ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Dual feature extraction based convolutional neural network classifier for magnetic resonance imaging tumor detection using U-Net and three-dimensional convolutional neural network

Analysis and monitoring of disease development rely heavily on automated segmentation of brain tumors using MRI data. Because gliomas are aggressive and diverse, effective and precise segmentation techniques are utilized to divide tumors into intra-tumoral groups. In the proposed work, the Gray Level Co-occurrence Matrix feature extraction is used to extract characteristics from the image. In the segmentation problem of Brain tumor, Convolutional Neural Networks, which are widely employed in biomedical image segmentation, have greatly enhanced the stateof-the-art accuracy. In this work, we propose a major but simple combinative approach that results in improved and more precise estimates by combining two segmentation networks: a U-Net and a 3D CNN. For each model, it was assessed independently on the dataset to produce segmentation maps that varied greatly in terms of segmented tumor sub-regions and were then ensembled to reach the final prediction. On the validation set, the accuracy (percentage) of 98.29, 98.45, and 99.4 for tumor core, enhanced tumor and whole tumor, respectively, performed well in contrast to state-of-the-art designs presently existing.
Myocardial Function Imaging in Echocardiography Using Deep Learning

Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been questioned, and a large inter-vendor variability has been demonstrated. In this work, we propose a novel deep learning based framework for motion estimation in echocardiography, and use this to fully automate myocardial function imaging. A motion estimator was developed based on a PWC-Net architecture, which achieved an average end point error of (0.06 +/- 0.04) mm per frame using simulated data from an open access database, on par or better compared to previously reported state of the art. We further demonstrate unique adaptability to image artifacts such as signal dropouts, made possible using trained models that incorporate relevant image augmentations. Further, a fully automatic pipeline consisting of cardiac view classification, event detection, myocardial segmentation and motion estimation was developed and used to estimate left ventricular longitudinal strain in vivo. The method showed promise by achieving a mean deviation of (-0.7 +/- 1.6)% compared to a semi-automatic commercial solution for N=30 patients with relevant disease, within the expected limits of agreement. We thus believe that learning-based motion estimation can facilitate extended use of strain imaging in clinical practice.
Darinaparsin (ZIO-101) enhances the sensitivity of small-cell lung cancer to PARP inhibitors

Small-cell lung cancer (SCLC) is an aggressive high-grade neuroendocrine carcinoma of the lung associated with early metastasis and an exceptionally poor prognosis. Little progress has been made in developing efficacious targeted therapy for this recalcitrant disease. Herein, we showed that H3.3, encoded by two genes (H3F3A and H3F3B), was prominently overexpressed in SCLC. Darinaparsin (ZIO-101), a derivative of arsenic trioxide, dose- and time-dependently inhibited the viability of SCLC cells in an H3.3-dependent manner. More importantly, ZIO-101 treatment resulted in substantial accumulation of H3.3 and PARP1 besides induction of G2/M cell cycle arrest and apoptosis in SCLC cells. Through integrative analysis of the RNA-seq data from Cancer Cell Line Encyclopedia dataset, JNCI and Genomics of Drug Sensitivity in Cancer 2 datasets, we found that H3F3A expression was negatively correlated with the IC50 values of PARP inhibitors (PARPi). Furthermore, co-targeting H3.3 and PARP1 by ZIO-101 and BMN673/olaparib achieved synergistic growth inhibition against SCLC in vitro and in vivo. In conclusion, it is feasible to target H3.3 by ZIO-101 to potentiate the response rate of PARPi in SCLC patients.
Human trajectory prediction and generation using LSTM models and GANs

Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent tracking sys-tems. This complex subject comes with different challenges, such as human-space interaction, human-human interaction, multimodality, and generalizability. Currently, these challenges, especially generaliz-ability, have not been completely explored by state-of-the-art works. This work attempts to fill this gap by proposing and defining new methods and metrics to help understand trajectories. In particular, new deep learning models based on Long Short-Term Memory and Generative Adversarial Network architec-tures are used in both unimodal and multimodal contexts. These approaches are evaluated with new error metrics, which normalize some biases in standard metrics. Tests have been assessed using newly collected datasets characterized by a higher diversity and lower linearity than those used in state-of-the -art works. The results prove that the proposed models and datasets are comparable to and yield better generalizability than state-of-the-art works. Moreover, we also prove that our datasets better represent multimodal scenarios (allowing for multiple possible behaviors) and that human trajectories are moder-ately influenced by their spatial region and slightly influenced by their date and time. (c) 2021 Elsevier Ltd. All rights reserved.
Adaptive-Tree Multicast: Efficient Multidestination Support for CMP Communication Substrate

Multidestination communications are a highly necessary capability for many coherence protocols in order to minimize on-chip hit latency. Although CMPs share this necessity, up to now few suitable proposals have been developed. The combination of resource scarcity and the common idea that multicast support requires a substantial amount of extra resources is responsible for this situation. In this work, we propose a new approach suitable for on-chip networks capable of managing multidestination traffic via hardware in an efficient way with negligible complexity. We introduce a novel multicast routing mechanism, able to circumvent many of the limitations of conventional multicast schemes. Adaptive-tree multicasting is able to maintain correctness for multiflit multicast messages without routing restrictions, while also coupling correctness and performance in a natural way. Replication restrictions not only guarantee the presence of enough resources to avoid deadlock, but also dynamically adapt tree shape to network conditions, routing multicast messages through noncongested paths. The performance results, using a state-of-the-art full system simulation framework, show that it improves the average full system performance of a CMP by 20 percent and network ED2P by 15 percent, when compared to a state-of-the-art router with conventional multicast support and similar implementation cost.
Arguments for the biological and predictive relevance of the proportional recovery rule

The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values - an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery.
Sequence analysis of Epstein-Barr virus RPMS1 gene in malignant hematopathy of Northern China

The RPMS1 gene is the only member of the BamHI-A rightward transcripts (BARTs) family for which a full-length complementary DNA has been identified, and RPMS1 transcript has been confirmed in many Epstein-Barr virus (EBV)-positive malignancies. However, the effects of sequence variations of RPMS1 in hematological malignancies and their biological significance are unclear. To explore the association between RPMS1 gene variations and hematological malignancy, the RPMS1 gene of 391 EBV-positive samples from patients with EBV-positive leukemia, myelodysplastic syndromes and lymphoma in northern China were sequenced. On the basis of phylogenetic tree and mutation characteristics of RPMS1, all the sequences were divided into five major types: RPMS1-A, RPMS1-B, RPMS1-C, RPMS1-E, and RPMS1-F. RPMS1-A type, similar to the prototype B95-8, was identified in 71.87% (281/391) of samples and was the major type in all subpopulations. The frequency of RPMS1-F type was significantly higher in all malignant hematopathy groups than in healthy donors. The Hodgkin lymphoma group contained more RPMS1-F than other malignant hematopathy groups, and acute myeloid leukemia contained more RPMS1-C type than other malignant hematopathy groups. Therefore, RPMS1-A is the main type of RPMS1 gene in northern China, and RPMS1-F may be associated with hematologic malignancies.
Low-Complexity State-Space-Based System Identification and Controller Auto-Tuning Method for Multi-Phase DC-DC Converters

The importance of online system identification (SI) in power electronics is ever increasing. It enables the tracking of system parameters, which in turn can be used for online controller tuning. Hence, SI is a key element for improving a converter's dynamic performance, stability, reliability. In this paper, a novel state-space-based SI approach utilizing the step-adaptive approximate least squares estimation algorithm with observation matrix randomization is proposed. The presented concept yields an accurate state-space model of the converter while simultaneously achieving a fast convergence rate and low computational complexity. Consequently, the estimated state-space model is utilized to automatically tune a full state feedback controller. This results in an improved converter performance in terms of overshoots, undershoots, and settling times. The proposed concept is verified by a prototype system comprising a two-phase buck converter and a field-programmable gate array. The provided measurement results highlight the effectiveness and benefits of the presented method over the state-of-the-art algorithms, as well as z-domain estimation. It is shown that the number of required estimation iterations is more than halved in comparison with the state-of-the-art parametric SI approaches, while accuracy is improved.
Texture and rheological features of strain and pH sensitive chitosan-imine graphene-oxide composite hydrogel with fast self-healing nature

Here we report a smart Chitosan-isophthalaldehyde-Graphene oxide (CS-ISD-GO) hydrogel as a "multicomponent hydrogel". We witnessed an unprecedented pH responsive changes in viscoelasticity, stretchability, adhesiveness, self-healing and self-adaptability upon changing the pH and concentration of CS and ISD that was authenticated by texture and rheological analysis. The GO provides physical crosslinks and antibacterial properties to the hydrogel. Taking the advantage of dynamic nature of covalent and non-covalent interactions, we tuned the hydrogel adhesion and stretchability in response to the pH changes. Further self-healing of hydrogels was fully investigated by measuring thixotropic response over more than three cycles of strain sweep and real time optical imaging and video recording techniques. The recorded videos display 100 % self-healing response within a time frame of 2-6 min. These properties were observed only over small range of pH (4.5-5.5). The hydrogel becomes mechanically strong above pH 5.5 and becomes unstable above pH 7 leading to subsequent disintegration. The characterization of hydrogel was carried by FTIR, FESEM and TGA analysis. In addition, the hydrogel was reduced using NaBH4 for drug release. The reduced gel appears to be stable at lower pH values also. The reduced hydrogel may potentially be used for drug release purpose with low toxicity.
Artistic Image Analysis Using Graph-Based Learning Approaches

We introduce a new methodology for the problem of artistic image analysis, which among other tasks, involves the automatic identification of visual classes present in an art work. In this paper, we advocate the idea that artistic image analysis must explore a graph that captures the network of artistic influences by computing the similarities in terms of appearance and manual annotation. One of the novelties of our methodology is the proposed formulation that is a principled way of combining these two similarities in a single graph. Using this graph, we show that an efficient random walk algorithm based on an inverted label propagation formulation produces more accurate annotation and retrieval results compared with the following baseline algorithms: bag of visual words, label propagation, matrix completion, and structural learning. We also show that the proposed approach leads to a more efficient inference and training procedures. This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where we show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures.
Assisting Example-Based API Misuse Detection via Complementary Artificial Examples

Application Programming Interfaces (APIs) allow their users to reuse existing software functionality without implementing it by themselves. However, using external functionality can come at a cost. Because developers are decoupled from the API's inner workings, they face the possibility of misunderstanding, and therefore misusing APIs. Prior research has proposed state-of-the-art example-based API misuse detectors that rely on existing API usage examples mined from existing code bases. Intuitively, without a varied dataset of API usage examples, it is challenging for the example-based API misuse detectors to differentiate between infrequent but correct API usages and API misuses. Such mistakes lead to false positives in the API misuse detection results, which was reported in a recent study as a major limitation of the state-of-the-art. To tackle this challenge, in this paper, we first undertake a qualitative study of 384 falsely detected API misuses. We find that around one third of the false-positives are due to missing alternative correct API usage examples. Based on the knowledge gained from the qualitative study, we uncover five patterns which can be followed to generate artificial examples for complementing existing API usage examples in the API misuse detection. To evaluate the usefulness of the generated artificial examples, we apply a state-of-the-art example-based API misuse detector on 50 open source Java projects. We find that our artificial examples can complement the existing API usage examples by preventing the detection of 55 false API misuses. Furthermore, we conduct a pre-designed experiment in an automated API misuse detection benchmark (MUBench), in order to evaluate the impact of generated artificial examples on recall. We find that the API misuse detector covers the same true positive results with and without the artificial example, i.e., obtains the same recall of 94.7 percent. Our findings highlight the potential of improving API misuse detection by pattern-guided source code transformation techniques.
Enhanced Peptide Stability Against Protease Digestion Induced by Intrinsic Factor Binding of a Vitamin B12 Conjugate of Exendin-4

Peptide digestion from proteases is a significant limitation in peptide therapeutic development. It has been hypothesized that the dietary pathway of vitamin B12 (B12) may be exploited in this area, but an open question is whether B12-peptide conjugates bound to the B12 gastric uptake protein intrinsic factor (IF) can provide any stability against proteases. Herein, we describe a new conjugate of B12 with the incretin peptide exendin 4 that demonstrates picomolar agonism of the glugacon-like peptide-1 receptor (GLP1-R). Stability studies reveal that Ex-4 is digested by pancreatic proteases trypsin and chymotrypsin and by the kidney endopeptidase meprin β. Prebinding the B12 conjugate to IF, however, resulted in up to a 4-fold greater activity of the B12-Ex-4 conjugate relative to Ex-4, when the IF-B12-Ex-4 complex was exposed to 22 μg/mL of trypsin, 2.3-fold greater activity when exposed to 1.25 μg/mL of chymotrypsin, and there was no decrease in function at up to 5 μg/mL of meprin β.
Enzymatic potato starch modification and structure-function analysis of six diverse GH77 4-alpha-glucanotransferases

4-α-glucanotransferase (EC 2.4.1.25) mediated glucan transfer in starch provides opportunities for production of clean label starch ingredients with unique gelling properties. 4-α-glucanotransferases can be found in glycoside hydrolase (GH) family GH13, GH57, and in the monospecific glycoside hydrolase family 77 (GH77). Here, pH-temperature optima, steady-state kinetics, potato starch modifying properties and structural folds are reported for six phylogenetically distinct GH77 members, representing four different domain architectures including a novel multi-domain 4-α-glucanotransferase from Lactococcus lactis. Four of the enzymes exhibited starch modifying activity leading to a gradual decrease of the amylose content, elongation of amylopectin chains, and enabled formation of firm starch gels. Unexpectedly, these diverse enzymes catalyzed similar changes in chain length distributions. However, the amylose depletion and amylopectin elongation rates spanned more than two orders of magnitude between the enzyme showing very different specific activities. Tt4αGT from Thermus thermophilus had highest temperature optimum (73 °C) and superior potato starch modifying efficacy compared to the other five enzymes.
Bitline Charge Sharing Suppressed Bitline and Cell Supply Collapse Assists for Energy-Efficient 6T SRAM

This paper proposes a bitline charge sharing suppressed bitline read assist (BCS RA) and a cell supply collapse write assist (BCS WA). The proposed BCS RA suppresses the bitline (BL) voltage to half of the supply voltage (V-DD) using the charge sharing BL precharger for improving read stability and energy efficiency. In the proposed BCS WA, the charge on cell V-DD (CVDD) is shared with that on the BL precharged to half-V-DD by the charge sharing write driver, which causes the collapse in CVDD. In cells with poor writability, CVDD can be collapsed more by the self-collapse paths when the write operation is performed. Thus, the BCS WA improves writability and reduces write energy consumption. The simulation results using 22-nm FinFET technology show that static random access memory (SRAM) using BCS RA and WA consumes much less read and write energy than SRAMs using state-of-the-art assists while achieving a comparable minimum operating V-DD to SRAMs using state-of-the-art assists. Even compared to the SRAM without assists, the read and write energy consumption is reduced by 31% and 26%, respectively.
Multielement determination (essential and potentially toxic elements) in eye shadows exposed to consumption in Brazil using ICP OES

Worldwide, cosmetics (especially eye shadows) are widely consumed and have a great impact on the economy. The aim of this study was to determine the multielement composition, focusing on essential and potentially toxic elements, in cosmetics (eye shadow) exposed to consumption in Brazil. Concentrations of 17 elements (Al, As, Ba, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Se, Sr, Ti, V and Zn) were determined in samples (produced in China and Brazil) using a sequential optical emission spectrometer with inductively coupled plasma (ICP OES) after acid digestion, assisted by a closed digester block (6 mL of HNO3 + 2 mL of H2O2 + 1 mL of Triton ×-100 + 1 mL of ultrapure water). The method was validated by linearity, precision, accuracy, limits of detection (LoD) and quantification (LoQ). The elements were quantified (in µg g-1): Al (852-21,900), Ba (3.47-104), Cd (1.70-6.93), Cr (< 8.53-66.6), Cu (< 0.480-14.5), Mn (92.20-1,190), Ni (< 4.23-40.7), Pb (< 2.16-5.06), Sb (1.10-10.5), Sr (0.760-46.0), Ti (32.0-440), V (< 0.85-1.7) and Zn (24.90-2,600). As, Co, Mo and Se in all the investigated samples were found to be below the LoQ values of ICP OES. In this study, regardless of sample compositions and origins (Brazilian or Chinese), high levels of Al, Cd, Cr, Cu, Mn, Ni, Pb, Sb, Ti, V and Zn were observed, exceeding the recommended maximum tolerable limits, according to Brazilian and global legislations, which may present potential risks to human health and the environment.
Lymphoepithelioma-Like Carcinoma of the Breast: A Case Report of a Rare Type of Invasive Carcinoma

Lymphoepithelioma carcinoma (LELC) is an extremely rare type of mammary cancer. Based on the histology, it can be misdiagnosed with inflammatory lesions like mastitis and medullary carcinoma or other hematopoietic neoplasms like lymphoma in the breast. Since LELC has a good response to chemotherapy with a good prognosis, t is prognostically important to recognize LELC. We report a rare case of LELC in a 51-year-old pre-menopausal female with a left breast mass, diagnosed with invasive ductal carcinoma (IDC), LELC type, treated with mastectomy, followed by adjuvant chemotherapy and radiotherapy, with a disease-free interval of 10 months. Herein, we present the case with its clinical presentation, radiologic imaging, histopathological features, and immunohistochemistry (IHC) findings. The rarity of this type of breast tumor warrants studying the behavior of these uncommon tumors to avoid misdiagnosis and establish well-defined criteria for diagnosis.
Mountain erosion and mitigation: global state of art

Mountain erosion mapping is one of the important aspects for monitoring environmental degradation. Global climate change coupled with human activities is eroding the mountain regions of the world at an alarming rate. The various types of erosion can be classified as water erosion, aeolian erosion, glaciered erosion, gravity erosion and man-made erosion. Tectonic activity also plays a major role towards mountain erosion. Soil erosion along with debris flow and rock falling not only causes loss of human life and property, but also affects the climatic condition of the mountain regions. Mountain erosion mitigation and protection methods include wire mesh fencing with inclusion of brush layers and brush mats on fascines, constructing check dams and slope benches and application of bioengineering works such as plantation and vegetation growth. This study consists of the global scenario with some of the state-of-the-art methods for mitigation and mappings of mountain erosion. The erosion in mountainous regions can be controlled with the help of suitable vegetation and plant growth, with installation of special bioengineered materials. However, public awareness is also considered as an important factor. Moreover, it is found that mapping the rate of erosion greatly helps to make proper action plan against the erosion process.