Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Next Generation Sequencing for Precision Medicine in Cancer

Rapid adoption of next generation sequencing (NGS) in genomic medicine has been driven by low cost, high throughput sequencing and rapid advances in our understanding of the genetic bases of human diseases. Today, the NGS method has dominated sequencing space in genomic research, and quickly entered clinical practice. Because unique features of NGS perfectly meet the clinical reality (need to do more with less), the NGS technology is becoming a driving force to realize the dream of precision medicine. This article describes the strengths of NGS, NGS panels used in precision medicine, current applications of NGS in cytology, and its challenges and future directions for routine clinical use.
ACSM STRENGTH TRAINING GUIDELINES: Role in Body Composition and Health Enhancement

OBESITY AND CHRONIC DISEASE Body mass index (BMI), which is based on height and weight relationships, is the most frequently used predictor for classifications of overweight (BMI = 25.0 to 29.9) and obesity (BMI = 30 or higher). Using BMI, a recent report by the National Health and Nutrition Examination Survey (NHANES IV) indicated that about 66% of the population is overweight, with more than 32% being obese (36). Because BMI does not account for the loss of lean weight associated with aging (12,13), it may underestimate the percentage of middleaged and older adults who have excess body fat (G22% for males, G32% for females) (2). Consider a 40-year-old woman who weighs 150 lbs and is 30% fat (45 lbs fat weight, 105 lbs lean weight). If she weighs the same at age 60 years, her BMI has not changed. However, if she has not engaged in resistance exercise, she most likely has lost 10 lbs of lean weight and added 10 lbs of fat weight making her 37% fat (55 lbs fat weight, 95 lbs lean weight) (11). When lean weight loss is taken into account, it is likely that even more American adults than the 66% identified by BMI calculations as overweight actually have too much body fat. Because excess fat is associated with a number of chronic diseases, such as Type 2 diabetes, hypertension, and coronary heart disease (2), as well as reduced life expectancy (37), it is essential to implement successful intervention strategies.
Dispositional Mindfulness, Posttraumatic Stress Disorder Symptoms and Academic Burnout in Chinese Adolescents Following a Tornado: The Role of Mediation through Regulatory Emotional Self-Efficacy

ABSTRACT Limited research has been conducted on dispositional mindfulness, posttraumatic stress disorder (PTSD) symptoms and academic burnout in Chinese adolescents following a tornado. The present study investigated the ways in which dispositional mindfulness is related to PTSD symptoms and academic burnout in Chinese adolescents following a tornado by considering the role of regulatory emotional self-efficacy. A total of 431 Chinese adolescents (mean age: 14.75 years) who had experienced a severe tornado 9 months prior to this study were recruited for this study. The results indicated that our model fit the data well [χ2/df = 2.774, CFI = 0.952, TLI = 0.934, RMSEA (90% CI) = 0.064 (0.051–0.077)], and revealed that regulatory emotional self-efficacy partially mediates the relationships between dispositional mindfulness and PTSD symptoms and academic burnout, respectively. The clinical implications and limitations of our research, and recommendations for future research, are discussed in this paper.
Collision-free navigation of forklifts by points-of-interest switching

In this paper, a collision-free navigation method for multiple wheeled vehicles is investigated. The path tracking control utilizes kinematic representation. Moreover, a switching algorithm is implemented to attack the problem of obstacle avoidance. As the center point of the vehicle is used to navigate the vehicle to the goal position, two more points are used for the obstacle avoidance. These points are on the left front, and right front of the vehicle. Depending upon the position of the obstacle with respect to these points, the closest point to the obstacle gets activated and control design takes the parameters according to that point. The main advantage of this approach is the obstacle avoidance by keeping the safe distance from the closest point of the vehicle to the obstacle. The effectiveness of the proposed control algorithm is demonstrated by simulations.
Investigation of effectiveness of microphone arrays for in car use based on sound field simulation

The effectiveness of small microphone arrays for in car use is investigated. These arrays are designed for speech enhancing in a noise background within the cabin of a moving car. Speech and noise simulation based on a designed mathematical algorithm of sound field modeling within a car cavity is applied for predicting the effectiveness of the spatial-time processing algorithms. The mathematical model takes into account complicated cabin geometry, the difference of sizes in the wavelength scale at low and high frequencies, frequency dependent sound absorption of the cabin surfaces and distributed noise sources with their cross correlation. Theoretical estimates of the microphone array effectiveness and output simulated signals (for subjective estimation) are presented.
Increasing Buffer-Locality for Multiple Index Based Scans through Intelligent Placement and Index Scan Speed Control

Decision support systems are characterized by large concurrent scan operations. A significant percentage of these scans are executed as index based scans of the data. This is especially true when the data is physically clustered on the index columns using the various clustering schemes employed by database engines. Common database management systems have only limited ability to reuse buffer content across multiple running queries due to their treatment of queries in isolation. Previous attempts to coordinate scans for better buffer reuse were limited to table scans only. Attempts for index based scan sharing were non existent or were less than satisfactory due to drifting between scans. In this paper, we describe a mechanism to keep scans using the same index closer together on scan position during scanning. This is achieved via intelligent placement of index scans at scan start time based on their scan ranges and speeds. This is then augmented by adaptive throttling of scan speeds based on the index scans runtime behavior during scan execution. We discuss the challenges in doing it for index scans in comparison to the more common table scan sharing. We show that this can be done with minimal changes to an existing database management system as demonstrated in our DB2 UDB prototype. Our experiments show significant gains in end-to-end response times and disk I/O for TPC-H workloads.
ThioFinder: A Web-Based Tool for the Identification of Thiopeptide Gene Clusters in DNA Sequences

Thiopeptides are a growing class of sulfur-rich, highly modified heterocyclic peptides that are mainly active against Gram-positive bacteria including various drug-resistant pathogens. Recent studies also reveal that many thiopeptides inhibit the proliferation of human cancer cells, further expanding their application potentials for clinical use. Thiopeptide biosynthesis shares a common paradigm, featuring a ribosomally synthesized precursor peptide and conserved posttranslational modifications, to afford a characteristic core system, but differs in tailoring to furnish individual members. Identification of new thiopeptide gene clusters, by taking advantage of increasing information of DNA sequences from bacteria, may facilitate new thiopeptide discovery and enrichment of the unique biosynthetic elements to produce novel drug leads by applying the principle of combinatorial biosynthesis. In this study, we have developed a web-based tool ThioFinder to rapidly identify thiopeptide biosynthetic gene cluster from DNA sequence using a profile Hidden Markov Model approach. Fifty-four new putative thiopeptide biosynthetic gene clusters were found in the sequenced bacterial genomes of previously unknown producing microorganisms. ThioFinder is fully supported by an open-access database ThioBase, which contains the sufficient information of the 99 known thiopeptides regarding the chemical structure, biological activity, producing organism, and biosynthetic gene (cluster) along with the associated genome if available. The ThioFinder website offers researchers a unique resource and great flexibility for sequence analysis of thiopeptide biosynthetic gene clusters. ThioFinder is freely available at http://db-mml.sjtu. edu.cn/ThioFinder/.
International Meshing Roundtable ( IMR 24 ) Recurrent Neural Networks for Geometric Problems

We introduce a new architecture using Recurrent Neural Networks (RRNs) to learn approximate solutions to three geometric problems – finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem – using training examples alone. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete geometric problems, including mesh generation. c © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24).
Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations

We propose a novel tree classification system called Treelogy, that fuses deep representations with hand-crafted features obtained from leaf images to perform leaf-based plant classification. Key to this system are segmentation of the leaf from an untextured background, using convolutional neural networks (CNNs) for learning deep representations, extracting hand-crafted features with a number of image processing techniques, training a linear SVM with feature vectors, merging SVM and CNN results, and identifying the species from a dataset of 57 trees. Our classification results show that fusion of deep representations with hand-crafted features leads to the highest accuracy. The proposed algorithm is embedded in a smart-phone application, which is publicly available. Furthermore, our novel dataset comprised of 5408 leaf images is also made public for use of other researchers.
Creative Systems as Dynamical Systems

In this paper, we discuss ideas for characterizing a case- based generative system as \creative". Focusing on a specic generator of graphics, we performed a qualitative exploration of the space of solu- tions. The emerged intuition is that the set of congurations generated by the program can be viewed both as the conceptual space of a creative system and the phase space of a dynamical system. In the context of this analogy, we hypothesize that a higher degree of creativity can be ascribed to the search paths allowing the system to reach new basins of attractions.
All Mixed Up? Finding the Optimal Feature Set for General Readability Prediction and Its Application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, although NLP-inspired research has focused on adding more complex readability features, there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and crowdsourcing, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring deep linguistic processing, resulting in ten different feature groups. Both a regression and classification set-up are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task that provides considerable insights in which feature combinations contribute to the overall readability prediction. Because we also have gold standard information available for those features requiring deep processing, we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully automatic readability prediction pipeline is on par with the pipeline using gold-standard deep syntactic and semantic information.
Symposium 1: Nutrition and epigenetics Effect of maternal diet on the epigenome: implications for human metabolic disease

The rapid increase in the incidence of chronic non-communicable diseases over the past two decades cannot be explained solely by genetic and adult lifestyle factors. There is now considerable evidence that the fetal and early postnatal environment also strongly influences the risk of developing such diseases in later life. Human studies have shown that low birth weight is associated with an increased risk of CVD, type II diabetes, obesity and hypertension, although recent studies have shown that over-nutrition in early life can also increase susceptibility to future metabolic disease. These findings have been replicated in a variety of animal models, which have shown that both maternal under- and over-nutrition can induce persistent changes in gene expression and metabolism within the offspring. The mechanism by which the maternal nutritional environment induces such changes is beginning to be understood and involves the altered epigenetic regulation of specific genes. The demonstration of a role for altered epigenetic regulation of genes in the developmental induction of chronic diseases raises the possibility that nutritional or pharmaceutical interventions may be used to modify long-term cardio-metabolic disease risk and combat this rapid rise in chronic non-communicable diseases.
Joint Beamforming and Power Control in the Downlink of Cognitive Radio Networks

We consider a system where a licensed radio spectrum is shared by two networks, namely the primary network and the secondary network. The spectrum is licensed to the primary network, however the secondary network makes opportunistic access of the unused and/or underutilized portions of it by using cognitive radio technology. For such a system, we study the problem of joint power control and beamforming in the downlink of the cognitive radio network and formulate two iterative algorithms considering two different scenarios of spectrum sharing. In the first case, the primary network cooperates with the secondary network to jointly optimize the transmit beamforming weights and total transmitted power by both networks subject to signal to interference plus noise ratio (SINR) constraints of both primary and secondary users. In the second case, the secondary network jointly optimizes the transmit beamforming weights and its total transmitted power satisfying the SINR constraint of the secondary users and maximum tolerable interference constraint to the primary user. Simulation results are provided to show convergence behavior of the iterative algortihms.
Circular Polarization and Polarization Losses

Circularly polarized microwave radiation is frequently used in EME (Earth-Moon-Earth) communication on the 23, 13 and 6 cm bands. Recently published findings [1] suggest that favorable properties of CP (circular polarization) should also enable utilization on the 3cm band. The main advantage of CP versus linear polarization is that CP eliminates polarization mismatch losses caused by Faraday’s rotation and varies the squint angle of polarization vectors between stations on the Earth. This article is intended to familiarize and clarify the application of CP in conjunction with parabolic reflector antennas and to publicize problems that CP antenna designers may encounter.
Neuronal Inwardly Rectifying K+ Channels Differentially Couple to PDZ Proteins of the PSD-95/SAP90 Family

Several signaling proteins clustered at the postsynaptic density specialization in neurons harbor a conserved C-terminal PDZ domain recognition sequence (X-S/T-X-V/I) that mediates binding to members of the PSD-95/SAP90 protein family. This motif is also present in the C termini of some inwardly rectifying K+ (Kir) channels. Constitutively active Kir2 channels as well as G protein-gated Kir3 channels, which are fundamental for neuronal excitability, were analyzed as candidates for binding to PSD-95/SAP90 family members. Therefore C termini of Kir2.1(+), Kir2.3(+), Kir2.4(−), Kir3.1(−), Kir3.2(+), Kir3.3(+) and Kir3.4(−) subunits (+, motif present; −, motif absent) were used as baits in the yeast two-hybrid assay to screen for in vivo interaction with PDZ domains 1–3 of PSD-95/SAP90. In contrast to Kir2.1 and Kir2.3, all Kir3 fragments failed to bind PSD-95 in this assay, which was supported by the lack of coimmunoprecipitation and colocalization of the entire proteins in mammalian cells. A detailed analysis of interaction domains demonstrated that the C-terminal motif in Kir3 channels is insufficient for binding PDZ domains. Kir2.1 and Kir2.3 subunits on the other hand coprecipitate with PSD-95. When coexpressed in a bicistronic internal ribosome entry site expression vector in HEK-293 cells macroscopic and elementary current analysis revealed that PSD-95 suppressed the activity of Kir2.3 channels by >50%. This inhibitory action of PSD-95, which predominantly affects the single-channel conductance, is likely attributable to a molecular association with additional internal interaction sites in the Kir2.3 protein.
Robust security network association adjusted hybrid authentication schema

Wireless network, whether it's ad-hoc or at enterprise level is vulnerable due to its features of open medium, and usually due to weak authentication, authorization, encryption, monitoring and accounting mechanisms. Various wireless vulnerability situations as well as the minimal features that are required in order to protect, monitor, account, authenticate, and authorize nodes, users, computers into the network are examined. Also, aspects of several IEEE Security Standards, which were ratified and which are still in draft are described.
Analysis of Heuristics for Number Partitioning

We illustrate the use of phase transition behavior in the study of heuristics. Using an “annealed” theory, we define a parameter that measures the “constrainedness” of an ensemble of number partitioning problems. We identify a phase transition at a critical value of constrainedness. We then show that constrainedness can be used to analyze and compare algorithms and heuristics for number partitioning in a precise and quantitative manner. For example, we demonstrate that on uniform random problems both the Karmarkar–Karp and greedy heuristics minimize the constrainedness, but that the decisions made by the Karmarkar–Karp heuristic are superior at reducing constrainedness. This supports the better performance observed experimentally for the Karmarkar–Karp heuristic. Our results refute a conjecture of Fu that phase transition behavior does not occur in number partitioning. Additionally, they demonstrate that phase transition behavior is useful for more than just simple benchmarking. It can, for instance, be used to analyze heuristics, and to compare the quality of heuristic solutions.
NRC Publications Archive Archives des publications du CNRC

Na¨ıve Bayes Tree uses decision tree as the general structure and deploys na¨ıve Bayesian classiﬁers at leaves. The intuition is that na¨ıve Bayesian classiﬁers work better than decision trees when the sample data set is small. Therefore, after several attribute splits when constructing a decision tree, it is better to use na¨ıve Bayesian classiﬁers at the leaves than to continue splitting the attributes. In this paper, we propose a learning algorithm to improve the conditional probability estimation in the diagram of Na¨ıve Bayes Tree. The motivation for this work is that, for cost-sensitive learning where costs are associated with conditional probabilities, the score function is optimized when the estimates of conditional probabilities are accurate. The additional beneﬁt is that both the classiﬁcation accuracy and Area Under the Curve (AUC) could be improved. On a large suite of benchmark sample sets, our experiments show that the CLL tree outperforms the state-of-art learning algorithms, such as Na¨ıve Bayes Tree and na¨ıve Bayes signiﬁcantly in yielding accurate conditional probability estimation and improving clas-siﬁcation accuracy and AUC.
Trend mining in social networks: from trend identification to visualization

A four‐stage social network trend mining framework, the Identification, Grouping, Clustering and Visualization framework, is described. The framework extracts trends from social network data and then applies a sequence of techniques (‘tools’) to this data to facilitate interpretation of the identified trends. Of particular note is the visualization of trend migrations (changes) that feature within time‐stamped network data. The framework is illustrated using a sequence of four social networks extracted from the Cattle Tracing System in operation in Great Britain, although it could equally well be applied to other forms of temporal data. The presented analysis of the Identification, Grouping, Clustering and Visualization framework indicates advantages, with respect to network trend mining, that can be gained, especially when the framework is applied to real data.
Relationship aspects

The relationships between objects in object-oriented programs are as important as the objects themselves. Unfortunately, most object-oriented programming languages provide little support for such relationships, leaving the task of implementing them entirely to the programmer. Relationships are typically hard-coded into the participating classes, resulting in tangled code that unnecessarily couples these classes together. The classes become harder to understand and cannot be reused independently. Aspect-oriented programs can model relationships explicitly, treating them as separate concerns that cross-cut their participants. We show how relationships can be implemented as a library of aspects in Aspect/J. Aspects keep relationships independent of their participants, making the resulting programs easier to read, write and reuse, and as efficient as hand-written code.
On the Relation between the Kautsky Effect (chlorophyll a Fluorescence Induction) and Photosystem Ii: Basics and Applications of the Ojip Fluorescence Transient Q

Chlorophyll a fluorescence is a highly sensitive, non-destructive, and reliable tool for measuring, rather quickly, photosynthetic efficiency, particularly of Photosystem II (PSII), the water-plastoquinone oxidore-ductase. We briefly review here the connection between the fast (up to 2 s) chlorophyll fluorescence rise and PSII, as well as the empirical use of the fluorescence rise kinetics in understanding photosynthetic reactions, particularly of PSII. When dark-adapted photosynthetic samples are exposed to light, a fluores-cence induction is observed, known as the Kautsky effect, after Hans Kautsky, the discoverer of the phenomenon showing the existence of variable fluorescence. The chlorophyll fluorescence intensity rises from a minimum level (the O level), in less than 1 s, to a maximum level (the P-level) via two intermediate steps labeled J and I. This is followed by a decline to a lower semi-steady state level, the S level, which is reached in about one minute. We provide here an educational review on how this phenomenon has been exploited through analysis of the fast OJIP fluorescence transient, by discussing basic assumptions , derivation of equations, as well as application to PSII-related questions.