Miracle-dz/UniFAR-FTUs huggingface dataset:
GPS Trace Mining for Discovering Behaviour Patterns

There are diverse sensor applications built into different personal devices, which have the ability to record data related to various aspects of the user. With the ever increasing popularity and lowering costs of such personal devices such as Smart Phones, collecting data from the mobile sensors available in these devices becomes feasible. A wealth of information can be gleaned from such data collected from these sensors which reveals various aspects of the individual's behaviour and activity. Existing approaches for analyzing such data mainly focuses on inferring semantic context and detecting associations from such data. For example, GPS enabled devices allow users to record their movements in the form of spatio-temporal stream points, and meaningful information can be extracted based on different research objectives. In this paper, we have investigated a computation framework in order to identify users' activity categories and their event's associations from GPS trajectory data. This framework has several progressive stages and is designed based on different approaches in each stage, which will facilitate to analyse people's everyday lifestyles that are related to outdoor behaviours. Moreover, we have proposed an approach to improve the performance of the semantic annotation process of this framework, by combining different sources of mobile sensor data (i.e. GPS and audio data). The proposed framework and approaches have been validated on actual data sets which include the Microsoft's Geolife data set and a data set collected by ourselves.
Frequency of fragmented QRS on ECG is increased in patients with rheumatoid arthritis without cardiovascular disease: a pilot study

Myocardial fibrosis causes the fragmentation of QRS complexes (fQRS) on ECGs. We hypothesized that the frequency of fQRS could be more common in patients with rheumatoid arthritis (RA) than in control subjects. A total of 56 patients with RA were compared with 35 age- and gender-matched fibromyalgia subjects for fQRS. The fQRS was defined as the presence of an additional R wave, or notching of the R or S wave, or the presence of fragmentation in 2 contiguous leads corresponding to the territory of a major coronary artery. Patients with bundle block on ECG and cardiovascular disease were excluded. Twenty-one patients (37.5%) in the RA group had fQRS, while two patients in the control group (5.7%) had fQRS (p = 0.001). No differences were found between the groups in terms of age, gender, or drug use. Duration of disease—years (interquartile range [IQR])—was 10 (8) in the fQRS (+) group, while it was 5 (2) in the fQRS (−) group (p < 0.001). Multivariate logistic regression analysis revealed that duration of disease was associated with the presence of fQRS (B = 1.5, odds ratio = 4.5, p = 0.004, 95% confidence interval = 1.6–12.7). We found that fQRS on ECG was more common in patients with RA without cardiovascular disease than in age- and gender-matched control subjects.
Automatic detection of nocuous coordination ambiguities in natural language requirements

Natural language is prevalent in requirements documents. However, ambiguity is an intrinsic phenomenon of natural language, and is therefore present in all such documents. Ambiguity occurs when a sentence can be interpreted differently by different readers. In this paper, we describe an automated approach for characterizing and detecting so-called nocuous ambiguities, which carry a high risk of misunderstanding among different readers. Given a natural language requirements document, sentences that contain specific types of ambiguity are first extracted automatically from the text. A machine learning algorithm is then used to determine whether an ambiguous sentence is nocuous or innocuous, based on a set of heuristics that draw on human judgments, which we collected as training data. We implemented a prototype tool for Nocuous Ambiguity Identification (NAI), in order to illustrate and evaluate our approach. The tool focuses on coordination ambiguity. We report on the results of a set of experiments to assess the performance and usefulness of the approach.
Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as ‘cell adhesion’ and ‘cell migration’. Furthermore, they are most significantly correlated with ‘tissue invasion and metastasis’, a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.
Death and dying in America: the prison industrial complex's impact on women's health.

Copyright © 2003, The Regents of the University of California. I. Cynthia Chandler received an M.Phil. in Criminology from the University of Cambridge and a J.D. from Harvard Law School. She is currently the Co-Founder and Co-Director of Justice Now, a non-profit organization based in Oakland, California that provides direct legal services to women prisoners in conjunction with organizing grassroots campaigns and supporting prisoner peer organizing efforts in resistance to the prison industrial complex. She would like to express her thanks to activists imprisoned in California's state prisons, and also to Amira Day and Deirdre Bourdet for their editorial assistance.
Lignocellulose decomposition by selected streptomyces strains

From 30 actinomycete cultures isolated by enrichment technique on agar media containing newsprint as a primary carbon and energy source, three Streptomyces strains were selected for characterization of their lignocellulose-decomposing abilities. All three streptomycetes were capable of oxidizing specifically 14C-labeled lignocelluloses to 14CO2. These Streptomyces were shown to attack primarily the cellulosic (glucan) components, of which between 25 to 40% evolved as 14CO2 during 1,025 h of incubation depending upon the culture used. Lignin labeled lignocelluloses were also attacked, but to a lesser degree, with up to about 3.5% being oxidized to 14CO2 depending upon the culture used. Additionally, it was shown that purified 14C-labeled milled-wood lignin was attacked, with recoveries of up to 17.7% of the label was 14CO2. This is the first conclusive evidence to show that streptomycetes can decompose lignin.
Best Approach for Low-Risk Superficial Bladder Cancer

In order to create better criteria for different grades of superficial bladder tumor, the WHO and ISUOP develop a new classification in 1998, which was modified in 2004. Although the new classification might be more reproducible, it has not yet been widely accepted. The low risk groups include patients with single Ta, G1, ≤3cm diameter tumors. This group has high recurrence and low progression rates. The standard treatment is the complete resection and postoperative single immediate instillation of whatever chemotherapy agent should be considered.
Wave Climate Hindcasts for the Bohai Sea, Yellow Sea, and East China Sea

ABSTRACT Liang, B. ; Liu, X. ; Li, H. ; Wu, Y. , and Lee, D. , 2016. Wave climate hindcasts for the Bohai Sea, Yellow Sea, and East China Sea. To investigate wave climate of the Bohai Sea, Yellow Sea, and East China Seas, the third-generation wave model Simulated Waves Nearshore (SWAN) is used to simulate waves for the period 1990 to 2011. The spatial resolution of BYECS wave modelling is 1′ in both longitude and latitude. The wind parameters used to simulate the waves are obtained from the Weather Research and Forecasting Model. The results are validated using observational data from seven measurement stations. The spatial distributions of the largest and mean significant wave height within the 22 year period are given, and the spatial distribution of the largest significant wave height of the four seasons is specified. Furthermore, significant wave height is analyzed under mean annual averaged wave conditions for all four seasons. As an example of the application of the hindcast wave data, further details of the wave parameters in Zhangjiapu are presented.
Full-Information Item Bifactor Analysis of Graded Response Data

A plausible factorial structure for many types of psychological and educational tests exhibits a general factor and one or more group or method factors. This structure can be represented by a bifactor model. The bifactor structure results from the constraint that each item has a nonzero loading on the primary dimension and, at most, one of the group factors. The authors develop estimation procedures for fitting the graded response model when the data follow the bifactor structure. Using maximum marginal likelihood estimation of item parameters, the bifactor restriction leads to a major simplification of the likelihood equations and (a) permits analysis of models with large numbers of group factors, (b) permits conditional dependence within identified subsets of items, and (c) provides more parsimonious factor solutions than an unrestricted full-information item factor analysis in some cases. Analysis of data obtained from 586 chronically mentally ill patients revealed a clear bifactor structure.
Rapid and Weight-Independent Improvement of Glucose Tolerance Induced by a Peptide Designed to Elicit Apoptosis in Adipose Tissue Endothelium

A peptide designed to induce apoptosis of endothelium in white adipose tissue (WAT) decreases adiposity. The goal of this work is to determine whether targeting of WAT endothelium results in impaired glucose regulation as a result of impaired WAT function. Glucose tolerance tests were performed on days 2 and 3 of treatment with vehicle (HF-V) or proapoptotic peptide (HF-PP) and mice pair-fed to HF-PP (HF-PF) in obese mice on a high-fat diet (HFD). Serum metabolic variables, including lipid profile, adipokines, individual fatty acids, and acylcarnitines, were measured. Microarray analysis was performed in epididymal fat of lean or obese mice treated with vehicle or proapoptotic peptide (PP). PP rapidly and potently improved glucose tolerance of obese mice in a weight- and food intake–independent manner. Serum insulin and triglycerides were decreased in HF-PP relative to HF-V. Levels of fatty acids and acylcarnitines were distinctive in HF-PP compared with HF-V or HF-PF. Microarray analysis in AT revealed that pathways involved in mitochondrial dysfunction, oxidative phosphorylation, and branched-chain amino acid degradation were changed by exposure to HFD and were reversed by PP administration. These studies suggest a novel role of the AT vasculature in glucose homeostasis and lipid metabolism.
Generation of Accurate Integral Surfaces in Time-Dependent Vector Fields

We present a novel approach for the direct computation of integral surfaces in time-dependent vector fields. As opposed to previous work, which we analyze in detail, our approach is based on a separation of integral surface computation into two stages: surface approximation and generation of a graphical representation. This allows us to overcome several limitations of existing techniques. We first describe an algorithm for surface integration that approximates a series of time lines using iterative refinement and computes a skeleton of the integral surface. In a second step, we generate a well-conditioned triangulation. Our approach allows a highly accurate treatment of very large time-varying vector fields in an efficient, streaming fashion. We examine the properties of the presented methods on several example datasets and perform a numerical study of its correctness and accuracy. Finally, we investigate some visualization aspects of integral surfaces.
The design of a chopped current-feedback instrumentation amplifier

This paper presents the design of a chopped fully-differential current-feedback instrumentation amplifier (CFIA). The current-feedback topology facilitates ground sensing and the use of independent input and output common- mode voltages. The amplifier is intended for the readout of a high-precision thermistor bridge. The required temperature sensing resolution is 1 muK (3sigma) in a 3 mHz - 50 mHz bandwidth, which translates to an input referred noise density of 31nV/VHz. Since these frequencies are very low, the input stages of the amplifier are chopped to suppress their 1/f noise. Moreover, to achieve optimum power efficiency at low noise, the first stage is designed to dissipate the most power (75%). Simulations show that the amplifier's input-referred noise density is 17 nV/VHz at a supply current of 226 muA. The amplifier has been implemented in 0.7 mum CMOS technology.
Transfer of group A streptococcal pyrogenic exotoxin production to nontoxigenic strains of lysogenic conversion

Production of group A streptococcal pyrogenic exotoxins (SPE) type A and C was transferred from toxigenic streptococcal strains to nontoxigenic strains by lysogeny. Lysogens were tested for SPE with Ouchterlony immunodiffusion on Todd-Hewitt agar plates; toxin diffusing from isolated colonies reacted with specific hyperimmune antisera to SPE. Phage prepared from strains T25(3) (T12gl) and 3GL16, both yielding SPE type A, formed plaques on T25(3) (NONLYSOGENIC) lawns. Over 90% of the colonies picked from the plaque centers yielded A toxin, suggesting SPE type A was transferred by lysogenic conversion. SPE type C formation was transferred to nontoxigenic strains T25(3) and K56 with supernatant fluids from mitomycin C-induced cultures of CS112, producing SPE types B and C. All lysogens tested were positive for SPE type C, indicating that C toxin induction also was transferred by lysogenic conversion. SPE type B formation was not transferable by lysogeny with the strains tested.
Localization using signal strength: to range or not to range?

Received Signal Strength (RSS) data collected within a wireless network can be used to obtain either range estimates or connectivity information. Both approaches lead to localization schemes that require no additional hardware. It is not clear, however, when a range-based scheme should be used in favor of a connectivity-based one. We use analysis of the Fisher information and the Cramér-Rao Bound (CRB) to characterize the error of both approaches. We find the existence of a critical connectivity value, below which the use of RSS data for range-based localization is counter-productive. We show that an approximation of the critical connectivity value can be computed as a function of the network size and the parameters of the propagation model.
A 3.52 Gb/s mmWave baseband with delayed decision feedback sequence estimation in 40 nm

We present a digital baseband ASIC for 60 GHz single-carrier (SC) transmission that is optimized for communication scenarios in which most of the energy is concentrated in the first few channel taps. Such scenarios occur for example in office environments with strong reflections. Our circuit targets close-to-optimum maximum-likelihood performance under such conditions. To this end, we show for the first time how a reduced-state-sequence-estimation algorithm can be realized for the 1760 MHz bandwidth of the IEEE 802.11ad standard. The equalizer is complemented in the frontend by a synchronization unit for frequency offset compensation as well as a Golay-sequence based channel estimator and in the backend by an low density parity check (LDPC) decoder. In 40nm CMOS we achieve a measured data rate of up to 3.52 Gb/s using QPSK modulation.
Molecular Imaging of Drug-Modulated Protein-Protein Interactions in Living Subjects

Networks of protein interactions mediate cellular responses to environmental stimuli and direct the execution of many different cellular functional pathways. Small molecules synthesized within cells or recruited from the external environment mediate many protein interactions. The study of small molecule-mediated interactions of proteins is important to understand abnormal signal transduction pathways in cancer and in drug development and validation. In this study, we used split synthetic renilla luciferase (hRLUC) protein fragment-assisted complementation to evaluate heterodimerization of the human proteins FRB and FKBP12 mediated by the small molecule rapamycin. The concentration of rapamycin required for efficient dimerization and that of its competitive binder ascomycin required for dimerization inhibition were studied in cell lines. The system was dually modulated in cell culture at the transcription level, by controlling nuclear factor κB promoter/enhancer elements using tumor necrosis factor α, and at the interaction level, by controlling the concentration of the dimerizer rapamycin. The rapamycin-mediated dimerization of FRB and FKBP12 also was studied in living mice by locating, quantifying, and timing the hRLUC complementation-based bioluminescence imaging signal using a cooled charged coupled device camera. This split reporter system can be used to efficiently screen small molecule drugs that modulate protein-protein interactions and also to assess drugs in living animals. Both are essential steps in the preclinical evaluation of candidate pharmaceutical agents targeting protein-protein interactions, including signaling pathways in cancer cells.
PubChem BioAssay: A Decade’s Development toward Open High-Throughput Screening Data Sharing

High-throughput screening (HTS) is now routinely conducted for drug discovery by both pharmaceutical companies and screening centers at academic institutions and universities. Rapid advance in assay development, robot automation, and computer technology has led to the generation of terabytes of data in screening laboratories. Despite the technology development toward HTS productivity, fewer efforts were devoted to HTS data integration and sharing. As a result, the huge amount of HTS data was rarely made available to the public. To fill this gap, the PubChem BioAssay database (https://www.ncbi.nlm.nih.gov/pcassay/) was set up in 2004 to provide open access to the screening results tested on chemicals and RNAi reagents. With more than 10 years’ development and contributions from the community, PubChem has now become the largest public repository for chemical structures and biological data, which provides an information platform to worldwide researchers supporting drug development, medicinal chemistry study, and chemical biology research. This work presents a review of the HTS data content in the PubChem BioAssay database and the progress of data deposition to stimulate knowledge discovery and data sharing. It also provides a description of the database’s data standard and basic utilities facilitating information access and use for new users.
Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity

The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus genotype information to improve the identification of polymorphism combinations associated with disease risk. The MDR approach is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model‐free (i.e., assumes no particular inheritance model), and is directly applicable to case‐control and discordant sib‐pair study designs. Both empirical and theoretical studies suggest that MDR has excellent power for identifying high‐order gene‐gene interactions. However, the power of MDR for identifying gene‐gene interactions in the presence of common sources of noise is not currently known. The goal of this study was to evaluate the power of MDR for identifying gene‐gene interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic or locus heterogeneity. Using simulated data, we show that MDR has high power to identify gene‐gene interactions in the presence of 5% genotyping error, 5% missing data, or a combination of both. However, MDR has reduced power for some models in the presence of 50% phenocopy, and very limited power in the presence of 50% genetic heterogeneity. Extending MDR to address genetic heterogeneity should be a priority for the continued methodological development of this new approach. Genet. Epidemiol. 24:150–154, 2003. © 2003 Wiley‐Liss, Inc.
Entropy of Overcomplete Kernel Dictionaries

In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevant sparse representations are obtained by relaxing the orthogonality condition of the atoms, yielding overcomplete dictionaries with an extended number of atoms. More generally than the linear decomposition, overcomplete kernel dictionaries provide an elegant nonlinear extension by defining the atoms through a mapping kernel function (e.g., the gaussian kernel). Models based on such kernel dictionaries are used in neural networks, gaussian processes and online learning with kernels. The quality of an overcomplete dictionary is evaluated with a diversity measure the distance, the approximation, the coherence and the Babel measures. In this paper, we develop a framework to examine overcomplete kernel dictionaries with the entropy from information theory. Indeed, a higher value of the entropy is associated to a further uniform spread of the atoms over the space. For each of the aforementioned diversity measures, we derive lower bounds on the entropy. Several definitions of the entropy are examined, with an extensive analysis in both the input space and the mapped feature space.
Querying LUBM with Non-monotonic Features in Protege using NoHR

The Protege plug-in NoHR, which allows the user to combine an OWL 2 EL ontology with a set of non-monotonic (logic programming) rules – suitable e.g. to express defaults and exceptions – and query the combined knowledge base (KB), has recently been extended to OWL 2 QL in a non-trivial way. In this paper, we showcase this extension using an OWL 2 QL version of the well-known LUBM benchmark ontology that preserves all its reasoning features and, in addition, introduce meaningful non-monotonic rules. We can then query this extended knowledge base making use of the data sets that can be generated with LUBM. The demo will thus not only showcase the extension(s) of the tool NoHR, but also its usability with potentially large data sets.