Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic approaches in myelofibrosis and myelodysplastic/myeloproliferative overlap syndromes

The discovery of JAK2V617F a decade ago led to optimism for a rapidly developing treatment revolution in Ph− myeloproliferative neoplasms. Unlike BCR–ABL, however, JAK2 was found to have a more heterogeneous role in carcinogenesis. Therefore, for years, development of new therapies was slow, despite standard treatment options that did not address the overwhelming symptom burden in patients with primary myelofibrosis (MF), post-essential thrombocythemia MF, post-polycythemia vera MF, and myelodysplastic syndrome (MDS)/myeloproliferative neoplasm (MPN) syndromes. JAK–STAT inhibitors have changed this, drastically ameliorating symptoms and ultimately beginning to show evidence of impact on survival. Now, the genetic foundations of myelofibrosis and MDS/MPN are rapidly being elucidated and contributing to targeted therapy development. This has been empowered through updated response criteria for MDS/MPN and refined prognostic scoring systems in these diseases. The aim of this article is to summarize concisely the current and rationally designed investigational therapeutics directed at JAK–STAT, hedgehog, PI3K–Akt, bone marrow fibrosis, telomerase, and rogue epigenetic signaling. The revolution in immunotherapy and novel treatments aimed at previously untargeted signaling pathways provides hope for considerable advancement in therapy options for those with chronic myeloid disease.
Factors associated with the social competence and emotional well-being among young children in an Asian urban city

ABSTRACT This cross-sectional observational study aims to examine the current status and familial factors associated with social competence and emotional well-being among young children in an urban city in the East Asia region. Early childhood teachers assessed the social competence and the emotional state of preschool children with the Social Competence and Behavioural Evaluation (SCBE-30) Scale. Data were analysed with multiple regression modelling. Results suggested few variables were significantly associated with social competence. These included being a boy, aged 5 years or older, had more than one sibling, whose mother spoke a language other than the local language, and whose carers were not biological parents. Being a male with a mother who spoke a language other than the local language was associated with anger–aggression, while parental work arrangement was the only variable associated with anxiety–withdrawal. Results have a direct implication on the individual and structural levels of child care and early childhood education.
VEGF–neuropilin-2 signaling promotes stem-like traits in breast cancer cells by TAZ-mediated repression of the Rac GAP β2-chimaerin

The growth factor VEGF induces a Rac1-TAZ–mediated positive feedback loop that sustains the proliferation of stem-like cells in breast cancer. A Rac-TAZ loop drives cancer stem cells In various cancers, stem-like cells called CSCs may drive tumor growth, metastasis, recurrence, and drug resistance; thus, blocking the survival and proliferation of CSCs may enhance the efficacy of anticancer therapies in patients. Elaimy et al. found that CSC phenotypes in breast cancer cells are supported by VEGF, a growth factor that is associated with poor prognosis in breast cancer patients. Through its receptor NRP2 and subsequent activation of the kinase FAK and the protein Rac1, VEGF activated the Hippo pathway transcription cofactor TAZ, which bound and repressed the promoter of the gene encoding the enzyme β2-chimaerin. β2-chimaerin effectively shuts off Rac1; thus, its loss promoted sustained Rac1 activity in a self-perpetuating loop that promoted CSC-like behavior in cells and correlated with stem-like and metastatic markers in patient tumors. The role of vascular endothelial growth factor (VEGF) signaling in cancer is not only well known in the context of angiogenesis but also important in the functional regulation of tumor cells. Autocrine VEGF signaling mediated by its co-receptors called neuropilins (NRPs) appears to be essential for sustaining the proliferation and survival of cancer stem cells (CSCs), which are implicated in mediating tumor growth, progression, and drug resistance. Therefore, understanding the mechanisms involved in VEGF-mediated support of CSCs is critical to successfully treating cancer patients. The expression of the Hippo effector TAZ is associated with breast CSCs and confers stem cell–like properties. We found that VEGF-NRP2 signaling contributed to the activation of TAZ in various breast cancer cells, which mediated a positive feedback loop that promoted mammosphere formation. VEGF-NRP2 signaling activated the GTPase Rac1, which inhibited the Hippo kinase LATS, thus leading to TAZ activity. In a complex with the transcription factor TEAD, TAZ then bound and repressed the promoter of the gene encoding the Rac GTPase-activating protein (Rac GAP) β2-chimaerin. By activating GTP hydrolysis, Rac GAPs effectively turn off Rac signaling; hence, the TAZ-mediated repression of β2-chimaerin resulted in sustained Rac1 activity in CSCs. Depletion of β2-chimaerin in non-CSCs increased Rac1 activity, TAZ abundance, and mammosphere formation. Analysis of a breast cancer patient database revealed an inverse correlation between β2-chimaerin and TAZ expression in tumors. Our findings highlight an unexpected role for β2-chimaerin in a feed-forward loop of TAZ activation and the acquisition of CSC properties.
Applying NIALM technology to predictive maintenance for industrial machines

The disadvantage of the predictive maintenance approach is to increase the investment in diagnostic system and staff training, and the saving potential may not be seen when the machines are working in their good condition. Moreover, a predictive maintenance system so far can monitor only one machine that puts this solution in only very important and sensitive machines. This paper proposes an innovation method for monitoring in real-time and predicting simultaneously the status of many industrial inductive machines. Moreover, this solution can also provide an energy efficiency service to improve performance of machine and to optimize the operation cost.
Is government contestability an integral part of the definition of democracy?

Is government contestability an integral part of the definition of democracy? The answer to this question affects the way we classify political systems in which, despite a formally open political structure, a dominant political group faces weak opposition from other political parties and civil society organizations – an indication of a low degree of government contestability. In Robert Dahl’s polyarchy, contestability is an essential dimension of democracy and, consequently, one-party dominance is classified as an ‘inclusive hegemony’ outside his conception of democracy. For procedural definitions of democracy, however, dominant party systems are legitimate outcomes of electoral competition provided that there have been no formal restrictions to the exercise of civil and political rights. The article examines the boundaries between democracy and authoritarianism, broadens the notion of authoritarian controls to include soft manipulative practices and explains why government contestability should be regarded as a constitutive property of democracy.
BGFS: Design and Development of Brain Genetic Fuzzy System for Data Classification

Abstract Recently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.
Lecture-based versus problem-based learning in ethics education among nursing students

Background: Moral reasoning is a vital skill in the nursing profession. Teaching moral reasoning to students is necessary toward promoting nursing ethics. Objectives: The aim of this study was to compare the effectiveness of problem-based learning and lecture-based methods in ethics education in improving (1) moral decision-making, (2) moral reasoning, (3) moral development, and (4) practical reasoning among nursing students. Research design: This is a repeated measurement quasi-experimental study. Participants and research context: The participants were nursing students in a University of Medical Sciences in west of Iran who were randomly assigned to the lecture-based (n = 33) or the problem-based learning (n = 33) groups. The subjects were provided nursing ethics education in four 2-h sessions. The educational content was similar, but the training methods were different. The subjects completed the Nursing Dilemma Test before, immediately after, and 1 month after the training. The data were analyzed and compared using the SPSS-16 software. Ethical considerations: The program was explained to the students, all of whom signed an informed consent form at the baseline. Findings: The two groups were similar in personal characteristics (p > 0.05). A significant improvement was observed in the mean scores on moral development in the problem-based learning compared with the lecture-based group (p < 0.05). Although the mean scores on moral reasoning improved in both the problem-based learning and the lecture-based groups immediately after the training and 1 month later, the change was significant only in the problem-based learning group (p < 0.05). The mean scores on moral decision-making, practical considerations, and familiarity with dilemmas were relatively similar for the two groups. Conclusion: The use of the problem-based learning method in ethics education enhances moral development among nursing students. However, further studies are needed to determine whether such method improves moral decision-making, moral reasoning, practical considerations, and familiarity with the ethical issues among nursing students.
Does Parent–Child Interaction Therapy Reduce Future Physical Abuse? A Meta-Analysis

Objective: To use meta-analytic techniques to evaluating the effectiveness of parent–child interaction therapy (PCIT) at reducing future physical abuse among physically abusive families. Methods: A systematic search identified six eligible studies. Outcomes of interest were physical abuse recurrence, child abuse potential, and parenting stress. Results: Parents receiving PCIT had significantly fewer physical abuse recurrences and significantly greater reductions on the Parenting Stress Index than parents in comparison groups. Reductions in child abuse potential were nonsignificant, although 95% confidence intervals suggest clinically meaningful treatment effects. The studies examining physical abuse recurrence had a medium treatment effect (g = 0.52), while results from pooled effect size estimates for child abuse potential (g = 0.31) and parenting stress (g = 0.35) were small. Conclusions: PCIT appears to be effective at reducing physical abuse recurrence and parenting stress for physically abusive families, with the largest treatment effects seen on long-term physical abuse recurrence. Applications to social work practice are discussed.
A NOVEL APPROACH TO EFFICIENT EXPLORATION AND MINING OF STUDENTS ’ DATA

Several undergraduate students most especially the new students do have an unacceptable Grade Point Average (GPA) at the end of their first year. Research has shown that during this period, stress by this set of students sometime responsible for their poor performance as they are faced with many challenges. Having knowledge about their previous academic knowledge can bring about a workable solution to prevent this trend. In this paper, a novel approach that efficiently explores students’ data for the prediction of their performance based on their historical data is proposed. A prediction system is designed in line with the concept of the Unified Modelling Language (UML) and it is implemented using PHP; while MySQL serves as the back-end. The developed model is tested and a more satisfactory result is achieved when compared with similar models emanated from using machine learning techniques. The resulting outputs of this study unveil the academic achievement of each student prior to their first registration in the university. The model developed is also found to be useful for efficient planning and quick decision making that can ensure a sustainable educational growth.
Final Report for Final Year Project Deep Learning for Text Classification in Azure Infrastructure

The emerging deep learning technologies and trending of big data have enabled many research studies as well as applications of natural language processing (NLP). However, deep learning NLP models for Chinese processing are still underdeveloped. Inspired by the potential of Chinese NLP with deep learning, this project aims at exploring and developing different deep learning models that can classify short Chinese sentences according to their underlying sentiments. Cooperating with Microsoft, this project has already designed, implemented, and evaluated two families of deep learning models, namely LSTM models and CNN models, together with their variations. Experiments on three real-world datasets including two large-scale datasets showed that both the LSTM models and the CNN models are capable of capturing sentence-level sentiment features. A comparison of LSTM models and CNN models showed that CNN models outperformed LSTM models, and gave rise to the hypothesis that local patterns play a decisive role in text classification. Future directions for this project include a further investigation of LSTM models as well as hybrid models, and building real-world applications based on the outcomes of this project.
Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives

Partially-observable Markov decision processes (POMDPs) with discounted-sum payoff are a standard framework to model a wide range of problems related to decision making under uncertainty. Traditionally, the goal has been to obtain policies that optimize the expectation of the discounted-sum payoff. A key drawback of the expectation measure is that even low probability events with extreme payoff can significantly affect the expectation, and thus the obtained policies are not necessarily risk averse. An alternate approach is to optimize the probability that the payoff is above a certain threshold, which allows to obtain risk-averse policies, but ignore optimization of the expectation. We consider the expectation optimization with probabilistic guarantee (EOPG) problem where the goal is to optimize the expectation ensuring that the payoff is above a given threshold with at least a specified probability. We present several results on the EOPG problem, including the first algorithm to solve it.
MODULARITY: U: Relations in Role-Based Data Modeling, Navigation and Updates

Object-oriented programming languages support concise navigation of relations represented by references. However, relations are not first-class citizens and bidirectional navigation is not supported. The relational paradigm provides first-class relations, but with bidirectional navigation through verbose queries. We present a systematic analysis of approaches to modeling and navigating relations. By unifying and generalizing the features of these approaches, we developed the design of a data modeling language that features first-class relations, n-ary relations, native multiplicities, bidirectional relations and concise navigation. This paper is a shortened version of work published in [9]. We extend our previous work by ongoing work on multiplicity-safe update operations.
End-to-End Residual CNN with L-GM Loss Speaker Verification System

We propose an end-to-end speaker verification system based on the neural network and trained by a loss function with less computational complexity. The end-to-end speaker verification system in this paper consists of a ResNet architecture to extract features from utterance, then produces utterance-level speaker embeddings, and train using the large-margin Gaussian Mixture loss function. Influenced by the large-margin and likelihood regularization, large-margin Gaussian Mixture loss function benefits the speaker verification performance. Experimental results demonstrate that the Residual CNN with large-margin Gaussian Mixture loss outperforms DNN-based i-vector baseline by more than 10% improvement in accuracy rate.
A Framework for the Experience of Product Aesthetics

Abstract An important part of design expertise is the ability to design products that produce aesthetic affection. The design literature provides different explanations of aesthetic experience, yet its exact meaning remains unclear. This paper addresses this topic by developing a framework for understanding the aesthetic experience of products. The framework contributes to the design literature by: (1) connecting the main processes involved in the aesthetic experience; (2) clarifying how product experiences can be affected by factors other than the product in focus; and (3) deriving different types of experiences of aesthetic emotions. Finally, the implications of these contributions for practice and future research are discussed.
Prey preferences of the leopard (Panthera pardus)

Leopards Panthera pardus have a catholic diet and are generally thought to prey on medium-sized ungulates; however, knowledge on which species are actually preferred and avoided is lacking, along with an understanding of why such preferences arise. Twenty-nine published and four unpublished studies of leopard diet that had relative prey abundance estimates associated with them were analysed from 13 countries in 41 different spatial locations or temporal periods throughout the distribution of the leopard. A Jacobs’ index value was calculated for each prey species in each study and the mean of these was then tested against a mean of 0 using t or sign tests for preference or avoidance. Leopards preferentially prey upon species within a weight range of 10–40 kg. Regression plots suggest that the most preferred mass of leopard prey is 25 kg, whereas the mean body mass of significantly preferred prey is 23 kg. Leopards prefer prey within this body mass range, which occur in small herds, in dense habitat and afford the hunter minimal risk of injury during capture. Consequently, impala, bushbuck and common duiker are significantly preferred, with chital likely to also be preferred with a larger sample size from Asian sites. Species outside the preferred weight range are generally avoided, as are species that are restricted to open vegetation or that have sufficient anti-predator strategies. The ratio of mean leopard body mass with that of their preferred prey is less than 1 and may be a reflection of their solitary hunting strategy. This model will allow us to predict the diet of leopards in areas where dietary information is lacking, also providing information to assist wildlife managers and conservation bodies on predator carrying capacity and predator– prey interactions.
Executive functioning in preschool children born very preterm: Relationship with early white matter pathology

Despite evidence for executive dysfunction in school-aged preterm children, less is known about the early development of these difficulties or their underlying neuropathology. This study used prospective longitudinal data from a regional cohort of 88 very preterm and 98 full-term comparison children to examine the executive functioning (EF) of preschool children born very preterm. The relationship between the severity of neonatal cerebral white matter (WM) abnormalities on magnetic resonance imaging (MRI) at term equivalent and children's EF at ages two and four years (corrected age) was examined. At age four, very preterm children with WM abnormalities performed less well than full-term children on the Detour Reaching Box, a measure of behavioral inhibition and cognitive flexibility, even after controlling for child IQ, SES, and medical background. Examination of patterns of EF performance between the ages of 2 and 4 years showed that the performance of all groups improved with age. However, very preterm children with mild and moderate-severe WM abnormalities were characterized by higher rates of consistent performance impairments. These findings support the presence of early and persistent executive difficulties in preschool children born very preterm, and highlight the importance of white matter pathology in the development of executive impairments. (JINS, 2008, 14, 90–101.)
《大学图书馆学报》与《College＆Research Libraries》比较

《大学图书馆学报》与《College＆Research Libraries》分别代表中国与美国的大学图书馆学专业期刊。 通过比较大学图书馆学报《College＆Research Libraries》主要研究议题的差别，可以从一个侧面分析二者在研究主题上的差异和特色，为我国大学图书馆学专业期刊在编辑出版方面带来不同的思考方向与参考价值。
Profiling Graphs: Order from Chaos

Graphs are being increasingly adopted as a flexible data model in scenarios (e.g., Google's Knowledge Graph, Facebook's Graph API, Wikidata, etc.) where multiple editors are involved in content creation, where the schema is ever changing, where data are incomplete, where the connectivity of resources plays a key rolescenarios where relational models traditionally struggle. But with this flexibility comes a conceptual cost: it can be difficult to summarise and understand, at a high level, the content that a given graph contains. Hence profiling graphs becomes of increasing importance to extract order, a posteriori, from the chaotic processes by which such graphs are generated. This talk will motivate the use of graphs as a data model, abstract recent trends in graph data management, and then turn to the issue of profiling and summarising graphs: what are the goals of such profiling, the principles by which graphs can be summarised, the main techniques by which this can/could be achieved The talk will emphasise the importance of profiling graphs while highlighting a variety of open research questions yet to be tackled.