Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue Epidemic

Social media platforms are widely used by people to report, access, and share information during outbreaks and epidemics. Although government agencies and healthcare institutions in developed regions are increasingly relying on social media to develop epidemic forecasts and outbreak response, there is a limited understanding of how people in developing regions interact on social media during outbreaks and what useful insights this dataset could offer during public health crises. In this work, we examined 28,688 tweets to identify public health issues during dengue epidemic in Bangladesh and found several insights, such as irregularities in dengue diagnosis and treatment, shortage of blood supply for Rh negative blood groups, and high local transmission of dengue during Eid-ul-Adha, that impact disease preparedness and outbreak response. We discuss the opportunities and challenges in analyzing tweets and outline how government agencies and healthcare institutions can use social media health data to inform policy making during public health crises.
Novel Approach for Microbiome Analysis Using Bacterial Replication Rates and Causal Inference with Applications

Motivation Metagenomics sequencing data can be used to compute not just the relative abundance profile, but also the replication rates of every taxon in the microbiome sample. We investigate how the dynamics implied by the replication rates can be used to understand the antibiotic response in microbiomes, given the significant variation in the types of antibiotics and the types of response by different taxa. The analysis is further expanded by factoring in the resistome of the microbiomes, which can be readily profiled from the metagenomic sequence data. The fact that some antibiotics such as β-lactams target replicating cells makes it even more critical to use replication rates to analyze the antibiotic response. Results We introduce a novel approach for metagenomic analysis that integrates microbial community profiling, replication rate calculation, and causal structural learning to analyze the antibiotic response. First, we developed PeTRi, which involves efficient cluster computation of bacterial replication rates from metagenomic sequence data. Second, we integrate the abundance profile, replication profile, resistome profile, and environmental variables to perform causality analysis. Finally, we applied the integrated analysis to the data from an infant gut microbiome study. Conclusions from our analysis are as follows: (i) Microbes tend to lower their replication rates in response to β-lactams; (ii) The presence of antibiotic resistance genes combined with the causality analysis strongly suggest that genes fosA5, oqxA, kpnF, arnA, and acrA provides resistance for the taxon K. pneumoniae, allowing it to replicate and dominate the microbiome after the drug ticarcillin-clavulanate was administered; and (iii) Human and donor milk strongly influence the resistome of the infant gut microbiome. Availability The pipeline for calculation of bacterial replication rates using the high performance computing framework Slurm is available from https://biorg.cs. fiu.edu/petri/.
Exploiting the Trust Between Boundaries: Discovering Memory Corruptions in Printers via Driver-Assisted Testing

TrustScope is a new, a practical approach to identifying vulnerabilities in printer firmware without actually touching the firmware. By exploiting the trust between the firmware and the device drivers, TrustScope analyzes driver software to identify the driver endpoints that output the page description language (PDL) code to be sent to the printer, extracts key constraints for this output, generates new inputs violating these constraints, and fuzzes the printer firmware with malicious PDL code composed with these inputs yet conforming to the grammar of the PDL accepted by the printer. To accommodate the black-box nature of printers, printer behavior is observed strictly externally, allowing TrustScope to detect more vulnerabilities than only those that produce crashes. A variety of key optimizations, such as fuzzing without consuming paper and ink, and offline test case generation, make printer vulnerability detection feasible and practical. An implementation of TrustScope tested with 8 different printers reveals at least one test case causing anomalous behavior in every printer tested. For most printers it finds multiple vulnerabilities, 6 of which have been assigned CVE numbers, including buffer overflow and information disclosure.
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data

Machine Learning (ML) models are difficult to maintain in production settings. In particular, deviations of the unseen serving data (for which we want to compute predictions) from the source data (on which the model was trained) pose a central challenge, especially when model training and prediction are outsourced via cloud services. Errors or shifts in the serving data can affect the predictive quality of a model, but are hard to detect for engineers operating ML deployments. We propose a simple approach to automate the validation of deployed ML models by estimating the model's predictive performance on unseen, unlabeled serving data. In contrast to existing work, we do not require explicit distributional assumptions on the dataset shift between the source and serving data. Instead, we rely on a programmatic specification of typical cases of dataset shift and data errors. We use this information to learn a performance predictor for a pretrained black box model that automatically raises alarms when it detects performance drops on unseen serving data. We experimentally evaluate our approach on various datasets, models and error types. We find that it reliably predicts the performance of black box models in the majority of cases, and outperforms several baselines even in the presence of unspecified data errors.
Hidden components of microbiome diversity revealed by cell wall lysis with Adaptive Focused Acoustics

Within the rapidly evolving field of microbiome sequencing, a primary need exists for experimentally capturing microbiota in a manner as close as possible to their in vivo composition. During microbiome profiling, the first step necessarily involves lysis of the cell wall, releasing nucleic acids for next-generation sequencing. Microbial cell wall thicknesses can vary between 5nm to 80nm; while some species are quite easy to lyse, others are particularly resistant to lysis. Despite this, current chemical/mechanical lysis protocols ignore the possibility that species with different cell wall thicknesses are lysed at differential rates. This creates noise in species compositions and possibly skews current microbiome results in ways that are not currently understood. To develop a cell wall thickness-agnostic lysis protocol, we used Adaptive Focused Acoustics (AFA), a tunable acoustic methodology for processing of biological samples. Using identical aliquots of mouse stool homogenate as the lysis substrate, we compared AFA with chemical/mechanical lysis methodology routinely used in microbiome studies and found that AFA-mediated lysis substantially increases both microbial DNA yield as well as alpha and beta diversity. By starting with lower AFA energy levels, sequentially removing aliquots at each step, and subjecting the remainder to progressively stronger AFA treatment, we developed a sequential lysis method that accounts for differences in cell wall thickness. This method revealed even greater levels of diversity than single-timepoint AFA treatment. 16S sequencing results from the above experiments were verified by shotgun metagenome sequencing of a subset of the AFA samples. We found that lysis-induced noise affects not just species compositaions, but also functional characterization of shotgun metagenome data. AFA samples also showed a higher detection of eukaryotic and fungal DNA. We suggest that AFA-mediated lysis produces a truer representation of the native microbiota, and that this method deserves consideration as a potential addition to microbiome lysis protocols.
Does the Motor Cortex Want the Full Story? The Influence of Sentence Context on Corticospinal Excitability in Action Language Processing

The reading of action verbs has been shown to activate motor areas, whereby sentence context may serve to either globally strengthen this activation or to selectively sharpen it. To investigate this issue, we manipulated the presence of manual actions and sentence context, assessing the level of corticospinal excitability by means of transcranial magnetic stimulation. We hypothesized that context would serve to sharpen the neural representation of the described actions in the motor cortex, reflected in context-specific modulation of corticospinal excitability. Participants silently read manual action verbs and non-manual verbs, preceded by a full sentence (rich context) or not (minimal context). Transcranial magnetic stimulation pulses were delivered at rest or shortly after verb presentation. The coil was positioned over the cortical representation of the right first dorsal interosseous (pointer finger). We observed a general increase of corticospinal excitability while reading both manual action and non-manual verbs in minimal context, whereas the modulation was action-specific in rich context: corticospinal excitability increased while reading manual verbs, but did not differ from baseline for non-manual verbs. These findings suggest that sentence context sharpens motor representations, activating the motor cortex when relevant and eliminating any residual motor activation when no action is present.
Analysis of Cognitive Radio Networks with Cooperative Communication

This paper proposes and analyzes a two-orbit single server queueing model for cognitive wireless networks with cooperative communication. In our model, two secondary users opportunistically utilize a channel which is originally granted to primary users. If a secondary user cannot occupy the channel it returns to the orbit and retries for server after an exponentially distributed time. The retrial rates of the SUs are coupled. We derive a boundary problem for the joint generating functions of the numbers of jobs in the orbits and the state of the server.
Entrain, exploring new territorial user interfaces

We aim at exploring the different approaches to design interactive applications for groups of users using a set of interacting surfaces to perform their tasks with an optimal user experience. Participants are invited to present both the models and/or design methods as well as the case studies and applications they are studying in this context. We would like to set up a discussion group in order to put each person's work in perspective with the notion of territoriality applied to ambient computing and multiple devices. The territoriality theory may serve as a basis for the design of complex interactive applications of quality. From these discussions will emerge a mapping of models and design methods that could be mutualized and combined.
Chinese FOG Index: the Readability of Information Disclosure in Chinese Listed Companies

The disclosure of non-financial information from listed companies has a significant impact on Chinese market. How can we apply advanced techniques (e.g: big data analysis ) to analyze text-format non-financial information? The FOG index, proposed by Robert Gunning in 1952, is the most commonly adopted text readability index; unfortunately, it is only suitable for English text. In this study, we choose annual financial reports of 200 listed Chinese companies, using machine learning and text-mining methods, to build a new index which is suitable for measuring the readability of Chinese text. Statistical methods such as Cluster Analysis, Ridge Regression, and LARS regression are also utilized to get the final model. Then we apply the proposed index to prospectuses of Chinese listed companies in the Sci-Tech innovation board to measure their readability and thus the quality of their information disclosure. We tentatively build an assessment model for Chinese text readability which may enhance the intelligibility and observability of non-financial information disclosure quality in the Chinese market.
Mapping the architecture of regulatory variation provides insights into the evolution of complex traits

Background Organisms evolve complex traits by recruiting existing programs to new contexts, referred as co-option. Within a species, single upstream regulators can trigger full differentiation programs. Distinguishing whether co-option of differentiation programs results from variation in single regulator, or in multiple genes, is key for understanding how complex traits evolve. As an experimentally accessible model for studying this question we turned to budding yeast, where a differentiation program (filamentous) is activated in S. cerevisiae only upon starvation, but used by the related species S. paradoxus also in rich conditions. Results To define expression variations associated with species-specific activation of the filamentous program, we profiled the transcriptome of S. cerevisiae, S. paradoxus and their hybrid along two cell cycles at 5-minutes resolution. As expected in cases of co-option, expression of oscillating genes varies between the species in synchrony with their growth phenotypes and was dominated by upstream trans-variations. Focusing on regulators of filamentous growth, we identified gene-linked variations (cis) in multiple genes across regulatory layers, which propagated to affect expression of target genes, as well as binding specificities of downstream transcription factor. Unexpectedly, variations in regulators essential for S. cerevisiae filamentation were individually too weak to explain activation of this program in S. paradoxus. Conclusions Our study reveals the complex architecture of regulatory variation associated with species-specific use of a differentiation program. Based on these results, we suggest a new model in which evolutionary co-option of complex traits is stabilized in a distributed manner through multiple weak-effect variations accumulating throughout the regulatory network.
Two Approaches to Inner Estimations of the Optimal Solution Set in Interval Linear Programming

We consider a linear programming problem with uncertain input coefficients. The only information we have are lower and upper bounds for the uncertain values. This gives rise to the so called interval linear programming. The challenging problem here is to characterize and determine the set of all possible optimal solutions. Most of the scholars were focus on computing outer bounds for the optimal solution. Herein, we will be interested with computing inner bounds. We propose a local search algorithm and a genetic algorithm. We compare both methods numerically on random data to ascertain what is their real time complexity and quality of inner estimations.
Endothelial CXCL12 regulates neovascularization during tissue repair and tumor progression

CXC chemokine ligand 12 (CXCL12; stromal cell-derived factor 1 [SDF-1]), primarily known for its role in embryogenesis and hematopoiesis, has also been implicated in tumor biology and neovascularization. However, its specific role and mechanism of action remain poorly understood. We previously demonstrated that CXCL12 expression is Hypoxia-Inducible Factor (HIF)-1 responsive. Here we use a conditional CXCL12 knockout mouse to show that endothelial-specific deletion of CXCL12 (eKO) does not affect embryogenesis, but reduces the survival of ischemic tissue, altering tissue repair and tumor progression. Loss of vascular endothelial CXCL12 disrupts endothelial – fibroblast crosstalk necessary for stromal growth and vascularization. Single-cell gene expression analysis in combination with a parabiosis model reveals a specific population of non-inflammatory circulating cells, defined by genes regulating neovascularization, which is recruited by endothelial CXCL12. These findings indicate an essential role for endothelial CXCL12 expression during the adult neovascular response in tissue injury and tumor progression.
A sensor-fusion-system for tracking sheep location and behaviour

The growing interest in precision livestock farming is prompted by a desire to understand the basic behavioural needs of the animals and optimize the contribution of each animal. The aim of this study was to develop a system that automatically generated individual animal behaviour and localization data in sheep. A sensor-fusion-system tracking individual sheep position and detecting sheep standing/lying behaviour was proposed. The mean error and standard deviation of sheep position performed by the ultra-wideband location system was 0.357 ± 0.254 m, and the sensitivity of the sheep standing and lying detection performed by infrared radiation cameras and three-dimenional computer vision technology were 98.16% and 100%, respectively. The proposed system was able to generate individual animal activity reports and the real-time detection was achieved. The system can increase the convenience for animal behaviour studies and monitoring of animal welfare in the production environment.
Molecular mechanisms of forgetting

The field of learning and memory has largely focused their efforts on memory formation, deeming the act of forgetting as a necessary and unavoidable error of the brain's storage system. However, recent studies show that forgetting might be a purposeful mechanism with dedicated molecular pathways. This has opened new avenues for the study of memory processes and their manipulation. This review article focuses on the proposed molecular pathways that could underlie forgetting (postsynaptic glutamate receptors and their subunit composition, structural proteins, kinases, phosphatases, and neuromodulators), and reviews the latest contributions to this topic. The discussed molecular pathways are then related to synaptic depotentiation, which is a proposed mechanism underlying forgetting.
Training Feedforward Neural Networks Using Hybrid Particle Swarm Optimization, Multi-Verse Optimization

—The learning process of artiﬁcial neural networks is an important and complex task in the supervised learning ﬁeld. The main difﬁculty of training a neural network is the process of ﬁne-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efﬁciency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with ﬁve evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.
MicroRNA-93 contributes to the suppression of lung inflammatory responses in LPS-induced acute lung injury in mice via the TLR4/MyD88/NF-κB signaling pathway

Acute lung injury (ALI) is a severe inflammatory lung disease with a rapid onset. The anti-inflammatory functions of microRNA-93 (miRNA/miR-93) have been described in various types of tissue injury and disease. However, the biological role of miR-93 and its molecular mechanisms underlying the initiation and progression of ALI have not yet been reported, at least to the best of our knowledge. The present study aimed to investigate the regulatory effects exerted by miR-93 in ALI. Using an in vivo murine model of ALI induced by lipopolysaccharide (LPS), miR-93 expression was found to be downregulated in the lung tissues and bronchoalveolar lavage fluid (BALF) compared with the control group. Following agomiR-93 injection, it was observed that agomiR-93 attenuated lung injury, as evidenced by decreased lung permeability, a reduced lung wet/dry weight ratio and an increased survival rate of the mice. Concomitantly, agomiR-93 significantly reduced LPS-induced the interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α levels in BALF. Of note, Toll-like receptor 4 (TLR4), an upstream regulator of the nuclear factor (NF)-κB signaling pathway, was directly suppressed by miR-93 in RAW 264.7 cells. Importantly, agomiR-93 induced a significant suppression of the TLR4/myeloid differentiation primary response 88 (MyD88)/NF-κB signaling pathway, as demonstrated by the downregulation of MyD88, and the phosphorylation of IκB-α and p65 in the lung tissues of mice with ALI. Taken together, the findings of the present study indicate that miR-93 attenutes LPS-induced lung injury by regulating the TLR4/MyD88/NF-κB signaling pathway, suggesting that miR-93 may prove to be a potential therapeutic target for ALI.
Anti-obesity effects of herbal extract YY312 in C57BL/6 mice fed a high-fat diet and 3T3-L1 cells

Objectives : The purpose of this study was to determine the anti-obesity effect and molecular mechanism of YY312, a herbal extract composed of Imperatae Rhizoma, Citri Unshius Pericarpium Immaturus, and Evodiae Fructus, on a high-fat diet-induced animal model and on 3T3-L1 cells. Methods : C57BL/6 mice were fed for 6 weeks with a normal diet or a high-fat diet (HFD). Then they orally administered daily with 300 mg/kg YY312 for next 10 weeks. Body weight and food consumption were recorded weekly and daily, respectively. Tissue weights, serum lipid, and glucose levels were analyzed at the end of the study. Additionally, the effects of YY312 on adipocyte differentiation in 3T3-L1 cells were examined. After differentiating 3T3-L1 cells were treated with YY312, Oil-red O staining, RT-PCR, and Western blotting were performed for lipid accumulation, mRNA expression of adipogenesis gene, and AMP-activated protein kinase (AMPK) phosphorylation, respectively. Results : YY312-administered mice showed a significant reduction of body weights and abdominal adipose tissue weights. YY312 also reduced the serum levels of triglycerides and total cholesterol, compared with the HFD group. Treatment with YY312 inhibited lipid accumulation and blocked expression of adipogenic transcription factors and lipogenesis genes, including peroxisome proliferator-activated receptor , CCAT/enhancer binding protein and fatty acid synthase. YY312 increased AMPK phosphorylation in 3T3-L1 adipocytes. Conclusions : This study showed that herbal extract YY312 has an anti-obesity effect in vitro and in vivo. Thus, YY312 could be developed as a supplement for reduction of body weight gain induced by an HFD.
Prognostic role of extracellular vesicles in squamous cell carcinoma of the lung

Research on diagnosing recurrent non‐small cell lung cancer (NSCLC) and applying target gene treatment using exosomes in a less invasive way is very important. Recently, however, it has been argued that exosomes do not contain double‐stranded DNA (dsDNA) or histones. In this study, we describe the expression of extracellular vesicle (EV) markers in specimens from squamous cell carcinoma (SCC) of the lung and analyze their relationship with the prognosis of patients.