ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Computational biophysics approach towards the discovery of multi-kinase blockers for the management of MAPK pathway dysregulation

The MAPK pathway is important in human lung cancer and is improperly activated in a substantial proportion through number of ways. Strategies on dual-targeting RAF and MEK are an alternative option to diminish the limitations in this pathway inhibition. Hence, we implemented parallel pharmacophore screening of 11,808 DrugBank compounds against RAF and MEK. ADHRR and DHHRR were modeled as a pharmacophore hypothesis for RAF and MEK respectively. Importantly, these hypotheses resulted an AUC value of > 0.90 with the external data set. As a result of phase screening, glide docking, and prime-MM/GBSA scoring, it is determined that DB08424 and DB08907 have the best chances of acting as multi-kinase inhibitors. The pi-cation interaction with key amino acid residues of both target receptors may responsible for the stronger binding with these kinases. Cumulative 600 ns MD simulation studies validate the binding ability of these compounds. Significantly, the hit compounds resulted higher number of stable conformational state with less atomic movements than the reference compound against both targets. The anti-cancer efficacy of the lead compounds was validated through machine learning-based approaches. These findings suggest that DB08424 and DB08907 might be novel molecules to be explored further experimentally to block the MAPK signaling in lung cancer patients.
Uncommon isolated distant subcutaneous tissue and skeletal muscle metastasis from oesophageal cancer diagnosed by PET/CT (18)F-FDG

Distant soft-tissue metastases (subcutaneous tissues and skeletal muscle) are extremely rare, particularly in oesophageal carcinoma. The case is described of a patient who was treated for oesophageal adenocarcinoma 2.5 years previously. A PET/CT was performed showing metastatic spread due to a solitary focus of increased tracer uptake corresponding to one subcutaneous node in the upper abdomen. An excisional biopsy showed a metastasis from the carcinoma. Restaging PET/CT (18)F-FDG study was performed 2 year later, demonstrating foci of increased uptake within several muscles as isolated distant haematogenous spread of metastases, histopathologically confirmed. As most of soft-tissue metastases are asymptomatic, the physicians should recommend a histopathological study of focal FDG uptake at subcutaneous tissues and/or skeletal muscles, because they may be the first sign of disease spread, so therapeutic management of these patients could be changed.
Catatonia in inpatients with psychiatric disorders: A comparison of schizophrenia and mood disorders

This study aimed to evaluate the symptom threshold for making the diagnosis of catatonia. Further the objectives were to (1) to study the factor solution of Bush Francis Catatonia Rating Scale (BFCRS); (2) To compare the prevalence and symptom profile of catatonia in patients with psychotic and mood disorders among patients admitted to the psychiatry inpatient of a general hospital psychiatric unit. 201 patients were screened for presence of catatonia by using BFCRS. By using cluster analysis, discriminant analysis, ROC curve, sensitivity and specificity analysis, data suggested that a threshold of 3 symptoms was able to correctly categorize 89.4% of patients with catatonia and 100% of patients without catatonia. Prevalence of catatonia was 9.45%. There was no difference in the prevalence rate and symptom profile of catatonia between those with schizophrenia and mood disorders (i.e., unipolar depression and bipolar affective disorder). Factor analysis of the data yielded 2 factor solutions, i.e., retarded and excited catatonia. To conclude this study suggests that presence of 3 symptoms for making the diagnosis of catatonia can correctly distinguish patients with and without catatonia. This is compatible with the recommendations of DSM-5. Prevalence of catatonia is almost equal in patients with schizophrenia and mood disorders.
A Comparative Study on Various State of the Art Face Recognition Techniques under Varying Facial Expressions

Through face we can know the emotions and feelings of a person. It can also be used to judge a person's mental aspect and psychomatic aspects. There are 5 state of the art approaches for recognizing faces under varying facial expressions. These 5 approaches are overlapping Discrete Cosine Transform (DCT), Hierarchical Dimensionality Reduction (HDR), Local and Global combined Computational Features (LGCF), Combined Statistical Moments (CSM), and Score Level Fusion Techniques (SLFT). Matlab code has been developed for all the 5 systems and tested using common set of train and test images. The train and test images are considered from standard public face databases ATT, JAFFE, and FEI. The key contribution of this article is, we have developed and analyzed the 5 state of the art approaches for recognizing faces under varying facial expressions using a common set of train and test images. This evaluation gives us the exact face recognition rates of the 5 systems under varying facial expressions. The face recognition rate of overlap DCT on ATT database was 95% and FEI 99% which was better than HDR, LGCM, CSM and SLFT. But the face recognition rate of CSM on JAFE database, which contains major facial expression variations, was 100% which was better than overlap DCT, HDR, LGCM, and SLFT.
Wetting Ridge-Guided Directional Water Self-Transport

Directional water self-transport plays a crucial role in diverse applications such as biosensing and water harvesting. Despite extensive progress, current strategies for directional water self-transport are restricted to a short self-driving distance, single function, and complicated fabrication methods. Here, a lubricant-infused heterogeneous superwettability surface (LIHSS) for directional water self-transport is proposed on polyimide (PI) film through femtosecond laser direct writing and lubricant infusion. By tuning the parameters of the femtosecond laser, the wettability of PI film can be transformed into superhydrophobic or superhydrophilic. After trapping water droplets on the superhydrophilic surface and depositing excess lubricant, the asymmetrical wetting ridge drives water droplets by an attractive capillary force on the LIHSS. Notably, the maximum droplet self-driving distance can approach ≈3 mm, which is nearly twice as long as the previously reported strategies for direction water self-transport. Significantly, it is demonstrated that this strategy makes it possible to achieve water self-transport, anti-gravity pumping, and chemical microreaction on a tilted LIHSS. This work provides an efficient method to fabricate a promising platform for realizing directional water self-transport.
Demonstration of pH imaging in acute stroke with endogenous ratiometric chemical exchange saturation transfer magnetic resonance imaging at 2 ppm

pH change is often considered a hallmark of metabolic disruption in diseases such as ischemic stroke and cancer. Chemical exchange saturation transfer (CEST) MRI, particularly amide proton transfer (APT), has emerged as a noninvasive pH imaging approach. However, there are changes in multipool CEST effects besides APT MRI. Our study investigated radiofrequency (RF) amplitude-based ratiometric CEST pH imaging in acute stroke. Briefly, adult male Wistar rats underwent CEST MRI under two RF saturation (B1 ) levels of 0.75 and 1.5 μT following middle cerebral artery occlusion. Magnetization transfer (MT), direct water saturation, CEST at 2 ppm (CEST@2 ppm), amine (2.75 ppm), and APT (3.5 ppm) effects were resolved with the multipool Lorentzian fitting approach. The ratiometric analysis was measured in the ischemic lesion and the contralateral normal area, which was also correlated with pH-specific MT and the relaxation normalized APT (MRAPT) index. MT, amine CEST effect, and their respective ratiometric indices did not show significant changes in ischemic regions (p > 0.05), as expected. Whereas APT decreased in the ischemic lesion for B1 of 1.5 μT (p < 0.01), the correlation between the amide ratio with MRAPT index was moderate (r = 0.52, p = 0.02). By comparison, the ischemic tissue showed a significantly increased CEST@2 ppm for both saturation levels from the contralateral normal area (p ≤ 0.01). Importantly, the CEST@2 ppm ratio decreased in the ischemic lesion (p < 0.01), which highly correlated with the MRAPT index (r = 0.93, p < 0.001). To summarize, our study demonstrated the feasibility of endogenous CEST@2 ppm ratiometric imaging of pH upon acute stroke, promising to detect pH changes in metabolic diseases.
State of the art in flow visualization in the environmental sciences

Flow plays a major role in environmental sciences, because many of the Earth's physical and biological processes involve movement. Yet, there are major differences between theoretically available and practically applied visualization techniques to represent flow. This paper surveys various techniques in computational and environmental flow visualization. Techniques from the computational flow visualization community are classified into geometric, texture-based, topology-based, and feature-based approaches. Environmental flow applications are categorized into four application domains (atmospheric science, ecology, geosciences, and urban environments). Computational and environmental visualization approaches are compared to exhibit gaps and suggest solutions on how to bridge the gap. Outcomes from this literature review will inform the development of strategic initiatives for both future flow visualization research and flow visualization in the environmental sciences.
Estimation of monkeypox spread in a nonendemic country considering contact tracing and self-reporting: A stochastic modeling study

In May 2022, monkeypox started to spread in nonendemic countries. To investigate contact tracing and self-reporting of the primary case in the local community, a stochastic model is developed. An algorithm based on Gillespie's stochastic chemical kinetics is used to quantify the number of infections, contacts, and duration from the arrival of the primary case to the detection of the index case (or until there are no more local infections). Different scenarios were set considering the delay in contact tracing and behavior of infectors. We found that the self-reporting behavior of a primary case is the most significant factor affecting outbreak size and duration. Scenarios with a self-reporting primary case have an 86% reduction in infections (average: 5-7, in a population of 10 000) and contacts (average: 27-72) compared with scenarios with a non-self-reporting primary case (average number of infections and contacts: 27-72 and 197-537, respectively). Doubling the number of close contacts per day is less impactful compared with the self-reporting behavior of the primary case as it could only increase the number of infections by 45%. Our study emphasizes the importance of the prompt detection of the primary case.
MPPT in Dynamic Condition of Partially Shaded PV System by Using WODE Technique

This paper introduces a humpback whale hunting behavior inspired whale optimization with differential evolution (WODE) technique-based tracking algorithm for the maximum power point tracking in the dynamic as well as the steady-state conditions of a partially shaded solar photovoltaic (PV) system. This "WODE" technique is used for quick and oscillation-free tracking of the global best peak position in a few steps. The unique advantage of this algorithm for maximum power point tracking in partially shaded condition is as, it is free from common and generalized problems of other evolutionary techniques, like longer convergence duration, a large number of search particles, steadystate oscillation, heavy computational burden, etc., which creates power loss and oscillations in output. This hybrid algorithm is tested in MATLAB simulation and verified on a developed hardware of the solar PV system, which consists of multiple peaks in voltage-power curve. Moreover, the tracking ability is compared with the state-of-the-art methods. The satisfactory steady-state and dynamic performances of the new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.
X-ray spectrometry imaging and chemical speciation assisting to understand the toxic effects of copper oxide nanoparticles on zebrafish ( Danio rerio)

Currently, copper nanoparticles are used in various sectors of industry, agriculture, and medicine. To understand the effects induced by these nanoparticles, it is necessary to assess the environmental risk and safely expand their use. In this study, we evaluated the toxicity of copper oxide (nCuO) nanoparticles in Danio rerio adults, their distribution/concentration, and chemical form after exposure. This last assessment had never been performed on copper-exposed zebrafish. Such evaluation was done through the characterization of nCuO, acute exposure tests and analysis of distribution and concentration by microstructure X-ray fluorescence spectroscopy (µ-XRF) and atomic absorption spectroscopy (GF-AAS). Synchrotron X-ray absorption spectroscopy (XAS) was performed to find out the chemical form of copper in hotspots. The results show that the toxicity values of fish exposed to nCuO were 2.4 mg L-1 (25 nm), 12.36 mg L-1 (40 nm), 149.03 mg L-1 (80 nm) and 0.62 mg L-1 (CuSO4, used as a positive control). The total copper found in the fish was in the order of mg kg-1 and it was not directly proportional to the exposure concentration; most of the copper was concentrated in the gastric system. However, despite the existence of copper hotspots, chemical transformation of CuO into other compounds was not detected.
In vivo biocompatibility of SrO and MgO doped brushite cements

The addition of dopants in biomaterials has emerged as a critical regulator of bone formation and regeneration due to their imminent role in the biological process. The present work evaluated the role of strontium (Sr) and magnesium (Mg) dopants in brushite cement (BrC) on in vivo bone healing performance in a rabbit model. Pure, 1 wt% SrO (Sr-BrC), 1 wt% MgO (Mg-BrC), and a binary composition of 1.0 wt% SrO + 1.0 wt% MgO (Sr + Mg-BrC) BrCs were implanted into critical-sized tibial defects in rabbits for up to 4 months. The in vivo bone healing of three doped and pure BrC samples was examined and compared using sequential radiological examination, histological evaluations, and fluorochrome labeling studies. The results indicated excellent osseous tissue formation for Sr-BrC and Sr + Mg-BrC and moderate bone regeneration for Mg-BrC compared to pure BrC. Our findings indicated that adding small amounts of SrO, MgO, and binary dopants to the BrC can significantly influence new bone formation for bone tissue engineering.
Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and nonlocal priors simultaneously. First, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background nonlocal self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entrywise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the elementwise reweighted higher order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.
Unsupervised Semantic Similarity Computation between Terms Using Web Documents

In this work, Web-based metrics that compute the semantic similarity between words or terms are presented and compared with the state of the art. Starting from the fundamental assumption that similarity of context implies similarity of meaning, relevant Web documents are downloaded via a Web search engine and the contextual information of words of interest is compared (context-based similarity metrics). The proposed algorithms work automatically, do not require any human-annotated knowledge resources, e.g., ontologies, and can be generalized and applied to different languages. Context-based metrics are evaluated both on the Charles-Miller data set and on a medical term data set. It is shown that context-based similarity metrics significantly outperform co-occurrence-based metrics, in terms of correlation with human judgment, for both tasks. In addition, the proposed unsupervised context-based similarity computation algorithms are shown to be competitive with the state-of-the-art supervised semantic similarity algorithms that employ language-specific knowledge resources. Specifically, context-based metrics achieve correlation scores of up to 0.88 and 0.74 for the Charles-Miller and medical data sets, respectively. The effect of stop word filtering is also investigated for word and term similarity computation. Finally, the performance of context-based term similarity metrics is evaluated as a function of the number of Web documents used and for various feature weighting schemes.
Studying digital imagery of ancient paintings by mixtures of stochastic models

This paper addresses learning-based characterization of fine art painting styles. The research has the potential to provide a powerful tool to art historians for studying connections among artists or periods in the history of art. Depending on specific applications, paintings can be categorized in different ways. In this paper, we focus on comparing the painting styles. of artists. To profile the style of an artist, a mixture of stochastic models is estimated using training images. The two-dimensional (2-D) multiresolution hidden Markov model (MHMM) is used in the experiment. These models form an artist's distinct digital signature. For certain types of paintings, only strokes provide reliable information to distinguish artists. Chinese ink paintings are a prime example of the above phenomenon; they do not have colors or even tones. The 2-D MHMM analyzes relatively large regions in an image, which in turn makes it more likely to capture properties of the painting strokes. The mixtures of 2-D MHMMs established for artists can be further used to classify paintings and compare paintings or artists. We implemented and tested the system using high-resolution digital photographs of some of China's most renowned artists. Experiments have demonstrated good potential of our approach in automatic analysis of paintings. Our work can be applied to other domains.
Masked Dual-Rail Precharge Logic Encounters State-of-the-Art Power Analysis Methods

Latest evaluation of the state-of-the-art iMDPL logic style has shown small information leakage compared to its predecessor version MDPL. Concurrently, new advanced power analysis attacks specifically targeting iMDPL have been proposed. Up to now, these attacks are purely theoretic and have not been applied to an implementation. We present a comprehensive analysis of iMDPL, backed by real measurements collected from a 180 nm iMDPL prototype chip. We thoroughly study the extent of remaining information leakage of iMDPL by applying all relevant attacks. Our investigation shows the vulnerability of the target device, a standalone AES core, to several of the advanced attack methods. In comparison to conventional power analysis attacks, the advanced attacks need less power measurements to obtain meaningful results. With the help of logic level simulations routing imbalances between complementary mask trees are identified as a major source of leakage.
Solar irradiation accelerates the oxidation of Cr(III) by δ-manganese dioxide

Cr(VI) has been observed to be released from Cr(III)-bearing natural sources or residues when they are found alongside manganese and manganese oxides. However, relevant mechanism studies normally ignore the effect of simulated solar irradiation on this oxidation reaction. Therefore, we investigated the photochemical reaction between Cr(OH)3 and δ-MnO2, the common species of chromium and manganese oxide found in the environment. At pH 11, the oxidation of Cr(OH)3 by δ-MnO2 was accelerated under simulated solar irradiation, which had an oxidation rate 2.7-fold greater than that in the dark condition. Further investigation revealed that δ-MnO2, an n-type semiconductor with a 2.7 eV band gap, can be excited by light with wavelengths < 459 nm and produce photogenic electrons and holes. These photo-induced carriers reacted with surrounding molecules to form free radicals and participate the redox reactions. Free-radical quenching experiments indicated that hydroxyl radicals (•OH) are the main oxidants of Cr(III) under simulated solar irradiation. This work provides new mechanistic insight into the oxidation of Cr(III) to Cr(VI), which may help clarifying the environmental fate of Cr and the potential solar-triggered release of Cr(VI).
Three-Dimensional Covalent Organic Framework with scu-c Topology for Drug Delivery

Three-dimensional (3D) covalent organic frameworks (COFs) exemplify a new generation of crystalline extended solids with intriguing structures and unprecedented porosity. Notwithstanding substantial scope, the reticular synthesis of 3D COFs from pre-designed building units leading to new network topologies yet remains a demanding task owing to the shortage of 3D building units and inadequate reversibility of the linkages between the building units. In this work, by linking a tetragonal prism (8-connected) node with a square planar (4-connected) node, we report the first 3D COF with scu-c topology. The new COF, namely, TUS-84, features a two-fold interpenetrated structure with well-defined porosity and a Brunauer-Emmett-Teller surface area of 679 m2 g-1. In drug delivery applications, TUS-84 shows efficient drug loading and sustained release profile.
SWAP: A Server-Scale Communication-Aware Chiplet-Based Manycore PIM Accelerator

Processing-in-memory (PIM) is a promising technique to accelerate deep learning (DL) workloads. Emerging DL workloads (e.g., ResNet with 152 layers) consist of millions of parameters, which increase the area and fabrication cost of monolithic PIM accelerators. The fabrication cost challenge can be addressed by 2.5-D systems integrating multiple PIM chiplets connected through a network-on-package (NoP). However, server-scale scenarios simultaneously execute multiple compute-heavy DL workloads, leading to significant interchiplet data volume. State-of-the-art NoP architectures proposed in the literature do not consider the nature of DL workloads. In this article, we propose a novel server scale 2.5-D manycore architecture called SWAP that accounts for the traffic characteristics of DL applications. Comprehensive experimental evaluations with different system sizes as well as diverse emerging DL workloads demonstrate that SWAP achieves significant performance and energy consumption improvements with much lower fabrication cost than state-of-the-art NoP topologies.