ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
An efficient approach for sub-image separation from large-scale multi-panel images using dynamic programming

Multi-panel images are increasingly used in research and medical domains for describing complicated situations like results' comparison in paper; or case depiction of a patient by combining all his medical images into a consolidated image. However, the content based image retrieval (CBIR) systems face the issue of performance decline in terms of poor retrieval accuracy because the individual sub-images of the multi-panel images cannot be accessed during the searching process. Representing multi-panel images in the form of sub-images is a necessary step for improving the retrieval accuracy of CBIR systems. The state-of-the-art multi-panel image segmentation approaches use recursive approach for sub-image separation, which detects the location of the sub-lines of a line in the multi-panel image appearing in its sub-images repeatedly. This characteristic of the available approaches makes the CBIR incapable to provide the intended results to the end users in real time. In this work, a line detection-based method using dynamic programming is proposed for sub-image separation, which detects the position of every line in the multi-panel image only once, instead of several times as in the case of state-of-art approaches. We evaluated the proposed method on a subset of the imageCLEFmed 2013 dataset, containing 1050 images belonging to different groups. The experimental results depict the effectiveness of the proposed method in term of generating the results quickly without losing the accuracy as compare to the state-of-the-art approaches.
Incremental learning of human activity models from videos

Learning human activity models from streaming videos should be a continuous process as new activities arrive over time. However, recent approaches for human activity recognition are usually batch methods, which assume that all the training instances are labeled and present in advance. Among such methods, the exploitation of the inter-relationship between the various objects in the scene (termed as context) has proved extremely promising. Many state-of-the-art approaches learn human activity models continuously but do not exploit the contextual information. In this paper, we propose a novel framework that continuously learns both of the appearance and the context models of complex human activities from streaming videos. We automatically construct a conditional random field (CRF) graphical model to encode the mutual contextual information among the activities and the related object attributes. In order to reduce the amount of manual labeling of the incoming instances, we exploit active learning to select the most informative training instances with respect to both of the appearance and the context models to incrementally update these models. Rigorous experiments on four challenging datasets demonstrate that our framework outperforms state-of-the-art approaches with significantly less amount of manually labeled data. (C) 2015 Elsevier Inc. All rights reserved.
Low-Dimensional Shaping for High-Dimensional Lattice Codes

We propose two low-complexity lattice code constructions that have competitive coding and shaping gains. The first construction, named systematic Voronoi shaping, maps short blocks of integers to the dithered Voronoi integers, which are dithered integers that are uniformly distributed over the Voronoi region of a low-dimensional shaping lattice. Then, these dithered Voronoi integers are encoded using a high-dimensional lattice retaining the same shaping and coding gains of low-and high-dimensional lattices. A drawback to this construction is that there is no isomorphism between the underlying message and the lattice code, preventing its use in applications such as compute-and-forward. Therefore, we propose a second construction, called mixed nested lattice codes, in which a high-dimensional coding lattice is nested inside a concatenation of low-dimensional shaping lattices. This construction not only retains the same shaping/coding gains as first construction but also provides the desired algebraic structure. We numerically study these methods, for point-to-point channels as well as compute-and-forward using low-density lattice codes as coding lattices and E-8 and Barnes-Wall as shaping lattices. Numerical results indicate a shaping gain of up to 0.86 dB, compared with the state-of-the-art of 0.4 dB; furthermore, the proposed method has lower complexity than the state-of-the-art approaches.
Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments

This paper presents and analyzes an alternative formulation of the locally low-rank (LLR) regularization framework formagnetic resonance image (MRI) reconstruction. Generally, LLR-based MRI reconstruction techniques operate by dividing the underlying image into a collection of matrices formed from image patches. Each of these matrices is assumed to have low rank due to the inherent correlations among the data, whether along the coil, temporal, or multi-contrast dimensions. The LLR regularization has been successful for various MRI applications, such as parallel imaging and accelerated quantitative parameter mapping. However, a major limitation of most conventional implementations of the LLR regularization is the use of multiple sets of overlapping patches. Although the use of overlapping patches leads to effective shift-invariance, it also results in high-computational load, which limits the practical utility of the LLR regularization for MRI. To circumvent this problem, alternative LLR-based algorithms instead shift a single set of non-overlapping patches at each iteration, thereby achieving shift-invariance and avoiding block artifacts. A novel contribution of this paper is to provide a mathematical framework and justification of LLR regularization-with iterative random patch adjustments (LLR-IRPA). This method is compared with a state-of-the-art LLR regularization algorithm based on overlapping patches, and it is shown experimentally that results are similar but with the advantage of much reduced computational load. We also present theoretical results demonstrating the effective shift invariance of the LLR-IRPA approach, and we show reconstruction examplesand comparisons in both retrospectively and prospectively undersampled MRI acquisitions, and in T1 parameter mapping.
Laser beam machining (LBM), state of the art and new opportunities

An overview is given of the state of the art of laser beam machining in general with special emphasis on applications of short and ultrashort lasers. In laser welding the trend is to apply optical sensors for process control. Laser surface treatment is mostly used to apply corrosion and wear resistant layers, but also for repair of engine and machine parts. In micro-machining, shorter pulses reduce heat-affected damage of the material and opens new ways for nanometer accuracy. Even 40 years after the development of the laser there is a lot of effort in developing new and better performing lasers. The driving force is higher accuracy at reasonable cost, which is realised by compact systems delivering short laser pulses of high beam quality. Another trend is the shift towards shorter wavelengths, which are better absorbed by the material and which allows smaller feature sizes to be produced. Examples of new products, which became possible by this technique, are given. The trends in miniaturisation as predicted by Moore and Taniguchi are expected to continue over the next decade too thanks to short and ultrashort laser machining techniques. (C) 2004 Elsevier B.V. All rights reserved.
Intense long-term training impairs brain health compared with moderate exercise: Experimental evidence and mechanisms

The consequences of extremely intense long-term exercise for brain health remain unknown. We studied the effects of strenuous exercise on brain structure and function, its dose-response relationship, and mechanisms in a rat model of endurance training. Five-week-old male Wistar rats were assigned to moderate (MOD) or intense (INT) exercise or a sedentary (SED) group for 16 weeks. MOD rats showed the highest motivation and learning capacity in operant conditioning experiments; SED and INT presented similar results. In vivo MRI demonstrated enhanced global and regional connectivity efficiency and clustering as well as a higher cerebral blood flow (CBF) in MOD but not INT rats compared with SED. In the cortex, downregulation of oxidative phosphorylation complex IV and AMPK activation denoted mitochondrial dysfunction in INT rats. An imbalance in cortical antioxidant capacity was found between MOD and INT rats. The MOD group showed the lowest hippocampal brain-derived neurotrophic factor levels. The mRNA and protein levels of inflammatory markers were similar in all groups. In conclusion, strenuous long-term exercise yields a lesser improvement in learning ability than moderate exercise. Blunting of MOD-induced improvements in CBF and connectivity efficiency, accompanied by impaired mitochondrial energetics and, possibly, transient local oxidative stress, may underlie the findings in intensively trained rats.
Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases

There is ongoing research for the automatic diagnosis of Alzheimer's disease (AD) based on traditional machine learning techniques, and deep learning-based approaches are becoming a popular choice for AD diagnosis. The state-of-the-art techniques that consider multimodal diagnosis have been shown to have accuracy better than a manual diagnosis. However, collecting data from different modalities is time-consuming and expensive, and some modalities may have radioactive side effects. Our study is confined to structural magnetic resonance imaging (sMRI). The objectives of our attempt are as follows: 1) to increase the accuracy level that is comparable to the state-of-the-art methods; 2) to overcome the overfitting problem, and; 3) to analyze proven landmarks of the brain that provide discernible features for AD diagnosis. Here, we focused specifically on both the left and right hippocampus areas. To achieve the objectives, at first, we incorporate ensembles of simple convolutional neural networks (CNNs) as feature extractors and softmax cross-entropy as the classifier. Then, considering the scarcity of data, we deployed a patch-based approach. We have performed our experiment on the Gwangju Alzheimer's and Related Dementia (GARD) cohort dataset prepared by the National Research Center for Dementia (GARD), Gwangju, South Korea. We manually localized the left and right hippocampus and fed three view patches (TVPs) to the CNN after the preprocessing steps. We achieve 90.05% accuracy. We have compared our model with the state-of-the-art methods on the same dataset they have used and found our result comparable.
The impact of targeted ablation of one row of outer hair cells and Deiters' cells on cochlear amplification

The mammalian cochlea contains three rows of outer hair cells (OHCs) that amplify the basilar membrane traveling wave with high gain and exquisite tuning. The pattern of OHC loss caused by typical methods of producing hearing loss in animal models (noise, ototoxic exposure, or aging) is variable and not consistent along the length of the cochlea. Thus, it is difficult to use these approaches to understand how forces from multiple OHCs summate to create normal cochlear amplification. Here, we selectively removed the third row of OHCs and Deiters' cells in adult mice and measured cochlear amplification. In the mature cochlear epithelia, expression of the Wnt target gene Lgr5 is restricted to the third row of Deiters' cells, the supporting cells directly underneath the OHCs. Diphtheria toxin administration to Lgr5DTR-EGFP/+ mice selectively ablated the third row of Deiters' cells and the third row of OHCs. Basilar membrane vibration in vivo demonstrated disproportionately lower reduction in cochlear amplification by about 13.5 dB. On a linear scale, this means that the 33% reduction in OHC number led to a 79% reduction in gain. Thus, these experimental data describe the impact of reducing the force of cochlear amplification by a specific amount. Furthermore, these data argue that because OHC forces progressively and sequentially amplify the traveling wave as it travels to its peak, the loss of even a relatively small number of OHCs, when evenly distributed longitudinally, will cause a substantial reduction in cochlear amplification.NEW & NOTEWORTHY Normal cochlear physiology involves force production from three rows of outer hair cells to amplify and tune the traveling wave. Here, we used a genetic approach to target and ablate the third row of outer hair cells in the mouse cochlea and found it reduced cochlear amplification by 79%. This means that the loss of even a relatively small number of OHCs, when evenly distributed, causes a substantial reduction in cochlear amplification.
Design Methodology to Explore Hybrid Approximate Adders for Energy-Efficient Image and Video Processing Accelerators

This paper proposes a new design methodology to explore the state-of-the-art approximate adders for accelerator architectures conceived in the realm of multiplier-less multiple constant multiplication optimization problem. The proposed methodology is composed of: 1) a search heuristic to seek faster and feasible approximate configurations for the architectures under evaluation; 2) low-power techniques regarding hybrid approximate adders design for accelerators based on trees of shift-and-add operations; 3) high-performance evaluation by exploring parallel prefix adders and low power analysis through the use of the adder optimized by a commercial synthesis tool in the precise part of the approximate adders; and 4) energy efficiency analysis by considering both the approximate techniques and voltage over scaling estimation. Furthermore, improvements are proposed for the state-of-the-art approximate adders under evaluation in this paper. Two case studies are considered to assess the proposed methodology: 1) Gaussian image filter and 2) Sobel operator. The precise and approximate image filters were described in very high-speed integrated circuits hardware description language regarding the proposed methodology. Results are shown after synthesis to a 45-nm standard cell-based technology, where energy reductions ranging from 7.7% up to 73.2% were experienced for multiple levels of quality considering the applications under analysis.
Identification of risk genes in Chinese nonobstructive azoospermia patients based on whole-exome sequencing

Nonobstructive azoospermia (NOA) is a severe condition in infertile men, and increasing numbers of causative genes have been identified during the last few decades. Although certain causative genes can explain the presence of NOA in some patients, a proportion of NOA patients remain to be addressed. This study aimed to investigate potential high-risk genes associated with spermatogenesis in idiopathic NOA patients by whole-exome sequencing. Whole-exome sequencing was performed in 46 male patients diagnosed with NOA. First, screening was performed for 119 genes known to be related to male infertility. Next, further screening was performed to determine potential high-risk causative genes for NOA by comparisons with 68 healthy male controls. Finally, risk genes with high/specific expression in the testes were selected and their expression fluctuations during spermatogenesis were graphed. The frequency of cystic fibrosis transmembrane conductance regulator (CFTR) gene pathogenic variant carriers was higher in the NOA patients compared with the healthy controls. Potential risk genes that may be causes of NOA were identified, including seven genes that were highly/specifically expressed in the testes. Four risk genes previously reported to be involved in spermatogenesis (MutS homolog 5 [MSH5], cilia- and flagella-associated protein 54 [CFAP54], MAP7 domain containing 3 [MAP7D3], and coiled-coil domain containing 33 [CCDC33]) and three novel risk genes (coiled-coil domain containing 168 [CCDC168], chromosome 16 open reading frame 96 [C16orf96], and serine protease 48 [PRSS48]) were identified to be highly or specifically expressed in the testes and significantly different in the 46 NOA patients compared with 68 healthy controls. This study on clinical NOA patients provides further evidence for the four previously reported risk genes. The present findings pave the way for further functional investigations and provide candidate risk genes for genetic diagnosis of NOA.
Computational modeling of redox enzymes

A computational methodology is briefly described, which appears to be able to accurately describe the mechanisms of redox active enzymes. The method is built on hybrid density functional theory where the inclusion of a fraction of exact exchange is critical. Two examples of where the methodology has been applied are described. The first example is the mechanism for water oxidation in photosystem II, and the second one is the mechanism for N2 activation by nitrogenase. The mechanism for PSII has obtained very strong support from subsequent experiments. For nitrogenase, the calculations suggest that there should be an activation process prior to catalysis, which is still strongly debated.
Absorption mechanism and performance characterization of CuO nanostructured absorbers

We introduce copper oxide (CuO) nanostructured selective solar absorbers having a broadband solar absorption over visible to near infrared wavelengths while suppressing long wavelength emission. The detailed mechanism enabling the enhanced solar absorption was investigated using the finite-difference-time-domain (FDTD) simulations. Both indoor and outdoor solar absorption experiments were conducted to investigate the performance of the suggested absorber in a well-controlled and actual operating conditions, respectively. The combined effects of the suggested absorber and double-glazing layout were also investigated. The results show that the sharp morphology of the CuO structures and its random size distribution enable the enhanced broadband optical absorption, and the resulting performance was measured to be approximately 95% and 97% of that of the current state-of-the-art TiNOX absorber for single and double glazing case, respectively. The suggested absorbers can provide a near-equivalent performance with the current-state-of-the-art solar absorber with much simpler and faster fabrication process. Furthermore, the numerical and experimental frameworks provided in this study will help develop high efficiency solar collectors.
Profile of HIV-Infected Hispanics with Pancytopenia

Pancytopenia is seen in late HIV infection; it is associated with medical complications and with decreased survival. We determined the prevalence of pancytopenia at baseline in a cohort of HIV-positive Hispanics living in Puerto Rico, and compared their socio-demographic, immunological and clinical characteristics. A total of 1202 patients enrolled between 2000 and 2010 were included. They were grouped according to pancytopenia status, defined by having: platelets <150,000 mu L, white cell count <4000 mu L, and hemoglobin <12 g/dL (women) or <13 g/dL (men). Differences were evaluated using Student's t-test, Chi-square test and Kaplan-Meier method. The prevalence of pancytopenia was 8.7%. Patients with pancytopenia had lower BMI and lower CD4 count, as well as higher HIV viral load and higher proportions of unemployment, clinical AIDS and antiretroviral treatment (ART) use (p < 0.05). One-year mortality rate was significantly higher in patients with pancytopenia (18.1% vs. 5.1%, p < 0.001). When stratifying for ART this association persisted for patients who did not receive ART (41.4% vs. 5.2%, p < 0.001), but it was not seen in patients who received treatment (9.2% vs. 5.6%, p = 0.196). Pancytopenia was associated with elements of advanced stages of HIV. ART could reduce the mortality of HIV-patients with pancytopenia to levels comparable to patients without the disorders.
Cloning and characterization of Aedes aegypti juvenile hormone epoxide hydrolases (JHEHs)

Juvenile hormone epoxide hydrolase (JHEH) plays an important role in the metabolism of juvenile hormone III (JH III) in insects. To study the role that JHEH plays in female Aedes aegypti JHEH 1, 2, and 3 complementary DNA (cDNAs) were cloned and sequenced. Northern blot analyses show that the three transcripts are expressed in the head thorax, the gut, the ovaries, and the fat body of females. Molecular modeling shows that the enzyme is a homodimer that binds JH III acid (JH IIIA) at the catalytic groove better than JH III. The cDNA of JHEH 1 and 2 are very similar indicating close relationship. Knocking down of jheh 1, 2, and 3 in adult female and larval Ae. aegypti using double-stranded RNA (dsRNA) did not affect egg development or caused adult mortality. Larvae that were fed bacterial cells expressing dsRNA against jheh 1, 2, and 3 grew normally. Treating blood-fed female Ae. aegypti with [12-3 H](10R) JH III and analyzing the metabolites by C18 reversed phase chromatography showed that JHEH preferred substrate is not JH III but JH IIIA. Genomic analysis of jheh 1, 2, and 3 indicate that jheh 1 and 2 are transcribed from a 1.53 kb DNA whereas jheh 3 is transcribed from a 10.9 kb DNA. All three genes are found on chromosome two at distinct locations. JHEH 2 was expressed in bacterial cells and purified by Ni affinity chromatography. Sequencing of the recombinant protein by MS/MS identified JHEH 2 as the expressed recombinant protein.