Miracle-dz/UniFAR-FTUs huggingface dataset:
Verification of the Addiction Severity Index Japanese Version (ASI-J) as a Treatment-Customization, Prediction, and Comparison Tool for Alcohol-Dependent Individuals

Objective: To demonstrate the usefulness of the Addiction Severity Index Japanese Version (ASI-J) in Japanese alcohol-dependent individuals. The ASI is a frequently used clinical and research instrument that measures severities in seven functional domains in people with substance abuse disorders. Methods: A total of 370 male inpatients with a history of alcohol dependence participated in the study. Forty-nine participants were excluded in the final analysis due to lack of reliability (i.e., patient misrepresentation or inability to understand). We used the ASI-J and a series of indexes that determined patient states during and post-treatment. Results: The correlations between ASI Composite Scores (CSs), which were calculated through a weighted formula and indicated the severity of each problem area, were significant but low in eight relations and not significant in 13 relations, indicating substantial independence of the problem areas. Significant differences were found in Family/Social CSs between abstinent and relapsed alcohol-dependent individuals. The questions of undesirable attitude were significantly related to the CSs of Employment, Drug use, Family/Social, and Psychiatric sections. Significant differences were observed in patient demographics, CS, and ASI Severity Rating (SR) and interviewer’s subjective scoring between alcohol-dependent individuals and drug abusers. CSs in Japanese alcohol-dependent individuals were generally similar to corresponding CSs in individuals from other countries, with the exception of The Netherlands. Conclusions: This study demonstrated that the ASI-J is useful for understanding individual profiles of problems for each patient and planning customized treatment. The ASI-J served as a predictive tool for relapse and compliance to treatment afterward and was shown to be useful as a comparison tool in clarifying similarities and differences between substance abuser groups.
Prediction of MPEG Traffic Data Using a Bilinear Recurrent Neural Network with Adaptive Training

A time-series prediction model using a Bilinear Recurrent Neural Network (BRNN) is proposed in this paper. The BRNN model used in this paper is the Multiresolution architecture with an adaptive training mode. The Multiresolution Bilinear Recurrent Neural Network (MBRNN) is based on the BLRNN that has been proven to have robust abilities in modeling and predicting time series. The proposed MBRNN-based predictor is applied to real-time MPEG video traffic data. The performance of the proposed MBRNN-based predictor is evaluated and compared with the conventional MultiLayer Perceptron Type Neural Network (MLPNN)-based predictor and BRNN-based predictor. When compared with the MLPNN-based predictor and theBRNN-based predictor, the proposed MBRNN-based predictor shows significant improvement in terms of the Normalized Mean Square Error (NMSE) criterion.
Dynamic suspicious reputation based collaborative sensing in cognitive radio networks

Collaborative sensing is an available technology to increase access opportunity for unused spectrum and avoid interference to the primary users in cognitive radio networks. Due to the complexity of wireless environment and the primary user's location is unknown, the sensing performance will be affected by many unfavorable factors such as multipath fading, shadowing, and so on. Especially, some secondary users (SUs), defined as attackers, could cause security problem by reporting false information to other SUs. In this paper, we proposed a Dynamic Suspicious Reputation Scheme to detect attackers in collaborative sensing. In the proposed scheme, a SU with suspicious reputation makes the sensing decision by evaluating the correlation value between its own sensing reports and those from other SUs. If the correlation value exceed a given threshold, the compared SU is regarded as an attacker. The suspicious reputation is updated according to its own sensing reports in each collaborative sensing iteration. Once the suspicious reputation of a SU exceed a certain threshold, the SU is regarded as Malicious Attacker, and removed out the collaborative sensing group. Numerical analysis show that the proposed scheme can achieve a considerable performance improvement compared with a User-centric Misbehavior Detection Scheme for secure collaborative sensing. In addition, we also discuss the affection of the suspicious reputation to the probability of false alarm and detection.
A Robust Procedure for Distinctively Visualizing Zebrafish Retinal Cell Nuclei Under Bright Field Light Microscopy

To simultaneously visualize individual cell nuclei and tissue morphologies of the zebrafish retina under bright field light microscopy, it is necessary to establish a procedure that specifically and sensitively stains the cell nuclei in thin tissue sections. This necessity arises from the high nuclear density of the retina and the highly decondensed chromatin of the cone photoreceptors, which significantly reduces their nuclear signals and makes nuclei difficult to distinguish from possible high cytoplasmic background staining. Here we optimized a procedure that integrates JB4 plastic embedding and Feulgen reaction for visualizing zebrafish retinal cell nuclei under bright field light microscopy. This method produced highly specific nuclear staining with minimal cytoplasmic background, allowing us to distinguish individual retinal nuclei despite their tight packaging. The nuclear staining is also sensitive enough to distinguish the euchromatin from heterochromatin in the zebrafish cone nuclei. In addition, this method could be combined with in situ hybridization to simultaneously visualize the cell nuclei and mRNA expression patterns. With its superb specificity and sensitivity, this method may be extended to quantify cell density and analyze global chromatin organization throughout the retina or other tissues.
Clustering of XML documents based on structure and aggregated content

The main objective of the work is to improve the clustering efficiency and performance when we deal with very big datasets. This paper aims to improve the quality of XML data clustering by exploiting more features extracted from source schemas. In particular, it proposes clustering approach that gathers both content and structure of XML documents to determine similarity between them. The content and structure information are concluded using two different similarity methods that are then grouped via weight factor to compute the overall document similarity. The structural similarity of XML data are derived from edge summaries while content features similarity are derived from aggregate of set of similarity measures; Jaccard, Cosine measure and Jensen-Shannon divergence in one algorithm. However, we also experimented using Jaccard distance as content measure with edge summaries to prove that using an aggregation of content similarity measures can further improve the results. The experiments prove that clustering of XML documents based on structure only information produce worse solution in homogenous environment, while in heterogeneous environment clustering of XML document produce better result when the structure and the content are combined. Results have shown that performance and quality of the proposed approach is better in comparison of both XEdge and XCLSC approaches.
Is There a Metabolic Program in the Skeletal Muscle of Obese Individuals?

Severe obesity (BMI ≥ 40 kg/m2) is associated with multiple defects in skeletal muscle which contribute to insulin resistance and a reduction in fatty acid oxidation (FAO) in this tissue. These metabolic derangements are retained in human skeletal muscle cells raised in culture. Together, these findings are indicative of a dysfunctional global metabolic program with severe obesity which is of an epigenetic or genetic origin. Weight loss via gastric bypass surgery can “turn off” and/or correct components of this metabolic program as insulin sensitivity is restored; however, the impairment in FAO in skeletal muscle remains evident. Physical activity can improve FAO and insulin action, indicating that this patient population is not exercise resistant and that exercise offers a pathway to circumvent the abnormal program. Findings presented in this review will hopefully increase the understanding of and aid in preventing and/or treating the severely obese condition.
Integrating perceived risk and trust with technology acceptance model: An empirical assessment of customers' acceptance of online shopping in Jordan

This study is set to explore the influence of perceived risk and trust on the consumers' acceptance of online channel for purchase in Jordan. The study proposes a theoretical model based upon the technology acceptance model (TAM), by extending its boundary with perceived risk and trust variables. A questionnaire developed from a previously validated measurement was administered to collect the data using purposeful sampling technique. The model identifies nine hypotheses, which were examined through partial least squares (PLS) statistical tool. Empirically, this study revealed that trust, perceived usefulness, and perceived ease-of-use have direct and positive impact on the consumers' behavioral intention to use online channel for shopping. Also, perceived ease-of-use was found to positively influence the behavioral intention to use online shopping indirectly via perceived usefulness and trust variables. Surprisingly, the results of this study showed that perceived risk has no direct influence on the consumers' behavioral intention, however it has indirect effect via trust variable. Therefore, both perceived risk and trust can be considered influential in increasing instability in the online shopping environment. Managerial implications are provided.
High-sensitivity enzyme immunoassay for C-reactive protein in dried blood spots.

C-Reactive protein (CRP), the prototypical acute-phase protein, is produced by liver hepatocytes and regulated by cytokines, particularly interleukin-6 (1)(2). Circulating concentrations of CRP indicate inflammatory activity, and the recent development of highly sensitive CRP assays (3)(4)(5) has led to the discovery that slight increases in CRP (>1–2 mg/L) are indicative of low-grade inflammatory processes that may be related to the pathophysiology of cardiovascular disease. More than a dozen population-based studies have demonstrated that increased CRP is an independent risk factor for future cardiovascular disease, with adjusted odds ratios >2.0 (6)(7)(8)(9). The American Heart Association and the CDC have recommended measurements of CRP in clinical practice and called for additional population-based research (10). A potential obstacle to the measurement of CRP (as well as other biomarkers) in large epidemiologic, community-based studies is the requirement for venous blood. Venipuncture is a relatively invasive procedure that must be performed by a trained phlebotomist (usually in a clinical setting), and it requires readily accessible facilities where blood samples can be promptly processed and stored under controlled conditions. Assays using whole blood dried on filter paper may provide a viable alternative: Several community-based applications have shown this to be a convenient and reliable means to facilitate sample collection, storage, and transportation, and laboratory methods have been validated for a growing number of analytes (11)(12)(13)(14)(15)(16). “Guthrie papers” have been a core component of US hospital-based newborn-screening programs since the 1960s and are subject to a rigorous quality-control program (17). Samples can be collected on filter paper easily by nonmedical personnel: The patient’s finger is pricked with a sterile, disposable lancet (commonly used by diabetics), and up to five drops of blood (∼50 …
Building a faceted classification for the humanities: principles and procedures

Purpose – This paper aims to provide an overview of principles and procedures involved in creating a faceted classification scheme for use in resource discovery in an online environment.Design/methodology/approach – Facet analysis provides an established rigorous methodology for the conceptual organization of a subject field, and the structuring of an associated classification or controlled vocabulary. This paper explains how that methodology was applied to the humanities in the FATKS project, where the objective was to explore the potential of facet analytical theory for creating a controlled vocabulary for the humanities, and to establish the requirements of a faceted classification appropriate to an online environment. A detailed faceted vocabulary was developed for two areas of the humanities within a broader facet framework for the whole of knowledge. Research issues included how to create a data model which made the faceted structure explicit and machine‐readable and provided for its further develop.
A kernel-based monitoring approach for analyzing malicious behavior on Android

This paper proposes a new technique that monitors important events at the kernel level of Android and analyzes malicious behavior systematically. The proposed technique is designed in two ways. First, in order to analyze malicious behavior that might happen inside one application, it monitors file operations by hooking the system calls to create, read from, and write to a file. Secondly, in order to analyze malicious behavior that might happen in the communication between colluding applications, it monitors IPC messages (Intents) by hooking the binder driver. Our technique can detect even the behavior of obfuscated malware using a run-time monitoring method. In addition, it can reduce the possibility of false detection by providing more specific analysis results compared to the existing methods on Android. Experimental results show that our technique is effective to analyze malicious behavior on Android and helpful to detect malware.
Differential Expression of Matrix Metalloproteinases and Tissue Inhibitors of Metalloproteinases in Thioacetamide-Induced Chronic Liver Injury

Hepatic fibrogenesis, a complex process that involves a marked accumulation of extracellular matrix components, activation of cells capable of producing matrix materials, cytokine release, and tissue remodeling, is regulated by matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs). The MMP-TIMP balance can regulate liver fibrogenesis. The aim of this study was to evaluate the expression patterns of MMPs and TIMPs during thioacetamide (TAA)-induced liver fibrogenesis. Chronic liver injury was induced with TAA (200 mg/kg i.p.) for 4 or 7 weeks in male Sprague-Dawley rats. Hepatic injury and fibrosis were assessed by hematoxylin-eosin (H&E) staining, and collagen deposition was confirmed by Sirius Red staining. The level of hepatic injury was quantified by serological analysis. The transcriptional and translational levels of α-smooth muscle actin (α-SMA), MMPs, and TIMPs in the liver were measured by Western blotting, RT-PCR, and immunohistochemistry. MMP, TIMP, and α-SMA were observed along fibrotic septa and portal spaces around the lobules. TAA treatment increased transcription of both MMPs and TIMPs, but only TIMPs showed increased translation. The dominant expression of TIMPs may regulate the function of MMPs to maintain liver fibrosis induced by TAA.
An empirical investigation around the finance-growth puzzle in China with a particular focus on causality and efficiency considerations

The paper explores a coherent perspective for understanding the multifaceted puzzle of China‘s financial development. Specifically, it tests competing finance-growth nexus hypotheses using Granger causality tests in a VECM framework for China over the period 1980–2002. The empirical results support a complex set of bidirectional causality between the financial development proxies and economic growth variables. Additionally, bidirectional causality shows the Chinese financial system to be more driven by and closely aligned with real sector activities than exposed to speculative finance. Study findings have several policy implications. Notably, the development of financial institutions should not be emphasized unilaterally. Rather, attention should be given to the complementary and coordinated development of financial reforms and changes in other areas.
MICS: Mingling Chained Storage Combining Replication and Erasure Coding

High reliability, low space cost, and efficient read/write performance are all desirable properties for cloud storage systems. Due to the inherent conflicts, however, simultaneously achieving optimality on these properties is unrealistic. Since reliable storage is indispensable prerequisite for services with high availability, tradeoff should therefore be made between space and read/write efficiency when storage scheme is designed. N-way Replication and Erasure Coding, two extensively-used storage schemes with high reliability, adopt opposite strategies on this tradeoff issue. However, unbalanced tradeoff designs of both schemes confine their effectiveness to limited types of workloads and system requirements. To mitigate such applicability penalty, we propose MICS, a MIngling Chained Storage scheme that combines structural and functional advantages from both N-way replication and erasure coding. Qualitatively, MICS provides efficient read/write performance and high reliability at reasonably low space cost. MICS stores each object in two forms: a full copy and certain amount of erasure-coded segments. We establish dedicated read/write protocols for MICS leveraging the unique structural advantages. Moreover, MICS provides high read/write efficiency with Pipeline Random-Access Memory consistency to guarantee reasonable semantics for services users. Evaluation results demonstrate that under same fault tolerance and consistency level, MICS outperforms N-way replication and pure erasure coding in I/O throughput by up to 34.1% and 51.3% respectively. Furthermore, MICS shows superior performance stability over diverse workload conditions, in which case the standard deviation of MICS is 70.1% and 29.3% smaller than those of other two schemes.
A Hybrid Evolutionary Approach for Combinatorial Problems in Dynamic Environments

There is a growing interest in the use of evolutionary algorithms in time-varying environments where the information is revealed progressively with time to the decision maker. However, most existing research basically targets continuous optimization, while little work is directed to discrete optimization even though many real-world problems are both discrete and time-varying. This paper seeks to enhance the ability of genetic algorithms to track the optima shifting due to environmental changes: first, parameters of the genetic operators react to changes in the environment and to changes in the population diversity in order to persevere after obsolete convergence and overcome premature convergence. Second, multi-populations are introduced to cooperatively maintain the search diversity. Third, the algorithm is hybridized with local search heuristics for better tuning. The final algorithm is tested on dynamic combinatorial benchmark problems from the literature. Collectively, results from the conducted experiments favor the strategies proposed in this paper
Unsupervised Dimensionality Reduction for Transfer Learning

We investigate the suitability of unsupervised dimensionality reduction (DR) for transfer learning in the context of different represen- tations of the source and target domain. Essentially, unsupervised DR establishes a link of source and target domain by representing the data in a common latent space. We consider two settings: a linear DR of source and target data which establishes correspondences of the data and an ac- cording transfer, and its combination with a non-linear DR which allows to adapt to more complex data characterised by a global non-linear structure.
Linking toxicity and adaptive responses across the transcriptome, proteome, and phenotype of Chlamydomonas reinhardtii exposed to silver

Significance Comprehending the responses of organisms to pollutants by a systems-based approach allows characterization of molecular events and the cellular pathways that have been perturbed. However, mapping only adverse outcomes of a toxicant in an organism falls short of describing the defense response that is mounted to maintain homeostasis and resistance to the toxic insult. Our study provides the understanding of molecular mechanisms of algae in response to silver, which in turn indicates how the algae might behave in a silver contamination scenario. We have used complementary information obtained from the transcriptome, proteome, and physiology to gain mechanistic insights into the responses of Chlamydomonas reinhardtii. We show here the importance of stress and adaptive responses, especially at sublethal concentrations of pollutant. Understanding mechanistic and cellular events underlying a toxicological outcome allows the prediction of impact of environmental stressors to organisms living in different habitats. A systems-based approach aids in characterizing molecular events, and thereby the cellular pathways that have been perturbed. However, mapping only adverse outcomes of a toxicant falls short of describing the stress or adaptive response that is mounted to maintain homeostasis on perturbations and may confer resistance to the toxic insult. Silver is a potential threat to aquatic organisms because of the increasing use of silver-based nanomaterials, which release free silver ions. The effects of silver were investigated at the transcriptome, proteome, and cellular levels of Chlamydomonas reinhardtii. The cells instigate a fast transcriptome and proteome response, including perturbations in copper transport system and detoxification mechanisms. Silver causes an initial toxic insult, which leads to a plummeting of ATP and photosynthesis and damage because of oxidative stress. In response, the cells mount a defense response to combat oxidative stress and to eliminate silver via efflux transporters. From the analysis of the perturbations of the cell’s functions, we derived a detailed mechanistic understanding of temporal dynamics of toxicity and adaptive response pathways for C. reinhardtii exposed to silver.
Real-time Prediction of Respiratory Motion based on Nonparametric Local Regression Methods

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using those techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy (IGRT) systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on nonparametric local regression. There are three major ingredients of this approach: (1) augmentation of the state space to capture system dynamics; (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion; (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumour motion and its observed location for 10 patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths. Submitted to: Phys. Med. Biol. Prediction with Nonparametric Local Regression 2