Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary Walnuts Protect Against Obesity-Driven Intestinal Stem Cell Decline and Tumorigenesis

Obesity can negatively impact intestinal homeostasis, and increase colon cancer risk and related mortality. Thus, given the alarmingly high rates of obesity in the US and globally, it is critical to identify practical strategies that can break the obesity-cancer link. Walnuts have been increasingly recognized to mitigate cancer risk, and contain many bioactive constituents with antioxidant and anti-inflammatory properties that could potentially counteract pathways thought to be initiators of obesity-related cancer. Therefore, the purpose of this study was to determine if walnuts could preserve intestinal homeostasis, and attenuate tumorigenesis and growth in the context of obesity and a high calorie diet. To this end, we studied effects of walnuts on these parameters under different dietary conditions in wildtype mice, two independent Apc models (Apc1638N/+ and ApcΔ14), and in MC38 colon cancer cells in vivo, respectively. Walnuts did not alter the metabolic phenotype or intestinal morphology in normal mice fed either a low-fat diet (LFD), LFD with 6% walnuts (LFD+W), high-fat diet (HFD), or HFD with 7.6% walnuts (HFD+W). However, walnuts did lead to a significant reduction in circulating CCL5 and preserved intestinal stem cell (ISC) function under HFD-fed conditions. Furthermore, walnuts reduced tumor multiplicity in Apc1638N/+ male HFD+W animals, as compared to HFD controls (3.7 ± 0.5 vs. 2.5 ± 0.3; P = 0.015), tended to reduce the number of adenocarcinomas (0.67 ± 0.16 vs. 0.29 ± 0.12; P = 0.07), and preferentially limited tumor growth in ApcΔ14 male mice (P = 0.019) fed a high-calorie western-style diet. In summary, these data demonstrate that walnuts confer significant protection against intestinal tumorigenesis and growth and preserve ISC function in the context of a high-calorie diet and obesity. Thus, these data add to the accumulating evidence connecting walnuts as a potentially effective dietary strategy to break the obesity-colon cancer link.
An in silico pipeline to filter the Toxoplasma gondii proteome for proteins that could traffic to the host cell nucleus and influence host cell epigenetic regulation

Toxoplasma gondii uses epigenetic mechanisms to regulate both endogenous and host cell gene expression. To identify genes with putative epigenetic functions, we developed an in silico pipeline to interrogate the T. gondii proteome of 8313 proteins. Step 1 employs PredictNLS and NucPred to identify genes predicted to target eukaryotic nuclei. Step 2 uses GOLink to identify proteins of epigenetic function based on Gene Ontology terms. This resulted in 611 putative nuclear localised proteins with predicted epigenetic functions. Step 3 filtered for secretory proteins using SignalP, SecretomeP, and experimental data. This identified 57 of the 611 putative epigenetic proteins as likely to be secreted. The pipeline is freely available online, uses open access tools and software with user-friendly Perl scripts to automate and manage the results, and is readily adaptable to undertake any such in silico search for genes contributing to particular functions.
What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis

This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impression of a movie, describing aspects such as its genre, plot, mood, or artistic style. We create a dataset that consists of movie scripts, attribute-value pairs for the movies’ aspects, as well as overviews, which we extract from an online database. We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. Automatic and human evaluation show that the encoder is able to reliably assign good labels for the movie’s attributes, and the overviews provide descriptions of the movie’s content which are informative and faithful.
Wobbling Forth and Drifting Back: The Evolutionary History and Impact of Bacterial tRNA Modifications

Abstract Along with tRNAs, enzymes that modify anticodon bases are a key aspect of translation across the tree of life. tRNA modifications extend wobble pairing, allowing specific (“target”) tRNAs to recognize multiple codons and cover for other (“nontarget”) tRNAs, often improving translation efficiency and accuracy. However, the detailed evolutionary history and impact of tRNA modifying enzymes has not been analyzed. Using ancestral reconstruction of five tRNA modifications across 1093 bacteria, we show that most modifications were ancestral to eubacteria, but were repeatedly lost in many lineages. Most modification losses coincided with evolutionary shifts in nontarget tRNAs, often driven by increased bias in genomic GC and associated codon use, or by genome reduction. In turn, the loss of tRNA modifications stabilized otherwise highly dynamic tRNA gene repertoires. Our work thus traces the complex history of bacterial tRNA modifications, providing the first clear evidence for their role in the evolution of bacterial translation.
An Axially Variant Kernel Imaging Model Applied to Ultrasound Image Reconstruction

Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circulant boundary conditions. We discard both restrictions and introduce an image formation model applicable to ultrasound imaging and deconvolution based on an axially varying kernel, which accounts for arbitrary boundary conditions. Our model has the same computational complexity as the one employing spatially invariant convolution and has negligible memory requirements. To accommodate the state-of-the-art deconvolution approaches when applied to a variety of inverse problem formulations, we also provide an equally efficient adjoint expression for our model. Simulation results confirm the tractability of our model for the deconvolution of large images. Moreover, in terms of accuracy metrics, the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution.
Particle swarm optimization models applied to neural networks using the R language

There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A Grammatical Swarm model is applied to obtain the Neural Network topology of a given problem, training the net with a Particle Swarm algorithm in R. This paper just shows some ideas in order to obtain an automatic way to define the most suitable neural network topology for a given patter set. High dimension problem is also mentioned when dealing with the particle swarm algorithm.
Comparative genomics of Anaplasma marginale - a preliminary examinaton of factors involved in tick transmission

With the recent completion of numerous sequenced bacterial genomes, notable advances have been made in understanding the level of conservation between various species. However, relatively little is known about the genomic diversity among strains. We determined the complete genome sequence of the Florida strain of Anaplasma marginale, and near complete (>96%) sequences for an additional three strains, for comparative analysis with the previously fully sequenced St. Maries strain genome. These comparisons revealed that A. marginale has a closed-core genome with few highly plastic regions, which include the msp2 and msp3 genes, as well as the aaap locus. Comparison of the Florida and St. Maries genome sequences found that SNPs comprise 0.8% of the longer Florida genome, with 33.5% of the total SNPs between all five strains present in at least two strains and 3.0% of SNPs present in all strains except Florida. Comparison of genomes from three strains of Mycobacterium tuberculosis, Bacillus anthracis, and Nessieria meningiditis, as well as four Chlamydophila pneumoniae strains found that 98.8%-100% of SNPs are unique to each strain, suggesting A. marginale, with 76.0%, has an intermediate level of strain-specific SNPs. Comparison of genomes from other organisms revealed variation in diversity that did not segregate with the environmental niche the bacterium occupies, ranging from 0.00% to 8.00% of the larger pairwise-compared genome. Analysis of multiple A. marginale
How does the transformation of an avatar face giving a favorable impression affect human recognition of the face?

We investigated how different appearances in the favorable impressions of 3D avatar faces affect face-recognition performances by humans. We conducted an encoding and testing experiment using synthesized facial images and artificially manipulated the strength of the perceived impressions in three different dimensions. We also subjectively assessed the favorability of the synthesized faces that were used as visual stimuli in face-recognition tests and found that facial transformation, which decreased the favorability impressions, generally deteriorates human face-recognition performance.
AN EFFICIENT ASSOCIATION RULE BASED HIERARCHICAL ALGORITHM FOR TEXT CLUSTERING

In this modern era, the amount of information available has become too large. But are we getting useful information still remain a question. Text clustering is one of the techniques that helps organize information and hence obtain information in a more efficient manner. This paper presents a new technique for clustering text documents based on association rule based systems. In this approach, the text documents are preprocessed and the association between the text files are found using Apriori algorithm. The associated text files are clustered using hierarchical clustering algorithm. The text files are also clustered using hierarchical algorithm. The results of both the methods are evaluated. The algorithms are tested on benchmark data set Reuters-21578. The experimental results prove that the Association Rule Based Hierarchical clustering method (ARBHC) produce better results and also improved cluster quality over hierarchical method. KEYWORDSText clustering, Association rule, Hierarchical algorithm, Apriori.
Estimating the wage and employment effects of a large increase in South Africa ’ s agricultural minimum wage

What were the effects of a 52% increase in the minimum wage in the agricultural sector in South Africa in 2013? We estimate the short run effects of this policy change on the employment and income of rural farmworkers, using both repeated cross-sectional data as well as individual level longitudinal data from the Quarterly Labour Force Surveys (QLFS). We find that the law had a substantial effect on the earnings of rural respondents who were employed as farmworkers after the law came into effect, but that there were also substantial job losses. The descriptive evidence from the cross-sections indicates an increase in real mean income of 19% per month amongst rural farmworkers, about a year after the law came into effect. At the same time, we observe a mean decrease in adult employment by this industry of about 20%, or over 6 percentage points in absolute terms. Our regression results, which makes use of longitudinal data from the rotating panel component of the QLFS, supports the claim that the law caused these changes. These results suggest that caution is warranted when considering minimum wage policies, particularly when large increases are being considered in a low wage sector. 1 Chief Research Officer, SALDRU, School of Economics, University of Cape Town. Corresponding author: vimal.ranchhod@gmail.com 2 Graduate research assistant, SALDRU, School of Economics, University of Cape Town. 3 The authors are grateful for financial assistance for this work from the REDI 3x3 project. Ranchhod also acknowledges more general support from the NRF’s RCA Fellowship. This work would not have been possible without the assistance of various members of the StatsSA team, particularly Monet Durieux.
A Central Extended Amygdala Circuit That Modulates Anxiety

Both the amygdala and the bed nucleus of the stria terminalis (BNST) have been implicated in maladaptive anxiety characteristics of anxiety disorders. However, the underlying circuit and cellular mechanisms have remained elusive. Here we show that mice with Erbb4 gene deficiency in somatostatin-expressing (SOM+) neurons exhibit heightened anxiety as measured in the elevated plus maze test and the open field test, two assays commonly used to assess anxiety-related behaviors in rodents. Using a combination of electrophysiological, molecular, genetic, and pharmacological techniques, we demonstrate that the abnormal anxiety in the mutant mice is caused by enhanced excitatory synaptic inputs onto SOM+ neurons in the central amygdala (CeA), and the resulting reduction in inhibition onto downstream SOM+ neurons in the BNST. Notably, our results indicate that an increase in dynorphin signaling in SOM+ CeA neurons mediates the paradoxical reduction in inhibition onto SOM+ BNST neurons, and that the consequent enhanced activity of SOM+ BNST neurons is both necessary for and sufficient to drive the elevated anxiety. Finally, we show that the elevated anxiety and the associated synaptic dysfunctions and increased dynorphin signaling in the CeA–BNST circuit of the Erbb4 mutant mice can be recapitulated by stress in wild-type mice. Together, our results unravel previously unknown circuit and cellular processes in the central extended amygdala that can cause maladaptive anxiety. SIGNIFICANCE STATEMENT The central extended amygdala has been implicated in anxiety-related behaviors, but the underlying mechanisms are unclear. Here we found that somatostatin-expressing neurons in the central amygdala (CeA) controls anxiety through modulation of the stria terminalis, a process that is mediated by an increase in dynorphin signaling in the CeA. Our results reveal circuit and cellular dysfunctions that may account for maladaptive anxiety.
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition

We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data. Our model uses bilingual character representation and transfer learning to address out-of-vocabulary words. In order to mitigate data noise, we propose to use token replacement and normalization. In the 3rd Workshop on Computational Approaches to Linguistic Code-Switching Shared Task, we achieved second place with 62.76% harmonic mean F1-score for English-Spanish language pair without using any gazetteer and knowledge-based information.
EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet

Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other hand, politicians, companies, and businesses are interested in analyzing automatically people’s opinions and emotions. In the last decade, a lot of efforts has been put into extracting sentiment polarity from texts. Recently, the focus has expanded to also cover emotion recognition from texts. In this work, we expand an existing emotion lexicon, DepecheMood, by leveraging semantic knowledge from English WordNet (EWN). We create an expanded lexicon, EmoWordNet, consisting of 67K terms aligned with EWN, almost 1.8 times the size of DepecheMood. We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood. EmoWordNet is publicly available to speed up research in the field on http://oma-project.com.
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection

This paper describes MuTuX, our system that is designed for task 1-5a, emotion classification analysis of tweets on SemEval2018. The system aims at exploring the potential of context information of terms for emotion analysis. A Recurrent Neural Network is adopted to capture the context information of terms in tweets. Only term features and the sequential relations are used in our system. The results submitted ranks 16th out of 35 systems on the task of emotion detection in English-language tweets.
Harms to Consumers of Inpatient Psychiatric Facilities in the United States: An Analysis of News Articles

ABSTRACT Inpatient psychiatric facilities in the United States lack systematic regulation and monitoring of a variety of patient safety concerns. We conducted a qualitative analysis of 61 news articles to identify common causes and types of harms within inpatient psychiatric facilities, with a focus on physical harm. The news articles reported on patient self-harm, patient-patient violence, and violence between patients and staff, noting that youth, older adults, and veterans were especially vulnerable. Harms occurred throughout the care continuum — at admission, during the inpatient stay, and at discharge — and retaliation towards whistleblowers deterred facility accountability. We recommend 1) addressing staffing shortages, 2) instituting systematic monitoring of critical incidents and the experiences of consumers and staff, 3) improving both inpatient safety and post-discharge community supports, and 4) continued journalistic coverage of harms within inpatient psychiatric facilities.
Learning Domain Representation for Multi-Domain Sentiment Classification

Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on different domains. We investigate this problem by learning domain-specific representations of input sentences using neural network. In particular, a descriptor vector is learned for representing each domain, which is used to map adversarially trained domain-general Bi-LSTM input representations into domain-specific representations. Based on this model, we further expand the input representation with exemplary domain knowledge, collected by attending over a memory network of domain training data. Results show that our model outperforms existing methods on multi-domain sentiment analysis significantly, giving the best accuracies on two different benchmarks.
The norepinephrine transporter deserves more attention.

N orepinephrine released from adrenergic nerve terminals in heart, kidney, and vasculature is crucial for blood pressure (BP) regulation. Both, too little and too much norepinephrine perturbs cardiovascular regulation. Lifewithout norepinephrine ismiserable as exemplified by patients with genetic dopamine-beta-hydroxylase deficiency [1]. These rare patients lack the enzyme required for dopamine to norepinephrine conversion and suffer from profound orthostatic hypotension among other hypoadrenergic symptoms. Conversely, excess norepinephrine action can contribute to arterial hypertension (AH) and predisposes to cardiovascular and renal damage. The hyperadrenergic state may be particularly pronounced in patients with treatment-resistant AH [2]. These mechanism provide the pathophysiological rational for using adrenoreceptor blockers, sympatholytics, and interventions targeting the sympathetic nervous system in hypertension management. Yet, adrenergic contributions to hypertension vary between patients, thus, explaining the heterogeneous response to antiadrenergic treatments.Given the important role ofnorepinephrine in BP regulation, mechanisms regulating its availability deserve our attention. Theoretically, increases in peripheral norepinephrine availability could be explained by increased sympathetic nerve traffic and norepinephrine release from adrenergic nerves, reduction in norepinephrine degradation, or both mechanisms combined. Much of the hypertension research in the last decades has focused on sympathetic nerve traffic and norepinephrine release. Norepinephrine uptake and metabolism has been neglected. The neuronal norepinephrine transporter reclaims much of the norepinephrine from the synaptic cleft. Then, norepinephrine can be either recycled or degraded by monoamine oxidases. Reduced norepinephrine transporter function could conceivably contribute to AH [3].
Illumination estimation based on human visual characteristics for AR

Realistic image synthesis that users do not feel the artificiality is important for AR(Augmented Reality) technology. Although illumination environment in the real world needs to be estimated in order to realize the photometric consistency, the estimation precision is enough to satisfy human perception. A questionnaire is taken from users to analyze factors which affect the users to feel artificiality for synthetic AR images in advance. In the result of the questionnaire, more than 73% of the users perceived photometric consistency to shadow deviation of 30 degrees or less. This paper proposes a simple illumination estimation based method on the human visual characteristics. A light source position is estimated by using a monocular camera with a cylindrical object. First, images with and without a cylindrical object are captured. Next, the cylindrical object region and its cast shadow region are extracted by using subtraction of these two images. Finally, the proposed method estimates the direction of the light source from the extracted regions. AR images are generated with the estimated light source direction. In the user survey, more than 70% of users feel the AR images consistent. Usefulness of the proposed method is presented by the user survey.
Expressions of Olfactory Proteins in Locust Olfactory Organs and a Palp Odorant Receptor Involved in Plant Aldehydes Detection

The main chemosensory organs of locusts consisted of the antennae and the mouthparts (maxillary and labial palps), which are suggested to perform different functions. However, very few are known about the differences of these two organs at molecular level. To understand the differences of locust antennae and palps in olfaction, the electrophysiological response and olfactory gene expression of these two organs were conducted. Our electrophysiological experiments with Locusta migratoria showed that the responses of mouthpart palps and antennae to odorants are quite different. Only a few odorants, such as (E,E)-2,4-hexadienal and (E,E)-2,4-heptadienal, elicited stronger electrophysiological responses of both maxillary and labial palps in comparison to the antennae. Additionally, we obtained 114 and 11 putative odorant receptor (OR) gene segments from the antennal and palp transcriptomes, respectively. Two novel odorant-binding proteins (OBPs; OBP15 and OBP16) and one novel OR (OR142) were identified for the first time. Out of the 16 OBP genes tested in RT-PCR and qPCR analyses, OBP8 was highly expressed in the nymphal palps. OBP4, OBP10, and OBP16 were only detected in the antennae. The other 11 OBP genes were jointly expressed in both antennae and palps. The relative expression level of OBP6 in male palps was much higher than that of female palps. Furthermore, for the 11 OR genes identified in palp transcriptome, the expression levels of OR12, OR13, OR14, and OR18 in the palps were significantly higher than those in the antennae. The OR12 in palps was demonstrated to be involved in detection of hexanal and E-2-hexenal, as well as (E,E)-2,4-heptadienal. Our results provide information on the different olfactory roles of locust antennae and palps at the molecular level.