ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting

Due to the chaotic nature of the underlying physical processes, even state-of-the-art models cannot perfectly forecast the solar irradiance at the surface of the earth. There is, therefore, a growing interest in the research community for forecasting methods that can quantify their own uncertainty. This paper proposes a novel probabilistic framework for forecasting day-ahead hourly solar irradiance. A principal component analysis (PCA) is used to tightly combine a high-resolution mesoscale numerical weather prediction (NWP) model with a quantile gradient boosting algorithm. A thorough evaluation of the deterministic and probabilistic properties of the model is conducted for a full year in the tropical island of Singapore. The impact of the sky conditions on its performance is also considered. Furthermore, a rigorous statistical framework is employed to systematically benchmark our model against two state of the art methods, a Lasso model output statistic procedure and an analog ensemble (AnEn). Our model significantly improves the numerical weather prediction model: it achieves a 41% reduction of the MAE and 39% reduction of the RMSE. It is also slightly more accurate than Lasso and has a CRPS 4% lower than that of AnEn.
Neurite Tracing With Object Process

In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information about neuronal branches from the microscopy data into connected minimum spanning trees. Such digital reconstruction is represented in standard SWC format, prevalent for archiving, sharing, and further analysis in the neuroimaging community. Our proposed pipeline outperforms state of the art methods in tracing accuracy and minimizes the subjective variability in reconstruction, inherent to semi-automatic methods.
Effect of empirical antifungal treatment on mortality in non-neutropenic critically ill patients: a propensity-matched retrospective cohort study

To evaluate the effect of empirical antifungal treatment (EAFT) on mortality in critically ill patients without invasive fungal infections (IFIs). This was a single-center propensity score-matched retrospective cohort study involving non-transplanted, non-neutropenic critically ill patients with risk factors for invasive candidiasis (IC) in the absence of IFIs. We compared all-cause hospital mortality and infection-attributable hospital mortality in patients who was given EAFT for suspected IC as the cohort group and those without any systemic antifungal agents as the control group. Among 640 eligible patients, 177 patients given EAFT and 177 control patients were included in the analyses. As compared with controls, EAFT was not associated with the lower risks of all-cause hospital mortality [odds ratio (OR), 0.911; 95% CI, 0.541-1.531; P = 0.724] or infection-attributable hospital mortality (OR, 1.149; 95% CI, 0.632-2.092; P = 0.648). EAFT showed no benefit of improvement of infection at discharge, duration of mechanical ventilation, and antibiotic-free days. However, the later initiation of EAFT was associated with higher risks of all-cause hospital mortality (OR, 1.039; 95% CI, 1.003 to 1.076; P = 0.034) and infection-attributable hospital mortality (OR, 1.046; 95% CI, 1.009 to 1.085; P = 0.015) in patients with suspected IC. This effect was also found in infection-attributable hospital mortality (OR, 1.042; 95% CI, 1.005 to 1.081; P = 0.027) in septic patients with suspected IC. EAFT failed to decrease hospital mortality in non-neutropenic critically ill patients without IFIs. The timing may be critical for EAFT to improve mortality in these patients with suspected IC. ChiCTR2000038811, registered on Oct 3, 2020.
Impact of gulf war toxic exposures after mild traumatic brain injury

Chemical and pharmaceutical exposures have been associated with the development of Gulf War Illness (GWI), but how these factors interact with the pathophysiology of traumatic brain injury (TBI) remains an area of study that has received little attention thus far. We studied the effects of pyridostigmine bromide (an anti-nerve agent) and permethrin (a pesticide) exposure in a mouse model of repetitive mild TBI (r-mTBI), with 5 impacts over a 9-day period, followed by Gulf War (GW) toxicant exposure for 10 days beginning 30 days after the last head injury. We then assessed the chronic behavioral and pathological sequelae 5 months after GW agent exposure. We observed that r-mTBI and GWI cumulatively affect the spatial memory of mice in the Barnes maze and result in a shift of search strategies employed by r-mTBI/GW exposed mice. GW exposure also produced anxiety-like behavior in sham animals, but r-mTBI produced disinhibition in both the vehicle and GW treated mice. Pathologically, GW exposure worsened r-mTBI dependent axonal degeneration and neuroinflammation, increased oligodendrocyte cell counts, and increased r-mTBI dependent phosphorylated tau, which was found to colocalize with oligodendrocytes in the corpus callosum. These results suggest that GW exposures may worsen TBI-related deficits. Veterans with a history of both GW chemical exposures as well as TBI may be at higher risk for worse symptoms and outcomes. Subsequent exposure to various toxic substances can influence the chronic nature of mTBI and should be considered as an etiological factor influencing mTBI recovery.
A Low-Power Photoplethysmogram-Based Heart Rate Sensor Using Heartbeat Locked Loop

In this paper, we present an ultralow power heart rate (HR) monitoring photoplethysmography (PPG) sensor using a heartbeat locked loop (HBLL). The HBLL generates a narrow window that turns on the LED and analog-front-end only when a peak is expected in the PPG signal. The prototype PPG sensor implemented in 0.18 mu m CMOS has an effective duty-cycle of 0.01% and consumes only 43.4 mu W at a HR of 60 b/m, which is the lowest power consumption compared with previous state-of-the-art PPG sensors. The HR error of the proposed sensor is less than 2.1 b/m for HR below 180 b/m.
Texture classification using block intensity and gradient difference (BIGD) descriptor

In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and gradient differences of pairwise blocks to construct the local BIGD descriptor. The random sampling strategy and the multi-scale framework help BIGD descriptors capture the distinctive patterns of patches at different orientations and spatial granularity levels. We use vectors of locally aggregated descriptors (VLAD) or improved Fisher vector (IFV) to encode local BIGD descriptors into a full image descriptor, which is then fed into a linear support vector machine (SVM) classifier for texture classification. We compare the proposed descriptor with typical and state-of-the-art ones by evaluating their classification performance on five public texture data sets including Brodatz, CUReT, KTH-TIPS, and KTH-TIPS-2a and -2b. Experimental results show that the proposed BIGD descriptor with stronger discriminative power yields 0.12% similar to 6.43% higher classification accuracy than the state-of-the-art texture descriptor, dense microblock difference (DMD).
Knowledge Transfer with Citizen Science: Luft-Leipzig Case Study

Community-based participatory research initiatives such as "hackAir", "luftdaten.info", "senseBox", "CAPTOR", "CurieuzeNeuzen Vlaanderen", "communityAQ", and "Healthy Air, Healthier Children" campaign among many others for mitigating short-lived climate pollutants (SLCPs) and improving air quality have reported progressive knowledge transfer results. These research initiatives provide the research community with the practical four-element state-of-the-art method for citizen science. For the preparation-, measurements-, data analysis-, and scientific support-elements that collectively present the novel knowledge transfer method, the Luft-Leipzig project results are presented. This research contributes to science by formulating a novel method for SLCP mitigation projects that employ citizen scientists. The Luft-Leipzig project results are presented to validate the four-element state-of-the-art method. The method is recommended for knowledge transfer purposes beyond the scope of mitigating short-lived climate pollutants (SLCPs) and improving air quality.
HEADS-JOIN: Efficient Earth Mover's Distance Similarity Joins on Hadoop

The Earth Mover's Distance (EMD) similarity join has a number of important applications such as near duplicate image retrieval and distributed based pattern analysis. However, the computational cost of EMD is super cubic and consequently the EMD similarity join operation is prohibitive for datasets of even medium size. We propose to employ the Hadoop platform to speed up the operation. Simply porting the state-of-the-art metric distance similarity join algorithms to Hadoop results in inefficiency because they involve excessive distance computations and are vulnerable to skewed data distributions. We propose a novel framework, named HEADS-JOIN, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these EMD lower bounds has constant or linear complexity. We investigate both range and top-k joins, and design efficient algorithms on three popular Hadoop computation paradigms, i.e., MapReduce, Bulk Synchronous Parallel, and Spark. We conduct extensive experiments on both real and synthetic datasets. The results show that HEADS-JOIN outperforms the state-of-the-art metric similarity join technique, i.e., Quickjoin, by up to an order of magnitude and scales out well.
Photo-catalytic and biomedical applications of one-step, plant extract-mediated green-synthesized cobalt oxide nanoparticles

In the present work, for the first time, green chemically synthesized and stabilized Co3O4 nanoparticles were employed for catalytic conversion of isopropyl alcohol to acetone by dehydrogenation of IPA. Plant extract of Rosmarinus officinalis was used as a reducing and stabilizing agent for this synthesis. The biosynthesized Co3O4 nanoparticles were annealed at 450℃ followed by their physiochemical characterizations through XRD, SEM, AFM, and FTIR. Size distribution information collected through XRD and AFM back each other, and it was found to be 6.5 nm, having the highest number of nanoparticles in this size range. While SEM confirms the self-arranging property of synthesized nanoparticles due to their magnetic nature, furthermore, the biogenic Co3O4 nanoparticles were studied for their catalytic potential to convert isopropyl alcohol to acetone with the help of a UV-Visible spectrophotometer. The highest photocatalytic conversion of 99% was obtained in time period of 48 s. For the first time ever, nanoparticles were used for 5 cycles to evaluate their recyclable nature and conversion fell from 99 to 86% and the end of the 5th cycle. Later anti-bacterial activity against 3 Gram-positive and 3 Gram-negative strains gave the highest inhibition value of 99% against Streptococcus pneumoniae at 500 µg/mL. Finally, a cytotoxicity study on synthesized nanomaterials was carried out by exposing freshly drawn human macrophages to them. It was found that even at the highest concentration of 500 µg/mL, the nanoparticles showed only 28% lysis.
ConvSequential-SLAM: A Sequence-Based, Training-Less Visual Place Recognition Technique for Changing Environments

Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place under changing viewpoints and appearances. A large number of handcrafted and deep-learning-based VPR techniques exist, where the former suffer from appearance changes and the latter have significant computational needs. In this paper, we present a new handcrafted VPR technique, namely ConvSequential-SLAM, that achieves state-of-the-art place matching performance under challenging conditions. We utilise sequential information and block-normalisation to handle appearance changes, while using regional-convolutional matching to achieve viewpoint-invariance. We analyse content-overlap in-between query frames to find a minimum sequence length, while also re-using the image entropy information for environment-based sequence length tuning. State-of-the-art performance is reported in contrast to 9 contemporary VPR techniques on 4 public datasets. Qualitative insights and an ablation study on sequence length are also provided.
Quality Analysis of Autologous Platelet-Rich Plasmapheresis

To summarized the technology of autologous platelet-rich plasmapheresis and analyzed the product quality, in order to provide safe and effective product guarantee service for clinical treatment. Technical parameters were set according to patient age, weight, height, and preoperative routine blood indices. Autologous platelet-rich plasma (PRP) was collected, and the product quality and adverse reactions of patients were statistically analyzed. Autologous PRP had platelet (PLT), white blood cell (WBC), and red blood cell (RBC) counts of (1250.26 ± 435.88) × 109/L, (1.19 ± 1.95) × 109/L, and (0.05 ± 0.04) × 1012/L, respectively. The PLT enrichment ratio in PRP was 5.66 ± 1.66. There was no significant difference in PLT, RBC, WBC, or hematocrit before and after apheresis (P > 0.05). The incidence of adverse reactions was 8%, and all were mild. When clinical patients use PRP in the treatment of diseases, autologous platelet-rich plasmapheresis technology was used to apheresis PRP, which has good product quality and few adverse reactions, and thus can be adopted more widely.
Self-reported voice difficulties in educational professionals during COVID-19 in Quebec: a cross-sectional mixed-methods study

Background: The health measures imposed by COVID-19 on workplaces created adverse communication settings. Our cross-sectional study aimed to document the nature and severity of the vocal difficulties experienced by educational professionals a few weeks after the implementation of health measures in schools and early childhood settings in Quebec, Canada while teaching in class.Methods: To this end, we conducted a self-report survey containing nine close-ended questions and one open-ended question regarding self-reported vocal difficulties and the implementation of health measures. The survey was answered by 194 educational professionals in October 2020.Results: Since the introduction of the health measures, respondents reported often or always: having difficulty making themselves heard (66.5%), needing to strain their voice (68.1%), having throat pain after work (38.1%), and being concerned about their vocal health (25.2%). 35.6% perceived that their voice changed moderately or a lot and 75.3% did not feel equipped to take care of their vocal health. Fisher's exact tests revealed the difficulties overall were more present in women (p < 0.05).Discussion: The qualitative analysis of open-ended question answers shows a circular process at play, where the vocal responses to the COVID-19-induced communication barriers contribute to creating more problematic communication settings, thus increasing the challenges for vocal health. Better equipping the professionals to take care of their vocal health by developing resources in their professional settings to help them face vocal challenges in both every day as well as extreme situations, should be a priority.
A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription

Automatic music transcription (AMT) is a critical problem in the field of music information retrieval (MIR). When AMT is faced with deep neural networks, the variety of timbres of different instruments can be an issue that has not been studied in depth yet. The goal of this work is to address AMT transcription by analyzing how timbre affect monophonic transcription in a first approach based on the CREPE neural network and then to improve the results by performing polyphonic music transcription with different timbres with a second approach based on the Deep Salience model that performs polyphonic transcription based on the Constant-Q Transform. The results of the first method show that the timbre and envelope of the onsets have a high impact on the AMT results and the second method shows that the developed model is less dependent on the strength of the onsets than other state-of-the-art models that deal with AMT on piano sounds such as Google Magenta Onset and Frames (OaF). Our polyphonic transcription model for non-piano instruments outperforms the state-of-the-art model, such as for bass instruments, which has an F-score of 0.9516 versus 0.7102. In our latest experiment we also show how adding an onset detector to our model can outperform the results given in this work.
Deep Convolutional Neural Networks for Unconstrained Ear Recognition

This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear recognition on the unconstrained EarVN1.0 dataset. As the dataset size is still insufficient to train deep CNNs from scratch, we utilize transfer learning and propose different domain adaptation strategies. The experiments show that our networks, which are fine-tuned using custom-sized inputs determined specifically for each CNN architecture, obtain state-of-the-art recognition performance where a single ResNeXt101 model achieves a rank-1 recognition accuracy of 93.45%. Moreover, we achieve the best rank-1 recognition accuracy of 95.85% using an ensemble of fine-tuned ResNeXt101 models. In order to explain the performance differences between models and make our results more interpretable, we employ the t-SNE algorithm to explore and visualize the learned features. Feature visualizations show well-separated clusters representing ear images of the different subjects. This indicates that discriminative and ear-specific features are learned when applying our proposed learning strategies.
Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.
Genome-wide selection signatures reveal widespread synergistic effects of two different stressors in Drosophila melanogaster

Experimental evolution combined with whole-genome sequencing (evolve and resequence (E&R)) is a powerful approach to study the adaptive architecture of selected traits. Nevertheless, so far the focus has been on the selective response triggered by a single stressor. Building on the highly parallel selection response of founder populations with reduced variation, we evaluated how the presence of a second stressor affects the genomic selection response. After 20 generations of adaptation to laboratory conditions at either 18°C or 29°C, strong genome-wide selection signatures were observed. Only 38% of the selection signatures can be attributed to laboratory adaptation (no difference between temperature regimes). The remaining selection responses are either caused by temperature-specific effects, or reflect the joint effects of temperature and laboratory adaptation (same direction, but the magnitude differs between temperatures). The allele frequency changes resulting from the combined effects of temperature and laboratory adaptation were more extreme in the hot environment for 83% of the affected genomic regions-indicating widespread synergistic effects of the two stressors. We conclude that E&R with reduced genetic variation is a powerful approach to study genome-wide fitness consequences driven by the combined effects of multiple environmental factors.
A simple single-sensor MPPT solution

Maximum power point trackers (MPPTs) are used to ensure optimal utilization of solar cells. The implementation essentially involves sensing input current and voltage. An MPPT algorithm uses this information to maximize power drawn from the solar cells. Understandably, such realization is costly. Current state of the art allows replacing one of the sensors by complicated computations. In the present work, an empirical observation is used to develop a strategy, which employs a single voltage sensor and carries out simple computations for a buck converter-based MPPT.
Structure-Driven Unsupervised Domain Adaptation for Cross-Modality Cardiac Segmentation

Performance degradation due to domain shift remains a major challenge in medical image analysis. Unsupervised domain adaptation that transfers knowledge learned from the source domain with ground truth labels to the target domain without any annotation is the mainstream solution to resolve this issue. In this paper, we present a novel unsupervised domain adaptation framework for cross-modality cardiac segmentation, by explicitly capturing a common cardiac structure embedded across different modalities to guide cardiac segmentation. In particular, we first extract a set of 3D landmarks, in a self-supervised manner, to represent the cardiac structure of different modalities. The high-level structure information is then combined with another complementary feature, the Canny edges, to produce accurate cardiac segmentation results both in the source and target domains. We extensively evaluate our method on the MICCAI 2017 MM-WHS dataset for cardiac segmentation. The evaluation, comparison and comprehensive ablation studies demonstrate that our approach achieves satisfactory segmentation results and outperforms state-of-the-art unsupervised domain adaptation methods by a significant margin.