Miracle-dz/UniFAR-FTUs huggingface dataset:
Computerised clinical decision support systems to improve medication safety in long-term care homes: a systematic review

Objectives Computerised clinical decision support systems (CCDSS) are used to improve the quality of care in various healthcare settings. This systematic review evaluated the impact of CCDSS on improving medication safety in long-term care homes (LTC). Medication safety in older populations is an important health concern as inappropriate medication use can elevate the risk of potentially severe outcomes (ie, adverse drug reactions, ADR). With an increasing ageing population, greater use of LTC by the growing ageing population and increasing number of medication-related health issues in LTC, strategies to improve medication safety are essential. Methods Databases searched included MEDLINE, EMBASE, Scopus and Cochrane Library. Three groups of keywords were combined: those relating to LTC, medication safety and CCDSS. One reviewer undertook screening and quality assessment. Results Overall findings suggest that CCDSS in LTC improved the quality of prescribing decisions (ie, appropriate medication orders), detected ADR, triggered warning messages (ie, related to central nervous system side effects, drug-associated constipation, renal insufficiency) and reduced injury risk among older adults. Conclusions CCDSS have received little attention in LTC, as attested by the limited published literature. With an increasing ageing population, greater use of LTC by the ageing population and increased workload for health professionals, merely relying on physicians’ judgement on medication safety would not be sufficient. CCDSS to improve medication safety and enhance the quality of prescribing decisions are essential. Analysis of review findings indicates that CCDSS are beneficial, effective and have potential to improve medication safety in LTC; however, the use of CCDSS in LTC is scarce. Careful assessment on the impact of CCDSS on medication safety and further modifications to existing CCDSS are recommended for wider acceptance. Due to scant evidence in the current literature, further research on implementation and effectiveness of CCDSS is required.
UniProt Knowledgebase: a hub of integrated protein data

The UniProt Knowledgebase (UniProtKB) acts as a central hub of protein knowledge by providing a unified view of protein sequence and functional information. Manual and automatic annotation procedures are used to add data directly to the database while extensive cross-referencing to more than 120 external databases provides access to additional relevant information in more specialized data collections. UniProtKB also integrates a range of data from other resources. All information is attributed to its original source, allowing users to trace the provenance of all data. The UniProt Consortium is committed to using and promoting common data exchange formats and technologies, and UniProtKB data is made freely available in a range of formats to facilitate integration with other databases. Database URL: http://www.uniprot. org/
Building an Interpretable Model of Predicting Student Performance Using Comment Data Mining

Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.
Additive Models for Survey Data via Penalized Least Squares

The use of penalized least squares in the sampling context is studied as a tool for the analysis of a general trend in a nite population. Ties in covariates abound for large scale surveys resulting in the application of scatterplot smoothers to means. Smoothing techniques are motivated in the usual way by assuming the nite population is a realization of a classical model. However, to study the properties of estimates this model is abandoned as unreasonable and, rather than replacing it with another model that reeects the ner structure of the nite population, we couple nite population central limit theorems with a limiting sequence of nite populations. The advantage is simplicity. For example, this allows us to assert y _ N(g; V) and so to estimate g we simply smooth y. In a sense central limit theorems render survey data in a familiar form suitable for existing technology. The machinery for estimation and testing is always the same. Only the inputs, like sampling weights, depend on the ner structure of the nite population and not the machinery itself. Although crucial to variance calculations, the role of the sampling design is minimized. This approach has many advantages in terms of graphical display, estimation, testing , bandwidth selection, and so forth. Standard tools can be used in the usual way for estimation where emphasis is placed on \interpretation in the sampling context". Tests of hypotheses are developed as those reported by such standard software are inappropriate due to the structure of survey data. Illustrative examples are given using the Ontario health survey
Experimental Comparison of Face detection Techniques on the basis of Intrinsic and Extrinsic Parameters

Face detection is an important contributing factor to make computer vision applicable to problems of everyday life. Of the techniques used for face detection, two are more efficacious i.e. skin detection and violajones based face detection. These two techniques have limitations of their own. One of the most restricting factors to these techniques is the color cluster of the image. Face detection algorithms are trained on a set of images. Those images are sure to be occupying a particular color cluster of the color plane because it is not possible for the set to contain encompass the full color space. If an image occupying a color space other than that on which the algorithms is trained is an input to the algorithm, the result is not optimal then. This paper explores the effect of different color clusters on the performance of both techniques. Images of color clusters other than that on which the algorithms are trained are given to the algorithms and the results are analyzed to decide the optimality of the techniques in face of images on which the algorithms are not trained. Key-words: Viola-jones, Face detection, Color cluster, Color spaces.
Carrier Aggregation for High Speed Data in LTE Advanced System

One of the most important features to migrate from LTE system to LTE-A system is Carrier aggregation. It is used to achieve high data rates of the order of 1Gbps. Furthermore an LTE Advanced user is backward compatible with LTE. Carrier aggregation is a multi carrier technique, where we will be combining unused SCC with the PCC that is allocated to the user equipment. By combining five component carriers, each of 20 Mhz, we can extend the bandwidth to 100 Mhz and hence achieve high data rates. In this paper we will analyze the performance of carrier aggregation and compare it with the conventional LTE system. Keywords—Carrier Aggregation, eNodeB, Inter-Band Aggregation, Intra-Band Aggregation, LTE Advanced Abbreviations—International Telecommunication Union (ITU), Long term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), Primary Component Carriers (PCC), Secondary Component Carriers (SCC)
Regulation of Microglial Proliferation during Chronic Neurodegeneration

An important component of chronic neurodegenerative diseases is the generation of an innate inflammatory response within the CNS. Microglial and astroglial cells play a key role in the development and maintenance of this inflammatory response, showing enhanced proliferation and activation. We studied the time course and regulation of microglial proliferation, using a mouse model of prion disease. Our results show that the proliferation of resident microglial cells accounts for the expansion of the population during the development of the disease. We identify the pathway regulated by the activation of CSF1R and the transcription factors PU.1 and C/EBPα as the molecular regulators of the proliferative response, correlating with the chronic human neurodegenerative conditions variant Creutzfeldt-Jakob disease and Alzheimer's disease. We show that targeting the activity of CSF1R inhibits microglial proliferation and slows neuronal damage and disease progression. Our results demonstrate that microglial proliferation is a major component in the evolution of chronic neurodegeneration, with direct implications for understanding the contribution of the CNS innate immune response to disease progression.
Metabolism and Pharmacokinetics of Allitinib in Cancer Patients: The Roles of Cytochrome P450s and Epoxide Hydrolase in its Biotransformation

Allitinib, a novel irreversible selective inhibitor of the epidermal growth factor receptor (EGFR) 1 and human epidermal receptor 2 (ErbB2), is currently in clinical trials in China for the treatment of solid tumors. It is a structural analog of lapatinib but has an acrylamide side chain. Sixteen metabolites of allitinib were detected by ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. The pharmacologically active α,β-unsaturated carbonyl group was the major metabolic site. The metabolic pathways included O-dealkylation, amide hydrolysis, dihydrodiol formation, hydroxylation, and secondary phase 2 conjugation. The metabolite of amide hydrolysis (M6) and 27,28-dihydrodiol allitinib (M10) were the major pharmacologically active metabolites in the circulation. The steady-state exposures to M6 and M10 were 11% and 70% of that of allitinib, respectively. The biotransformation of allitinib was determined using microsomes and recombinant metabolic enzymes. In vitro phenotyping studies demonstrated that multiple cytochrome P450 (P450) isoforms, mainly CYP3A4/5 and CYP1A2, were involved in the metabolism of allitinib. Thiol conjugates (M14 and M16) and dihydrodiol metabolites (M5 and M10) were detected in humans, implying the formation of reactive intermediates. The formation of a glutathione conjugate of allitinib was independent of NADPH and P450 isoforms, but was catalyzed by glutathione-S-transferase. P450 enzymes and epoxide hydrolase were involved in M10 formation. Overall, our study showed that allitinib was metabolized by the O-dealkylation pathway similar to lapatinib, but that amide hydrolysis and the formation of dihydrodiol were the dominant metabolic pathways. The absorbed allitinib was extensively metabolized by multiple enzymes.
Predictors of liver fibrosis in HIV-infected patients with chronic hepatitis C virus (HCV) infection: assessment using transient elastometry and the role of HCV genotype 3.

BACKGROUND Liver fibrosis is accelerated in patients coinfected with hepatitis C virus (HCV) and human immunodeficiency virus (HIV). The reasons for this faster liver disease progression are unclear, although higher plasma HCV RNA levels and distinct HCV genotype distribution in this population, compared with in HCV-monoinfected subjects, could play a role. METHODS Liver fibrosis was assessed using elastometry in all consecutive HIV-infected patients with chronic hepatitis C who attended our institution (Hospital Carlos III, Madrid) during the past 12 months. Hepatic stiffness was measured in kiloPascal units (kPa) and was interpreted on the basis of Metavir score: no or mild fibrosis (score, F0-F1) when liver stiffness is < or =7.1 kPa, and fibrosis with septa or cirrhosis (F2-F4) when >7.1 kPa. RESULTS A total of 283 patients (71% were male; mean age, 42 years; 94% were injection drug users and 94% were receiving antiretrovirals; mean CD4 cell count, 554 cells/microL; 72% with plasma HIV RNA level of <50 copies/mL) were analyzed. The mean alanine aminotransferase level was 68 IU/L, and the mean plasma HCV RNA level was 5.9 log IU/mL. HCV genotype distribution was as follows: genotype 1, 60% of patients; genotype 2, 2%; genotype 3, 26%; and genotype 4, 12%. Overall, 164 (58%) of the patients had scores indicating advanced liver fibrosis (F2-F4), as determined using elastometry. In the univariate and multivariate analyses, respectively, a significant odds ratio (OR) for score F2-F4 was found for HCV genotype 3, compared with the other genotypes (OR, 1.9 [95% confidence interval {CI}, 1.1-3.4] vs. 4.3 [95% CI, 1.4-13.3]); for older age (OR, 1.1 [95% CI, 1.03-1.17] vs. 1.1 [95% CI, 1.01-1.25]); and for elevated alanine aminotransferase levels (OR, 1.02 [95% CI, 1.01-1.03] vs. 1.03 [95% CI, 1.01-1.04]). Although patients with HCV genotype 1 had higher mean serum HCV RNA levels than did those with HCV genotype 3 (6.1 log IU/mL vs. 5.7 log IU/mL; P=.01), patients with HCV genotype 3 tended to have F2-F4 scores more frequently than did those with HCV genotype 1 (69% vs. 58%; P = not significant). CONCLUSIONS HCV genotype 3, older age, and elevated alanine aminotransferase levels are independent predictors of advanced liver fibrosis in HCV-HIV-coinfected patients.
Combining clustering and pattern detection for the reengineering of component-based software systems

During the software lifecycle, software systems have to be continuously maintained to counteract architectural deterioration and retain their software quality. In order to maintain a software it has to be understood first which can be supported by (semi-)automatic reverse engineering approaches. Reverse engineering is the analysis of software for the purpose of recovering its design documentation, e.g., in form of the conceptual architecture. Today, the most prevalent reverse engineering approaches are (1) the clustering based approach which groups the elements of a given software system based on metric values in order to provide an overview of the system and (2) the pattern-based approach which tries to detect pre-defined patterns in the software which can give insight about the original developers' intentions. In this paper, we present an approach towards combining these techniques: we show how the detection and removal of certain bad smells in a software system can improve the results of a clustering-based analysis. We propose to integrate this combination of reverse engineering approaches into a reengineering process for component-based software systems.
IMPROVING THE LEARNING EFFECTIVENESS OF MANAGEMENT INFORMATION SYSTEMS COURSE WITH COOPERATIVE LEARNING: LESSONS OF EMPIRICAL STUDY

This paper reports the empirical results of implementing a cooperative learning method in a core undergraduate MIS course taken by business students with diverse backgrounds and interests. The results confirmed the effectiveness of cooperative learning but also showed interesting findings of students’ perception of the method. The paper also reports factors identified by students that might be detrimental to the successful use of cooperative learning, which can be used by instructors to improve their implementation of cooperative learning.
An energy-efficient data security system for wireless sensor network

Wireless sensor networks are a new type of networked systems, characterized by severely constrained computational and energy resources, and an ad hoc operational environment. Because sensor networks may interact with sensitive data and operate in hostile unattended environments, it is imperative that these security concerns be addressed from the beginning of the system design. However, due to inherent resource and computing constraints, security in sensor networks poses different challenges than traditional network security. There is currently enormous research potential in the field of wireless sensor network security. In this paper we introduce a message expantion problem which can be solved by a data link-layer security architecture called dasiaCipher-text Stealing methodpsila for wireless sensor networks. Conventional security protocols tend to be conservative in their security guarantees, typically adding 16-32 bytes of overhead. With small memories, weak processors, limited energy, and 30 byte packets, sensor networks cannot afford this luxury. TinySec addresses these extreme resource constraints with careful design; we explore the tradeoffs among different cryptographic primitives and use the inherent sensor network limitations to our advantage when choosing parameters to find a sweet spot for security, packet overhead, and resource requirements.
MultiVCRank With Applications to Image Retrieval

In this paper, we propose and develop a multi-visual-concept ranking (MultiVCRank) scheme for image retrieval. The key idea is that an image can be represented by several visual concepts, and a hypergraph is built based on visual concepts as hyperedges, where each edge contains images as vertices to share a specific visual concept. In the constructed hypergraph, the weight between two vertices in a hyperedge is incorporated, and it can be measured by their affinity in the corresponding visual concept. A ranking scheme is designed to compute the association scores of images and the relevance scores of visual concepts by employing input query vectors to handle image retrieval. In the scheme, the association and relevance scores are determined by an iterative method to solve limiting probabilities of a multi-dimensional Markov chain arising from the constructed hypergraph. The convergence analysis of the iteration method is studied and analyzed. Moreover, a learning algorithm is also proposed to set the parameters in the scheme, which makes it simple to use. Experimental results on the MSRC, Corel, and Caltech256 data sets have demonstrated the effectiveness of the proposed method. In the comparison, we find that the retrieval performance of MultiVCRank is substantially better than those of HypergraphRank, ManifoldRank, TOPHITS, and RankSVM.
P-time Unique State Coding Algorithms for Signal Transition Graphs P-time Unique State Coding Algorithms for Signal Transition Graphs

Current algorithms to force the Complete State Coding (CSC) property for Signal Transition Graphs (STGs) work on the State Graph and, therefore require exponential time and space. Polynomial algorithms have been only proposed for Marked Graphs. In this paper, a P-time algorithm for Unique State Coding (USC) is presented. Although more restrictive than CSC, it is shown that the USC property can be eeciently guaranteed for large STGs. Several experiments evidence that the obtained results are even better than those generated by exponential-time techniques.
Dual Regulation of the Mitotic Exit Network (MEN) by PP2A-Cdc55 Phosphatase

Exit from mitosis in budding yeast is triggered by activation of the key mitotic phosphatase Cdc14. At anaphase onset, the protease separase and Zds1 promote the downregulation of PP2ACdc55 phosphatase, which facilitates Cdk1-dependent phosphorylation of Net1 and provides the first wave of Cdc14 activity. Once Cdk1 activity starts to decline, the mitotic exit network (MEN) is activated to achieve full Cdc14 activation. Here we describe how the PP2ACdc55 phosphatase could act as a functional link between FEAR and MEN due to its action on Bfa1 and Mob1. We demonstrate that PP2ACdc55 regulates MEN activation by facilitating Cdc5- and Cdk1-dependent phosphorylation of Bfa1 and Mob1, respectively. Downregulation of PP2ACdc55 initiates MEN activity up to Cdc15 by Bfa1 inactivation. Surprisingly, the premature Bfa1 inactivation observed does not entail premature MEN activation, since an additional Cdk1-Clb2 inhibitory signal acting towards Dbf2-Mob1 activity restrains MEN activity until anaphase. In conclusion, we propose a clear picture of how PP2ACdc55 functions affect the regulation of various MEN components, contributing to mitotic exit.
YAGO-QA: Answering Questions by Structured Knowledge Queries

We present a natural-language question-answering system that gives access to the accumulated knowledge of one of the largest community projects on the Web â€" Wikipedia â€" via an automatically acquired structured knowledge base. Key to building such a system is to establish mappings from natural language expressions to semantic representations. We propose to acquire these mappings by data-driven methods â€" corpus harvesting and paraphrasing â€" and present a preliminary empirical study that demonstrates the viability of our method.
The First Generation of Stars: First Steps toward Chemical Evolution of Galaxies

We argue that extreme metal-poor stars show a high dispersion in metallicity, because their abundances are the outcome of very few supernova events. Abundance anomalies should appear because of the discrete range of progenitor masses. There is a natural metallicity threshold of Z/Z☉ ~ 10-4 below which one would expect to find very few, if any, halo stars. Similar reasoning is applied to lower mass systems, such as metal-poor compact blue galaxies and Lyman-alpha absorption-line clouds seen toward high-redshift quasars, where a somewhat higher threshold is inferred.
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (11) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (11)-PAES is intended to be a baseline approach against which more involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1) and () variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows standard statistical analysis to be carried out for comparative purposes. Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks.
School Climate, Deployment, and Mental Health Among Students in Military-Connected Schools

Research has found that when compared with civilian students, military-connected students in the United States have more negative mental health outcomes, stemming from the stress of military life events (i.e., deployment). To date, studies on military-connected youth have not examined the role of protective factors within the school environment, such as school climate, in the mental health and well-being of military-connected adolescents. Given this gap in the research on military adolescents, this study draws from a large sample of military and non-military secondary adolescents in military-connected schools (N = 14,943) and examines associations between school climate, military connection, deployment, and mental health. Findings show that multiple components of school climate are associated with a lower likelihood of depressive symptoms and suicidal ideation and increased likelihood of well-being among students in military-connected schools, after controlling for student demographics, military connection, and deployments. The authors conclude with a discussion of school climate interventions for military-connected youth.