ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A Supervised Framework for the Registration and Segmentation of White Matter Fiber Tracts

A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method.
Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon

In this article, a handwriting recognition model whose complexity does not depend on the lexicon size is proposed. It is an alternative to lexicon-driven decoding, based on alexicon verificationprocess that allows to deal with millions of words, without any time consuming decoding stage. This lexicon verification is included in a cascade framework that uses complementary LSTM RNN classifiers. An original and very efficient method to obtain hundreds of complementary LSTM RNN extracted from a single training, calledcohort, is proposed. The proposed approach achieves new state-of-the art performance on the Rimes and IAM datasets, and provides 90% of accuracy on the Rimes dataset when dealing with a gigantic lexicon record of 3 millions of words. The last contribution extends the idea of cohort and lexicon verification in a ROVER combination for handwriting line recognition, and achieves state-of-the-art results on the Rimes dataset.
Betulinic acid alleviates zearalenone-induced uterine injury in mice

Zearalenone (ZEA) is a mycotoxin with estrogen-like biological activity, which widely present in feed and raw materials, with strong reproductive system toxicity and a major threat to animal reproduction. Betulinic acid (BA) is a natural plant compound with antioxidant, anti-inflammatory and other pharmacological activities. However, the mechanism of ZEA-induced uterine injury and the protective effect of BA have not been reported. Our results show that ZEA could cause uterine histopathological damage and cellular ultrastructural damage, affecting the secretion of sex hormones, such as estradiol (E2) and progesterone (P4), and increase the mRNA and protein expression of estrogen receptor α (ERα). ZEA could inhibit the activities of catalase (CAT) and superoxide dismutase (SOD), increase the production of malondialdehyde (MDA) and reactive oxygen species (ROS), and cause uterine oxidative stress. Furthermore, ZEA affected the homeostasis of uterine cell proliferation and death by regulating the expression of proliferating cell nuclear antigen (PCNA) and activating the mitochondrial apoptotic pathway. ZEA-induced uterine injury might be related to the activation of p38/ERK MAPK signaling pathway. However, the regulatory effect of ZEA on the uterus was reversed after BA treatment. In conclusion, the uterus is an important target organ attacked by ZEA, and BA showed a good therapeutic effect.
Adaptive Weighting Landmark-Based Group-Wise Registration on Lung DCE-MRI Images

Image registration of lung dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging because the rapid changes in intensity lead to non-realistic deformations of intensity-based registration methods. To address this problem, we propose a novel landmark-based registration framework by incorporating landmark information into a group-wise registration. Robust principal component analysis is used to separate motion from intensity changes caused by a contrast agent. Landmark pairs are detected on the resulting motion components and then incorporated into an intensity-based registration through a constraint term. To reduce the negative effect of inaccurate landmark pairs on registration, an adaptive weighting landmark constraint is proposed. The method for calculating landmark weights is based on an assumption that the displacement of a good matched landmark is consistent with those of its neighbors. The proposed method was tested on 20 clinical lung DCE-MRI image series. Both visual inspection and quantitative assessment are used for the evaluation. Experimental results show that the proposed method effectively reduces the non-realistic deformations in registration and improves the registration performance compared with several state-of-the-art registration methods.
The BACE1-generated C-terminal fragment of the neural cell adhesion molecule 2 (NCAM2) promotes BACE1 targeting to Rab11-positive endosomes

Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), also known as β-secretase, is an aspartic protease. The sorting of this enzyme into Rab11-positive recycling endosomes regulates the BACE1-mediated cleavage of its substrates, however, the mechanisms underlying this targeting remain poorly understood. The neural cell adhesion molecule 2 (NCAM2) is a substrate of BACE1. We show that BACE1 cleaves NCAM2 in cultured hippocampal neurons and NCAM2-transfected CHO cells. The C-terminal fragment of NCAM2 that comprises the intracellular domain and a small portion of NCAM2's extracellular domain, associates with BACE1. This association is not affected in cells with inhibited endocytosis, indicating that the interaction of NCAM2 and BACE1 precedes the targeting of BACE1 from the cell surface to endosomes. In neurons and CHO cells, this fragment and BACE1 co-localize in Rab11-positive endosomes. Overexpression of full-length NCAM2 or a recombinant NCAM2 fragment containing the transmembrane and intracellular domains but lacking the extracellular domain leads to an increase in BACE1 levels in these organelles. In NCAM2-deficient neurons, the levels of BACE1 are increased at the cell surface and reduced in intracellular organelles. These effects are correlated with increased levels of the soluble extracellular domain of BACE1 in the brains of NCAM2-deficient mice, suggesting increased shedding of BACE1 from the cell surface. Of note, shedding of the extracellular domain of Sez6, a protein cleaved exclusively by BACE1, is reduced in NCAM2-deficient animals. These results indicate that the BACE1-generated fragment of NCAM2 regulates BACE1 activity by promoting the targeting of BACE1 to Rab11-positive endosomes.
RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-Lesion Segmentation

Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead of considering the pathological association for lesions. Through investigating the pathogenic causes of DR lesions in advance, we found that certain lesions are closed to specific vessels and present relative patterns to each other. Motivated by the observation, we propose a relation transformer block (RTB) to incorporate attention mechanisms at two main levels: a self-attention transformer exploits global dependencies among lesion features, while a cross-attention transformer allows interactions between lesion and vessel features by integrating valuable vascular information to alleviate ambiguity in lesion detection caused by complex fundus structures. In addition, to capture the small lesion patterns first, we propose a global transformer block (GTB) which preserves detailed information in deep network. By integrating the above blocks of dual-branches, our network segments the four kinds of lesions simultaneously. Comprehensive experiments on IDRiD and DDR datasets well demonstrate the superiority of our approach, which achieves competitive performance compared to state-of-the-arts.
Using Temporal Convolutional Networks to estimate ball possession in soccer games

The use of tracking data in the field of sport analytics has increased in the last years as a starting point for in-depth tactical analyses. This work investigates the use of Temporal Convolutional Networks (TCNs), a powerful architecture for sequential data analysis, to extract ball possession information from tracking data. This task is a crucial step for many tactical analyses and is nowadays carried out manually by a human operator in the stadium, which is costly, difficult to implement, and prone to errors. In this work, several classification approaches are explored to classify the game state as dead, ball owned by the home team, or by the away team: as a single-branch, ternary prediction, or as two binary predictions, first detecting whether the game is dead or alive and then which team owns the ball. TCNs are exploited to create independent trajectory embeddings from tracking data of each object; since there is no semantic ordering among the tracked objects, we investigate different permutation-invariant layers to combine the embeddings, namely, an element-wise sum over the embeddings, a self-attention module, and the use of 2D convolutions. Performance evaluation on tracking data from professional soccer games shows that the proposed method outperforms state-of-the-art rule-based methods, achieving 86.2% accuracy in possession estimation (+7.3% compared to the state of the art) and 89.2% accuracy in dead-alive classification (+33.2% compared to the state of the art). Extensive ablation studies were conducted to investigate how different input data concur to the final prediction.
Indole-3-carbinol ameliorated the neurodevelopmental deficits in neonatal anoxic injury in rats

Neonatal anoxia is linked to long-lasting neurodevelopmental deficits. Due to the lack of pharmacological intervention to treat neonatal anoxia, there is interest in finding new molecules for its treatment. Indole-3-carbinol (I3C) has shown neuroprotective effects in some disease conditions. However, the neuroprotective role of I3C in neonatal anoxia has not been explored. Consequently, we have investigated the effect of I3C on neonatal anoxia-induced brain injury and neurodevelopmental deficits. Rat pups after 30 h of birth were subjected to two episodes of anoxia (10 min in each) at a time interval of 24 h by flowing 100% nitrogen. I3C was administered within 30 min of the second episode of anoxia on a postnatal day (PND) 3 and continued for PND 9. Neurodevelopmental deficits, cortical mitochondrial membrane potential (MMP), opening of mitochondrial permeability transition pore (MPTP), electron transport chain (ETC) enzyme activities, oxidative stress, hypoxia-inducible factor-1α (HIF-1α) levels, histopathological changes, and apoptosis were measured. I3C treatment dose-dependently ameliorated the neurodevelopmental deficits and somatic growth in anoxic pups. I3C improved mitochondrial function by enhancing the MMP, mitochondrial ETC enzymes, and antioxidants. It blocked the MPTP opening and release of cytochrome C in anoxic pups. Further, I3C reduced the elevated cortical HIF-1α in neonatal anoxic pups. Furthermore, I3C ameliorated histopathological abnormalities and mitochondrial-mediated apoptotic indicators Cyt C, caspase-9, and caspase-3. Our study concludes that I3C improved neuronal development in anoxic pups by enhancing mitochondrial function, reducing HIF-1α, and mitigating apoptosis.
Fecal microbiota transplantation improves intestinal inflammation in mice with ulcerative colitis by modulating intestinal flora composition and down-regulating NF-kB signaling pathway

Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine. It is characterized with recurrent. The pathogenesis is mainly associated with environmental factors, genetic susceptibility, dysbiosis of the intestinal flora and autoimmunity. The role of intestinal flora disorders in the pathogenesis and progression of UC is becoming increasingly prominent. More and more studies have confirmed that fecal microbiota transplantation (FMT) could reshape the composition of UC intestinal flora and it is expected to be a new strategy for UC treatment. In this study, we used 2% Dextran sulfate sodium (DSS) for 7 days to induce acute colitis model in mice, and interfere with FMT and Enterotoxigenic Escherichia coli (ETEC). ELISA and immunohistochemistry were applied to detect the concentration and expression of NF-κB p65, STAT3 and IL-6. 16SrRNA high-throughput sequencing was performed to explore the composition of intestinal flora. The aim was to study the treatment effect of FMT on UC mice and explore its potential mechanism by observing the changes of intestinal flora composition and diversity, and its relationship with NF-κB p65, STAT3 and IL-6 expression. We conclude that FMT could improve intestinal flora disorder in mice with ulcerative colitis, regulate NF-κB signaling pathway, and significantly reduce intestinal inflammation in UC mice.
Neuroimaging Correlates of Post-Traumatic Stress Disorder in Traumatic Brain Injury: A Systematic Review of the Literature

Neuroimaging is widely utilized in studying traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD). The risk for PTSD is greater after TBI than after non-TBI trauma, and PTSD is associated with worse outcomes after TBI. Studying the neuroimaging correlates of TBI-related PTSD may provide insights into the etiology of both conditions and help identify those TBI patients most at risk of developing persistent symptoms. The objectives of this systematic review were to examine the current literature on neuroimaging in TBI-related PTSD, summarize key findings, and highlight strengths and limitations to guide future research. A Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) compliant literature search was conducted in PubMed (MEDLINE®), PsycINFO, Embase, and Scopus databases prior to January 2022. The database query yielded 4486 articles, which were narrowed based on specified inclusion criteria to a final cohort of 16 studies, composed of 854 participants with TBI. There was no consensus regarding neuroimaging correlates of TBI-related PTSD among the included articles. A small number of studies suggest that TBI-related PTSD is associated with white matter tract changes, particularly in frontotemporal regions, as well as changes in whole-brain networks of resting-state connectivity. Future studies hoping to identify reliable neuroimaging correlates of TBI-related PTSD would benefit from ensuring consistent case definition, preferably with clinician-diagnosed TBI and PTSD, selection of comparable control groups, and attention to imaging timing post-injury. Prospective studies are needed and should aim to further differentiate predisposing factors from sequelae of TBI-related PTSD.
Real-time 3D reconstruction using point-dependent pose graph optimization framework

As an essential core of structure from motion, full optimization and pose graph optimization are widely used in most of state-of-the-art 3D reconstruction systems, to estimate the motion trajectory of camera during scanning. Comparing to full optimization, the pose graph optimization has the advantages of low computational complexity and fast convergence, while the practical accuracy of pose graph optimization in applications is intrinsically limited by simple loss function independent of points in scene. In this paper, we proposed a point-dependent pose graph optimization (PDPGO) to address this problem and take it as core to construct a 3D high-precision reconstruction system. In our pipeline, we first construct a hierarchical pose graph by aligning the input frame to its overlapping frames searched by a spatial hashing scheme, which reduces the computational complexity of pairwise alignment. We then derive a loss function of PDPGO from global geometry loss, which improves the accuracy of previous methods. Our system is validated on public benchmarks, and experimental results demonstrate the competing performance against the state-of-the-art systems. And the average reconstruction accuracy in all scenes of ICL-NUIM is up to 0.9 cm.
Combating desertification: Building on traditional knowledge systems of the thar desert communities

The Thar Desert of western India is known for its rich and ancient culture system and traditions. The communities have long been part of the Thar Desert ecosystem and have evolved specific strategies to live in harmony with its hostile environment. This culture has provided several miracle plants of immense food and medicinal value to modem civilisation. The ancient rural livelihood knowledge system reflects time-tested techno-scientific knowledge with a proven track record of sustainability, especially during natural hazards like drought and famines. In addition, several of the traditional skills of local communities in arts and crafts, music and instruments have made modem man aware of the art and techniques of sustainably utilising local biological resources and preserving their biodiversity along with using waste products of the forests, without harming the desert ecosystem. Traditional cultural and socio-religious values are fast dwindling under the impact of materialistic approach, industrialisation and development. This paper endeavours to illustrate the need to assist and propagate indigenous rural livelihood systems rather than mindlessly replace or abandon them as a result of state bureaucracies.
Reconstructing 3D Shapes From Multiple Sketches Using Direct Shape Optimization

3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modeling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application.
First report on the probiotic potential of Mammaliicoccus sciuri isolated from raw goat milk

Probiotics are considered effective microbial dietary supplements that provide beneficial effects to consumers, usually by restoring or improving gut microflora. Goat milk is one of the rich sources of probiotics as well as nutrients. Therefore, the primary aim of this research was to isolate and evaluate the potential of novel indigenous probiotic strains present in goat milk. Six different raw goat milk samples were collected from different areas of Multan, Pakistan. For bacterial characterization, samples were cultured and isolated on MRS agar plates for different morphological and biochemical tests. The probiotic potential of the six isolates, all of which were gram positive (G1, G2, G3, G4, G5, and G6) and five of which were catalase negative (all except G1), were assessed via a milk coagulation assay and antimicrobial activity, pH tolerance, phenol tolerance, and sodium chloride (NaCl) tolerance tests, which revealed that all the isolates coagulated in milk and showed protease and lipase activity, except G3. All six isolates showed tolerance against 0.2% phenol and 2-4% NaCl and were able to survive in both alkaline and acidic conditions. Only five isolates showed antimicrobial activity against indicator strain Aspergillus niger strain STA9, validating their probiotic nature. The most potent bile-tolerant and bacteriocin-producing isolate, G1, also showed γ-hemolytic activity and resistance to penicillin but showed susceptibility to other antibiotics. The lactic acid-producing (0.60% titratable acidity) G1 isolate was identified as a novel strain of Mammaliicoccus sciuri based on 16S rDNA sequencing. The above findings suggest that the potent M. sciuri GMN01 strain can serve as a potential probiotic strain. A potent probiotic strain isolated from raw goat milk could be utilized as a dietary supplement, and goat milk could become an alternative to other sources of milk, particularly cow milk. However, safety aspects of this strain require further investigation because the present safety tests are insufficient to conclude that the GMN01 isolate is safe.
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed stateof-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.
Research and Development on Phase-Shifting Surfaces (PSSs)

Phase-shifting surfaces (PSSs) developed in recent years are reported. Research and development on these phase-shifting surfaces are set in the historical context of prior-art free-standing lensing devices, as well as in the context of recent studies performed by other research groups. In addition, initial work on a phase-and amplitude-shifting surface (PASS), an extension of the phase-shifting surface, is demonstrated in a beam-shaping application.