ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Early-life adversity programs emotional functions and the neuroendocrine stress system: the contribution of nutrition, metabolic hormones and epigenetic mechanisms

Clinical and pre-clinical studies have shown that early-life adversities, such as abuse or neglect, can increase the vulnerability to develop psychopathologies and cognitive decline later in life. Remarkably, the lasting consequences of stress during this sensitive period on the hypothalamic-pituitary-adrenal axis and emotional function closely resemble the long-term effects of early malnutrition and suggest a possible common pathway mediating these effects. During early-life, brain development is affected by both exogenous factors, like nutrition and maternal care as well as by endogenous modulators including stress hormones. These elements, while mostly considered for their independent actions, clearly do not act alone but rather in a synergistic manner. In order to better understand how the programming by early-life stress takes place, it is important to gain further insight into the exact interplay of these key elements, the possible common pathways as well as the underlying molecular mechanisms that mediate their effects. We here review evidence that exposure to both early-life stress and early-life under-/malnutrition similarly lead to life-long alterations on the neuroendocrine stress system and modify emotional functions. We further discuss how the different key elements of the early-life environment interact and affect one another and next suggest a possible role for the early-life adversity induced alterations in metabolic hormones and nutrient availability in shaping later stress responses and emotional function throughout life, possibly via epigenetic mechanisms. Such knowledge will help to develop intervention strategies, which gives the advantage of viewing the synergistic action of a more complete set of changes induced by early-life adversity.
Investigating the allosteric response of the PICK1 PDZ domain to different ligands with all-atom simulations

The PDZ family is comprised of small modular domains that play critical roles in the allosteric modulation of many cellular signaling processes by binding to the C-terminal tail of different proteins. As dominant modular proteins that interact with a diverse set of peptides, it is of particular interest to explore how different binding partners induce different allosteric effects on the same PDZ domain. Because the PICK1 PDZ domain can bind different types of ligands, it is an ideal test case to answer this question and explore the network of interactions that give rise to dynamic allostery. Here, we use all-atom molecular dynamics simulations to explore dynamic allostery in the PICK1 PDZ domain by modeling two PICK1 PDZ systems: PICK1 PDZ-DAT and PICK1 PDZ-GluR2. Our results suggest that ligand binding to the PICK1 PDZ domain induces dynamic allostery at the αA helix that is similar to what has been observed in other PDZ domains. We found that the PICK1 PDZ-ligand distance is directly correlated with both dynamic changes of the αA helix and the distance between the αA helix and βB strand. Furthermore, our work identifies a hydrophobic core between DAT/GluR2 and I35 as a key interaction in inducing such dynamic allostery. Finally, the unique interaction patterns between different binding partners and the PICK1 PDZ domain can induce unique dynamic changes to the PICK1 PDZ domain. We suspect that unique allosteric coupling patterns with different ligands may play a critical role in how PICK1 performs its biological functions in various signaling networks.
Introducing skip mode in distributed video coding

Although it was proven in the 1970s already by Wyner and Ziv and Slepian and Wolf that, under certain conditions, the same rate-distortion boundaries exist for distributed video coding (DVC) systems as for traditional predicting systems, until now no practical DVC system has been developed that even comes close to the performance of state-of-the-art video codecs such as H.264/AVC in terms of rate-distortion. Some important factors for this are the lower accuracy of the motion estimation performed at the decoder, the inaccurate modeling of the correlation between the side information and the original frame, and the absence in most state-of-the-art DVC systems of anything conceptually similar to the notion of skipped macroblocks in predictive coding systems. This paper proposes an extension of a state-of-the-art transform domain residual DVC system with an implementation of skip mode. The skip mode has an impact at two different places: in the turbo decoder, more specifically the soft input, soft output (SISO) convolutional decoder, and in the puncturing of the parity bits. Results show average bitrate gains up to 39% (depending on the sequence) achieved by combining both approaches. Furthermore, a hybrid video codec is presented where the motion estimation task is shifted back to the encoder. This results in a drastic increase in encoder complexity, but also in a drastic performance gain in terms of rate-distortion, with average bitrate savings up to 60% relative to the distributed video codec. in the hybrid video codec, smaller but still important average bitrate gains are achieved by implementing skip mode: up to 24%. (C) 2008 Elsevier B.V. All rights reserved.
One-Dimensional Deep Low-Rank and Sparse Network for Accelerated MRI

Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed ODLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, ODLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1D deep learning scheme is memory-efficient and robust in fast MRI.
Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data

Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i.e., retrogress) after each communication and the intractable class imbalance. Thus, in this paper, we propose a novel personalized FL framework to tackle these two problems. For the retrogress problem, we first devise a Progressive Fourier Aggregation (PFA) at the server side to gradually integrate parameters of client models in the frequency domain. Then, at the client side, we design a Deputy-Enhanced Transfer (DET) to smoothly transfer global knowledge to the personalized local model. For the class imbalance problem, we propose the Conjoint Prototype-Aligned (CPA) loss to facilitate the balanced optimization of the FL framework. Considering the inaccessibility of private local data to other participants in FL, the CPA loss calculates the global conjoint objective based on global imbalance, and then adjusts the client-side local training through the prototype-aligned refinement to eliminate the imbalance gap with such a balanced goal. Extensive experiments are performed on real-world dermoscopic and prostate MRI FL datasets. The experimental results demonstrate the advantages of our FL framework in real-world medical scenarios, by outperforming state-of-the-art FL methods with a large margin. The source code is available at < uri > https://github.com/CityU-AIM-Group/PRR-Imbalance.
A Spline-High Dimensional Model Representation for SRAM Yield Estimation in High sigma and High Dimensional Scenarios

Traditional Static Random-Access Memory (SRAM) yield estimation through Monte Carlo analysis is an extremely time-consuming process since it runs millions of expensive transistor-level simulations to get the yield results with the specified precision, especially for the large-scale circuits. In this paper, we develop an efficient yield analysis framework by integrating our novel performance metamodel into a state-of-art importance sampling method. The performance meta-model, named Spline-High Dimensional Model Representation (SP-HDMR), is used to substitute the expensive transistor-level simulations in yield estimation. The proposed SP-HDMR model provides a high computationally efficient formula expansion. It uses spline functions as the kernels to describe the various relations between the process parameters and SRAM read access delay. And an adaptive sampling method with sparsity analysis is developed to support SP-HDMR modeling. The experiments on the 40nm SRAM circuits validate the accuracy and the efficiency of the proposed yield analysis framework based on our SP-HDMR model with 1.3X similar to 5X speedup over the other state-of-art methods within 9% relative error.
A sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environments

The integration of 5G and Beyond 5G (B5G)/6G in Machine-to-Machine (M2M) communications, is making Industry 4.0 smarter. However, the goal of having a sustainable self-monitored industry has not been reached yet. State-of-the-art deep learning-based Fault Detection algorithms cannot handle heterogeneous data, meaning that more than one fault detection computational device has to be used for each data format, in addition to the inability to take advantage of the combination of all the information available in different formats to derive more accurate conclusions. Moreover, these algorithms rely on inefficient hyper-parameters tuning strategies. In this paper, we propose an Advanced Deep Learning framework for Fault Diagnosis in Industry 4.0 (ADL-FDI4), which combines Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN) and graph CNN (GNN), to handle heterogeneous data. Furthermore, our novel framework uses a Branch-and-Bound procedure to guide the learning process. Our experimental results show that ADL-FDI4 outperforms the state-of-the-art solutions in terms of detection rate and running time, and for that, it consumes less energy. In addition to handling heterogeneous data, which implies that one computational device is sufficient to handle all data formats.
A trauma-informed approach to understanding firearm decision-making among Black adolescents: Implications for prevention

Firearm violence remains a public health crisis in marginalized, urban communities, with Black adolescents bearing the burden of firearm homicides and injuries. As such, the prevention of firearm violence among adolescents has moved to a high priority of the U.S. public health agenda. The current paper reviews recent literature to highlight the heterogeneity in firearm behavior among Black adolescents and underscore the need for additional research on decision-making and firearm behavior to better understand how adolescents make decisions to acquire, carry, and use firearms. Through a discussion of the disproportionate levels of trauma exposure and trauma symptoms experienced by Black adolescents, the current paper also proposes a trauma-informed approach to understanding decision-making for risky firearm behavior. We discuss the broader impacts of this approach, including the development of a more comprehensive and contextually relevant understanding of the variability in risky firearm behavior and improvements in risk screening capabilities and preventive intervention strategies.
Comparison study of pattern-synthesis techniques using neural networks

In this paper, a comparison study among three neural-network algorithms for the synthesis of array patterns is presented. The neural networks are used to estimate the array elements' excitations for an arbitrary pattern. The architecture of the neural networks is discussed and simulation results art presented. Two new neural networks, based on radial basis functions (RBFs) and wavelet neural networks (WNNs), are introduced. The proposed networks offer a more efficient synthesis procedure, as compared to other available techniques. (C) 2004 Wiley Periodicals, Inc.
Age-invariant face recognition using gender specific 3D aging modeling

The age-invariant face recognition (AIFR) is a relatively new area of research in the face recognition domain which has recently gained substantial attention due to its great potential and importance in real-world applications. However, the AIFR is still in the process of emergence and development, offering a large room for further investigation and accuracy improvement. The key challenges in the AIFR are considerable changes of appearance of facial skin (wrinkles, jaw lines), facial shape, and skin tone in combination with the variations of pose and illumination. These challenges impose limitations on the current AIFR systems and complicate the recognition task for identity verification especially for temporal variation. In order to address this problem, we need a temporally invariant face verification system that would be robust vis-a-vis several factors, such as aging (shape, texture), pose, and illumination. In this study, we present a 3D gender-specific aging model that is robust to aging and pose variations and provides a better recognition performance than the conventional state-of-the-art AIFR systems. The gender-specific age modeling is performed in a 3D domain from 2D facial images of various datasets, such as PCSO, BROWNS, Celebrities, Private, and FG-NET. The evaluation of the proposed approach is performed on FG-NET (the most referred database in the AIFR studies) and MORPH-Album2 (the largest aging database) by using the VGG face CNN descriptor for matching. In addition, we also test the effects of linear discriminant analysis (LDA) and principal component analysis (PCA) subspaces learning in our face verification experiments. The proposed AIFR system is evaluated both on the pose corrected and background composited age-simulated images. The experimental results demonstrate that the proposed system provides state-of-the-art performance on FG-NET (83.89% of rank-1, 43.24% of TAR) and comparable performance to the state-of-the-art on MORPH-Album2 (75.27% of rank-1, 96.93% of TAR).
The use of cerebral oximetry in cardiac surgery: A systematic review and meta-analysis of randomized controlled trials

High prevalence of cerebral desaturation is associated with postoperative neurological complications in cardiac surgery. However, the evidence use of cerebral oximetry by correcting cerebral desaturation in the reduction of postoperative complications remains uncertain in the literature. This systematic review and meta-analysis aimed to examine the effect of cerebral oximetry on the incidence of postoperative cognitive dysfunction in cardiac surgery. Databases of MEDLINE, EMBASE, and CENTRAL were searched from their inception until April 2021. All randomized controlled trials comparing cerebral oximetry and blinded/no cerebral oximetry in adult patients undergoing cardiac surgery were included. Observational studies, case series, and case reports were excluded. A total of 14 trials (n = 2,033) were included in this review. Our pooled data demonstrated that patients with cerebral oximetry were associated with a lower incidence of postoperative cognitive dysfunction than the control group (studies = 4, n = 609, odds ratio [OR]: 0.15, 95% confidence interval [CI]: 0.04 to 0.54, P = 0.003, I2 = 88%; certainty of evidence = very low). In terms of postoperative delirium (OR: 0.75, 95%CI: 0.50-1.14, P = 0.18, I2 = 0%; certainty of evidence = low) and postoperative stroke (OR: 0.81 95%CI: 0.37-1.80, P = 0.61, I2 = 0%; certainty of evidence = high), no significant differences (P > 0.05) were reported between the cerebral oximetry and control groups. In this meta-analysis, the use of cerebral oximetry monitoring in cardiac surgery demonstrated a lower incidence of postoperative cognitive dysfunction. However, this finding must be interpreted with caution due to the low level of evidence, high degree of heterogeneity, lack of standardized cognitive assessments, and cerebral desaturation interventions.
Pulmonary Artery-Vein Classification in CT Images Using Deep Learning

Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time consuming, difficult to standardize, and thus not feasible for large clinical studies or useful in real-world clinical decision making. Therefore, automatic separation of arteries and veins in CT images is becoming of great interest, as it may help physicians to accurately diagnose pathological conditions. In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins. The algorithm follows three main steps: first, a scale-space particles segmentation to isolate vessels; then a 3-D convolutional neural network (CNN) to obtain a first classification of vessels; finally, graph-cuts' optimization to refine the results. To justify the usage of the proposed CNN architecture, we compared different 2-D and 3-D CNNs that may use local information from bronchus-and vessel-enhanced images provided to the network with different strategies. We also compared the proposed CNN approach with a randomforests (RFs) classifier. The methodology was trained and evaluated on the superior and inferior lobes of the right lung of 18 clinical cases with noncontrast chest CT scans, in comparison with manual classification. The proposed algorithm achieves an overall accuracy of 94%, which is higher than the accuracy obtained using other CNN architectures and RF. Our method was also validated with contrast-enhanced CT scans of patients with chronic thromboembolic pulmonary hypertension to demonstrate that our model generalizes well to contrast-enhanced modalities. The proposed method outperforms state-of-the-art methods, paving the way for future use of 3-D CNN for artery/vein classification in CT images.
Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.
A Germline-Specific Regulator of Mitochondrial Fusion is Required for Maintenance and Differentiation of Germline Stem and Progenitor Cells

Maintenance and differentiation of germline stem and progenitor cells (GSPCs) is important for sexual reproduction. Here, the authors identify zebrafish pld6 as a novel germline-specific gene by cross-analyzing different RNA sequencing results, and find that pld6 knockout mutants develop exclusively into infertile males. In pld6 mutants, GSPCs fail to differentiate and undergo apoptosis, leading to masculinization and infertility. Mitochondrial fusion in pld6-depleted GSPCs is severely impaired, and the mutants exhibit defects in piRNA biogenesis and transposon suppression. Overall, this work uncovers zebrafish Pld6 as a novel germline-specific regulator of mitochondrial fusion, and highlights its essential role in the maintenance and differentiation of GSPCs as well as gonadal development and gametogenesis.
Driving forces of China's provincial bilateral carbon emissions and the redefinition of corresponding responsibilities

The carbon transfers caused by inter-provincial commodity flows account for about 35 % of the total carbon emissions in China. There are great differences between the production-side and consumption-side carbon emissions for each province. Therefore, under the constraints of carbon peak and carbon neutralization, bilateral carbon emissions management is crucial to mitigate carbon emissions and the driving forces of bilateral carbon emissions must first be identified. Based on China's inter-provincial input-output data and carbon emissions data released by China Emissions Accounts and Datasets (CEADs), this paper uses a multi-regional input-output model (MRIO) to calculate the bilateral carbon emissions in 30 China's provinces from 2007 to 2017 and then apply structural decomposition analysis (SDA) to measure the influencing factors of these emissions. We also use counterfactual analysis to investigate the adjustment of provincial responsibilities for carbon emissions. The results show that the provinces in central and northern China undertake major net carbon inflows from other provinces in the eastern and southern coastal region. According to the results of SDA, the technological effect is an important factor in curbing the bilateral carbon emissions and the demand effect promotes the bilateral carbon emissions, but their contribution rates show a downward trend. By contrast, the variation in structural effect has significantly restrictive effects on the bilateral carbon emissions. Based on the provincial contribution to emissions mitigation, the adjusted consumption-side carbon emission embodies the principle of "more emission reduction, more compensation". We suggest implementing differentiated bilateral carbon emission management, taking the adjusted consumption-side carbon emission as the evaluation standard, and promoting inter-provincial carbon compensation.