Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of tumor mutation burden in breast cancer based on the expression of ER, PR, HER-2, and Ki-67

Objective Cancer immunoediting is the process of eliminating highly immunogenic tumor cells by somatic evolution and protecting the host from tumor development in the host immune system. Frequencies of somatic mutations or tumor mutation burden (TMB) were associated with immunogenicity of breast cancer. This study aimed to predict the level of TMB in patients with breast cancer by the expression of estrogen (ER), progesterone (PR), HER-2, and Ki-67, thereby anticipating the prognosis of patients and the possible response to immunotherapy. Patients and methods In 53 patients with breast cancer, the 453 multigenes panel based on NGS was used to determine the TMB value of breast cancer in the patient’s primary tumor tissues. The predicted TMB value was divided into 4 groups: A (0–3.33), B (3.33–5.56), C (5.56–8.89), and D (>8.89), according to the quartile method, with group A as reference level. Logistic regression was used to analyze the risk ratio of each molecule type, and the prediction model was established. Survival probabilities by covariates were assessed using Kaplan–Meier estimator survival analysis and Cox’s proportional hazards models. Results In 53 patients, the TMB value measured by the NGS polygenic panel was between 0 and 14.4/Mb. TMB distribution in 53 cases of breast cancer tissue: 18 cases in A group, 22 cases in B group, 10 cases in C group, and 3 cases in D group. HER-2 expression positivity was significantly associated with TMB (HER-2 positive vs HER-2 negative, odds ratio [OR] =34.81, 95% confidence interval [CI]: 3.711–821.689, P=0.0065). Higher TMB was distributed in the patients who were Ki-67 expression positive (>14%) than those who were Ki-67 expression negative (≤14%) (OR =0.217, 95% CI: 0.054–0.806, P=0.0242). However, no significant differences of TMB were found between ER-positive group and ER-negative group (OR =3.133, 95% CI: 0.124–127.687, P=0.4954) and between PR-positive group and PR-negative group in terms of TMB (OR =1.702, 95% CI: 0.162–20.335, P=0.6492). The predicted model is TMB = −1.14×ER +0.53×PR +3.55×HER-2-1.53×Ki-67+ CONSTANT (INTERCEPT). Patients with low TMB had a better disease-free survival (DFS) than those with high TMB (83 vs 59 m, P=0.002). In a multivariate analysis, high TMB (>5.56) was an independent predictive factor for decreased DFS (adjusted hazard ratio [HR], 5.594; 95% CI: 1.694–18.473; P = 0.005). Conclusion The preliminary results suggest that the level of TMB value in patients with breast cancer can be predicted based on the expression levels of ER, PR, HER-2, and Ki-67, which may indicate the prognostic and predictive value of immunotherapy in patients with breast cancer.
What becomes true after arbitrary announcements

Public announcement logic is an extension of multi-agent epistemic logic with dynamic operators to model the informational consequences of announcements to the entire group of agents. We propose an extension of public announcement logic with a dynamic modal operator that expresses what is true after arbitrary announcements. Intuitively, [! ] ϕ expresses that ϕ is true after an arbitrary announcement ψ . We show completeness for a Hilbert-style axiomatization of this logic, and also provide a labelled tableau-calculus.
GeoFolk: latent spatial semantics in web 2.0 social media

We describe an approach for multi-modal characterization of social media by combining text features (e.g. tags as a prominent example of short, unstructured text labels) with spatial knowledge (e.g. geotags and coordinates of images and videos). Our model-based framework GeoFolk combines these two aspects in order to construct better algorithms for content management, retrieval, and sharing. The approach is based on multi-modal Bayesian models which allow us to integrate spatial semantics of social media in a well-formed, probabilistic manner. We systematically evaluate the solution on a subset of Flickr data, in characteristic scenarios of tag recommendation, content classification, and clustering. Experimental results show that our method outperforms baseline techniques that are based on one of the aspects alone. The approach described in this contribution can also be used in other domains such as Geoweb retrieval.
Salivary cortisol responses to mental stress are associated with coronary artery calcification in healthy men and women.

AIMS Psychosocial stress is a risk factor for coronary heart disease (CHD), although the mechanisms are incompletely understood. We examined the cross-sectional association between the cortisol response to laboratory-induced mental stress and a marker of sub-clinical coronary atherosclerosis. METHODS AND RESULTS Participants were 514 healthy men and women (mean age = 62.9 +/- 5.7 years), without history or objective signs of CHD, drawn from the Whitehall II epidemiological cohort. Salivary cortisol was measured in response to mental stressors, consisting of a 5 min Stroop task and a 5 min mirror tracing task. Coronary artery calcification (CAC) was measured using electron beam computed tomography. Approximately 40% of the sample responded to the stress tasks with a notable (>or=1 nmol/L) increase in cortisol. Significant CAC (Agatston score >or= 100) was recorded in 23.9% of the sample. The cortisol response group demonstrated a higher risk of significant CAC (odds ratio = 2.20, 95% CI, 1.39-3.47) after adjustments for age, gender, baseline cortisol, employment grade, and conventional risk factors, although cortisol was unrelated to the presence of detectable CAC. Among participants with detectable CAC, the cortisol response group also demonstrated higher log Agatston scores compared with non-responders (age and sex adjusted scores; 4.51 +/- 0.15 vs. 3.94 +/- 0.13, P = 0.004). CONCLUSION In healthy, older participants without history or objective signs of CHD, heightened cortisol reactivity is associated with a greater extent of CAC. These data support the notion that heightened hypothalamic pituitary adrenal activity is a risk factor for CHD.
Two Distinct Size Classes of Immature and Mature Subviral Particles from Tick-Borne Encephalitis Virus

ABSTRACT Flaviviruses assemble in the endoplasmic reticulum by a mechanism that appears to be driven by lateral interactions between heterodimers of the envelope glycoproteins E and prM. Immature intracellular virus particles are then transported through the secretory pathway and converted to their mature form by cleavage of the prM protein by the cellular protease furin. Earlier studies showed that when the prM and E proteins of tick-borne encephalitis virus are expressed together in mammalian cells, they assemble into membrane-containing, icosahedrally symmetrical recombinant subviral particles (RSPs), which are smaller than whole virions but retain functional properties and undergo cleavage maturation, yielding a mature form in which the E proteins are arranged in a regular T = 1 icosahedral lattice. In this study, we generated immature subviral particles by mutation of the furin recognition site in prM. The mutation resulted in the secretion of two distinct size classes of particles that could be separated by sucrose gradient centrifugation. Electron microscopy showed that the smaller particles were approximately the same size as the previously described mature RSPs, whereas the larger particles were approximately the same size as the virus. Particles of the larger size class were also detected with a wild-type construct that allowed prM cleavage, although in this case the smaller size class was far more prevalent. Subtle differences in endoglycosidase sensitivity patterns suggested that, in contrast to the small particles, the E glycoproteins in the large subviral particles and whole virions might be in nonequivalent structural environments during intracellular transport, with a portion of them inaccessible to cellular glycan processing enzymes. These proteins thus appear to have the intrinsic ability to form alternative assembly products that could provide important clues about the role of lateral envelope protein interactions in flavivirus assembly.
Desingularization of nonredundant serial manipulator trajectories using Puiseux series

It is shown that smooth spatial paths for nonredundant serial robots can always be smoothly reparametrized in the vicinity of kinematic singularities with finite root multiplicity using a Puiseux series. This reparametrization, based on the algebraic structure of the manipulator kinematics, is formed using the /spl nu/th root of the path parameter distance to the singularity, where /spl nu/ is some integer not exceeding the root multiplicity of the singularity. By contrast, self-motion singularities, which have infinite root multiplicity, do not possess such a reparametrization. However, smooth motions directly through such singularities are generally possible because approaching path solutions are unaffected by them. These results help quantify how joint derivatives blow up at singularities, and can readily be used to generate singularity-robust motions along any path, with no spatial error. This is illustrated with an example involving the PUMA manipulator.
Mental Health Effects of Changes in Psychosocial Work Characteristics: A Prospective Cohort Study

Objective: To explore a possible causal relationship between psychosocial work characteristics and mental health. Methods: Using longitudinal data from the Maastricht Cohort Study on “Fatigue at Work” (n = 2332), the effects of changes in job demands and decision latitude on subsequent changes in need for recovery and prolonged fatigue were studied. Results: Increasing job demands are a significant predictor of a subsequent increase in need for recovery (β = 0.063) and prolonged fatigue (β = 0.057). An increase in decision latitude predicted a subsequent decrease in need for recovery (β = −0.078) and prolonged fatigue (β = −0.063). After adjusting for changes in other work characteristics, the effects on changes in prolonged fatigue were no longer significant. Conclusion: These findings support a possible causal relationship between work characteristics and mental health and can be used for designing effective prevention and intervention strategies.
Targeting the Mechanisms of Resistance to Chemotherapy and Radiotherapy with the Cancer Stem Cell Hypothesis

Despite advances in treatment, cancer remains the 2nd most common cause of death in the United States. Poor cure rates may result from the ability of cancer to recur and spread after initial therapies have seemingly eliminated detectable signs of disease. A growing body of evidence supports a role for cancer stem cells (CSCs) in tumor regrowth and spread after initial treatment. Thus, targeting CSCs in combination with traditional induction therapies may improve treatment outcomes and survival rates. Unfortunately, CSCs tend to be resistant to chemo- and radiation therapy, and a better understanding of the mechanisms underlying CSC resistance to treatment is necessary. This paper provides an update on evidence that supports a fundamental role for CSCs in cancer progression, summarizes potential mechanisms of CSC resistance to treatment, and discusses classes of drugs currently in preclinical or clinical testing that show promise at targeting CSCs.
Ancorapipe: a New Tool for Corpora Annotation Working Paper 1: Text-mess 2.0 (text-knowledge 2.0) Ancorapipe: a New Tool for Corpora Annotation

This paper describes AnCoraPipe, an environment for the creation, edition and analysis of linguistic corpora and lexicons. AnCoraPipe has been used in the development of different linguistic resources: AnCora, CesCa, ClInt, Amazighe corpora, and the verbal and nominal AnCora lexicons. We present the functionalities of AnCoraPipe, the way in which the data and metadata is structure, as well as some implementation details.
Interest Groups Vie for Public Support

Although affirmative action in college admissions has not been declared unconstitutional by the Supreme Court, the consideration of race in admissions has been banned in nine states—in six of them by public vote. This article analyzes the campaigns to ban affirmative action in California and Michigan as a battle between interest groups. The course of events in these states demonstrates that public opinion is a threat to the legality of affirmative action, should interest groups continue to take advantage of it by pursuing bans by state initiative.
Biometrical Models as Tools for Forest Ecosystem Management

This paper sketches how forest management functions in Europe and how knowledge about forest ecosystems can be instilled into the planning process via models. Simulation models support decisions by scenario analysis, which display the consequences of management options with respect to decisive system variables. While elsewhere plantations for intensive wood-production are separated from forests for conservation, in European forests a multitude of functions is integrated. This concept requires models, which quantify the ecological, economical, and social consequences of treatment options. The required information can be delivered by hybrid models, which are on the one hand simple enough with respect to input data, driving variables, and application, on the other hand sufficiently mechanistic in order to display matter and energy flow. Recommendations are given for a convergence of empirical and mechanistic models, for integration of models into the information flow of management, and for a successful transfer of scientific knowledge to end-users.
Supporting fault-tolerance for time-critical events in distributed environments

In this paper, we consider the problem of supporting fault tolerance for adaptive and time-critical applications in heterogeneous and unreliable grid computing environments. Our goal for this class of applications is to optimize a user-specified benefit function while meeting the time deadline. Our first contribution in this paper is a multi-objective optimization algorithm for scheduling the application onto the most efficient and reliable resources. In this way, the processing can achieve the maximum benefit while also maximizing the success-rate, which is the probability of finishing execution without failures. However, for the cases where failures do occur, we have developed a hybrid failure-recovery scheme to ensure that the application can complete within the pre-specified time interval. Our experimental results show that our scheduling algorithm can achieve better benefit when compared to several heuristics-based greedy scheduling algorithms, while still having a negligible overhead. Benefit is further improved when we apply the hybrid failure recovery scheme, and the success-rate becomes 100%.
Coronary Calcium Score Predicts Cardiovascular Mortality in Diabetes

OBJECTIVE In type 2 diabetes mellitus (T2DM), it remains unclear whether coronary artery calcium (CAC) provides additional information about cardiovascular disease (CVD) mortality beyond the Framingham Risk Score (FRS) factors. RESEARCH DESIGN AND METHODS A total of 1,123 T2DM participants, ages 34–86 years, in the Diabetes Heart Study followed up for an average of 7.4 years were separated using baseline computed tomography scans of CAC (0–9, 10–99, 100–299, 300–999, and ≥1,000). Logistic regression was performed to examine the association between CAC and CVD mortality adjusting for FRS. Areas under the curve (AUC) with and without CAC were compared. Net reclassification improvement (NRI) compared FRS (model 1) versus FRS+CAC (model 2) using 7.4-year CVD mortality risk categories 0% to <7%, 7% to <20%, and ≥20%. RESULTS Overall, 8% of participants died of cardiovascular causes during follow-up. In multivariate analysis, the odds ratios (95% CI) for CVD mortality using CAC 0–9 as the reference group were, CAC 10–99: 2.93 (0.74–19.55); CAC 100–299: 3.17 (0.70–22.22); CAC 300–999: 4.41(1.15–29.00); and CAC ≥1,000: 11.23 (3.24–71.00). AUC (95% CI) without CAC was 0.70 (0.67–0.73), AUC with CAC was 0.75 (0.72–0.78), and NRI was 0.13 (0.07–0.19). CONCLUSIONS In T2DM, CAC predicts CVD mortality and meaningfully reclassifies participants, suggesting clinical utility as a risk stratification tool in a population already at increased CVD risk.
A FEATURE BASED APPROACH TO AUTOMATIC CHANGE DETECTION FROM LIDAR DATA IN URBAN AREAS

Automatic change detection particularly in urban areas is the important tools for their management and planning. This paper presents a feature based method to detect changes in urban area using two LIDAR data sets acquired at different times. The main processes in the method are to detect the change areas through the subtraction between the two DSMs generated from the two individual LIDAR sets, to organize the LIDAR points within the detected areas into surface patches, to classify each patch to one of the pre-defined classes such as ground, vegetation, or building, and to determine the types of the change based on the classes and properties of the patches. The results from the application of the method to real data were verified with the reference data manually acquired from the visual inspection of the orthoimages in the same area. With the proposed method, we were able to detect not only the change area but also the types of the changes in a sufficient degree of accuracy with a reasonable processing time.
Relations of parenting style to Chinese children's effortful control, ego resilience, and maladjustment

Abstract The purpose of the study was to examine the relations of authoritative parenting and corporal punishment to Chinese first and second graders' effortful control (EC), impulsivity, ego resilience, and maladjustment, as well as mediating relations. A parent and teacher reported on children's EC, impulsivity, and ego resilience; parents reported on children's internalizing symptoms and their own parenting, and teachers and peers reported on children's externalizing symptoms. Authoritative parenting and low corporal punishment predicted high EC, and EC mediated the relation between parenting and externalizing problems. In addition, impulsivity mediated the relation of corporal punishment to externalizing problems. The relation of parenting to children's ego resilience was mediated by EC and/or impulsivity, and ego resilience mediated the relations of EC and impulsivity to internalizing problems.
Algorithm for clustering analysis of gene expression data using MapReduce framework

Bioinformatics is a fast growing field in data mining techniques to solve biological problems. Few decades' fast developments in genomic and other molecular research in information technologies have combined to produce an enormous amount of information relevant to molecular biology. The study of gene expression data investigation has developed in the few years from being purely data-centric to interrelate. The developments in gene expression based analysis methods are association, classification, clustering, and prediction studies. In recent times, industries have a rapid growth of data; a data analysis tool is required to satisfy the need for analyzing a huge volume of data. The MapReduce framework is designed to compute data demanding applications to support effective decision making. This document afford an outline of the MapReduce programming model, different methods to implement MapReduce models to process large-scale datasets and used to cluster the given gene dataset based on specified features.
Semantic Hadith: Leveraging Linked Data Opportunities for Islamic Knowledge

While the linked data paradigm has gathered much attention over the recent years, the domain of Islamic knowledge has yet to cache upon its full potential. The web-scale integration of Islamic texts and knowledge sources at large is currently not well facilitated. The two primary sources of the Islamic legislation are the Qur’an and the Hadith (collections of Prophetic Narrations) and form the basis of laying the foundation for anyone wanting to learn Islam. This paper presents ongoing design and development eorts to semantically model and publish the Hadith, which holds a primary position as the next most important knowledge source, after the Qur’an. We present the design of the linked data vocabulary for not only publishing these narrations as linked data, but also delineate upon the mechanism for linking these narrations with the verses of the Qur’an. We establish how the links between the Hadith and the Qur’anic verses may be captured and published using this vocabulary, as derived from the secondary and tertiary sources of knowledge. We present detailed insights into the potential, the design considerations and the use cases of publishing this wealth of knowledge as linked data.
Eye State Prediction from EEG Data Using Boosted Rotational Forests

With the advent of brain-computer interfacing (BCI) technology, the need for fast and accurate classification and prediction of brain activity from electrode recording data is becoming more critical. The classifier systems developed in the current study improve upon the previous state-of-the-art developed by Rösler & Suendermann's (2013) for detecting whether an individual's eyes are open or closed based on EEG recordings. It was hypothesized that a system could be constructed with comparatively accurate classifications to Rösler & Suendermann's K*-based system, but with enough speed to be utilized within a BCI by constructing ensembles from eager learners (e.g. decision trees). To meet these requirements, three ensemble learners were developed: a rotational forest that implements random forests as its base classifiers (RRF), a rotational forest that implements J48 trees as its base classifiers and is boosted by adaptive boosting (ada(RJ48F)), and an ensemble of the RRF model with Rösler & Suendermann's K* model (RRF+K*). The RRF and ada(RJ48F) models were shown to have a classification speed faster than the K* model by a factor of seventeen and thirteen respectively, though the RRF model was significantly less accurate and the ada(RJ48F) model had an accuracy comparable to K*. The results of this study indicate that instance-based learners like the K* algorithm are likely to be too slow to be used within a BCI device, while the ada(RJ48F) model performed accurate classifications well within the time constraints of real-time classification and control.