ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Assessing engagement of scheduled tribe communities in the functioning of village health sanitation & nutrition committees in India

India is home to the largest population of indigenous tribes in the world. Despite initiative of the National Rural Health Mission, now National Health Mission (NHM) and various tribal development programmes since India's Independence, disparity in healthcare for Scheduled Tribes (STs) prevails. The constitution of Village Health Sanitation and Nutrition Committees (VHSNCs) in 2007 by the NHM is a step towards decentralized planning and community engagement to improve health, nutrition and sanitation services. VHSNCs are now present in almost all States of the country. However, several reports including the 12th Common Review Mission report have highlighted that these committees are not uniformly following guidelines and lack clarity about their mandates, with no clear visibility of their functioning in tribal areas. We therefore conducted a review of studies to assess the participation of the VHSNCs in tribal dominated States in order to know in detail about their functioning and gaps if any that require intervention. Several deviations from the existing guidelines of NHM were identified and we concluded that in order to sustain and perform well, VHSNCs not only require, mobilization and strict monitoring but also motivation and willingness of its members to bring in a radical change at the grassroot level. With continuous supervision and support from both the Government and various non-governmental organizations, handholding, strategic deployment of workforce, community participation and sustained financial support, VHSNCs would be able to facilitate delivery of better healthcare to the indigenous population.
A measurement based feasibility study of space-frequency MIMO detection and decoding techniques for next generation wireless LANs

This article presents a performance evaluation of various multi-antenna concepts based on OFDM for Wireless LANs. The studies are based on state-of-the-art measured channel data in the 5GHz band. The investigated techniques include: spatial multiplexing (BLAST), space frequency trellis coded modulation, their concatenation, turbo bit interleaved coded modulation and turbo space frequency trellis coded modulation. The studies aim to assess the MIMO concepts for future high speed WLANs.
Novel Fisher discriminant classifiers

At the present, several applications need to classify high dimensional points belonging to highly unbalanced classes. Unfortunately, when the training set cardinality is small compared to the data dimensionality ("small sample size" problem) the classification performance of several well-known classifiers strongly decreases. Similarly, the classification accuracy of several discriminative methods decreases when non-linearly separable, and unbalanced, classes are treated. In this paper we firstly survey state of the art methods that employ improved versions of Linear Discriminant Analysis (LDA) to deal with the above mentioned problems; secondly, we propose a family of classifiers based on the Fisher subspace estimation, which efficiently deal with the small sample size problem, non-linearly separable classes, and unbalanced classes. The promising results obtained by the proposed techniques on benchmark datasets and the comparison with state of the art predictors show the efficacy of the proposed techniques. (C) 2012 Elsevier Ltd. All rights reserved.
[The place of users and their families in tomorrow's healthcare system]

Partnership in health is an innovative concept that brings about change. It is part of an attempt to synthesize support practices and actions of political and citizen scope. Its application poses challenges and issues for the actors of tomorrow's health system.
Bi-modal biometric authentication on mobile phones in challenging conditions

This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 63% and 1.9% for Female and Male trials, respectively. (C) 2013 Elsevier B.V. All rights reserved.
Switching linear dynamical systems for noise robust speech recognition

Real world applications such as hands-free dialling in cars may have to deal with potentially very noisy environments. Existing state-of-the-art solutions to this problem use feature-based HMMs, with a preprocessing stage to clean the noisy signal. However, the effect that raw signal noise has on the induced HMM features is poorly understood, and limits the performance of the HMM system. An alternative to feature-based HMMs is to model the raw signal, which has the potential advantage that including an explicit noise model is straightforward. Here we jointly model the dynamics of both the raw speech signal and the noise, using a Switching Linear Dynamical System (SLDS). The new model was tested on isolated digit utterances corrupted by Gaussian noise. Contrary to the Autoregressive HMM and its derivatives, which provides a model of uncorrupted raw speech, the SLDS is comparatively noise robust and also significantly outperforms a state-of-the-art feature-based HMM. The computational complexity of the SLDS scales exponentially with the length of the time series. To counter this we use Expectation Correction which provides a stable and accurate linear-time approximation for this important class of models, aiding their further application in acoustic modeling.
Plant Growth Hormones and Nanomaterial Interface: Exploring the connection from development to defense

The global increase in nanotechnology applications has been unprecedented and has now moved into the area of agriculture and food production. Applications with promising potential in sustainable agriculture include nanobiosensors, nanofertilizers, nanopesticides, nano-mediated remediation strategies for contaminated soils and nanoscale strategies to increase crop production and protection. Given this, the impact of nanomaterials/nanoparticles (NPs) on plant species needs to be thoroughly evaluated as this represents a critical interface between the biosphere and the environment. Importantly, phytohormones represent a critical class of biomolecules to plant health and productivity; however, the impact of NPs on these molecules is poorly understood. In addition, phytohormones, and associated pathways, are widely explored in agriculture to influence several biological processes for the improvement of plant growth and productivity under natural as well as stressed conditions. However, the impact of exogenous applications of phytohormones on NP-treated plants has not been explored. The importance of hormone signaling and cross-talk with other metabolic systems makes these biomolecules ideal candidates for a thorough assessment of NP impacts on plant species. This article presents a critical evaluation of the existing yet limited literature available on NP-phytohormone interactions in plants. In addition, the developing strategy of nano-enabled precision delivery of phytohormones via nanocarriers will be explored. Finally, directions for future research and critical knowledge gaps will be identified for this important aspect of nano-enabled agriculture.
Vitamin D and Colorectal Cancer: Current Perspectives and Future Directions

Vitamin D is considered to be the main mediator of the beneficial effects of sun exposure. In humans, highest expression of Vitamin D receptors is found in the intestinal tract. In addition, 1α,25-dihydroxyvitamin D3 (or calcitriol), the most active Vitamin D metabolite, plays important homeostatic roles in the intestine, particularly calcium absorption. Vitamin D deficiency is defined as a serum 25-hydroxyvitamin D [25(OH)D] level of < 20 ng/mL. Previous studies show that higher circulating 25(OH)D levels are associated with reduced risk of colorectal cancer (CRC) and improved survival. Most research to date has been conducted in animals, specifically mice. Although human studies have a limited number of participants, one study recruiting a large cohort of patients with advanced or metastatic CRC revealed that higher plasma 25(OH)D levels are associated with improved overall and progression-free survival. However, the effects of Vitamin D supplementation on incidence and mortality of CRC remain inconclusive. Although Vitamin D may help to prevent cancer, there is a paucity of research demonstrating conclusively that Vitamin D alters prognosis after chemotherapy. Here, we review the mechanisms by which Vitamin D affects CRC, as well as the results of clinical, epidemiological, and human intervention studies. We also discuss current perspectives and future directions regarding Vitamin D and CRC.
Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction.
The opportunistic nature of gut commensal microbiota

The abundance of gut commensals has historically been associated with health-promoting effects despite the fact that the definition of good or bad microbiota remains condition-specific. The beneficial or pathogenic nature of microbiota is generally dictated by the dimensions of host-microbiota and microbe-microbe interactions. With the increasing popularity of gut microbiota in human health and disease, emerging evidence suggests opportunistic infections promoted by those gut bacteria that are generally considered beneficial. Therefore, the current review deals with the opportunistic nature of the gut commensals and aims to summarise the concepts behind the occasional commensal-to-pathogenic transformation of the gut microbes. Specifically, relevant clinical and experimental studies have been discussed on the overgrowth and bacteraemia caused by commensals. Three key processes and their underlying mechanisms have been summarised to be responsible for the opportunistic nature of commensals, viz. improved colonisation fitness that is dictated by commensal-pathogen interactions and availability of preferred nutrients; pathoadaptive mutations that can trigger the commensal-to-pathogen transformation; and evasion of host immune response as a survival and proliferation strategy of the microbes. Collectively, this review provides an updated concept summary on the underlying mechanisms of disease causative events driven by gut commensal bacteria.
ConsInstancy: learning instance representations for semi-supervised panoptic segmentation of concrete aggregate particles

We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelled data by enforcing consistency between predicted instance representations and semantic segmentations during training in order to improve the segmentation performance. To this end, we also propose new types of instance representations that can be predicted by one simple forward path through a fully convolutional network (FCN), delivering a convenient and simple-to-train framework for panoptic segmentation. More specifically, we propose the prediction of a three-dimensional instance orientation map as intermediate representation and two complementary distance transform maps as final representation, providing unique instance representations for a panoptic segmentation. We test our method on two challenging data sets of both, hardened and fresh concrete, the latter being proposed by the authors in this paper demonstrating the effectiveness of our approach, outperforming the results achieved by state-of-the-art methods for semi-supervised segmentation. In particular, we are able to show that by leveraging completely unlabelled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labelled data. Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised methods by up to 1.5%.
[Measuring the emergency psychiatric units activity: An experimental application of the Delphi Method]

Psychiatric emergency units (UUP) are nowadays important gateways to healthcare. Whether integrated into general emergency departments or not, these units have very heterogeneous resources and organisations which are not always in line with a populations' needs. The increasing activity of emergency departments in recent years and the recurrent psychiatric bed shortages have shed light upon the weaknesses of this key link in the mental healthcare process. The Seine-Saint-Denis is a department of France located in the Grand Paris metropolis in the Île-de-France region. Ranked third in terms of population size in France, it is marked by social precariousness. With regard to mental health, it has one of the lowest rates of psychiatric beds per capita in France. A great deal of thought has been ongoing for five years on how best to upgrade the offer of unscheduled psychiatric care, particularly the management of emergencies. The growing imbalance between demand and supply depending on living areas urges a rapid equalization of resources. This operation requires an accurate activity characterization, allowing more effective organizations and adequate resource allocation. We sought to characterize the activity of psychiatric emergencies by selecting quantitative and qualitative indicators by means of a consensus method, the Delphi Method, which consists of iterative questioning of an expert group. We first submitted 36 potential criteria to twenty-five experts. Twenty obtained a weak to a strong consensus. Seventeen were then selected as potentially useful for activity characterization. In a second time, we tested the consensus on selected indicators by interviewing a panel of 19 experts. A strong consensus was found on four criteria: "Number of visits for psychiatric advice>2000/year", "Number of emergency room visits>40,000/year", "Density of adult hospital beds<150 per 100,000 inhabitants", "Passage rate for homeless patients and/or outside the sector>10%". Using these criteria in the classification of UUPs would test their validity and provide a potentially helpful tool for improving organizations and resource allocation.
Protective effect of biogenic selenium nanoparticles against diquat-induced acute toxicity via regulation of gut microbiota and its metabolites

Selenium nanoparticles (SeNPs) with unique biological properties have been suggested as a safer and more effective platform for delivering of Selenium for biological needs. In this study, we investigated the association between gut microbiota altered by SeNPs supplementation and its metabolites under oxidative stress conditions through 16S rDNA gene sequencing analysis and untargeted metabolomics. The results showed that dietary supplementation with SeNPs attenuated diquat-induced acute toxicity in mice, as demonstrated by lower levels of inflammatory effector cells, and biochemical markers in serum such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). SeNPs also reversed the perturbed gut microbiota composition induced by diquat, decreased the Firmicutes/Bacteroidetes ratio, and increased the abundance of beneficial bacteria such as Akkermansia, Muribaculaceae, Bacteroides and Parabacteroides. Untargeted fecal metabolomics showed that SeNPs can regulate the production of steroids and steroid derivatives, organonitrogen compounds, pyridines and derivatives and other metabolites. Microbiome-metabolome correlation analysis suggested that Parabacteroides was the key bacteria for the SeNPs intervention, which might upregulate the levels of metabolites such as trimethaphan, emedastine, berberine, desoxycortone, tetrahydrocortisone. This study demonstrated that dietary SeNPs supplementation can extensively modulate the gut microbiota and its metabolism, thereby alleviating diquat-induced acute toxicity.
Investigation of the Role of Feature Selection and Weighted Voting in Random Forests for 3-D Volumetric Segmentation

This paper describes a novel 3-D segmentation technique posed within the Random Forests (RF) classification framework. Two improvements over the traditional RF framework are considered. Motivated by the high redundancy of feature selection in the traditional RF framework, the first contribution develops methods to improve voxel classification by selecting relatively "strong" features and neglecting "weak" ones. The second contribution involves weighting each tree in the forest during the testing stage, to provide an unbiased and more accurate decision than provided by the traditional RF. To demonstrate the improvement achieved by these enhancements, experimental validation is performed on adult brain MRI and 3-D fetal femoral ultrasound datasets. In a comparison of the new method with a traditional Random Forest, the new method showed a notable improvement in segmentation accuracy. We also compared the new method with other state-of-the-art techniques to place it in context of the current 3-D medical image segmentation literature.
Robust Approach for Disparity Map Estimation Based on Multilevel Decomposition

A novel approach for dense disparity map (DM) estimation is designed. The proposed frameworks include together processing of stereo pairs using the following blocks: CIEL*a*b*color space conversion, stereo matching via multilevel scheme, employing Normalized Cross-Correlation or Structural Similarity Index Measure, color segmentation by k-means on a*b*color plane, and adaptive post-filtering. The Bad Matching Pixels and Structural Similarity Index Measure criteria are applied in order to compare the performance of the proposed approach against state-of-the-art techniques. Two designed frameworks appear outperform existing state-of-the-art techniques in terms of objective criteria as well as in subjective visual perception.
Reconfigurable Network Systems and Software-Defined Networking

Modern high-speed networks have evolved from relatively static networks to highly adaptive networks facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability and configuration flexibility. This influence has extended from the lowest level of physical hardware interfaces to the highest level of network management by software. A key representative of this evolution is the emergence of software-defined networking (SDN). In this paper, we review the current state of the art in reconfigurable network systems, covering hardware reconfiguration, SDN, and the interplay between them. We take a top-down approach, starting with a tutorial on software-defined networks. We then continue to discuss programming languages as the linking element between different levels of software and hardware in the network. We review electronic switching systems, highlighting programmability and reconfiguration aspects, and describe the trends in reconfigurable network elements. Finally, we describe the state of the art in the integration of photonic transceiver and switching elements with electronic technologies, and consider the implications for SDN and reconfigurable network systems.
Nasopharyngeal microbiome of COVID-19 patients revealed a distinct bacterial profile in deceased and recovered individuals

The bacterial co-infections in SARS-CoV-2 patients remained the least explored subject of clinical manifestations that may also determine the disease severity. Nasopharyngeal microbial community structure within SARS-CoV-2 infected patients could reveal interesting microbiome dynamics that may influence the disease outcomes. Here, in this research study, we analyzed distinct nasopharyngeal microbiome profile in the deceased (n = 48) and recovered (n = 29) COVID-19 patients and compared it with control SARS-CoV-2 negative individuals (control) (n = 33). The nasal microbiome composition of the three groups varies significantly (PERMANOVA, p-value <0.001), where deceased patients showed higher species richness compared to the recovered and control groups. Pathogenic genera, including Corynebacterium (LDA score 5.51), Staphylococcus, Serratia, Klebsiella and their corresponding species were determined as biomarkers (p-value <0.05, LDA cutoff 4.0) in the deceased COVID-19 patients. Ochrobactrum (LDA score 5.79), and Burkholderia (LDA 5.29), were found in the recovered group which harbors ordinal bacteria (p-value <0.05, LDA-4.0) as biomarkers. Similarly, Pseudomonas (LDA score 6.19), and several healthy nasal cavity commensals including Veillonella, and Porphyromonas, were biomarkers for the control individuals. Healthy commensal bacteria may trigger the immune response and alter the viral infection susceptibility and thus, may play important role and possible recovery that needs to be further explored. This research finding provide vital information and have significant implications for understanding the microbial diversity of COVID-19 patients. However, additional studies are needed to address the microbiome-based therapeutics and diagnostics interventions.