Miracle-dz/UniFAR-FTUs huggingface dataset:
Telomere length heterogeneity in ALT cells is maintained by PML-dependent localization of the BTR complex to telomeres

Telomeres consist of TTAGGG repeats bound by protein complexes that serve to protect the natural end of linear chromosomes. Most cells maintain telomere repeat lengths by utilizing the enzyme telomerase, although there are some cancer cells that use a telomerase-independent mechanism of telomere extension, termed Alternative Lengthening of Telomeres (ALT). Cells that employ ALT are characterized, in part, by the presence of specialized PML nuclear bodies called ALT-associated PML-Bodies (APBs). APBs localize to and cluster telomeric ends together with telomeric and DNA damage factors, which led to the proposal that these bodies act as a platform on which ALT can occur. However, the necessity of APBs and their function in the ALT pathway has remained unclear. Here, we used CRISPR/Cas9 to delete PML and APB components from ALT-positive cells to cleanly define the function of APBs in ALT. We find that PML is required for the ALT mechanism, and that this necessity stems from APBs’ role in localizing the BLM-TOP3A-RMI (BTR) complex to ALT telomere ends. Strikingly, recruitment of the BTR complex to telomeres in a PML-independent manner bypasses the need for PML in the ALT pathway, suggesting that BTR localization to telomeres is sufficient to sustain ALT activity.
Distributed fusion Kalman filtering under binary sensors

Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.
Containing classroom discussions of current social and political issues

ABSTRACT This paper attends to the emotional and affective relations that manifest through political discussions in secondary classrooms. In particular, the focus is on the dynamics of classrooms during discussions of political issues. Specific attention is given to interpretations of movements and manifestations of emotions as well as the ways that teachers work with, avoid, navigate and help students make meaning from such discussions. Two case examples are provided that took place in the US South in the Spring of 2018 in separate high school classrooms; the first a discussion about Confederate Memorialization and the second a conversation about gun violence that took place 1 week after the massacre in Parkland, Florida. The idea of containment, borrowed from psychoanalytic theory, is used to theorize how students and teachers engage with and work through difficult emotional content. The ways that emotional significance and political knowledge are linked become increasingly necessary to understand in the context of increasing political divisions across democratic countries.
A survey on semanticized and personalized health recommender systems

Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.
Research on Early Warning Model of College Students' Psychological Crisis Based on Genetic BP Neural Network

With the continuous advancement of information construction in colleges and universities, a large number of student data have been accumulated and precipitated in the campus center database of colleges and universities. On the basis of social constructivist psychology, Maslow and Mittelmann's mental health standards and related research results of psychological crisis early warning, three first-level indicators and 15 second-level indicators of college student’s psychological crisis were established. The campus data of 1504 college students were collected on one data center of a college, and the weight of each indicator was determined on the basis of correlation analysis of each indicator and psychological status indicators through SPSS21.0 and the expert opinion. The early warning model of college students’ psychological crisis was basically constructed. With experimental simulation of 250 sets of real data, the early warning model based on the genetic BP Neural network for its initial weight and threshold with MATLAB was improved. The results indicated that the indicator system of college student’s psychological crisis in this paper was effective and feasible, and the early warning model based on genetic BP neural network had high accuracy and certain application value.
Curriculum Deep Reinforcement Learning with Different Exploration Strategies: A Feasibility Study on Cardiac Landmark Detection

Transcatheter aortic valve implantation (TAVI) is associated with conduction abnormalities and the mechanical interaction between the prosthesis and the atrioventricular (AV) conduction path cause these life-threatening arrhythmias. Pre-operative assessment of the location of the AV conduction path can help to understand the risk of post-TAVI conduction abnormalities. As the AV conduction path is not visible on cardiac CT, the inferior border of the membranous septum can be used as an anatomical landmark. Detecting this border automatically, accurately and efficiently would save operator time and thus benefit pre-operative planning. This preliminary study was performed to identify the feasibility of 3D landmark detection in cardiac CT images with curriculum deep Q-learning. In this study, curriculum learning was used to gradually teach an artificial agent to detect this anatomical landmark from cardiac CT. This agent was equipped with a small field of view and burdened with a large ac tion-space. Moreover, we introduced two novel action-selection strategies: α-decay and action-dropout. We compared these two strategies to the already established e-decay strategy and observed that α-decay yielded the most accurate results. Limited computational resources were used to ensure reproducibility. In order to maximize the amount of patient data, the method was cross-validated with k-folding for all three action-selection strategies. An inter-operator variability study was conducted to assess the accuracy of the method
Study into the short and long term (re)production of relations between communities, inorganic solid waste and the Surabaya River, Indonesia

In Indonesia, river pollution due to inorganic solid waste has become an enormous issue. This article explores the (re)production of relations between communities, river and inorganic solid waste in the Surabaya River Basin. It does so by analysing activities based on interviews, observations and measurements. It tries to explain the current situation in the Surabaya River Basin and shows how ’river water’ and ’waste’ shape ontologies. Furthermore, how these two nonhuman actors act differently (or the same) in different ontologies. The definition used in this study for an ontology is "a construction of how concepts of reality are constructed". Several analyses are used. Firstly, statistics are used to support narratives where deemed necessary. In addition, QGIS is used to visualise data. Lastly, a flow diagram is made to show the flow of waste into the Surabaya River. Existence of multiple ontologies in the Surabaya River Basin is described. It is found that both reproduction of actions is present (on the short term) as well as production of different actions, which becomes more visible in the long term. For example, waste burning every afternoon is a reoccurring action. However, the reduction of single-use plastics is an evolved state. The riverbank is an important location in the relations and could serve as a suitable location for such actions. Lack of information, communication and facilities are identified to be the most important issues. In general, people are optimistic and motivated concerning the environment. However there are some people not engaged. This causes a divide and together with social control leads to tensions in the community. Recommendations include, among others, setting up an information sharing platform between institutions and community members and making use of the cultural resources to work towards a common goal. Including the multiple ontologies in decision-making processes could lead to inclusive and sustainable river management and waste management.
Exposure to PM2.5 during pregnancy or lactation increases methylation while reducing the expression of Pdx1 and NEUROG3 in mouse pancreatic islets

Air pollution is comprised of several substances, including particulate matter (PM). Exposure to air pollution may trigger alterations in DNA methylation thus modifying gene expression patterns. This phenomenon is likely to mediate the relationship between exposure to air pollution and adverse health effects. The purpose of this study was analyzing the effects of exposure to PM2.5 during pregnancy or lactation and whether it would cause multigenerational epigenetic alterations in the promoter region of the genes Pdx1 and NEUROG3 within mouse pancreatic islets. Our results show that maternal exposure to PM2.5 led to an elevation in blood glucose levels within the two following generations (F1 and F2). There was also an increase in DNA methylation in the aforementioned promoter regions accompanied by reduced gene expression in generations F1 and F2 upon F0 exposure to PM2.5 during pregnancy. These data suggest that maternal exposure to PM2.5 from air pollution, particularly during pregnancy, may lead to a multigenerational and lifelong negative impact on glucose homeostasis mediated by an increase in DNA methylation within the promoter region of the genes Pdx1 and NEUROG3 in pancreatic islets.
Using different processors as predictors to detect a data packet logging into the Snort network with minimal delay time

The growing number of Internet threats has increased demand for better defence and information security in computer systems. Snort is a Network Intrusion Detection System (NIDS) used in network security by staying on the edge of a network and performing deep packet checking on any packet logging in to the protected domain. The question of how much overhead is introduced into the network traffic by the introduction of real-time NIDS was thus investigated. A simulation methodology was used to implement experiments to evaluate the effect of Snort, measured by end-to-end delay-time introduced by the engine. These experiments proved that there is no noticeable effect on network traffic from such an introduction. IBM SPSS software version 24 was used in this study.
Optimization Algorithms to Solve Feature Selection Problem: A Review

Feature Selection (FS) method is one of the most important data pre-processing steps in data mining domain, it is used to find the essential features subset in order to make a new subset of informative features. The model that used the informative subset such that a classification model built only with this subset would get better predictive accuracy than the model that used a complete set of features. In this paper, we provide a summary of almost all of the methods present in the literature of 2018. The goal is to provide a general presentation to variant optimization algorithms that can be applied to a wide array of machine learning problems. We converge on Filter, Wrapper and Embedded methods. Finally, we analyzed the results of several feature selection techniques on standard datasets to illustrate the applicability of feature selection techniques.
Complete chloroplast genome sequences of Lagotis yunnanensis (Scrophulariaceae): an Endangered species endemic to the Hengduan Mountains region

Abstract Lagotis yunnanensis is a perennial plant in the Scrophulariaceae family with a high value of medicinal in Tibetan medicine. In this study, we assembled and characterized the complete chloroplast genome of L. yunnanensis as a resource for future studies on this species. The chloroplast genome was 152,789 bp in size, with a large single-copy (LSC) region of 83,642 bp, a small single-copy (SSC) region of 17,795 bp, separated by two inverted repeat (IR) regions of 25,676 bp each. A total of 131 genes were predicted. Phylogenetic analysis showed a close relationship between L. yunnanensis and Veronicastrum sibiricum with 100% bootstrap value.
Can the Integration of a Tax Compliant Transfer Pricing System Into the Management Control System Be Successful? Yes, It Can!

This paper shifts the approach of transfer pricing research from a traditional transaction-based focus to a focus on the overall transfer pricing system. More precisely, we investigate the relationship between the integration of the transfer pricing system into the management control system, an enabling use of the transfer pricing system and the success of the transfer pricing system. Results from structural equation modeling using the partial least squares technique indicate that the level of transfer pricing system integration is significantly related to its success. This relationship is mediated through an enabling use of the transfer pricing system as indicated by the variables repair and internal transparency. Therefore, based on our results, we recommend strong integration of the transfer pricing system into the management control system. However, the users of highly integrated transfer pricing systems need to be able to truly understand the transfer pricing system and to be able to initiate revisions to it in the event of fundamental problems.
Gut Microbiome Pattern Reflects Healthy Aging and Predicts Extended Survival in Humans

The gut microbiome has important effects on human health, yet its importance in human aging remains unclear. Using two independent cohorts comprising 4582 individuals across the adult lifespan we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique with age. This uniqueness pattern is strongly associated with gut microbial amino acid derivatives circulating within the bloodstream, many of which have been previously identified as longevity biomarkers. At the latest stages of human life, two distinct patterns emerge wherein individuals in good health show continued microbial drift toward a unique compositional state, while the same drift is absent in individuals who perform worse on a number of validated health measures. The identified healthy aging pattern is characterized by an overall depletion of core genera found across most humans - primarily a depletion in the nearly ubiquitous genus Bacteroides. Consistently, retaining a high Bacteroides dominance into extreme age, or, equivalently, having a low gut microbiome uniqueness score, predicts decreased survival in a four-year follow-up. Our comprehensive analysis identifies the gut microbiome as a novel component of healthy aging, with important implications for the world’s growing older population.
Non-monogamous mating system and evidence of lekking behaviour in the Helmeted Manakin (Aves: Pipridae)

ABSTRACT Manakins (Pipridae) are a group of Neotropical birds well known for their spectacular lekking displays and non-monogamous mating system. Nevertheless, the two species of Antilophia have been traditionally considered monogamous and, therefore, an exception to this rule. In this paper, we studied the home range and the mating system of a colour-ringed population of the Helmeted Manakin Antilophia galeata in southeastern Brazil. We propose that it is a non-monogamous species that ranges widely during the breeding season, presenting a lekking behaviour in the form of unspectacular but aggressive chasing courtship displays.
Clustering Categorical Data: A Survey

Clustering is a complex unsupervised method used to group most similar observations of a given dataset within the same cluster. To guarantee high efficiency, the clustering process should ensure high accuracy and low complexity. Many clustering methods were developed in various fields depending on the type of application and the data type considered. Categorical clustering considers segmenting a dataset in which the data are categorical and were widely used in many real-world applications. Thus several methods were developed including hard, fuzzy and rough set-based methods. In this survey, more than 30 categorical clustering algorithms were investigated. These methods were classified into hierarchical and partitional clustering methods and classified in terms of their accuracy, precision and recall to identify the most prominent ones. Experimental results show that rough set-based clustering methods provided better efficiency than hard and fuzzy methods. Besides, methods based on the initialization of the centroids also provided good results.
Pre-Service and In-Service Science Teachers’ Technological Acceptance of 3-D, Haptic-Enabled Virtual Reality Instructional Technology

Combined three-dimensional, haptic-enabled, virtual reality (3D HE VR) systems allow students to actively engage and explore various science concepts by leveraging user-friendly and immersive interfaces. Successful implementation of these learning tools in science classrooms hinges upon teachers' perceptions of the technology’s potential as a viable pedagogical tool. Prior studies using the Technology Acceptance (TA) Model (TAM) suggest pre-service teachers have greater TA compared to in-service teachers. This study sought to explore how 3D HE VR designed to diminish Ease of Use (EOU) issues, influenced TA (through reported preferences) between pre-service and in-service science teachers. Five pre-service and five in-service teachers reported Perceived Utility (PU) and EOU upon using a 3D HE VR system (zSpace) to learn science concepts. Quantitative data were collected from preand post-test content assessments. Qualitative data were collected and transcribed from field notes and interviews. Both teacher groups evidenced learning gains and reported EOU using zSpace. However, preference for the technology compared to traditional methods varied between teacher groups. Sampled pre-service teachers held a significant preference for hands-on activities for instruction whereas in-service teachers reported greater TA, citing its potential to increase student interest in science and opportunity for personalized learning. This research suggests that when perceived EOU is mitigated, PU may more readily mediate TA among in-service teachers as they can envision the use of 3D HE VR technology use in teaching practices. Further exploration is needed to leverage in-service teachers’ classroom experience to implement novel forms of technology into their science instruction. Hite, Jones, Childers, Chesnutt, Corin & Pereyra 2 Electronic Journal of Science Education ejse.southwestern. edu
Mathematical Models and Inverse Algorithms for Childhood Infectious Diseases with Vaccination - Case Studies in Measles

Children are at a high risk of infection since they have not yet developed mature immunity. Childhood infectious diseases, such as measles, chicken pox and mumps, remain epidemic and endemic around the world. Yet, their dynamics are still not fully understood. SIR-type models have been proposed and widely applied to understand and control infectious diseases, and the SEIR model has been frequently applied to study childhood infectious diseases. In this thesis, we improve the classic SEIR model by separating the juvenile group and the adult group to better describe the dynamics of childhood infectious diseases. We perform stability analysis to study the asymptotic dynamics of the new model, and perform sensitivity analysis to uncover the relative importance of the parameters on infection. The transmission rate is a key parameter in controlling the spread of an infectious disease as it directly determines the disease incidence. However, it is essentially impossible to measure the transmission rate due to ethical reasons. We introduce an inverse method for our new model, which can extract the time-dependent transmission rate from either prevalence data or incidence data in existing open databases. Preand post-vaccination measles data sets from Liverpool and London are applied to estimate the time-varying transmission rate. The effectiveness of vaccination has been widely discussed and studied in epidemiology. Outbreaks can still occur if the percentage of susceptible individuals who take the vaccination is low or the vaccination itself is not sufficiently effective. We further extend our model by adding a vaccination term for all children to predict the date and the infection number of a possible measles outbreak peak in the Province of Alberta.
Cell signaling in high risk childhood B cell precursor acute lymphoblastic leukemia: high-throughput dissection and targeting strategies.

89 INTRODUCTION 90 MATERIALS AND METHODS 93 RESULTS 105 DISCUSSION 126 REFERENCES 132 CHAPTER 4: THE HISTONE DEACETYLASE INHIBITOR GIVINOSTAT (ITF2357) HAS A POTENT ANTI-TUMOR ACTIVITY AGAINST CRLF2 REARRANGED BCP-ALL 141 ABSTRACT 143 INTRODUCTION 144 MATERIALS AND METHODS 147143 INTRODUCTION 144 MATERIALS AND METHODS 147 RESULTS 152 DISCUSSION 167
Positive Deviance Case Selection as a Method for Organizational Communication: A Rationale, How-to, and Illustration

Case study remains a foundation of past and present organizational communication scholarship. In this article, we show the value of supplementing traditional case-selection methods with positive deviance case selection (PDCS). PDCS is about identifying and investigating individuals, teams, and organizations whose communication is intentional, nonnormative, and honorable. PDCS supports the creation of analytic generalizations and transferable concepts that can be recommended or imitated to bolster communicative excellence and thriving in the workplace. The article explores the benefits of and techniques for PDCS and illustrates the unique strengths of PDCS with two recent examples in the organizational communication literature. Implications for method, theory building, and practice are discussed throughout the article.