Miracle-dz/UniFAR-FTUs huggingface dataset:
SPARC Inhibits Metabolic Plasticity in Ovarian Cancer

The tropism of ovarian cancer (OvCa) to the peritoneal cavity is implicated in widespread dissemination, suboptimal surgery, and poor prognosis. This tropism is influenced by stromal factors that are not only critical for the oncogenic and metastatic cascades, but also in the modulation of cancer cell metabolic plasticity to fulfill their high energy demands. In this respect, we investigated the role of Secreted Protein Acidic and Rich in Cysteine (SPARC) in metabolic plasticity of OvCa. We used a syngeneic model of OvCa in Sparc-deficient and proficient mice to gain comprehensive insight into the paracrine effect of stromal-SPARC in metabolic programming of OvCa in the peritoneal milieu. Metabolomic and transcriptomic profiling of micro-dissected syngeneic peritoneal tumors revealed that the absence of stromal-Sparc led to significant upregulation of the enzymes involved in glycolysis, TCA cycle, and mitochondrial electron transport chain (ETC), and their metabolic intermediates. Absence of stromal-Sparc increased reactive oxygen species and perturbed redox homeostasis. Recombinant SPARC exerted a dose-dependent inhibitory effect on glycolysis, mitochondrial respiration, ATP production and ROS generation. Comparative analysis with human tumors revealed that SPARC-regulated ETC-signature inversely correlated with SPARC transcripts. Targeting mitochondrial ETC by phenformin treatment of tumor-bearing Sparc-deficient and proficient mice mitigated the effect of SPARC-deficiency and significantly reduced tumor burden, ROS, and oxidative tissue damage in syngeneic tumors. In summary, our findings provide novel insights into the role of SPARC in regulating metabolic plasticity and bioenergetics in OvCa, and shines light on its potential therapeutic efficacy.
Learning Type-2 Fuzzy Rule-Based Systems through Memetic Algorithms

Fuzzy Rule-Based Systems (FRBSs) are powerful tools for handling uncertainty in complex real world problems. Unfortunately, designing an optimal set of fuzzy membership functions and rules is not an easy task. The complexity of this task increases furthermore when the design involves type-2 fuzzy memberships because of the higher number of parameters to identify. For this reason, researches have been carried out to automatically extract the most suitable parameters and the hidden rules for type-2 FRBSs by using historical data. However, very few approaches exist based on a hybrid optimization between global and local search. This paper is aimed at overcoming this gap by proposing to automatically generate type-2 FRBSs through the so-called memetic algorithms. As shown in the experiments, the exploitation of memetic algorithms leads to generate type-2 FRBSs more accurate than those obtained by well-known approaches in literature.
A “Deeper” Look at Detecting Cyberbullying in Social Networks

As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, a detailed look at the current state-of-the-art in cyberbullying detection reveals that deep learning techniques have seldom been used to tackle this problem, despite growing reputation in other text-based classification tasks. Motivated by neural networks' documented success, three architectures are implemented from similar works: a simple CNN, a hybrid CNN-LSTM and a mixed CNN-LSTM-DNN. In addition, three text representations are trained from three different sources, via the word2vec model: Google-News, Twitter and Formspring. The experiment shows that these models with one of the above embeddings beat other benchmark classifiers (Support Vector Machines and Logistic Regression) both in an unbalanced and balanced version of the same dataset.
Low Latency Spiking ConvNets with Restricted Output Training and False Spike Inhibition

Deep convolutional neural networks (ConvNets) have achieved the state-of-the-art performance on many real-world applications. However, significant computation and storage demands are required by ConvNets. Spiking neural networks (SNNs), with sparsely activated neurons and event-driven computations, show great potential to take advantage of the ultra- low power spike-based hardware architectures. Yet, training SNN with similar accuracy as ConvNets is difficult. Recent researchers have demonstrated the work of converting ConvNets to SNNs (CNN-SNN conversion) with similar accuracy. However, the energy-efficiency of the converted SNNs is impaired by the increased classification latency. In this paper, we focus on optimizing the classification latency of the converted SNNs. First, we propose a restricted output training method to normalize the converted weights dynamically in the CNN-SNN training phase. Second, false spikes are identified and the false spike inhibition theory is derived to speedup the convergence of the classification process. Third, we propose a temporal max pooling method to approximate the max pooling operation in ConvNets without accuracy loss. The evaluation shows that the converted SNNs converge in about 30 time-steps and achieve the best classification accuracy of 94% on CIFAR -10 dataset.
Free Radical-Scavenging Capacities, Phenolics and Capsaicinoids in Wild Piquin Chili (Capsicum annuum var. Glabriusculum)

The total phenolic compounds content, free radical-scavenging capacity and capsaicinoid content in populations of wild Piquin chili (C. annuum) were studied. Aqueous and hydroalcoholic extracts from nine ecotypes were evaluated. High contents of phenolic compounds and free radical-scavenging capacities were observed for both extracts; however, the values that were found for the hydroalcoholic phase were substantially higher. LC-MS analysis allowed for the detection of 32 compounds, where apigenin-8-C-glucoside followed by vanillic acid 1-O-β-o-glucopyranosylester (Isomer I or II) and 7-ethoxy-4-methylcoumarin were the most widely distributed; they were found in more than 89% of the ecotypes. The diversity of identified phenolic compounds was different among ecotypes, allowing them to be distinguished by chemical diversity, free radical-scavenging capacities and heat Scoville units. The total capsaicinoid content was higher in Population I (23.5 mg/g DW) than in Populations II and III, which had contents of 15.3 and 10.7 mg/g DW, respectively. This variability could lead to phytochemical exploitation and the conservation of the natural populations of wild chili.
Interferon- Gamma- Inducible Guanosine Triphosphate Cyclohydrolase 1 (GTP-CH1) Pathway Is Associated with Frailty in Egyptian Elderly.

Background Chronic low-grade inflammation may be a cardinal pathophysiologic feature in the pathogenesis of frailty. Interferon-gamma (INF-γ) is an understudied proinflammatory cytokine in frailty that induces many inflammatory pathways including the guanosine triphosphate cyclohydrolase 1 (GTP-CH1) pathway. Our aim was to evaluate the GTP-CH1 pathway in Egyptian frail elderly subjects. Methods INF-γ, neopterin, and nitric oxide (NO) levels were measured in 80 participants. Results Both pre-frail and frail subjects had significantly higher levels of INF-γ, neopterin and lower levels of NO than the control group. These biomarkers were associated with the risk of frailty with significant odds ratio. Conclusion Elevated INF-γ levels in frail subjects may activate the GTP-CH1 pathway. Elevated neopterin and reduced NO levels correlated with an active GTP-CH1 pathway. The risk of frailty increased with elevated INF-γ and neopterin and decreased with elevated NO levels.
Second-Order Consensus for Multiagent Systems via Intermittent Sampled Data Control

In this paper, a periodic intermittent sampled data control strategy, which both reduces the load of updating rate of the controller and cuts down the working time of the controller in each sampling interval, is proposed to investigate the second-order consensus of multiagent systems. In order to reach consensus, a necessary and sufficient condition depending on the coupling gains, the communication width, the sampling period, and the spectrum of the Laplacian matrix, is established. Furthermore, the feasible region of communication width is derived for given coupling gains and structure of network. Besides, the coupling gains are also carefully designed. On the other hand, when the time delay exists in the sampling process, a time delayed protocol is proposed. A necessary and sufficient condition based on the communication width, the sampling period, the coupling gains, the time delay, and the network structure is also derived for achieving consensus. It is amazing found that the sampling period should have both lower and upper bounds for given time delay and communication width for the sake of reaching consensus. Finally, several numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
Awareness about Management of Tooth Avulsion among Dentists in Jazan, Saudi Arabia

AIM: This study aimed to evaluate the awareness amongst dentists working in Jazan, a city in K.S. A. , about their potential role in the treatment of traumatic tooth avulsion injuries. MATERIAL AND METHODS: A cross-sectional study was performed and all dentists in Jazan, K.S. A. were invited to participate in this study. Data was collected through a self-administrated questionnaire. A total of 200 dental practitioners answered the questionnaire. The intern/general practitioners were 75%, and the specialists were 25%. The variables comprised socio-demographic information, professional characteristics and information regarding the awareness towards the management of tooth avulsion. RESULTS: A total of 200 dental practitioners answered the questionnaire. The intern/general practitioners were 75%, and the specialists were 25%. (44.3%) were aware of the critical time for avulsed tooth replantation while 71.8% of females did not know about the critical time for avulsed tooth replantation. 50.0% of the specialists reported that less than 30 min was needed for avulsed tooth replantation, while 59.3% of interns/general dentists advised less than 60 minutes. CONCLUSION: A statistically significant result was observed regarding the best storage medium, a critical time for avulsed tooth replantation and tooth management before replantation.
End-to-End Hierarchical Fuzzy Inference Solution

Hierarchical Fuzzy System (HFS) is a popular approach for handling curse of dimensionality problem occurred in complex fuzzy rule-based systems with various and numerous inputs. However, the processes of modeling and reasoning of HFS have some critical issues to be considered. In this study, the effect of these issues on the accuracy and stability of the resulting system has been investigated, and an end-to-end HFS framework has been proposed. The proposed framework has three main steps such as single system modeling, rule partitioning and HFS reasoning. It is fully automated, generic, almost independent from data, and applicable for any kind of inference problem. In addition, the proposed framework preserves accuracy and stability during the HFS reasoning. These judgments have been ensured by a number of experimental studies on several datasets about software faulty prediction (SFP) problem with a large feature space. The main contributions of this paper are as follows: (i) it provides the entire HFS implementation from problem definition to calculation of final output, (ii) it increases the accuracy of recently proposed rule generation scheme in the literature, (iii) it presents the only possible fuzzy system solution for SFP problem containing a large feature space with reasonable accuracy.
Recognizing Partial Biometric Patterns

Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching. In this area, deep learning based methods are widely applied to match these partial captured objects caused by occlusions, variations of postures or just partial out of view in person re-identification and partial face recognition. However, most current methods are not able to identify an individual in case that some parts of the object are not obtainable, while the rest are specialized to certain constrained scenarios. To this end, we propose a robust general framework for arbitrary biometric matching scenarios without the limitations of alignment as well as the size of inputs. We introduce a feature post-processing step to handle the feature maps from FCN and a dictionary learning based Spatial Feature Reconstruction (SFR) to match different sized feature maps in this work. Moreover, the batch hard triplet loss function is applied to optimize the model. The applicability and effectiveness of the proposed method are demonstrated by the results from experiments on three person re-identification datasets (Market1501, CUHK03, DukeMTMC-reID), two partial person datasets (Partial REID and Partial iLIDS) and two partial face datasets (CASIA-NIR-Distance and Partial LFW), on which state-of-the-art performance is ensured in comparison with several state-of-the-art approaches. The code is released online and can be found on the website: this https URL.
Detection and Characterization of Human Pegivirus 2, Vietnam

We report human pegivirus 2 (HPgV-2) infection in Vietnam. We detected HPgV-2 in some patients with hepatitis C virus/HIV co-infection but not in patients with HIV or hepatitis A, B, or C virus infection, nor in healthy controls. HPgV-2 strains in Vietnam are phylogenetically related to global strains.
Person Head Detection in Multiple Scales Using Deep Convolutional Neural Networks

Person detection is an important problem in computer vision with many real-world applications. The detection of a person is still a challenging task due to variations in pose, occlusions and lighting conditions. The purpose of this study is to detect human heads in natural scenes acquired from a publicly available dataset of Hollywood movies. In this work, we have used state-of-the-art object detectors based on deep convolutional neural networks. These object detectors include region-based convolutional neural networks using region proposals for detections. Also, object detectors that detect objects in the single-shot by looking at the image only once for detections. We have used transfer learning for fine-tuning the network already trained on a massive amount of data. During the fine-tuning process, the models having high mean Average Precision (mAP) are used for evaluation of the test dataset. Experimental results show that Faster R-CNN [18] and SSD MultiBox [13] with VGG16 [21] perform better than YOLO [17] and also demonstrate significant improvements against several baseline approaches.
Arachne: Core-Aware Thread Management

Arachne is a new user-level implementation of threads that provides both low latency and high throughput for applications with extremely short-lived threads (only a few microseconds). Arachne is core-aware: each application determines how many cores it needs, based on its load; it always knows exactly which cores it has been allocated, and it controls the placement of its threads on those cores. A central core arbiter allocates cores between applications. Adding Arachne to memcached improved SLO-compliant throughput by 37%, reduced tail latency by more than 10x, and allowed memcached to coexist with background applications with almost no performance impact. Adding Arachne to the RAMCloud storage system increased its write throughput by more than 2.5x. The Arachne threading library is optimized to minimize cache misses; it can initiate a new user thread on a different core (with load balancing) in 320 ns. Arachne is implemented entirely at user level on Linux; no kernel modifications are needed.
Measuring and monitoring health-related quality of life responsiveness in systemic lupus erythematosus patients: current perspectives

Objective Little is known about health-related quality of life (HRQoL) responsiveness in systemic lupus erythematosus (SLE) patients, compared to other chronic diseases. This review summarizes the available data in HRQoL responsiveness and sensitivity to change in SLE, and recommends directions for research and clinical application. Methods A review of the literature was conducted reporting on HRQoL responsiveness in adult SLE patients between 1984 and 2018. HRQoL studies were assessed for responsiveness, sensitivity of change, minimal important differences, minimal clinical important differences, or change in improvement or deterioration. Results Responsiveness or sensitivity to change in health-related status was observed in Medical Outcome Survey Short Form-36, SLE Symptom Checklist, EuroQoL, and Medical Outcomes Study Short Form 6D. SLE-specific quality of life questionnaire demonstrated greater responsiveness than the individual domains of SF-36. Lupus quality of life showed large responsiveness when there was improvement. LupusPRO and its derivative Lupus Impact Tracker were found to be responsive to change in disease activity, reflecting both improvement and worsening. Lupus Impact Tracker and physical health and pain domains of Lupus quality of life were responsive to SLE composite responder index. Conclusion This review highlights the need for further studies that capture responsiveness and change in HRQoL that are clinically meaningful and sustained. Most importantly, the choice of one measure over another is influenced by the purpose of the HRQoL measure, the particular HRQoL domain, and the SLE disease state that are relevant to the research question.
Image Purification Networks: Real-time Style Transfer with Semantics through Feed-forward Synthesis

Image synthesis has been widely accepted as a cost effective way to learn models because it provides training sets that are large, diverse and accurately labeled. However, the realism of the synthetic image is not enough, this affects generalization on naturalistic test image. In an attempt to address this issue, previous methods learn a model to improve the realism of synthetic image. Differently, from previous methods, we take the first step towards purifying the naturalistic image to weaken the influence of light and convert the distribution of an outdoor naturalistic image through a real-time style transfer task to that of indoor synthetic image. This paper proposes, therefore a real-time image purification networks that transfer style information with semantics through a feed-forward synthesis. Results from our experiments demonstrate that images purified through the proposed networks architecture trained models for gaze estimation more accurately on cross-datasets over using raw naturalistic images and when compared to baseline methods.
High concordance of TMPRSS-ERG fusion between primary prostate cancer and its lymph node metastases

Approximately 50% of prostate cancer types harbor the transmembrane protease, serine 2: Erythroblast transformation-specific-related gene (ERG) fusion, resulting in oncogenic expression of the ERG transcription factor. ERG represents an attractive target for potential future anticancer therapy in advanced and metastatic prostate cancer. To better understand whether the analysis of the primary cancer is sufficient to estimate the ERG expression status of the lymph node metastases, the present study examined patterns of immunohistochemical ERG expression in a tissue microarray created from multiple primary and metastatic sites of 77 prostate cancer tissues. Among the identified tumor types, 80% were either entirely ERG-positive (38%) or ERG-negative (42%) across all (at least 9) analyzed different tumor sites. The results were heterogeneous in 20% of the tumor types and typically resulted from small ERG-negative areas within otherwise ERG-positive tumor types. Comparison of the ERG expression status in 51 primary cancer types with at least three interpretable lymph node metastases revealed an entirely identical ERG status in all tumor sites in 75% of the cases, including 16 ERG-positive and 22 ERG-negative cancer types. The remaining 13 cancer types exhibited ERG heterogeneity within the primary tumor, while all metastases had an identical (12 positive and 1 negative) ERG status. The results of the present study revealed a high degree of concordance of the ERG expression status between primary prostate cancer types and their lymph node metastases. Therefore, potential anti-ERG therapy may also be effective against lymph node metastases in the majority of cases of ERG-positive metastatic prostate cancer.
Semi-Supervised Clustering With Multiresolution Autoencoders

In most real world clustering scenarios, experts generally dispose of limited background information, but such knowledge is valuable and may guide the analysis process. Semi-supervised clustering can be used to drive the algorithmic process with prior knowledge and to enable the discovery of clusters that meet the analyst’s expectations. Usually, in the semi-supervised clustering setting, the background knowledge is converted to some kind of constraint and, successively, metric learning or constrained clustering are adopted to obtain the final data partition. Conversely, we propose a new semi-supervised clustering algorithm that directly exploits prior knowledge, under the form of labeled examples, avoiding the necessity to derive constraints. Our algorithm employs a multiresolution strategy to generate an ensemble of semi-supervised autoencoders that fit the data together with the background knowledge. Successively, the network models are employed to supply a new embedding representation on which clustering is performed. The proposed strategy is evaluated on a set of real-world benchmarks also in comparison with well-known state-of-the-art semi-supervised clustering methods. The experimental results highlight the benefit of directly leveraging the prior knowledge and show the quality of the representation learnt by the multiresolution schema.
Leptin and its receptor in glucose metabolism of T-cell lymphoma

T-cell lymphoma (TCL) is a group of heterogeneous disorders with a poor response to conventional treatment. In order to identify novel therapeutic targets, the present study investigated the effect of leptin and its receptor on glucose metabolism in TCL. The expression of the leptin receptor (ObR), and glucose transporter (Glut)1 and 4 was detected in TCL and reactive lymphoid hyperplasia (RLH) tissues by immunohistochemical analysis. A higher level of ObR expression was observed in the TCL tissues than in the RLH tissues (58.3 vs. 22.2%; P=0.012), and ObR overexpression was associated with high expression of Glut1 (P=0.007). In vitro analysis using the human TCL MOLT-3 cell line demonstrated that leptin stimulated cell glucose uptake via promoting recruitment and expression of Glut1, effects which were abolished by ObR-specific small interfering RNA (siRNA). Additionally, MOLT-3 cell viability was also increased following leptin treatment. ObR-specific siRNA abolished these responses. In conclusion, these results suggested that leptin serves a critical role in TCL glucose uptake via the ObR.