Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimize Illumination Parameter of Low-Level Laser Therapy for Hemorrhagic Stroke by Monte Carlo Simulation on Visible Human Dataset

Stroke is the second leading cause of death and disability worldwide. The incidence of hemorrhagic stroke increases dramatically with the increasingly aging population. Recently, technology of low-level light/laser therapy (LLLT) is emerging as a novel noninvasive therapeutic approach to treat stroke based on effective photobiomodulation. To obtain optimal therapeutic effects, several LLLT illumination parameters such as beam size and beam type need to be optimized. However, the quantitative optimization of LLLT illumination parameters for stroke therapeutics is impractical to test directly on human subjects. In this paper, we employed a precise voxelized three-dimensional Monte Carlo method (MCVM) to simulate photon propagation within Visible Chinese human (VCH) head at different level of stroke with varied parameters of beams. By evaluation with criteria of the total fluence flux in lesion region and the maximal penetration depth, we found that Gaussian beam with larger or the same size of hemorrhagic region generates the highest and relative homogeneous therapeutic outcomes, while the Top-hat beam performed better when hemorrhagic region is much bigger than beam size. These results demonstrate the great potential of using VCH and MCVM in optimizing LLLT treatment parameters for stroke and in guiding future instrumentation of LLLT on hemorrhagic stroke.
Refractory Hypertension Is not Attributable to Intravascular Fluid Retention as Determined by Intracardiac Volumes

Refractory hypertension (RfHTN) is an extreme phenotype of antihypertensive treatment failure defined as lack of blood pressure control with ≥5 medications, including a long-acting thiazide and a mineralocorticoid receptor antagonist. RfHTN is a subgroup of resistant hypertension (RHTN), which is defined as blood pressure >135/85 mm Hg with ≥3 antihypertensive medications, including a diuretic. RHTN is generally attributed to persistent intravascular fluid retention. It is unknown whether alternative mechanisms are operative in RfHTN. Our objective was to determine whether RfHTN is characterized by persistent fluid retention, indexed by greater intracardiac volumes determined by cardiac magnetic resonance when compared with controlled RHTN patients. Consecutive patients evaluated in our institution with RfHTN and controlled RHTN were prospectively enrolled. Exclusion criteria included advanced chronic kidney disease and masked or white coat hypertension. All enrolled patients underwent biochemical testing and cardiac magnetic resonance. The RfHTN group (n=24) was younger (mean age, 51.7±8.9 versus 60.6±11.5 years; P=0.003) and had a greater proportion of women (75.0% versus 43%; P=0.02) compared with the controlled RHTN group (n=30). RfHTN patients had a greater left ventricular mass index (88.3±35.0 versus 54.6±12.5 g/m2; P<0.001), posterior wall thickness (10.1±3.1 versus 7.7±1.5 mm; P=0.001), and septal wall thickness (14.5±3.8 versus 10.0±2.2 mm; P<0.001). There was no difference in B-type natriuretic peptide levels and left atrial or ventricular volumes. Diastolic dysfunction was noted in RfHTN. Our findings demonstrate greater left ventricular hypertrophy without chamber enlargement in RfHTN, suggesting that antihypertensive treatment failure is not attributable to intravascular volume retention.
Some maternal lineages of domestic horses may have origins in East Asia revealed with further evidence of mitochondrial genomes and HVR-1 sequences

Objectives There are large populations of indigenous horse (Equus caballus) in China and some other parts of East Asia. However, their matrilineal genetic diversity and origin remained poorly understood. Using a combination of mitochondrial DNA (mtDNA) and hypervariable region (HVR-1) sequences, we aim to investigate the origin of matrilineal inheritance in these domestic horses. Methods To investigate patterns of matrilineal inheritance in domestic horses, we conducted a phylogenetic study using 31 de novo mtDNA genomes together with 317 others from the GenBank. In terms of the updated phylogeny, a total of 5,180 horse mitochondrial HVR-1 sequences were analyzed. Results Eightteen haplogroups (Aw-Rw) were uncovered from the analysis of the whole mitochondrial genomes. Most of which have a divergence time before the earliest domestication of wild horses (about 5,800 years ago) and during the Upper Paleolithic (35–10 KYA). The distribution of some haplogroups shows geographic patterns. The Lw haplogroup contained a significantly higher proportion of European horses than the horses from other regions, while haplogroups Jw, Rw, and some maternal lineages of Cw, have a higher frequency in the horses from East Asia. The 5,180 sequences of horse mitochondrial HVR-1 form nine major haplogroups (A-I). We revealed a corresponding relationship between the haplotypes of HVR-1 and those of whole mitochondrial DNA sequences. The data of the HVR-1 sequences also suggests that Jw, Rw, and some haplotypes of Cw may have originated in East Asia while Lw probably formed in Europe. Conclusions Our study supports the hypothesis of the multiple origins of the maternal lineage of domestic horses and some maternal lineages of domestic horses may have originated from East Asia.
Health and Well-Being of International University Students, and Comparison with Domestic Students, in Tasmania, Australia

International students comprise an increasingly larger proportion of higher education students globally. Empirical evidence about the health and well-being of these students is, however, limited. We sought to examine the health and well-being of international students, primarily from Asian countries, attending the University of Tasmania, Australia, using domestic students as a comparison group. Ethics approval was given to invite (via email) all currently enrolled students to participate in the study by completing a pilot-tested, online survey. The survey was completed by 382 international students (response rate = 8.9%) and 1013 domestic students (9.2%). Independent samples t-tests, analysis of variance (ANOVA) and chi-square tests were used for bivariate comparisons between international and domestic students, and between subgroups of international students. Regression models were used to examine the associations between student status (international vs. domestic) and health outcomes, controlling for demographic and enrolment variables. International students, particularly male students, were found to be at increased risk of several adverse health outcomes while also being less likely to seek help for mental health and related problems. The findings indicate the need for accessible, targeted, culturally-sensitive health promotion and early intervention programs.
Treatment with Obestatin—A Ghrelin Gene-Encoded Peptide—Reduces the Severity of Experimental Colitis Evoked by Trinitrobenzene Sulfonic Acid

Obestatin is a 23-amino acid peptide derived from proghrelin, a common prohormone for ghrelin and obestatin. Previous studies showed that obestatin exhibited some protective and therapeutic effects in the gut. The aim of our presented study was to examine the effect of treatment with obestatin on trinitrobenzene sulfonic acid (TNBS)-induced colitis. In rats anesthetized with ketamine, colitis was induced through intrarectal administration of 25 mg of 2,4,6-trinitrobenzene sulfonic acid (TNBS). Obestatin was administered intraperitoneally at doses of 4, 8, or 16 nmol/kg, twice per day for four consecutive days. The first dose of obestatin was given one day before the induction of colitis, and the last one was given two days after administration of TNBS. Fourteen days after the induction of colitis, rats were anesthetized again with ketamine, and the severity of colitis was determined. The administration of obestatin had no effect on the parameters tested in rats without the induction of colitis. In rats with colitis, administration of obestatin at doses of 8 or 16 nmol/kg reduced the area of colonic damage, and improved mucosal blood flow in the colon. These effects were accompanied by a reduction in the colitis-evoked increase in the level of blood leukocytes, and mucosal concentration of pro-inflammatory interleukin-1β. Moreover, obestatin administered at doses of 8 or 16 nmol/kg reduced histological signs of colonic damage. The administration of obestatin at a dose of 4 nmol/kg failed to significantly affect the parameters tested. Overall, treatment with obestatin reduced the severity of TNBS-induced colitis in rats. This effect was associated with an improvement in mucosal blood flow in the colon, and a decrease in local and systemic inflammatory processes.
Shallow Decision-Making Analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game-playing, such as evolutionary computation techniques, tree search algorithms, hyper-heuristic-based or knowledge-based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent’s decision-making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS-based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent’s behaviours.
Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics

For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.
Engineering and Modulating Functional Cyanobacterial CO2-Fixing Organelles

Bacterial microcompartments (BMCs) are proteinaceous organelles widespread among bacterial phyla and provide a means for compartmentalizing specific metabolic pathways. They sequester catalytic enzymes from the cytoplasm, using an icosahedral proteinaceous shell with selective permeability to metabolic molecules and substrates, to enhance metabolic efficiency. Carboxysomes were the first BMCs discovered and their unprecedented capacity of CO2 fixation allows cyanobacteria to make a significant contribution to global carbon fixation. There is an increasing interest in utilizing synthetic biology to construct synthetic carboxysomes in new hosts, i.e., higher plants, to enhance carbon fixation and productivity. Here, we report the construction of a synthetic operon of the β-carboxysome from the cyanobacterium Synechococcus elongatus PCC7942 to generate functional β-carboxysome-like structures in Escherichia coli. The protein expression, structure, assembly, and activity of synthetic β-carboxysomes were characterized in depth using confocal, electron and atomic force microscopy, proteomics, immunoblot analysis, and enzymatic assays. Furthermore, we examined the in vivo interchangeability of β-carboxysome building blocks with other BMC components. To our knowledge, this is the first production of functional β-carboxysome-like structures in heterologous organisms. It provides important information for the engineering of fully functional carboxysomes and CO2-fixing modules in higher plants. The study strengthens our synthetic biology toolbox for generating BMC-based organelles with tunable activities and new scaffolding biomaterials for metabolic improvement and molecule delivery.
A new autonomous data transmission reduction method for wireless sensors networks

The inherent limitation in energy resources and computational power for sensor nodes in a Wireless Sensor Network, poses the challenge of extending the lifetime of these networks. Since radio communication is the dominant energy consuming activity, most presented approaches focused on reducing the number of data transmitted to the central workstation. This can be achieved by deploying both on the workstation and the sensor node a synchronized prediction model capable of forecasting future values. Thus, enabling the sensor node to transmit only the values that surpasses a predefined error threshold. This mechanism offers a decrease in the cost of transmission energy for a price of an increase in the cost of computational energy. Therefore, finding the right trade­off between complexity and efficiency is very important to achieve optimal results. In this paper, we present a novel data reduction method that outperforms other state of the art data reduction approaches. We demonstrated the efficiency of our algorithm using simulation on real-world data sets collected at our laboratory. The obtained results show that our method was able to achieve a data suppression ratio ranging between 93.2% and 99.8%.
Silicon nanoparticle charge trapping memory cell

A charge trapping memory with 2 nm silicon nanoparticles (Si NPs) is demonstrated. A zinc oxide (ZnO) active layer is deposited by atomic layer deposition (ALD), preceded by Al2O3 which acts as the gate, blocking and tunneling oxide. Spin coating technique is used to deposit Si NPs across the sample between Al2O3 steps. The Si nanoparticle memory exhibits a threshold voltage (Vt) shift of 2.9 V at a negative programming voltage of –10 V indicating that holes are emitted from channel to charge trapping layer. The negligible measured Vt shift without the nanoparticles and the good re‐ tention of charges (>10 years) with Si NPs confirm that the Si NPs act as deep energy states within the bandgap of the Al2O3 layer. In order to determine the mechanism for hole emission, we study the effect of the electric field across the tunnel oxide on the magnitude and trend of the Vt shift. The Vt shift is only achieved at electric fields above 1 MV/cm. This high field indicates that tunneling is the main mechanism. More specifically, phonon‐assisted tunneling (PAT) dominates at electric fields between 1.2 MV/cm < E < 2.1 MV/cm, while Fowler–Nordheim tunneling leads at higher fields (E > 2.1 MV/cm). (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Development of a Fault Monitoring Technique for Wind Turbines Using a Hidden Markov Model

Regular inspection for the maintenance of the wind turbines is difficult because of their remote locations. For this reason, condition monitoring systems (CMSs) are typically installed to monitor their health condition. The purpose of this study is to propose a fault detection algorithm for the mechanical parts of the wind turbine. To this end, long-term vibration data were collected over two years by a CMS installed on a 3 MW wind turbine. The vibration distribution at a specific rotating speed of main shaft is approximated by the Weibull distribution and its cumulative distribution function is utilized for determining the threshold levels that indicate impending failure of mechanical parts. A Hidden Markov model (HMM) is employed to propose the statistical fault detection algorithm in the time domain and the method whereby the input sequence for HMM is extracted is also introduced by considering the threshold levels and the correlation between the signals. Finally, it was demonstrated that the proposed HMM algorithm achieved a greater than 95% detection success rate by using the long-term signals.
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the first membership inference attack has shown that extraction of information on the training set is possible in such MLaaS settings, which has severe security and privacy implications. However, the early demonstrations of the feasibility of such attacks have many assumptions on the adversary, such as using multiple so-called shadow models, knowledge of the target model structure, and having a dataset from the same distribution as the target model's training data. We relax all these key assumptions, thereby showing that such attacks are very broadly applicable at low cost and thereby pose a more severe risk than previously thought. We present the most comprehensive study so far on this emerging and developing threat using eight diverse datasets which show the viability of the proposed attacks across domains. In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.
Explainable Social Contextual Image Recommendation with Hierarchical Attention

Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users' preferences to the user-generated images and recommending them to users have become an urgent need. However, this is a challenging task. On one hand, we have to overcome the extremely data sparsity issue in image recommendation. On the other hand, we have to model the complex aspects that influence users' preferences to these highly subjective content from the heterogeneous data. In this paper, we develop an explainable social contextual image recommendation model to simultaneously explain and predict users' preferences to images. Specifically, in addition to user interest modeling in the standard recommendation, we identify three key aspects that affect each user's preference on the social platform, where each aspect summarizes a contextual representation from the complex relationships between users and images. We design a hierarchical attention model in recommendation process given the three contextual aspects. Particularly, the bottom layered attention networks learn to select informative elements of each aspect from heterogeneous data, and the top layered attention network learns to score the aspect importance of the three identified aspects for each user. In this way, we could overcome the data sparsity issue by leveraging the social contextual aspects from heterogeneous data, and explain the underlying reasons for each user's behavior with the learned hierarchial attention scores. Extensive experimental results on real-world datasets clearly show the superiority of our proposed model.
Miniaturized planar Si-nanowire micro-thermoelectric generator using exuded thermal field for power generation

Abstract For harvesting energy from waste heat, the power generation densities and fabrication costs of thermoelectric generators (TEGs) are considered more important than their conversion efficiency because waste heat energy is essentially obtained free of charge. In this study, we propose a miniaturized planar Si-nanowire micro-thermoelectric generator (SiNW-μTEG) architecture, which could be simply fabricated using the complementary metal–oxide–semiconductor–compatible process. Compared with the conventional nanowire μTEGs, this SiNW-μTEG features the use of an exuded thermal field for power generation. Thus, there is no need to etch away the substrate to form suspended SiNWs, which leads to a low fabrication cost and well-protected SiNWs. We experimentally demonstrate that the power generation density of the SiNW-μTEGs was enhanced by four orders of magnitude when the SiNWs were shortened from 280 to 8 μm. Furthermore, we reduced the parasitic thermal resistance, which becomes significant in the shortened SiNW-μTEGs, by optimizing the fabrication process of AlN films as a thermally conductive layer. As a result, the power generation density of the SiNW-μTEGs was enhanced by an order of magnitude for reactive sputtering as compared to non-reactive sputtering process. A power density of 27.9 nW/cm2 has been achieved. By measuring the thermal conductivities of the two AlN films, we found that the reduction in the parasitic thermal resistance was caused by an increase in the thermal conductivity of the AlN film and a decrease in the thermal boundary resistance.
1 An Efficient Recommender System Using Locality Sensitive Hashing

Recommender systems are widely used for personalized recommendation in many business applications such as online shopping websites and social network platforms. However, with the tremendous growth of recommendation space (e.g., number of users, products, etc.), traditional systems suffer from time and space complexity issues and cannot make real-time recommendations when dealing with large-scale data. In this paper, we propose an efficient recommender system by incorporating the locality sensitive hashing (LSH) strategy. We show that LSH can approximately preserve similarities of data while significantly reducing data dimensions. We conduct experiments on synthetic and real-world datasets of various sizes and data types. The experiment results show that the proposed LSH-based system generally outperforms traditional item-based collaborative filtering in most cases in terms of statistical accuracy, decision support accuracy, and efficiency. This paper contributes to the fields of recommender systems and big data analytics by proposing a novel recommendation approach that can handle large-scale data efficiently.
L'activité de la NADPH oxydase : une nouvelle cible pour la curcumine ?

89 Introduction . 90 Material and methods . 92 Results . 97 Discussion . 104 Article II . 109 Curcumin and resveratrol act by different ways on NADPH oxidase activity and reactive oxygen species produced by equine neutrophils . 109 Abstract . 109. 109 Introduction . 110 Material and methods . 112