ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The Brakeless co-regulator can directly activate and repress transcription in early Drosophila embryos

The Brakeless protein performs many important functions during Drosophila development, but how it controls gene expression is poorly understood. We previously showed that Brakeless can function as a transcriptional co-repressor. In this work, we perform transcriptional profiling of brakeless mutant embryos. Unexpectedly, the majority of affected genes are down-regulated in brakeless mutants. We demonstrate that genomic regions in close proximity to some of these genes are occupied by Brakeless, that over-expression of Brakeless causes a reciprocal effect on expression of these genes, and that Brakeless remains an activator of the genes upon fusion to an activation domain. Together, our results show that Brakeless can both repress and activate gene expression. A yeast two-hybrid screen identified the Mediator complex subunit Med19 as interacting with an evolutionarily conserved part of Brakeless. Both down- and up-regulated Brakeless target genes are also affected in Med19-depleted embryos, but only down-regulated targets are influenced in embryos depleted of both Brakeless and Med19. Our data provide support for a Brakeless activator function that regulates transcription by interacting with Med19. We conclude that the transcriptional co-regulator Brakeless can either activate or repress transcription depending on context.
Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors

Attenuation correction for positron-emission tomography (PET)/magnetic resonance(MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 +/- 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 +/- 1.15 dB, a relative MAE of 1.56 +/- 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 +/- 0.107% in D-98%, as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.
The application of clustering analysis for the critical areas on TFT-LCD panel

For thin film transistor-liquid crystal displays (TFT-LCD) factories in Taiwan, yield performance had become as an important competitiveness determinant during the competitive environment. As we known, the market for LCDs has grown at over 20% on average per annum and the downward pricing trend had also promoted LCD applications. However, only few studies were proposed to address the related issues for process analysis in TFT-LCD industry from the viewpoint of systems. Particularly, the defect status (i.e. abnormal position) on TFT-LCD panel may represent the clustering effect when there are many defect counts on it. Hence, performing the clustering analysis for those abnormal positions will be an important issue to be addressed in TFT-LCD process. In this study, we will propose an approach incorporating fuzzy adaptive resonance theory (Fuzzy ART) and stepwise regression techniques to achieve such process analysis. Besides, an illustrative case owing to TFT-LCD manufacturer at Tainan Science Park in Taiwan will be applied to verifying the rationality and feasibility of our proposed procedure. (c) 2006 Elsevier Ltd. All rights reserved.
Next generation grid storage "HYDRAstor"

HYDRAstor is a grid storage product developed in accordance with the NEC's three REAL IT PLATFORM concepts of "Flexibility," "Security" and "Comfort," in order to resolve serious issues surrounding storage in recent years. This paper introduces the state-of-the-art grid storage architecture of HYDRAstor and introduces its core technologies, Dynamic Topology, DataRedux and Distributed Resilient Data.
Mobility scaling in short-channel length strained Ge-on-insulator P-MOSFETs

The hole transport characteristics in partially strained (0.5%) Ge p-channel MOSFETs formed on silicon-germanium-on-insulator (SGOI) substrates were investigated for gate lengths down to 65 min. We demonstrate that high hole mobility is maintained down to the shortest channel lengths. The channel conductance from these devices is measured and compared to state-of-the-art high-performance Si channel P-MOSFETs.
Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography

Computed tomography (CT) is widely used to locate pulmonary nodules for preliminary diagnosis of the lung cancer. However, due to high visual similarities between malignant (cancer) and benign (non-cancer) nodules, distinguishing malignant from malign nodules is not an easy task for a thoracic radiologist. In this paper, a novel convolutional neural network (ConvNet) architecture is proposed to classify the pulmonary nodules as either benign or malignant. Due to the high variance of nodule characteristics in CT scans, such as size and shape, a multi-path, multi-scale architecture is proposed and applied in the proposed ConvNet to improve the classification performance. The multi-scale method utilizes filters with different sizes to more effectively extracted nodule features from local regions, and the multi-path architecture combines features extracted from different ConvNet layers thereby enhancing the nodule features with respect to global regions. The proposed ConvNet is trained and evaluated on the LUNGx Challenge database, and achieves a sensitivity of 0.887 and a specificity of 0.924 with an area under the curve (AUC) of 0.948. The proposed ConvNet achieves a 14% AUC improvement compared to the state-of-the-art unsupervised learning approach. The proposed ConvNet also outperforms the other state-of-the-art ConvNets explicitly designed for pulmonary nodule classification. For clinical usage, the proposed ConvNet could potentially assist the radiologists to make diagnostic decisions in CT screening.
Image fusion method based on the advection equation

We present an innovative method based on the linear advection equation, an important partial differential equation, to perform the fusion of images. The basic idea of this method is to insert the relevant information from other source images into the current source image through an advection process. Furthermore, we present the discrete scheme of this model and compare it with classical fusion approaches, the diffusion equation-based method, and some state-of-the-art fusion approaches on three groups of fusion images that are often used in the image fusion research. The results of experiments show that the fusion method based on the advection equation is comparable with the best of the classical, diffusion-based, and state-of-the-art methods. The high "weighted performance metric" Q(AB/F) of fused images certifies that the relevant information is well injected from the input to the output images. Moreover, this method has fewer adjustable parameters with settings that affect the metric Q(AB/F) less than other methods, and the evolution from input to output is also faster than the diffusion-based method. In addition, this model allows us to cope with noisy source image fusion by adding a diffusion term in the equation, thereby combining the denoising process with the fusion process. (C) 2014 SPIE and IS&T
Investigating the effects of demand flexibility on electricity retailers' business through a tri-level optimisation model

The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state-of-the-art, bi-level optimisation modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This study proposes a tri-level optimisation model, which drops this assumption and represents the wholesale market clearing process endogenously, thus capturing the realistic implications of a retailer's pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is three-fold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers' time-shifting flexibility on the retailer's revenue from the consumers, its cost in the wholesale market and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer's relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri-level model by comparing its outcomes against the state-of-the-art bi-level modelling approach.
Promoting microbiology education through the iGEM synthetic biology competition

Synthetic biology has developed rapidly in the 21st century. It covers a range of scientific disciplines that incorporate principles from engineering to take advantage of and improve biological systems, often applied to specific problems. Methods important in this subject area include the systematic design and testing of biological systems and, here, we describe how synthetic biology projects frequently develop microbiology skills and education. Synthetic biology research has huge potential in biotechnology and medicine, which brings important ethical and moral issues to address, offering learning opportunities about the wider impact of microbiological research. Synthetic biology projects have developed into wide-ranging training and educational experiences through iGEM, the International Genetically Engineered Machines competition. Elements of the competition are judged against specific criteria and teams can win medals and prizes across several categories. Collaboration is an important element of iGEM, and all DNA constructs synthesized by iGEM teams are made available to all researchers through the Registry for Standard Biological Parts. An overview of microbiological developments in the iGEM competition is provided. This review is targeted at educators that focus on microbiology and synthetic biology, but will also be of value to undergraduate and postgraduate students with an interest in this exciting subject area.
Low-Complexity Linear Zero-Forcing for the MIMO Broadcast Channel

Maximizing the sum capacity in the multiple-input multiple-output (MIMO) broadcast channel requires the use of dirty paper coding (DPC). However, practical implementations of DPC which are nearly optimum exhibit high computational complexity. As an alternative to DPC linear zero-forcing can be used where the multiuser interference is completely canceled by linear beamforming. Determining the optimum user allocation, transmit and receive filters thereby constitutes a combinatorial and nonconvex optimization problem. To circumvent its direct solution and therefore reduce complexity, we propose an algorithm that successively allocates data streams to users and, in contrast to state-of-the-art approaches, includes the receive filters into the optimization. We then show several steps that reduce the complexity of the algorithm at marginal performance losses. Thus, performance of state-of-the-art approaches can be maintained while the computational complexity is reduced considerably, as it is shown by a detailed complexity analysis and simulation results.
Delay-dependent forgetting in object recognition and object location test is dependent on strain and test

The object recognition and object location task (ORT and OLT, respectively) have been applied in preclinical research to evaluate the effects of treatments on memory. Although both tasks look quite similar, they differ with respect to the brain structures involved in the memory performance. The characterization of the memory performance in both tasks is important to understand treatment effects. Since there are no previous studies that compared strain differences in delay-dependent forgetting in both tasks, Wistar and Long Evans rats were tested in both the ORT and the OLT at different intervals. The data showed that in the ORT the delay-dependent forgetting was similar for Wistar and Long Evans rats. However, the forgetting curve was different for both strains in the OLT: the Long Evans rats the forgetting took a longer interval. This study indicates that delay-dependent forgetting in the ORT and OLT is strain and test dependent. It is suggested that before testing treatments the forgetting curve of a specific strain should be tested in this type of tasks.
Research on Open Practice Teaching of Off-Campus Art Appreciation Based on ICT

Art appreciation is an effective way to promote artistic literacy and is also an important component of aesthetic education in school. With the help of information and communication technology, the authors organized open practice teaching for students to learn art appreciation outside school. During the COVID-19 epidemic, local art appreciation education could not be carried out in the city where the authors' school is located. With the support of mobile positioning technology and information platforms, students were able to carry out 32 art appreciation activities in their hometowns during this period. Through the mobile positioning information submitted by students, feedback questionnaires, after-view data, and other data, learning achievements were identified. A correlation analysis of the data submitted by the students on the information platform confirmed that satisfaction with the art appreciation activity directly affected their interest in art. The correlation reached 0.78. Satisfaction was strongly correlated with psychological expectations (0.67) and art information obtained in the early stage (0.61). The authors propose that using information and communication technology to carry out art appreciation education outside the school is the way to promote the sustainable development of aesthetic education in school.
Predict, Share, and Recycle Your Way to Low-power Nanophotonic Networks

High static power consumption is widely regarded as one of the largest bottlenecks in creating scalable optical NoCs. The standard techniques to reduce static power are based on sharing optical channels and modulating the laser. We show in this article that state-of-the-art techniques in these areas are suboptimal, and there is a significant room for further improvement. We propose two novel techniques-a neural network-based method for laser modulation by predicting optical traffic and a distributed and altruistic algorithm for channel sharing-that are significantly closer to a theoretically ideal scheme. In spite of this, a lot of laser power still gets wasted. We propose to reuse this energy to heat micro-ring resonators (achieve thermal tuning) by efficiently recirculating it. These three methods help us significantly reduce the energy requirements. Our design consumes 4.7x lower laser power as compared to other state-of-the-art proposals. In addition, it results in a 31% improvement in performance and 39% reduction in ED2 for a suite of Splash2 and Parsec benchmarks.
Enhanced Microcystis Aeruginosa removal and novel flocculation mechanisms using a novel continuous co-coagulation flotation (CCF)

Co-coagulation flotation (CCF) is a novel flotation technology that renders more efficient algal removal compared to traditional mechanical coagulation flotation (MCF) due to a short residence time (< 30 s) and fast rising behavior of algal flocs (> 250 m·h-1). This study compared the algal removal performance using continuous CCF and MCF using water samples taken from Lake Dianchi with severe Microcystis aeruginosa blooms. Removal efficiency, dosage of coagulant/flocculant, rising velocity and structural characteristics of the resulting flocs in the two processes were systematically compared. The results show that CCF could save >50 % polyaluminum chloride (PAC) and polyacrylamide (PAM) compared with MCF when the removal efficiency was both over 95 %. The average rising velocity of flocs in CCF could reach 254.3 m·h-1, much higher than that in MCF (154.5 m·h-1). In the respective optimal coagulation conditions, the flocs formed in CCF (G = 164.8 s-1) were larger (1843 ± 128 μm) and more spherical with a higher fractal dimension (Df = 1.85 ± 0.01) than those generated in MCF (G = 34.1 s-1). The Stokes's Law was found to correctly predict the rising velocity of spherical flocs with large fractal dimensions (Df > 1.7). In contrast, the Haarhoff and Edzwald's extended equation was more suitable for calculating the rising velocity of irregular flocs with small fractal dimension. This study provides new insights into the mechanisms of the enhanced algal removal by CCF and lays foundation for developing cost-efficient algal mitigation processes.
Cryptanalysis of MD2

This paper considers the hash function MD2 which was developed by Ron Rivest in 1989. Despite its age, MD2 has withstood cryptanalytic attacks until recently. This paper contains the state-of-the-art cryptanalytic results on MD2, in particular collision and preimage attacks on the full hash function, the latter having complexity 2(73), which should be compared to a brute-force attack of complexity 2(128).
ATLANTIS: A benchmark for semantic segmentation of waterbody images

Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water-related categories prevents researchers from studying water-related issues in the computer vision field. To tackle this problem, we present ATLANTIS, a new benchmark for semantic segmentation of waterbodies and related objects. ATLANTIS consists of 5,195 images of waterbodies, as well as high quality pixel-level manual annotations of 56 classes of objects, including 17 classes of man-made objects, 18 classes of natural objects and 21 general classes. We analyze ATLANTIS in detail and evaluate several state-of-the-art semantic segmentation networks on our benchmark. In addition, a novel deep neural network, AQUANet, is developed for waterbody semantic segmentation by processing the aquatic and non-aquatic regions in two different paths. AQUANet also incorporates low-level feature modulation and cross-path modulation for enhancing feature representation. Experimental results show that the proposed AQUANet outperforms other state-of-the-art semantic segmentation networks on ATLANTIS. We claim that ATLANTIS is the largest waterbody image dataset for semantic segmentation providing a wide range of water and water-related classes and it will benefit researchers of both computer vision and water resources engineering.
Algorithm and Architecture Co-Design of Hardware-Oriented, Modified Diamond Search for Fast Motion Estimation in H.264/AVC

In this paper, we present a new hardware-oriented, modified diamond search (HMDS) algorithm, for fast integer pel, motion estimation in H. 264/AVC. We also present our co-designed, low power very large scale integration (VLSI) architecture for HMDS. The goal of HMDS is to enable the support of high quality video on low power mobile devices and low bit rate applications which typically use H.264/AVC baseline profile at levels 1-2. Our experiments use standard test sequences ranging from QCIF to high-definition 1280 x 720p video. The proposed VLSI architecture is prototyped as an field-programable gate array (FPGA)-based field programable system-on- chip. Our results show that HMDS on average has better rate-distortion performance and speedup, compared to previous state-of-the-art fast motion estimation algorithms, while its losses compared to full search motion estimation, are insignificant. Our prototyped architecture is more hardware-efficient than previous FPGA-based architectures in terms of power consumption, area, throughput, and memory utilization. We also show that its performance in terms of maximum frequency, minimum frequency, transistor count, and power consumption are comparable to that of state-of-the-art architectures implemented on application-specific integrated circuits.
A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.</p>
o-Quinodimethane Atropisomers: Enantioselective Synthesis and Stereospecific Transformation

o-Quinodimethanes have remarkable utility as reactive intermediates in Diels-Alder reactions, enabling significantly accelerated routes to complex polycyclic compounds. The discovery of different discrete precursors to thermally generate o-quinodimethanes thereby greatly augmented their availability and versatility. However, due to the required high temperatures and the immense reactivity of o-quinodimethanes, stereoselectivity to afford isomerically defined products still constitutes a critical challenge. Herein, we describe the accessibility of atropisomeric o-quinodimethanes, the enantioselective synthesis of their precursors, their remarkable configurational stability and the stereospecific transformation by the benzannulation of dienophiles. A catalyst-stereocontrolled [2+2+2] cycloaddition, the generation of o-quinodimethane atropisomers and ensuing stereospecific Diels-Alder reactions enabled enantioselectivities through these transient intermediates with of up to 96 : 4 e.r.
[Exploration and Thinking on Fine Management of Cost Accounting of Medical Consumables]

With the gradual advancement of The Reform Plan to Control High-value Medical Consumables published by the State Council, the reform policies such as purchase with quantity, charging consumables" zero bonus" were born, the operating pressure of medical institutions on medical consumables increased sharply, and the fine cost accounting management demands were improving. Due to the manage features of medical consumables, this will lead to the inaccurate and cross-cycle of cost accounting. In order to achieve the refined cost accounting management, the related business system and process adjustment are studied.