Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-based segmentation of individual brain structures from MRI data

This paper proposes a methodology that enables an arbitrary 3-D MRI brain image-volume to be automatically segmented and classified into neuro-anatomical components using multiresolution registration and matching with a novel volumetric brain structure model (VBSM). This model contains both raster and geometric data. The raster component comprises the mean MRI volume after a set of individual volumes of normal volunteers have been transformed to a standardized brain-based coordinate space. The geometric data consists of polyhedral objects representing anatomically important structures such as cortical gyri and deep gray matter nuclei. The method consists of iteratively registering the data set to be segmented to the VBSM using deformations based on local image correlation. This segmentation process is performed hierarchically in scale-space. Each step in decreasing levels of scale refines the fit of the previous step and provides input to the next. Results from phantom and real MR data are presented.
Molecular Analysis of katG Encoding Catalase-Peroxidase from Clinical Isolate of Isoniazid-Resistant Mycobacterium tuberculosis

Isoniazid (INH) is a drug for the treatment of tuberculosis in patients infected with Mycobacterium tuberculosis. The katG enzyme, or catalase-peroxidase, activates the pro-drug INH that is coded by the katG gene in M. tuberculosis. Mutations of the katG gene in M. tuberculosis are a major INH resistance mechanism. The M. tuberculosis clinical isolate R2 showed INH resistance at a high level of 10 µg/mL. However, the molecular basis for the resistance is unclear. The identification of a mutation in the katG gene of the clinical isolate R2 showed four mutations, i.e., C1061T, G1261 A, G1388T, G2161A, which correspond to the amino acid substitutions T354I, G421S, R463L, and V721M, respectively. The mutant katG gene, along with the wild-type were cloned, expressed and purified. The mutant enzyme showed 86.5% of catalase and 45% of peroxidase activities in comparison to the wild type. The substitutions of T354I and G421S in mutant katG R2 created significant instability in the adduct triad complex (Trp107-Tyr229-Met255), a part of the active site of the catalase-peroxidase enzyme in the model structure analysis. The events could be based on the high resistance of the clinical isolate R2 toward INH as the molecular basis.
Approach for Machine-Printed Arabic Character Recognition: the-state-of-the-art deep-learning method

Optical character recognition (OCR) automatically recognizes texts in an image and converts them into machine codes such as ASCII or Unicode. Compared to many research studied on OCR for other languages, recognizing Arabic language is still a challenging problem due to character connection and segmentation issues. In this work, we propose a deep-learning framework of recognizing Arabic characters based on the multidimensional bi-direction long short-term memory (MD-BLSTM) with connectionist temporal classification (CTC). To train this framework, we generate over one-million Arabic text-line images dataset that contains Arabic digits, basic Arabic forms with isolated shape and connected forms. To compare the results, we also measure the performance of other OCR software such as Tesseract made by Hewlett-Packard and Google Inc. Tesseract version 3 and version 4 are used. Results show that deep-learning method outperforms the conventional methods in terms of recognition error rate, although the Tesseract_3. 0 system was faster.
Integrating screening for non-communicable diseases and their risk factors in routine tuberculosis care in Delhi, India: A mixed-methods study

Background Evidence supports the integration of prevention and management for tuberculosis (TB) with non-communicable diseases (NCDs). Bi-directional screening for TB and diabetes mellitus (DM) is already implemented in India, a country with a dual burden of TB and NCDs. However, very limited programmatic data are available on the feasibility of adding other NCDs and their risk factors in such screening programme. Objective To assess the yield, feasibility, and acceptability of a two-stage integrated screening for NCDs and risk factors for NCDs among patients with TB ≥20 years and treated in DOTS centres of two medical colleges in Delhi, between October 2016 and March 2017. Methods It was a mixed-methods, triangulation study with a quantitative component (cross-sectional study using questionnaires, anthropometric measurements and records review) and a qualitative component (descriptive study using interview data). Results Amongst 403 patients screened, the prevalence of hypertension was 7% (n = 28) with 20 new cases detected and 8% for DM (n = 32) with 6 new cases diagnosed. The number needed to screen to find a new case was 20 and 63 for hypertension and DM respectively. The most frequent NCD-risk factors were inadequate vegetable (80%) and fruits (72%) intake, alcohol use (34%), use of smokeless tobacco (33%) and smoking (32%). Clustering of four or more risk factors was associated with increasing age and male sex (p<0.05). Both patients and health providers considered the screening relevant and acceptable. However, waiting time and costs involved in blood tests were considered as bothersome by the patients, while health providers perceived increased workload, inadequate medical supplies and inadequate skills and knowledge as key challenges in implementation of the screening. Conclusion Integrating screening for NCDs and their risk factors in the existing TB programme produces high yield and it is feasible and acceptable by patients and health providers provided the challenges are overcome.
Effect of Unstable Loads on Stabilizing Muscles and Bar Motion During the Bench Press.

Lawrence, MA, Ostrowski, SJ, Leib, DJ, and Carlson, LA. Effect of unstable loads on stabilizing muscles and bar motion during the bench press. J Strength Cond Res XX(X): 000-000, 2018-Unstable loads are anecdotally used to increase muscle stabilizer strength and utilization; however, there is little evidence to support these claims. Twelve male powerlifters (age 28.6 ± 5.2 years, body mass 105.6 ± 14.5 kg, lifting experience 9.8 ± 6.0 years, and bench press 5 repetition maximum [5RM] 133.6 ± 30.9 kg) pressed their 5RM in one stable condition with a standard barbell, and 3 unstable conditions using a flexible barbell with weights suspended from elastic bands. Mean integrated muscle activity for each repetition of the stabilizers and prime movers were measured. A Lyapunov exponent and sample entropy indicated unstable loads were less predictable and more variable in the superior/inferior direction, thus more challenging to control, despite decreased loads. The main finding was increased bicep activity in all unstable conditions (light bands and plates, +57%; mini bands and plates, +67%; and mini bands and kettlebells, +88%). Although less weight was used in the unstable 5RMs, most stabilizer muscle activity was not different between conditions, suggesting that the amount of weight used during unstable load training (up to 32.3% less load) may be limited by the ability of stabilizer muscles to control the load.
Correspondence between primary and secondary care about patients with cancer: A qualitative mixed‐methods analysis

Abstract Cancer care is complex and involves many different healthcare providers, especially during diagnosis and initial treatment, and it has been reported that both general practitioners and oncology specialists experience difficulties with interdisciplinary communication. The aim of this qualitative study was to explore information sharing between primary and secondary care for patients with lung, breast or colorectal cancer. A qualitative content analysis of 50 medical files (419 documents) was performed, which identified 70 correspondence‐related items. Six main topics were identified in most referral letters from primary to secondary care, but it was particularly notable that highly relevant information regarding the past medical history was often mixed with less relevant information. To lesser extents, the same held true for the medication list and presenting history. In the letters from specialists, nine topics were identified in most letters. Although information about actual treatment was always present, only limited detail, if any, was given about the intent of the treatment (curative or palliative) or the treatment alternatives. Interviews with nine healthcare providers confirmed these issues. These findings indicate that neither the initial referral nor the specialist correspondence is tailored to the needs of the recipient.
Robust Counterfactual Inferences using Feature Learning and their Applications

In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we chose to intervene or not. Typically randomized experiments are carried out to understand whether an intervention is overall better than no intervention. Here we present a feature learning algorithm to learn from a randomized experiment where the intervention in consideration is most effective and where it is least effective rather than only focusing on the overall impact, thus adding a context to our learning mechanism and extract more information. From the randomized experiment, we learn the feature representations which divide the population into subpopulations where we observe statistically significant difference in average customer feedback between those who were subjected to the intervention and those who were not, with a level of significance l, where l is a configurable parameter in our model. We use this information to derive the value of the intervention in consideration for each instance in the population. With experiments, we show that using this additional learning, in future interventions, the context for each instance could be leveraged to decide whether to intervene or not.
Sentiment Index of the Russian Speaking Facebook

A sentiment index measures the average emotional level in a corpus. We introduce four such indexes and use them to gauge average " positiveness" of a population during some period based on posts in a social network. This article for the first time presents a text-, rather than word-based sentiment index. Furthermore, this study presents the first large-scale study of the sentiment index of the Russian-speaking Facebook. Our results are consistent with the prior experiments for English language.
Role of Intonation in Scoring Spoken English

In this paper, we have introduced and evaluated intonation based feature for scoring the English speech of nonnative English speakers in Indian context. For this, we created an automated spoken English scoring engine to learn from the manual evaluation of spoken English. This involved using an existing Automatic Speech Recognition (ASR) engine to convert the speech to text. Thereafter, macro features like accuracy, fluency and prosodic features were used to build a scoring model. In the process, we introduced SimIntonation, short for similarity between spoken intonation pattern and "ideal" i.e. training intonation pattern. Our results show that it is a highly predictive feature under controlled environment. We also categorized interword pauses into 4 distinct types for a granular evaluation of pauses and their impact on speech evaluation. Moreover, we took steps to moderate test difficulty through its evaluation across parameters like difficult word count, average sentence readability and lexical density. Our results show that macro features like accuracy and intonation, and micro features like pause-topography are strongly predictive. The scoring of spoken English is not within the purview of this paper.
Propagation Measurement System and Approach at 140 GHz-Moving to 6G and Above 100 GHz

With the relatively recent realization that millimeter wave frequencies are viable for mobile communications, extensive measurements and research have been conducted on frequencies from 0.5 to 100 GHz, and several global wireless standard bodies have proposed channel models for frequencies below 100 GHz. Presently, little is known about the radio channel above 100 GHz where there are much wider unused bandwidth slots available. This paper summarizes wireless communication research and activities above 100 GHz, overviews the results of previously published propagation measurements at D-band (110–170 GHz), provides the design of a 140 GHz wideband channel sounder system, and proposes indoor wideband propagation measurements and penetration measurements for common materials at 140 GHz which were not previously investigated.
New approach to 3-D registration of multimodality medical images by surface matching

Multimodality images obtained from medical imaging systems such as computed tomography (CT), magnetic resonance (MR) imaging, positron emission tomography (PET), and single photon emission computed tomography (SPECT), generally provide complementary characteristic and diagnostic information. Synthesis of these image data sets into a single composite image containing these complementary attributes in accurate registration and congruence would provide truly synergistic information about the object(s) under examination. We have developed a new method which produces such correlation using parametric Chamfer matching. The method is fast, accurate, and reproducible. Surfaces ar initially extracted from two different images to be matched using semi-automatic segmentation techniques. These surfaces are represented as contours with common features to be matched. A distance transformation is performed for one surface image, and a cost function for the matching process is developed using the distance image. The geometric transformation includes three- dimensional translation, rotation, and scaling to accommodate images of different position, orientation, and size. The matching process involves searching this multi-parameter space to find the best fit which minimizes the cost function. The local minima problem is addressed by using a large number of starting points. A pyramid multi-resolution approach is employed to speed up both the distance transformation and the multi-parameter minimization processes. Robustness in noise handling is accomplished using multiple thresholds embedded in the multi- resolution search. The algorithm can register both partially overlapped and fragmented surfaces. Manual intervention is generally not necessary. Preliminary results suggest registration accuracy on the order of the voxel size used in the registration process. Computational time scales with the number of matching elements used, with about five minutes typical for 2563 images using a modern desktop workstation.
Probabilistic segmentation using edge detection and region growing

A new segmentation algorithm is described which incorporates both region and edge information. The algorithm allows simultaneous segmentation of multiple anatomical objects given one or more user-specified disc-shaped seed regions which sample the density characteristics of the underlying anatomy. The algorithm is iterative in nature, using the seed discs to grow out the specified region(s), for the initial image slice, through a type of connected component labeling. The final segmentation from the previous image slice seeds the segmentation for the next adjoining slice until the entire image volume is processed. The algorithm requires no training, is adaptive, demonstrating good performance for differing data types including CT and MRI, and requires minimal user input. The output of the segmentation algorithm is a three-dimensional (3-D) n-ary scene (where n specifies the number of segmented regions) which is amenable to surface rendering, via surface tracking, or volume rendering by masking the n-ary scene against the original image volume.
Shape computations with NURB curves

Abstract Freeform curves are commonly used in contemporary design practices, especially with digital modeling tools. We investigate facilitating shape subtraction and addition with two-dimensional (planar) non-uniform rational basis-spline (NURB) curves with the codes and conventions of modeling while preserving the visual continuity of curved shapes. Our proposed tool, developed in a common digital modeling environment, automates the adjustment of parameters for tangential continuity of curves in shape rule applications. When the user designates a curve range to subtract from an initial shape and provides a new curved shape to add to it, the tool splits the initial shape, scales and aligns the curve to be added to fit into this range, introduces additional control points at the joining ends of the new curve to preserve continuity and redraws the new curve. We present a sample set of design variations produced using this practical approach which can be utilized as a method or become part of an automated NURB curve manipulation tool for designers.
The Role of Oxidative Stress in the Development of Systemic Sclerosis Related Vasculopathy

Systemic sclerosis (SSc) is a rare connective tissue disease characterized by autoimmunity, vasculopathy, and progressive fibrosis typically affecting multiple organs including the skin. SSc often is a lethal disorder, because effective disease-modifying treatment still remains unavailable. Vasculopathy with endothelial dysfunction, perivascular infiltration of mononuclear cells, vascular wall remodeling and rarefaction of capillaries is the hallmark of the disease. Most patients present with vasospastic attacks of the digital arteries referred to as ‘Raynaud’s phenomenon,’ which is often an indication of an underlying widespread vasculopathy. Although autoimmune responses and inflammation are both found to play an important role in the pathogenesis of this vasculopathy, no definite initiating factors have been identified. Recently, several studies have underlined the potential role of oxidative stress in the pathogenesis of SSc vasculopathy thereby proposing a new aspect in the pathogenesis of this disease. For instance, circulating levels of reactive oxygen species (ROS) related markers have been found to correlate with SSc vasculopathy, the formation of fibrosis and the production of autoantibodies. Excess ROS formation is well-known to lead to endothelial cell (EC) injury and vascular complications. Collectively, these findings suggest a potential role of ROS in the initiation and progression of SSc vasculopathy. In this review, we present the background of oxidative stress related processes (e.g., EC injury, autoimmunity, inflammation, and vascular wall remodeling) that may contribute to SSc vasculopathy. Finally, we describe the use of oxidative stress related read-outs as clinical biomarkers of disease activity and evaluate potential anti-oxidative strategies in SSc.
Retrieval and Richness when Querying by Document

A single relevant document can be viewed as a long query for ad hoc retrieval, or a tiny training set for supervised learning. We tested techniques for QBD (query by document), with an eye toward their eventual use in active learning of text classifiers in a legal context. Richness (prevalence of relevant documents) varies widely in our tasks of interest. We used 658 categories from the RCV1-v2 collection to study the impact of richness on QBD variants supported by Elasticsearch. BM25 weighting on full query documents dominated other methods. However, its absolute and relative effectiveness depended strongly on richness, raising broader questions about common test collection practices. We ported Elasticsearch’s version of BM25 to the machine learning package scikit-learn and we discuss some lessons learned about the replicability of retrieval results.
Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter

For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior-and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods.
Effects of Combination of Anti-CTLA-4 and Anti-PD-1 on Gastric Cancer Cells Proliferation, Apoptosis and Metastasis

Background/Aims: Gastric cancer (GC) is one of the most common and lethal varieties of cancers. Anticancer activities of anti-CTLA-4 and anti-PD-1 antibodies have been explored in different cancers, including GC. The study aimed to explore the role of combination therapy with anti-CTLA-4 and anti-PD-1 antibodies in GC cells, and understand the possible underlying molecular mechanism. Methods: MKN-45 and MGC-803 cells were divided into four groups, namely control, CTLA-4, PD-1, and CTLA-4&PD-1. Cell viability, cell cycle, apoptosis, migration and invasion were measured by MTT, flow cytometry, and transwell assays, respectively. Expression levels of different mRNAs and proteins associated with apoptosis, epithelial mesenchymal transition (EMT), β-catenin, MAPK, and PI3K/AKT pathways were assessed by RT-qPCR and western blot analysis, respectively. The tumor formation in vivo was examined by tumor Xenograft model assay. Results: Combination with anti-CTLA-4 and anti-PD-1 antibodies significantly suppressed cell proliferation, induced apoptosis, as well as inhibited migration, invasion, and EMT in MKN-45 and MGC-803 cells. Western blotting revealed that combination with anti-CTLA-4 and anti-PD-1 antibodies declined the activation of β-catenin, MAPK and PI3K/AKT signal pathways. Moreover, combination of anti-CTLA-4 and anti-PD-1 antibodies inhibited tumor formation in vivo. Furthermore, the mRNA levels of CTLA-4 and PD-1 were significantly decreased in si-CTLA and si-PD-1 transfected cells, and combination with si-CTLA and si-PD-1 also suppressed cell proliferation, migration, invasion, EMT and induced apoptosis in MKN-45 cells. Conclusion: Combination therapy with anti-CTLA-4 and anti-PD-1 antibodies presented the promising outcomes in GC, although further investigations are warranted.