Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Circulating Long Noncoding 7S RNA with Deep Vein Thrombosis

Abstract Mitochondrial dysfunction is a recognized factor in the pathogenesis of deep vein thrombosis (DVT). The role of 7S RNA, a long noncoding RNA that plays an important role in mitochondrial function, in DVT remains unclear. In this study, we aimed to investigate the potential use of 7S RNA as a biomarker in DVT. Plasma samples were obtained from 237 patients (aged 16–95 years) with suspected DVT recruited in a prospective multicenter management study (SCORE) where 53 patients were objectively confirmed with a diagnosis of DVT and the rest were diagnosed as non-DVT. 7S RNA was measured using quantitative real-time polymerase chain reaction in plasma samples. The plasma expression of 7S RNA was significantly lower in DVT compared with non-DVT (0.50 vs. 0.95, p = 0.043). With the linear regression analysis, we showed that the association between the plasma expression of 7S RNA and DVT (β = −0.72, p = 0.007) was independent of potential confounders. Receiver-operating characteristic curve analysis showed the area under the curve values of 0.60 for 7S RNA. The findings of the present study showed a notable association between 7S RNA and DVT. However, further investigations are needed to fully elucidate the exact role of 7S RNA in the pathophysiology of DVT and its diagnostic value.
Better long-term learning ability is predicted by higher surface folding of the human premotor cortex

The capacity to learn enabled the human species to adapt to various challenging environmental conditions and pass important achievements on to the next generation. A growing body of research suggests links between neocortical folding and numerous aspects of human behaviour, but their impact on enhanced human learning capacity remains unexplored. Here we leverage multiple training cohorts to demonstrate that higher levels of premotor cortical folding reliably predict individual long-term learning gains in a challenging new motor task, above and beyond initial performance differences. Individual folding-related predisposition to motor learning was found to be independent of cortical thickness and several intracortical microstructural parameters, but dependent on larger cortical surface area. We further show that learning-relevant features of cortical folding occurred in close spatial proximity to practice-induced structural plasticity and were primarily localized in hominoid-specific frontal tertiary sulci. Our results suggest a new link between neocortical surface folding and human behavioural adaptability.
Regulatory variation controlling architectural pleiotropy in maize

An early event in plant organogenesis is establishment of a boundary between the meristem and differentiating lateral organ. In maize (Zea mays), evidence suggests a common gene network functions at boundaries of distinct organs and contributes to pleiotropy between leaf angle and tassel branch number, two agronomic traits. To identify developmental regulators and their sub-networks at the nexus of these two traits, we used regulatory network topologies derived from specific developmental contexts to guide multivariate genome-wide association analyses. In addition to defining network plasticity around core pleiotropic loci, we identified new transcription factors that contribute to small trait effects in canopy architecture, likely through redundancy and dose-dependency with interconnected gene family members. We also showed that structural variation in the promoter of one factor contributes to cis-regulatory control of pleiotropy between tassel branching and leaf angle across maize diversity. Results demonstrate the power of informing statistical genetics with context-specific gene networks to pinpoint small effect genetic loci and their cis-regulatory components, which can be used to fine-tune plant architecture for crop improvement.
New insights into the systematics of the Afrotropical Amblyomma marmoreum complex (Acari, Ixodidae) and a novel Rickettsia africae strain using morphological and metagenomic approaches

The Amblyomma marmoreum complex includes some Afrotropical species, such as Amblyomma sparsum, a three-host tick that parasitizes reptiles, birds, and mammals, and is a recognized vector of Ehrlichia ruminatum. However, the lack of morphological, genetic and ecological data on A. sparsum has caused considerable confusion in its identification among A. marmoreum complex members. In this study, we used microscopy and metagenomic approaches to analyze A. sparsum ticks collected from a puff adder snake (Bitis arietans) in southwest Senegal (an endemic rickettsioses area) in order to supplement previous morphological descriptions, provide novel genomic data for the A. marmoreum complex, and search for some associated spotted fever agent. Based on stereoscope and scanning electron microscopy (SEM) morphological evaluations, we provided high-quality images and new insights about punctation and enameling in the male of A. sparsum to facilitate identification for future studies. Additionally, the metagenomic approach allowed us assembly the complete mitochondrial genome of A. sparsum, as well as the nearly entire chromosome and complete plasmid sequences of a novel Rickettsia africae strain. Phylogenomic analyses showed a close relationship between A. sparsum and A. nuttalli for the first time and confirmed the position of A. sparsum within the A. marmoreum complex. Our results provide new insights into the systematic of A. sparsum and A. marmoreum complex, as well as the genetic diversity of R. africae in Afrotropical region. Future studies should consider the possibility that A. sparsum may be a competent vector for R. africae.
Mining Interesting Outlier Subgraphs in Attributed Graphs

A common task in local pattern mining on attributed graphs revolves around detecting subgraphs with predefined similarity measures on the attributes of the nodes. In this work we focus on mining cohesive subgraphs with attribute values that stand out when compared to the rest of the graph. We tackle a more specific problem: given a vertex-attributed graph, we aim to detect a cohesive set of connected vertices , such that for a maximal set of the attributes, at least one of the vertices in the subgraph shows an abnormal value. Such patterns are very relevant in the biological field, where anomaly detection is used to identify parts of a corrupt gene network. We develop a pattern syntax and an interestingness score to mine such subgraphs, and implement it using a branch-and-bound algorithm. Furthermore, we design an experimental setup to qualitatively assess the results, discuss our findings, limitations and future improvements.
en-Security Coming of Age

eup, in the same way a mathematician enjoys a brilliant demonstration: an intellectual pleasure more akin to appreciation of art than anything else no matter how narrow the applicability, no matter how obscure the trick (in fact, the more, the better), no matter the prerequirements or the real-world impact. Capture-the-flag (CtF) exercises and competitions are somehow exemplar in this: There is absolutely no comparison between the ones we used to play in 2003–2004 and those my students play today. Besides becoming a mainstream tool for education and a competitive form of entertainment (as opposed to a curiosity for a small group of nerdy students worldwide), A S F OR M A N Y colleagues of a similar age to mine, my path into computer security research was all but academic. In the late 1990s, there were very few academic research centers on security, and even less dedicated courses or degrees, especially outside the U.S. We—many of us, at least—came from a hacking background, experimenting with things such as vulnerabilities and exploitation on our own. We would find our brethren in obscure alleyways of the Internet, and then browse through e-zines and (if lucky) attend hacker meetups with a score of attendees. I remember fondly the feeling of attending DefCon for the first time, 20 years ago, and seeing a few thousand kindred souls together. It should not come as a surprise that for the “hackademics,” as a colleague once half-jokingly defined us, offensive security research has a definite thrill. In a discipline that lacks a fundamental, unified theory of how to build “secure things,”1 and where in fact most properties are defined in negative terms (how “not to build”), this makes rational sense. After all, we define robustness of encryption based on resilience to attacks: We routinely first propose attacks, and then offer mitigations. Even in the applied, corporate world, we use penetration testing and red teaming exercises to assess security level. The strictest security evaluation standards, such as the Common Criteria, define security on the basis of resilience to attack attempts. As the saying goes, in security defense is the child of offense. Not the other way around. But this is just partially true. The fact is, those with a similar background to mine would not want it any other way. We take pride and delight in clever hacks and bypasses. We enSecurity Coming of Age
African & American: West Africans in Post-Civil Rights America

Contents Preface: Griots from Different Shores ix Introduction: The Newest African Americans? 1 1. West Africa and West Africans: Imagined Communities in Africa and the Diaspora 35 2. Occupational Detour: New Paths to Making a Living 75 3. Capturing a Niche: The West African Enclave Economy 115 4. Transnational Ties/Translocal Connections: Traversing Nations, Cities, and Cultures 141 5. More Than Black: Resistance and Rapprochement 179 6. Young, Gifted, and West African: Transnational Migrants Growing Up in America 211 Conclusion: Further into the Twenty-First Century 255 Notes 259 Bibliography 295 Index 323 About the Authors 335
Fast Regional Convolutional Neural Network Integrated with Multiclass Linear Support Vector Machine to Classify Brain Tumour from MRI Images.

: Segmentation of brain tumour is the pivotal task in medical image processing. Manual segmentation of brain tumour is an ambitious task and also time-consuming process. Numerous Computer Aided Diagnosis (CAD) methods have been proposed in medical imaging applications. Recently, deep learning approaches have been used to magnify the performance of existing machine learning techniques. In this paper, localisation of brain tumour in MRI images based on deep learning mechanism is proposed. Convolutional Neural Network with Multiclass Linear SVM is used for brain tumour classification and Fast Regional Convolutional Neural Network is used to localise the brain tumour. First phase, consists of 5 layer CNN architecture is used for segmentation and feature extraction. MCLSVM classifier is used to classify brain disease into normal, edema, enhancing tumour, non-enhancing tumour and necrosis. Second phase, FRCNN model is proposed for localisation of brain tumour. The performance of localisation methods is quantified with the help of different evaluation metrics. Our proposed model approach 89.21% accuracy and 0.89 Dice Index score.
Energy Economics

Energy economics is a vast topic. Many applied and agricultural economists today work on fuel economy, fossil fuel energy issues, energy sector economic analysis, electricity sector economic and policy issues, techno-economic analyses of energy alternatives as well as agricultural energy issues such as biofuels, and energy use in agriculture. Energy economists also employ a wide variety of modeling and analytical tools. We cover six broad topics: externalities, policy analysis, energy demand and supply analyses, electricity pricing, biofuels, and techno-economic analysis. Applied and agricultural economists have made and will continue to make major contributions to the literature and policy analysis in these areas.
Robust audiovisual speech recognition using noise-adaptive linear discriminant analysis

Automatic speech recognition (ASR) has become a widespread and convenient mode of human-machine interaction, but it is still not sufficiently reliable when used under highly noisy or reverberant conditions. One option for achieving far greater robustness is to include another modality that is unaffected by acoustic noise, such as video information. Currently the most successful approaches for such audiovisual ASR systems, coupled hidden Markov models (HMMs) and turbo decoding, both allow for slight asynchrony between audio and video features, and significantly improve recognition rates in this way. However, both typically still neglect residual errors in the estimation of audio features, so-called observation uncertainties. This paper compares two strategies for adding these observation uncertainties into the decoder, and shows that significant recognition rate improvements are achievable for both coupled HMMs and turbo decoding.
Business School Discipline of Finance Discussion Paper 2013-002 Waiting Costs and Limit Order Book Liquidity : Evidence from the Ex-Dividend Deadline in Australia

Recently developed theoretical models suggest a link between order aggressiveness, spreads and waiting time. We directly test these models using an experimental setting where waiting time is likely to be important for traders, namely the ex-dividend day. Consistent with theoretical predictions, we show that order placement is more aggressive before stocks begin trading exdividend and that spreads decline. Stocks with higher expected costs of delaying execution experience larger declines in order aggressiveness from the cum-day to the ex-day. Waiting costs also impact effective bid-ask spreads, which fall on the cum-day before rising on the ex-day.
Clusters of Dark Patterns Across Popular Websites in New Zealand

“Dark patterns” are interface design techniques that aim to trick or mislead Internet users. Most dark-patterns research has been undertaken in the United States and Europe and by user experience or human computer interaction researchers. In this study, we adopt a media and communication studies and science and technology studies approach to investigate where dark patterns “cluster” in online environments. A walkthrough of the top 100 New Zealand websites leads us to the following findings: (1) dark patterns cluster around financial transactions; (2) the most common types of dark patterns constitute a form of interface interference; and (3) dark patterns are often deployed as mechanisms to drive revenue, facilitate customer surveillance, and reduce business operations costs, and appear to be largely imported from overseas markets.
MOSQUITO PESTICIDE SPRAYING

The Salt Lake City Mosquito Abatement District (SLCMAD)’s Environmental Assessment (EA) states, “[SLCMAD] was created in 1923 to protect Salt Lake City residents from the large numbers of these pestiferous mosquitoes. ” The EA seems to be using a mandate that is nearly 100 years old as part of their justification for their spraying strategy. Obviously much has changed in the last 100 years. This mandate, and the mere continued presence of mosquitoes in their natural habitat in the Northwest Quadrant is not enough to justify pesticide spraying.
Introducing telemedicine as a strategic intent

In many settings, the introduction of new technologies has failed. In the healthcare industry, the implementation of telemedicine-which uses technology in ways that revolutionize the delivery process of healthcare-has failed more often than not. Reasons abound, but one of the most critical features when introducing a technology or process that will profoundly alter an organization is that the technology or process be implemented as a strategic intent; deliberate, championed and strongly led from the top, not from within a single department or functional area. The paper discusses the introduction of telemedicine as strategic intent.
Virtual Machines: Versatile Platforms for Systems and Processes

Virtual Machine technology applies the concept of virtualization to an entire machine, circumventing real machine compatibility constraints and hardware resource constraints to enable a higher degree of software portability and flexibility. Virtual machines are rapidly becoming an essential element in computer system design. They provide system security, flexibility, cross-platform compatibility, reliability, and resource efficiency. Designed to solve problems in combining and using major computer system components, virtual machine technologies play a key role in many disciplines, including operating systems, programming languages, and computer architecture. For example, at the process level, virtualizing technologies support dynamic program translation and platform-independent network computing. At the system level, they support multiple operating system environments on the same hardware platform and in servers. Historically, individual virtual machine techniques have been developed within the specific disciplines that employ them (in some cases they aren’t even referred to as “virtual machines? ), making it difficult to see their common underlying relationships in a cohesive way. In this text, Smith and Nair take a new approach by examining virtual machines as a unified discipline. Pulling together cross-cutting technologies allows virtual machine implementations to be studied and engineered in a well-structured manner. Topics include instruction set emulation, dynamic program translation and optimization, high level virtual machines (including Java and CLI), and system virtual machines for both single-user systems and servers. * Examines virtual machine technologies across the disciplines that use them—operating systems, programming languages and computer architecture—defining a new and unified discipline. * Reviewed by principle researchers at Microsoft, HP, and by other industry research groups. * Written by two authors who combine several decades of expertise in computer system research and development, both in academia and industry.
Effectiveness of Virtual Reality–Based Pain Control With Multiple Treatments

ObjectiveThe current study explored whether immersive virtual reality continues to reduce pain (via distraction) with repeated use. SettingThe study was conducted in a burn care unit at a regional trauma center. PatientsSeven patients aged 9–32 years (mean age of 21.9 years; average of 23.7% total body surface area burned [range, 3–60%]) performed range-of-motion exercises of their injured extremity under an occupational therapist's direction on at least 3 separate days each. InterventionFor each physical therapy session, each patient spent equal amounts of time in virtual reality and in the control condition (no distraction). The mean duration of physical therapy in virtual reality was 3.5, 4.9, and 6.4 minutes for the first, second, and third session, respectively. Condition order was randomized and counterbalanced. Outcome MeasuresFor each of the three physical therapy sessions, five visual analog pain scores for each treatment condition served as the dependent variables. ResultsPain ratings were statistically lower when patients were in virtual reality, and the magnitude of pain reduction did not diminish with repeated use of virtual reality. The results of this study may be examined in more detail at www.vrpain. com. ConclusionsAlthough the small sample size limits generalizability, results provide converging preliminary evidence that virtual reality can function as a strong nonpharmacological pain reduction technique for burn patients during physical therapy. Results suggest that virtual reality does not diminish in analgesic effectiveness with three (and possibly more) uses. Virtual reality may also have analgesic potential for other painful procedures or pain populations. Practical implications are discussed.
Assessing implementation of veterinary extension on control of cattle parasites, in Moretele district, Northwest Province

There is currently no independent monitoring and evaluation structure for state agricultural or veterinary services to support the “Batho Pele” principle of effective and efficient service delivery to the people of South Africa. Participatory rural appraisal was used in Moretele District, North West Province, to design, implement and assess veterinary extension on the tick and worm parasites of cattle. iv University of Pretoria etd – Sekokotla, M J (2005)
Applied Economics Workshop Business 33610 Autumn Quarter 2010 Managing Self-confidence: Theory and Experimental Evidence *

A large literature in social psychology suggests that agents process information about their own ability in a biased manner. While this finding has generated excited research in behavioral economics, some of the fundamental evidence has also been criticized as being potentially consistent with Bayesian updating. We reevaluate the evidence from social psychology by conducting a large-scale experiment with 656 undergraduate students. Subjects perform an IQ-test and receive feedback on their performance while we track the evolution of their beliefs. Our experimental design allows us to precisely measure the relevant belief distribution of subjects simply and repeatedly. Our experiment confirms that subjects are asymmetric and put greater weight on positive performance signals compared to negative signals. However, we also find that subjects are conservative and update too little in response to both positive and negative signals. Subjects appear much more Bayesian when receiving feedback about someone else's performance. We show that these seemingly related phenomena naturally arise in a simple model of biased information processing based on the anticipatory utility model of Brunnermeier and Parker (2005) (henceforth BP). Our model is parsimonious and adds just a single parameter to the standard model. We also find that women are more conservative than men, but not more asymmetric. High-confidence women have a higher value for information than men. List, Al Roth and Lise Vesterlund for helpful discussions. We would like to thank seminar participants at the Institute for Advanced Study (Princeton) and 2006 North American Meetings of the Economic Science Association for their feedback. Niederle and Rosenblat are grateful for the hospitality of the Institute for Advanced Study where part of this paper was written. We thank the
Threshold Phenomena and Influence, with Some Perspectives from Mathematics, Computer Science, and Economics

Threshold phenomena refer to settings in which the probability for an event to occur changes rapidly as some underlying parameter varies. Threshold phenomena play an important role in probability theory and statistics, physics, and computer science, and are related to issues studied in economics and political science. Quite a few questions that come up naturally in those fields translate to proving that some event indeed exhibits a threshold phenomenon, and then finding the location of the transition and how rapid the change is. The notions of sharp thresholds and phase transitions originated in physics, and many of the mathematical ideas for their study came from mathematical physics. In this chapter, however, we will mainly discuss connections to other fields. A simple yet illuminating example that demonstrates the sharp threshold phenomenon is Condorcet’s Jury Theorem (CJT), which can be described as follows. Say one is running an election process, where the results are determined by simple majority, between two candidates, Alice and Bob. If every voter votes