ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks

The communication between a person from the impaired community with a person who does not understand sign language could be a tedious task. Sign language is the art of conveying messages using hand gestures. Recognition of dynamic hand gestures in American Sign Language (ASL) became a very important challenge that is still unresolved. In order to resolve the challenges of dynamic ASL recognition, a more advanced successor of the Convolutional Neural Networks (CNNs) called 3-D CNNs is employed, which can recognize the patterns in volumetric data like videos. The CNN is trained for classification of 100 words on Boston ASL (Lexicon Video Dataset) LVD dataset with more than 3300 English words signed by 6 different signers. 70% of the dataset is used for Training while the remaining 30% dataset is used for testing the model. The proposed work outperforms the existing state-of-art models in terms of precision (3.7%), recall (4.3%), and f-measure (3.9%). The computing time (0.19 seconds per frame) of the proposed work shows that the proposal may be used in real-time applications.
From Materials to Technique: A Complete Non-Invasive Investigation of a Group of Six Ukiyo-E Japanese Woodblock Prints of the Oriental Art Museum E. Chiossone (Genoa, Italy)

In the present work, a complete non-invasive scientific investigation of six Utagawa Kunisada's woodblock prints (nishiki-e) belonging to the Oriental Art Museum "E. Chiossone" (Genoa, Italy), was performed in situ. The campaign started with high resolution multiband imaging (visible, multiband fluorescence, near infrared) followed by reflectance transformation imaging (RTI) to characterize and highlight the peculiar printing techniques and the condition of the support. Then fiber optics reflectance spectroscopy (FORS), spectrofluorimetry, Raman and reflectance Fourier-transform infrared (FTIR) spectroscopies were successfully applied in synergy for the investigation of the printing materials (pigments, binders, support). The results obtained represent a set of very important information for these never-before-studied works of art, useful to the different professionals involved: historians, conservators and curators. The materials identified were completely in agreement with those traditionally used in the Edo period in the 19th century, while the computational imaging technique RTI gave an additional amount of information in terms of surface characterization that could not be overlooked when studying these works of art. RTI data were further processed to enhance the texture visualization.
Transcriptome analysis provides novel insights into the immune mechanisms of Macrobrachium nipponense during molting

Molting is a basic physiological behavior of the Oriental river prawn (Macrobrachium nipponense), however, the gene expression patterns and immune mechanisms during the molting process of Oriental river prawn are unclear. In the current study, the gene expression levels of the hepatopancreas of the Oriental river prawn at different molting stages (pre-molting, Prm; mid-molting, Mm; and post-molting, Pom) were detected by mRNA sequencing. A total of 1721, 551, and 1054 differentially expressed genes (DEGs) were identified between the Prm hepatopancreas (PrmHe) and Mm hepatopancreas (MmHe), MmHe and Pom hepatopancreas (PomHe) and PrmHe and PomHe, respectively. The results showed that a total of 1151 DEGs were annotated into 316 signaling pathways, and the significantly enriched immune-related pathways were "Lysosome", "Hippo signaling pathway", "Apoptosis", "Autophagy-animal", and "Endocytosis". The qRT-PCR verification results of 30 randomly selected DEGs were consistent with RNA-seq. The expression patterns of eight immune related genes in different molting stages of the Oriental river prawn were analyzed by qRT-PCR. The function of Caspase-1 (CASP1) was further investigated by bioinformatics, qRT-PCR, and RNAi analysis. CASP1 has two identical conserved domains: histidine active site and pentapeptide motif, and the expression of CASP1 is the highest in ovary. The expression levels of triosephosphate isomerase (TPI), Cathepsin B (CTSB) and Hexokinase (HXK) were evaluated after knockdown of CASP1. This research provides a valuable basis to improve our understanding the immune mechanisms of Oriental river prawns at different molting stages. The identification of immune-related genes is of great significance for enhancing the immunity of the Oriental river prawn, or other crustaceans, by transgenic methods in the future.
Induction heating in transversal magnetic field - mathematical modeling and experimental verification

Based upon a state-of art on induction heating an analysis of induction devices for surface heating of thin workpieces in transverse flux magnetic field was done in the paper. A short overview of calculation methods for such induction heating systems applied for was elaborated. (Induction heating in transversal magnetic field - mathematical modeling and experimental verification).
RPEOD: A Real-Time Pose Estimation and Object Detection System for Aerial Robot Target Tracking

Pose estimation and environmental perception are the fundamental capabilities of autonomous robots. In this paper, a novel real-time pose estimation and object detection (RPEOD) strategy for aerial robot target tracking is presented. The aerial robot is equipped with a binocular fisheye camera for pose estimation and a depth camera to capture the spatial position of the tracked target. The RPEOD system uses a sparse optical flow algorithm to track image corner features, and the local bundle adjustment is restricted in a sliding window. Ulteriorly, we proposed YZNet, a lightweight neural inference structure, and took it as the backbone in YOLOV5 (the state-of-the-art real-time object detector). The RPEOD system can dramatically reduce the computational complexity in reprojection error minimization and the neural network inference process; Thus, it can calculate real-time on the onboard computer carried by the aerial robot. The RPEOD system is evaluated using both simulated and real-world experiments, demonstrating clear advantages over state-of-the-art approaches, and is significantly more fast.
Spiking Deep Residual Networks

Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess the potential of achieving energy-efficient machine intelligence while keeping comparable performance to ANNs. However, it is still a big challenge to train a very deep SNN. In this brief, we propose an efficient approach to build deep SNNs. Residual network (ResNet) is considered a state-of-the-art and fundamental model among convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons named spiking ResNet (S-ResNet). We propose a residual conversion model that appropriately scales continuous-valued activations in ANNs to match the firing rates in SNNs and a compensation mechanism to reduce the error caused by discretization. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet 2012 with low latency. This work is the first time to build an asynchronous SNN deeper than 100 layers, with comparable performance to its original ANN.
Mental Health Risks Among Informal Waste Workers in Kathmandu Valley, Nepal

Informal waste workers are a vulnerable population group who are often socio-economically marginalized and disadvantaged, with more likelihood of experiencing ill health than the general population. To explore the determinants of mental ill health in this group, we conducted a cross-sectional survey of 1278 informal waste-workers in Nepal in 2017, using a demographic health assessment questionnaire and a modified Patient Health Questionnaire (PHQ-9). We looked at the potential associations between various exposure factors and mental health outcomes and found that 27.4% of waste-workers had depressive symptoms, more likely to be reported by female (OR 2.290), older person (OR 7.757), divorced/separated (5.859), and those with ill health (OR 2.030), or disability (OR 3.562). Waste-workers with access to social protection (OR 0.538) and financial savings (OR 0.280) were less likely to have depressive symptoms. There are key risk factors that may enable identification of particularly vulnerable persons within this group and also protective factors that may help improve their mental health resilience.
Historic building materials from Alhambra: Nanoparticles and global climate change effects

Advanced microscopy analyses are capable of revealing particles that are existent in the Alhambra, Spain, a UNESCO World Heritage Site, at minimal levels owing to the detailed directing on ultra-fine (UFPs) and nano-particles (NPs) of importance. This applied technique is one of the most used to natural samples. Between the diverse archaeological places in the planet, Alhambra is one of the most important representations of Hispano-Islamic architecture and art significant Muslim virtuosity in its conclusive European periods, thus helping to offer scientific material about the still little studied Islamic art. In this study, advanced microscopy analyses has been applied to understand minor portions of UFPs and NPs of pollutants in external walls exposed to weathering. Several materials identified by X-Ray Diffraction can be detected using HR-TEM/FE-SEM and vice versa. The occurrence of anglesite, gypsum, hematite containing PHEs, and several organic compounds associated with modifications due to moisture and pollution was also demonstrated. (C) 2019 Elsevier Ltd. All rights reserved.
Primary membranous nephropathy: an endless story

Primary membranous nephropathy (PMN) is an autoimmune disease caused by the attack of autoantibodies against podocyte antigens leading to the in situ production of immune complexes. However, the etiology is unknown and the pathogenesis is still far from being completely elucidated. MN is prevalently idiopathic or primary, but in about 20-30% of cases it is secondary to chronic infections, systemic diseases, exposure to drugs, or malignancy. The differentiation between primary and secondary MN may be difficult, particularly when MN precedes signs and symptoms of the original disease, as in some cases of cancer or systemic lupus erythematosus. The natural course of PMN is variable, but in the long term 40-60% of patients with nephrotic syndrome progress to end-stage renal disease (ESRD) or die from thrombotic or cardiovascular events. PMN is a treatable disease. Patients with asymptomatic proteinuria should receive supportive care. Immunosuppressive treatments should be given to patients with nephrotic syndrome or risk of progression. The most frequently adopted treatments rely on cyclical therapy alternating steroids with a cytotoxic agent every other month, i.e., rituximab at different doses, or calcineurin inhibitors plus low-dose steroids. A good rate of response may be obtained but relapses can occur. Randomized controlled trials, with adequate size, long-term follow-up, and fair definition of endpoints are needed to identify treatment with the best therapeutic index.
Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing

Snapshot spectral imaging (SSI) is a cutting-edge technology for enabling the efficient acquisition of the spatio-spectral content of dynamic scenes using miniaturized platforms. To achieve this goal, SSI architectures associate each spatial pixel with a specific spectral band, thus introducing a critical trade-off between spatial and spectral resolutions. In this paper, we propose a computational approach for the recovery of high spatial and spectral resolution content from a single exposure or a small number of exposures. We formulate the problem in a novel framework of spectral measurement matrix completion and we develop an efficient low-rank and graph regularized method for SSI demosaicing. Furthermore, we extend state-of-the-art approaches by considering more realistic sampling paradigms that incorporate information related to the spectral profile associated with each pixel. In addition to reconstruction quality, we also investigate the impact of recovery on subsequent analysis tasks, such as classification using state-of-the-art convolutional neural networks. We experimentally validate the merits of the proposed recovery scheme using synthetically generated data from indoor and satellite observations and real data obtained with an Interuniversity MicroElectronics Center (IMEC) visible range SSI camera.
Oral arsenic and retinoic acid for high-risk acute promyelocytic leukemia

Acute promyelocytic leukemia (APL) has become curable over 95% patients under a complete chemo-free treatment with all-trans retinoic acid (ATRA) and arsenic trioxide in low-risk patients. Minimizing chemotherapy has proven feasible in high-risk patients. We evaluated oral arsenic and ATRA without chemotherapy as an outpatient consolidation therapy and no maintenance for high-risk APL. We conducted a multicenter, single-arm, phase 2 study with consolidation phases. The consolidation therapy included Realgar-Indigo naturalis formula (60 mg/kg daily in an oral divided dose) in a 4-week-on and 4-week-off regimen for 4 cycles and ATRA (25 mg/m2 daily in an oral divided dose) in a 2-week-on and 2-week-off regimen for 7 cycles. The primary end point was the disease-free survival (DFS). Secondary end points included measurable resident disease, overall survival (OS), and safety. A total of 54 participants were enrolled at seven centers from May 2019. The median age was 40 years. At the median follow-up of 13.8 months (through April 2022), estimated 2-year DFS and OS were 94% and 100% in an intention-to-treat analysis. All the patients achieved complete molecular remission at the end of consolidation phase. Two patients relapsed after consolidation with a cumulative incidence of relapse of 6.2%. The majority of adverse events were grade 1-2, and only three grade 3 adverse events were observed. Oral arsenic plus ATRA without chemotherapy was active as a first-line consolidation therapy for high-risk APL.Trial registration: chictr.org.cn number, ChiCTR1900023309.
Flow Adversarial Networks: Flowrate Prediction for Gas-Liquid Multiphase Flows Across Different Domains

The solution of how to accurately and timely predict the flowrate of gas-liquid mixtures is the key to help petroleum and other related industries to reduce costs, improve efficiency, and optimize management. Although numerous studies have been carried out over the past decades, the problem is still significantly challenging due to the complexity of multiphase flows. This paper attempts to seek new possibilities for multiphase flow measurement and novel application scenarios for state-of-the-art machine learning (ML) techniques. Convolutional neural networks (CNNs) are applied to predict the flowrate of multiphase flows for the first time and can achieve promising performance. In addition, considering the difference between data distributions of training and testing samples and its negative impact on prediction accuracy of the CNN models on testing samples, we propose flow adversarial networks (FANs) that can distill both domain-invariant and flowrate-discriminative features from the raw input. The method is evaluated on dynamic experimental data of different multiphase flows on different flow conditions and operating environments. The experimental results demonstrate that FANs can effectively prevent the accuracy degradation caused by the gap between training and testing samples and have better performance than state-of-the-art approaches in the flowrate prediction field.
Bottleneck-Stationary Compact Model Accelerator With Reduced Requirement on Memory Bandwidth for Edge Applications

State-of-the-art compact models such as MobileNets and EfficientNets are structured using a linear bottleneck and inverted residuals. Hardware architecture using a single dataflow strategy fails to balance the required memory bandwidth with the given computational resources. This work presents a heterogeneous dual-core accelerator that performs a block-wise pipelined process as a unit using a bottleneck-stationary (BS) dataflow. The BS greatly relieves the requirement on DRAM bandwidth and on-chip SRAM capacity. A look-behind-only attention is also proposed as a co-optimized algorithm. Compared to the state-of-the-art hardware scheme, the proposed accelerator demonstrates a reduction of 1.8-2.9 x in latency and 2.2-3 x in energy consumption, respectively.For verification, the accelerator with a 16-bit integer precision was implemented using 28nm CMOS process. Measurements show energy efficiencies of 0.5-to-3.75 TOPS/W in a supply voltage range of 0.55-to-1.15V.
Pentoxifylline treatment had no detrimental effect on sperm DNA integrity and clinical characteristics in cases with non-obstructive azoospermia

The aim of this study was to assess the consequences of treatment with pentoxifylline (PTX), an inducer of sperm motility, on sperm DNA fragmentation (SDF) and clinical characteristics in non-obstructive azoospermia (NOA) patients. The pilot study included 15 NOA patients. Half of each sperm sample before and after rapid freezing, was treated with PTX (3.6 mM /l, 30 min) as the PTX group and the remaining samples were considered as the control. SDF and sperm motility were assessed in each group. The clinical study comprised 30 fresh testicular sperm extractions (TESE) and 22 post-thawed TESE intracytoplasmic sperm injection cycles. Half of the mature oocytes from each patient were injected with PTX-treated spermatozoa and the remaining oocytes were injected with non-treated spermatozoa. Fertilization was assessed at 16 h post injection. Embryo transfer was carried out on day 2 after fertilization. Chemical pregnancy was assessed 2 weeks after transfer. PTX was found to significantly increase (P < 0.05) sperm motility. There was an insignificant difference in SDF rates between the groups (P > 0.05). In patient ovaries given fresh TESE, there was not any significant difference in clinical characteristics (P > 0.05). In patient ovaries given post-thawed TESE, there was a significant difference in the number of 2PN and in embryo formation (P < 0.05). Differences in the results of chemical pregnancy were insignificant (P > 0.05) between the groups. In addition, there was not any correlation between DNA fragmentation index and sperm motility and laboratory outcomes. Therefore, obtaining viable spermatozoa using PTX was more effective in post-thawed TESE regime patients in terms of 2PN and in embryo formation, deprived of damaging effects on sperm DNA integrity.
A Complex Convolution Kernel-Based Optical Displacement Sensor

During the last decade, a variety of optical displacement measurement techniques have been developed, commercialized and widely used. This paper presents a new type of optical displacement sensor that improves on the prior art with higher sub-pixel resolution, better accuracy, and better computation efficiency. The Complex Convolution Kernel-Based Optical Sensor (KBOS) developed in this paper leverages the phase-magnitude representation of a complex valued kernel function to achieve a superior optical displacement sensor for 2D motion measurements. The sensor's performance and properties are quantified in terms of its ability to compute the marker motions. Evaluation of this metric will explain how the sensor is able to achieve superior performance over the state of the art. The design is then validated with a set of experiments over a range of operating conditions to demonstrate the effectiveness of the approach over other existing displacement measurement techniques. Finally, the sensor's capability is compared qualitatively against other state of the art optical displacement sensors found in the literature. The sensor is shown to possess a subpixel resolution of between 1/100 and 1/2500 of a pixel depending upon spatial sampling density. The sensors dynamic range was investigated and shown to be only limited by the camera sampling speeds.
Microcrystalline Silicon Photodiode For Large Area NIR Light Detection Applications

This letter reports on microcrystalline silicon near infrared (NIR) photodiode detector. The fabricated device shows dynamic ratio of 200 at 850 nm wavelength per 0.2 mW/cm(2) of incident power density at reverse bias voltage of -1 V with response time of 400 mu s. The dynamic ratio achieved here is 15 times higher than the state of art large area inorganic a-SiGe: H phototransistor and twice the one for state of art organic cyanine based NIR detector. The speed of this device is 40 times faster than the a-SiGe: H detector. The overall advantage of high dynamic ratio and fast response alongside with compatibility with standard a-Si: H thin film transistor industry makes this device suitable for large area NIR detection applications.
A Chopped Neural Front-End Featuring Input Impedance Boosting With Suppressed Offset-Induced Charge Transfer

Modern neuromodulation systems typically provide a large number of recording and stimulation channels, which reduces the available power and area budget per channel. To maintain the necessary input-referred noise performance despite growingly rigorous area constraints, chopped neural front-ends are often the modality of choice, as chopper-stabilization allows to simultaneously improve (1/f) noise and area consumption. The resulting issue of a drastically reduced input impedance has been addressed in prior art by impedance boosters based on voltage buffers at the input. These buffers precharge the large input capacitors, reduce the charge drawn from the electrodes and effectively boost the input impedance. Offset on these buffers directly translates into charge-transfer to the electrodes, which can accelerate electrode aging. To tackle this issue, a voltage buffer with ultra-low time-averaged offset is proposed, which cancels offset by periodic reconfiguration, thereby minimizing unintended charge transfer. This article explains the background and circuit design in detail and presents measurement results of a prototype implemented in a 180 nm HV CMOS process. The measurements confirm that signal-independent, buffer offset induced charge transfer occurs and can be mitigated by the presented buffer reconfiguration without adversely affecting the operation of the input impedance booster. The presented neural recorder front-end achieves state of the art performance with an area consumption of 0.036 mm (2), an input referred noise of 1.32 mu V-rms (1 to 200 Hz) and 3.36 mu V-rms (0.2 to 10 kHz), power consumption of 13.7 mu W from 1.8 V supply, as well as CMRR and PSRR >= 83 dB at 50 Hz.