ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning

Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera-installed on the ground or possibly a drone-and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.
Chroma 422 subsampling for Bayer pattern in H.264 video coding

The optimal strategy of chroma 422 subsampling for Bayer pattern in H.264 video coding is proposed. The state-of-the-art work uses a predetermined conversion method from colour filter array to YCbCr 422. Derived from this structure, first, 64 subsampling methods are designed to exploit more possibilities. Secondly, among which, the four optimal methods are found by the pre-screening process, which show their better performances than the state-of-the-art method by up to 2.57 dB in CPSNR averaged for different videos, demonstrating the practicability of the proposed methods. Finally, for the experiment consisting of the whole conversion and H.264 video encoding/decoding procedures, the proposed optimal methods perform better than the state-of-the-art method under all different operating bitrates and different tested videos, by up to 2.25 dB in BDPSNR.
Strategies to overcome the main challenges of the use of exosomes as drug carrier for cancer therapy

Exosomes are naturally occurring nanosized particles that aid intercellular communication by transmitting biological information between cells. Exosomes have therapeutic efficacy that can transfer their contents between cells as natural carriers. In addition, the exosomal contents delivered to the recipient pathological cells significantly inhibit cancer progression. However, exosome-based tumor treatments are inadequately precise or successful, and various challenges should be adequately overcome. Here, we discuss the significant challenges that exosomes face as drug carriers used for therapeutic targets and strategies for overcoming these challenges in order to promote this new incoming drug carrier further and improve future clinical outcomes. We also present techniques for overcoming these challenges.
An overview of process systems engineering approaches for process intensification: State of the art

Process intensification offers the potential to drastically reduce the energy consumption and cost of producing chemicals from both bulk and distributed feedstocks. This review article aims to offer an extensive survey on state-of-the-art process systems engineering (PSE) approaches for process intensification. From both academic and industrial perspectives, this paper provides an overview of the development of various process intensification technologies, specifically those under the categories of separation, reaction, hybrid reaction/separation, and alternative energy sources. A current status analysis in the areas of modeling and simulation is then provided. An indicative list of PSE publications specialized on process intensification is presented to illustrate the progresses made so far towards the deployment of novel process intensification technologies. We also highlight some recent advances for the modeling, design, and synthesis of intensified systems, as well as for the assessment of their controllability/operability/safety performance. Key open questions in these areas include: (i) how to systematically derive intensified designs, and (ii) how to ensure the operability and optimality of the derived intensified structures while delivering their expected functionality.
Discriminalve cluster adaptive training

Multiple-cluster schemes, such as cluster adaptive training (CAT) or eigenvoice systems, are a popular approach for rapid speaker, and environment adaptation. Interpolation I weights are used to transform a multiple-cluster, canonical, mode (HMM) set representative to a standard hidden Markov model e, of an individual speaker or acoustic environment. Maximum likelihood training for CAT has previously been investigated. However, in state-of-the-art large vocabulary continuous. speech ' recognition systems, discriminative training is commonly employed. This paper investigates applying discriminative training to multiple-cluster systems. In particular,. minimum phone error (MPE) update formulae for CAT systems are derived. In order to use MPE in this case, modifications to the standard MPE smoothing function and the prior. distribution associated with MPE training are required. A more complex adaptive training near trans scheme combining both interpolation weights and in forms, a structured transform (ST), is also discussed within the MPE training framework. Discriminatively trained CAT and ST systems were evaluated on a state-of-the-art conversational telephone speech task. These Multiple-cluster systems ,were found to outperform both standard and adaptively trained systems.
Laparoscopic Cholecystectomy with Intraoperative Cholangiogram and Antegrade Biliary Stenting in Acute Gallstone Pancreatitis: A Pilot Study

Aims: To demonstrate feasibility and efficacy of simultaneous intraoperative cholangiogram (IOC) and antegrade biliary stenting (ABS) with laparoscopic cholecystectomy (LC) compared with preoperative biliary investigation and delayed LC in acute gallstone pancreatitis (AGP). Methods: A retrospective case-control study was performed comparing patients who had a simultaneous IOC ± ABS with LC at index admission with those who had delayed LC in the treatment of AGP. 74 patients were included in this study from January 2016 to October 2018. All patients who underwent LC for AGP were included in a prospective database with 1 year follow-up. Results: 30 (40.5%) patients underwent simultaneous IOC ± ABS with LC, 11 of these required ABS insertion. 2 (6.7%) patients also underwent magnetic resonance cholangiopancreatography (MRCP). No patients underwent endoscopic retrograde cholangiopancreatography (ERCP). No patients were readmitted with AGP or symptomatic gallbladder. Mean length of total hospital admission was 10.1 days. 44 (59.5%) patients underwent delayed LC. Of this cohort, 7 (15.9%) patients underwent ERCP and 19 (43.2%) underwent MRCP. In total, there were 19 (43.2%) readmissions in this group with pancreatitis or symptomatic gallbladder. Mean length of total hospital admission was 13 days. Conclusions: In our pilot study we demonstrated that performing simultaneous IOC ± ABS with LC is a feasible option in the secondary care setting. Using this surgical technique, we have demonstrated a reduction in readmissions with AGP and symptomatic gallbladder while also reducing the number of CBD investigations required. Using simultaneous IOC ± ABS with LC reduced the mean total length of stay in hospital.
Are predictions of cancer response to targeted drugs, based on effects in unrelated tissues, the 'Black Swan' events?

Adverse effects of targeted drugs on normal tissues can predict the cancer response. Rash correlates with efficacy of erlotinib, cetuximab and gefitinib and onset of arterial hypertension with response to bevacizumab, sunitinib, axitinib and sorafenib, possible examples of 'Black Swan' events, unexpected scientific observations, as described by Karl Popper in 1935. The proposition is that our patients have individual intrinsic variants of cell growth control, important for tumor response and adverse effects on tumor-unrelated tissue. This means that the lack of predictive side effects in healthy tissue is linked with poor results of tumor therapy when tumor resistance is caused by mechanisms that protect all cells of that patient from the targeted drug effects.
Efficient single image super-resolution via graph-constrained least squares regression

We explore in this paper an efficient algorithmic solution to single image super-resolution (SR). We propose the gCLSR, namely graph-Constrained Least Squares Regression, to super-resolve a high-resolution (HR) image from a single low-resolution (LR) observation. The basic idea of gCLSR is to learn a projection matrix mapping the LR image patch to the HR image patch space while preserving the intrinsic geometric structure of the original HR image patch manifold. Even if gCLSR resembles other manifold learning-based SR methods in preserving the local geometric structure of HR and LR image patch manifolds, the innovation of gCLSR lies in that it preserves the intrinsic geometric structure of the original HR image patch manifold rather than the LR image patch manifold, which may be contaminated by image degeneration (e.g., blurring, down-sampling and noise). Upon acquiring the projection matrix, the target HR image can be simply super-resolved from a single LR image without the need of HR-LR training pairs, which favors resource-limited applications. Experiments on images from the public database show that gCLSR method can achieve competitive quality as state-of-the-art methods, while gCLSR is much more efficient in computation than some state-of-the-art methods.
A Simplified Adaptive Neural Network Prescribed Performance Controller for Uncertain MIMO Feedback Linearizable Systems

In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design complexity with respect to the current state of the art. The proposed scheme achieves prescribed bounds on the transient and steady-state performance of the output tracking errors despite the uncertainty in system nonlinearities. Contrary to the current state of the art, however, only a single neural network is utilized to approximate a scalar function that partly incorporates the system nonlinearities. Furthermore, the loss of model controllability problem, typically introduced owing to approximation model singularities, is avoided without attaching additional complexity to the control or adaptive law. Simulations are performed to verify and clarify the theoretical findings.
What is known about the care and support provided for an ageing population with lived experience of chronic viral hepatitis as they near end-of-life: A scoping review

Ageing with a chronic hepatitis B (HBV) or hepatitis C (HCV) infection is an emerging public health priority. For people living with chronic viral hepatitis, their disease progression into old age is both underpinned by their existing blood borne virus and the potential emergence of other infectious and non-infectious conditions. These twinned pathways bring additional challenges to the care and support for people as they near end of life. This scoping review sought to examine what is known about the experiences of the end-of-life phase of an increasing population ageing with HBV and HCV in studies conducted in high-income settings and published in peer reviewed literature (2010-2021). In interpreting this literature, we found that challenges in determining the end-of life phase for people with lived experience of HBV or HCV are exacerbated by the conflation of aetiologies into a singular diagnosis of end-stage liver disease. Studies overwhelmingly reported the clinical aspects of end-of-life care (i.e. prognosis assessment and symptom management) with less attention paid to educative aspects (i.e. advance care directives and surrogate decision makers, discussion of treatment options and determining goals of care). Psychosocial interventions (i.e. quality of life beyond symptom management, including emotional/spiritual support and family and bereavement support) received limited attention in the literature, though there was some recognition that psychosocial interventions should be part of end-of-life care provision. Given the focus on the prominent disease presentation of liver cirrhosis and/or end-stage liver disease, the social and cultural dimensions of these infections have received less attention in the literature on end-of-life in the context of chronic viral hepatitis.
Chimpanzee susceptibility to hepatitis C virus infection correlates with presence of Pt-KIR3DS2 and Pt-KIR2DL9: paired activating and inhibitory natural killer cell receptors

Infection of humans and chimpanzees with Hepatitis C virus (HCV) results in either the resolution of the acute infection or its progression to a persistent infection associated with chronic liver disease. In cohorts of human patients, resolution of HCV infection has been associated with homozygosity for both C1(+)HLA-C and its cognate inhibitory receptor, KIR2DL3. Compared here are the killer cell immunoglobulin-like receptors (KIR) and major histocompatibility complex (MHC) class I factors of chimpanzees who resolve, or resist, HCV infection with those chimpanzees who progress to chronic infection. Analysis of Pt-KIR gene content diversity associated two of the 12 Pt-KIR with clinical outcome. Activating Pt-KIR3DS2 and inhibitory Pt-KIR2DL9 are strong receptors specific for the C2 epitope. They are encoded by neighboring genes within the Pt-KIR locus that are in strong linkage disequilibrium. HCV-infected chimpanzees with KIR genotypes containing Pt-KIR3DS2 and KIR2DL9 are significantly more likely to progress to chronic infection than infected chimpanzees lacking the genes (p = 0.0123 and p = 0.0045, respectively), whereas human HLA-B allotypes having the C1 epitope are unusual, such allotypes comprise about one quarter of the chimpanzee Patr-B allotypes. Homozygous C1 (+) Patr-B are enriched in chimpanzees with chronic HCV infection, and the compound genotype of homozygous C1 (+) Patr-B combined with either Pt-KIR3DS2 or Pt-KIR2DL9 is more strongly associated with disease progression than either factor alone (p = 0.0031 and p = 0.0013, respectively). Thus, despite similarities suggesting a common basis in disease resistance, there are substantial differences in the KIR and MHC class I correlations observed for HCV-infected humans and chimpanzees, consistent with the divergence of their KIR and MHC class I systems.
Factors Associated With Visual Acuity in Non-arteritic Ischemic Optic Neuropathy Patients: A Five-Year Cross-Sectional Study

Background and objective Non-arteritic ischemic optic neuropathy (NAION) is a common cause of optic neuropathy in elderly patients. Currently, there is no definitive treatment for this condition, and the factors influencing visual outcomes have not yet been conclusively identified. In this study, we aimed to evaluate factors that affect visual outcomes and those that are predictors of the development of NAION in a Thai population. Methods All patients diagnosed with NAION at the Rajavithi Hospital between January 1, 2016, and December 31, 2020, were retrospectively reviewed to evaluate the improvement in their best-corrected visual acuity (BCVA) and determine the factors that are predictive of visual outcomes. Results The 80 patients reviewed were predominantly male (55%) with a mean age of 55.8 ±9.89 years. Their most common comorbidities were dyslipidemia (DLP) (67.5%), diabetes mellitus (DM) (61.3%), and hypertension (HT) (48.8%). At the 12-week follow-up visit, there was a significant improvement of at least 0.2 logarithm of the minimum angle of resolution (logMAR) in BCVA (p=0.001). A significantly greater percentage of patients with higher age, DM, and HT was observed in the unfavorable visual recovery (UVR) group (p=0.002, p=0.001, and p=0.005 respectively). In contrast, neither baseline visual acuity nor cup-to-disc ratio (CDR) affected the result of visual recovery (p=0.275 and p=0.076, respectively). In multivariate logistic analysis, older age increased the odds of worse visual recovery [odds ratio (OR): 4.014; 95% CI: 1.038-15.515; p=0.044], as did having DM (OR: 3.809; 95% CI: 1.168-12.421; p=0.027), and HT (OR: 4.577; 95% CI: 1.491-14.049; p=0.008). Conclusions None of the baseline visual status parameters (visual acuity, CDR, or visual field defect) was able to determine the outcome of visual recovery at 12 weeks in our NAION patients. Regarding systemic vascular diseases, diabetes and HT are significant risk factors and also predictors of poor visual improvement in Thai populations. NAION patients who are elderly or have vascular diseases such as DM or HT should be closely followed up and advised about the likelihood of having inferior visual recovery.
Skin rash associated with combined cytotoxic chemotherapy and immunotherapy for cancer: A retrospective single-center case series

In recent years, the development of combination therapies with immune checkpoint inhibitors (ICIs) and cytotoxic anticancer drugs has radically changed the management of diverse malignancies and significantly improved patient outcomes. Several clinical trials have shown that skin rash caused by combination therapy with ICIs and cytotoxic drugs may be more frequent and severe than that developing after administration of ICIs alone or cytotoxic drug monotherapy. However, most reports provide little information on severity, treatment, post-diagnosis course, and recurrence of rashes on drug rechallenges. We aimed to describe the experience of skin rashes developing within 2 weeks from the first administration of combination therapy with ICIs and cytotoxic drugs in 11 patients visiting our dermatology department. This study included seven men and four women, and the patients' median age was 52 years. The primary disease was non-small-cell lung cancer in eight patients, cervical cancer in two patients, and esophageal cancer in one patient. Nine patients had a maculopapular rash and two patients developed erythema multiforme-like eruptions. The skin rash was often accompanied by extracutaneous symptoms, such as fever (n = 9), mucositis (n = 4), and liver dysfunction (n = 2). In all cases, the symptoms improved with topical steroid therapy alone, with no patients exhibiting severe symptoms requiring systemic steroids or immunosuppressive agents. In addition, when the causative drugs were re-administered after recovery from the rash, only two patients relapsed with accompanying systemic symptoms, and all patients except one were able to continue treatment using the same drug regimen. Although it was suggested that the rash caused by the combination therapy of ICIs and cytotoxic drugs may be more prominent than that caused by each agent alone, comprehensive judgment, including histopathological examination, may indicate the feasibility of continuing the treatment regimen for cancer.
A Adaptive Algorithm for Online Interference Cancellation in EMG Sensors

One of the biggest issues encountered in the analysis of sensitive electromyography (EMG) sensor data is the power line interference (PLI). Conventional methods in literature either lose valuable sensor data or inadequately decrease the power line noise. Instead of filtering out predetermined frequencies, adaptively estimating the spectrum of PM can provide better performance. This paper introduces an online adaptive algorithm that removes the power line interference in real time without disturbing the true EMG data. Our method sequentially processes the biomedical signal to properly estimate and remove the PLI component. Through experiments with real EMG data, we compared our method to the five state-of-the-art techniques. Our algorithm outperformed all of them with the highest SNR gain (3.6 dB on average) and with the least disturbance of the true EMG signal (0.0152 dB loss on average). Our method reduces the PLI the most while keeping the valuable sensor data loss at its minimum in comparison to the state-of-the-art. Reducing the noise without disturbing the valuable sensor data provides higher quality signals with decreased interference, which can be better processed and used in biomedical research.
Real-Time Optical Flow for Vehicular Perception With Low- and High-Resolution Event Cameras

Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional frame-based sensors show their limits with blur and over- or under-exposed pixels. Thanks to these unique properties, they represent nowadays an highly attractive sensor for ITS-related applications. Event-based optical flow (EBOF) has been studied following the rise in popularity of these neuromorphic cameras. The recent arrival of high-definition neuromorphic sensors, however, challenges the existing approaches, because of the increased resolution of the events pixel array and a much higher throughput. As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras. We formulate a novel dense representation for the sparse events flow, in the form of the "inverse exponential distance surface". It serves as an interim frame, designed for the use of proven, state-of-the-art frame-based optical flow computation methods. We evaluate our approach on both low- and high-resolution driving sequences, and show that it often achieves better results than the current state of the art, while also reaching higher frame rates, 250Hz at 346 x 260 pixels and 77Hz at 1280 x 720 pixels.
Fuzzy multilevel graph embedding

Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. (c) 2012 Elsevier Ltd. All rights reserved.