ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Graph Attention for Automated Audio Captioning

State-of-the-art audio captioning methods typically use the encoder-decoder structure with pretrained audio neural networks (PANNs) as encoders for feature extraction. However, the convolution operation used in PANNs is limited in capturing the long-time dependencies within an audio signal, thereby leading to potential performance degradation in audio captioning. This letter presents a novel method using graph attention (GraphAC) for encoder-decoder based audio captioning. In the encoder, a graph attention module is introduced after the PANNs to learn contextual association (i.e. the dependency among the audio features over different time frames) through an adjacency graph, and a top-k mask is used to mitigate the interference from noisy nodes. The learnt contextual association leads to a more effective feature representation with feature node aggregation. As a result, the decoder can predict important semantic information about the acoustic scene and events based on the contextual associations learned from the audio signal. Experimental results show that GraphAC outperforms the state-of-the-art methods with PANNs as the encoders, thanks to the incorporation of the graph attention module into the encoder for capturing the long-time dependencies within the audio signal.
Heating temperature and water activity of alfalfa seeds affect thermal inactivation of Salmonella and maintaining seed viability

Sprouts have been involved in many outbreaks of salmonellosis where seeds were identified as the likely source of contamination. This study aimed to develop an effective heat treatment that could achieve a >5-log reduction of Salmonella inoculated on alfalfa seeds while maintaining seed viability and vigor. Effects of seeds' water activity (aw) and heat treatment temperature on Salmonella inactivation and seed viability were determined. Alfalfa seeds were dip-inoculated with a four-strain Salmonella cocktail and dried to aw of 0.05-0.20. The inoculated seeds were then placed in sealed glass tubes and heated at 65.9, 71.0, and 76.6 °C for up to 180 h. Increasing aw of seeds greatly improved thermal inactivation of Salmonella. For example, to achieve a >5-log reduction of Salmonella on seeds, treatment times of 140 and 60 h at 71.0 °C were required for aw of 0.1 and 0.2, respectively. Treatment temperature also greatly affected inactivation of Salmonella on alfalfa seeds. For example, to achieve a >5-log reduction of Salmonella on seeds with aw of 0.2, treatment times of 180 and 60 h were required for temperatures of 65.9 and 71.0 °C, respectively. Seeds' aw was critical for preserving seed viability. When seeds were treated at 71.0 °C for 60 h, increasing aw from 0.1 to 0.2 decreased the sprout yield ratio from 103.9 % to 73.7 %. Treatment of seeds with aw of 0.1 at 71.0 °C was found to be optimum for achieving a desirable Salmonella inactivation level while maintaining seed viability, resulting in 4.2 and 6.0 log reductions of Salmonella and yield ratios of 100.7 % and 96.1 % after 100- and 140-h treatments, respectively. This optimum heat treatment was compared with the traditional 20,000-ppm chlorine wash in terms of Salmonella inactivation and preservation of seed viability and found to be a far superior disinfection method. The chlorine treatment achieved 1.8 and 2.0 log reductions of Salmonella and yield ratios of 70.9 % and 65.1 % after 15- and 20-min treatments, respectively.
On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey

This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning. It is showing interesting results in the computer vision field and, recently, it has been applied to the visual tracking problem yielding to the rapid development of novel tracking strategies. After providing an introduction to reinforcement learning, this paper compares recent visual tracking approaches based on deep reinforcement learning. Analysis of the state-of-the-art suggests that reinforcement learning allows modeling varying parts of the tracking system including target bounding box regression, appearance model selection, and tracking hyper-parameter optimization. The DRL framework is elegant and intriguing, and most of the DRL-based trackers achieve state-of-the-art results.
Improving Face-Based Age Estimation With Attention-Based Dynamic Patch Fusion

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.
Developmental exposure of aflatoxin B1 reversibly affects hippocampal neurogenesis targeting late-stage neural progenitor cells through suppression of cholinergic signaling in rats

To elucidate the maternal exposure effects of aflatoxin B1 (AFB1) and its metabolite aflatoxin M1, which is transferred into milk, on postnatal hippocampal neurogenesis, pregnant Sprague-Dawley rats were provided a diet containing AFB1 at 0, 0.1, 0.3, or 1.0 ppm from gestational day 6 to day 21 after delivery on weaning. Offspring were maintained through postnatal day (PND) 77 without AFB1 exposure. Following exposure to 1.0 ppm AFB1, offspring showed no apparent systemic toxicity at weaning, whereas dams showed increased liver weight and DNA repair gene upregulation in the liver. In the hippocampal dentate gyrus of male PND 21 offspring, the number of doublecortin(+) progenitor cells were decreased, which was associated with decreased proliferative cell population in the subgranular zone at ≥ 0.3 ppm, although T-box brain 2(+) cells, tubulin beta III(+) cells, gamma-H2A histone family, member X(+) cells, and cyclin-dependent kinase inhibitor 1A(+) cells did not fluctuate in number. AFB1 exposure examined at 1.0 ppm also resulted in transcript downregulation of the cholinergic receptor subunit Chrna7 and dopaminergic receptor Drd2 in the dentate gyrus, although there was no change in transcript levels of DNA repair genes. In the hippocampal dentate hilus, interneurons expressing CHRNA7 or phosphorylated tropomyosin receptor kinase B (TRKB) decreased at ≥ 0.3 ppm. On PND 77, there were no changes in neurogenesis-related parameters. These results suggested that maternal AFB1 exposure reversibly affects hippocampal neurogenesis targeting type-3 progenitor cells. This mechanism likely involves suppression of cholinergic signals on hilar GABAergic interneurons and brain-derived neurotrophic factor-TRKB signaling from granule cells. The no-observed-adverse-effect level for offspring neurogenesis was determined to be 0.1 ppm (7.1-13.6 mg/kg body weight/day).
Combination of Kullback-Leibler divergence and Manhattan distance measures to detect salient objects

Salient object detection is a computer vision technique that filters out redundant visual information and considers potentially relevant parts of our visual field. In this paper, we modify the Liu et al. model for salient object detection, which combines multi-scale contrast, center-surround histogram and color spatial distribution with conditional random fields. A combination of Symmetric Kullback-Leibler divergence and Manhattan distance instead of chi-square measure is employed to determine center-surround histogram difference. The modified Liu et al. model also uses a less computational intensive color spatial distribution map. To check the efficacy of the modified Liu et al. model, the performance is evaluated in terms of precision, recall, F-measure, area under curve and computation time. Experiment is carried out on a publicly available image datasets, and performance is compared with Liu et al. model and six other popular state-of-the-art models. Experimental results demonstrate that the modified Liu et al. model outperforms Liu et al. model and other existing state-of-the-art methods in terms of precision, F-measure, area under curve and has comparable performance in terms of recall with Liu et al. model.
Injectable Immunotherapeutic Hydrogel Containing RNA-Loaded Lipid Nanoparticles Reshapes Tumor Microenvironment for Pancreatic Cancer Therapy

Pancreatic cancer immunotherapy is becoming a promising strategy for improving the survival rate of postsurgical patients. However, the low response rate to immunotherapy suggests a low number of antigen-specific T cells and a high number of immunosuppressive tumor-associated macrophages in the pancreatic tumor microenvironment. Herein, we developed an in situ injectable thermosensitive chitosan hydrogel loaded with lipid-immune regulatory factor 5 (IRF5) mRNA/C-C chemokine ligand 5 (CCL5) siRNA (LPR) nanoparticle complexes (LPR@CHG) that reprogram the antitumoral immune niche. The LPR@CHG hydrogel upregulates IRF5 and downregulates CCL5 secretion, which contribute to a significant increase in M1 phenotype macrophages. Tumor growth is controlled by effective M1 phenotype macrophage that initiate T cell-mediated immune responses. Overall, the LPR@CHG hydrogel is expected to be a meaningful immunotherapy platform that can reshape the immunosuppressive tumor microenvironment and improve the efficacy of current pancreatic immunotherapies while minimizing systemic toxicity.
Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.
Ambiguity-aware robust teacher (ART): Enhance d self-knowle dge distillation framework with pruned teacher network

Self-knowledge distillation (self-KD) methods, which use a student model itself as the teacher model instead of a large and complex teacher model, are currently a subject of active study. Since most previ-ous self-KD approaches relied on the knowledge of a single teacher model, if the teacher model incor-rectly predicted confusing samples, poor-quality knowledge was transferred to the student model. Unfor-tunately, natural images are often ambiguous for teacher models due to multiple objects, mislabeling, or low quality. In this paper, we propose a novel knowledge distillation framework named ambiguity-aware robust teacher knowledge distillation (ART-KD) that provides refined knowledge, that reflects the ambigu-ity of the samples with network pruning. Since the pruned teacher model is simply obtained by copying and pruning the teacher model, re-training process is unnecessary in ART-KD. The key insight of ART-KD lies in the predictions of a teacher model and pruned teacher model for ambiguous samples providing different distributions with low similarity. From these two distributions, we obtain a joint distribution considering the ambiguity of the samples as teacher's knowledge for distillation. We comprehensively evaluate our method on public classification benchmarks, as well as more challenging benchmarks for fine-grained visual recognition (FGVR), achieving much superior performance to state-of-the-art counter-parts.(c) 2023 Elsevier Ltd. All rights reserved.
Viewpoints of Residency Program Directors Regarding Depressive Symptoms in Pharmacy Residents

Objective: Several publications have highlighted residency-specific factors being associated with depressive symptoms in pharmacy residents, but no studies have investigated the viewpoint of residency program directors (RPDs) regarding this issue. This study's primary objectives were to identify potential contributing factors, determine current resources, and outline possible solutions to decrease depressive symptoms among pharmacy residents from the point of view of RPDs. Methods: RPDs were asked to participate in a 45-60-minute semi-structured interview conducted via phone by the primary investigator, recorded, and transcribed using NVivo. Interviews were manually analyzed by study investigators to identify emerging themes. The investigators discussed findings and discrepancies to agree upon thematic interpretations of the transcripts. Results: Ten interviews were conducted between May 2019 and February 2020. RPD experience ranged from 2-15 years. Pharmacy practice PGY1 programs comprised 60% of interviews, 20% for community practice, and 10% each for managed care and ambulatory care. All RPDs indicated increased workload as a contributing factor to depressive symptoms in residents. The inability to accept and utilize constructive feedback and difficulties transitioning from student to resident were identified as contributing factors by 50% of the RPDs. Nine RPDs reported having employee assistance programs, stating the resource was underutilized, and identified the need for additional education regarding identification and triage, not necessarily management, to help residents. Conclusion: This study highlights consistency among RPDs regarding potential contributors to depressive symptoms in pharmacy residents and emphasizes the need for additional RPD and preceptor training to identify and help residents more effectively with these issues.
DDX60 selectively reduces translation off viral type II internal ribosome entry sites

Co-opting host cell protein synthesis is a hallmark of many virus infections. In response, certain host defense proteins limit mRNA translation globally, albeit at the cost of the host cell's own protein synthesis. Here, we describe an interferon-stimulated helicase, DDX60, that decreases translation from viral internal ribosome entry sites (IRESs). DDX60 acts selectively on type II IRESs of encephalomyocarditis virus (EMCV) and foot and mouth disease virus (FMDV), but not by other IRES types or by 5' cap. Correspondingly, DDX60 reduces EMCV and FMDV (type II IRES) replication, but not that of poliovirus or bovine enterovirus 1 (BEV-1; type I IRES). Furthermore, replacing the IRES of poliovirus with a type II IRES is sufficient for DDX60 to inhibit viral replication. Finally, DDX60 selectively modulates the amount of translating ribosomes on viral and in vitro transcribed type II IRES mRNAs, but not 5' capped mRNA. Our study identifies a novel facet in the repertoire of interferon-stimulated effector genes, the selective downregulation of translation from viral type II IRES elements.
Slum tourism in the context of the tourism and poverty (relief) debate

The paper examines the role of slum tourism in poverty relief. To do so, it surveys the state-of-the-art literature on tourism and poverty and investigates the ways in which slum tourism research relates to this literature. Slum tourism research has addressed the question of how the poor may benefit from this practice; however, these efforts have not systematically considered the general debate on tourism and poverty relief. The survey of slum tourism research also contributes to the conceptual development of the tourism-poverty nexus. The predominant choice of approaches in this field relies on quantitative indicators of poverty relief, but these do not sufficiently account for the multi-dimensional character of poverty. The study of slum tourism research points to the multi-dimensional valorisation of poverty in tourism which is an aspect often overlooked in the state-of-the-art research on tourism and poverty.
Body Mass Index in Adolescence and Long-Term Risk of Early Incident Atrial Fibrillation and Subsequent Mortality, Heart Failure, and Ischemic Stroke

Background We sought to determine the role of obesity in adolescent men on development of atrial fibrillation (AF) and subsequent associated clinical outcomes in subjects diagnosed with AF. Methods and Results We conducted a nationwide, register-based, cohort study of 1 704 467 men (mean age, 18.3±0.75 years) enrolled in compulsory military service in Sweden from 1969 through 2005. Height and weight, blood pressure, fitness, muscle strength, intelligence quotient, and medical disorders were recorded at baseline. Records obtained from the National Inpatient Registry and the Cause of Death Register were used to determine incidence and clinical outcomes of AF. During a median follow-up of 32 years (interquartile range, 24-41 years), 36 693 cases (mean age at diagnosis, 52.4±10.6 years) of AF were recorded. The multivariable-adjusted hazard ratio (HR) for AF increased from 1.06 (95% CI, 1.03-1.10) in individuals with body mass index (BMI) of 20.0 to <22.5 kg/m2 to 3.72 (95% CI, 2.44-5.66) among men with BMI of 40.0 to 50.0 kg/m2, compared with those with BMI of 18.5 to <20.0 kg/m2. During a median follow-up of ≈6 years in patients diagnosed with AF, we identified 3767 deaths, 3251 cases of incident heart failure, and 921 cases of ischemic stroke. The multivariable-adjusted HRs for all-cause mortality, incident heart failure, and ischemic stroke in AF-diagnosed men with baseline BMI >30 kg/m2 compared with those with BMI <20 kg/m2 were 2.86 (95% CI, 2.30-3.56), 3.42 (95% CI, 2.50-4.68), and 2.34 (95% CI, 1.52-3.61), respectively. Conclusions Increasing BMI in adolescent men is strongly associated with early AF, and with subsequent worse clinical outcomes in those diagnosed with AF with respect to all-cause mortality, incident heart failure, and ischemic stroke.
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.
High-Speed Low-Complexity Guided Image Filtering-Based Disparity Estimation

Stereo vision is a methodology to obtain depth in a scene based on the stereo image pair. In this paper, we introduce a discrete wavelet transform (DWT)-based methodology for a state-of-the-art disparity estimation algorithm that resulted in significant performance improvement in terms of speed and computational complexity. In the initial stage of the proposed algorithm, we apply DWT to the input images, reducing the number of samples to be processed in subsequent stages by 50%, thereby decreasing computational complexity and improving processing speed. Subsequently, the architecture has been designed based on this proposed methodology and prototyped on a Xilinx Virtex-7 FPGA. The performance of the proposed methodology has been evaluated against four standard Middlebury Benchmark image pairs viz. Tsukuba, Venus, Teddy, and Cones. The proposed methodology results in the improvement of about 44.4% cycles per frame, 52% frames/s, and 61.5% and 59.6% LUT and register utilization, respectively, compared with state-of-the-art designs.
Renewable energy technology innovation and inclusive low-carbon development from the perspective of spatiotemporal consistency

As an emerging driving factor, the positive impact of renewable energy technology innovation (RETI) on inclusive low-carbon development (ILCD) may be undervalued or even neglected. This paper develops an evaluation system to measure China's ILCD by using provincial panel data from 2006 to 2020. Based on the combined perspective of spatial spillover effect and threshold effect, this paper examines the spatial spillover effects and the regional boundary of RETI on ILCD in different periods and further analyzes five heterogeneities. The results show that (1) RETI and ILCD are increasing steadily, presenting a spatial pattern of "high in the east and low in the west." (2) Overall, RETI significantly promotes ILCD in local and neighboring areas. RETI in the growth period inhibits local ILCD, which in the mature period promotes local and neighboring ILCD. (3) The spatial spillover boundary of the whole RETI is 1400 km, that of RETI in the growth period is 1000 km, and that of RETI in the mature period is 1600 km. (4) The promotion effect of RETI on ILCD enhances over time and shows a spatial pattern of "eastern > central > south > north > western." It is further found that RETI strongly promotes ILCD in non-resource-based, high marketization, and strong environmental regulation areas. Therefore, it is necessary to break down administrative and market barriers, strengthen inter-regional cooperation and interconnection of resource elements, and establish a dynamic management mechanism of "one province, one policy" according to the regional heterogeneity for providing decision-making reference in promoting global energy transition and climate governance.