ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
An investigation of the impact of the Global Gag Rule on women's sexual and reproductive health outcomes in Uganda: a difference-in-differences analysis

In 2017, the Trump administration reinstated the Global Gag Rule (GGR), making non-U.S. non-governmental organisations ineligible for US government global health assistance if they provide access to or information about abortion. Little is known about the impact of the Trump administration's GGR on women's outcomes. Data for this analysis come from a panel of women surveyed in 2018 and 2019 in Uganda (n = 2755). We also used data from meetings with key stakeholders to create a detailed measure of exposure to the GGR within Uganda, classifying districts as more or less exposed to the GGR. Multivariable regression models were used to assess changes in contraceptive use, all births, unplanned births, and abortion from before to during implementation of the GGR. Difference-in-differences (DID) estimates were determined by calculating predicted probabilities from interaction terms for exposure/survey round. Descriptive analyses showed long-acting reversible contraceptive use increased more rapidly among women in less exposed districts after GGR implementation. DID estimates for contraceptive use were small. We observed a DID estimate of 3.5 (95% CI -0.9, 7.9) for all births and 2.9 (95% CI -0.2, 6.0) for unplanned births for women in more exposed districts during the period the policy was in effect. Our results suggest that the GGR may have attenuated Uganda's recent progress in improving SRHR outcomes, with women in less exposed districts continuing to benefit from this progress, while previously increasing trends for women in more exposed districts levelled off. Although the GGR was rescinded in January 2021, the impact of these disruptions may be felt for years to come.
Delayed access of low body weight-selected chicks to food at hatch is associated with up-regulated pancreatic glucagon and glucose transporter gene expression

Chickens selected for low (LWS) and high (HWS) juvenile body weight (BW) for 55 generations differ in BW by 10-fold at selection age. High (HWR) and low (LWR) body weight-relaxed lines have been random-bred since the 46th generation. Our objective was to evaluate the developmental and nutritional regulation of pancreatic mRNA abundance of pancreatic and duodenal homeobox 1 (PDX1), preproinsulin (PPI), preproglucagon (PPG), and glucose transporter 2 (GLUT2). At day of hatch (DOH) and days 1, 3, 7, and 15 (D1, 3, 7 and 15, respectively), pancreas was collected and real time PCR was performed in Experiment 1. In Experiment 2, HWS and LWS were fed or delayed access to food for 72 h post-hatch, and pancreas collected at D15. There was an interaction of line and age for GLUT2 (P=0.001), PPI (P<0.0001), PPG (P=0.034), and PDX1 (P<0.0001). Expression was greater in chicks from LWR and LWS than HWR and HWS. There was an interaction of line and nutrition on PPG (P<0.0001) and GLUT2 (P=0.001) mRNA, where expression was similar among chicks that were fed but greater in LWS than HWS when chicks were delayed access to food. Thus, the first two weeks is important for maturation of pancreatic endocrine function. Long-term selection for BW is associated with differences in pancreas development, and delaying access to food at hatch may have persisting effects on glucose regulatory function.
Sustainable Development Assessment of Cultural and Creative Industries in Casino Cities: A Case Study of Macao

In Macao, the government established the Cultural and Creative Industry Promotion Office and Cultural Industries Committee in 2010, which nominated eight to-be-developed cultural and creative industries (CCIs): design, visual arts, performing arts, clothing, pop music, film and video, animation, and publishing. However, because each CCI has its unique pattern and environmental resources are very limited in Macao, an industrial chain analysis for these eight industries should be conducted prior to policy implementation. Therefore, this study organized an industrial feasibility analysis for these eight CCIs. The methodologies included in-depth interviews, a literature analysis, and knowledge-discovery in databases. On the other hand, this study adopted the concept of creative industries, "the relationship between production and reproduction", and "the three-circle hypothetical interactive consumption" model for positioning these eight CCIs to choose existing industries in Macao, such as the exhibition, gambling, and cultural tourism industries, that are likely to promote CCIs. Next, the orientations of these CCIs are determined. Finally, it is suggested that the performing arts, design, and visual arts industries should be prioritized currently, and the heritage management and digital media industries are advised as to-be-developed ones. In contrast, the clothing, pop music, film and video, animation, and publishing industries are not so beneficial for Macao's development.
Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval

Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination, and lighting conditions. The accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper, a local gradient hexa pattern is proposed that identifies the relationship among the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. The proposed descriptor effectively transforms these relationships into binary micropatterns discriminating inter-class facial images with optimal precision. The recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors, namely, local derivative pattern, local tetra pattern, multiblock local binary pattern, and local vector pattern over the most challenging and benchmark facial image databases, i.e., Cropped Extended Yale B, CMU-PIE, color-FERET, LFW, and Ghallager database. The proposed descriptor has better recognition as well as retrieval rates compared with state-of-the-art descriptors.
Conditional motion in-betweening

Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking. Although state-of-the-art MIB methods are capable of producing plausible mo-tions given sparse key-poses, they often lack the controllability to generate motions satisfying the se-mantic contexts required in practical applications. We focus on the method that can handle pose or se-mantic conditioned MIB tasks using a unified model. We also present a motion augmentation method to improve the quality of pose-conditioned motion generation via defining a distribution over smooth tra-jectories. Our proposed method outperforms the existing state-of-the-art MIB method in pose prediction errors while providing additional controllability. Our code and results are available on our project web page: https://jihoonerd.github.io/Conditional- Motion- In- Betweening . (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Applying the food multimix concept for sustainable and nutritious diets

Despite a rich and diverse ecosystem, and biodiversity, worldwide, more than 2 billion people suffer from micronutrient malnutrition or hidden hunger. Of major concern are a degradation of our ecosystems and agricultural systems which are thought to be unsustainable thereby posing a challenge for the future food and nutrition security. Despite these challenges, nutrition security and ensuring well balanced diets depend on sound knowledge and appropriate food choices in a complex world of plenty and want. We have previously reported on how the food multimix (FMM) concept, a food-based and dietary diversification approach can be applied to meet energy and micronutrient needs of vulnerable groups through an empirical process. Our objective in this paper is to examine how the concept can be applied to improve nutrition in a sustainable way in otherwise poor and hard-to-reach communities. We have reviewed over 100 FMM food recipes formulated from combinations of commonly consumed traditional candidate food ingredients; on average five per recipe, and packaged as per 100 g powders from different countries including Ghana, Kenya, Botswana, Zimbabawe and Southern Africa, India, Mexico, Malaysia and the UK; and for different age groups and conditions such as older infants and young children, pregnant women, HIV patients, diabetes and for nutrition rehabilitation. Candidate foods were examined for their nutrient strengths and nutrient content and nutrient density of recipes per 100 g were compared with reference nutrient intakes for the different population groups. We report on the nutrient profiles from our analysis of the pooled and age-matched data as well as sensory analysis and conclude that locally produced FMM foods can complement local diets and contribute significantly to meet nutrient needs among vulnerable groups in food-insecure environments.
Unabridged adjacent modulation for clothing parsing

Clothing parsing has made tremendous progress in the domain of computer vision recently. Most state-ofthe-art methods are based on the encoder-decoder architecture. However, the existing methods mainly neglect problems of feature uncalibration within blocks and semantics dilution between blocks. In this work, we propose an unabridged adjacent modulation network (UAM-Net) to aggregate multi-level features for clothing parsing. We first build an unabridged channel attention (UCA) mechanism on feature maps within each block for feature recalibration. We further design a top-down adjacent modulation (TAM) for decoder blocks. By deploying TAM, high-level semantic information and visual contexts can be gradually transferred into lower-level layers without loss. The joint implementation of UCA and TAM ensures that the encoder has an enhanced feature representation ability, and the low-level features of the decoders contain abundant semantic contexts. Quantitative and qualitative experimental results on two challenging benchmarks (i.e., colorful fashion parsing and the modified fashion clothing) declare that our proposed UAM-Net can achieve competitive high-accurate performance with the state-of-the-art methods. The source codes are available at: https://github.com/ctzuo/UAM-Net .(c) 2022 Elsevier Ltd. All rights reserved.
Breathing pattern and its evaluation by muscle dynamometer md03

The purpose of the study was to determine the proportion of engagements of individual breathing sectors during 1-min physically active breathing at rest in 163 healthy, physically active participants (students of Physical Education and Sport). The research analyzed breathing movements through the muscle dynamometer MD03 (Hitron, Plzeň, Czech Republic). The proportion of engagements of the individual breathing sector in the group analyzed was determined based on measurement results. The lower breathing sector was engaged at 29.2%, the middle breathing sector at 31.0%, and the upper breathing sector at 39.8%. The largest observed difference between the involvement of individual breath sectors was 10.6% between the lower and upper breathing sectors. The muscle dynamometer MD03 may be instrumental for practising both localized breathing and full breath.
Gradient-based global features for seam carving

We propose a gradient-based global feature and its application to seam carving. We focus on areas, rather than points and lines, to be assigned as important elements for expressing the rough location of salient objects in an image. The proposed feature is calculated with a low computational load based on gray-scale intensity. The superior performance of the proposed gradient-based global feature, as compared to state-of-the-art salient features for seam carving, is demonstrated experimentally.
Optimization of cervical cage and analysis of its base material: A finite element study

Nowadays, cervical disorders are common due to human lifestyles. Accordingly, the cage design should be optimized as an essential issue. For an optimal design, an objective function is utilized to calculate the proper geometrical parameters. Additionally, the base material of the cage plays a key role in its functionality and final cost. Novel materials are currently introduced with more compatibility with the bone in terms of mechanical and chemical properties. In this study, a cervical cage was modeled based on PEEK material with three types of tooth designs on its surface. The cervical cage is assumed to be implanted between C6 and C7 vertebrae. The geometric parameters of the cage were optimized to minimize the mass by determining allowable stress and subsidence. The effect of complete cortical removal was investigated as a surgical mistake. Finally, a new composition of PEEK/titanium was introduced as the base material of the cage. Ansys 18.2 was used for FEA. The cage with a straight tooth was chosen due to its lower stress and subsidence compared with other designs. Furthermore, the optimized structures of all three tooth designs were determined. The mass and volume of the optimal cages were reduced by 41.47% and 41.52% respectively. Besides, complete cortical resection should not be carried out during fusion surgery, since it may lead to higher subsidence. The composition of PEEK/titanium was chosen as an appropriate base material due to its better performance compared with PEEK or titanium alone.
An efficient and low power one-lambda crosstalk avoidance code design for network on chips

Crosstalk faults occurring in wires of Networks on Chip (NoCs) can seriously threaten the reliability of data transfer. One efficient way to tackle crosstalk faults is numeral-based Crosstalk Avoidance Codes (CACs). Numeral-based CACs reduce crosstalk faults by preventing specific transition patterns to occur. One-Lambda Codes (OLCs) are the most efficient types of CACs. However, the codec of OLCs imposes overheads including power consumption, critical path and area occupation to the routers of NoCs. To find overhead-efficient OLCs, this paper proposes an Algorithm for Generating OLC Numeral systems (AGON). AGON provides a tradeoff for designers in selecting overhead-efficient OLCs. Using AGON, an efficient numeral-based OLC called Subtraction based-Numeral (Sub-Num) is proposed that benefits the Numeral system that can omit OLC-induced transition patterns completely. In addition, the mapping algorithm of Sub-Num can reduce the overheads of codec more efficiently than the other state-of-the art OLCs. Evaluation results using SPICE and VHDL simulations show that Sub-Num reduces power consumption and average delay of wires by 10% and 9%, and also overheads of codecs including dynamic power consumption, critical path and area occupation by 52%, 51% and 21%, respectively as compared to the state-of-the-art numeral-based OLC.
The Clinical Implications of the Academic Performance of the Siblings of Individuals With Autism Spectrum Disorder

We all know that autism spectrum disorder (ASD) can affect academic performance. Many children with autism face different challenges at school. However, less attention is paid to the siblings of autistic children, who are at a high risk of ASD or the broad autism phenotype (BAP). Recent data also shows that many siblings of ASD children suffer from neurodevelopmental disorders, mental health problems as well as poor academic performance. This review will look at the possible etiologies of the poor school performance of autistic children's siblings, with an emphasis on the challenges they face. We will also highlight the clinical implications of these findings, and the possible solutions that can help this vulnerable group.
Deep Gait Recognition: A Survey

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.
The development of the amnion in mice and other amniotes

The amnion is an extraembryonic tissue that evolutionarily allowed embryos of all amniotes to develop in a transient and local aquatic environment. Despite the importance of this tissue, very little is known about its formation and its molecular characteristics. In this review, we have compared the basic organization of the extraembryonic membranes in amniotes and describe the two types of amniogenesis, folding and cavitation. We then zoom in on the atypical development of the amnion in mice that occurs via the formation of a single posterior amniochorionic fold. Moreover, we consolidate lineage tracing data to better understand the spatial and temporal origin of the progenitors of amniotic ectoderm, and visualize the behaviour of their descendants in the extraembryonic-embryonic junctional region. This analysis provides new insight on amnion development and expansion. Finally, using an online-available dataset of single-cell transcriptomics during the gastrulation period in mice, we provide bioinformatic analysis of the molecular signature of amniotic ectoderm and amniotic mesoderm. The amnion is a tissue with unique biomechanical properties that deserves to be better understood. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
Prediction Error Expansion (PEE) based Reversible polygon mesh watermarking scheme for regional tamper localization

This paper proposes a block based semi-fragile reversible authentication scheme that achieves regional localization for different classes of geometry and topology based mesh attacks. First of all, the model bounding volume is spatially partitioned into sub-volumes using Octree data structure and for each sub-volume, the embeddable units are computed. Each embeddable unit is comprises of three vertices and one bit can be embedded to an embeddable unit using a Prediction Error Histogram (PEH) shifting strategy. In PEH shifting, few of the embeddable units are expandable and few are shiftable. To compute PEH, Vertex Normal Value Ordering (VNVO) is performed and the maximum prediction error value is expanded. During PEH generation, to achieve sharper histograms and to get minimum numbers of shiftable units, adaptive bin-width selection step is also added. Verification of each block is performed by computing CRC-8 using vertex information from the block and embedding to the expandable units of the corresponding block itself. The proposed method could generate a sharper histogram than the state of the art methods and the proposed embedding function incurred very low distortion to mesh surface. It also outperforms the prior arts by achieving regional taper localization for both geometrical as well as topological attacks. The results analysis justifies the superiority of the proposed work than state of the art methods.
The challenges of growing orchids from seeds for conservation: An assessment of asymbiotic techniques

Lewis Knudson first successfully germinated orchid seeds asymbiotically on artificial medium in 1922. While many orchid species have since been grown asymbiotically, the tremendous variation in how species respond to artificial medium and growth conditions ex situ has also become apparent in the past century. In this study, we reviewed published journal articles on asymbiotic orchid seed germination to provide a summary of techniques used and to evaluate if these differ between terrestrial and epiphytic species, to identify areas where additional research is needed, and to evaluate whether asymbiotic germination could be used more often in ex situ conservation. We found articles reporting successful asymbiotic germination of 270 species and 20 cultivars across Orchidaceae. Researchers often used different techniques with epiphytic versus terrestrial species, but species-specific responses to growth media and conditions were common, indicating that individualized protocols will be necessary for most species. The widespread success in generating seedlings on artificial media suggests that asymbiotic techniques should be another tool for the conservation of rare orchid species. Further advances are needed in understanding how to introduce mycorrhizae to axenically grown orchids and to maximize the viability of seedlings reintroduced into natural habitats to fully utilize these methods for conservation.
Robust online video super-resolution using an efficient alternating projections scheme

Video super-resolution reconstruction (SRR) algorithms attempt to reconstruct high-resolution (HR) video sequences from low-resolution observations. Although recent progress in video SRR has significantly improved the quality of the reconstructed HR sequences, it remains challenging to design SRR algorithms that achieve good quality and robustness at a small computational complexity, being thus suitable for online applications. In this paper, we propose a new adaptive video SRR algorithm that achieves state-of-the-art performance at a very small computational cost. Using a nonlinear cost function constructed considering characteristics of typical innovation outliers in natural image sequences and an edge-preserving regularization strategy, we achieve state-of-the-art reconstructed image quality and robustness. This cost function is optimized using a specific alternating projections strategy over non-convex sets that is able to converge in a very few iterations. An accurate and very efficient approximation for the projection operations is also obtained using tools from multidimensional multirate signal processing. This solves the slow convergence issue of stochastic gradient-based methods while keeping a small computational complexity. Simulation results with both synthetic and real image sequences show that the performance of the proposed algorithm is similar or better than state-of-the-art SRR algorithms, while requiring only a small fraction of their computational cost. (C) 2020 Elsevier B.V. All rights reserved.