Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiac differentiation in Xenopus is initiated by mespa.

AIMS Future cardiac repair strategies will require a profound understanding of the principles underlying cardiovascular differentiation. Owing to its extracorporal and rapid development, Xenopus laevis provides an ideal experimental system to address these issues in vivo. Whereas mammalian MesP1 is currently regarded as the earliest marker for the cardiovascular system, several MesP1-related factors from Xenopus-mespa, mespb, and mespo-have been assigned only to somitogenesis so far. We, therefore, analysed these genes comparatively for potential contributions to cardiogenesis. METHODS AND RESULTS RNA in situ hybridizations revealed a novel anterior expression domain exclusively occupied by mespa during gastrulation, which precedes the prospective heart field. Correspondingly, when overexpressed mespa most strongly induced cardiac markers in vivo as well as ex vivo. Transference to murine embryonic stem (ES) cells and subsequent FACS analyses for Flk-1 and Troponin I confirmed the high potential of mespa as a cardiac inducer. In vivo, Morpholino-based knockdown of mespa protein led to a dramatic loss of pro-cardiac and sarcomeric markers, which could be rescued either by mespa itself or human MesP1, but neither by mespb nor mespo. Epistatic analysis positioned mespa upstream of mespo and mespb, and revealed positive autoregulation for mespa at the time of its induction. CONCLUSIONS Our findings contribute to the understanding of conserved events initiating vertebrate cardiogenesis. We identify mespa as functional amphibian homologue of mammalian MesP1. These results will enable the dissection of cardiac specification from the very beginning in the highly versatile Xenopus system.
A Folding Contraction Method for Community Partition Detection

Recently, network topology has become a pop research field in complex networks. The small-world and scalefree character can be used in community detection. In this paper, we propose a folding contraction method (FCM) for community partition detection based on scale-free networks feature. And a framework of this method has been provided. Experimental results show that the proposed method has high accuracy and good performance.
Paying Attention to SQuAD : Exploring Bidirectional Attention Flow Heather

With the goal of automated reading comprehension, we apply a neural network with Bidirectional Attention Flow (BiDAF) to the Stanford Question Answering Dataset (SQuAD) and achieve F1 and Exact Match (EM) scores close to the original paper with a single model. We obtain a test F1 score of 76.037 and test EM score of 66.663. Our model includes Character-level CNN embeddings, a Highway Network layer, a Phrase Embedding layer, a Modeling layer, and smart span selection. We also explored expanding the model with feature engineering and an Answer Pointer output layer, which did not further improve our best model. We analyze our model’s performance across categories of contexts, questions, and answers, and compare baseline attention with BiDAF.
Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm.
Detection of lanes and traffic signs painted on road using on-board camera

Road safety was always an area that concerned many people around the world and systems that aid the drivers have been appearing ever since cars and computers were combined to make driving safer and more efficient. There are plenty of systems that are able to warn drivers about different types of dangers: lane departure, collision possibility and various traffic signs. However, there is still room for development, because modern technologies, like the rising vision about the internet of things, allow us to create much more efficient systems. Also, the detections can be improved to perform better in various situations, such as different light conditions, road quality, etc. In this paper, we present the plans of a driver-assistance system, which will be capable of lane and painted traffic sign detection by using a car's on-board camera and is integrated in a network which connects different users to enhance the efficiency of these detections. The current version is capable of lane and painted traffic sign detection and is able to warn the driver about a possible lane departure on videos captured in daylight, with medium quality road markings. Our system uses a lane detection method that is based on the Hough transformation and contour detection for the painted traffic sign detection.
Distributed Parellel MOEA/D on Spark

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has shown remarkable performance for multi-objective optimization problems (MOPs). However, MOEA/D still consumes long time to solve MOPs with computationally intensive objective functions. This paper proposes two distributed parallel MOEA/Ds based on the popular distributed framework, Spark, to further reduce the running time of the sequential MOEA/D for MOPs. The first entirely evolved MOEA/D evolves an entire population, while the second partially evolved MOEA/D based on Spark evolves a partial subpopulation equal in size to a partition in each transformation-action process. Experimental results on DTLZ benchmark MOPs with three objectives indicate that both distributed MOEA/Ds on Spark obtains better speedup than the distributed MOEA/Ds on MapReduce and achieve the quality of solutions similar to the sequential MOEA/D.
A New Lower Bound of Privacy Budget for Distributed Differential Privacy

Distributed data aggregation via summation (counting) helped us to learn the insights behind the raw data. However, such computing suffered from a high privacy risk of malicious collusion attacks. That is, the colluding adversaries infer a victim's privacy from the gaps between the aggregation outputs and their source data. Among the solutions against such collusion attacks, Distributed Differential Privacy (DDP) shows a significant effect of privacy preservation. Specifically, a DDP scheme guarantees the global differential privacy (the presence or absence of any data curator barely impacts the aggregation outputs) by ensuring local differential privacy at the end of each data curator. To guarantee an overall privacy performance of a distributed data aggregation system against malicious collusion attacks, part of the existing work on such DDP scheme aim to provide an estimated lower bound of privacy budget for the global differential privacy. However, there are two main problems: low data utility from using a large global function sensitivity; unknown privacy guarantee when the aggregation sensitivity of the whole system is less than the sum of the data curator's aggregation sensitivity. To address these problems while ensuring distributed differential privacy, we provide a new lower bound of privacy budget, which works with an unconditional aggregation sensitivity of the whole distributed system. Moreover, we study the performance of our privacy bound in different scenarios of data updates. Both theoretical and experimental evaluations show that our privacy bound offers better global privacy performance than the existing work.
Application of Phase Space Reconstruction in a Few-Channel EEG-NIRS Bimodal Brain-Computer Interface System

We developed a highly accurate, few-channel, bimodal electroencephalograph (EEG) and near-infrared spectroscopy (NIRS) brain-computer interface (BCI) system by developing new methods for signal processing and feature extraction. For data processing, we performed source analysis of EEG and NIRS signals to select the best channels from which to build a few-channel system. For EEG feature extraction, we used phase space reconstruction to convert EEG few-channel signals into multichannel signals, facilitating the extraction of EEG features by common spatial pattern. The Hurst exponent of the selected 10 channels constituted the extracted NIRS data feature. For pattern classification, we fused EEG and NIRS features together and used the support vector machine classification method. The average accuracy of bimodal EEG-NIRS was significantly higher than that of either EEG or NIRS as unimodal techniques.
Parametric Study Of Dry Sliding Wear Behaviour Of Functionally Graded Al Lm25/Si3n4 Composite By Response Surface Methodology

Functionally graded aluminium LM25/silicon nitride composite was produced through stir casting followed by centrifugal casting and obtained a hollow cylindrical cast component with dimensions of 150 × 150 × 20 mm. The microstructural examination and the hardness test were carried out on the outer (1 mm) and inner surface (17 mm) as the function of radial distance from the outer periphery. The outer surface was observed with particle enriched region compared to inner surface and exhibited higher hardness. Hence the outer surface of the functionally graded composite was only further subjected to sliding wear test in pin-on-disc tribometer. The Central Composite Design in Response Surface Methodology was used to design the experiments for the selected parameters such as load (15–45 N), velocity (0.5–2.5 m/s) and sliding distance (500–2000 m). Regression test and Analysis of Variance were conducted to check the adequacy of the constructed model. The surface plots for wear rate showed that wear rate increased .
Internal Fusion Functions

In this paper, we investigate a mechanism for fusing a set of inputs (values) in such a way that the procedure does not create new information during the process. In order to do so, we introduce internal fusion functions, a family of fusion functions in which the output always corresponds to some of the given inputs. We perform an in-depth theoretical study of internal fusion functions and, furthermore, we propose three different construction methods, which are based on 1) an arbitrary fusion function and a partition of the domain; 2) a linear order; and 3) a minimization mechanism using penalty functions. Finally, we illustrate this paper with the application of internal fusion functions in two image processing algorithms where a set of images must be fused, namely multifocus image and denoised image fusion, as well as in an example of multiclass problem, where we fuse a set of score matrices obtained by several classification algorithms.
A Fifth-Order Butterworth OTA-C LPF With Multiple-Output Differential-Input OTA for ECG Applications

This brief proposes a fifth-order Butterworth operational transconductance amplifier-C (OTA-C) low-pass filter (LPF) with multiple-output differential-input (MODI) OTA structure and metal–insulator–metal capacitors for electrocardiography applications. The current division technology is used as an alternative output pair to provide multiple outputs and achieve high linearity. This technique reduces the number of OTAs of the fifth-order LPF from 11 to 6 as compared with the conventional structure. The design issue of linearity is also considered in the implementation of MODI OTA. The proposed filter is fabricated in a 0.18 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> complementary metal–oxide–semiconductor technology with an area of 0.12 mm<sup>2</sup>. The LPF achieved a total harmonic distortion of 49.8 dBc under a bandwidth of 50 Hz and input voltage of 86 mV<inline-formula> <tex-math notation="LaTeX">$_\text{pp}$ </tex-math></inline-formula> at a 1-V supply voltage. The total power dissipation is 350 nW.
Inference Attack-Resistant E-Healthcare Cloud System with Fine-Grained Access Control

The e-healthcare cloud system has shown its potential to improve the quality of healthcare and individuals’ quality of life. Unfortunately, security and privacy impede its widespread deployment and application. There are several research works focusing on preserving the privacy of the electronic healthcare record (EHR) data. However, these works have two main limitations. First, they only support the ‘black or white’ access control policy. Second, they suffer from the inference attack. In this paper, for the first time, we design an inference attack-resistant e-healthcare cloud system with fine-grained access control. We first propose a two-layer encryption scheme. To ensure an efficient and fine-grained access control over the EHR data, we design the first-layer encryption, where we devise a specialized access policy for each data attribute in the EHR, and encrypt them individually with high efficiency. To preserve the privacy of role attributes and access policies used in the first-layer encryption, we systematically construct the second-layer encryption. To take full advantage of the cloud server, we propose to let the cloud execute computationally intensive works on behalf of the data user without knowing any sensitive information. To preserve the access pattern of data attributes in the EHR, we further construct a blind data retrieving protocol. We also demonstrate that our scheme can be easily extended to support search functionality. Finally, we conduct extensive security analyses and performance evaluations, which confirm the efficacy and efficiency of our schemes.
Depth map estimation with unknown fixed pattern projection

Depth map estimation is important in many computer vision applications. Various systems and algorithms have been proposed for depth map estimation. In this paper, we focus on a fixed random dot projection approach. An invisible near infrared (NIR) is usually used for the random dot projection. In many cases, the depth map estimation system of the fixed random dot projection approach consists of an NIR projector, an NIR camera and an RGB camera. First, we propose a simple calibration for the fixed NIR random dot projection and the NIR camera. Then, the depth map estimation algorithm is described. We also propose a depth map refinement algorithm using the RGB camera image. Experimental results demonstrate that the simple calibration is good enough to estimate the depth map. It is also shown that the depth map can be refined by the RGB camera image.
Tomatidine Is a Lead Antibiotic Molecule That Targets Staphylococcus aureus ATP Synthase Subunit C

ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of deadly hospital-acquired infections. The discovery of anti-Staphylococcus antibiotics and new classes of drugs not susceptible to the mechanisms of resistance shared among bacteria is imperative. We recently showed that tomatidine (TO), a steroidal alkaloid from solanaceous plants, possesses potent antibacterial activity against S. aureus small-colony variants (SCVs), the notoriously persistent form of this bacterium that has been associated with recurrence of infections. Here, using genomic analysis of in vitro-generated TO-resistant S. aureus strains to identify mutations in genes involved in resistance, we identified the bacterial ATP synthase as the cellular target. Sequence alignments were performed to highlight the modified sequences, and the structural consequences of the mutations were evaluated in structural models. Overexpression of the atpE gene in S. aureus SCVs or introducing the mutation found in the atpE gene of one of the high-level TO-resistant S. aureus mutants into the Bacillus subtilis atpE gene provided resistance to TO and further validated the identity of the cellular target. FC04-100, a TO derivative which also possesses activity against non-SCV strains, prevents high-level resistance development in prototypic strains and limits the level of resistance observed in SCVs. An ATP synthesis assay allowed the observation of a correlation between antibiotic potency and ATP synthase inhibition. The selectivity index (inhibition of ATP production by mitochondria versus that of bacterial ATP synthase) is estimated to be >105-fold for FC04-100.
The HIV protease inhibitor, nelfinavir, as a novel therapeutic approach for the treatment of refractory pediatric leukemia

Purpose Refractory pediatric leukemia remains one of the leading causes of death in children. Intensification of current chemotherapy regimens to improve the outcome in these children is often limited by the effects of drug resistance and cumulative toxicity. Hence, the search for newer agents and novel therapeutic approaches are urgently needed to formulate the next-generation early-phase clinical trials for these patients. Materials and methods A comprehensive library of antimicrobials, including eight HIV protease inhibitors (nelfinavir [NFV], saquinavir, indinavir, ritonavir, amprenavir, atazanavir, lopinavir, and darunavir), was tested against a panel of pediatric leukemia cells by in vitro growth inhibition studies. Detailed target modulation studies were carried out by Western blot analyses. In addition, drug synergy experiments with conventional and novel antitumor agents were completed to identify effective treatment regimens for future clinical trials. Results Several of the HIV protease inhibitors showed cytotoxicity at physiologically relevant concentrations (half-maximal inhibitory concentration values ranging from 1–24 µM). In particular, NFV was found to exhibit the most potent antileukemic properties across all cell lines tested. Mechanistic studies show that NFV leads to the induction of autophagy and apoptosis possibly through the induction of endoplasmic reticulum stress. Furthermore, interference with cell signaling pathways, including Akt and mTOR, was also noted. Finally, drug combination studies have identified agents with potential for synergy with NFV in its antileukemic activity. These include JQ1 (BET inhibitor), AT101 (Bcl-2 family inhibitor), and sunitinib (TK inhibitor). Conclusion Here, we show data demonstrating the potential of a previously unexplored group of drugs to address an unmet therapeutic need in pediatric oncology. The data presented provide preclinical supportive evidence and rationale for future studies of these agents for refractory leukemia in children.
Original Research Paper FORMULATION DEVELOPMENT OF SELF NANOEMULSIFYING DRUG DELIVERY SYSTEM (SNEDDS) OF CELECOXIB FOR IMPROVEMENT OF ORAL BIOAVAILABILITY

Celecoxib is a hydrophobic and highly permeable drug which belongs to class II of biopharmaceutics classification system (BCS). Low aqueous solubility of celecoxib leads to high variability in absorption after oral administration. The present study was carried out for the formulation development of celecoxib loaded self nanoemulsifying drug delivery system (SNEDDS) with the aim of enhancement of oral bioavailability. The SNEDDS formulation was optimized using pseudo-ternary phase diagram composed of capryol 90, cremophor RH 40 and propylene glycol. Six formulations (F1, F2, F3, F4, F5 and F6) were developed with varying concentration of oil, surfactant and co-surfactant by sonication technique. The self nano-emulsifying drug delivery system of celecoxib was characterized for content uniformity, particle size, poly dispersity index, viscosity determination and robustness of dilution, drug loading efficiency and shape of globules, thermodynamic stability and self emulsification efficiency. In vitro dissolution and in vitro diffusion studies were also carried out to get final optimized formulation. The optimized celecoxib SNEDDS (F6) found to contain surfactant content less than 30% and yielded nanoemulsion of mean droplet size 169.4nm, which was not affected by the pH of dilution media. The zeta potential of the optimized SNEDDS (F6) found to be -32.9 mV. The drug loading efficiency of the formulation F6 was high at 98.98%. The developed celecoxib loaded SNEDDS formulation exhibited a complete in vitro drug release in 1hr as compared with the plain drug. Thus, the self nanoemulsifying drug delivery system (SNEDDS) using celecoxib as model drug found to achieve effective therapeutic concentration and intended to provide relief from pain associated with inflammation by single dose administration as compared to conventional tablets as well as prevent the cardiac risks due to lower dose size.
Fine-Grained Product Feature Extraction in Chinese Reviews

Fine-grained product feature extraction is the most important task in opinion mining. To realize the fine-grained product feature extraction in Chinese reviews, three main tasks have been solved in this paper. Firstly, we propose a dependency parsing based method to directly extract the explicit feature-opinion pairs. Then, by analyzing the characteristics of two synonyms features and the relations with opinion words, we calculate the similarities to cluster features. Finally, we propose a novel implicit feature extraction method by combining review context information and two kind opinions to extract implicit features. Experiments show that the dependency parsing based method can get high precision, by considering verbs as product feature can improve the recall obviously. Besides, several proven pruning strategies can improve the accuracy. The comparison demonstrates that our implicit feature extraction method outperforms existing method, and feature clustering before implicit feature mining can get better results.
Torsion strength of continuous drive friction weld joint of round bar aluminum A6061 affected by single cone geometry of friction area

This paper describes the effect of single cone geometry of friction area on torsion strength of continuous drive friction weld (CDFW) joint of round bar aluminum alloys A6061. Single cone geometry on friction area was determined with ratio of upper diameter (D1) and lower diameter (D2), D1/D2 of cone. Ratios of D1/D2 used were 0.02, 0.25, 0.65, 0.8 and 1. Friction time was 120 seconds. Torsion strength test, macro and microstructure, micro-hardness test were conducted. The results show that D1/D2 of 0.25 gives maximum torsion strength of CDFW joint. It is thought that it occurs due to the wider area of Zpl zone, smaller grain size and more Mg2Si precipitate in the Zpl and Zpd zone and nil or minimum porosity in the CDFW joint.
Countering Noise-based Splicing Detection Using Noise Density Transfer

Image splicing is a common and widespread type of manipulation, which is defined as pasting a portion of an image onto a second image. Several forensic methods have been developed to detect splicing, using various image properties. Some of these methods exploit the noise statistics of the image to try and find discrepancies. In this paper, we propose a new counter-forensic approach to eliminate the noise differences that can appear in a spliced image. This approach can also be used when creating computer graphics images, in order to endow them with a realistic noise. This is performed by changing the noise statistics of the spliced elements so that they are closer to those of the original image. The proposed method makes use of a novel way to transfer density functions. We apply this to image noise in order to impose identical noise density functions from a source to a destination image. This method can be used with arbitrary noise distributions. The method is tested against several noise-based splicing detection methods, in order to prove its efficacy.