ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Single Nucleotide Polymorphism-Based Real-Time PCR Screening Assay for Rapid Tracking of Bacterial Infection Clusters To Complement Whole-Genome Sequencing Efforts during Outbreak Investigations

Infection clusters of multidrug-resistant bacteria increase mortality and entail expensive infection control measures. Whereas whole-genome sequencing (WGS) is the current gold standard to confirm infection clusters, PCR-based assays targeting cluster-specific signatures, such as single nucleotide polymorphisms (SNPs) derived from WGS data, are more suitable to initially screen for cluster isolates within large sample sizes. Here, we evaluated four software tools (SeqSphere+, RUCS, Gegenees, and Find Differential Primers) regarding their efficiency to find SNPs within WGS data sets that were specific for two bacterial monospecies infection clusters but were absent from a WGS reference data set comprising several hundred diverse genotypes of the same bacterial species. Cluster-specific SNPs were subsequently used to establish a probe-based real-time PCR screening assay for in vitro differentiation between cluster and noncluster isolates. SeqSphere+ and RUCS found 2 and 24 SNPs for clusters 1 and 14 and 24 SNPs for cluster 2, respectively. However, some signatures detected by RUCS were not cluster specific. Interestingly, all SNPs identified by SeqSphere+ were also detected by RUCS. In contrast, analyses with the remaining tools either resulted in no SNPs (with Find Differential Primers) or failed (Gegenees). Design of six cluster-specific real-time PCR assays enabled reliable cluster screening in vitro. Our evaluation revealed that SeqSphere+ and RUCS identified cluster-specific SNPs that could be used for large-scale screening in surveillance samples via real-time PCR, thereby complementing WGS efforts. This faster and simplified approach for the surveillance of bacterial clusters will improve infection control measures and will enhance protection of patients and physicians. IMPORTANCE Infection clusters of multidrug-resistant bacteria threaten medical facilities worldwide and cause immense health care costs. In recent years, whole-genome sequencing (WGS) has been increasingly applied to detect and to further control bacterial clusters. However, as WGS is still expensive and time-consuming, its exclusive application for screening and confirmation of bacterial infection clusters contributes to high costs and enhanced turnaround times, which many hospitals cannot afford. Therefore, there is need for alternative methods that can enable further surveillance of bacterial clusters that are initially detected by WGS in a faster and more cost-efficient way. Here, we established a system based on real-time PCR that enables rapid large-scale sample screening for bacterial cluster isolates within 7 days after the initial detection of an infection cluster, thereby complementing WGS efforts. This faster and simplified surveillance of bacterial clusters will improve infection control measures and will enhance protection of patients and physicians.
3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from raw depth sequences. The described 3D-CNN allows actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people's privacy, since their identities can not be recognized from these data. The proposed 3DFCNN has been optimized to reach a good performance in terms of accuracy while working in real-time. Then, it has been evaluated and compared with other state-of-the-art systems in three widely used public datasets with different characteristics, demonstrating that 3DFCNN outperforms all the non-DNN-based state-of-the-art methods with a maximum accuracy of 83.6% and obtains results that are comparable to the DNN-based approaches, while maintaining a much lower computational cost of 1.09 seconds, what significantly increases its applicability in real-world environments.
Three-Dimensional Vessels-on-a-Chip Based on hiPSC-derived Vascular Endothelial and Smooth Muscle Cells

Blood vessels are composed of endothelial cells (ECs) that form the inner vessel wall and mural cells that cover the ECs to mediate their stabilization. Crosstalk between ECs and VSMCs while the ECs undergo microfluidic flow is vital for the function and integrity of blood vessels. Here, we describe a protocol to generate three-dimensional (3D) engineered vessels-on-chip (VoCs) composed of vascular cells derived from human induced pluripotent stem cells (hiPSCs). We first describe protocols for robust differentiation of vascular smooth muscle cells (hiPSC-VSMCs) from hiPSCs that are effective across multiple hiPSC lines. Second, we describe the fabrication of a simple microfluidic device consisting of a single collagen lumen that can act as a cell scaffold and support fluid flow using the viscous finger patterning (VFP) technique. After the channel is seeded sequentially with hiPSC-derived ECs (hiPSC-ECs) and hiPSC-VSMCs, a stable EC barrier covered by VSMCs lines the collagen lumen. We demonstrate that this 3D VoC model can recapitulate physiological cell-cell interaction and can be perfused under physiological shear stress using a microfluidic pump. The uniform geometry of the vessel lumens allows precise control of flow dynamics. We have thus developed a robust protocol to generate an entirely isogenic hiPSC-derived 3D VoC model, which could be valuable for studying vessel barrier function and physiology in healthy or disease states. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Differentiation of hiPSC-VSMCs Support Protocol 1: Characterization of hiPSC-NCCs and hiPSC-VSMCs Support Protocol 2: Preparation of cryopreserved hiPSC-VSMCs and hiPSC-ECs for VoC culture Basic Protocol 2: Generation of 3D VoC model composed of hiPSC-ECs and hiPSC-VSMCs Support Protocol 3: Structural characterization of 3D VoC model.
Deterioration processes affecting prehistoric rock art engravings in granite in NW Spain

Prehistoric rock art sites are endangered despite conservation efforts. The lack of scientific documentation regarding weathering agents affecting rock art and the absence of specific diagnostic protocols hinder the development of conservation strategies. The aim of this research was to investigate active deterioration processes in a granite petroglyph site located in Mougas (Galicia, NW Spain) by characterizing the granite, conducting a geotechnical study of the outcrop and describing and analysing the main weathering processes. Two main deterioration factors were identified. First, water favours block disjunction at the massif scale and causes pitting and surface erosion at the millimetre scale that affects the readability of the engravings. Second, high temperatures associated with wildfires cause mineral transformations that increase the susceptibility of the rock to weathering. Identifying deterioration factors is a first step in developing appropriate preventive conservation measures, which should aim to reduce rock contact time with water (technically affordable in the short term) and to reduce the probability of wildfire occurrence (technically more complex and possibly with longer-term results). Copyright (c) 2018 John Wiley & Sons, Ltd.
Bandwidth-enhanced differential VCO and varactor-coupled quadrature VCO for mmWave applications

This paper presents a bandwidth-enhanced differential LC-Voltage Controlled Oscillator (LC-VCO) and varactor-coupled Quadrature VCO (QVCO) designed and simulated in high-performance SiGe HBTs of the GlobalFoundries (GFs) 130 nm SiGe BiCMOS8HP process for mmWave applications. Using the proposed varactor circuit, the designed VCO is operated in wideband and also the two-identical free-running differential VCOs (DVCOs) are coupled through the proposed varactor to oscillate in quadrature. The device sizes in the coupling network are properly set to achieve low I/Q errors. In addition, all the VCOs are designed in class-C to further improve their performances both in term of low power and low phase noise. The post-simulation results show that the differential oscillator is tunable from 20.3- to 31.3-GHz as the tuning voltage is varied from 0 to 1.2 V, while consuming only 10 mA from 1.2 V supply, presenting the best state-of-the-art figure-of-merit (FoM(T)) of -188 dBc/Hz. Similarly, the QVCO can cover 40.2% oscillation bandwidth around 25.5 GHz oscillating frequency which is significantly wider than other state-of-the-art VCOs operating at comparable frequencies. (C) 2018 Elsevier GmbH. All rights reserved.
Multiple influences of working memory capacity on number comprehension: The interplay with metacognition and number-specific prerequisites

A wide literature has studied the predictors of number comprehension and early math learning by considering both domain-general and number-specific prerequisites. However, a consensus has not been reached regarding the specific contribution of these prerequisites. This study aimed to analyze the contribution and interplay of two domain-general functions, working memory (WM) and metacognitive abilities, and number-specific prerequisites in determining number comprehension. The participants, 126 Italian first-graders, were tested on two WM capacity tasks, an early metacognition questionnaire, five number-specific prerequisites tasks (e.g., quantity and/or size comparison; placement of Arabic numeral), and the Number Knowledge Test for whole-number comprehension. We hypothesized that WM capacity would predict number comprehension both directly and indirectly via metacognition and domain-specific prerequisites. This is because both metacognition and domain-specific prerequisites might place an information load on WM to establish schemes for declarative metamemory and metacognitive monitoring and for emerging counting skills, respectively. The results confirmed these hypotheses. WM capacity was positively associated with number comprehension both directly and via increased metacognition and domain-specific prerequisites. These findings offer a model for interpreting the interplay between domain-general and number-specific predictors of whole-number comprehension, but they also underline the multiple ways in which WM capacity affects it.
Skeletal interoception in bone homeostasis and pain

Accumulating evidence indicates that interoception maintains proper physiological status and orchestrates metabolic homeostasis by regulating feeding behaviors, glucose balance, and lipid metabolism. Continuous skeletal remodeling consumes a tremendous amount of energy to provide skeletal scaffolding, support muscle movement, store vital minerals, and maintain a niche for hematopoiesis, which are processes that also contribute to overall metabolic balance. Although skeletal innervation has been described for centuries, recent work has shown that skeletal metabolism is tightly regulated by the nervous system and that skeletal interoception regulates bone homeostasis. Here, we provide a general discussion of interoception and its effects on the skeleton and whole-body metabolism. We also discuss skeletal interoception-mediated regulation in the context of pathological conditions and skeletal pain as well as future challenges to our understanding of these process and how they can be leveraged for more effective therapy.
Medical students' perception of e-learning approach (MeSPeLA) - a mixed method research

There is a discrepancy between the research exploring e-learning at medical universities in Central/Eastern and Western European countries. The aim of the MeSPeLA study was to explore the understanding, experience and expectations of Polish medical students in terms of e-learning. Questionnaire containing open-ended and closed questions supplemented by focus group discussion was validated and performed among 204 medical students in Poland before COVID-19 pandemia. Several domains: understanding of e-learning definitions; students' experience, preferences, expectations and perceptions of e-learning usefulness, advantages and disadvantages were addressed. The qualitative data were analyzed using an inductive approach. 46.0% of students chose a communication-oriented definition as the most appropriate. 7.4% claimed not to have any experience with e-learning. 76.8% of respondents indicated they had contact with e-learning. The main reported e-learning advantages were time saving and easier time management. The most common drawback was limited social interactions. The acceptance of the usage of e-learning was high. Medical undergraduates in Poland regardless of the year of studies, gender or choice of future specialization showed positive attitudes towards e-learning. Students with advanced IT skills showed a better understanding of the e-learning definition and perceived e-learning to be a more useful approach. The expectations and perceptions about e-learning in Polish medical schools seems similar to some extent to that in Western European and the United States so we can be more confident about applying some lessons from these research to Poland or other post-communist countries. Such application has been accelerated due to COVID-19 pandemia.
A Neandertal dietary conundrum: Insights provided by tooth enamel Zn isotopes from Gabasa, Spain

The characterization of Neandertals' diets has mostly relied on nitrogen isotope analyses of bone and tooth collagen. However, few nitrogen isotope data have been recovered from bones or teeth from Iberia due to poor collagen preservation at Paleolithic sites in the region. Zinc isotopes have been shown to be a reliable method for reconstructing trophic levels in the absence of organic matter preservation. Here, we present the results of zinc (Zn), strontium (Sr), carbon (C), and oxygen (O) isotope and trace element ratio analysis measured in dental enamel on a Pleistocene food web in Gabasa, Spain, to characterize the diet and ecology of a Middle Paleolithic Neandertal individual. Based on the extremely low δ66Zn value observed in the Neandertal's tooth enamel, our results support the interpretation of Neandertals as carnivores as already suggested by δ15N isotope values of specimens from other regions. Further work could help identify if such isotopic peculiarities (lowest δ66Zn and highest δ15N of the food web) are due to a metabolic and/or dietary specificity of the Neandertals.
Development of transmission-reducing behaviour adherence measure (TRAM) for monitoring and predicting transmission-reducing behaviours during the pandemic

There is a need for a measure to monitor adherence to transmission-reducing behaviours (TRBs) during pandemics. An adherence measure can monitor current TRBs, assess change over time and, potentially, predict later behaviours. The TRB adherence measure (scale consisting of seven items) includes questions based on government behavioural directives in Scotland that were common internationally, i.e., physical distancing, face covering and hand hygiene. Data were collected weekly for 6 weeks at the beginning of the pandemic, including a later follow-up repeated measure of some participants, in 20-minute structured telephone surveys with a nationally representative random sample of adults in Scotland. A total of 2969 people completed the adherence items and were highly adherent. Confirmatory factor analysis supported a unidimensional scale (CFI = .95; TLI = .93; RMSEA = .08; SRMR = .08), although internal consistency was low (Cronbach's alpha = .49). The adherence score significantly predicted adherence to a validity test item (ΔR2 = .114, F(1,2964) = 379.76, p < .001). It also predicted adherence to TRBs later over and above personal habitual styles (Creature of Habit Scale: COHS). The adherence score has been developed for routine monitoring of adherence to TRBs during the COVID-19 pandemic. It can be used to predict future similar behaviours and adherence to other behaviours, although it may be necessary to explore adherence to the specific behaviours occasionally. Adherent behaviour for one TRB is likely to be associated with adherence to government directives to other TRBs. Importantly, these TRBs are likely to be crucial in reducing COVID-19 case numbers, as well as protecting against other infectious diseases including influenza and the common cold.
Goal-oriented top-down probabilistic visual attention model for recognition of manipulated objects in egocentric videos

We propose a new top down probabilistic saliency model for egocentric video content. It aims to predict top-down visual attention maps focused on manipulated objects, that are then used for psycho-visual weighting of features in the problem of manipulated object recognition. The model is probabilistically defined using both global and local appearance features extracted from automatically segmented arm areas and objects. A psycho-visual experiment has been conducted in a guided framework that compares our proposal and other popular state-of-the-art models with respect to human gaze fixations. The obtained results show that our approach outperforms several popular bottom-up saliency approaches in a well-known egocentric dataset Furthermore, an additional task-driven assessment for object recognition in egocentric video reveals that the proposed method improves the performance of several state-of-the-art techniques for object detection. (C) 2015 Elsevier B.V. All rights reserved.
Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

We design a centralized and a decentralized variational Bayesian learning (C- and D-VBL) algorithms for the base station (BS) of a frequency division duplex massive multiple input multiple output (mMIMO) cellular system, wherein users send compressed information for it to estimate their downlink channels. The BS in the decentralized algorithm consists of multiple processing units (PUs), and each PU separately estimates the channels of a group of users, by employing the proposed D-VBL algorithm. To reduce channel estimation error, the PUs exploit the structured sparsity inherent in multi-user mMIMO channels by exchanging information among themselves. We investigate the proposed C-VBL and low-complexity D-VBL algorithms and show that i) they substantially outperform the state-of-the-art centralized and decentralized algorithms in terms of the normalized mean squared error and the bit error rate. This is because they beneficially exploit the channel sparsity, while the existing state-of-the-art solutions fail to do so. The proposed D-VBL is also robust to PU failures, and provides a similar performance as its centralized counterpart (C-VBL), but with a much reduced complexity.
Neural and behavioral plasticity across the female reproductive cycle

Sex is fundamental for the evolution and survival of most species. However, sex can also pose danger, because it increases the risk of predation and disease transmission, among others. Thus, in many species, cyclic fluctuations in the concentration of sex hormones coordinate sexual receptivity and attractiveness with female reproductive capacity, promoting copulation when fertilization is possible and preventing it otherwise. In recent decades, numerous studies have reported a wide variety of sex hormone-dependent plastic rearrangements across the entire brain, including areas relevant for female sexual behavior. By contrast, how sex hormone-induced plasticity alters the computations performed by such circuits, such that collectively they produce the appropriate periodic switches in female behavior, is mostly unknown. In this review, we highlight the myriad sex hormone-induced neuronal changes known so far, the full repertoire of behavioral changes across the reproductive cycle, and the few examples where the relationship between sex hormone-dependent plasticity, neural activity, and behavior has been established. We also discuss current challenges to causally link the actions of sex hormones to the modification of specific cellular pathways and behavior, focusing on rodents as a model system while drawing a comparison between rodents and humans wherever possible.
A Hierarchical Criticality-Aware Architectural Synthesis Framework for Multicycle Communication

In deep submicron era, wire delay is no longer negligible and is becoming a dominant factor of the system performance. To cope with the increasing wire delay, several state-of-the-art architectural synthesis flows have been proposed for the distributed register architectures by enabling on-chip multicycle communication. In this article, we present a new performance-driven criticality-aware synthesis framework CriAS targeting regular distributed register architectures. To achieve high system performance, CriAS features a hierarchical binding-then-placement for minimizing the number of performance-critical global data transfers. The key ideas are to take time criticality as the major concern at earlier binding stages before the detailed physical placement information is available, and to preserve the locality of closely related critical components in the later placement phase. The experimental results show that CriAS can achieve an average of 14.26% overall performance improvement with no runtime overhead as compared to the previous art.
Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates

Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via using simpler object representation model and less regression parameters. In P-RSDet method, arbitrary-oriented object detection can be achieved by predicting the center point and regressing one polar radius and two polar angles. Besides, in order to express the geometric constraint relationship between the polar radius and the polar angle, a Polar Ring Area Loss function is proposed to improve the prediction accuracy of the corner position. Experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show that our P-RSDet achieves state-of-the-art performances with simpler model and less regression parameters.