ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Precise Dynamic Consensus under Event-Triggered Communication

This work addresses the problem of dynamic consensus, which consists of estimating the dynamic average of a set of time-varying signals distributed across a communication network of multiple agents. This problem has many applications in robotics, with formation control and target tracking being some of the most prominent ones. In this work, we propose a consensus algorithm to estimate the dynamic average in a distributed fashion, where discrete sampling and event-triggered communication are adopted to reduce the communication burden. Compared to other linear methods in the state of the art, our proposal can obtain exact convergence under continuous communication even when the dynamic average signal is persistently varying. Contrary to other sliding-mode approaches, our method reduces chattering in the discrete-time setting. The proposal is based on the discretization of established exact dynamic consensus results that use high-order sliding modes. The convergence of the protocol is verified through formal analysis, based on homogeneity properties, as well as through several numerical experiments. Concretely, we numerically show that an advantageous trade-off exists between the maximum steady-state consensus error and the communication rate. As a result, our proposal can outperform other state-of-the-art approaches, even when event-triggered communication is used in our protocol.
Prediction of focal image for solar parabolic dish concentrator with square facets-an analytical model

Solar parabolic dish concentrator is one of the high-temperature applications of more than 400 °C for thermal and electrical power generation. In the solar parabolic dish concentrator, the arrangement of reflectors over the surface area is the significant factor for effective concentration of solar radiation. Also, focal image is one of the most influencing parameters in the design of receiver. Among the various reflectors, the square-shaped reflectors (facets) are comparatively effective in converging the incoming radiations to attain better focal image. In this regard, an attempt has been made to predict the focal image diameter of a solar parabolic dish concentrator with a square facet of different influencing parameters using a novel mathematical model. The influencing parameters considered for the study are aperture diameter, rim angle, and facet length of the dish concentrator. Using the model, the focal image dimension and aperture area of a solar parabolic dish concentrator with square facets can be predicted accurately for efficient design of a solar parabolic dish collector system. Finally, the model is validated with the experimentally obtained focal image diameter. The current model is in good agreement with the experimental value, with a deviation of 8.84%. Hence, the proposed model can be used for the design of parabolic dish systems.
Evaluation of an inflammation-based score for identification of appropriate patients for comprehensive genomic profiling

Performance status (PS) is widely used as an assessment of general condition in patients before performing comprehensive genomic profiling (CGP). However, PS scoring is dependent on each physician, and there is no objective and universal indicator to identify appropriate patients for CGP. Overall, 263 patients were scored using the modified Glasgow prognostic score (mGPS) from 0 to 2 based on the combination of serum albumin and c-reactive protein (CRP): 0, albumin ≥ 3.5 g/dl and CRP ≤ 0.5 mg/dl; 1, albumin < 3.5 g/dl or CRP > 0.5 mg/dl; and 2, albumin < 3.5 g/dl and CRP > 0.5 mg/dl. Overall survival was compared between mGPS 0-1 and mGPS 2 groups. The prognosis of patients with PS 0-1 and mGPS 2 was also evaluated. Thirty-nine patients (14.8%) were mGPS 2. Patients with mGPS 2 had significant shorter survival (14.7 months vs 4.6 months, p < 0.01). Twenty-eight patients were PS 0-1 and mGPS 2, and their survival was also short (5.6 months). Evaluation of mGPS is a simple and useful method for identifying patients with adequate prognosis using CGP.
Regulated Restructuring of Mucins During Secretory Granule Maturation In Vivo

Mucins are large, highly glycosylated transmembrane and secreted proteins that line and protect epithelial surfaces. However, the details of mucin biosynthesis and packaging in vivo are largely unknown. Here, we demonstrate that multiple distinct mucins undergo intragranular restructuring during secretory granule maturation in vivo, forming unique structures that are spatially segregated within the same granule. We further identify temporally-regulated genes that influence mucin restructuring, including those controlling pH (Vha16-1), Ca2+ ions (fwe) and Cl- ions (Clic and ClC-c). Finally, we show that altered mucin glycosylation influences the dimensions of these structures, thereby affecting secretory granule morphology. This study elucidates key steps and factors involved in intragranular, rather than intergranular segregation of mucins through regulated restructuring events during secretory granule maturation. Understanding how multiple distinct mucins are efficiently packaged into and secreted from secretory granules may provide insight into diseases resulting from defects in mucin secretion.
Robust stack-run coding for low bit-rate image transmission over noisy channels

We propose a multiresolution algorithm to jointly optimize a source coder and channel coder. The variable-rate source coder combines an optimal bit-allocation strategy and efficient stack-run coding. This compression scheme, concatenated with appropriate rate-compatible punctured convolutional codes, provides a competitive approach to recent state-of-the-art extensions of zerotree methods to noisy channels. Results show that under the assumption of almost zero probability of decoding error, the proposed scheme provides good performance for a lower complexity.
Preliminary investigation of a hypertonic saline nasal rinse as a hygienic intervention in dairy workers

Livestock workers experience an increased burden of bioaerosol-induced respiratory disease including a high prevalence of rhinosinusitis. Dairy operations generate bioaerosols spanning the inhalable size fraction (0-100 μm) containing bacterial constituents such as endotoxin. Particles with an aerodynamic diameter between 10 and 100 μm are known to deposit in the nasopharyngeal region and likely affect the upper respiratory tract. We evaluated the effectiveness of a hypertonic saline nasal lavage in reducing inflammatory responses in dairy workers from a high-volume dairy operation. Inhalable personal breathing zone samples and pre-/post-shift nasal lavage samples from each participant over five consecutive days were collected. The treatment group (n = 5) received hypertonic saline while the control group (n = 5) received normotonic saline. Personal breathing zone samples were analyzed for particulate concentrations and endotoxin using gravimetric and enzymatic methods, respectively. Pro- and anti-inflammatory cytokines (i.e., IL-8, IL-10, and TNF-α) were measured from nasal lavage samples using a multiplex assay. Inhalable dust concentrations ranged from 0.15 to 1.9 mg/m3. Concentrations of both pro- and anti-inflammatory cytokines, specifically IL-6, IL-8, and IL-10, were significantly higher in the treatment group compared to the control group (p < 0.02, p < 0.04, and p < 0.01, respectively). Further analysis of IL-10 anti-inflammatory indicates a positive association between hypertonic saline administration and IL-10 production. This pilot study demonstrates that hypertonic saline nasal lavages were successful in upregulating anti-inflammatory cytokines to support larger interventional studies.
Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed lowdimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.
Tuning the Micro-coordination Environment of Al in Dealumination Y Zeolite to Enhance Electron Transfer at the Cu-Mn Oxides Interface for Highly Efficient Catalytic Ozonation of Toluene at Low Temperatures

The development of stable, highly active, and inexpensive catalysts for the ozone catalytic oxidation of volatile organic compounds (VOCs) is challenging but of great significance. Herein, the micro-coordination environment of Al in commercial Y zeolite was regulated by a specific dealumination method and then the dealuminated Y zeolite was used as the support of Cu-Mn oxides. The optimized catalyst Cu-Mn/DY exhibited excellent performance with around 95% of toluene removal at 30 °C. Besides, the catalyst delivered satisfactory stability in both high-humidity conditions and long-term reactions, which is attributed to more active oxygen vacancies and acidic sites, especially the strong Lewis acid sites newly formed in the catalyst. The decrease in the electron cloud density around aluminum species enhanced electron transfer at the interface between Cu-Mn oxides. Moreover, extra-framework octahedrally coordinated Al in the support promoted the electronic metal-support interaction (EMSI). Compared with single Mn catalysts, the incorporation of the Cu component changed the degradation pathway of toluene. Benzoic acid, as the intermediate of toluene oxidation, can directly ring-open on Cu-doped catalysts rather than being further oxidized to other byproducts, which increased the rate of the catalytic reaction. This work provides a new insight and theoretical guidance into the rational design of efficient catalysts for the catalytic ozonation of VOCs.
FDD: a deep learning-based steel defect detectors

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.
Emerging approaches for decoding neuropeptide transmission

Neuropeptides produce robust effects on behavior across species, and recent research has benefited from advances in high-resolution techniques to investigate peptidergic transmission and expression throughout the brain in model systems. Neuropeptides exhibit distinct characteristics which includes their post-translational processing, release from dense core vesicles, and ability to activate G-protein-coupled receptors (GPCRs). These complex properties have driven the need for development of specialized tools that can sense neuropeptide expression, cell activity, and release. Current research has focused on isolating when and how neuropeptide transmission occurs, as well as the conditions in which neuropeptides directly mediate physiological and adaptive behavioral states. Here we describe the current technological landscape in which the field is operating to decode key questions regarding these dynamic neuromodulators.
Practical factors affecting the application of interactive environmental panorama art in diverse urban exploration: A case study of San Francisco

When touring multicultural cities, planning everything out in advance can enrich the visiting experience. Advances in the internet and computer graphics technology have resulted in the trend of using virtual tourism for trip planning. However, the mere presentation of virtual street views of a geographical location does not highlight its cultural elements and characteristics. The lack of imaginative space and extensive guidance result in the lack of aesthetic artistic visual stimulation and interest. Accordingly, this study developed a novel experience model and an experimental platform where images with panoramic oil painting art styles were used to provide participants with interactive tours that presented different cultural characteristics and elements. This platform enabled the re-creation of crucial scenic urban spots in spherical, panoramic, and three-dimensional spaces, which allowed the participants to explore and experience these spots, aroused their interest, and stimulated their senses. Moreover, timelines were used and the beauty of visual art was highlighted to achieve efficient virtual tourism. A total of 100 students were invited to experience the designed virtual tourism system and fill out questionnaires. After the participants operated the system, statistical analyses were conducted to determine the categories of and the factors that influenced participant assessment. The categories of participant assessment were assessment based on design elements, subjective assessment based on feelings, assessment based on multicultural exploration, and assessment based on system improvement expectations. Moreover, the eight factors influencing participant assessment were immersive presence, ease of use, connectivity of diverse cultures, content appealing, visual pleasure, interesting graphic design, adaptive UI, and UI affordance. A modular design model was used to perform project analyses, design relevant items, and assess the participants' experiences when visiting multicultural scenic spots. On the basis of the aforementioned steps, conclusions were drawn, and system design recommendations are proposed for platforms offering multicultural experiences.
EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy

Present architecture of convolution neural network for diabetic retinopathy (DR-Net) is based on normal convolution (NC). It incurs high computational cost as NC uses a multiplicative weight that measures a combined correlation in both cross-channel and spatial dimension of layer's inputs. This might cause the overall DR-Net architecture to be over-parameterised and computationally inefficient. This paper proposes EDR-Net a new end-to-end, DR-Net architecture with depth-wise separable convolution module. The EDR-Net architecture was trained with DRKaggle-train dataset (35,126 images), and tested on two datasets, i.e. DRKaggle-test (53,576 images) andMessidor-2 (1,748 images). Results showed that the proposed EDR-Net achieved predictive performance comparable with current state-of-the-arts in detecting referable diabetic retinopathy (rDR) from fundus images and outperformed other light weight architectures, with at least two times less computation cost. This makes it more amenable for mobile device based computer-assisted rDR screening applications.
Lateral and Vertical Scaling of In0.7Ga0.3As HEMTs for Post-Si-CMOS Logic Applications

In this paper, we have experimentally investigated the impact of lateral and vertical scaling of In0.7Ga0.3As high-electron-mobility transistors (HEMTs) onto their logic performance. We have found that reducing the In0.52Al0.48As insulator thickness results in much better electrostatic integrity and improved short-channel behavior down to a gate length of around 60 nm. Our nearly enhancement-mode 60-nm HEMTs feature V-T = -0.02 V, DIBL = 93 mV/V, S = 88 mV/V, and ION/ I-OFF = 1.6 x 10(4), at V-DD = 0.5 V. We also estimate a gate delay of CV/1 = 1.6 ps at VDD = 0.5 V. We have bench-marked these devices against state-of-the-art Si CMOS. For the same leakage current, which includes the gate leakage current, the InGaAs HEMTs exhibit 1.2X more current drive (ION) than the state-of-the-art 65-nm low-power CMOS technology at V-DD=0.5 V.
Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions

In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter-and intra-subject image registration.
Person Re-Identification by Iterative Re-Weighted Sparse Ranking

In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft-and hard-re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single-and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.
Artificially Layered CoSe2 Nanosheets by a Dual-Templating Strategy for High-Performance Lithium-Sulfur Batteries

Owing to the attractive merits of layered transition metal dichalcogenides (LTMDs) with van der Waals interactions, it is significant to modulate electronic structures and endow them with fascinating physiochemical properties by converting a nonlayered metal dichalcogenide into an atomic layered one. Herein, a dual-templating strategy is designed to prepare artificially layered CoSe2 nanosheets on carbon fiber cloth (L-CoSe2/CFC). It is found that not only the nanosheet morphology but also the layered structure is well inherited from the precursor of layered Co(OH)2 nanosheets through a wet-solution ion-exchange approach. The as-prepared L-CoSe2/CFC serves as an efficient multifunctional interlayer to solve the challenges of "shuttling effect" and slow multistep reaction kinetics in lithium-sulfur batteries (LSBs), thus dramatically improving their electrochemical performance. Benefiting from the L-CoSe2 nanosheets with large interlayer spacing, strong chemical adsorption, and superior catalytic activity, L-CoSe2/CFC promotes the anchoring of lithium polysulfides (LiPSs) and their catalytic conversion. Consequently, the L-CoSe2/CFC cell yields a large reversible capacity of 1584 mAh g-1 at 0.2C and a high rate capability of 987 mAh g-1 at 4C. A high areal capacity of 4.38 mAh cm-2 after 100 cycles at 0.2C is achieved for the high-S-loading LSB (4.6 mg cm-2) using the L-CoSe2/CFC interlayer.
Identification of genes associated with male sterility in a mutant of white birch (Betula platyphylla Suk.)

White birch (Betula platyphylla Suk.) is a monoecious tree species with unisexual flowers. In this study, we used a spontaneous mutant genotype that produced normal-like male (NLM) inflorescences and mutant male (MM) inflorescences at different locations within the tree to investigate the genes necessary for pollen development. A cDNA-amplified fragment length polymorphism (cDNA-AFLP) analysis was used to identify genes differentially expressed between the two types of inflorescences. Of approximately 5000 transcript-derived fragments (TDFs) obtained, 323 were significantly differentially expressed, of which 141 were successfully sequenced. BLAST analyses revealed 51.8% of the sequenced TDFs showed significant homology with proteins of known or predicted functions, 10.6% showed significant homology with putative proteins without any known or predicted function, and the remaining 37.6% had no hits in the NCBI database. Further, in a functional categorization based on the BLAST analyses, the protein fate, metabolism, energy categories had in order the highest percentages of the proteins. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the known TDFs were mainly involved in metabolic (28.4%), signal transduction (23.5%) and folding, sorting and degradation (13.6%) pathways. Ten genes from the NLM and MM development stages in the mutant were analyzed by quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR). The information generated in this study can provide some useful clues to help understand male sterility in B. platyphylla.