Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results

Genetic testing of cancer susceptibility genes is now widely applied in clinical practice to predict risk of developing cancer. In general, sequence‐based testing of germline DNA is used to determine whether an individual carries a change that is clearly likely to disrupt normal gene function. Genetic testing may detect changes that are clearly pathogenic, clearly neutral, or variants of unclear clinical significance. Such variants present a considerable challenge to the diagnostic laboratory and the receiving clinician in terms of interpretation and clear presentation of the implications of the result to the patient. There does not appear to be a consistent approach to interpreting and reporting the clinical significance of variants either among genes or among laboratories. The potential for confusion among clinicians and patients is considerable and misinterpretation may lead to inappropriate clinical consequences. In this article we review the current state of sequence‐based genetic testing, describe other standardized reporting systems used in oncology, and propose a standardized classification system for application to sequence‐based results for cancer predisposition genes. We suggest a system of five classes of variants based on the degree of likelihood of pathogenicity. Each class is associated with specific recommendations for clinical management of at‐risk relatives that will depend on the syndrome. We propose that panels of experts on each cancer predisposition syndrome facilitate the classification scheme and designate appropriate surveillance and cancer management guidelines. The international adoption of a standardized reporting system should improve the clinical utility of sequence‐based genetic tests to predict cancer risk. Hum Mutat 29(11), 1282–1291, 2008. © 2008 Wiley‐Liss, Inc.
Multi-Paradigm Approach for Teaching Programming

Selecting an appropriate programming paradigm in which to teach the first programming and problem solving course in a Computer Science undergraduate program has been discussed extensively. Procedural programming, functional programming, and object oriented programming are the most widelyused programming paradigms both in institutes of higher learning and in the high-tech industry. In recent years, a number of authors have suggested object oriented programming as the first programming paradigm for undergraduate curriculum. We argue the critical role of using more than one programming paradigm in the typical undergraduate program in Computer Science and Information Technology. We address the issue of integrating multi-paradigm programming in undergraduate curricula. We identify core computer science courses where appropriate programming paradigms could be embedded. We then propose an outline of multi-paradigm programming topics and their mapping to appropriate core courses in an undergraduate computer science curriculum.
Patterns of Epistasis between Beneficial Mutations in an Antibiotic Resistance Gene

Understanding epistasis is central to biology. For instance, epistatic interactions determine the topography of the fitness landscape and affect the dynamics and determinism of adaptation. However, few empirical data are available, and comparing results is complicated by confounding variation in the system and the type of mutations used. Here, we take a systematic approach by quantifying epistasis in two sets of four beneficial mutations in the antibiotic resistance enzyme TEM-1 β-lactamase. Mutations in these sets have either large or small effects on cefotaxime resistance when present as single mutations. By quantifying the epistasis and ruggedness in both landscapes, we find two general patterns. First, resistance is maximal for combinations of two mutations in both fitness landscapes and declines when more mutations are added due to abundant sign epistasis and a pattern of diminishing returns with genotype resistance. Second, large-effect mutations interact more strongly than small-effect mutations, suggesting that the effect size of mutations may be an organizing principle in understanding patterns of epistasis. By fitting the data to simple phenotype resistance models, we show that this pattern may be explained by the nonlinear dependence of resistance on enzyme stability and an unknown phenotype when mutations have antagonistically pleiotropic effects. The comparison to a previously published set of mutations in the same gene with a joint benefit further shows that the enzyme's fitness landscape is locally rugged but does contain adaptive pathways that lead to high resistance.
Methods of Handling the Tolerance and Test-Point Selection Problem for Analog-Circuit Fault Diagnosis

Soft-fault diagnosis and tolerance are two challenging problems in analog-circuit fault diagnosis. Although many analog faults can be diagnosed theoretically, they cannot be accurately diagnosed due to the influence of component tolerance. This paper proposes approaches to the tolerance-handling method in soft-fault diagnosis. First, the slope fault model and its theoretical proof are presented. In linear analog circuits, the voltage equation between two nodes is linear and can be expressed by a point-slope-form equation in which the point is determined by the nominal voltage values on the two selected nodes, and the slope, which is invariant, can be used as the fault model, which is applicable to both hard (open or short) and soft (parametric) faults. Then, the parameter tolerance is taken into consideration. The point-slope-form equation is governed by a point and a slope value. The tolerance influences the fault diagnosis by shifting both the point and the nominal slope value. These influences can be mitigated in two ways: 1) The point is measured when an actual circuit is under steady state and free of fault, and 2) the maximum slope deviation is readily obtained by using optimization theory if the equation governing the voltage between two nodes is available; otherwise, a threshold proportion coefficient is used to calculate the maximum deviation. Based on these methods, a novel test-point selection approach for the fault dictionary technique is proposed. The effectiveness of the proposed approaches is verified by both simulated and experimental results.
CL-SNA: social network analysis with Lisp

Social relations have been a research interest in many areas of social sciences. In sociology, for example, individuals up in the power hierarchy in a group can be determined quantitatively by looking at the number of direct relations to others in the group[1]. Similarly, in management and economics, alliance relationships between firms are examined carefully to explain phenomenon such as innovation and market leadership[6]. Similar problems were encountered in Anthropology, Zoology, and many other fields. In most studies regarding social relations, one must look into the content of social relations in addition to their structure, an endeavor which demands ethnographic methods. Nevertheless, analysis of the structure alone has been a powerful tool which not only enables quantitative assessment of social structures, but also prompts elaboration of interpretive process.
“Mere Measurement Plus”: How Solicitation of Open-Ended Positive Feedback Influences Customer Purchase Behavior

In two studies (a longitudinal field experiment with an established business-to-consumer national chain, and a field experiment with a business-to-business software manufacturer), the authors demonstrate that starting a survey with an open-ended positive solicitation increases customer purchase behavior. Study 1, a longitudinal field experiment, shows that one year after the completion of a survey that began by asking customers what went well during their purchase experience, those customers spent 8.25% more than customers who had completed a survey that did not include the positive solicitation. Study 2 utlizes multiple treatment groups to assess the stepwise gains of solicitation, measurement, and solicitation frame. The results demonstrate (1) a mere solicitation effect, (2) a traditional mere measurement effect, and (3) an additional “mere measurement plus” effect of an open-ended positive solicitation; all effects increased customer spending. Specifically, starting a survey with an open-ended positive solicitation resulted in a 32.88% increase in customer spending relative to a survey with no open-ended positive solicitation. The findings suggest that firms can proactively influence the feedback process. Soliciting open-ended positive feedback can create positively biased memories of an experience; the subsequent expression of those memories in an open-ended feedback format further reinforces them, making them more salient and accessible in guiding future purchase behavior.
Genome-Wide Analysis of Long Noncoding RNA (lncRNA) Expression in Hepatoblastoma Tissues

Long noncoding RNAs (lncRNAs) have crucial roles in cancer biology. We performed a genome-wide analysis of lncRNA expression in hepatoblastoma tissues to identify novel targets for further study of hepatoblastoma. Hepatoblastoma and normal liver tissue samples were obtained from hepatoblastoma patients. The genome-wide analysis of lncRNA expression in these tissues was performed using a 4×180 K lncRNA microarray and Sureprint G3 Human lncRNA Chips. Quantitative RT-PCR (qRT-PCR) was performed to confirm these results. The differential expressions of lncRNAs and mRNAs were identified through fold-change filtering. Gene Ontology (GO) and pathway analyses were performed using the standard enrichment computation method. Associations between lncRNAs and adjacent protein-coding genes were determined through complex transcriptional loci analysis. We found that 2736 lncRNAs were differentially expressed in hepatoblastoma tissues. Among these, 1757 lncRNAs were upregulated more than two-fold relative to normal tissues and 979 lncRNAs were downregulated. Moreover, in hepatoblastoma there were 420 matched lncRNA-mRNA pairs for 120 differentially expressed lncRNAs, and 167 differentially expressed mRNAs. The co-expression network analysis predicted 252 network nodes and 420 connections between 120 lncRNAs and 132 coding genes. Within this co-expression network, 369 pairs were positive, and 51 pairs were negative. Lastly, qRT-PCR data verified six upregulated and downregulated lncRNAs in hepatoblastoma, plus endothelial cell-specific molecule 1 (ESM1) mRNA. Our results demonstrated that expression of these aberrant lncRNAs could respond to hepatoblastoma development. Further study of these lncRNAs could provide useful insight into hepatoblastoma biology.
Pretrial Bargaining with Self-Serving Bias and Asymmetric Information

We introduce self-serving bias into the Bebchuk (1984) model in which trials result from asymmetric information and characterize the equilibrium. An increase in the self-serving bias of a defendant who receives an offer can, under some circumstances, reduce the incidence of trial. More typically, however, we find that an increase in the self-serving bias of either player increases the incidence of trial. An increase in the self-serving bias of a player who receives the offer has ambiguous effects on that player's welfare. Self-serving bias serves as a commitment mechanism not to accept offers which are too unfavorable, but players with such a bias typically end up in trial more often. For the player making the offer, we find that an increase in self-serving bias unambiguously lowers welfare.
PREPARING STUDENTS FOR CHANGING WORK DEMANDS THROUGH EMPOWERING INTERNSHIPS - A LONGITUDINAL STUDY

Employers expect universities to play a key role in generating work-ready graduates who are able to act in complex and dynamic working environments, thus, are employable. The employers’ expectation places high pressure onto universities to prepare students for unknown future demands by equipping them with employability enhancing competences. One accepted and often applied method to support the development of these competences are internships. However, empirical evidence on the linkage of individual with contextual factors of the working environment regarding the students’ competence development remains fragmented. To address this issue, a quantitative longitudinal study in a Solomonfour-group-design with 143 German undergraduate students was conducted. The findings reveal that internships are highly promotive for the development of employability enhancing competence, particularly for reflection and learning. The individual’s perception of being empowered and an enabling working environment moderate the latter effect. Moreover, self-efficacy is significantly improved. Based on this, it is recommended to make internships obligatory and improve learning, for example, through progress reports and learning journals and by providing enactive mastery, vicarious experience, social persuasion and physiological arousal. Moreover, empowering learning conditions should be provided, including facilitative leadership, access to information and resources, opportunities for mobility and growth and socio-political support. Additionally, interns should perceive meaningfulness of work and impact on work results, have autonomy and enlarge their perception of self-efficacy, Thereby, the paper contributes to a systematic approach for empowering internships and derives implications for universities, industry and their collaboration.
Structural manifold analysis: A new method for dimensionality reduction based on a computational theory of human concept learning

A new non-parametric method for reducing the number of dimensions in binary and continuous data, and for measuring the complexity of binary and continuous datasets, is introduced. The method, named Structural Manifold Analysis (SMA), is based on “Generalized Invariance Structure Theory” [1-6], a theory that has been successful in characterizing and accurately predicting human concept learning and categorization performance. SMA is unique among data reduction and classification methods because it determines the degree of “diagnosticity” or classification potential of each of the dimensions in a multidimensional dataset up to a pre-specified discrimination resolution threshold. We compared SMA to five classical and recent dimensionality reductions methods using supervised and unsupervised classification techniques on 304 actual and simulated datasets. Overall, SMA performed as well as or better than any of the five methods tested while providing significant advantages over each.
An Approach to Precise Nitrogen Management Using Hand-Held Crop Sensor Measurements and Winter Wheat Yield Mapping in a Mediterranean Environment

Regardless of the crop production system, nutrients inputs must be controlled at or below a certain economic threshold to achieve an acceptable level of profitability. The use of management zones and variable-rate fertilizer applications is gaining popularity in precision agriculture. Many researchers have evaluated the application of final yield maps and geo-referenced geophysical measurements (e.g., apparent soil electrical conductivity-ECa) as a method of establishing relatively homogeneous management zones within the same plot. Yield estimation models based on crop conditions at certain growth stages, soil nutrient statuses, agronomic factors, moisture statuses, and weed/pest pressures are a primary goal in precision agriculture. This study attempted to achieve the following objectives: (1) to investigate the potential for predicting winter wheat yields using vegetation measurements (the Normalized Difference Vegetation Index—NDVI) at the beginning of the season, thereby allowing for a yield response to nitrogen (N) fertilizer; and (2) evaluate the feasibility of using inexpensive optical sensor measurements in a Mediterranean environment. A field experiment was conducted in two commercial wheat fields near Seville, in southwestern Spain. Yield data were collected at harvest using a yield monitoring system (RDS Ceres II-volumetric meter) installed on a combine. Wheat yield and NDVI values of 3498 ± 481 kg ha−1 and 0.67 ± 0.04 nm nm−1 (field 1) and 3221 ± 531 kg ha−1 and 0.68 ± 0.05 nm nm−1 (field 2) were obtained. In both fields, the yield and NDVI exhibited a strong Pearson correlation, with rxy = 0.64 and p < 10−4 in field 1 and rxy = 0.78 and p < 10−4 in field 2. The preliminary results indicate that hand-held crop sensor-based N management can be applied to wheat production in Spain and has the potential to increase agronomic N-use efficiency on a long-term basis.
Pulmonary Renal Syndrome: Update Article

The pulmonary–renal syndrome (PRS) refers to the combination of diffuse alveolar haemorrhage and rapidly progressive glomerulonephritis (RPGN). Pulmonary-renal syndrome can originate from various systemic autoimmune diseases. ANCA-associated vasculitides account for approximately 60%, Goodpasture's Syndrome for approximately 20% of the cases. It is almost always autoimmune in nature, therefore steroids and other immunosupressants have role in its treatment. The underlying renal pathology is a form of focal proliferative glomerulonephritis. The lung pathology is in form of diffuse alveolar hemorrhages.
Renal Interstitial Fluid Concentrations of Angiotensins I and II in Anesthetized Rats

Previous studies have indicated that angiotensin II (Ang II) concentrations in renal interstitial fluid are much higher than plasma levels. In the present study, we performed experiments to explore renal interstitial fluid concentrations of Ang I and Ang II further and to determine whether these levels are altered by acute arterial infusion of an ACE inhibitor (enalaprilat) or by volume expansion. Microdialysis probes (molecular weight cutoff point: 30 000 Da) were implanted in the renal cortex of anesthetized rats and were perfused at a rate of 2 &mgr;L/min. Using relative equilibrium rates, the basal renal interstitial fluid Ang II concentration averaged 3.07±0.43 nmol/L, a value much higher than the plasma Ang II concentration of 107±8 pmol/L (n=7). Interstitial fluid Ang I concentrations (0.84±0.04 nmol/L) were consistently lower than the Ang II concentrations but higher than the plasma Ang I concentrations (112±14 pmol/L). Intra-arterial infusion of enalaprilat (7.5 &mgr;mol/kg/min, n=5) for 120 minutes resulted in a significant decrease in mean arterial pressure (from 114±4 to 68±4 mm Hg) along with reductions in plasma and renal ACE activity (by −99% and −52%, respectively). Enalaprilat resulted in a significant increase in plasma Ang I from 133±21 to 1167±328 pmol/L and a decrease in plasma Ang II from 110±12 to 67±9 pmol/L. During enalaprilat infusion, interstitial fluid concentration of Ang I was significantly increased from 0.78±0.06 to 0.97±0.08 nmol/L; however, Ang II concentrations were not altered significantly (3.67±0.28 versus 3.67±0.25 nmol/L). Acute volume loading with Ringer’s solution containing 1% bovine serum albumin at a rate of 150 &mgr;L/min for 2 hours (6% to 7% of body weight) lowered plasma concentrations of Ang I from 110±23 to 16±2 pmol/L and Ang II from 100±23 to 36±6 pmol/L; however, renal interstitial fluid concentrations of Ang I and Ang II were not altered significantly during volume expansion (Ang I, from 0.77±0.05 to 0.69±0.03 nmol/L; Ang II, from 3.76±0.43 to 3.59±0.39 nmol/L, n=5). These data indicate that renal interstitial fluid concentrations of Ang I and Ang II are substantially higher than the corresponding plasma concentrations. Furthermore, the fact that the high interstitial fluid concentrations of Ang II are not responsive to acute ACE inhibition or volume expansion suggests the compartmentalization and independent regulation of renal interstitial fluid Ang II.
Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm

Early detection of cancer is crucial for successful treatments. In this paper, we propose a multiclass Logistic Partial Least Squares (LPLS) algorithm for classification of normal vs. cancer using Mass Spectrometry (MS). LPLS combines the multiclass logistic regression with Partial Least Squares (PLS) algorithm. Wavelet decomposition is also proposed for pre-processing of original data. Wavelet decomposition and the proposed LPLS are applied to real life cancer data. Experimental comparisons show that LPLS with wavelet decomposition outperforms other methods in the analysis of MS data.
THE THREE-DIMENSIONAL EVOLUTION TO CORE COLLAPSE OF A MASSIVE STAR

We present the first three-dimensional (3D) simulation of the final minutes of iron core growth in a massive star, up to and including the point of core gravitational instability and collapse. We capture the development of strong convection driven by violent Si burning in the shell surrounding the iron core. This convective burning builds the iron core to its critical mass and collapse ensues, driven by electron capture and photodisintegration. The non-spherical structure and motion generated by 3D convection is substantial at the point of collapse, with convective speeds of several hundreds of km s−1. We examine the impact of such physically realistic 3D initial conditions on the core-collapse supernova mechanism using 3D simulations including multispecies neutrino leakage and find that the enhanced post-shock turbulence resulting from 3D progenitor structure aids successful explosions. We conclude that non-spherical progenitor structure should not be ignored, and should have a significant and favorable impact on the likelihood for neutrino-driven explosions. In order to make simulating the 3D collapse of an iron core feasible, we were forced to make approximations to the nuclear network making this effort only a first step toward accurate, self-consistent 3D stellar evolution models of the end states of massive stars.
Robust noisy speech recognition with adaptive frequency bank selection

With the development of automatic speech recognition technology, the robustness problem of speech recognition systems is becoming more and more important. This paper addresses the problem of speech recognition in an additive background noise environment. Since the frequency energy of different types of noise focuses on different frequency banks, the effects of additive noise on each frequency bank are different. The seriously obscured frequency banks have little word signal information left, and are harmful for subsequence speech processing. Wu and Lin (2000) applied the frequency bank selection theory to robust word boundary detection in a noisy environment, and obtained good detection results. In this paper, this theory is extended to noisy speech recognition. Unlike the standard MFCC which uses all frequency banks for cepstral coefficients, we only use the frequency banks that are slightly corrupted and discard the seriously obscured ones. Cepstral coefficients are calculated only on the selected frequency banks. Moreover, an acoustic model is also adapted to match the modification of the acoustic feature. Experiments on continuous digital speech recognition show that the proposed algorithm leads to better performance than spectral subtraction and cepstral mean normalization at low SNRs.
Formal UML Support for the semi-automatic Application of object-oriented Refactorings

With the recent success of refactoring, UML tool vendors seek ways to support programmers in applying a (sequence of) refactoring(s). The problem with such tools is that the UML metamodel – on which their repository is based – is inadequate to maintain the consistency between the model and the code while one of them gets refactored. Therefore, we propose a set of minimal extensions to the UML metamodel, which is sufficient to reason about refactoring for all common OO languages. For instance, by specifying preand postconditions in OCL, we are able to compose primitive refactorings, verify preservation of program behavior, and trigger refactorings based on code smells. This way, we provide future MDA tools with the ability to improve existing UML designs, yet keeping them in synch with the underlying code base.