Miracle-dz/UniFAR-FTUs huggingface dataset:
Hookah Use among U.S. Youth: A Systematic Review of the Literature from 2009-2017.

Introduction Given the U.S. Food and Drug Administration (FDA)'s authority to regulate hookah, more research is needed to inform regulations intended to prevent youth from using hookah. This systematic review summarizes and assesses the literature related to hookah use among adolescents (11 to ≤18 years of age) in the U.S. from 2009 to 2017. Methods Database searches yielded 867 peer-reviewed articles. After duplicates were removed, authors reviewed 461 articles for inclusion. Included articles (n=55) were coded for study themes, study quality and their relevance to FDA's research priorities. A qualitative synthesis is presented. Results The following themes were identified: 1) prevalence of hookah use (n=42); 2) tobacco use transitions (n=7); 3) sociodemographic correlates (n=35); 4) psychosocial risk factors (n=21); 5) concurrent use of other tobacco products (n=31); 6) concurrent use of other substances (n=9); and 7) other (n=15)-which includes low prevalence themes. The qualitative synthesis showed increasing rates of hookah use. Older age, male gender, positive social normative beliefs, higher peer use as well as lower perceived risk was associated with hookah use. Longitudinal studies of youth hookah use showed bidirectional relationships between use of hookah and other tobacco products. All articles fell within FDA's research priority related to "behavior," and three priorities ("impact analysis," "health effects," and "toxicity") have not been explored for hookah use among U.S. youth since 2009. Conclusions The prevalence of hookah use among youth in the U.S. is increasing, thus more research is needed to inform policies targeted to protect this vulnerable population. Implications This study represents a novel contribution to our understanding of hookah use among youth in the United States (U.S.) from 2009-the year that the Family Smoking Prevention and Tobacco Control Act was passed-to 2017. In recent years, hookah has become a more popular tobacco product among U.S. youth; however, to date, no systematic reviews of hookah use among this population exist. Results highlight implications for future U.S. Food and Drug Administration regulatory policy and identify gaps in research to be addressed in future studies.
Computer Science Identity and Sense of Belonging: A Case Study in Ireland

The study described in this paper investigates the role that gender plays in making the decision to study Computer Science in University College Dublin in Ireland (background influences) and investigates whether there is a difference in the perceived sense of belonging between the genders. The aim is to improve diversity and sense of belonging amongst Computer Science students, in order to ensure that our school is an inclusive space, where anyone can feel a sense of belonging regardless their gender.
Metabolomic and lipidomic analysis of the effect of pioglitazone on hepatic steatosis in a rat model of obese Type 2 diabetes

Thiazolidinediones, acting as PPAR‐γ ligands, reduce hepatic steatosis in humans and animals. However, the underlying mechanism of this action remains unclear. The purpose of this study was to investigate changes in hepatic metabolites and lipids in response to treatment with the thiazolidinedione pioglitazone in an animal model of obese Type 2 diabetes.
Smart Optical Catheters for Epidurals

Placing the needle inside the epidural space for locoregional anesthesia is a challenging procedure, which even today is left to the expertise of the operator. Recently, we have demonstrated that the use of optically sensorized needles significantly improves the effectiveness of this procedure. Here, we propose an optimized configuration, where the optical fiber strain sensor is directly integrated inside the epidural catheter. The new design allows the solving of the biocompatibility issues and increases the versatility of the former configuration. Through an in vivo study carried out on a porcine model, we confirm the reliability of our approach, which also opens the way to catheter monitoring during insertion inside biological spaces.
Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses’ Perceptions and Lessons Learned

RÉSUMÉ Les personnes âgées hospitalisées présentent un haut risque de chute. Le système HELPER est un système de détection des chutes fixé au plafond qui envoie une alerte à un téléphone intelligent lorsqu’une chute est détectée. Cet article décrit la performance du système HELPER, qui a été testé dans un projet pilote mené dans un centre de santé mentale gériatrique. La précision du système pour la détection des chutes a été comparée aux données de l’hôpital liées à la documentation des chutes. Au terme du projet pilote, le personnel infirmier a été interviewé afin de documenter comment cette technologie était perçue. Dans cette étude, le système HELPER n’a pas permis de détecter une chute qui a été documentée par le personnel, mais en a détecté 4 autres qui n’avaient pas été documentées. Bien que la sensibilité du système soit élevée (0.80), les fausses alarmes qu’il génère diminuent sa valeur prédictive (0.01). Les entrevues avec le personnel infirmier ont permis de recueillir plusieurs informations utiles liées au fonctionnement de cette technologie dans un environnement réel; ces données seront utiles aux ingénieurs travaillant sur de tels systèmes et sur des technologies associées aux soins de santé et aux services sociaux. ABSTRACT Hospitalized older adults are at high risk of falling. The HELPER system is a ceiling-mounted fall detection system that sends an alert to a smartphone when a fall is detected. This article describes the performance of the HELPER system, which was pilot tested in a geriatric mental health hospital. The system’s accuracy in detecting falls was measured against the hospital records documenting falls. Following the pilot test, nurses were interviewed regarding their perceptions of this technology. In this study, the HELPER system missed one documented fall but detected four falls that were not documented. Although sensitivity (.80) of the system was high, numerous false alarms brought down positive predictive value (.01). Interviews with nurses provided valuable insights based on the operation of the technology in a real environment; these and other lessons learned will be particularly valuable to engineers developing this and other health and social care technologies.
Early cirrhosis and a preserved bone marrow niche favour regenerative response to growth factors in decompensated cirrhosis

Exogenous growth factor‐mobilized bone marrow (BM) stem cells have shown a differential response in the management of decompensated cirrhosis (DC). This study was designed to evaluate potential clinical benefit of adding Erythropoietin (EPO) in granulocyte‐colony stimulating factor (G‐CSF)‐mobilized stem cell therapy, possible mechanisms of regeneration and predictive factors of regenerative response.
Prevalence and Genetic Analysis of mcr-3-Positive Aeromonas Species from Humans, Retail Meat, and Environmental Water Samples

The mobile colistin resistance gene mcr-3 is globally disseminated in both Enterobacteriaceae and Aeromonas species, with the latter potentially serving as a reservoir for this gene. Here, we investigated the prevalence of mcr-3 in rectal swabs from humans, in food-producing animals and their products, and in the aquatic environment, and we investigated the genetic relationships between the mcr-3-positive isolates. ABSTRACT The mobile colistin resistance gene mcr-3 is globally disseminated in both Enterobacteriaceae and Aeromonas species, with the latter potentially serving as a reservoir for this gene. Here, we investigated the prevalence of mcr-3 in rectal swabs from humans, in food-producing animals and their products, and in the aquatic environment, and we investigated the genetic relationships between the mcr-3-positive isolates. An enriched broth screening method was used to detect mcr-3 in samples, and species identification of isolates from positive samples was carried out by matrix-assisted laser desorption ionization–time of flight mass spectrometry and shotgun sequencing. All mcr-3-positive isolates were subjected to antimicrobial susceptibility testing, conjugation, and whole-genome sequencing. Ten Aeromonas isolates, including 2 from human rectal swabs, 1 from pork, 3 from chicken meat, and 4 from the aquatic environment, were positive for mcr-3, but only 2 showed resistance to colistin. In addition to the mcr-3 variants identified previously (the novel variants were termed mcr-3.13 to mcr-3.18), all isolates harbored mcr-3-like genes downstream of the mcr-3 variants. The MCR-3.13 to MCR-3.18 proteins exhibited only 89.2% to 96.1% amino acid identity to the original MCR-3 protein. Whole-genome sequence analysis indicated diversity within the genetic environments of mcr-3-positive Aeromonas isolates and possible transmission between different sources in China and even worldwide. Close relationships between mcr-3-positive and mcr-3-negative Aeromonas isolates suggested that mcr-3 might be common in Aeromonas species, which are not inherent hosts of mcr-3 but may act as an important reservoir of this mobile colistin resistance gene.
Single, Dual, and Poly Use of Flavored Tobacco Products Among Youths

Introduction Flavoring has become the leading reason for current tobacco use among adolescents. This study sought to evaluate patterns of flavored tobacco product use and associated risk factors among youths. Methods Weighted estimates of single, dual, and poly use of flavored tobacco products were calculated from the 2014 National Youth Tobacco Survey (n = 21,926). Multinomial logistic regression was performed to assess factors associated with flavored product use. Results Among current tobacco users (n = 3,805), 70.0% of students were current users of flavored tobacco products: 42.6% used a single flavored product, 16.8% used 2 flavored products (dual users), and 10.6% used more than 2 flavored products (poly users). Flavored product use, especially dual and poly use, was higher among high school students compared with middle school students. Compared with single flavored tobacco product users (36%), dual (57%) and poly users (79%) of flavored tobacco products had higher prevalences of using flavored e-cigarettes (P < .001). Non-Hispanic blacks and those of other races had lower prevalences than non-Hispanic whites of using flavored products but not nonflavored products. Tobacco use by household members, no perception of harm from tobacco products, and more frequent exposure to tobacco advertisement in newspapers/magazines and stores were associated with increased odds of flavored product use. Conclusion The concurrent use of flavored tobacco products is prevalent among youths. E-cigarettes were the leading flavored product and often concurrently used with other flavored tobacco products. Comprehensive control and prevention strategies to reduce flavored tobacco use among youths are needed.
Subsystem organization of axonal connections within and between the right and left cerebral cortex and cerebral nuclei (endbrain)

Significance The right and left cerebral hemispheres (together forming the endbrain) support cognition and affect, and, structurally, each hemisphere consists of a cortical sheet and set of deep nuclei (often called the basal ganglia). Experimental evidence in the literature identified more than 10,000 axonal macroconnections between the 244 gray matter regions of the endbrain, and the global organizing principles of the network formed by these connections were subjected to multiresolution consensus clustering analysis. The result was a hierarchy of subsystems that has only four components at the top level and 60 components at the bottom level. Furthermore, a region’s status as a connectivity hub in a network is not absolute; it depends on the size and coverage of its anatomical neighborhood. The endbrain (telencephalon) is at the rostral end of the central nervous system and is primarily responsible for supporting cognition and affect. Structurally, it consists of right and left cerebral hemispheres, each parceled into multiple cortical and nuclear gray matter regions. The global network organization of axonal macroconnections between the 244 regions forming the endbrain was analyzed with a multiresolution consensus clustering (MRCC) method that provides a hierarchical description of community clustering (modules or subsystems) within the network. Experimental evidence was collated from the neuroanatomical literature for the existence of 10,002 of a possible 59,292 connections within the network, and they cluster into four top-level subsystems and 60 bottom-level subsystems arranged in a 50-level hierarchy. Two top-level subsystems are bihemispheric: One deals with auditory and visual information, and the other corresponds broadly to the default mode network. The other two top-level subsystems are bilaterally symmetrical, and each deals broadly with somatic and visceral information. Because the entire endbrain connection matrix was assembled from multiple subconnectomes, it was easy to show that the status of a region as a connectivity hub is not absolute but, instead, depends on the size and coverage of its anatomical neighborhood. It was also shown numerically that creating an ultradense connection matrix by converting all “absent” connections to a “very weak” connection weight has virtually no effect on the clustering hierarchy. The next logical step in this project is to complete the forebrain connectome by adding the thalamus and hypothalamus (together, the interbrain) to the endbrain analysis.
Neurogenic decisions require a cell cycle independent function of the CDC25B phosphatase

A fundamental issue in developmental biology and in organ homeostasis is understanding the molecular mechanisms governing the balance between stem cell maintenance and differentiation into a specific lineage. Accumulating data suggest that cell cycle dynamics plays a major role in the regulation of this balance. Here we show that the G2/M cell cycle regulator CDC25B phosphatase is required in mammals to finely tune neuronal production in the neural tube. We show that in chick neural progenitors, CDC25B activity is both required and sufficient to stimulate neurogenic divisions and to promote neuronal differentiation. We design a mathematical model showing that within a limited period of time, cell cycle length modifications cannot account for changes in the ratio of the mode of division. Using a CDC25B point mutation that cannot interact with CDK, we show that part of CDC25B activity on neurogenic divisions is independent of its action on the cell cycle.
Detecting malicious authentication events trustfully

Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.
Nothing But Net: Invading Android User Privacy Using Only Network Access Patterns

We evaluate the power of simple networks side-channels to violate user privacy on Android devices. Specifically, we show that, using blackbox network metadata alone (i.e., traffic statistics such as transmission time and size of packets) it is possible to infer several elements of a user's location and also identify their web browsing history (i.e, which sites they visited). We do this with relatively simple learning and classification methods and basic network statistics. For most Android phones currently on the market, such process-level traffic statistics are available for any running process, without any permissions control and at fine-grained details, although, as we demonstrate, even device-level statistics are sufficient for some of our attacks. In effect, it may be possible for any application running on these phones to identify privacy-revealing elements of a user's location, for example, correlating travel with places of worship, point-of-care medical establishments, or political activity.
Ca2+, Astrocyte Activation and Calcineurin/NFAT Signaling in Age-Related Neurodegenerative Diseases

Mounting evidence supports a fundamental role for Ca2+ dysregulation in astrocyte activation. Though the activated astrocyte phenotype is complex, cell-type targeting approaches have revealed a number of detrimental roles of activated astrocytes involving neuroinflammation, release of synaptotoxic factors and loss of glutamate regulation. Work from our lab and others has suggested that the Ca2+/calmodulin dependent protein phosphatase, calcineurin (CN), provides a critical link between Ca2+ dysregulation and the activated astrocyte phenotype. A proteolyzed, hyperactivated form of CN appears at high levels in activated astrocytes in both human tissue and rodent tissue around regions of amyloid and vascular pathology. Similar upregulation of the CN-dependent transcription factor nuclear factor of activated T cells (NFAT4) also appears in activated astrocytes in mouse models of Alzheimer’s disease (ADs) and traumatic brain injury (TBI). Major consequences of hyperactivated CN/NFAT4 signaling in astrocytes are neuroinflammation, synapse dysfunction and glutamate dysregulation/excitotoxicity, which will be covered in this review article.
Common mental disorders and risk factors in rural India: baseline data from the SMART mental health project

Background About 10% Indians suffer from stress, depression or substance use disorders. Few receive care for these problems, especially in rural areas. Aims As part of a broader initiative to deliver technology-enabled mental health services for rural communities (adults ≥18 years), information was collected about the prevalence of depression, anxiety and suicide risk. Method The study was conducted in 12 villages in the West Godavari district of Andhra Pradesh. Depression and anxiety were assessed using the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively. Additionally, data were collected about sociodemographic factors and stressful events, among others. Results Anxiety, depression and suicidal ideation affected 10.8, 14.4 and 3.5% of participants, respectively (N = 22 377). These were more common among women, and among those who were aged 30–59 years, uneducated, or divorced/ separated/ widowed. Stress due to financial loss was significant. Conclusions The study identified a significant number of people at risk of depression, anxiety and suicide, and needing care. Declaration of interest None.
Unsupervised depth estimation using feature matching method

Depth estimation from single-view images is significant in 3D reconstruction. The previous unsupervised depth estimation method is affected by the ambient illumination changes and the occlusions between adjacent frames. In this paper, feature matching method is combined to the unsupervised depth estimation network to acquire accurate corresponding position information between adjacent frames. The view synthesis loss function is then updated based on feature matching results to enhance the convergence of the training pipline. Finally the proposed method is tested on KITTI dataset and acquire more accurate estimation result compared with the previous work.
An integrated neural model for sentence classification

Text classification is one of the fundamental problems in natural language processing. The difficulty of text classification still lies in the ambiguity and rhetorical of natural language. In order to classify sentences more effectively, we propose a hybrid L-MCNN model to represent the semantics of sentences, and this model consists of two parts, bidirectional long short-term memory (LSTM) and multichannel convolutional neural networks (CNNs). Different from the existing methods, our model utilizes the bidirectional LSTM to acquire the max contextual information of every position in sentences, and then the abstract semantics would be fed into the multichannel convolution layer which uses several filters to extract active local n-gram features. By combining the advantages of both architectures, the L-MCNN model can capture both long-distance dependencies and local information within sentences to improve the classification performance. Several experiments are conducted, and the results indicate that our system outperforms the baseline algorithms on sentiment analysis and question classification tasks.
A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data

Alzheimer's disease (AD) is an irreversible, pro­gressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this paper, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer's us­ing a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer's disease (AD). The model was imple­mented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.
Deep neural networks for terrain recognition task

This paper focuses on the problem of using artificial, deep neural networks in terrain recognition task based on data from vision sensor. Information about a terrain class is valuable for mobile robots, as it can improve their motion control algorithm performance through the use of information about surface properties. In this work RGB-D sensor was used for providing vision data, which comprise a depth map and infrared image in addition to the standard RGB data. Our own model of the artificial neural network is presented in this work. It was trained using the latest machine learning libraries. The results of this work demonstrate the performance of artificial neural networks in the terrain recognition task and give some hints how to improve classification in the future.