ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
TP53, NOTCH2, and STK11 Mutations in a Rare Tumor of non-Small Cell Lung Carcinoma with Diffuse Coexpression of TTF1 and p40 in the Same Tumor Cells

Introduction. Five cases of non-small cell lung carcinoma (NSCLC) with diffuse coexpression of TTF1 and p40 in the same tumor cells (hereafter referred to as TTF1/P40-NSCLC) have been reported since 2015. It was considered a new entity of NSCLC occurred in aged male smokers with poorly differentiated carcinomas and a similar molecular signature harboring a TP53 mutation. Methods. We report an extremely rare tumor of TTF1/P40-NSCLC. Morphological observation and immunohistochemical examination were performed, clinical and molecular features were summarized, and a review of the relevant literature was provided. Results. The tumor showed a solid growth pattern with patchy necrosis, and glandular and squamous pattern were not obvious. The tumor cells proliferated within the bronchial epithelium. Spreading through air spaces of tumor cells were observed. A peculiar immunohistochemical phenotype of diffuse and strong positivity for TTF1 (8G7G3/1) and p40 in the same tumor cells was detected. Additionally, the tumor cells were positive for KRT7 and KRT20, while negative for PD-L1 (22C3). Negative P53 (null) Immunohistochemistry (IHC) staining indicated mutational status and the Ki67 index was 80%. Molecular investigation was performed using whole exome sequencing, and TP53, NOTCH2, and STK11 mutations were detected. The patient remained alive over a follow-up period of 22 months without tumor recurrence or metastasis. Conclusions. We describe an unusual tumor of TTF1/P40-NSCLC harboring TP53, NOTCH2 and STK11 mutations. These gene mutations may be helpful in providing additional therapeutic possibilities. Our report offers further insight into this rare tumor.
Regulation of the aryl hydrocarbon receptor in cancer and cancer stem cells of gynecological malignancies: An update on signaling pathways

Gynecological malignancies are a female type of cancers that affects the reproductive system. Cancer metastasis or recurrence mediated by cellular invasiveness occurs at advanced stages of cancer progression. Cancer Stem Cells (CSCs) enrichment in tumors leads to chemoresistance, which results in cancer mortality. Exposure to environmental pollutants such as polycyclic aromatic hydrocarbons is associated with an increased the risk of CSC enrichment in gynecological cancers. One of the important pathways that mediates the metabolism and bioactivation of these environmental chemicals is the transcription factor, aryl hydrocarbon receptor (AhR). The present review explores the molecular mechanisms regulating the crosstalk and interaction of the AhR with cancer-related signaling pathways, such as apoptosis, epithelial-mesenchymal transition, immune checkpoints, and G-protein-coupled receptors in several gynecological malignancies such as ovarian, uterine, endometrial, and cervical cancers. The review also discusses the potential of targeting the AhR pathway as a novel chemotherapy for gynecological cancers.
A hybrid list-based task scheduling scheme for heterogeneous computing

Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task grid represented using a Directed Acyclic Graph (DAG). Solving such DAG representing a scheduling problem is an NP-complete task. The primary concern in this problem domain is to reduce the schedule length with minimum complexity. This work presents a Hybrid List-based Task Scheduling using Duplication Scheme (HLTSD) algorithm for heterogeneous processors. The proposed HLTSD algorithm has the same time complexity as that of the recent state-of-the-art algorithms. However, it produces a minimum cost schedule in comparison with other related methods. This work also presents a mathematical formulation to find task priorities. The processor selection phase is improved by utilizing the techniques, like entry task duplication, insertion-based policy, duplication of parent task on other levels, and balancing the load on each processor. The current proposal minimizes the overall makespan of execution by reasonable levels. Performance of the proposed algorithm is evaluated using DAGs adopted from various state-of-the-art algorithms, real-world problems, like Gaussian elimination (GE) and fast Fourier transformation (FFT) task graph and randomly generated graphs with diverse characteristics. The proposed scheme is compared with four state-of-the-art list-based scheduling algorithms, namely Heterogeneous Earliest Finish Time (HEFT), Predict Earliest Finish Time (PEFT), Heterogeneous Scheduling with Improved task Priority (HSIP), and Task Scheduling for Heterogeneous Computing Systems (TSHCS). Based on the best quality schedule, the obtained results suggest that HLTSD has better results in 87% cases.
Suspicious-Region Segmentation From Breast Thermogram Using DLPE-Based Level Set Method

Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for the identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the high-intensity-pixels and the centroid-knowledge of SRs (SCH-CS), is proposed to approximately locate the SRs, whose contours are later used as the initial evolving curves of the level set method (LSM). Second, a new energy functional, called different local priorities embedded (DLPE), is proposed regarding the level set function. DLPE is then minimized using the interleaved level set evolution to segment the potential SRs in TBI more accurately. Finally, a new stopping criterion is incorporated into the proposed LSM. The proposed LSM not only increases the segmentation speed but also ameliorates the segmentation accuracy. The performance of our SR segmentation method was evaluated on two TBI databases, namely, DMR-IR and DBT-TU-JU, and the average segmentation accuracies obtained on these databases are 72.18% and 71.26% respectively, which are better than the other state-of-the-art methods. Beside this, a novel framework to analyze TBIs is proposed for differentiating abnormal and normal breasts on the basis of the segmented SRs. We have also shown experimentally that investigating only the SRs instead of the whole breast is more effective in differentiating abnormal and normal breasts.
Information Fusion of Conflicting Input Data

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the mu BalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.
End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.
Is native aortic valvuloplasty at time of Norwood operation in infants with hypoplastic left heart syndrome and aortic stenosis safe?

In hypoplastic left heart syndrome, the size and function of the left ventricle vary and are dependent on the patency of the aortic valve. A patent native aortic valve, permitting left ventricular ejection, can augment cardiac output. We performed a retrospective chart review of patients with hypoplastic left heart syndrome and a stenotic aortic valve who underwent native aortic valvuloplasty at the time of Norwood and found that none of the eight patients identified had clinically significant aortic insufficiency. This case series suggests that surgical aortic valvuloplasty at Norwood is associated with aortic valve patency/augmented systemic cardiac output without the development of clinically significant aortic regurgitation at intermediate follow-up in a limited cohort.
Fast Multidimensional Ellipsoid-Specific Fitting by Alternating Direction Method of Multipliers

Many problems in computer vision can be formulated as multidimensional ellipsoid-specific fitting, which is to minimize the residual error such that the underlying quadratic surface is a multidimensional ellipsoid. In this paper, we present a fast and robust algorithm for solving ellipsoid-specific fitting directly. Our method is based on the alternating direction method of multipliers, which does not introduce extra positive semi-definiteness constraints. The computation complexity is thus significantly lower than those of semi-definite programming (SDP) based methods. More specifically, to fit n data points into a p dimensional ellipsoid, our complexity is O(p(6) + np(4)) + O(p(3)), where the former O results from preprocessing data once, while that of the state-of-the-art SDP method is O(p(6) + np(4) + n(3/2)p(2)) for each iteration. The storage complexity of our algorithm is about 1/2np(2), which is at most 1/4 of those of SDP methods. Extensive experiments testify to the great speed and accuracy advantages of our method over the state-of-the-art approaches. The implementation of our method is also much simpler than SDP based methods.
Enhancement of thioethers removal by pre-oxidation-coagulation: Effects of background organic matter

Swampy/septic odor caused by thioethers has become the main taste and odor (T&O) problem in drinking water of China. Improving its removal performance by commonly traditional water treatment process is significant. In our study, we have found that pre-oxidation could modify the background dissolved organic matter (DOM) properties and thus improve the coagulation performance of thioethers, increasing the coagulation removal rates by 1.5-3 times. Particularly, after pre-ozonation only protein-like substances remained, and thioethers removal was 1.5 times higher than that after pre-chlorination (only coagulation not including oxidation). Compared with humic acid (HA), the thioethers compounds removal efficiencies under bovine serum albumin (BSA) as background DOM was increased by 0.3-3 times. Through Freundlich model analysis, the binding strength of BSA (KF = 20.712, at 298 K) to dimethyl disulfide (DMDS) was enhanced by 60 % compared to HA (KF = 12.778, at 298 K). According to thermodynamic parameters, the binding effect between HA/BSA and thioethers compounds was mainly van der Waals forces and hydrogen bond. BSA with more amino structure and oxygen groups was more easily to adsorb DMDS through hydrogen bond and thus achieved better coagulation performance. Therefore, pre-ozonation combined with coagulation was suggested to be more suitable for thioethers compounds control.
A framework for suspect face retrieval using linguistic descriptions

Facial sketch plays a vital role in suspect's identification and apprehension by law enforcement agencies. The sketch artist develops sketches based on the memory of the eye witness. However, sketch artists are less in number and limited to availability. It is also observed that as time passes, the onlooker even forgets many of the critical attributes, which proves costly in time-sensitive investigations. State-of-the-art sketch-photo retrieval methods have used the sketch to retrieve the suspect's photograph and ignore the importance of time sensitivity. In this paper, we have proposed a linguistic description based approach for suspect's photo retrieval. In the proposed approach, the facial attributes and their descriptions are extracted from the input linguistic information using parts of speech and attributes ontology. Attributes are ranked according to their importance in the identification of a suspect. An ensemble classifier which aggregates the predictions of multiple classifiers is used to retrieve a suspect's image. Experiments are performed on standard datasets. The performance of the proposed approach is evaluated by comparing it with the existing methods of linguistic sketch-based retrieval as well as with sketch to photo retrieval method. Experimental results illustrate that the proposed linguistic-base method using ensemble classification attains auspicious performance as compared to state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
Associations of dispositional forgivingness with facets of well-being among Colombian adults: A longitudinal outcome-wide analysis

Many studies have reported evidence suggesting that dispositional forgivingness has positive implications for different domains of well-being. However, relatively few methodologically rigorous studies have been conducted in the Global South, particularly in post-conflict settings where forgiveness could play an important role in supporting individual well-being. In this three-wave cohort study of predominantly young adult Colombians (n = 1575), we examined the associations of dispositional forgivingness with 20 well-being outcomes across several domains of well-being: psychological distress, psychological well-being, physical health, social well-being, and character strengths/virtues. Using an outcome-wide analytic design that included extensive control for potential confounding and reverse causation by adjusting for a range of covariates assessed in Wave 1 (2017), we found that dispositional forgivingness assessed in Wave 2 (2018) was at least modestly associated with improvements in one or more facet of each well-being domain assessed approximately 1 year later in Wave 3 (2019). Associations tended to be stronger for outcomes in the domains of psychological distress, psychological well-being, social well-being, and character strengths/virtues compared to the physical health domain. These longitudinal findings suggest that dispositional forgivingness might promote individual well-being in societies with a long history of ongoing civil unrest, conflict, and war.
A curvelet-based multi-sensor image denoising for KLT-based image fusion

The transform-based multi-sensor image denoising methods are inefficient in restoring fine details and texture information of noisy images. The fixed and non-adaptive curvelet transform (CT) design limits its performance in image denoising tasks. Moreover, the Karhunen-Loeve Transform (KLT)-based multi-sensor image fusion techniques premise that high variance's first two principal components are an excellent option for weights used for the weighted average multi-sensor source images. However, the selected weights are non-optimal in this method, considering the less relevant information of source images. The experimental section has several examples showing the key advantages of the proposed optimized CT-based natural image denoising technique over seven existing denoising methods. First, our image denoising method introduces a modified Meyer window (used in unequally-spaced fast Fourier transform (USFFT))-based novel optimized USFFT CT (OUSFFT CT) and a modified wrapping window (WW)-based novel optimized WW CT (OWW CT) to address non-adaptive nature of curvelet transform. These windows are used for the decomposition of noisy source images into low- and high-frequency coefficients. The coefficients are hard thresholds to remove the noisy artifacts in the source image. Moreover, the denoised images are used for fusion purposes to obtain fused images with less noise. Secondly, our proposed image fusion method presents an optimized algorithm to fuse multi-sensor source images. In this method, KLT based weights are optimized by considering more relative information of source images and improve the fused image's information interpretation capability. The qualitative and quantitative evaluations of fused images show that our method provides better fusion results than five different state-of-the-art medical, multi-focus, and infrared image fusion methods. The proposed image denoising method has 1% and 2.2% increment on average of PSNR and SSIM values, respectively, compared to existing state-of-the-art methods. The proposed image fusion method has a 9.04% increment on the average value of image fusion metrics compared to existing state-of-the-art methods.
The beginning of the Neolithic in the mid-Ebro valley and in Iberia's Inland (Northern and Southern submeseta), Spain

This paper is a state-of-art of the Neolithisation process and Early Neolithic in Iberia's inlands, specifically in the mid and upper Ebro Valley and Central Meseta of Spain. Firstly, a historiographical review of the hypothesis and models of the last decades are presented. In this section, the paper shows that the main theoretical concepts and ideas remain similar for a long time ago but they are nuanced by the last data and analysis. Next, these new data and revisions are analysed, in particular the existence of Neolithic settler pioneer communities and "Mesolithic context with Neolithic elements", the role of colonization phenomena (DNA analysis, leapfrog colonization and landscape occupation), the pottery studies like main interpretative tool and, finally, the variable of rock art. The chronology is reviewed in the last section. (C) 2017 Elsevier Ltd and INQUA. All rights reserved.
Developing a Naïve Bayesian Classification Model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations

Phosphatidylinositol 3-kinase gamma (PI3Kγ) plays a critical role in immune signaling, thus identifying PI3Kγ as a potential therapeutic target. However, developing selective PI3Kγ inhibitors is hampered by the highly conserved structure of the ATP-binding pocket. Focused effort would be needed to improve upon the γ-subtype selectivity of the inhibitors; therefore, in the present study, a naïve Bayesian classification (NBC) model with PI3Kγ structural features that integrates molecular docking and pharmacophore based on multiple PI3Kγ conformations was developed for virtual screening against PI3Kγ to find novel selective PI3Kγ inhibitors. First, the active PI3Kγ inhibitors/decoy dataset was used to prove whether molecular docking or pharmacophore, integrating multiple PI3Kγ conformations always has higher prediction accuracy than that of any single conformation. Second, both internal cross-validation and external prediction revealed that the NBC model combining molecular docking and pharmacophore could significantly improve the enrichment of active PI3Kγ inhibitors. Then, an analog dataset based on JN-PK1 (a reference compound) was constructed and submitted to virtual screening using the optimal NBC model. Finally, a novel inhibitor with higher PI3Kγ inhibitory activity than JN-PK1 was identified through a series of biological assays, showing both good accuracy and significant reliability of the NBC model with the PI3Kγ structural features. We hope that the developed virtual screening strategy will provide valuable guidance for the discovery of novel selective PI3Kγ inhibitors.
Point Set Registration Using Havrda-Charvat-Tsallis Entropy Measures

We introduce a labeled point set registration algorithm based on a family of novel information-theoretic measures derived as a generalization of the well-known Shannon entropy. This generalization, known as the Havrda-Charvat-Tsallis entropy, permits a fine-tuning between solution types of varying degrees of robustness of the divergence measure between multiple point sets. A variant of the traditional free-form deformation approach, known as directly manipulated free-form deformation, is used to model the transformation of the registration solution. We provide an overview of its open source implementation based on the Insight Toolkit of the National Institutes of Health. Characterization of the proposed framework includes comparison with other state of the art kernel-based methods and demonstration of its utility for lung registration via labeled point set representation of lung anatomy.
Sensations of skin infestation linked to abnormal frontolimbic brain reactivity and differences in self-representation

Some patients experience skin sensations of infestation and contamination that are elusive to proximate dermatological explanation. We undertook a functional magnetic resonance imaging study of the brain to demonstrate, for the first time, that central processing of infestation-relevant stimuli is altered in patients with such abnormal skin sensations. We show differences in neural activity within amygdala, insula, middle temporal lobe and frontal cortices. Patients also demonstrated altered measures of self-representation, with poorer sensitivity to internal bodily (interoceptive) signals and greater susceptibility to take on an illusion of body ownership: the rubber hand illusion. Together, these findings highlight a potential model for the maintenance of abnormal skin sensations, encompassing heightened threat processing within amygdala, increased salience of skin representations within insula and compromised prefrontal capacity for self-regulation and appraisal.