Miracle-dz/UniFAR-FTUs huggingface dataset:
Driver gene mutations based clustering of tumors: methods and applications

Motivation Somatic mutations in proto‐oncogenes and tumor suppressor genes constitute a major category of causal genetic abnormalities in tumor cells. The mutation spectra of thousands of tumors have been generated by The Cancer Genome Atlas (TCGA) and other whole genome (exome) sequencing projects. A promising approach to utilizing these resources for precision medicine is to identify genetic similarity‐based sub‐types within a cancer type and relate the pinpointed sub‐types to the clinical outcomes and pathologic characteristics of patients. Results We propose two novel methods, ccpwModel and xGeneModel, for mutation‐based clustering of tumors. In the former, binary variables indicating the status of cancer driver genes in tumors and the genes' involvement in the core cancer pathways are treated as the features in the clustering process. In the latter, the functional similarities of putative cancer driver genes and their confidence scores as the ‘true' driver genes are integrated with the mutation spectra to calculate the genetic distances between tumors. We apply both methods to the TCGA data of 16 cancer types. Promising results are obtained when these methods are compared to state‐of‐the‐art approaches as to the associations between the determined tumor clusters and patient race (or survival time). We further extend the analysis to detect mutation‐characterized transcriptomic prognostic signatures, which are directly relevant to the etiology of carcinogenesis. Availability and implementation R codes and example data for ccpwModel and xGeneModel can be obtained from http://webusers.xula. edu/kzhang/ISMB2018/ccpw_xGene_software. zip.
Predicting the risk of attrition for undergraduate students with time based modelling

Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist universities by proactively supporting and retaining these students as their situations and risk change over time. The study evaluated different models for predicting student attrition at four different time periods throughout a semester study period: pre-enrolment, enrolment, in-semester and end-of-semester models. A dataset of 23,291 students who enrolled in their first semester between 2011-2013 was extracted from various data sources. Three supervised machine learning techniques were tested to develop the predictive models: logistic regression, decision trees and random forests. The performance of these models were evaluated using the precision and recall metrics. The model achieved the best performance and user utility using logistic regression (67% precision, 29% recall). A web application was developed for users to visualise and interact with the model results to assist in the targeting of student intervention responses and programs.
A multi-task learning approach for meal assessment

Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet. The conventional dietary assessment methods are time-consuming, expensive and prone to errors. New technology-based methods that provide reliable and convenient dietary assessment, have emerged during the last decade. The advances in the field of computer vision permitted the use of meal image to assess the nutrient content usually through three steps: food segmentation, recognition and volume estimation. In this paper, we propose a use one RGB meal image as input to a multi-task learning based Convolutional Neural Network (CNN). The proposed approach achieved outstanding performance, while a comparison with state-of-the-art methods indicated that the proposed approach exhibits clear advantage in accuracy, along with a massive reduction of processing time.
A Formal Analysis of 5G Authentication

Mobile communication networks connect much of the world's population. The security of users' calls, SMSs, and mobile data depends on the guarantees provided by the Authenticated Key Exchange protocols used. For the next-generation network (5G), the 3GPP group has standardized the 5G AKA protocol for this purpose. We provide the first comprehensive formal model of a protocol from the AKA family: 5G AKA. We also extract precise requirements from the 3GPP standards defining 5G and we identify missing security goals. Using the security protocol verification tool Tamarin, we conduct a full, systematic, security evaluation of the model with respect to the 5G security goals. Our automated analysis identifies the minimal security assumptions required for each security goal and we find that some critical security goals are not met, except under additional assumptions missing from the standard. Finally, we make explicit recommendations with provably secure fixes for the attacks and weaknesses we found.
Microfabricated plastic chips by hot embossing methods and their applications for DNA separation and detection

Design and fabrication of microfluidic devices on polymethylmethacrylate (PMMA) substrates using novel microfabrication methods are described. The image of microfluidic devices is transferred from quartz master templates possessing inverse image of the devices to plastic plates by using hot embossing method. The micro channels on master templates are formed by the combination of metal etch mask and wet chemical etching. The micromachined quartz templates can be used repeatedly to fabricate cheap and disposable plastic devices. The reproducibility of the hot embossing method is evaluated after using 10 channels on different plastics. The relative standard deviation of the plastic channel profile from ones on quartz templates is less than 1%. In this study, the PMMA chips have been demonstrated as a micro capillary electrophoresis ((mu) -CE) device for DNA separation and detection. The capability of the fabricated chip for electrophoretic injection and separation is characterized via the analysis of DNA fragments (phi) X174. Results indicate that all of the 11 DNA fragments of the size marker could be identified in less than 3 minutes with relative standard deviations less than 0.4% and 8% for migration time and peak area, respectively. Moreover, with the use of near IR dye, fluorescence signals of the higher molecular weight fragments ($GTR 603 bp in length) could be detected at total DNA concentrations as low as 0.1 (mu) g/mL. In addition to DNA fragments (phi) X174, DNA sizing of hepatitis C viral (HCV) amplicon is also achieved using microchip electrophoresis fabricated on PMMA substrate.
Modular meta-learning

Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined in different ways. We train different modular structures on a set of related tasks and generalize to new tasks by composing the learned modules in new ways. By reusing modules to generalize we achieve combinatorial generalization, akin to the "infinite use of finite means" displayed in language. Finally, we show this improves performance in two robotics-related problems.
Semantic network skeletons - a tool to analyze spreading activation effects

—Spreading Activation algorithms are a well-known tool to determine relevance of nodes in a semantic network. Although often used, the conﬁguration of a spreading activation algorithm is usually very problem-speciﬁc, and experience-driven. There are practically no guidelines or tools to help with the task. In this paper, we present semantic network skeletons, which are essentially a structural summary of a semantic network. We show how to derive the skeleton from a given semantic network, and how to derive conclusions about good conﬁgurations from it. Our results are then demonstrated in a case study from the automotive domain
Clinical course predicts long-term outcomes in bipolar disorder

Abstract Background The long-term outcomes of bipolar disorder range from lasting remission to chronic course or frequent recurrences requiring admissions. The distinction between bipolar I and II disorders has limited utility in outcome prediction. It is unclear to what extent the clinical course of bipolar disorder predicts long-term outcomes. Methods A representative sample of 191 individuals diagnosed with bipolar I or II disorder was recruited and followed for up to 5 years using a life-chart method. We previously described the clinical course over the first 18 months with dimensional course characteristics and latent classes. Now we test if these course characteristics predict long-term outcomes, including time ill (time with any mood symptoms) and hospital admissions over a second non-overlapping follow-up period in 111 individuals with available data from both 18 months and 5 years follow-ups. Results Dimensional course characteristics from the first 18 months prospectively predicted outcomes over the following 3.5 years. The proportion of time depressed, the severity of depressive symptoms and the proportion of time manic predicted more time ill. The proportion of time manic, the severity of manic symptoms and depression-to-mania switching predicted a greater likelihood of hospital admissions. All predictions remained significant after controlling for age, sex and bipolar I v. II disorder. Conclusions Differential associations with long-term outcomes suggest that course characteristics may facilitate care planning with greater predictive validity than established types of bipolar disorders. A clinical course dominated by depressive symptoms predicts a greater proportion of time ill. A clinical course characterized by manic episodes predicts hospital admissions.
Incentivizing Rider Time-Shift in a Multi-Leg Public Transportation System

We develop an incentive scheme for a hub-toshuttle campus transit system, encouraging riders to shift travel times to improve system efficiency. Riders are modeled as agents with loss functions in order to evaluate social welfare in the incentive scheme. Incentives are calculated based on a predictive wait-time model trained via Gaussian process regression. Experiments show that the system’s total loss decreases, but the average wait time increases. This leads to the conclusion that there is room for improvement in the predictive wait time model.
Subradiant, Superradiant and Fano Resonances in Dimer and Ring/Disk Nanocavities

Fano Resonance in Dimers and Ring/Disk Nanocavities by Heidar Sobhani Khakestar Subradiant and superradiant plasmon modes in ring/disk nanocavities and dimers are investigated theoretically and experimentally. The subradiance is obtained through an overall reduction of the total dipole moment of the hybridized mode due to antisymmetric coupling of the dipole moments of the parent plasmons. We observe the appearance of Fano resonances in the optical response of plasmonic nanocavities due to the coherent coupling between their superradiant and subradiant plasmon modes due to structural symmetry breaking. Both subradiant modes and Fano resonances exhibit substantia] reductions in linewidth compared to the parent plasmon resonances, opening up possibilities in optical and near IR sensing via plasmon lineshape design. Four reducedsymmetry nanostructures Studied via Plasmon Hybridization and FDTD (Finite Difference Time Domain), a dolmen-style slab arrangement , a ring/disk dimer, a ring/disk cavity and a colloid dimer, clearly exhibit the strong polarization and geometry dependence expected for this behavior at the individual nanostructure level, confirmed by experimental result in each case, multiple Fano resonances occur as structure size is increased.
A comparative study of changes in vertical dimension of occlusion using four different investing methods

Statement of problem Acrylic resin complete dentures undergo dimensional changes during polymerization. Alternation in type of investment mediums and laboratory procedures can affect vertical dimension of occlusion. Purpose The purpose of this study was to compare changes in vertical dimension of occlusion of simulated complete dentures using four different investing methods in compression molding technique. Materials and Methods Four test groups with 10 sets of complete dentures each were evaluated. Flasking were done in four groups: Group 1: PlasterMixed, Group 2: PlasterStoneMixed, Group 3: PlasterMixedStone, Group 4: PlasterStonePlaster. All experimental groups were compression molded with denture base resin. Changes in occlusal vertical dimension were measured before and after denture processing. Collected data were analyzed with analysis of variance and Tukey's test at 95% level of confidence (p=0.05). Results The Mean of VDO changes in different groups were: group 1 (0.860 ± 0.839), group 2 (0.710 ± 0.585), group 3 (1.548 ± 0.951) and group 4 (0.578 ± 0.686). Conclusion There was significant difference between all tested groups. Less dimensional change was observed in the simulated dentures tested when they were invested with PlasterStonePlaster than when flasked with other investing techniques.
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

A combination of systematic density-functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications. This study presents an application of the combination of systematic DFT calculations and regression techniques to the prediction of the melting temperature for single and binary compounds. Here we adopt the ordinary least-squares regression, partial least-squares regression, support vector regression, and Gaussian process regression. Among the four kinds of regression techniques, SVR provides the best prediction. The inclusion of physical properties computed by the DFT calculation to a set of predictor variables makes the prediction better. In addition, limitation of the predictive power is shown when extrapolation from the training dataset is required. Finally, a simulation to find the highest melting temperature toward the efficient materials design using kriging is demonstrated. The kriging design finds the compound with the highest melting temperature much faster than random designs. This result may stimulate the application of kriging to efficient materials design for a broad range of applications.
DIRECT PORE-SCALE MODELING OF TWO-PHASE FLOW THROUGH NATURAL MEDIA

Pore-scale modeling of multiphase flow of petroleum reservoir fluids through real natural porous media is a true challenge for engineering and scientific society. Because the pore-scale description is a fundamental approach in hydrodynamics it is addressed most frequently to improve our understanding of flow and transport phenomena in such settings. Besides, it can be used to obtain macro-scale constitutive equations, to provide multiphase flow properties for large scale models, to predict how these properties may vary with rock type, wettability etc. One of the important aspects of this problem is the construction of adequate 3D numerical models of flow through pore space. With the recent development of computed micro-tomography (μCT) technique it becomes evident that direct numerical simulations (DNS) at pore-scale will be widely used in the porous medium modeling and particularly, in petroleum applications. The geometrical description of flow region via X-ray μCT, the generation of a 3D grid in real pore space and the corresponding flow computations, constitute the principal objective of our current work. For a simulation of fluid distribution inside pores the models based on incompressible Navier-Stokes equations and their extension to diffuse interface (CahnHilliard) model are used. Transport properties for singleand twophase flow, stationary and transient flow patterns at various viscosity ratio and capillary numbers, the offset of the displacement instability, numerical performance of diffuse interface model in a specific 3D framework, are presented and discussed in some details. The developed methodology of a porous medium properties computation is a valuable tool for both fundamental porous medium and applied petroleum and environmental applications.
Delivering improved patient and system outcomes for hospitalized older adults through an Acute Care for Elders Strategy

Acute care hospitals are widely recognized as potentially high-risk environments for older adults. In 2010, Mount Sinai Hospital conceived its Acute Care for Elders (ACE) Strategy as a multi-component intervention to improve the care of hospitalized older adults. In order to determine its effectiveness, we conducted a quasi-experimental time series analysis of 12,008 older patients admitted non-electively for acute medical issues over a 6-year period. Despite a 53% increase in annual admissions of older patients between 2009/2010 and 2014/2015, Mount Sinai decreased total lengths of stay and readmissions and reduced the direct cost of care per patient, leading to net savings of CDN$4.2 million in 2014/2015. This article presents Mount Sinai’s ACE Strategy and discusses the benefits of implementing integrated evidence-based models across the continuum of care and how it is supporting the implementation of ACE Strategy models of care and care practices across Canada and beyond.
Lower Bounds for Dynamic Programming on Planar Graphs of Bounded Cutwidth

Many combinatorial problems can be solved in time $O^*(c^{tw})$ on graphs of treewidth $tw$, for a problem-specific constant $c$. In several cases, matching upper and lower bounds on $c$ are known based on the Strong Exponential Time Hypothesis (SETH). In this paper we investigate the complexity of solving problems on graphs of bounded cutwidth, a graph parameter that takes larger values than treewidth. We strengthen earlier treewidth-based lower bounds to show that, assuming SETH, Independent Set cannot be solved in $O^*((2-\varepsilon)^{cutw})$ time, and Dominating Set cannot be solved in $O^*((3-\varepsilon)^{cutw})$ time. By designing a new crossover gadget, we extend these lower bounds even to planar graphs of bounded cutwidth or treewidth. Hence planarity does not help when solving Independent Set or Dominating Set on graphs of bounded width. This sharply contrasts the fact that in many settings, planarity allows problems to be solved much more efficiently.
Dynamics and rheology of a supercooled polymer melt in shear flow

Using molecular dynamics simulations, we study dynamics of a model polymer melt composed of short chains with bead number N=10 in supercooled states. In quiescent conditions, the stress relaxation function G(t) is calculated, which exhibits a stretched exponential relaxation on the time scale of the α relaxation time τα and ultimately follows the Rouse dynamics characterized by the time τR∼N2τα. After application of shear γ, transient stress growth σxy(t)/γ first obeys the linear growth ∫0tdt′G(t′) for strain less than 0.1 but saturates into a non-Newtonian viscosity for larger strain. In steady states, shear thinning and elongation of chains into ellipsoidal shapes take place for shear γ larger than τR−1. In such strong shear, we find that the chains undergo random tumbling motion taking stretched and compact shapes alternatively. We examine the validity of the stress–optical relation between the anisotropic parts of the stress tensor and the dielectric tensor, which are violated in transient states d.
Case-based Portraits of Contrasting Micro-Interaction Processes During Online Assessment of Collaborative Problem Solving

This study recognizes the role and the quality of social aspects in collaborative problem solving (CPS) processes and outcomes. The aim of this study, relying on multiple data and phases of analysis, is to explore and visualise, through contrasting case-based portraits, how micro-interaction processes at pair level evolve during CPS assessments in an online environment. The assessment is designed for a student pair in the STEM domain. The results show that in despite students’ similar CPS performance scores, variations in micro-interactions occurred across pairs. It is expected that studying these patterns at pair level may provide new insights into CPS processes and so to support acquiring these skills.
Volume and bounded cohomology

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Effectiveness of targeting the health promotion settings for non-communicable disease control in low/middle-income countries: systematic review protocol

Introduction Settings-based approaches to health promotion, involving holistic and multidisciplinary methods, which integrate action across risk factors are important. Major advantage of focusing on these settings is the continuous and intensive contact with the participant. Despite the apparent advantages of addressing non-communicable diseases (NCDs) using targeted interventions for several developed country settings, a relative lack of evidence of effectiveness of such interventions in low/middle-income countries has led to poor allocation of resources towards these interventions. The focus is therefore on the settings rather than any one condition, and we therefore expect the findings to generalise to NCD prevention and control efforts. We intend to estimate the effectiveness of targeted interventions in low/middle-income countries. Methods and analysis We will search PubMed, Excerpta Medica Database, OVID, WHO Library and The Cochrane Library from the year 2000 to March 2018 without language restrictions. Study designs to be included will be randomised controlled trials. The primary outcome of effectiveness will be the percentage change in population having different behavioural risk factors. Subgroup analyses will be performed, and sensitivity analyses will be conducted to assess the robustness of the findings. Ethics and dissemination No ethical issues are foreseen. The Institute Ethics Committee of the Post Graduate Institute of Medical Education and Research approved the doctoral research protocol under which this review is being done. Dissemination will be done by submitting scientific articles to academic peer-reviewed journals. We will present the results at relevant conferences and meetings. Study design Systematic review. PROSPERO registration number CRD42016042647; Pre-results.