ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation

Segmentation is a fundamental task in biomedical image analysis. Unlike the existing region-based dense pixel classification methods or boundary-based polygon regression methods, we build a novel graph neural network (GNN) based deep learning framework with multiple graph reasoning modules to explicitly leverage both region and boundary features in an end-to-end manner. The mechanism extracts discriminative region and boundary features, referred to as initialized region and boundary node embeddings, using a proposed Attention Enhancement Module (AEM). The weighted links between cross-domain nodes (region and boundary feature domains) in each graph are defined in a data-dependent way, which retains both global and local cross-node relationships. The iterative message aggregation and node update mechanism can enhance the interaction between each graph reasoning module's global semantic information and local spatial characteristics. Our model, in particular, is capable of concurrently addressing region and boundary feature reasoning and aggregation at several different feature levels due to the proposed multi-level feature node embeddings in different parallel graph reasoning modules. Experiments on two types of challenging datasets demonstrate that our method outperforms state-of-the-art approaches for segmentation of polyps in colonoscopy images and of the optic disc and optic cup in colour fundus images. The trained models will be made available at: https://github.com/smallmax00/Graph_Region_Boudnary
Locally Affine Invariant Descriptors for Shape Matching and Retrieval

This work proposes novel locally affine invariant descriptors for shape representation. The descriptors are theoretically simple and solid, derived from the matrix theories. They can be used for matching and retrieval of shapes under affine transformation, articulated motion or nonrigid deformation. Comparisons of the work with the state-of-the-art shape descriptors are performed based on synthetic and some well-known databases. The experiments validate that the proposed descriptors achieve higher retrieval accuracy and have faster running speed than most of other approaches.
Rinderidine and oblongifolidine new pyrrolizidine alkaloids from Rindera oblongifolia M. Popov and their absolute configurations

The alkaloid composition of Rindera oblongifolia was studied, in which the pyrrolizidine alkaloids echinatine and trachelanthamine N-oxide, as well as two new quaternary salts namely rinderidine and the oblongifolidine were isolated. The structures of the isolated new alkaloids were elucidated by NMR spectroscopy. The absolute configuration of lindelofine, trachelanthamine N-oxide, rinderidine and oblongifolidine was established by single crystal X-ray diffraction as: 1 R, 4 R, 8 R, 2'S, 3'R; 1 R, 4S, 8S, 2'S, 3'R; 4 R, 7S, 8 R, 2'S, 3'S; 4 R, 7S, 8 R, 2'S, 3'S (7''S, 8''R) respectively. Both new pyrrolizidine alkaloids showed no cytotoxicity against four cancer cell lines such as HeLa, НEр-2, HBL-100 and CCRF-CEM.
Making Polypharmacy Safer for Children with Medical Complexity

A 14-year-old patient who has severe cerebral palsy and seizures, requested that his parents speak to his pediatrician about a medication to help with sleep. He already uses 13 other medications, including anticonvulsants, analgesics, and respiratory medications, and 5 additional as needed (PRN) medications (Figure 1). He has a vagal nerve stimulator and a gastrostomy tube. His parents had researched several sleep medications, and they were interested in discussing trazodone therapy for his sleep issues. Clinicians who prescribe medications to children with medical complexity (CMC) frequently must consider the question: How does one safely prescribe a patient like ours a new medication, like the sleep medication trazodone, amidst an already complex background of polypharmacy?
Antithrombotic therapy for durable left ventricular assist devices - current strategies and future directions

Left ventricular assist devices (LVADs) improve survival and quality of life for patients with advanced heart failure but are associated with high rates of thromboembolic and hemorrhagic complications. Antithrombotic therapy is required following LVAD implantation, though practices vary. Identifying a therapeutic strategy that minimizes the risks of thromboembolic and hemorrhagic complications is critical to optimizing patient outcomes and is an area of active investigation. This paper reviews strategies for initiating and maintaining antithrombotic therapy in durable LVAD recipients, focusing on those with centrifugal-flow devices.
A Nonlocal Poisson Denoising Algorithm Based on Stochastic Distances

In this letter, a new version of the Nonlocal-Means(NLM) algorithm based on stochastic distances is proposed for Poisson denoising. NLM estimates a noise-free pixel as a weighted average of image pixels, where each pixel is weighted according to the similarity between image patches. In this work, stochastic distances are used as a new similarity measure. We explored the use of four stochastic distances for which closed-form solutions were found for Poisson distribution. This approach was demonstrated to be competitive with related state-of-the-art methods.
A 65-nm CMOS Lossless Bio-Signal Compression Circuit With 250 FemtoJoule Performance Per Bit

A 65nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel 'xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250fJ/bit at continuous conversion data-rates of 5MSa/sec. This is achieved with a diminutive circuit area of 155um(2). Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization.
Reinforcement learning combined with a fuzzy adaptive learning control network (FALCON-R) for pattern classification

Reinforcement learning has been widely-used for applications in planning. control. and decision making. Rather than using instructive feedback as in supervised learning. reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a FALCON-R network. FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). This paper serves as a guideline in formulating a classification problem as a reinforcement learning problem using FALCON-R. The strengths of applying the reinforcement learning method to the pattern classification application are demonstrated. We show that such a system can converge faster. is able to escape from local minima. and has excellent disturbance rejection capability. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new maximum a posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the-art algorithms.
SLAP: Simpler, Improved Private Stream Aggregation from Ring Learning with Errors

Private Stream Aggregation (PSA) protocols perform secure aggregation of time-series data without leaking information about users' inputs to the aggregator. Previous work in post-quantum PSA used the Ring Learning with Errors (RLWE) problem indirectly via homomorphic encryption (HE), leading to a needlessly complex and intensive construction. In this work, we present SLAP, the first PSA protocol that is directly constructed from the RLWE problem to gain post-quantum security. By nature of our white-box approach, SLAP is simpler and more efficient than previous PSA that uses RLWE indirectly through the black box of HE. We also show how to apply stateof-the-art optimizations for lattice-based cryptography to greatly improve the practical performance of SLAP. The communication overhead of SLAP is much less than in previous work, with decreases of up to 99.96% in ciphertext sizes as compared to previous work in RLWE-based PSA. We demonstrate a speedup of 20.76x over the previous state-of-the-art RLWE-based PSA work's aggregation and show that SLAP achieves a throughput of 390,691 aggregations per second for 1000 users. We also compare SLAP to other state-of-the-art post-quantum PSA and showthat SLAP is comparable in latency and shows improvement in throughput when compared to these works, and we compare the qualitative features of these schemes with regards to practical usability.
Modeling of a Novel Atmospheric SOFC/GT Hybrid Process and Comparison with State-of-the-Art SOFC System Concepts

A novel concept for an atmospheric solid oxide fuel cell (SOFC) hybrid system applying a sub-atmospheric gas turbine is proposed. Based on a developed process model suitable operating conditions are studied. The resulting performance is compared to different state-of-the-art SOFC system concepts. The comparison shows that pressurized SOFC/GT systems offer the highest electric efficiency. The novel concept as well as a two-stage SOFC system offer lower efficiencies, but still well above 60%. However, avoiding the complex system requirements of pressurized SOFC/GT systems, these concepts are promising for highly efficient electricity generation.
Differential expression and roles of Huntingtin and Huntingtin-associated protein 1 in the mouse and primate brains

Huntingtin-associated protein 1 (HAP1) is the first identified protein whose function is affected by its abnormal interaction with mutant huntingtin (mHTT), which causes Huntington disease. However, the expression patterns of Hap1 and Htt in the rodent brain are not correlated. Here we found that the primate HAP1, unlike the rodent Hap1, is correlatively expressed with HTT in the primate brains. CRISPR/Cas9 targeting revealed that HAP1 deficiency in the developing human neurons did not affect neuronal differentiation and gene expression as seen in the mouse neurons. However, deletion of HAP1 exacerbated neurotoxicity of mutant HTT in the organotypic brain slices of adult monkeys. These findings demonstrate differential HAP1 expression and function in the mouse and primate brains, and suggest that interaction of HAP1 with mutant HTT may be involved in mutant HTT-mediated neurotoxicity in adult primate neurons.
An effective 3-D fast fourier transform framework for multi-GPU accelerated distributed-memory systems

This paper introduces an efficient and flexible 3D FFT framework for state-of-the-art multi-GPU distributed-memory systems. In contrast to the traditional pure MPI implementation, the multi-GPU distributed-memory systems can be exploited by employing a hybrid multi-GPU programming model that combines MPI with OpenMP to achieve effective communication. An asynchronous strategy that creates multiple streams and threads to reduce blocking time is adopted to accelerate intra-node communication. Furthermore, we combine our scheme with the GPU-Aware MPI implementation to perform GPU-GPU data transfers without CPU involvement. We also optimize the local FFT and transpose by creating fast parallel kernels to accelerate the total transform. Results show that our framework outperforms the state-of-the-art distributed 3D FFT library, being up to achieve 2x faster in a single node and 1.65x faster using two nodes.
Pigeon pea penta- and hexapeptides with antioxidant properties also inhibit renin and angiotensin-I-converting enzyme activities

Pigeon pea protein was sequentially digested with pepsin followed by pancreatin and the hydrolysate separated into 18 fractions using reversed-phase high-performance liquid chromatography. Fractions were analyzed for in vitro antioxidant properties (radical scavenging, metal chelation, and ferric iron reducing ability) in addition to inhibition of renin and angiotensin-converting enzyme (ACE). The most active fractions were analyzed by mass spectroscopy followed by identification of 10 peptide sequences (7 pentapeptides and 3 hexapeptides). All the peptides showed a wide range of multifunctional activity by scavenging hydroxyl (31.9-66.8%) and superoxide (25.6-100.0%) radicals in addition to ACE inhibition (7.4-100%) with significant (p < .05) differences between the peptides. AGVTVS, TKDIG, TSRLG, GRIST, and SGEKI were the most active; however, AGVTVS had the highest hydrophobic residue and exhibited the strongest activity against ACE, renin as well as superoxide and hydroxyl radicals. PRACTICAL APPLICATIONS: There is an increasing attraction of researchers to food peptides especially from legume proteins. Enzymatic digestion as well as high performance liquid chromatography (HPLC) purification has become an important process used to separate peptides with significant biological activities and health-promoting effects. There is useful information regarding the bioactive and functional (in vitro antioxidant, antidiabetic, in vitro/in vivo antihypertensive) properties of hydrolyzed and ultra-filtered pigeon pea fractions but scant research output still exists for purified peptides from pigeon pea establishing their therapeutic potential. The present study aimed to separate peptide fractions from pigeon pea hydrolysate and identify available amino acid sequences from the parent protein. Therefore, peptide sequences generated from the most bioactive fractions showed prospects for the expanded industrial utilization of pigeon pea. Further promoting its application as functional ingredient or additive for alleviating angiotensin-converting enzyme-related diseases.
Stacked Sequential Scale-Space Taylor Context

We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.
Restricted random testing: Adaptive random testing by exclusion

Restricted Random Testing (RRT) is a new method of testing software that improves upon traditional Random Testing (RT) techniques. Research has indicated that failure patterns (portions of an input domain which, when executed, cause the program to fail or reveal an error) can influence the effectiveness of testing strategies. For certain types of failure patterns, it has been found that a widespread and even distribution of test cases in the input domain can be significantly more effective at detecting failure compared with ordinary RT. Testing methods based on RT, but which aim to achieve even and widespread distributions, have been called Adaptive Random Testing (ART) strategies. One implementation of ART is RRT. RRT uses exclusion zones around executed, but non-failure-causing, test cases to restrict the regions of the input domain from which subsequent test cases may be drawn. In this paper, we introduce the motivation behind RRT, explain the algorithm and detail some empirical analyses carried out to examine the effectiveness of the method. Two versions of RRT are presented: Ordinary RRT (ORRT) and Normalized RRT (NRRT). The two versions share the same fundamental algorithm, but differ in their treatment of non-homogeneous input domains. Investigations into the use of alternative exclusion shapes are outlined, and a simple technique for reducing the computational overheads of RRT, prompted by the alternative exclusion shape investigations, is also explained. The performance of RRT is compared with RT and another ART method based on maximized minimum test case separation (DART), showing excellent improvement over RT and a very favorable comparison with DART.
Low-recovery, -energy-consumption, -emission hybrid systems of seawater desalination: Energy optimization and cost analysis

Ultraconcentrated seawater disposal is detrimental to marine biota, and carbon emissions from desalination processes are detrimental to the atmosphere. These detrimental effects are expected to increase, given the continuously growing global water demand and the associated water stress problems caused by water scarcity and population and economic growth. Along with political inclination to impose strict environmental regulations and a carbon tax on the price of freshwater, developing low-energy-consumption, low-carbon-emission desalination systems operating at low recovery is the future of SW desalination. Such desalination systems can be achieved by integrating electrodialysis, which does not have driving force limitations, with nanofiltration and brackish water reverse osmosis (RO), which provide low-energy-consumption desalination regions, to decrease energy consumption below that of current state-of-the-art RO systems. In this study, iterative optimization algorithms were developed for hybrid desalination systems. As a result, energy consumptions as low as 1.3 kWh/m(3) were achieved at recoveries < 30%. Despite the higher cost of freshwater production compared with that of state-of-the-art RO systems, owing to utilization of larger membrane areas, the hybrid systems reduced carbon dioxide emissions and brine concentrations from 63 to 26 and 41 to 34%, respectively.
A Probabilistic Model for Automatic Segmentation of the Esophagus in 3-D CT Scans

Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a "detect and connect" approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods.
Endoplasmic reticulum stress in diabetic mouse or glycated LDL-treated endothelial cells: protective effect of Saskatoon berry powder and cyanidin glycans

Endoplasmic reticulum (ER) stress is associated with insulin resistance and diabetic cardiovascular complications, and mechanism or remedy for ER stress remains to be determined. The results of the present study demonstrated that the levels of ER stress or unfolded protein response (UPR) markers, the intensity of thioflavin T (ThT) fluorescence and the abundances of GRP78/94, XBP-1 and CHOP proteins were elevated in cardiovascular tissue of diabetic leptin receptor-deficient (db/db) mice. Cyanidin-3-glucoside (C3G) and cyanidin-3-galactoside (C3Ga) are major anthocyanins in Saskatoon berry (SB) powder. The administration of 5% SB powder for 4 weeks attenuated ThT fluorescence and the UPR markers in hearts and aortae of wild-type and db/db mice. Treatment with glycated low-density lipoprotein (gLDL) increased ThT intensity in human umbilical vein endothelial cells (ECs). Elevated UPR markers were detected in gLDL-treated EC compared to control cultures. The involvement of ER stress in gLDL-treated EC was supported by that the addition of 4-phenyl butyrate acid (a known ER stress antagonist) inhibited gLDL-induced increases in ER stress or UPR markers. C3G at 30 μM or C3Ga at 100 μM reached their maximal inhibition on gLDL-induced increases in ThT, GRP78/94, XBP-1 and CHOP in EC. The results demonstrated that ER stress was enhanced in cardiovascular tissue of db/db mice or gLDL-treated EC. SB powder or cyanidin glycans prevented the abnormal increases in ER stress and UPR markers in cardiovascular tissue of diabetic db/db mice or gLDL-treated EC.