ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Modelling biological puncture: a mathematical framework for determining the energetics and scaling

Biological puncture systems use a diversity of morphological tools (stingers, teeth, spines etc.) to penetrate target tissues for a variety of functions (prey capture, defence, reproduction). These systems are united by a set of underlying physical rules which dictate their mechanics. While previous studies have illustrated form-function relationships in individual systems, these underlying rules have not been formalized. We present a mathematical model for biological puncture events based on energy balance that allows for the derivation of analytical scaling relations between energy expenditure and shape, size and material response. The model identifies three necessary energy contributions during puncture: fracture creation, elastic deformation of the material and overcoming friction during penetration. The theoretical predictions are verified using finite-element analyses and experimental tests. Comparison between different scaling relationships leads to a ratio of released fracture energy and deformation energy contributions acting as a measure of puncture efficiency for a system that incorporates both tool shape and material response. The model represents a framework for exploring the diversity of biological puncture systems in a rigorous fashion and allows future work to examine how fundamental physical laws influence the evolution of these systems.
A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging

EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually "deep" in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging.
The outcomes of psychiatric inpatients by proportion of experienced psychiatrists and nurse staffing in hospital: New findings on improving the quality of mental health care in South Korea

Readmission rates for mental health care are higher in South Korea than other Organization for Economic Development (OECD) countries. Therefore, it is worthwhile to continue investigating how to reduce readmissions. Taking a novel approach, we determined the relationship between psychiatrist experience and mental health care readmission rates. We used National Health Insurance claim data (N=21,315) from 81 hospitals to analyze readmissions within 30 days of discharge for "mood disorders" or "schizophrenia, schizotypal and delusional disorders" during 2010-2013. In this study, multilevel models that included both patient and hospital-level variables were analyzed to examine associations with readmission. Readmissions within 30 days of discharge accounted for 1079 (5.1%) claims. Multilevel analysis demonstrated that the proportion of experienced psychiatrists at a hospital was inversely associated with risk of readmission (OR: 0.79, 95% CI: 0.74-0.84 per 10% increase in experienced psychiatrists). Readmission rates for psychiatric disorders within 30 days of discharge were lower in hospitals with a higher number of nurses (OR: 0.95, 95% CI: 0.94-0.96 per 10 nurses). In conclusion, health policymakers and hospital managers should make an effort to reduce readmissions for psychiatric disorders and other diseases by considering the role that physician experience plays and nurse staffing.
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training data sets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3-D lung nodule characteristics by decomposing a 3-D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trainedResNet-50 networks that characterize the nodules' overall appearance, voxel, and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI data set and compared it to the five state-of-the-artclassificationapproaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.
Anomaly Detection With Particle Filtering for Online Video Surveillance

With growing security threats, many online and offine frameworks have been proposed for anomaly detection in video sequences. However, existing online anomaly detection techniques are either computationally very expensive or lack desirable accuracy. This research work proposes a novel particle filtering based framework for online anomaly detection which detects video frames with anomalous activities based upon the posterior probability of activities in a video sequence. The proposed method also detects anomalous regions in anomalous video frames. We propose novel prediction and measurement models to accurately detect anomalous video frames and anomalous regions in video frames. Novel prediction model for particle prediction and likelihood model for assigning weights to these particles are proposed. These models efficiently utilise variable sized cell structure which creates variable sized sub-regions of scenes in video frames. Furthermore, they efficiently extract and utilise information from the video frame in the form of size, motion and location features. The proposed framework is tested on UCSD and LIVE datasets and compared with the existing state-of-the-art algorithms in the literature. The proposed anomaly detection algorithm outperforms the state-of-the art algorithms in terms of reduced Equal Error Rate (EER) with comparatively lesser processing time.
State-of-the-art in force and tactile sensing for minimally invasive surgery

Haptic perception plays a very important role in surgery. It enables the surgeon to feel organic tissue hardness, measure tissue properties, evaluate anatomical structures, and allows him/her to commit appropriate force control actions for safe tissue manipulation. However, in minimally invasive surgery, the surgeon's ability of perceiving valuable haptic information through surgical instruments is severely impaired. Performing the surgery without such sensory information could lead to increase of tissue trauma and vital organic tissue damage. In order to restore the surgeon's perceptual capability, methods of force and tactile sensing have been applied with attempts to develop instruments that can be used to detect tissue contact forces and generate haptic feedback to the surgeon. This paper reviews the state-of-the-art in force and tactile sensing technologies applied in minimally invasive surgery. Several sensing strategies including displacement-based, current-based, pressure-based, resistive-based, capacitive-based, piezoelectric-based, vibration-based, and optical-based sensing are discussed.
An efficient ir approach based semantic segmentation

Content Based Image Retrieval (CBIR) is the task of finding similar images from a query one. The state of the art mentions two main methods to solve the retrieval problem: (1) Methods dependent on visual description, for example, bag of visual words model (BoVW), Vector of Locally Aggregated Descriptors (VLAD) (2) Methods dependent on deep learning approaches in particular convolutional neural networks (CNN). In this article, we attempt to improve the CBIR algorithms with the proposition of two image signatures based on deep learning. In the first, we build a fast binary signature by utilizing a CNN based semantic segmentation. In the second, we combine the visual information with the semantic information to get a discriminative image signature denoted semantic bag of visual phrase. We study the performance of the proposed approach on six different public datasets: Wang, Corel 10k, GHIM-10K, MSRC-V1,MSRC-V2, Linnaeus. We significantly improve the mean of average precision scores (MAP) between 10% and 25% on almost all the datasets compared to state-of-the-art methods. Several experiments achieved on public datasets show that our proposal leads to increase the CBIR accuracy.
Fuzzy Logic and Additive Wavelet-Based Panchromatic Sharpening

A fuzzy logic and additive wavelet-based image fusion scheme is proposed. The scheme injects high-frequency information from a high-resolution panchromatic image into a low-resolution multispectral image taking both the intensity levels of each band and spatial information of panchromatic image into consideration. The proposed scheme preserves both spatial and spectral information. Quantitative analysis performed on the Ikonos and Quickbird data sets demonstrates that the proposed scheme outperforms state-of-the-art multiresolution image fusion schemes.
Art painting detection and identification based on deep learning and image local features

Many art paintings are placed in film scenes or TV programs as decoration. To prevent using unauthorized copyrighted art paintings, we propose a method that combines a deep learning based object detector and hand-crafted image local features to identify copyrighted art paintings from images that contain them. The object detector is trained with our collected data to be able to detect art paintings. If a query image is input, the object detector will detect the art painting regions, then, the copyrighted art paintings can be identified by matching image local features between the art painting regions and the original copyrighted art paintings that have already been stored in advance. To test the ability of the proposed method from different aspects, we prepared four different kinds of test images: Famous, Monitor Easy, Monitor Hard, and Print. Finally, we provide a practicability analysis of our method based on the experimental results on these test images. Additionally, compared with Scale Invariant Feature Transform (SIFT), our approach outperformed by more than 20%.
Dynamic Statistical-Timing-Analysis-Based VLSI Path Delay Test Pattern Generation

Nanoscale VLSI systems are subject to increasingly significant performance variability. Accurate timing analysis and effective silicon-based performance verification techniques are critical to successful nanoscale VLSI design. The state-of-the-art statistical static timing analysis (SSTA) techniques cannot capture performance variability due to primary inputs and sequential element states which, however, is critical to path delay test generation. In this paper, we present the first dynamic statistical timing analysis-based VLSI path delay test pattern generation technique. We observe that VLSI timing analysis and power estimation target the same signal toggling activity. By leveraging the existing power estimation techniques, we have developed signal-probability-based statistical timing analysis (SPSTA), and SPSTA-based VLSI delay test pattern generation (SPSTA-DTPG) techniques. Our experimental results based on ISCAS' 89 benchmark circuits show that the state-of-the-art statistical static timing analysis-based delay test pattern generation (SSTA-DTPG) achieves an average of 47.32%, 45.14%, and 57.98%, SPSTA-DTPG achieves an average of 57.41%, 61.43%, and 68.05%, while signal probability-based statistical timing analysis-based delay test pattern generation with (test pattern) compaction (SPSTA-DTPG-C) achieves an average of 83.09%, 87.48% and 90.30% coverage of the top 50, 100, and 200 timing-critical paths, respectively.
Sex-specific behavioral effects of acute exposure to the neonicotinoid clothianidin in mice

Although neonicotinoids are among the major classes of pesticides that affect mammalian nervous systems, little is known about sex differences in their effects. This study aimed to examine whether the neurobehavioral effects of a neonicotinoid, clothianidin (CLO), differed between sexes. Male and female C57BL/6N mice were orally administered CLO (5 or 50 mg/kg) at or below the chronic no-observed-adverse-effect-level (NOAEL) and subjected to behavioral tests of emotional and learning functions. Changes in neuroactivity in several brain regions and the concentrations of CLO and its metabolites in blood and urine were measured. Acute CLO exposure caused sex-related behavioral effects; decreases in locomotor activities and elevation of anxiety-like behaviors were more apparent in males than in females. In addition, male-specific impairment of short- and long-term learning memory by CLO exposure was observed in both the novel recognition test and the Barnes maze test. Male-dominant increases in the number of c-fos positive cells were observed in the paraventricular thalamic nucleus in the thalamus and in the dentate gyrus in the hippocampus, which are related to the stress response and learning function, respectively. The concentrations of CLO and most metabolites in blood and urine were higher in males. These results support the notion that male mice are more vulnerable than females to the neurobehavioral effects of CLO and provide novel insights into the risk assessment of neonicotinoids in mammalian neuronal function.
Radiative efficiency of state-of-the-art photovoltaic cells

Maximum possible photovoltaic performance is reached when solar cells are 100% radiatively efficient, with different photovoltaic technologies at different stages in their evolution towards this ideal. An external radiative efficiency is defined, which can be unambiguously determined from standard cell efficiency measurements. Comparisons between state-of-the-art devices from the representative cell technologies produce some interesting conclusions. Copyright (c) 2011 John Wiley & Sons, Ltd.
Effect of germinated and heat-moisture treated ancient wheat on some quality attributes and bioactive components of noodles

Utilization of germinated and heat-moisture treated wheat in noodle formulation to improve bioactive components and technologic quality was investigated. Flour of untreated, germinated, heat-moisture treated germinated wheat varieties (Esperia, einkorn and emmer) were used at five ratios (0-60 %) in noodles according to (3 × 3 × 5) × 2 factorial design. Forty-five different noodle formulations were produced. Cooking loss values of the noodles raised with increasing ratios of germinated wheat flours. Heat-moisture treatment partially decreased the cooking loss of noodle prepared with germinated wheat flour. The ash, protein, phytic acid, total yellow pigment, total phenolic content, minerals (except K) and antioxidant activity values of the noodle increased with the inclusion of ancient wheat compared to modern wheat. These increases were much more evident with the use of germinated flours of modern and ancient wheat. While the germination process increased the bioactive components content, the heat-moisture treatment improved the technological quality of the noodle prepared from germinated wheat.
Perioperative Telemedicine Utilization Among Geriatric Patients Being Evaluated for Abdominal Wall Reconstruction and Hernia Repair

Introduction: Perioperative telemedicine services have increasingly been utilized for ambulatory care, although concerns exist regarding the feasibility of virtual consultations for older patients. We sought to review telemedicine encounters for geriatric patients evaluated at a hernia repair and abdominal wall reconstruction center. Methods: A retrospective review of telemedicine encounters between May 2020 and May 2021 was performed. Patient characteristics and encounter-specific outcomes were compared among geriatric (older than65 years old) and nongeriatric patients. Clinical care plans for encounters were reviewed to determine potential downstream care utilization. Patient-derived benefits related to time saved in travel time was calculated using geo-mapping. Outcomes for postoperative encounters were assessed to determine if complication rates differed between geriatric and nongeriatric populations. Results: A total of 313 telemedicine encounters (geriatric: 41.9%) were conducted among 251 patients. Reviewing preoperative factors for hernia care, geriatric patients presented with higher rates of recurrent or incisional hernias (87.9% vs. 70.7%, p < 0.01). Potential travel time was longer for geriatric patients (104 min vs. 42 min, p = 0.03) in the preoperative setting. No differences in clinical care plans were found. Only 8.6% of preoperative encounters resulted in recommendations for supplemental in-person evaluation. Operative plans were coordinated for 42.5% of all preoperative telemedicine encounters. There was no difference in complication rate between geriatric and nongeriatric patients (p > 0.05) in the postoperative setting, with no complications directly attributable to telemedicine-based care. Conclusions: Telemedicine-based evaluations appear to function well among geriatric patients seeking hernia repair and abdominal wall reconstruction. Clinical care plans rendered following telemedicine-based encounters are appropriate with a low rate of supplemental in-person evaluations. Telemedicine use resulted in significantly more time saved in commuting to and from clinic for geriatric patients.
Amaretto: An Active Learning Framework for Money Laundering Detection

Monitoring financial transactions is a critical Anti-Money Laundering (AML) obligation for financial institutions. In recent years, machine learning-based transaction monitoring systems have successfully complemented traditional rule-based systems to reduce the high number of false positives and the effort needed to review all the alerts manually. Unfortunately, machine learning-based solutions also have disadvantages: while unsupervised models can detect novel anomalous patterns, they are usually characterized by a high number of false alarms; supervised models, instead, usually offers a higher detection rate but require a large amount of labeled data to achieve such performance. In this paper, we present Amaretto, an active learning framework for money laundering detection that combines unsupervised and supervised learning techniques to support the transaction monitoring processes by improving the detection performance and reducing the compliance management costs. Amaretto exploits novel selection strategies to target a subset of transactions for investigation, making more efficient use of the feedback provided by the analyst. We perform the experimental evaluation on a synthetic dataset provided by the industrial partner, which simulates the profiles of clients trading in international capital markets. We show that Amaretto outperforms state-of-the-art solutions by reducing money laundering risk and improving detection performance. In particular, we compare state-of-the-art unsupervised and supervised techniques commonly used in the AML domain with the ones implemented in this work. We show that the Isolation and Random Forests of Amaretto perform best in the task under analysis, with an AUROC of 0.9 for the first (20% better on average) and a detection rate of 0.793 for the second (30 % better on average). In addition, they are characterized by lower investigation costs computed in terms of the daily number of transactions to be examined and the number of false positives and false negatives. Finally, we compare Amaretto against a state-of-the-art active learning fraud detection system, achieving better detection performances and lower costs in all the analyzed scenarios. Worth mentioning, Amaretto improves the detection rate up to 50 % and reduces the overall cost by 20% in the most realistic scenario under analysis.
Iterative grouping median filter for removal of fixed value impulse noise

Due to the limitation of existing filters in detection and removal of fixed value impulse noise, the authors propose an iterative grouping median filter (IGMF) according to the characteristics of noise intensity and distribution. It sorts the noise-free pixels in neighbourhood by intensity, divides the sorted pixels into groups depending on the intensity differences of adjacent pixels, and finally takes the median of the maximum group as the intensity of noisy pixel. This noise removal strategy is performed iteratively and takes full advantage of the previous denoising results. Experiments show that IGMF outperforms the existing state-of-the-art filters in terms of visual perception, peak signal to noise ratio and structural similarity index at various noise densities.
Data analytic approach for bankruptcy prediction

Bankruptcy prediction problem has been intensively studied over the past decades. From traditional statistical models to state of the art machine learning models, various predictive models are developed and applied to various datasets. However, models that use machine learning are not used in the field of business, for two main reasons. First, the prediction accuracy does not far exceed the statistical models and second, the results are not interpretable. In this study, we focused on solving the skewness which is a characteristic of financial data. By solving this problem, we obtained 17% average improvement in AUC over existing models. To address the second shortcoming, we analyze the importance of features identified by the XGBoost model. The interpretation of the model differs among categories of data. Our bankruptcy prediction model has high predictive accuracy with clear explanations and is therefore directly applicable to the industry. (C) 2019 Elsevier Ltd. All rights reserved.