Miracle-dz/UniFAR-FTUs huggingface dataset:
A System for Assessment of Canine-Human Interaction during Animal-Assisted Therapies

Animal-assisted therapies (AAT) are becoming increasingly common to help hospitalized patients, especially in oncology units. There is a critical need for methods and technologies that can enable a quantifiable understanding of AAT to objectively demonstrate its efficacy and improve its efficiency. In this paper, we present our preliminary efforts towards the development of wireless sensor systems to simultaneously detect the related behavioral (activity level, movement, stroking) and physiological signals (heart rate/variability) of humans and animals during their interaction. To detect heart rate, we tested two different techniques based on wearable or contactless electrocardiography. In this preliminary evaluation, we were able to assess these parameters successfully and identify the design challenges towards deployment of these systems in larger clinical studies.
Recognizing Brain States Using Deep Sparse Recurrent Neural Network

Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain’s dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers, and a softmax output layer. The proposed framework has been tested on seven task fMRI data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain’s state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.
Randomized Placebo-controlled Trial of Droperidol and Ondansetron for Adult Emergency Department Patients With Nausea.

OBJECTIVE The objective was to separately compare effectiveness of 1.25 mg of intravenous (IV) droperidol and 8 mg of IV ondansetron with 0.9% saline placebo for adult emergency department (ED) patients with nausea. A novel primary outcome measure, expected to aid clinical interpretation of reported results, was employed. METHODS A randomized controlled trial was conducted at the three EDs of Monash Health, Melbourne, Australia. The design was to demonstrate superiority of the active drugs over placebo. The primary outcome measure of symptom improvement was defined as a visual analog scale (VAS) rating change of -8 mm or more from baseline at 30 minutes posttreatment. Mean VAS changes per group and percentages experiencing the desired treatment effect were also compared. The study was concluded after recruitment of 215 of the planned 378 patients, as interim analysis confirmed that continuation could not result in a finding of superiority. RESULTS Of 215 patients, 73 (34%), 71 (33%), and 71 (33%) received droperidol, ondansetron, and placebo. Symptom improvement occurred in 75% (95% confidence interval [CI] = 64% to 85%), 80% (95% CI = 69% to 89%), and 76% (95% CI = 64% to 85%), respectively. Mean VAS changes were -29 mm (95% CI = -36 to -23 mm), -34 mm (95% CI = -41 to -28 mm), and -24 mm (95% CI = -29 to -19 mm), respectively. Desired treatment effects were experienced by 77% (95% CI = 65% to 86%), 73% (95% CI = 61% to 83%), and 59% (95% CI = 47% to 71%), respectively. CONCLUSION For adult ED patients with nausea, superiority was not demonstrated for droperidol or ondansetron over placebo.
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing

This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.
HDA: Cross-Project Defect Prediction via Heterogeneous Domain Adaptation With Dictionary Learning

Cross-Project Defect Prediction (CPDP) is an active topic for predicting defects on projects (target projects) with scarce-labeled data by reusing the classification models from other projects (source projects). Traditional CPDP methods require common features between the data of two projects and utilize them to construct defect prediction models. However, when cross-project data do not satisfy the requirement, i.e., heterogeneous CPDP (HCPDP) scenario, these methods become infeasible. In this paper, we propose a novel HCPDP method called Heterogeneous Domain Adaptation (HDA) to address the issue. HDA treats the cross-project data as being from two different domains with heterogeneous feature sets. It employs the domain adaptation method to embed the data from the two domains into a comparable feature space with a lower dimension, then measures the difference between the two mapped domains of data using the dictionaries learned from them with the dictionary learning technique. We comprehensively evaluate HDA on 94 cross-project pairs of 12 projects from three open-source defect data sets with three performance indicators, i.e., F-measure, Balance, and AUC. Compared with the two state-of-the-art HCPDP methods, the experimental results indicate that HDA improves 0.219 and 0.336 in terms of F-measure, 0.185 and 0.215 in terms of Balance, and 0.131 and 0.035 in terms of AUC. In addition, HDA achieves comparable results compared with Within-Project Defect Prediction (WPDP) setting and a state-of-the-art unsupervised learning method in most cases.
DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding

Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks. However, existing approaches are designed mainly for Indo-European languages and fail to capture the unique properties of Chinese pronunciation. In this paper, we propose a high dimensional encoded phonetic similarity algorithm for Chinese, DIMSIM. The encodings are learned from annotated data to separately map initial and final phonemes into n-dimensional coordinates. Pinyin phonetic similarities are then calculated by aggregating the similarities of initial, final and tone. DIMSIM demonstrates a 7.5X improvement on mean reciprocal rank over the state-of-the-art phonetic similarity approaches.
Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice?

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.
A Cerebellar Spiking Neural Model for Phase Reversal of Vestibulo-ocular Reflex

Cerebellum possesses very rich motor control and learning capability which is critical for animals. In this study, we proposed a spiking neural network model of cerebellum for gain and phase adaptation in vestibulo-ocular reflex (VOR). VOR is a critical adaptive reflexive eye movement for maintaining a stable visual field. In this model (with neuron number at the order of 104), synaptic plasticity at parallel fiber-Purkinje cell synapses was considered. In particular, we have shown that the inhibitory inputs from molecular layer interneurons on Purkinje cells play a critical role in phase adaptation of VOR. The inhibitory input from interneurons indirectly affects the strength of long-term potentiation (LTP) and long-term depression (LTD), resulting in more drastic phase shift upon learning and hence allowing phase reversal of VOR. The strength of inhibitory input also affects the maximum phase shift that can be achieved. Our result is consistent with experiments in mutant mice with blocked inhibitory inputs.
Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice

Synthetic aperture radar (SAR) incidence angle has a significant effect on the microwave backscatter from sea ice. This paper investigates the incidence angle dependence of C- and L-band HH-polarized microwave backscatter coefficient over Arctic first-year sea ice (FYI) and multiyear sea ice (MYI) in winter. Advanced Land Observation Satellite Phased Array type L-band SAR (L-band) and RADARSAT-2 (C-band) images are used to derive ice type-specific incidence angle dependencies calculated using linear regression models. For L-band, mean ice type-specific incidence angle dependencies for FYI and MYI are −0.21 and −0.30 dB/1°, respectively; and for C-band, they are −0.22 and −0.16 dB/1°, respectively. To validate our results, we calculated root-mean-square deviation (RMSD) by comparing the ice type-specific dependence from 2010 with individual dependencies from 2009 based on ice types and frequencies. The RMSD is found to be smaller than the standard deviation of ice type-specific dependencies for both frequencies. The RMSD values for the L-band incidence angle dependencies are 0.03 and 0.04 dB/1° for FYI and MYI, respectively. For C-band, the RMSD values for the FYI and MYI dependencies are 0.03 and 0.01 dB/1°, respectively. Subsequently, we demonstrate that after applying incidence angle normalization, the variability of C- and L-band SAR backscatter reduces and separability of ice types increase substantially.
Effects of pre‐hatch thermal manipulation and post‐hatch acute heat stress on the mRNA expression of interleukin‐6 and genes involved in its induction pathways in 2 broiler chicken breeds

ABSTRACT In response to heat stress, interleukin‐6 (IL‐6) expression is upregulated in broiler chickens. The main aim of the present study was to evaluate the cumulative effects of thermal manipulation (TM) and subsequent acute heat stress (AHS) on the mRNA expression of IL‐6 and genes involved in its induction pathways. The studied genes include IL‐6, IL‐1&bgr;, TNF‐&agr;, TLR2, TLR4, NF&kgr;B50, NF&kgr;B65, Hsp70, and HSF3 in the spleen and liver tissues. TM was carried out at 39°C for 18 h and 65% relative humidity during days 10 to 18 of embryonic development, while AHS was stimulated by raising the temperature to 40°C for 7 h on post‐hatch day 28. During AHS at 0, 1, 3, 5, and 7 h, the spleen and liver were collected from all groups to measure the mRNA expression by relative‐quantitation real‐time RT‐PCR, and the blood was collected to measure plasma IL‐6 level. TM significantly reduced Tc during AHS for both breeds from 1 to 7 h time intervals. TM resulted in enhanced basal mRNA expression of IL‐6, HSF3, and Hsp70, but decreased the basal mRNA level of TLR4. During heat stress, TM enhanced the expression dynamics of Hsp70, HSF3, IL‐6, IL‐1&bgr;, TNF‐&agr;, TLR2, TLR4, NF&kgr;B50, and NF&kgr;B65. The results of the current study indicate that TM enhanced the heat tolerance through improving the protective immunological response to heat stress by enhancing the expression of IL‐6 and modulating the expression of genes important in its induction pathways.
Frequency of gallstones and mean BMI in decompensated cirrhosis

Introduction The aim of the study was to determine the frequency of gallstones in patients with decompensated cirrhosis and to know about mean Body mass index (BMI) in patients of decompensated cirrhosis i.e End stage liver disease (ESLD) with and without gallstones. Methods it is a cross sectional descriptive study, conducted at the department of Hepato-gastroenterology, Sindh Institute of Urology and Transplantation (SIUT), Karachi from 1st August 2014 to 28 February 2015. Two hundred patients were enrolled from outpatient clinics of Hepato-gastroenterology that fulfilled the defined selection criteria. Questionnaire was filled for data collection. SPSS version 20.0 was used to analyze data. Mean value of age and BMI was calculated by mean ± S.D. values. Mean ± SD was also calculated for BMI in patients with and without gallstones. Stratification of the age, gender, and liver disease severity were done and chi-Square test was applied. p-values less than 0.05 considered statistically significant. Results Two hundred consecutive patients were enrolled among them 112(56%) were male. Mean age was 46.89 ± 11.9, BMI 23.59 ± 4.7 and CTP score was 9.7 ± 1.9. Most of the patient had Child class 'B' cirrhosis 102(51%), most common etiology was found to be Hepatitis C 133 (66.5%), cholelithiasis was found in 59(29.5%), sludge in 36 (18%) and both stone and sludge in 24(12%) of the cases. Advanced liver disease that is, more CTP score and child class 'C' was associated with increased frequency of gall stone formation (p-value = 0.012), and advancing age on age stratification (p-value = 0.024) however no relation was observed with increase BMI, gender, ethnicity, cause or duration of disease in this population. Conclusion Gallstone formation is associated with advanced stage of cirrhosis and hepatitis C Virus related CLD, contrary to the established risk factors, no relation of gender or BMI was found in decompensated liver disease.
Sonographic Evaluation of Normal Liver, Spleen, and Renal Parameters in Adult Population: A Multicenter Study.

OBJECTIVE To determine the normal liver, spleen, and renal parameters in adult patients with no comorbidities. STUDY DESIGN Cross-sectional study. PLACE AND DURATION OF STUDY Dow Institute of Radiology, Ojha Campus, LEJ Campus, and Al-Mustafa Hospital Karachi, from October 2016 to March 2017. METHODOLOGY A total of 3,136 study participants with more than 16 years of age of either gender underwent ultrasound examination. All individuals with morbid conditions like hypertension (HTN), diabetes mellitus (DM), liver cirrhosis, hydronephrosis, renal cyst, and liver mass were excluded. Ultrasound scan was performed and longitudinal and transverse sections were obtained of both kidneys (in full inspiration), spleen and liver. RESULTS A significant positive correlation was observed between age and spleen size of the individuals (r=0.053, p=0.012). The correlation of BMI and liver size was also found significantly positive (r=0.237, p <0.001). The correlation of age and kidney size was found significantly negative in between age and right kidney (r=-0.074, p <0.001) and left kidney (r=-0.087, p <0.001). Similarly, the correlation of BMI and renal size was found significantly weak positive between BMI and right kidney (r=0.206, p <0.001) and BMI and left kidney (r=0.227, p <0.001). CONCLUSION BMI was found significantly positively correlated with liver size and both kidneys in study participants. Moreover, spleen was found directly and renal size inversely correlated with age of the individuals.
A Dynamic-Shape-Prior Guided Snake Model With Application in Visually Tracking Dense Cell Populations

This paper proposes a dynamic-shape-prior guided snake (DSP G-snake) model that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake’s snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from self-crossing, or automatically untie an already self-intersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as the state-of-the-art cell tracking frameworks.
Acute Pericarditis and Cancer Risk: A Matched Cohort Study Using Linked UK Primary and Secondary Care Data

Background We aimed to examine whether acute pericarditis is an indicator of undetected cancer and identify patient‐level factors associated with high cancer risk among patients presenting with pericarditis. Methods and Results A population‐based matched cohort study was conducted using primary care data from the UK Clinical Practice Research Datalink linked to Hospital Episode Statistics. Patients with acute pericarditis (n=6530) were matched to a comparison cohort (n=26 111) on age, sex, calendar time, and general practice. We estimated cumulative cancer incidences, and calculated hazard ratios using Cox regression. Effect modification by patients' characteristics and lifestyle factors was examined, and we fitted a parsimonious model to evaluate absolute excess risk of later cancer among pericarditis patients by key patient‐level factors. We identified 728 and 1379 incidents of cancer among pericarditis patients and the comparison cohort (median follow‐up, 2.8 and 3.5 years). Pericarditis was associated with an elevated subsequent risk of any cancer (hazard ratio=3.03; 95% confidence interval, 2.74–3.36). The association was particularly pronounced 0 to 3 months after pericarditis (hazard ratio=23.56; 95% confidence interval, 18.00–30.83), but a more‐modest association remained thereafter (hazard ratio=1.95; 95% confidence interval, 1.48–2.57 after 3–12 months, and hazard ratio=1.40; 95% confidence interval, 1.21–1.62 after >12 month). Older individuals hospitalized with pericarditis and with combinations of obesity and smoking were at the highest excess risk of having a cancer diagnosis 3 to 12 months later, reaching 4.8%. Conclusions Occult cancers may be going undiagnosed during the acute episode of pericarditis. Patients presenting with pericarditis and combinations of older age, obesity, smoking, and a need for hospitalization might warrant targeted investigations for cancer.
Targeting SOX2 in anticancer therapy

ABSTRACT Introduction: SOX2 is a transcription factor that is important in the development and maintenance of the stem cell state. Furthermore, SOX2 is associated with cancer progression because it promotes the migration, invasion, and proliferation of cancer cells. SOX2 is also expressed in cancer stem cells and appears to be involved in the resistance toward anticancer therapies. These features render SOX2 an attractive target for cancer therapy. Areas covered: In this review, we highlight the role of SOX2 in cancer and in the resistance toward anticancer therapies. We summarize recent studies dealing with SOX2 as a direct or indirect therapeutic target in cancer. Expert opinion: SOX2 is an attractive target in cancer therapy because of its role in cancer progression and therapy resistance. SOX2 is a transcription factor, hence direct targeting is difficult. Studies aimed at a functional depletion, for example by knock-down with siRNAs, are difficult to translate into clinical settings. Alternatively, the identification of SOX2 upstream or downstream regulators that are easier to target is of paramount importance.
The Biomechanical Effects of Sagittal Split Ramus Osteotomy on Temporomandibular Joint

Abstract The aim of this study was to evaluate the stress distributions and deformations of the temporomandibular joint (TMJ) during different periods before and after sagittal split ramus osteotomy (SSRO). A three-dimensional finite element model of the mandible and TMJ was established, based on the preoperative CT of a patient with mandibular prognathism. Numerical SSRO was performed and the models of three postoperative periods were established. Contact elements were used to simulate the interaction between the articular discs and the articular cartilages. Nonlinear cable elements were used to simulate the disc attachments and the ligaments. Muscle forces and boundary conditions corresponding to the central occlusion were applied on all the models. The results showed that the stress distributions of the patient’s TMJs were not the same as those of asymptomatic subjects. The stress distributions and deformations of the disc, condylar and temporal cartilage were changed at different periods after SSRO. The biomechanical parameters of TMJ were improved after SSRO. And the postoperative results showed that appropriate functional training could help to avoid TMJ diseases. Therefore SSRO could improve the stress distributions of the TMJ and relieve the symptoms of temporomandibular disorder (TMD).
SimulES-W: A Collaborative Game to Improve Software Engineering Teaching

There is empirical evidence concerning the effectiveness and benefits of game-based learning (GBL). Our mainly interest is to present a tool that can be used to complement teaching software engineering in a motivating and didactic way. This paper studies the use of a GBL tool called SimulES-W (Simulation in Software Engineering) to teach Software Engineering in an undergraduate engineering program. SimulES-W has three characteristics: it is based on real software cases, it can be customized during the learning process, and it is a collaborative game. These characteristics are important because they help us understand and propose a new learning scenario, and to research with this the learning processes in their environments According to it, the first characteristic of SimulES-W makes it a motivating and engaging game which brings up cases which usually are only present in real software projects. Thanks to the second characteristic, the educators can use SimuelES-W to customize the education material, and tune the game for specific software engineering courses. The third characteristic is related to the proposed game as activity that involves group discussions and decision making. This paper presents SimulES-W a digital version of SimulES and reports the results of an evaluation from a pedagogical perspective, where game adequacy for teaching a subject and positive potential impact in student’s academic performance are investigated.