Miracle-dz/UniFAR-FTUs huggingface dataset:
Subtypes of disordered gamblers: results from the National Epidemiologic Survey on Alcohol and Related Conditions.

AIMS To derive empirical subtypes of problem gamblers based on etiological and clinical characteristics described in the Pathways Model, using data from a nationally representative survey of US adults. DESIGN & MEASUREMENT: Data were collected from structured diagnostic face-to-face interviews using the Alcohol Use Disorder and Associated Disabilities Interview Schedule DSM-IV version IV (AUDADIS-IV). SETTING The study utilized data from US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). PARTICIPANTS All disordered gambling participants (n = 581) from a nationally representative cross-sectional sample of civilian non-institutionalized adults aged 18 years or older. FINDINGS Latent class analyses indicated that the best-fitting model was a three-class solution. Those in the largest class (class 1: 50.76%, n = 295) reported the lowest overall levels of psychopathology including gambling problem severity and mood disorders. In contrast, respondents in class 2 (20.06%, n = 117) had a high probability of endorsing past-year substance use disorders, moderate probabilities of having parents with alcohol/drug problems and of having a personality disorder, and the highest probability for past-year mood disorders. Respondents in class 3 (29.18%, n = 169) had the highest probabilities of personality and prior-to-past year mood disorders, substance use disorders, separation/divorce, drinking-related physical fights and parents with alcohol/drug problems and/or a history of antisocial personality disorder (ASPD). CONCLUSIONS Three subtypes of disordered gamblers can be identified, roughly corresponding to the subtypes of the Pathways Model, ranging from a subgroup with low levels of gambling severity and psychopathology to one with high levels of gambling problem severity and comorbid psychiatric disorders.
Algorithms for Fair Team Formation in Online Labour Marketplaces✱

As freelancing work keeps on growing almost everywhere due to a sharp decrease in communication costs and to the widespread of Internet-based labour marketplaces (e.g., guru.com, feelancer.com, mturk.com, upwork.com), many researchers and practitioners have started exploring the benefits of outsourcing and crowdsourcing [13, 14, 16, 23, 25, 29]. Since employers often use these platforms to find a group of workers to complete a specific task, researchers have focused their efforts on the study of team formation and matching algorithms and on the design of effective incentive schemes [2, 3, 4, 17]. Nevertheless, just recently, several concerns have been raised on possibly unfair biases introduced through the algorithms used to carry out these selection and matching procedures. For this reason, researchers have started studying the fairness of algorithms related to these online marketplaces [8, 19], looking for intelligent ways to overcome the algorithmic bias that frequently arises. Broadly speaking, the aim is to guarantee that, for example, the process of hiring workers through the use of machine learning and algorithmic data analysis tools does not discriminate, even unintentionally, on grounds of nationality or gender. In this short paper, we define the Fair Team Formation problem in the following way: given an online labour marketplace where each worker possesses one or more skills, and where all workers are divided into two or more not overlapping classes (for examples, men and women), we want to design an algorithm that is able to find a team with all the skills needed to complete a given task, and that has the same number of people from all classes. We provide inapproximability results for the Fair Team Formation problem together with four algorithms for the problem itself. We also tested the effectiveness of our algorithmic solutions by performing experiments using real data from an online labor marketplace.
Leveraging Label-Specific Discriminant Mapping Features for Multi-Label Learning

As an important machine learning task, multi-label learning deals with the problem where each sample instance (feature vector) is associated with multiple labels simultaneously. Most existing approaches focus on manipulating the label space, such as exploiting correlations between labels and reducing label space dimension, with identical feature space in the process of classification. One potential drawback of this traditional strategy is that each label might have its own specific characteristics and using identical features for all label cannot lead to optimized performance. In this article, we propose an effective algorithm named LSDM, i.e., leveraging label-specific discriminant mapping features for multi-label learning, to overcome the drawback. LSDM sets diverse ratio parameter values to conduct cluster analysis on the positive and negative instances of identical label. It reconstructs label-specific feature space which includes distance information and spatial topology information. Our experimental results show that combining these two parts of information in the new feature representation can better exploit the clustering results in the learning process. Due to the problem of diverse combinations for identical label, we employ simplified linear discriminant analysis to efficiently excavate optimal one for each label and perform classification by querying the corresponding results. Comparison with the state-of-the-art algorithms on a total of 20 benchmark datasets clearly manifests the competitiveness of LSDM.
Opico: A Study of Emoji-first Communication in a Mobile Social App

In the last two decades, Emoji have become a mainstay of digital communication, allowing ordinary people to convey ideas, concepts, and emotions with just a few Unicode characters. While emoji are most often used to supplement text in digital communication, they comprise a powerful and expressive vocabulary in their own right. In this paper, we study the affordances of “emoji-first” communication, in which sequences of emoji are used to describe concepts without textual accompaniment. To investigate the properties of emoji-first communication, we built and released Opico, a social media mobile app that allows users to create reactions — sequences of between one and five emoji — and share them with a network of friends. We then leveraged Opico to collect a repository of more than 3700 emoji reactions from more than 1000 registered users, each tied to one of 2441 physical places. We describe the design and architecture of the Opico app, present a qualitative and quantitative analysis of Opico’s reaction dataset, and discuss the implications of Emoji-first communication for future social platforms.
A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning

Healthcare Internet-of-things comprises a huge number of wearable sensors and interconnected computers. The high volume of IoT data is transacted over servers leading to servers overloading with high traffic causing network congestion. These cloud servers are typically for analyzing, retrieving and storing the large data generated from IoT devices. There exist challenges regarding sending real-time healthcare data from cloud servers to end-users. These challenges include the high computational latency, high communication latency, and high network latency. Due to these challenges, IoTs may not be able to send data in real-time to end-users. Fog nodes can be used to play a major role in reducing the high delay and high traffic. It can be a solution to increase system performance. In this paper, we proposed a 3-tier architecture, an analytical model for healthcare IoT using a hybrid approach consisting of fuzzy logic and reinforcement learning in a fog computing environment. The aim is to minimize network latency. The proposed model and 3-tier architecture are simulated using iFogSim simulator.
TPD52 and NFKB1 gene expression levels correlate with G2 chromosomal radiosensitivity in lymphocytes of women with and at risk of hereditary breast cancer

Purpose: To evaluate a transcriptomic approach to identify healthy women at increased risk of breast cancer due to G2-radiosensitivity and look at transcripts that are differentially expressed between individuals. Materials and methods: We perform the first study to assess the association of G2 radiosensitivity with basal gene expression in cultured T-lymphocytes from 11 women with breast cancer and 12 healthy female relatives using Affymetrix GeneChips. Results: Transcripts associated with radiosensitivity and breast cancer risk were predominantly involved in innate immunity and inflammation, such as interleukins and chemokines. Genes differentially expressed in radiosensitive individuals were more similarly expressed in close family members than in un-related individuals, suggesting heritability of the trait. The expression of tumour protein D52 (TPD52), a gene implicated in cell proliferation, apoptosis, and vesicle trafficking was the most strongly correlated with G2 score while nuclear factor (kappa) – B (NFKB1) was highly inversely correlated with G2 score. NFKB1 is known to be activated by irradiation and its inhibition has been previously shown to increase radiosensitivity. Conclusions: Gene expression analysis of lymphocytes may provide a quantitative measure of radiation response potential and is a promising marker of breast cancer susceptibility.
Shellfier: A Shellcode Detection Method based on Dynamic Binary Instrumentation and Convolutional Neural Network

An important method of detecting zero-day attacks is to identify the shellcode which is usually taken as part of the attacks. It is vital to detect programs that have the characteristics of shellcode behavior in the network traffic detection. In this paper, a shellcode detection method named Shellfier based on Dynamic Binary Instrumentation and Convolutional Neural Network (CNN) is proposed. The method of program instrumentation can obtain the behavior characteristics of shellcode in fine-grained manner. The CNN algorithm trains and classifies the sample data, and compares the classification effect of Support Vector Machine (SVM) algorithm based on n-grams model to extract feature vectors. The experimental results show that CNN has strong representation ability for behavioral characteristics, which is more accurate than SVM classification, and the false positive rate and vulnerability rate are lower.
CrowdPT: Summarizing Crowd Opinions as Professional Analyst

This paper demonstrates a novel analytics service, CrowdPT, for capturing the key information, price target (PT), of individual investors on social media. PT, which is mentioned as a conclusion in most of analysts' reports, indicates not only the market sentiment (bullish/bearish) of investors, but also the analysis results. In order to provide the latest opinions of individual investors, we monitor Twitter in real time and update the information in price chart daily. For all component stocks in Dow Jones Industrial Average, textual information from numerous tweets is summarized into a single number, PT, in CrowdPT. Case studies confirm the effectiveness of our analytics service in the financial domain, and show that capturing the PT of individual investors is promising for stock price prediction. The Web API of CrowdPT is also provided for academic purpose.
Plant stomatal closure improves aphid feeding under elevated CO2

Stomata help plants regulate CO2 absorption and water vapor release in response to various environmental changes, and plants decrease their stomatal apertures and enhance their water status under elevated CO2. Although the bottom‐up effect of elevated CO2 on insect performance has been extensively studied, few reports have considered how insect fitness is altered by elevated CO2‐induced changes in host plant water status. We tested the hypothesis that aphids induce stomatal closure and increase host water potential, which facilitates their passive feeding, and that this induction can be enhanced by elevated CO2. Our results showed that aphid infestation triggered the abscisic acid (ABA) signaling pathway to decrease the stomatal apertures of Medicago truncatula, which consequently decreased leaf transpiration and helped maintain leaf water potential. These effects increased xylem‐feeding time and decreased hemolymph osmolarity, which thereby enhanced phloem‐feeding time and increased aphid abundance. Furthermore, elevated CO2 up‐regulated an ABA‐independent enzyme, carbonic anhydrase, which led to further decrease in stomatal aperture for aphid‐infested plants. Thus, the effects of elevated CO2 and aphid infestation on stomatal closure synergistically improved the water status of the host plant. The results indicate that aphid infestation enhances aphid feeding under ambient CO2 and that this enhancement is increased under elevated CO2.
The effects of erythromycin on the electrocardiogram.

BACKGROUND Various cardiac electrophysiologic effects have been attributed to erythromycin. During the course of treating a pneumonia patient with IV erythromycin, the conversion of atrial fibrillation to a sinus rhythm, and its subsequent reversal, appeared to be causally related to the introduction and cessation of this antibiotic. OBJECTIVE This observation suggested the need for studying the changes in the ECG following the use of IV erythromycin in a typical clinical setting. DESIGN A prospective comparative drug study. SETTING A university-affiliated teaching hospital. PATIENTS Nineteen patients being treated for uncomplicated community-acquired pneumonia. INTERVENTION IV erythromycin, 500 mg, and/or IV cefuroxime, 750 mg, infused in 250 mL of saline over 20 min. In the 11 patients who were receiving both of the antibiotics, cefuroxime was administered immediately before erythromycin was infused. MEASUREMENTS The 12-lead ECG measurements were obtained before infusion, at 5-min intervals during each infusion, and at 5 and 10 min after the infusions had been completed. All of the ECG complexes and intervals were measured using a software program (Interpretive Cardiograph; Hewlett Packard; Palo Alto, CA). RESULTS The administration of IV erythromycin increased heart rate and prolonged the corrected QT (QTc) interval. These changes were significant at 15 min of the infusion, and were no longer evident 5 min after the infusion had been stopped. The administration of IV cefuroxime did not produce any ECG changes. CONCLUSIONS A single, standard dose of IV erythromycin prolongs the QTc interval; therefore, the drug should always be administered as a slow infusion. ECG monitoring should accompany erythromycin therapy in critically ill patients, in patients with electrolyte disorders, or in patients taking other drugs with similar cardiac effects.
From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug–drug interaction (DDI) potential.
Pancreatic stellate cells are an important source of MMP-2 in human pancreatic cancer and accelerate tumor progression in a murine xenograft model and CAM assay

The effect of the characteristic desmoplastic reaction of pancreatic cancer on tumor progression is largely unknown. We investigated whether pancreatic stellate cells, which are responsible for the desmoplastic reaction, support tumor progression. Immunohistology revealed that matrix metalloproteinase-2 (MMP-2), which is suggested to promote pancreatic cancer progression, is present in stellate cells adjacent to cancer cells. In vitro, stellate cells exhibited a much higher basal expression of MMP-2 compared with cancer cells. Panc1-, MiaPaCa2- and SW850-conditioned media stimulated MMP-2 release of stellate cells as detected by zymography. Cancer cells expressed and released basigin [BSG, extracellular matrix metalloproteinase inducer (EMMPRIN), CD147], a glycoprotein that is known to stimulate MMP-2 in mesenchymal cells, as detected by immunostaining, western blot and reverse transcription-polymerase chain reaction. Tumor cell-conditioned medium and BSG purified by affinity chromatography from supernatants of cancer cells, but not supernatants depleted from BSG, stimulated expression of MMP-1 and MMP-2 of stellate cells as demonstrated by western blot and zymography. Moreover, the interaction of stellate cells and cancer cells promoted the invasiveness of Panc-1 cells in the chorioallantoic membrane assay and increased the weight of tumors induced by all carcinoma cell lines in nude mice by 2.1-3.7-fold. Our findings support the assumption that the interaction of stellate cells and cancer cells promotes progression of pancreatic cancer.
GABAergic Local Interneurons Shape Female Fruit Fly Response to Mating Songs

Many animals use acoustic signals to attract a potential mating partner. In fruit flies (Drosophila melanogaster), the courtship pulse song has a species-specific interpulse interval (IPI) that activates mating. Although a series of auditory neurons in the fly brain exhibit different tuning patterns to IPIs, it is unclear how the response of each neuron is tuned. Here, we studied the neural circuitry regulating the activity of antennal mechanosensory and motor center (AMMC)-B1 neurons, key secondary auditory neurons in the excitatory neural pathway that relay song information. By performing Ca2+ imaging in female flies, we found that the IPI selectivity observed in AMMC-B1 neurons differs from that of upstream auditory sensory neurons [Johnston's organ (JO)-B]. Selective knock-down of a GABAA receptor subunit in AMMC-B1 neurons increased their response to short IPIs, suggesting that GABA suppresses AMMC-B1 activity at these IPIs. Connection mapping identified two GABAergic local interneurons that synapse with AMMC-B1 and JO-B. Ca2+ imaging combined with neuronal silencing revealed that these local interneurons, AMMC-LN and AMMC-B2, shape the response pattern of AMMC-B1 neurons at a 15 ms IPI. Neuronal silencing studies further suggested that both GABAergic local interneurons suppress the behavioral response to artificial pulse songs in flies, particularly those with a 15 ms IPI. Altogether, we identified a circuit containing two GABAergic local interneurons that affects the temporal tuning of AMMC-B1 neurons in the song relay pathway and the behavioral response to the courtship song. Our findings suggest that feedforward inhibitory pathways adjust the behavioral response to courtship pulse songs in female flies. SIGNIFICANCE STATEMENT To understand how the brain detects time intervals between sound elements, we studied the neural pathway that relays species-specific courtship song information in female Drosophila melanogaster. We demonstrate that the signal transmission from auditory sensory neurons to key secondary auditory neurons antennal mechanosensory and motor center (AMMC)-B1 is the first-step to generate time interval selectivity of neurons in the song relay pathway. Two GABAergic local interneurons are suggested to shape the interval selectivity of AMMC-B1 neurons by receiving auditory inputs and in turn providing feedforward inhibition onto AMMC-B1 neurons. Furthermore, these GABAergic local interneurons suppress the song response behavior in an interval-dependent manner. Our results provide new insights into the neural circuit basis to adjust neuronal and behavioral responses to a species-specific communication sound.
mage co-segmentation using dual active contours

In this article a novel algorithm is proposed to segment a pair of images simultaneously (co-segmentation) for extracting common objects. The task of co-segmentation has been performed using the dual geometric active contour model. Both the contours (of the objects) are initialized and evolved simultaneously in the given images. As the contours proceed towards the boundary of the common object(s) present in the images, energy value gets reduced. The contours are allowed to evolve until both the inner and the outer contours coincide at the object boundary. The resultant images formed are known as the co-segmented o-segmentation ctive contour model han–Vese active contour ual-geometric active contours nner contour uter contour images. The proposed approach is evaluated on 20 benchmark datasets and compared with the state-ofthe-art methods. Results show that the performance of the proposed method is better than the compared methods. © 2018 Elsevier B.V. All rights reserved.
The Impact of Origin and Host Country Schooling on the Economic Performance of Immigrants

This study examines the economic returns to schooling acquired in the country of origin and the country of destination. It uses large-scale survey data on Turkish, Moroccan, Surinamese and Antillean immigrants in the Netherlands, which contain direct measures of pre- and post-migration schooling. It is studied whether the returns to origin-country schooling depend on contextual factors: i.e., immigrant group and the region of living. Furthermore, we examine the importance of host-country schooling for labor market outcomes and if these can be partly explained by increasing contacts with natives. Results show that the returns to origin-country schooling are higher for Surinamese and Antillean immigrants (i.e., those originating from former Dutch colonies) than for immigrants from Turkey and Morocco. The returns to origin-country schooling are not affected by ethnic concentration in the region of living. Finally, it appears that the returns to host-country schooling are much larger than to origin-country schooling, and the higher returns to host-country schooling cannot be explained by increased social contacts with natives.
The Effects of Asymmetric Diurnal Warming on Vegetation Growth of the Tibetan Plateau over the Past Three Decades

Temperatures over the past three decades have exhibited an asymmetric warming pattern between night and day throughout the Tibetan Plateau. However, the implications of such diurnally heterogeneous warming on vegetation growth is still poorly understood. In this paper, we evaluate how vegetation growth has responded to daytime and night-time warming at the regional, biome, and pixel scales based on normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2015. We found a persistent increase in the growing seasonal minimum temperature (Tmin) and maximum temperature (Tmax) over the Tibetan Plateau between 1982–2015, whereas the rate of increase of Tmin was 1.7 times that of Tmax. After removing the correlations between Tmin, precipitation, and solar radiation, we found that the partial correlation between Tmax and NDVI was positive in wetter and colder areas and negative in semi-arid and arid regions. In contrast, the partial correlation between Tmin and NDVI was positive in high-cold steppe and meadow steppe and negative in montane steppe or wet forest. We also found diverse responses of vegetation type to daytime and night-time warming across the Tibetan Plateau. Our results provide a demonstration for studying regional responses of vegetation to climate extremes under global climate change.