Miracle-dz/UniFAR-FTUs huggingface dataset:
U-Net based automatic carotid plaque segmentation from 3D ultrasound images

Ultrasound image assessment plays an important role in the diagnosis of carotid artery atherosclerosis. The segmentation of plaques from carotid artery ultrasound images is critical for the atherosclerotic diagnosis. In this paper, a novel automatic plaque segmentation method is presented based on U-Net deep learning network which allows to train the network end-to-end for pixel-wise classification. A large number of labeled examples are required for traditional supervised learning techniques as to obtain the global optimization. However, in this task, it is unavailable to obtain so many labeled examples since manually segmentation of plaques is a time-consuming task and its reliability relies to the experience of experts. In order to solve the problem of lack of labeled samples, an unsupervised learning technique, the deep convolutional encoder-decoder architecture, was proposed to pre-train the parameters of U-Net by amount of unlabeled data. Then the parameters learned from the deep convolutional encoder-decoder network were applied to initialize a U-Net from the labeled images for fine-tuning. Algorithm accuracy was examined on the common carotid artery part of 26 3D carotid ultrasound images (34 plaques) by comparing the results of our algorithm with manual segmentations and the Dice similarity coefficient (DSC) is 90.72±6.2% which was better than the previous level set method with the DSC of 88.2±8.3%. The automatic method provides a more convenient way to segment carotid plaques in 3D ultrasound images.
Evaluation Of Expression Patterns Of Cytokeratin And Mucin Peptide Core Antigen For Diagnostic Role In Upper Gastrointestinal Tract Pre Neoplastic And Neoplastic Lesions

Introduction: Gastrointestinal tract (GIT) lesions include a wide variety of lesions, which may be diagnostically challenging on histopathology and may be difficult to differentiate without the aid of immunohistochemical stains. Objective: The present study was undertaken to histopathologically analyze the upper GIT lesions and determine the expression pattern of cytokeratin (CK) and mucin peptide core antigen (MUC) in these lesions and their usefulness in diagnosis. Material and Methods: A 135 cases with clinical diagnosis of upper GIT lesions including Barrett’s metaplasia (BM), carcinoma esophagus, gastric intestinal metaplasia (GIM) and gastric carcinoma were included. Standard diagnostic criteria were used in evaluating tissue sections and arriving at a diagnosis. Relevant clinical data including age, gender, complications and symptoms of disease were noted. Immunohistochemical evaluation of the lesions were done. The antibody panel included CK7,CK18, CK19, CK20, MUC1&MUC2. Results: Immunohistochemical pattern CK7-/CK20+ was seen in 85% gastric adenocarcinoma and 82% gastric intestinal metaplasia. CK7+/ CK20was characteristic of esophageal adenocarcinoma. CK7+/CK20+ pattern was characteristic of Barrett’s metaplasia. MUC1 and MUC2 expression was seen in both goblet and non goblet cells in Barrett’s metaplasia, MUC1 in both gastric intestinal metaplasia and MUC2 in only goblet cells. MUC2 is a good marker of mucinous carcinomas of esophagus and stomach. Conclusions: Pre-neoplastic and neoplastic lesions of the gastrointestinal tract are varied yet histologically challenging, often prove to be a diagnostic dilemma. Understanding unique immunohistochemical profiles of each, combined with histopathological and endoscopic correlation greatly assists in the diagnosis and management of these lesions.
Butoxy Mansonone G Inhibits STAT3 and Akt Signaling Pathways in Non-Small Cell Lung Cancers: Combined Experimental and Theoretical Investigations

Epidermal growth factor receptor (EGFR) is the key molecular target for non-small cell lung cancer (NSCLC) due to its major contribution to complex signaling cascades modulating the survival of cancer cells. Targeting EGFR-mediated signaling pathways has been proved as a potential strategy for NSCLC treatment. In the present study, mansonone G (MG), a naturally occurring quinone-containing compound, and its semi-synthetic ether derivatives were subjected to investigate the anticancer effects on human NSCLC cell lines expressing wild-type EGFR (A549) and mutant EGFR (H1975). In vitro cytotoxicity screening results demonstrated that butoxy MG (MG3) exhibits the potent cytotoxic effect on both A549 (IC50 of 8.54 μM) and H1975 (IC50 of 4.21 μM) NSCLC cell lines with low toxicity against PCS201-010 normal fibroblast cells (IC50 of 21.16 μM). Western blotting and flow cytometric analyses revealed that MG3 induces a caspase-dependent apoptosis mechanism through: (i) inhibition of p-STAT3 and p-Akt without affecting upstream p-EGFR and (ii) activation of p-Erk. The 500-ns molecular dynamics simulations and the molecular mechanics combined with generalized Born surface area (MM/GBSA)-based binding free energy calculations suggested that MG3 could possibly interact with STAT3 SH2 domain and ATP-binding pocket of Akt. According to principal component analysis, the binding of MG3 toward STAT3 and Akt dramatically altered the conformation of proteins, especially the residues in the active site, stabilizing MG3 mainly through van der Waals interactions.
Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module

Although successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts sometimes disagree with their decisions. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of experts’ decision-making. Automatic and precision classification for breast cancer histopathological image is of great importance in clinical application for identifying malignant tumors from histopathological images. Advanced convolution neural network technology has achieved great success in natural image classification, and it has been used widely in biomedical image processing. In this paper, we design a novel convolutional neural network, which includes a convolutional layer, small SE-ResNet module, and fully connected layer. We propose a small SE-ResNet module which is an improvement on the combination of residual module and Squeeze-and-Excitation block, and achieves the similar performance with fewer parameters. In addition, we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate. We use our model for the automatic classification of breast cancer histology images (BreakHis dataset) into benign and malignant and eight subtypes. The results show that our model achieves the accuracy between 98.87% and 99.34% for the binary classification and achieve the accuracy between 90.66% and 93.81% for the multi-class classification.
Ascophyllan Induces Activation of Natural Killer Cells in Mice In Vivo and In Vitro

Natural marine polysaccharides have demonstrated immune stimulatory effects in both mice and humans. Our previous study compared the ability of ascophyllan and fucoidan to activate human and mouse dendritic cells (DCs). In this study, we further examined the effect of ascophyllan on the activation of mouse natural killer (NK) cells in vivo and in vitro and compared it to that of fucoidan, a well-studied natural marine polysaccharide. Specifically, administration of ascophyllan to C57BL/6 mice increased the number of NK cells in the spleen when compared to the number in PBS-treated mice. Moreover, the number of IFN-γ-producing NK cells and expression of CD69 were markedly upregulated by ascophyllan treatment. Ascophyllan treatment also induced IFN-γ production and CD69 upregulation in isolated NK cells, but did not promote cell proliferation. Finally, ascophyllan treatment increased the cytotoxicity of NK cells against Yac-1 cells. The effects of ascophyllan on NK cell activation were considerably stronger than those of fucoidan. These data demonstrated that ascophyllan promotes NK cell activation both in mice and in vitro, and its stimulatory effect on NK cells is stronger than that of fucoidan.
A Comparative Study between Possibilistic and Probabilistic Approaches for Query Translation Disambiguation

We propose in this paper a new hybrid possibilistic query translation disambiguation approach combining a probability-to-possibility transformation-based approach with a discriminative possibilistic one in order to take advantage of their strengths. The disambiguation process in this approach requires a bilingual lexicon and a parallel text corpus. Given a source query terms, the first step consists of selecting the existing noun phrases (NPs) and the remaining single terms which are not included in any NPs. We have translated these identified NPs as units through the probability-to-possibility transformation-based approach, as a mean to introduce further tolerance, using a language model and translation patterns. Then, the remaining single source query terms are translated via the discriminative possibilistic approach. We have modelled in this step the translation relevance of a given single source query term via two measures: the possible relevance excludes irrelevant translations, while the necessary relevance reinforces the translations not removed by the possibility. We have developed a set of experiments using the CLEF-2003 French-English CLIR test collection and the French-English parallel text corpus Europarl. The reported results highlight some statistically significant improvements of the hybrid possibilistic approach in the CLIR effectiveness using diverse evaluation metrics and scenarios for both long and short queries.
Composition and Rheological Properties of Polysaccharide Extracted from Tamarind (Tamarindus indica L.) Seed

A polysaccharide was extracted in high yield from tamarind (Tamarindus indica L.) seed (TSP) by acidic hot water extraction and ethanol precipitation. It was composed of 86.2% neutral polysaccharide, 5.4% uronic acid and 1.3% protein. The molecular weight of TSP was estimated to be about 1735 kDa, with glucose, xylose, and galactose in a molar ratio of 2.9:1.8:1.0 as the major monosaccharides. The steady shear and viscoelastic properties of TSP aqueous solutions were investigated by dynamic rheometry. Results revealed that TSP aqueous solution at a concentration above 0.5% (w/v) exhibited non-Newtonian shear-thinning behavior. Dynamic oscillatory analysis revealed that 10% (w/v) TSP showed as a “weak gel” structure. Apparent viscosities and viscoelastic parameters of TSP solutions decreased drastically in an alkaline solution of pH > 10, but slightly influenced by acidic solution, high temperature and the presence of salt ions and sucrose. These results indicated that TSP possessed excellent pH-resistance and thermo-stability, which might be suitable for applications in acidic beverages and high-temperature processed foodstuffs.
Preclinical Investigation of 212Pb-DOTAMTATE for Peptide Receptor Radionuclide Therapy in a Neuroendocrine Tumor Model

Somatostatin analogues have been examined as a treatment for somatostatin receptor overexpressing tumors for years; specifically, octreotate (TATE) and octreotide (TOC). Several versions of these analogues coupled to beta or gamma nuclides are currently used as imaging agents, as treatments with peptide receptor radionuclide therapy (PRRT) for patients with neuroendocrine tumors or are being explored in preclinical and clinical settings. Our study describes the use of 212Pb-DOTAMTATE, the octreotate analogue, in combination with 212Pb, the parent of an alpha emitter. Preclinical studies demonstrated tumor targeting of 212Pb-DOTAMTATE of >20% ID/g up to 24 hours post drug injection. The addition of kidney protection agents, including l-lysine and l-arginine decreases drug accumulation in the kidneys and the addition of ascorbic acid to the chelation mixture reduces oxidation of the drug product. 212Pb-DOTAMTATE displays a favorable toxicity profile with single-dose injections of 20 μCi showing 100% survival and with nontoxic cumulative doses up to 45 μCi, when fractionated into three smaller doses of 15 μCi. In an initial efficacy study, a single 10 μCi injection of 212Pb-DOTAMTATE extended the mean survival 2.4-fold. Efficacy was enhanced by giving three treatment cycles of 212Pb-DOTAMTATE and reducing the time between injections to two weeks. Efficacy was optimized further by the addition of a chemo-sensitizing agent, 5-fluorouracil, given in combination with three cycles of 10 μCi 212Pb-DOTAMTATE. These conditions led to 79% of the animals being tumor free at the end of the 31-week study suggesting that 212Pb-DOTAMTATE alone or in combination with a chemotherapeutic may have positive clinical implications.
Towards Adaptive Deep Reinforcement Game Balancing

The experience of a player regarding the difficulty of a video game is one of the main reasons for he/she decide to keep playing the game or abandon it. Effectively, player retention is one of the primary concerns related to the game development process. However, the experience of a player with a game is unique, making impractical to anticipate how they will face the gameplay. This work leverages the recent advances in Reinforcement Learning (RL) and Deep Learning (DL) to create intelligent agents that are able to adapt to the abilities of distinct players. We focus on balancing the difficulty of the game based on the information that the agent observes from the 3D environment as well as the current state of the game. In order to design an agent that learns how to act while still maintaining the balancing, we propose a reward function based on a balancing constant. We require that the agent remains inside a range around this constant during the training. Our experimental results show that by using such a reward function and combining information from different types of players it is possible to have adaptable agents that fit the player.
Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: a nationwide multicentre study

Objective To develop a gastric cancer (GC) risk prediction rule as an initial prescreening tool to identify individuals with a high risk prior to gastroscopy. Design This was a nationwide multicentre cross-sectional study. Individuals aged 40–80 years who went to hospitals for a GC screening gastroscopy were recruited. Serum pepsinogen (PG) I, PG II, gastrin-17 (G-17) and anti-Helicobacter pylori IgG antibody concentrations were tested prior to endoscopy. Eligible participants (n=14 929) were randomly assigned into the derivation and validation cohorts, with a ratio of 2:1. Risk factors for GC were identified by univariate and multivariate analyses and an optimal prediction rule was then settled. Results The novel GC risk prediction rule comprised seven variables (age, sex, PG I/II ratio, G-17 level, H. pylori infection, pickled food and fried food), with scores ranging from 0 to 25. The observed prevalence rates of GC in the derivation cohort at low-risk (≤11), medium-risk (12–16) or high-risk (17–25) group were 1.2%, 4.4% and 12.3%, respectively (p<0.001). When gastroscopy was used for individuals with medium risk and high risk, 70.8% of total GC cases and 70.3% of early GC cases were detected. While endoscopy requirements could be reduced by 66.7% according to the low-risk proportion. The prediction rule owns a good discrimination, with an area under curve of 0.76, or calibration (p<0.001). Conclusions The developed and validated prediction rule showed good performance on identifying individuals at a higher risk in a Chinese high-risk population. Future studies are needed to validate its efficacy in a larger population.
SPA: Smart Parking Algorithm Based on Driver Behavior and Parking Traffic Predictions

Smart parking problems have received much attention in recent years. In literature, many smart parking allocation algorithms that considered the parking grid reservation and recommendation have been proposed. However, the parking policies for maximizing parking rate and benefits still can be improved. This paper proposes a smart parking allocation algorithm (SPA), which aims to maximize the benefits created by a given parking lot while guaranteeing the quality of parking services. The proposed SPA algorithm predicts the driver behavior and estimated parking traffic in the near future based on the historical parking records. These predictions help SPA to better match the parking demands and the resource of available parking grids and, hence, improve the utilization and the created benefit of each parking grid. The proposed SPA applies three policies, namely worst-fit (WF-SPA), best-fit (BF-SPA), and parking behavior forecast (PBF-SPA), to allocate the available grids to the vehicles. Performance evaluations reveal that the proposed SPA outperforms exiting work in terms of accumulated parking rate and service quality and, hence, improves the benefits of a given parking lot.
Radiation-Induced Helium Bubbles in Metals

Helium (He) bubbles are typical radiation defects in structural materials in nuclear reactors after high dose energetic particle irradiation. In the past decades, extensive studies have been conducted to explore the dynamic evolution of He bubbles under various conditions and to investigate He-induced hardening and embrittlement. In this review, we summarize the current understanding of the behavior of He bubbles in metals; overview the mechanisms of He bubble nucleation, growth, and coarsening; introduce the latest methods of He control by using interfaces in nanocrystalline metals and metallic multilayers; analyze the effects of He bubbles on strength and ductility of metals; and point out some remaining questions related to He bubbles that are crucial for design of advanced radiation-tolerant materials.
Recognizing Art Style Automatically with deep learning

The artistic style (or artistic movement) of a painting is a rich descriptor that captures both visual and historical information about the painting. Correctly identifying the artistic style of a paintings is crucial for indexing large artistic databases. In this paper, we investigate the use of deep residual neural to solve the problem of detecting the artistic style of a painting and outperform existing approaches to reach an accuracy of 62% on the Wikipaintings dataset (for 25 different style). To achieve this result, the network is first pre-trained on ImageNet, and deeply retrained for artistic style. We empirically evaluate that to achieve the best performance, one need to retrain about 20 layers. This suggests that the two tasks are as similar as expected, and explain the previous success of hand crafted features. We also demonstrate that the style detected on the Wikipaintings dataset are consistent with styles detected on an independent dataset and describe a number of experiments we conducted to validate this approach both qualitatively and quantitatively.
Contemporary management of essential thrombocythemia in children

ABSTRACT Introduction: Essential thrombocythemia (ET) is a disease which is extremely rare in children. Only recently, data on pediatric ET have become available. Areas covered: In children with sustained platelet count over 450 x 109/L, secondary thrombocytosis must be ruled out. ET workup comprehends research of JAK2V617F, CALR and MPL mutations and bone marrow biopsy (BM). In asymptomatic children wait and watch is the best option. Aspirin controls headache and other microvascular disturbances. Patients with venous thrombosis need anticoagulation. Cytoreductive drugs in children with ET should be prescribed as a last choice. Hydroxyurea and IFN-a are first-line therapy at any age including children; Anagrelide is not licensed as first-line therapy for ET in Europe. New JAK2-inhibitors are not clearly useful in ET and hence not approved for ET. Expert opinion: The most challenging problem is to understand if a child with prolonged not secondary thrombocytosis really has ET. Diagnostic workup requires molecular and histological studies. The rare children with clonal ET have features like those of adults. Patients with ET have long expected survival and the treatment in children must be long-term efficacious and well tolerated.
Data-Driven Point Cloud Objects Completion

With the development of the laser scanning technique, it is easier to obtain 3D large-scale scene rapidly. However, many scanned objects may suffer serious incompletion caused by the scanning angles or occlusion, which has severely impacted their future usage for the 3D perception and modeling, while traditional point cloud completion methods often fails to provide satisfactory results due to the large missing parts. In this paper, by utilising 2D single-view images to infer 3D structures, we propose a data-driven Point Cloud Completion Network (PCCNet), which is an image-guided deep-learning-based object completion framework. With the input of incomplete point clouds and the corresponding scanned image, the network can acquire enough completion rules through an encoder-decoder architecture. Based on an attention-based 2D-3D fusion module, the network is able to integrate 2D and 3D features adaptively according to their information integrity. We also propose a projection loss as an additional supervisor to have a consistent spatial distribution from multi-view observations. To demonstrate the effectiveness, first, the proposed PCCNet is compared to recent generative networks and has shown more powerful 3D reconstruction abilities. Then, PCCNet is compared to a recent point cloud completion methods, which has demonstrate that the proposed PCCNet is able to provide satisfied completion results for objects with large missing parts.
NKG2D Polymorphism in Melanoma Patients from Southeastern Spain

Background: Natural killer (NK) and CD8+ T cells are involved in the immune response against melanoma. C-Type lectin-like NK cell receptors are located in the Natural Killer Complex (NKC) region 12p13.2-p12.3 and play a critical role in regulating the activity of NK and CD8+ T cells. An association between polymorphisms in the NKC region, including the NKG2D gene and NKG2A promoter, and the risk of cancer has been previously described. The aim of this study was to analyze the association of polymorphisms in the NKC region with cutaneous melanoma in patients from southeastern Spain. Methods: Seven single-nucleotide polymorphisms (SNPs) in the NKG2D gene (NKC3,4,7,9,10,11,12), and one SNP in the NKG2A promoter (NKC17) were genotyped by a TaqMan 5′ Nuclease Assay in 233 melanoma patients and 200 matched healthy controls. Results: A linkage disequilibrium analysis of the SNPs performed in the NKC region revealed two blocks of haplotypes (Hb-1 and Hb-2) with 14 and seven different haplotype subtypes, respectively. The third most frequent haplotype from the block Hb-2—NK3 (CAT haplotype)—was significantly more frequent on melanoma patients than on healthy controls (p = 0.00009, Pc = 0.0006). No further associations were found when NKC SNPs were considered independently. Conclusions: Our results suggest an association between NKG2D polymorphisms and the risk of cutaneous malignant melanoma.