of the adversarial query in the opposite direction of the original feature. Albeit simple, ODFA leads to a larger drop in Recall@K and mAP than the close-set attack methods on two open set recognition datasets, \emph{i.e.,} Market-1501 and CUB-200-2011. We also demonstrate that the attack performance of ODFA is not evidently superior to the state-of-the-art methods under closed set recognition (Cifar-10), suggesting its specificity for open set problems.
Systematics and symmetry in molecular phylogenetic modelling: perspectives from physics

Phylogenetics is the suite of mathematical and computational methods which provide biologists with the means to infer past evolutionary relationships between observed species. The aim of this review is to present and analyze the probabilistic models of mathematical phylogenetics which have been intensively used in recent years, as the foundations on which the practical implementations are based. We outline the development of theoretical phylogenetics, from the earliest studies based on morphological characters, through to the use of molecular data in a wide variety of forms. We bring the lens of mathematical physics to bear on the formulation of theoretical models, focussing on the applicability of many methods from the toolkit of that tradition—techniques of groups and representations to guide model specification and to exploit the multilinear setting of the models in the presence of underlying symmetries; extensions to coalgebraic properties of the generators associated to rate matrices underlying the models, and possibilities to marry these with the graphical structures (trees and networks) which form the search space for inferring evolutionary trees. Particular aspects which we wish to present to a readership accustomed to thinking from physics, include relating model classes to structural data on relevant matrix Lie algebras, as well as using manipulations with group characters (especially the operation of plethysm, for computing tensor powers) to enumerate various natural polynomial invariants, which can be enormously helpful in tying down robust, low-parameter quantities for use in inference (some of which have only come to light through our perspective). Above all, we wish to emphasize the many features of multipartite entanglement which are shared between descriptions of quantum states on the physics side, and the multi-way tensor probability arrays arising in phylogenetics. In some instances, well-known objects such as the Cayley hyperdeterminant (the ‘tangle’) can be directly imported into the formalism—in this case, for models with binary character traits, and for providing information about triplets of taxa. In other cases new objects appear, such as the remarkable ‘squangle’ invariants for quartet tree discrimination, which for DNA data are of quintic degree, with their own unique interpretation in the phylogenetic modelling context. All this hints strongly at the natural and universal presence of entanglement as a phenomenon which reaches across disciplines. We hope that this broad perspective may in turn furnish new insights of use in physics.