Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinct Phenotypes of Shank2 Mouse Models Reflect Neuropsychiatric Spectrum Disorders of Human Patients With SHANK2 Variants

The SHANK scaffolding proteins are important organizers for signaling proteins in the postsynapse of excitatory neurons. The functional significance of SHANK proteins becomes apparent by the wide spectrum of neurodevelopmental and neuropsychiatric disorders associated with SHANK variants in human patients. A similar diversity of neuropsychiatric-like phenotypes is described for numerous Shank2 and Shank3 knockout (KO) mouse lines. In this review, we will focus on and discuss the experimental results obtained from different, but genetically related and therefore comparable, Shank2 mouse models. First, we will describe the distinct SHANK2 variant-mediated neurodevelopmental and neuropsychiatric disorders in human patients. Then we will discuss the current knowledge of the expressed SHANK2 isoforms in the mouse, and we will describe the genetic strategies used for generating three conventional and seven conditional Shank2 mouse lines. The distinct impairments i.e., autistic-like and mania-like behavior and the alterations on the molecular, electrophysiological and behavioral levels will be compared between the different Shank2 mouse models. We will present our view as to why in these mouse models a spectrum of phenotypes can arise from similar Shank2 gene manipulations and how Shank2 mutant mice can be used and should be analyzed on the behavioral level in future research.
DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.
Modeling Oscillatory Phase and Phase Synchronization With Neuronal Excitation and Input Strength in Cortical Network

Neuronal oscillatory phase is suggested to be associated with feature coding, carrying information for stimulus identity and neuronal activation, while phase synchronization is indicated to be correlated with signal routing, establishing flexible communication structures for neuronal interactions. Recent electrophysiological and computational studies have revealed that oscillatory phase has close relationships with neuronal excitation and input stimulus. To simulate and further investigate these issues, we simulated orientation columns with a spiking neural network and performed spectral computations according to physiological experiments. Besides, six network activity states, pre-stimulus, and stimulus periods were introduced in our simulation for both independent and comparative analyses. The simulation results demonstrated that gamma band neuronal oscillations existed in the network and even emerged during pre-stimulus period. An input stimulus orientation, if approximately preferred, could produce smaller and more concentrated oscillatory phases, but relatively stronger phase synchronization. In particular, the oscillatory phase and phase synchronization had quantifiable relationships with neuronal excitation and input strength. With the network activity state transforming gradually from strong oscillation to non-oscillation, the oscillatory phase became more and more scattered and the strength of phase synchronization declined significantly. Their relationships with neuronal excitation and input strength became increasingly unstable, and finally collapsed.
Towards New Approach to Enhance Learning Based on Internet of Things and Virtual Reality

The Internet of Things (IoT) and Virtual Reality (VR) are two of the most significant technologies of the last decades that will surely influence many technologies in the few coming years. According to the experts at Gartner, 20.8 billion new things will be connected by 2020 and researchers at kzero expect that only by 2018, there will be more than 170 million VR active user over the world. The IoT refers to the network of internet connected objects that are able to collect, exchange data and make decisions, while VR intends to provide a computer-generated 3D environment that can be used to build real-time tools that can monitor every response made by the user in a natural way. VR has the ability to change the world in some incredible ways, while the IoT already has did. Therefore, much promise and opportunities lay in these two technologies. Taken individually, IoT and VR find their applications in almost every field. In this paper we focus on the educational field, thus the confluence of IoT and VR technologies can bring more new opportunities and possibilities for the improvement and the effectiveness of both learning/training process and educational institution's infrastructure. In this sense, we propose a new approach that can allow learners to consolidate their conceptual learning with tangible experiences in a novel manner.
Adaptation to drought in arid and semi-arid environments: Case of the Zambezi Valley, Zimbabwe

Small-scale rain-fed agriculture is the main livelihood in arid to semi-arid regions of sub-Saharan Africa. The area is characterised by erratic rainfall and frequent droughts, making the capacity for coping with temporal water shortages essential for smallholder farmers. Focusing on the Zambezi Valley, Zimbabwe, this study investigates the impact of drought on food security and the strategies used by smallholder farmers to cope with drought. We used meteorological data and interviews to examine the rainfall variability in the study area and the drought-coping mechanisms employed by smallholder famers respectively. The results show that there are various strategies used by smallholder farmers to cope with the impact of drought. These strategies include drought-tolerant crop production, crop variety diversification, purchasing cereals through asset sales, non-governmental organisations’ food aid and gathering wild fruit. However, consecutive droughts have resulted in high food insecurity and depletion of household assets during droughts. Smallholder farmers in the valley have also resorted to a number of measures taken before, during and after the drought. Still, these strategies are not robust enough to cope with this uncertainty.
Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions

Background Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition. Objective The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data. Methods Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32). Results On average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability. Conclusions Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed.
Hyaluronic Acid Layered Chimeric Nanoparticles: Targeting MAPK-PI3K Signaling Hub in Colon Cancer Cells

Colon cancer has emerged as one of the most devastating diseases in the whole world. Mitogen-activated protein kinase (MAPK)-phosphatidylinsitol-3-kinase (PI3K) signaling hub has gained lots of attention due to its deregulation in colon cancer cells. However, selective targeting of oncogenic MAPK-PI3K hub in colon cancer has remained highly challenging, hence it has mostly been unexplored. To address this, we have engineered a hyaluronic acid layered lipid-based chimeric nanoparticle (HA-CNP) consisting of AZD6244 (MAPK inhibitor), PI103 (PI3K inhibitor), and cisplatin (DNA impairing drug) ratiometrically in a single particle. Electron microscopy (field emission scanning electron microscopy and atomic force microscopy) and dynamic light scattering were utilized to characterize the size, shape, morphology, and surface charge of the HA-CNPs. Fluorescent confocal laser scanning microscopy and flow cytometry analysis confirmed that HA-CNPs were taken up by HCT-116 colon cancer cells by merging of clathrin and CD44 receptor-mediated endocytosis along with macropinocytosis to home into acidic organelles (lysosomes) within 1 h. A gel electrophoresis study evidently established that HA-CNPs simultaneously inhibited MAPK-PI3K signaling hub with DNA damage in HCT-116 cells. These HA-CNPs stalled the cell cycle into G0/G1 phase, leading to induction of apoptosis (early and late) in colon cancer cells. Finally, these HA-CNPs exerted remarkable cytotoxicity in HCT-116 colon cancer cells at 24 h compared to that of the free triple drug cocktail as well as HA-coated dual drug-loaded nanoparticles without showing any cell death in healthy L929 fibroblast cells. These HA-coated CNPs have potential to be translated into clinics as a novel platform to perturb various oncogenic signaling hubs concomitantly toward next-generation targeted colon cancer therapy.
Relation of high neuroticism with increased methylation of the BDNF gene

Background Brain-derived neurotrophic factor (BDNF) is a neurotrophin that has an important function in neuroplasticity and neuronal development. It is suggested that increased methylation of the BDNF gene resulting in decreased BDNF activity is associated with depression. Meanwhile, neuroticism is a well-known risk factor for developing depression. In the present study, the relationship between methylation of the BDNF gene and personality traits including neuroticism was examined. Subjects and methods The subjects were 98 healthy Japanese. Methylation levels of the BDNF gene were determined by the bisulfite-pyrosequencing method. Personality traits including neuroticism were assessed by the NEO Personality Inventory-Revised. Results There was a positive correlation between neuroticism scores and methylation levels of the BDNF gene. The subjects with higher neuroticism scores had higher levels of BDNF gene methylation compared with those with lower neuroticism scores. Meanwhile, other personality traits were not associated with BDNF gene methylation. Conclusion The present study suggests that high neuroticism is related to increased methylation of the BDNF gene.
On the Distributed Complexity of Large-Scale Graph Computations

Motivated by the increasing need to understand the distributed algorithmic foundations of large-scale graph computations, we study some fundamental graph problems in a message-passing model for distributed computing where $k \geq 2$ machines jointly perform computations on graphs with n nodes (typically, $n \gg k$). The input graph is assumed to be initially randomly partitioned among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication \em rounds of the computation. Our main contribution is the \em General Lower Bound Theorem, a theorem that can be used to show non-trivial lower bounds on the round complexity of distributed large-scale data computations. The General Lower Bound Theorem is established via an information-theoretic approach that relates the round complexity to the minimal amount of information required by machines to solve the problem. Our approach is generic and this theorem can be used in a "cookbook" fashion to show distributed lower bounds in the context of several problems, including non-graph problems. We present two applications by showing (almost) tight lower bounds for the round complexity of two fundamental graph problems, namely \em PageRank computation and \em triangle enumeration. Our approach, as demonstrated in the case of PageRank, can yield tight lower bounds for problems (including, and especially, under a stochastic partition of the input) where communication complexity techniques are not obvious. Our approach, as demonstrated in the case of triangle enumeration, can yield stronger round lower bounds as well as message-round tradeoffs compared to approaches that use communication complexity techniques. We then present distributed algorithms for PageRank and triangle enumeration with a round complexity that (almost) matches the respective lower bounds; these algorithms exhibit a round complexity which scales superlinearly in k , improving significantly over previous results for these problems [Klauck et al., SODA 2015]. Specifically, we show the following results: \beginitemize ıtem \em PageRank: We show a lower bound of $\tildeØmega (n/k^2)$ rounds, and present a distributed algorithm that computes the PageRank of all the nodes of a graph in $\tildeO (n/k^2)$ rounds. ıtem \em Triangle enumeration: We show that there exist graphs with m edges where any distributed algorithm requires $\tildeØmega (m/k^5/3 )$ rounds. This result also implies the first non-trivial lower bound of $\tildeØmega(n^1/3 )$ rounds for the \em congested clique model, which is tight up to logarithmic factors. We then present a distributed algorithm that enumerates all the triangles of a graph in $\tildeO (m/k^5/3 + n/k^4/3 )$ rounds. \enditemize
Increased Numbers of Culturable Inhibitory Bacterial Taxa May Mitigate the Effects of Batrachochytrium dendrobatidis in Australian Wet Tropics Frogs

Symbiotic bacterial communities resident on amphibian skin can benefit their hosts. For example, antibiotic production by community members can control the pathogen Batrachochytrium dendrobatidis (Bd) and it is possible for these community members to be used as probiotics to reduce infection levels. In the early 1990s, the emergence of Bd caused declines and disappearances of frogs in the Australian Wet Tropics; the severity of its effects varied among species and sites. Some species have since recolonized despite enzootic Bd within their populations. This variation in history among species and sites provided an opportunity to investigate the role of anti-fungal cutaneous bacteria in protecting frogs against Bd infection. We collected cutaneous swab samples from three species of frogs at two upland and two lowland sites in the Wet Tropics, and used in vitro challenge assays to identify culturable Bd-inhibitory bacterial isolates for further analysis. We sequenced DNA from cultured inhibitory isolates to identify taxa, resulting in the classification of 16 Bd-inhibitory OTUs, and determined whether inhibitory taxa were associated with frog species, site, or intensity of infection. We present preliminary results showing that the upper limit of Bd infection intensity was negatively correlated with number of inhibitory OTUs present per frog indicating that increased numbers of Bd-inhibiting taxa may play a role in reducing the intensity of Bd infections, facilitating frog coexistence with enzootic Bd. One upland site had a significantly lower prevalence of Bd infection, a significantly higher proportion of frogs with one or more culturable Bd-inhibitory OTUs, a greater number of inhibitory bacterial genera present per frog, and statistically significant clustering of individual frogs with similar Bd-inhibitory signatures when compared to all other sites. This suggests that Bd-inhibitory taxa are likely to be particularly important to frogs at this site and may have played a role in their ability to recolonize following population declines. Our findings suggest that the use of multi-taxon Bd-inhibitory probiotics to support at-risk amphibian populations may be more effective than single-taxon alternatives.
Location, Location, Location: Exploring Amazon EC2 Spot Instance Pricing Across Geographical Regions

Cloud computing is a ubiquitous part of the computing landscape. For many companies today, moving their computing infrastructure to the cloud reduces time to deployment and saves money. Spot Instances, a subset of Amazon's cloud computing infrastructure (EC2), expands upon this. They allow a user to bid on spare compute capacity in EC2 at heavily discounted prices. If other bids for the spare capacity exceeds the user's own, the user's instance is terminated. In this paper, we conduct one of the first detailed analyses of how location affects the overall cost of deployment of a Spot Instance. We analyse pricing data across all available AWS regions for 60 days for a variety of Spot Instances. We relate the pricing data we find to the overall AWS region and examine any patterns we see across the week. We find that location plays a critical role in Spot Instance pricing and that pricing differs, sometimes markedly, from region to region. We conclude by showing that it is indeed possible to run workloads on Spot Instances with low risk of termination and a low overall cost.
Evaluation of the extent and reasons for increased non-prescription antibiotics use in a University town, Nsukka Nigeria

Objectives: The use of non-prescribed antibiotics has become a public health concern, and this behavior continues to thrive in many countries of the world including Nigeria. Evidence from previous studies suggests that increased antibiotic prescriptions and patients’ past experiences are some of the factors contributing to the use of non-prescribed antibiotics. The present investigation was aimed at determining the factors driving this behavior and the extent of usage among people living in a sub-urban Nigerian community. Methods: A community-based, cross-sectional study was conducted in Nsukka, South-East Nigeria using a semi-structured and self-administered questionnaire. An estimated sample size of approximately 400 respondents, with ages ranging from 18 to 60 years participated in the study. A number of males were 233 and 197 were females. Results: Demographics showed that the majority of respondents were well educated. The behavior appeared to be common regardless of gender, age, and educational level. Results showed that more than 86% of the respondents use non-prescribed antibiotics for treatment or prevention or both. A significant percentage uses it to treat cold and cough. The most frequently used antibiotics where the penicillins (58%) followed by quinolones (22%). Tetracycline, aminoglycosides, and cephalosporin represented 20.75%, 14.75%, and 11% usage, respectively. Conclusion: This study revealed a substantial use of over-the-counter antibiotics, with this behavior cutting across gender, age and levels of education. However, participants’ knowledge and awareness of the hazards associated with this behavior were lacking.
Efficient Transmit Covariance Design in MIMO Interference Channel

This paper is concerned with the sum-rate maximization problem in multiple-input multiple-output interference channels. We design the transmit covariance matrix, associated with each transmitter to maximize the sum-rate of the network when the number of data symbols is unknown. The design problem is nonconvex and then hard to be solved. We devise a semidistributed design methodology according to majorization–minimization technique to deal with the optimization problem. This leads us to a convex quadratically constrained quadratic program at each iteration with a semiclosed form solution. The proposed method is computationally efficient and converges to stationary points of the problem. The fact that the method is based on a distributed processing framework along with relatively low computational burden (<inline-formula><tex-math notation="LaTeX">$\mathcal{O}(\max \lbrace M,N\rbrace ^{2.3})$ </tex-math></inline-formula> per iteration with <inline-formula><tex-math notation="LaTeX">$M$</tex-math> </inline-formula> and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> being the number of antennas at the transmitter and receiver, respectively) for the user terminals can make it a potential candidate for practical implementations. Finally, we extend the proposed method for the case with channel estimation errors. Simulation results verify the efficiency of the proposed method, and in a typical scenario we have around 60 times lower run-time when compared to the counterparts of the method.
Pulse pressure variation and pleth variability index as predictors of fluid responsiveness in patients undergoing spinal surgery in the prone position

Background This study investigated the ability of pulse pressure variation (PPV) and pleth variability index (PVI) to predict fluid responsiveness of patients undergoing spinal surgery in the prone position. Patients and methods A total of 53 patients undergoing posterior lumbar spinal fusion in the prone position on a Jackson table were studied. PPV, PVI, and hemodynamic and respiratory variables were measured both before and after the administration of 6 mL/kg colloid in both the supine and prone positions. Fluid responsiveness was defined as a 15% or greater increase in stroke volume index, as assessed by esophageal Doppler monitor after fluid loading. Results In the supine position, 40 patients were responders. The areas under the receiver operating characteristic (ROC) curves for PPV and PVI were 0.783 [95% CI 0.648–0.884, P<0.001] and 0.814 (95% CI 0.684–0.908, P<0.001), respectively. The optimal cut-off values of PPV and PVI were 10% (sensitivity 75%, specificity 62%) and 8% (sensitivity 78%, specificity 77%), respectively. In the prone position, 27 patients were responders. The areas under the ROC curves for PPV and PVI were 0.781 (95% CI 0.646–0.883, P<0.001) and 0.756 (95% CI 0.618–0.863, P<0.001), respectively. The optimal cut-off values of PPV and PVI were 7% (sensitivity 82%, specificity 62%) and 8% (sensitivity 67%, specificity 69%), respectively. Conclusion Both PPV and PVI were able to predict fluid responsiveness; their predictive abilities were maintained in the prone position.