Miracle-dz/UniFAR-FTUs huggingface dataset:
Major clinical research advances in gynecologic cancer in 2018

Nineteen topics were selected as major clinical research advances in gynecologic oncology in 2018. For cervical cancer, the importance of human papillomavirus (HPV) testing alone as primary cervical cancer screening method and negative survival impact of minimally invasive surgery in early-stage cervical cancer were addressed. For ovarian cancer, cost-effectiveness of genetic testing to prevent cancer, use of analgesics and oral pill to reduce cancer risk, efficacy of secondary cytoreductive surgery and hyperthermic intraperitoneal chemotherapy, update in the use of poly (ADP-ribose) polymerase inhibitor, and efficacy of anti-angiogenic targeted treatments, including bevacizumab and tyrosine kinase inhibitors, were reviewed. For corpus cancer, sentinel lymph node mapping technique, adjuvant therapy in high-risk endometrial cancer (PORTEC-3), and targeted therapy in recurrent disease were covered. For the field of radiation oncology, survival outcomes of chemoradiation compared with chemotherapy alone in metastatic cervical cancer and new findings regarding the use of neoadjuvant chemotherapy in locally advanced cervical cancer were introduced. Lastly, for breast cancer, the use of talazoparib in patients with germline BRCA1/2 mutation, ovarian suppression for premenopausal patients, adjuvant chemotherapy guided by 21-gene assay, and combination therapy of atezolizumab and nab-paclitaxel for triple-negative cancer as well as promising overall survival results of palbociclib and fulvestrant in advanced breast cancer were briefly mentioned.
Deep Activation Feature Maps for Visual Object Tracking

Video object tracking is an important task with a broad range of applications. In this paper, we propose a novel visual tracking algorithm based on deep activation feature maps in correlation filter framework. Deep activation feature maps are generated from convolution neural network feature maps, which can discover the important part of the tracking target and overcome shape deformation and heavy occlusion. In addition, the scale variation is calculated by another correlation filter with histogram of oriented gradient (HoG) features. Moreover, we integrate the final tracking result in each frame based on the appearance model and scale model to further boost the overall tracking performance. We validate the effectiveness of our approach on a challenging benchmark, where the proposed method illustrates outstanding performance compared with the state-ofthe-art tracking algorithms
Nanomedical Relevance of the Intermolecular Interaction Dynamics—Examples from Lysozymes and Insulins

Insulin and lysozyme share the common features of being prone to aggregate and having biomedical importance. Encapsulating lysozyme and insulin in micellar nanoparticles probably would prevent aggregation and facilitate oral drug delivery. Despite the vivid structural knowledge of lysozyme and insulin, the environment-dependent oligomerization (dimer, trimer, and multimer) and associated structural dynamics remain elusive. The knowledge of the intra- and intermolecular interaction profiles has cardinal importance for the design of encapsulation protocols. We have employed various biophysical methods such as NMR spectroscopy, X-ray crystallography, Thioflavin T fluorescence, and atomic force microscopy in conjugation with molecular modeling to improve the understanding of interaction dynamics during homo-oligomerization of lysozyme (human and hen egg) and insulin (porcine, human, and glargine). The results obtained depict the atomistic intra- and intermolecular interaction details of the homo-oligomerization and confirm the propensity to form fibrils. Taken together, the data accumulated and knowledge gained will further facilitate nanoparticle design and production with insulin or lysozyme-related protein encapsulation.
Pharmacogenetic Studies of Antihypertensive Treatment : With Special Reference to the Renin-Angiotensin-Aldosterone System

Hypertension is common and constitutes an increased risk of morbidity and mortality of cardiovascular disease. Antihypertensive treatment will reduce this risk; the individual patient's response to treatment, however, is difficult to predict.Patients with hypertension and left ventricular hypertrophy were randomized to monotherapy with either the angiotensin II type 1 receptor antagonist irbesartan or the beta-adrenoreceptor blocker atenolol, and followed for three months. The aim was to determine whether gene polymorphisms in the renin-angiotensin-aldosterone system were related to the response to treatment.The ACE II genotype was associated with the most pronounced diastolic blood pressure response, while the aldosterone synthase (CYP11B2) -344 TT genotype showed the greatest systolic blood pressure response. The angiotensinogen 174 TM genotype showed the most pronounced regression in left ventricular mass, independent of the change in blood pressure. These associations were exhibited only in response to treatment with the angiotensin II type 1 receptor antagonist irbesartan.In a sample of apparently healthy subjects, those with both the D allele and the angiotensinogen 174 TM variant in combination showed a decreased endothelium-dependent vasodilation.These results suggest that the response to antihypertensive treatment is associated with polymorphisms in the genes reflective of the pathophysiological pathway the drug targets. The present study is an encouragement for future investigation, such as large scale studies of multiple polymorphisms and combinations thereof in an attempt to identify a panel of genotypes that can be used as a predictor of an individual patient's response to anithypertensive treatment.
Commentary Understanding the cognitive consequences of critical illness through experimental animal models

Tuon and colleagues have developed an animal model to examine the impact of sepsis on memory in rats. They report important data that expand the understanding of the cognitive consequences of critical illness. Future research should follow this path of inquiry and extend animal models beyond aversive conditioning to include recently developed paradigms that will permit assessment of complex and cognitive processes, such as attention, episodic memory and orientation to time and place. This has the potential to greatly increase the putative understanding of the homologous neurocognitive dysfunctions acquired during critical illness. Major progress has been made over the past 20 years in the understanding of the cognitive consequences of critical illness. In order to expand the knowledge how disease states such as sepsis have a causal impact on the central nervous system and cognition, experimental animal models are certainly required. In a previous issue of Critical Care, Tuon and colleagues [1] reported a study in which they developed such a model in order to simulate the cognitive and behavioral effects of septic illness on memory functioning. They further provide evidence that this memory impairment can be attenuated by the administration adrenergic agents, which suggests that this mnemonic pathway may be mediated by adrenoceptors.
Diagnostic Breast Imaging

Part 1 Methods: patient history and dialogue clinical findings mammography sonography magnetic resonance tomography trancutaneous biopsy operative localization. Part 2 Appearance of benign and malignant breast tissues normal breast displasias cycsts benign tumours and tumours-like lesions inflammatory changes in-situ carcinomas invasive carcinoma other malignant tumours scarriong skin changes. Part 3 Application of imaging in the diagnosis of breast disease: screening problem-solving of screening deteced and clinically symptomatic lesions.
Speech Emotion Classification using Raw Audio Input and Transcriptions

As new gadgets that interact with the user through voice become accessible, the importance of not only the content of the speech increases, but also the significance of the way the user has spoken. Even though many techniques have been developed to indicate emotion on speech, none of them can fully grasp the real emotion of the speaker. This paper presents a neural network model capable of predicting emotions in conversations by analyzing transcriptions and raw audio waveforms, focusing on feature extraction using convolutional layers and feature combination. The model achieves an accuracy of over 71% across four classes: Anger, Happiness, Neutrality and Sadness. We also analyze the effect of audio and textual features on the classification task, by interpreting attention scores and parts of speech. This paper explores the use of raw audio waveforms, that in the best of our knowledge, have not yet been used deeply in the emotion classification task, achieving close to state of art results.
SimBSI: An open-source Simulink library for developing closed-loop brain signal interfaces in animals and humans

Objective. A promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans is still lacking. The objective of this paper is to develop such a unifying neurotechnological software framework. Approach. Here we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform. Main results. We use several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment. Significance. With the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.
Seeing cells smell: Dynamic optical measurements of Ca2+ and cAMP signaling from Olfactory Receptors transiently expressed in HEK293TN cells

Olfactory receptors (ORs) constitute the largest family of G-protein coupled receptors. They are responsible for the perception of odor (olfaction) and also play important roles in other biological processes, including regulation of cell proliferation. Their increasing diagnostic and therapeutic potential, especially for cancer research, requests the ongoing development of methodologies that would allow their robust functional expression in non-olfactory cells, and dynamic analysis of their signaling pathways. To enable realtime detection of OR activity, we use single cell imaging with genetically encoded fluorescent biosensors, Yellow Cameleon or EPAC, which are routinely used for kinetic measurements of Ca2+ or cAMP signaling downstream of various G-protein coupled receptors. We demonstrate that the co-expression of Lucy-Rho tagged variants of ORs together with an accessory protein, RTP1s, in HEK293TN cells is sufficient to detect the activity of a panel of ORs. Using this methodology, we were able to detect both Ca2+ and cAMP signaling downstream of twelve ORs within 2 minutes from the application of odorant.
Serum antioxidant status and some selected trace metal levels in patients with cervical intraepithelial neoplasm in Benin City, Nigeria

Antioxidants are important in the detoxification of free radicals, which are the hazardous derivatives of metabolic processes in the body. However, the mop up of free radicals in the prevention and management of cervical intraepithelial neoplasm is not clear. This study presented an assessment of the status of catalase (CAT), glutathione peroxidase (GPx), superoxide dismutase (SOD), total antioxidant capacity (TAC) and some trace metals (Ca, Mg, Cu and Zn) in women diagnosed with Cervical Intraepithelial Neoplasm (CIN) lesions. The study was carried out at the Department of Obstetrics and Gynaecology, University of Benin Teaching Hospital Benin City and Department of Chemical Pathology, Edo University Iyamho, Edo state Nigeria. Study participants were grouped into two; those with CIN lesions (n = 144) and the control (n = 53). Venous blood was obtained from participants and serum antioxidant levels were determined using standardized laboratory methods. Results showed that CAT levels increased significantly from 23.88 u/μl/min in the control group to 31.79 u/μl/min in CIN. The incidence of cervical intraepithelial neoplasm lesions did not significantly affect total antioxidant capacity (TAC) of the study population (2.43 - 2.70 mM, P>0.05). There were minimal increases in antioxidant status between the control and CIN groups respectively (P> 0.05). Cu was implicated as the only trace metal within the CIN group without significant correlation to the antioxidants investigated. Keywords: Epithelial cell, cervical cancer, neoplasm, antioxidant status
Physiopathologie du diabète secondaire à la fibrose kystique : études transversales et prospectives

Abnormal glucose tolerance is a frequent co-morbidity in cystic fibrosis patients (CF), and is associated with a worse prognosis. However, the most pertinent factors to predict the clinical status and the physiopathology of glucose intolerance remain unclear. The aim of this study was to investigate the roles of impaired insulin secretion and insulin resistance in the progression of glucose intolerance from the normal state up to diabetes (CFRD) using an ongoing prospective observational cohort of non-diabetic CF patients. We demonstrated that CF patients displayed a reduced first phase insulin secretion across glucose tolerance categories (normal; intolerance and CFRD). Moreover, variation in insulin resistance has a significant impact on glucose tolerance in this population. Finally, early in the course of the disease, increased glucose area under the curve (AUC) and reduced first phase insulin secretion are better predictors of clinical status than conventional glucose tolerance categories. Numerous indices to evaluate insulin secretion have been proposed, but their validity has not been explored in cystic fibrosis (CF). The aim of this study was to validate surrogate indices of insulin secretion calculated from fasting values or the oral glucose tolerance test (OGTT) in CF patients against the gold standard, the intravenous glucose tolerance test (IVGTT). This is an essential step to study the physiopathological role of defective insulin secretion in the development of CFDR in large cohort. We showed that all insulin secretion indices correlated significantly with the IGVTT in control individuals. However, while OGTT-derived indices correlated significantly with the gold standard test in all CF groups, this was not the case for fasting-derived indices. Since the OGTT is required on a regular basis in CF patients to screen for CFRD, OGTT-derived indices should be used to evaluate insulin secretion.
SENet for Weakly-Supervised Relation Extraction

Sentence relation extraction aims to extract relational facts from sentences, which is an important task in natural language processing (NLP) field. A common pipeline in relation extraction (RE) task is to first encode a sentence into latent representation, and then classify it utilizing a combination of attention mechanism, fully connected (FC) layers and softmax layer. Therefore a good sentence encoder for relation extraction is essential. Previous models use shallow convolution neural networks or recurrent neural networks like bi-directional Long Short-Term Memory (LSTM) to extract features of a sentence. To enhance the representation power of network, in this paper we investigate the capability of Squeeze-and Excitation (SE) module on the task of distantly supervised noisy relation extraction. SE module could learn the importance of each filter, enhance important filters and restrain less important ones, therefore prevent overfitting. To enhance position information, we also demonstrated the power of a new scheme of pooling named double pooling. Experimental results demonstrate the effectiveness of our method compared with baseline models.
Efficient and density adaptive edge weight model for measuring semantic similarity

The measurement of semantic similarity between concepts is an important research topic in natural language processing. However, previous efforts suffered from the mismatch of the accuracy and efficiency. In this paper, we propose an edge weight model for improving the accuracy of edge-based measures that have an inherent high efficiency. It combines the edge counting model with the information theory and deduces a function of edge weight based on the number of direct hyponyms of the subsumer in the edge. This model doesn't require any additional parameter and can adapt the effect of different densities to edges. Extensive experiments on four test datasets for WordNet and SNOMED-CT demonstrate that the proposed edge weight model can significantly improve the accuracy of various edge-based similarity measures and has a wide coverage over different ontologies. Compared with IC-based measures, our model has a remarkable advantage in efficiency and is comparable to it in accuracy.
Training and User Perceptions of the Quality of Internet-Based Information

The potential for information quality problems on the Internet is generally recognized. There have been calls for training designed to increase awareness of these problems. However, little research has examined the effect of this training on perceptions of the quality of Internet-based information. This study surveys subjects who have and have not received training on information quality problems associated with Internet-based information using an instrument that builds on prior research by Wang and Strong (1996). The findings will provide a basis for the development of training programs designed to sensitize users of the Internet to information quality issues.
A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost

The impact of Hyper-Parameter optimization on the performance of a machine learning algorithm has been proved both theoretically and empirically by many studies reported in the literature. It is a tedious and a time-consuming task if one goes for Manual Search. Some of the common approaches to address this include Grid search and Random search. Another alternative is performing the Bayesian optimization using the Hyperopt library in Python. In this paper, we tune the hyperparameters of XGBoost algorithm on six real world datasets using Hyperopt, Random search and Grid Search. We then compare the performances of each of these three techniques for hyperparameter optimization using both accuracy and time taken. We find that the Hyperopt performs better than the Grid search and Random search approaches taking into account both accuracy and time. We conclude that Bayesian optimization using Hyperopt is the most efficient technique for hyperparameter optimization.
Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis): a case-control study protocol and GWAS in Ethiopia, Kenya, South Africa and Uganda

Introduction Schizophrenia and bipolar disorder account for a large proportion of the global burden of disease. Despite their enormous impact, little is known about their pathophysiology. Given the high heritability of schizophrenia and bipolar disorder, unbiased genetic studies offer the opportunity to gain insight into their neurobiology. However, advances in understanding the genetic architecture of schizophrenia and bipolar disorder have been based almost exclusively on subjects of Northern European ancestry. The Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis) project aims to expand our understanding of the causes of schizophrenia and bipolar disorder through large-scale sample collection and analyses in understudied African populations. Methods and analysis NeuroGAP-Psychosis is a case-control study of 34 000 participants recruited across multiple sites within Ethiopia, Kenya, South Africa and Uganda. Participants will include individuals who are at least 18 years old with a clinical diagnosis of schizophrenia or bipolar disorder (‘psychosis’) or those with no history of psychosis. Research assistants will collect phenotype data and saliva for DNA extraction. Data on mental disorders, history of physical health problems, substance use and history of past traumatic events will be collected from all participants. DNA extraction will take place in-country, with genotyping performed at the Broad Institute. The primary analyses will include identifying major groups of participants with similar ancestry using the computation-efficient programme single nucleotide polymorphisms (SNP) weights. This will be followed by a GWAS within and across ancestry groups. Ethics and dissemination All participants will be assessed for capacity to consent using the University of California, San Diego Brief Assessment of Capacity to Consent. Those demonstrating capacity to consent will be required to provide informed consent. Ethical clearances to conduct this study have been obtained from all participating sites. Findings from this study will be disseminated in publications and shared with controlled access public databases, such as the database of Genotypes and Phenotypes, dbGaP.
The Ceprei-Scape Dataset for the Validation of Autonomous Driving

Validation and test is an enabling factor of the autonomous driving for a wide range of applications, especially for vision-based applications. Deep learning methods has been widely deployed in vision-based applications of autonomous driving and has benefitted enormously from large scale dataset. However, no current dataset focuses on the validation of autonomous driving and captures the feature of the validation process. To address this, the Ceprei-Scape dataset is proposed in this paper. Ceprei-Scape dataset is consist of RGB videos and corresponding dense Lidar point cloud. Comparing with existing datasets, our dataset has following unique properties. First, the scale of our dataset is very big and it contains 50k images and its pixel-level semantic classes. Second, each image in the dataset is tagged with high-accuracy 3D attributes achieved from dense 3D point cloud data. Third, the standard validation process required in the laws and regulations (EURO-NCAP, CNCAP, etc.) are included in our dataset to address the feature of the validation of the autonomous driving. We expect our new dataset can promote the validation and test of autonomous driving and deeply benefit the research and application of autonomous driving.