Miracle-dz/UniFAR-FTUs huggingface dataset:
Adverse Drug Reaction Related Post Detecting Using Sentiment Feature

Background: The posts related to Adverse Drug Reaction (ADR) on social websites are believed to be valuable resource for post-marketing drug surveillance. Beyond domain feature, the aim of this study was to find a more effective method to detect ADR related post. Methods: We conducted experiment on posts using sentiment features from March 8 to May 20 in 2016 in Shanghai of China. Firstly, the diabetes posts were collected; the 1814 posts were annotated by hand. Secondly, sentiment features set were generated and the χ2 (CHI) statistics were used to select feature. Finally, we evaluated the effectiveness of our method using the different feature sets. Results: By comparing the posts detection performance of different feature sets, using sentiment features by CHI statistics can improve ADR related post detection performance. By comparing the ADR-related group with the non-ADR group, performance of ADR related post detection was better than the performance of non-ADR post detection. We could obtain highest performance owing to introducing sentiment feature and using CHI feature selection technique, and the method was proved to be effective during detecting post related to ADR. Conclusion: By using sentiment feature and CHI feature selection technique, we can get an effective method to detect post related to ADR.
Serotonergic Regulation of Corticoamygdalar Neurons in the Mouse Prelimbic Cortex

Neuromodulatory transmitters, such as serotonin (5-HT), selectively regulate the excitability of subpopulations of cortical projection neurons to gate cortical output to specific target regions. For instance, in the mouse prelimbic cortex, 5-HT selectively excites commissurally projecting intratelencephalic (COM) neurons via activation of 5-HT2A (2A) receptors, while simultaneously inhibiting corticofugally projecting pyramidal neurons targeting the pons via 5-HT1A (1A) receptors. Here we characterize the physiology, morphology, and serotonergic regulation of corticoamygdalar (CAm) projection neurons in the mouse prelimbic cortex. Layer 5 CAm neurons shared a number of physiological and morphological characteristics with COM neurons, including higher input resistances, smaller HCN-channel mediated responses, and sparser dendritic arbors than corticopontine neurons. Across cortical lamina, CAm neurons also resembled COM neurons in their serotonergic modulation; focally applied 5-HT (100 µM; 1 s) generated 2A-receptor-mediated excitation, or 1A- and 2A- dependent biphasic responses, in ipsilaterally and contralaterally projecting CAm neurons. Serotonergic excitation depended on extrinsic excitatory drive, as 5-HT failed to depolarize CAm neurons from rest, but could enhance the number of action potentials generated by simulated barrages of synaptic input. Finally, using dual tracer injections, we identified double-labeled CAm/COM neurons that displayed primarily excitatory or biphasic responses to 5-HT. Overall, our findings reveal that prelimbic CAm neurons overlap with COM neurons, and that both neuron subtypes exhibit 2A-dependent serotonergic excitation. Our findings suggest that 5-HT, acting at 2A receptors, will promote cortical output to the amygdala. Significance Statement Cortical projections to the amygdala allow for executive “top-down” control of emotional responses. Corticoamygdalar (CAm) neurons in the prelimbic cortex contribute to the learning and expression of conditioned fear responses, processes that may also be regulated by serotonin (5-HT). Our study provides a physiological and morphological characterization of prelimbic CAm neurons, and demonstrates that the excitability of CAm neurons is regulated by 5-HT acting at 5-HT2A receptors alone, or in combination with 5-HT1A receptors. Our results suggest that 5-HT may regulate corticoamygdalar circuits during the learning and expression of conditioned fear.
Virtual bronchoscopic navigation as an aid to CT‐guided transbronchial biopsy improves the diagnostic yield for small peripheral pulmonary lesions

Virtual bronchoscopic navigation (VBN) entails the provision of a virtual display of the bronchial routes that lead to small peripheral pulmonary lesions (PPL). It has been predicted that a combination of computed tomography (CT)‐guided transbronchial biopsy (CT‐TBB) with VBN might improve the diagnostic yield for small PPL. This study sought to investigate that prediction.
An Exploratory Study of Mild Cognitive Impairment of Retired Professional Contact Sport Athletes

Objective: To test the hypothesis that mild cognitive impairment (MCI) rates are higher among retired professional contact sport athletes than in noncontact athlete controls and compare history of contact sports with other MCI risk factors. Setting: University Concussion Management Clinic. Participants: Twenty-one retired National Football League and National Hockey League players and 21 aged-matched noncontact athlete controls. Designs: Case-control. Main Measures: Comprehensive criteria were used to assess MCI based on the following: Wisconsin Card Sorting Test, Delis-Kaplan Executive Function System; Trail Making Parts A and B; Wechsler Adult Intelligence Scale–Third Edition subtests; Neuropsychological Assessment Battery Memory Module List Learning, Story Learning, and Naming subtests; and Controlled Oral Word Association Test. The Wide Range Achievement Test was used as a proxy measure for IQ. Atherosclerotic cardiovascular disease risk factors were self-reported and blood cholesterol was measured. Depression was measured by the Beck Depression Inventory-II (BDI). Results: Eight contact sport athletes (38%) and 3 noncontact athletes (14%) met MCI criteria (P = .083). Contact sport athletes' scores were significantly worse on Letter Fluency and List B Immediate Recall. Contact athletes were more obese, had more vascular risk factors, and had higher scores on the BDI than controls. Conclusion: Athletes with a history of playing professional contact sports had more vascular risk factors and higher depression scores. MCI rates were somewhat higher, though not significant.
Ear canal microbiota – a comparison between healthy dogs and atopic dogs without clinical signs of otitis externa

BACKGROUND The canine skin microbiota has been evaluated using relatively new microbiological techniques; studies have shown a difference in the composition of bacterial populations between healthy dogs and those with canine atopic dermatitis (cAD). However, little has been reported concerning the ear canal microbiota despite the fact that otitis externa is a common problem in dogs with cAD. HYPOTHESIS/OBJECTIVES The study evaluated the ear canal microbiota of healthy dogs and atopic dogs without clinical signs of otitis. ANIMALS Nine healthy privately owned dogs and 11 dogs with a diagnosis of cAD. METHODS AND MATERIALS Ear swabs from the two groups were used for global bacterial DNA extraction. V1-V3 hypervariable 16S rDNA amplicon libraries were prepared for each sample and sequenced with MiSeq Illumina sequence (V3 kit). Taxonomical assignment and clustering were performed with Mothur using SILVA database. RESULTS The ear canal microbiota showed changes in diversity similar to those reported for canine skin and seemed to be stable during the period of the study (28 days). Evidence of dysbiosis was observed in the atopic dogs, with increased abundance of Staphylococcus spp. and Ralstonia spp. (P < 0.05). Escherichia spp. also were found to be more abundant in healthy dogs. CONCLUSIONS AND CLINICAL IMPORTANCE This study demonstrated a shift in bacterial populations between allergic and healthy dogs. The observed results in the ear canal are similar to those reported on the skin and could explain the propensity of allergic dogs to develop bacterial otitis.
Artificial intelligence in the diagnosis of Parkinson’s disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning

Objective The objective of this study was to identify the extent to which artificial intelligence could be used in the diagnosis of Parkinson’s disease from ioflupane-123 (123I) single-photon emission computed tomography (SPECT) dopamine transporter scans using transfer learning. Materials and methods A data set of 54 normal and 54 abnormal 123I SPECT scans was amplified 44-fold using a process of image augmentation. This resulted in a training set of 2376 normal and 2376 abnormal images. This was used to retrain the top layer of the Inception v3 network. The resulting neural network functioned as a classifier for new 123I SPECT scans as either normal or abnormal. A completely separate set of 45 123I SPECT scans were used for final testing of the network. Results The area under the receiver-operator curve in final testing was 0.87. This corresponded to a test sensitivity of 96.3%, a specificity of 66.7%, a positive predictive value of 81.3% and a negative predictive value of 92.3%, using an optimum diagnostic threshold. Conclusion This study has provided proof of concept for the use of transfer learning, from convolutional neural networks pretrained on nonmedical images, for the interpretation of 123I SPECT scans. This has been shown to be possible in this study even with a very small sample size. This technique is likely to be applicable to many areas of diagnostic imaging.
Multi-Scale Supervised Network for Human Pose Estimation

Human pose estimation is an important topic in computer vision with many applications including gesture and activity recognition. However, pose estimation from image is challenging due to appearance variations, occlusions, clutter background, and complex activities. To alleviate these problems, we develop a robust pose estimation method based on the recent deep conv-deconv modules with two improvements: (1) multi -scale supervision of body keypoints, and (2) a global regression to improve structural consistency of keypoints. We refine keypoint detection heatmaps using layer-wise multi-scale supervision to better capture local contexts. Pose inference via keypoint association is optimized globally using a regression network at the end. Our method can effectively disambiguate keypoint matches in close proximity including the mismatch of left-right body parts, and better infer occluded parts. Experimental results show that our method achieves competitive performance among state-of-the-art methods on the MPII and FLIC datasets.
Assessment of the Role of the Anti-Mullerian Hormone, Luteinizing Hormone/Follicle Stimulating Hormone Ratio in the Diagnosis of Polycystic Ovary Syndrome in Sudanese Women

BACKGROUND: The diagnosis of polycystic ovary syndrome (PCOS) is not an easy procedure, as the signs and symptoms are heterogeneous and of undefined aetiology. AIM: This study is aimed to evaluate serum anti-Mullerian hormone (AMH) level and luteinizing hormone (LH)/folic stimulating hormone (FSH) ratio in women with PCOS in Sudan and to assess the diagnostic efficiency for the diagnosis of PCOS. METHODS: In a case-control study, Serum AMH, LH, FSH was measured in the early follicular phase from Sudanese patients (N = 230) with PCOS and100 controls. The LH/FSH ratio was calculated, and its diagnostic power was evaluated by receiver operating characteristic curves. RESULTS: The means of serum AMH, serum LH level and LH/FSH ratio of the test, were significantly increased in the test group compared to the control group (P-value < 0.000). The AMH sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were found to be 83%, 99%, 99%, and 72% respectively. Serum AMH was considered adequate measures for the diagnosis of PCOS; its level showed an area under the ROC curve of 0.98 (95% confidence, P-value < 0.000). The best compromise between 98% specificity and 90% sensitivity was obtained with a cut-off value of 3.3 ng/mL for PCOS diagnosis. There was no correlation between age, body mass index (BMI) and AMH level in the test group. CONCLUSIONS: The Serum AMH level and LH/FSH ratio were higher in patients than in control. However AMH level has better discriminative power and good diagnostic potency for the diagnosis of the PCOS among Sudanese women.
Error Detection in a Large-Scale Lexical Taxonomy

Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevent its effective usage. Even though some KB cleansing algorithms have been proposed, they focus on the structure of the knowledge graph and neglect the relation between the concepts, which could be helpful to discover wrong relations in KB. Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets. For efficient and effective knowledge base cleansing, we first apply a distance-based model to determine the conflicting concept sets using two different methods. Then, we propose and analyze several algorithms on how to detect and repair the errors based on our model, where we use a hash method for an efficient way to calculate distance. Experimental results demonstrate that the proposed approaches could cleanse the knowledge bases efficiently and effectively.
The applications of wavelet transform on signals and imagery

The treatment of the signal is an essential discipline nowadays. It has for object the development or the interpretation of signals carrying information. Often, the signals resulting from the physical phenomena are of nature non-stationary their classical temporal representations do not give a good perception of the multiple oscillating components, while the frequently representation (The transform of Fourier) does not allow of it the temporal localization of these components. To cure this problem, the wavelet transform at summer shouted. It makes it possible to carry out an analysis of the local structures of a signal with a zoom which depends on the scale considered. I.e. an analysis of which the frequently resolution (temporal representation) varies with the temporal localization (frequently representation).
Intentionality of a co-actor influences sensorimotor synchronisation with a virtual partner

Interpersonal sensorimotor synchronisation requires individuals to anticipate and adapt to their partner’s movement timing. Research has demonstrated that the intentionality of a co-actor affects joint action planning, however, less is known about whether co-actor intentionality affects sensorimotor synchronisation. Explicit and implicit knowledge of a synchronisation partner’s intentionality may influence coordination by modulating temporal anticipation and adaptation processes. We used a computer-controlled virtual partner (VP) consisting of tempo-changing auditory pacing sequences to simulate either an intentional or unintentional synchronisation partner. The VP was programmed to respond to the participant with low or moderate degrees of error correction, simulating a slightly or moderately adaptive human, respectively. In addition, task instructions were manipulated so that participants were told they were synchronising with either another person or a computer. Results indicated that synchronisation performance improved with the more adaptive VP. In addition, there was an influence of the explicit partner instruction, but this was dependent upon the degree of VP adaptivity and was modulated by subjective preferences for either the human or the computer partner. Beliefs about the intentionality of a synchronisation partner may thus influence interpersonal sensorimotor synchronisation in a manner that is modulated by preferences for interacting with intentional agents.
Computational light painting and kinetic photography

We present a computational framework for creating swept volume light painting and kinetic photography. Unlike conventional light painting technique using hand-held point light source or LED arrays, we move a flat-panel display with robot in a curved path. The display shows real-time rendered contours of a 3D object being sliced by the display plane along the path. All light contours are captured in a long exposure and constitute the virtual 3D object augmented in the real space. To ensure geometric accuracy, we use hand-eye calibration method to precisely obtain the transformation between the display and the robot. A path generation algorithm is developed to automatically yield the robot path that can best accommodate the 3D shape of the target model. To further avoid shape distortion due to asynchronization between the display's pose and the image content, we propose a real-time slicing method for arbitrary slicing direction. By organizing the triangular mesh into Octree data structure, the approach can significantly reduce the computational time and improve the performance of real-time rendering. We study the optimal tree level for different ranges of triangle numbers so as to attain competitive computational time. Texture mapping is also implemented to produce colored light painting. We extend our methodologies to computational kinetic photography, which is dual to light painting. Instead of keeping the camera stationary, we move the camera with robot and capture long exposures of a stationary display showing light contours. We transform the display path for light painting to the camera path for kinetic photography. A variety of 3D models are used to verify that the proposed techniques can produce stunning long exposures with high-fidelity volumetric imagery. The techniques have great potential for innovative applications including animation, visible light communication, invisible information visualization and creative art.
Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many problem-specific optimization techniques have been proposed, alternating least square (ALS) remains popular due to its general applicability (e.g. easy to handle positive-unlabeled inputs), fast convergence and parallelization capability. Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficent. This is because a single machine provides limited compute power for large-scale data while multiple machines suffer from the network communication bottleneck. To address the aforementioned challenge, accelerating ALS on garphics processing units (GPUs) is a promising direction. We propose the novel approach in enhancing the MF efficiency via both memory optimization and approximate computing. The former exploits GPU memory hierarchy to increase data reuse, while the later reduces unneccessary computing without hurting the convergence of learning algorithms. Extensive experiments on large-scale datasets show that our solution not only outperforms the competing CPU solutions by a large margin but also has a 2x-4x performance gain compared to the state-of-the-art GPU solutions. Our implementations are open-sourced and publicly available.
Structure of the GH9 glucosidase/glucosaminidase from Vibrio cholerae

The structure of a Vibrio cholerae ‘exo’-glycosidase from CAZy family GH9 has been solved at 3.17 Å resolution. Preliminary activity assays show that the enzyme is active towards both chitosan-derived oligosaccharides and β-glucosides.
Media Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context

Health literacy describes skills and competencies that enable people to gain access to, understand and apply health information to positively influence their own health and the health of those in their social environments. In an increasingly media saturated and digitized world, these skill sets are necessary for accessing and navigating sources of health information and tools, such as television, the Internet, and mobile apps. The concepts of Media Health Literacy (MHL) and eHealth Literacy (eHL) describe the specific competencies such tasks require. This article introduces the two concepts, and then reviews findings on the associations of MHL and eHL with several contextual variables in the social environment such as socio-demographics, social support, and system complexity, as a structural variable. As eHL and MHL are crucial for empowering people to actively engage in their own health, there is a growing body of literature reporting on the potential and the effectiveness of intervention initiatives to positively influence these competencies. From an ethical standpoint, equity is emphasized, stressing the importance of accessible media environments for all—including those at risk of exclusion from (digital) media sources. Alignment of micro and macro contextual spheres will ultimately facilitate both non-digital and digital media to effectively support and promote public health.
AEGIS: An Automated Permission Generation and Verification System for SDNs

An important consideration in Software-defined Networks (SDNs), is that one SDN application, through a bug or API misuse, can break an entire SDN. While previous works have tried to mitigate such concerns by implementing access control mechanisms (permission models) for an SDN controller, they commonly require serious manual efforts in creating a permission model. Moreover, they do not support flexible permission models, and they are often tightly coupled with a specific SDN controller. To address such limitations, we introduce an automated permission generation and verification system called AEGIS. A distinguishing aspect of AEGIS is that it automatically generates flexible permission models and yet is completely separated from an SDN controller implementation. To demonstrate the feasibility of our approach, we implement a prototype, evaluate its completeness and soundness, and examine its usability in the context of popular SDN controllers.
Modeling IP-to-IP Communication using the Weighted Stochastic Block Model

The vision of self-driving networks integrates network measurements with network control. Processing data for each of the network control tasks separately might be prohibitive due to the large volume and waste of computational resources. In this work we make the case of using the Weighted Stochastic Block Model (WSBM), a probabilistic model, to learn a task independent representation. In particular, we consider a case study of real-world IP-to-IP communication. The learned representation provides higher level-features for traffic engineering, anomaly detection, or other tasks, and reduces their computational effort. We find that the WSBM is able to accurately model traffic and structure of communication in the considered trace.
Multi‐Scale Network Model Supported by Proteomics for Analysis of Combined Gemcitabine and Birinapant Effects in Pancreatic Cancer Cells

Gemcitabine combined with birinapant, an inhibitor of apoptosis protein antagonist, acts synergistically to reduce pancreatic cancer cell proliferation. A large‐scale proteomics dataset provided rich time‐series data on proteome‐level changes that reflect the underlying biological system and mechanisms of action of these drugs. A multiscale network model was developed to link the signaling pathways of cell cycle regulation, DNA damage response, DNA repair, apoptosis, nuclear factor‐kappa β (NF‐κβ), and mitogen‐activated protein kinase (MAPK)‐p38 to cell cycle progression, proliferation, and death. After validating the network model under different conditions, the Sobol Sensitivity Analysis was applied to identify promising targets to enhance gemcitabine efficacy. The effects of p53 silencing and combining curcumin with gemcitabine were also tested with the developed model. Merging proteomics analysis with systems modeling facilitates the characterization of quantitative relations among relevant signaling pathways in drug action and resistance, and such multiscale network models could be applied for prediction of combination efficacy and target selection.