Miracle-dz/UniFAR-FTUs huggingface dataset:
MALAT1 silencing suppresses prostate cancer progression by upregulating miR-1 and downregulating KRAS

Background Prostate cancer (PC) is the second leading cause of cancer-related deaths among men. Long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) performed as an oncogene in multiple cancers including PC. However, the molecular mechanisms of MALAT1 implicated in PC progression have not been thoroughly elaborated. Materials and methods Reverse transcription-quantitative polymerase chain reaction assay was used to detect the expressions of MALAT1 and microRNA-1 (miR-1). Protein levels of cleaved poly (ADP-ribose) polymerase, cleaved caspase-3, BAX, bcl-2, and KRAS were determined using a western blot assay. Cell proliferation was assessed by colony formation and MTS assays. Cell migration capacity was examined by transwell migration assay (Corning Incorporated, Corning, NY, USA). Apoptosis rate was measured by flow cytometry via double staining of annexin V-FITC and propidium iodide. Luciferase and RNA immunoprecipitation assays were employed to explore the relationship among miR-1, MALAT1, and KRAS. Results MALAT1 expression was upregulated and miR-1 expression was downregulated in PC tissues and cell lines. MALAT1 knockdown inhibited cell proliferation and migration, and promoted cell apoptosis in androgen receptor-negative DU145 and PC3 cells. Molecular mechanism explorations disclosed that MALAT1 acted as a molecular sponge of miR-1 in DU145 cells. Moreover, miR-1 downregulation partly abrogated MALAT1 silencing-mediated anti-proliferative, antimigratory, and proapoptotic effects in DU145 and PC3 cells. Further investigation revealed that KRAS was a target of miR-1 in DU145 cells. MALAT1 acted as a competing endogenous RNA of miR-1, resulting in the increase of KRAS expression in DU145 and PC3 cells. Furthermore, miR-1 overexpression hampered proliferation and migration and promoted apoptosis in DU145 and PC3 cells, while these effects were markedly weakened following KRAS upregulation. Conclusion MALAT1 knockdown inhibited proliferation and migration and facilitated apoptosis by upregulating miR-1 and downregulating KRAS in androgen receptor-negative PCa cells, providing a new insight into the molecular basis of MALAT1 and a potential biomarker or therapeutic target for suppressing castration-resistant PC.
Modeling and Forecasting of Fuel Selling Price Using Time Series Approach: Case Study

The liberalization of the petroleum sector in Morocco has a significant effect for petroleum product distributors. Since the beginning of December 2015, fuel prices are freely determined. This event presents many constraints affecting the balance of the sector plus the competition between its economic players. The lack of accompanying measures by the State makes this vital reform for public finances that stop subsidizing the price of gasoline vulnerable. With the halt of the competitive manufacturing's activity, Morocco's only refinery, distributors must, for their part, build up large stocks. As all fuel products are imported, we will be interested in the evolution by making forecasts of the price of fuels in the Moroccan market. In order to achieve their objectives, the oil companies must rely on precise forecasts. In this context, our paper aims mainly to study the time series of diesel and gasoline in order to provide precise forecasts to the company and to respect the permissible error margin of 3%. To this end, we worked with the ARIMA method. We found that the ARIMA model (1,1,1) gives forecasts of the price of gasoline near the margin to be met for the first quarter of the current year with an average error margin of 2,855%. In addition, the assumption that the residuals are a Gaussian white noise has always been verified.
A continuous water-level sensor based on load cell and floating pipe

Nowadays, the water level is of paramount importance to modern societies. The existing liquid-level-sensing techniques of monitoring systems have been widely used in various fields. The ultrasonic gauge and radar gauge have been adopted to replace the float gauge and pressure gauge in previous equipment. However, the water level warning system should be able to have stability, high resolution, and low cost. Hence, this paper presents the design of a prototype simple system, which has a water level sensor incorporated with a load cell and a floating body to provide an accurate water level measurement system. Our proposed system easily yields a 1-mm level resolution. Moreover, when the cumulative rainfall exceeds the threshold water level, remote users will receive a warning notification. In the future, the design with the early warning messages for people before the flood potentially gives people more time to evacuate from the flood.
A Model Centered Perspective on Software-Intensive Systems

The aim of this paper contributing to resurrect research interest in conceptual modeling as a means for designing and producing software-intensive systems, as there is still no comprehensive and consistent use of conceptual modeling in practice. The idea is to see any software and information system as a construct consisting of model handlers (model consumers and/or producers). This leads to the paradigm of “Model Centered Architecture”, which treats all processes, as well as the data they process, as instances of models. These models in turn are instances of metamodels, described using a particular domain specific modeling language (DSML), and represented using a corresponding domain specific representation language. Consequently, all system interfaces are defined through models (via an appropriate DSML) as well. The paper introduces the relevant MCA concepts and sketches open research questions in this field.
High V‐PPase activity is beneficial under high salt loads, but detrimental without salinity

Summary The membrane‐bound proton‐pumping pyrophosphatase (V‐PPase), together with the V‐type H+‐ATPase, generates the proton motive force that drives vacuolar membrane solute transport. Transgenic plants constitutively overexpressing V‐PPases were shown to have improved salinity tolerance, but the relative impact of increasing PPi hydrolysis and proton‐pumping functions has yet to be dissected. For a better understanding of the molecular processes underlying V‐PPase‐dependent salt tolerance, we transiently overexpressed the pyrophosphate‐driven proton pump (NbVHP) in Nicotiana benthamiana leaves and studied its functional properties in relation to salt treatment by primarily using patch‐clamp, impalement electrodes and pH imaging. NbVHP overexpression led to higher vacuolar proton currents and vacuolar acidification. After 3 d in salt‐untreated conditions, V‐PPase‐overexpressing leaves showed a drop in photosynthetic capacity, plasma membrane depolarization and eventual leaf necrosis. Salt, however, rescued NbVHP‐hyperactive cells from cell death. Furthermore, a salt‐induced rise in V‐PPase but not of V‐ATPase pump currents was detected in nontransformed plants. The results indicate that under normal growth conditions, plants need to regulate the V‐PPase pump activity to avoid hyperactivity and its negative feedback on cell viability. Nonetheless, V‐PPase proton pump function becomes increasingly important under salt stress for generating the pH gradient necessary for vacuolar proton‐coupled Na+ sequestration.
Kernel Low-Rank Multitask Learning in Variational Mode Decomposition Domain for Multi-/Hyperspectral Classification

Multitask learning (MTL) has recently yielded impressive results for classification of remotely sensed data due to its ability to incorporate shared information across multiple tasks. However, it remains a challenging issue to achieve robust classification results in the case that the data are from nonlinear subspaces. In this paper, we propose a kernel low-rank MTL (KL-MTL) method to handle multiple features from the 2-D variational mode decomposition (2-D-VMD) domain for multi-/hyperspectral classification. On the one hand, a nonrecursive 2-D-VMD method is applied to extract various features [i.e., intrinsic mode functions (IMFs)] of the original data concurrently. Compared with the existing 2-D empirical mode decomposition, 2-D-VMD has much stronger mathematical foundation and does not need any recursive sifting process. On the other hand, KL-MTL is proposed for classification by taking the extracted IMFs as features of multiple tasks. In KL-MTL, the low-rank representation formulated by nuclear norm can capture global structure of multiple tasks, while the kernel tricks are utilized for nonlinear extension of the low-rank MTL. Moreover, the optimization problem in KL-MTL is solved by the inexact augmented Lagrangian method. Compared with several state-of-the-art feature extraction and classification methods, the experimental results using both multi-/hyperspectral images demonstrate that the proposed method has satisfactory classification performance.
Gambling disorder and obsessive–compulsive personality disorder: A frequent but understudied comorbidity

Background and aims Epidemiological data have suggested that the prevalence of co-occurring personality disorders is particularly high in people with gambling disorder (GD). Among the personality disorders, obsessive–compulsive personality disorder (OCPD) appears to be the most common problem. The objective of this study was to investigate the clinical presentation of GD with and without co-occurring OCPD. Methods We studied 25 subjects with current GD and lifetime diagnosis of OCPD. They were matched for age and gender with 25 individuals with current GD but no lifetime diagnosis of any personality disorder. Results Subjects with GD and OCPD demonstrated (a) lower severity of gambling symptoms, (b) slower progression from recreational gambling to full-blown GD, (c) preferred individual forms of betting, (d) identified more triggers to gambling (specially the availability of money and stress); and (e) reported less negative impact on relational problems due to GD. Conclusions Our research provides further insight on GD co-occurring with OCPD, such as increasing social support and improvement of coping skills, especially to deal with financial difficulties and stress. Our findings may lead to more customized and effective therapeutic approaches to this frequent comorbidity.
The effectiveness of resilience training on life satisfaction among mothers with mentally retarded children

Article history: Received June 22, 2013 Received in revised format 28 August 2013 Accepted 21 September 2013 Available online September 26 2013 The present study investigates the effectiveness of resilience training on life satisfaction among the mothers with mentally retarded children. The method is semi experimental using pretest posttest with control group. Statistical population of research includes elementary mentally retarded student who were enrolled in Najafabad Sareban exceptional school over the period 2012-2013 educational year. Sample group includes 50 subjects who randomly replaced in control and experimental groups. Experimental group members participated in a 10-session resilience training. Finally, both groups completed post-tests where research scales were Diener life satisfaction questionnaire. The data are analyzed by co-variance analysis test. Results are significant and indicates that resilience training is effective on life satisfaction (p<0.05). © 2013 Growing Science Ltd. All rights reserved.
A memetic particle gravitation optimization algorithm for solving image segmentation

Significant objects can be obtained from the gray or color images via image segmentation by referring to the clustering problem. Inspired by the search strategy of GSA and the rapid convergence of PSO, this study proposes a memetic particle gravitation optimization (MPGO) algorithm, which is a hybrid approach for performing image segmentation. The proposed MPGO algorithm is verified using six well-known images, including Lena, Baboon, F16, Goldhill, Pepper, and Sailboat. MPGO significantly outperforms k-means, PSO and GSA in measures of the PSNR values.
Implemented the expert system of heart disease by using SVM

Heart sounds are the most significant parameter that can be used to represent the heart condition. However, the original heart sounds signal such as PCG still contained some unwanted information while the recording process happens that becomes a noise or extra sounds that will influence inside of the signal. This paper present signal processing technique and data analysis to suppress any noise in the recorded signal and classified it into two groups which are normal heart sounds and pathological heart sounds that contain Ventricular Septal Defect (VSD) inside. There are some steps in signal processing, first the signal will be processed through preprocessing technique which consists of the filter and smoothing by using IIR high pass filter in LabVIEW, secondly segmentation and feature extraction that is used to recognize the different parameter on each signal, and finally we use Support Vector Machine (SVM) to classify the heart sound signals. The result of the method capable to separate and classify the normal and pathological heart sound signals.
A prototype simulator for the simulation of complicated hadoop framework behaviors

Distributed computing and parallel computing have become the most effective tools for solving complex problems in amount of academia and industrial fields. Among a number of distributed and parallel computing technologies, MapReduce has been proved as one of the most efficient and reliable computing model to deal with distributed tasks. Especially, Hadoop framework, a Java based open source implementation of MapReduce has been widely used and studied. Hadoop supplies tremendously abilities to process large-scale tasks with large volume of data, which significantly facilitates the big data applications. However, Hadoop is lack of a function that to predict the job processing time before actually running a job. And also. Hadoop cannot tell that how the system performs under the condition that the system parameters have been tuned. Therefore, this paper presents a prototype Hadoop simulator which aims at simulating the parameters and interactions of the Hadoop framework using agent based simulation. The experimental results show that the simulator offers potentials to be an instrument to study complicated behaviors of the Hadoop framework.
Isoflurane decreases interleukin-2 production by increasing c-Cbl and Cbl-b expression in rat peripheral blood mononuclear cells

Objective To evaluate how isoflurane affects T-cell function by assaying interleukin (IL)-2 production and the expression of two Casitas B-lineage lymphoma (Cbl) family proto-oncogenes (c-Cbl and Cbl-b) in rat peripheral blood mononuclear cells (PBMCs). Methods Adult male Sprague–Dawley rats were randomly allocated to those that underwent blood collection after brief isoflurane anesthesia (control group), immediately after 4 hours of isoflurane general anesthesia (4I group), and 1 day after 4 hours of isoflurane general anesthesia (1D 4I group). IL-1, IL-2, and IL-6 mRNA levels and c-Cbl and Cbl-b levels in PBMCs were determined by polymerase chain reaction. Ubiquitination of protein kinase Cθ (PKCθ) and phospholipase C-γ1 (PLC-γ1) in PBMCs was assessed by immunoprecipitation. Results The IL-2 mRNA level in rat PBMCs was significantly lower in the 4I and 1D 4I groups than in the control group. c-Cbl, Cbl-b, and ubiquitin expression was significantly increased and zeta-chain-associated protein kinase 70, PLC-γ1, and PKCθ protein levels were significantly decreased in the 4I group. Ubiquitination of PLC-γ1 and PKCθ was significantly increased in the 4I group. Conclusion Isoflurane influences ubiquitin, c-Cbl, and Cbl-b expression in rat PBMCs, indicating suppression of receptor tyrosine kinase signaling pathways. These results suggest that isoflurane suppresses T-cell function.
Thiol–Ene Alginate Hydrogels as Versatile Bioinks for Bioprinting

Bioprinting is a powerful technique that allows precise and controlled 3D deposition of biomaterials in a predesigned, customizable, and reproducible manner. Cell-laden hydrogel (“bioink”) bioprinting is especially advantageous for tissue engineering applications as multiple cells and biomaterial compositions can be selectively dispensed to create spatially well-defined architectures. Despite this promise, few hydrogel systems are easily available and suitable as bioinks, with even fewer systems allowing for molecular design of mechanical and biological properties. In this study, we report the development of a norbornene functionalized alginate system as a cell-laden bioink for extrusion-based bioprinting, with a rapid UV-induced thiol–ene cross-linking mechanism that avoids acrylate kinetic chain formation. The mechanical and swelling properties of the hydrogels are tunable by varying the concentration, length, and structure of dithiol PEG cross-linkers and can be further modified by postprinting secondary cross-linking with divalent ions such as calcium. The low concentrations of alginate needed (<2 wt %), coupled with their rapid in situ gelation, allow both the maintenance of high cell viability and the ability to fabricate large multilayer or multibioink constructs with identical bioprinting conditions. The modularity of this bioink platform design enables not only the rational design of materials properties but also the gel’s biofunctionality (as shown via RGD attachment) for the expected tissue-engineering application. This modularity enables the creation of multizonal and multicellular constructs utilizing a chemically similar bioink platform. Such tailorable bioink platforms will enable increased complexity in 3D bioprinted constructs.
SiC trench IGBT with diode-clamped p-shield for oxide protection and enhanced conductivity modulation

In this paper, a diode-clamped p-shield is proposed as a feasible approach for SiC trench IGBT. The introduction of the p-shield effectively suppresses the high electric field in the gate oxide around the trench corner, which is a notorious feature for SiC trench gate devices. Additionally, the p-shield also results in a reduced Crss and thus better switching characteristics. The traditional grounded p-shield (widely adopted in SiC trench MOSFETs), however, significantly reduces the electron/hole density near the emitter side of the SiC trench IGBT. The diode-clamped p-shield structure proposed in this paper successfully solves this issue, since the potential of the p-shield in the on-state is elevated to near the turn-on voltage of the diode, and thus the extraction of holes from the p-shield is suppressed. Therefore, the proposed SiC trench IGBT with a diode-clamped p-shield reduces the high oxide field, improves the switching characteristics and maintains a low Kon simultaneously.
Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders

Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for employment early in a drug discovery project. Here, it is shown that the choice of chemical representation, such as strings from the simplified molecular-input line-entry system (SMILES), has a large influence on the properties of the latent space. It is further explored to what extent translating between different chemical representations influences the latent space similarity to the SMILES strings or circular fingerprints. By employing SMILES enumeration for either the encoder or decoder, it is found that the decoder has the largest influence on the properties of the latent space. Training a sequence to sequence heteroencoder based on recurrent neural networks (RNNs) with long short-term memory cells (LSTM) to predict different enumerated SMILES strings from the same canonical SMILES string gives the largest similarity between latent space distance and molecular similarity measured as circular fingerprints similarity. Using the output from the code layer in quantitative structure activity relationship (QSAR) of five molecular datasets shows that heteroencoder derived vectors markedly outperforms autoencoder derived vectors as well as models built using ECFP4 fingerprints, underlining the increased chemical relevance of the latent space. However, the use of enumeration during training of the decoder leads to a marked increase in the rate of decoding to different molecules than encoded, a tendency that can be counteracted with more complex network architectures.
Modulation-Domain Multichannel Kalman Filtering for Speech Enhancement

Compared with single-channel speech enhancement methods, multichannel methods can utilize spatial information to design optimal filters. Although some filters adaptively consider second-order signal statistics, the temporal evolution of the speech spectrum is usually neglected. By using linear prediction (LP) to model the inter-frame temporal evolution of speech, single-channel Kalman filtering (KF) based methods have been developed for speech enhancement. In this paper, we derive a multichannel KF (MKF) that jointly uses both interchannel spatial correlation and interframe temporal correlation for speech enhancement. We perform LP in the modulation domain, and by incorporating the spatial information, derive an optimal MKF gain in the short-time Fourier transform domain. We show that the proposed MKF reduces to the conventional multichannel Wiener filter if the LP information is discarded. Furthermore, we show that, under an appropriate assumption, the MKF is equivalent to a concatenation of the minimum variance distortion response beamformer and a single-channel modulation-domain KF and therefore present an alternative implementation of the MKF. Experiments conducted on a public head-related impulse response database demonstrate the effectiveness of the proposed method.
Low-Latency Detec on and Tracking of Aircra in Very High-Resolu on Video Feeds

Applying machine learning techniques for real-time detection and tracking of objects in very high-resolution video is a problem that has not been extensively studied. In this thesis, the practical uses of object detection for airport remote towers are explored. We present a Kalman filter-based tracking framework for low-latency aircraft tracking in very high-resolution video streams. The object detector is trained and tested on a dataset containing 3000 labelled images of aircraft taken at Swedish airports, reaching an mAP of 90.91% with an average IoU of 89.05% on the test set. The tracker is benchmarked on remote tower video footage from Örnsköldsvik and Sundsvall using slightly modified variants of the MOT-CLEAR and ID metrics for multiple object trackers, obtaining an IDF1 score of 91.9%, and a MOTA score of 83.3%. The prototype runs the tracking pipeline on seven high resolution cameras simultaneously at 10 Hz on a single thread, suggesting large potential speed gains being attainable through parallelization.