Miracle-dz/UniFAR-FTUs huggingface dataset:
Pigmented paravenous chorioretinal atrophy is associated with a mutation within the crumbs homolog 1 (CRB1) gene.

PURPOSE Pigmented paravenous chorioretinal atrophy (PPCRA) is an unusual retinal degeneration characterized by accumulation of pigmentation along retinal veins. The purpose of this study was to describe the phenotype of a family with PPCRA, determine the mode of inheritance, and identify the causal mutation. METHODS Ophthalmic examination was performed on seven family members and serially detailed in the proband over a 3-year period. Blood samples were collected and DNA extracted. All 12 coding exons and the 5' promoter region of the crumbs homologue 1 (CRB1) gene were PCR amplified and DNA sequenced. In silico homology modeling was performed on the mutated protein domain. RESULTS Subtle symmetrical chorioretinal atrophy in the inferior quadrant was the earliest clinical sign detectable within this family. Paravenous pigmentation occurred initially in the far periphery, progressing centrally, with atrophy later becoming more widespread, involving the nasal, then the temporal, and finally the upper quadrant. A novel, dominant Val162Met mutation within the fourth EGF-like domain of CRB1 cosegregates with the PPCRA phenotype. It is thought to affect domain structure, because codon 162 is involved in hydrogen bonding between the antiparallel beta-strands of the major beta-sheet, causing sufficient perturbation of the backbone that the domain-stabilizing hydrogen bond does not form or is weakened. CONCLUSIONS PPCRA was dominantly inherited in this family, but exhibited variable expressivity. Males are more likely to exhibit a severe phenotype, whereas females may remain virtually asymptomatic even in later years. The PPCRA phenotype is associated with a Val162Met mutation in CRB1 which is likely to affect the structure of the CRB1 protein.
19 A Collaborative Approach in Adaptive Management at a Large-landscape Scale

esource managers are increasingly confronted with the problem of how to make informed deciters and Holling 1990). A true adaptive process involves a rigorous scientific and repeatable approach to R sions that rely on new information or tools. This is especially true as we attempt to shift land management practices to a regional or ecosystem perspective. Federal agencies and others have variously defined ecosystem management, which has led to many debates over the concept (Haeuber 1996). Recently, increasing conflicts over resource management have resulted in a number of area ecosystem planning efforts (e.g., Great LakesSt. Lawrence River Basin, Interior Columbia Basin Ecosystem, Everglades-South Florida, Sierra Nevada Ecosystem Project, and Southern California Natural Communities [Johnson et al. 1999]). There have also been many well-publicized efforts to develop comprehensive landscape plans for managing individual species, focusing mainly on those that are federally listed under the Endangered Species Act (ESA) such as the northern spotted owl (Strix occidentalis caurina; USDA/USDI 1994a,b), California spotted owl (Strix occidentalis occidentalis; Verner et al. 1992), and grizzly bear (Ursus arctos; Burroughs and Clark 1995). For a variety of reasons, not all of these planning efforts have been successful, but all were costly. Adaptive management has been emerging as a central theme in the management of natural resources on federal lands in the United States, particularly as it applies to the concept of ecosystem management (Walresource planning (Holling 1978; Walters 1986). There is a distinction between active and passive adaptive management where in the former there is an active pursuit of information as an objective of the decision-making process (Nichols et al. 1995). Walters (1997) stated that "adaptive management should begin with a concerted effort to integrate existing interdisciplinary experience and scientific information into dynamic models that attempt to make predictions about the impacts o£ alternative policies. " He emphasized that this serves three functions: (1) problem clarification and enhanced communication among scientists, managers, and other stakeholders; (2) policy screening to eliminate options that are least likely to succeed; and (3) identification of key knowledge gaps. Adaptive management is complex and conceptual, and the methods are ambiguous and rarely or only partially applied (Lee 1993; Gunderson et al. 1995; Walters 1997; Carpenter 1998; Rogers 1998). Failures of traditional management that did not use an adaptive approach have occurred most obviously with problems in large complex ecosystems (Johnson 1999b). Many efforts to implement large-area management plans (including early attempts on the northern spotted owl, hereafter spotted owl, or owl) failed for a variety of reasons. Managers are often limited by one or more of the following: a lack of data,
Credit scoring and credit control : based on the proceedings of a conference on credit scoring and credit control, organized by the Institute of Mathematics and Its Applications and held at the University of Edinburgh in August 1989

Part 1 Development of credit scoring: credit scoring and credit control from four points of view, E.M. Lewis legal, social and economic issues in implementing scoring in the US, R.W. Johnson original French experiences in scoring, C. Guyon. Part 2 Theory and models for credit scoring: discriminant analysis and related topics, D.M. Titterington methods for credit scoring applied to slow payers, M. Boyle, et al cluster analysis in credit scoring, M. Lundy survival analysis and the credit granting decision, B. Narain measures for comparing scoring systems, A.D. Wilkie. Part 3 Implementing credit scoring systems: how credit scoring really works, G. Wilkinson the introduction of credit scoring into branch banking, D.B. Edelman updating scorecards - removing the mystique, A. Lucas turning data into information, G. Scallan. Part 4 New objectives in credit scoring systems: profit by the score, G. Bazley activity scoring - an optimistic and proactive view of credit assessment? , M.F. Graney and A.J. Wynn a comparison of discriminators under alternative definitions of credit default, J.N. Crook, et al "The score's the limit? ", M. O'Connor. Part 5 Behavioural scoring: behaviour scoring and adaptive control systems, M.A. Hopper and E.M. Lewis.
Analysis and Experimental Results of Interior DAC of SAR ADC using Cadence

Abstract: This paper focuses on analysis and experimental results of 6-bit charge-redistribution DAC and 6-bit charge-redistribution DAC using split array configuration. These DAC configurations are designed and simulated using GPDK 180nm CMOS technology. DAC is an important component within the SAR ADC. The charge redistribution DAC in a Split capacitor configuration has a total capacitance which is 87.5% smaller compared to a conventional design. Hence DAC gives the optimized architecture. Optimized design of DAC architecture ensures the accuracy of the components, which improves the performance of SAR ADC. The matching accuracy of integrated capacitors is excellent. The simulation results of both are compared. The delay required to get the output is 793.7E-15S and 793.6E-15S when all input bits are high for charge-redistribution DAC and split array DAC respectively. Dynamic range for these DACs is 35.98dB. The supply voltage is 1.8V.
PY-PITS: A Scalable Python Runtime System for the Computation of Partially Idempotent Tasks

The popularization of multi-core architectures and cloud services has allowed users access to high performance computing infrastructures. However, programming for these systems might be cumbersome due to challenges involving system failures, load balancing, and task scheduling. Aiming at solving these problems, we previously introduced SPITS, a programming model and reference architecture for executing bag-of-task applications. In this work, we discuss how this programming model allowed us to design and implement PY-PITS, a simple and effective open source runtime system that is scalable, tolerates faults and allows dynamic provisioning of resources during computation of tasks. We also discuss how PY-PITS can be used to improve utilization of multi-user computational clusters equipped with queues to submit jobs and propose a performance model to aid users to understand when the performance of PY-PITS scales with the number of Workers.
Adaptive Sparse Recovery by Parametric Weighted L$_{1}$ Minimization for ISAR Imaging of Uniformly Rotating Targets

It has been shown in the literature that, the inverse synthetic aperture radar (ISAR) echo can be seen as sparse and the ISAR imaging can be implemented by sparse recovery approaches. In this paper, we propose a new parametric weighted L1 minimization algorithm for ISAR imaging based on the parametric sparse representation of ISAR signals. Since the basis matrix used for sparse representation of ISAR signals is determined by the unknown rotation parameter of a moving target, we have to estimate both the ISAR image and basis matrix jointly. The proposed algorithm can adaptively refine the basis matrix to achieve the best sparse representation for the ISAR signals. Finally the high-resolution ISAR image is obtained by solving a weighted L1 minimization problem. Both numerical and real experiments are implemented to show the effectiveness of the proposed algorithm.
Potential Predictability of vehicles' visiting duration in different areas for large scale urban environment

The newly emerged vehicular communication network is seen as a key technology for solving the increasingly serious vehicular traffic congestion as well as improving road safety, and applications of vehicular networks are also emerging at the same time. Predicting vehicular visiting time is vital to both solving the vehicular system problem and building efficient vehicular networking. It is an open and unsolved problem that how much the vehicular staying duration of visits to different areas can be predicted. In this paper, we use real vehicular traces in Beijing and Shanghai and an area partition model to explore the limit of predictability of the vehicular visiting time in different areas in large scale cities and analyze the effect of different precision and slot time on predictability. We conclude that using a proper time slot is an efficient way of prediction and a higher predictability can be achieved if the requirement for precision is reduced. Among all the cases we study, we find the highest potential predictability in Beijing and Shanghai in the case with smallest slot time and lowest requirement for precision.
Clustering-based Approach to Multiword Expression Extraction and Ranking

We present a domain-independent clusteringbased approach for automatic extraction of multiword expressions (MWEs). The method combines statistical information from a general-purpose corpus and texts from Wikipedia articles. We incorporate association measures via dimensions of data points to cluster MWEs and then compute the ranking score for each MWE based on the closest exemplar assigned to a cluster. Evaluation results, achieved for two languages, show that a combination of association measures gives an improvement in the ranking of MWEs compared with simple counts of cooccurrence frequencies and purely statistical measures.
Glenda Kruss Innovation in South Africa Creating Knowledge Networks : Higher Education , Industry

This article focuses on a new organisational form that is emerging in the South African context—knowledge networks of higher education, industry and intermediary partners. The article focuses on seven case studies in two high-technology fields and their related industrial sectors—biotechnology, being relatively new, and new materials development, being relatively mature in South Africa. It shows the complex nature of the research partners at each node of a network, and of the structure and dynamics of the interaction that results. The study suggests that we need to open up the ideal enshrined in South African policy, of the desirability of research partnerships, to more informed analysis of the complexity of creating networks, in specific industrial sub-sectors, knowledge fields and institutional contexts. The major insight offered for developing countries like South Africa is the value of a contextualised analysis for informing cross-sectoral coordination of interventions within a national system of innovation.
From output regulation theory to flatness based tracking: a bridge for linear systems

The trajectory tracking problem for multivariable linear systems is considered. Two different techniques are examined: the output regulation theory (ORT) and the flatness based tracking (FBT). ORT and FBT are two different approaches to solve the tracking problem, and both methods have different restrictions. The tracking controller of the ORT furthermore depends on the solution of the so-called regulator equations. In this paper, a special analytic solution of the regulator equations is presented. Additionally, based on this analytic solution, a link from the ORT to the FBT approach is provided, and the connection of both tracking controllers is highlighted. It is shown how the ORT controller can be converted to the FBT controller and that both methods lead to identical control laws for a certain class of systems.
The Economics of Marijuana Consumption

In economic terms, marijuana is an important, yet little understood and controversial commodity. According to our estimates, spending on marijuana in Australia is about twice that on wine. But this commodity, which has been used by about one-third of the population, generates no tax revenue. This paper explores economic aspects by marijuana consumption, concentrating on the estimation of the amount consumed, its price sensitivity, its interaction with alcohol and the effects of any legalisation. Key findings are (i) the price elasticity of demand for marijuana is about –1/2; (ii) alcohol and marijuana seem to be substitutes, with cross-price elasticities ranging from . 1 (for beer with respect to the price of marijuana) to . 5 (spirits/marijuana); (iii) according to a survey at UWA, about 50 percent of first-year students have used marijuana; and (iv) legalisation would increase marijuana consumption by about 13 percent, with most of that accounted for by daily and weekly users, and alcohol consumption would fall.
A Survey and Evaluation of State‐of‐the‐Art Intelligent Question Routing Systems

In spite of great developments in artificial intelligence, human brain is still more powerful, concerning the comprehension and manipulation with partially known facts. One domain where this is prominent is related to a problem of question answering. When it comes to giving the answers, especially those that do not explicitly exist in the text corpus, the advantages of a human expert are abilities such as explaining, combining complex answers, and abstract reasoning. This paper represents a survey of the existing research in the domain of intelligent question routing. The survey starts from an original presentation paradigm that generalizes the essence of approaches found in the open literature. The presentation paradigm includes three basic processing stages related to the three major problems of system implementation: question analysis, question forwarding, and users’ knowledge profiling. Various research efforts use different approaches for implementation of each one of the basic processing stages. Each particular approach is described here using the same template. All these approaches are enlisted, discussed, and presented within a table, for easier comparison. The outcome of this analysis is a proposal for a new approach that tackles identified problems.
Generating natural-language narratives from activity recognition with spurious classification pruning

Continuous smartphone sensing offers the opportunity to perform recognition and logging of the user's activities, but a key challenge remains in conveying these activities in a manner that is both understandable by humans and useful for downstream consuming applications. We explore the viability of representing activity recognition through automatically-generated natural language English text, allowing the user to understand the activity recognition output and other software to operate on it, such as with text-to-speech software or an information retrieval search engine. To create narratives with smoother transitions and to help intelligently trigger the GPS in a power-conserving manner, we implemented a scheme called Spurious Sequential Classification Pruning that we show reduces real-time misclassifications by 76% and GPS requests by 38%. We describe a complete system for activity recognition and narrative generation and discuss its end-to-end operation.
HacA-Dependent Transcriptional Switch Releases hacA mRNA from a Translational Block upon Endoplasmic Reticulum Stress

ABSTRACT Activation of the unfolded protein response (UPR) in eukaryotes involves the splicing of an unconventional intron from the mRNA encoding the transcriptional activator of the pathway. In Saccharomyces cerevisiae a 252-nucleotide (nt) unconventional intron is spliced out of the transcript of HAC1, changing the 3′ end of the HAC1 open reading frame and relieving the transcript from a translational block in a single step. The translational block is caused by the base pairing of part of the unconventional intron with the 5′-untranslated region (5′UTR). In Aspergillus niger and other aspergilli, the unconventional intron in hacA mRNA is only 20 nt long. Since this intron is part of a stable stem-loop structure, base pairing with the 5′UTR, in contrast to the case with yeast HAC1, is not possible. However, analysis of the hacA mRNA revealed a GC-rich inverted repeat (18 base pairings). Upon the activation of the UPR, the 5′UTR of hacA mRNA is truncated by 230 nt, removing the left part of this inverted repeat. This implies a similar release of a translational block as in the case of S. cerevisiae HAC1 but in two steps. The mechanism behind the 5′ truncation, which does not take place in either yeast HAC1 or mammalian xbp1 mRNA, has been hitherto unknown. Here we show that during secretion stress in A. niger, hacA transcription starts from a new start site closer to the ATG, relieving the transcript from translational attenuation. This transcriptional switch is mediated by HacA itself and the unfolded protein response element 2 (UPRE2) in the hacA promoter.
Edinburgh Research Explorer Understanding counterfactuality

Cognitive and linguistic theories of counterfactual language comprehension assume that counterfactuals convey a dual meaning. Subjunctive-counterfactual conditionals (e.g., ‘ If Tom had studied hard, he would have passed the test ’ ) express a supposition while implying the factual state of affairs ( Tom has not studied hard and failed ). The question of how counterfactual dual meaning plays out during language processing is currently gaining interest in psycholinguistics. Whereas numerous studies using offline measures of language processing consistently support counterfactual dual meaning, evidence coming from online studies is less conclusive. Here, we review the available studies that examine online counterfactual language comprehension through behavioural measurement (self-paced reading times, eye-tracking) and neuroimaging (electroencephalography, functional magnetic resonance imaging). While we argue that these studies do not offer direct evidence for the online computation of counterfactual dual meaning, they provide valuable information about the way counterfactual meaning unfolds in time and influences successive information processing. Further advances in research on counterfactual comprehension require more specific predictions about how counterfactual dual meaning impacts incremental sentence processing.
Genetic risk factors in multiple sclerosis and 2000

Development of multiple sclerosis (MS) is believed to involve genetic as well as environmental factors. A complicating aspect to the study of aetiological factors in MS concerns the possible existence of genetically different subtypes of the disease. In addition, a relatively large number of susceptibility genes could be involved. Most likely, the contribution of the single genes to the susceptibility to MS is modest. However, interactions between different genes could result in a dramatic increase in disease susceptibility (synergistic gene effects). In this short review we focus upon genetic heterogeneity and gene interactions in MS. We also outline approaches to the genetic analysis of complex disease traits such as MS. Journal of NeuroVirology (2000) 6, S23 ± S27.
Distinct Signal Transduction Pathways Downstream of the (P)RR Revealed by Microarray and ChIP-chip Analyses

The (pro)renin receptor ((P)RR) signaling is involved in different pathophysiologies ranging from cardiorenal end-organ damage via diabetic retinopathy to tumorigenesis. We have previously shown that the transcription factor promyelocytic leukemia zinc finger (PLZF) is an adaptor protein of the (P)RR. Furthermore, recent publications suggest that major functions of the (P)RR are mediated ligand-independently by its transmembrane and intracellular part, which acts as an accessory protein of V-ATPases. The transcriptome and recruitmentome downstream of the V-ATPase function and PLZF in the context of the (P)RR are currently unknown. Therefore, we performed a set of microarray and chromatin-immunoprecipitation (ChIP)-chip experiments using siRNA against the (P)RR, stable overexpression of PLZF, the PLZF translocation inhibitor genistein and the specific V-ATPase inhibitor bafilomycin to dissect transcriptional pathways downstream of the (P)RR. We were able to identify distinct and overlapping genetic signatures as well as novel real-time PCR-validated target genes of the different molecular functions of the (P)RR. Moreover, bioinformatic analyses of our data confirm the role of (P)RŔs signal transduction pathways in cardiovascular disease and tumorigenesis.
Gaussian Process-Based Feature Selection for Wavelet Parameters: Predicting Acute Hypotensive Episodes from Physiological Signals

Physiological signals such as blood pressure might contain key information to predict a medical condition, but are challenging to mine. Wavelets possess the ability to unveil location-specific features within signals but there exists no principled method to choose the optimal scales and time shifts. We present a scalable, robust system to find the best wavelet parameters using Gaussian processes (GPs). We demonstrate our system by assessing wavelets as predictors for the occurrence of acute hypotensive episodes (AHEs) using over 1 billion blood pressure beats. We obtain an AUROC of 0.79 with wavelet features only, and the false positive rate when the true positive rate is fixed at 0.90 is reduced by 14% when the wavelet feature is used in conjunction with other statistical features. Furthermore, the use of GPs reduces the selection effort by a factor of 3 compared with a naive grid search.
Monte Carlo studies of critical behaviour of systems with long-range correlated disoder

Monte Carlo simulations of the short-time dynamic behaviour are reported for three-dimensional Ising model and XY-model with long-range spatially correlated disorder at criticality, in the case corresponding to linear defects. The static and dynamic critical exponents are computed with the use of the corrections to scaling. The obtained values of the exponents are in a good agreement with results of the field-theoretic description of the critical behaviour of this model in the two-loop approximation and with our results of Monte Carlo simulations of three-dimensional Ising model in equilibrium state.