Miracle-dz/UniFAR-FTUs huggingface dataset:
A Weighted Generalization of the Graham-Diaconis Inequality for Ranked List Similarity

The Graham-Diaconis inequality shows the equivalence between two well-known methods of measuring the similarity of two given ranked lists of items: Spearman's footrule and Kendall's tau. The original inequality assumes unweighted items in input lists. In this paper, we first define versions of these methods for weighted items. We then prove a generalization of the inequality for the weighted versions.
Converting a Common Document Scanner to a Multispectral Scanner

We propose the construction of a prototype scanner designed to capture multispectral images of documents. A standard sheet-feed scanner is modified by disconnecting its internal light source and connecting an external multispectral light source comprising of narrow band light emitting diodes (LED). A document is scanned by illuminating the scanner light guide successively with different LEDs and capturing a scan of the document. The system is portable and can be used for potential applications in verification of questioned documents, cheques, receipts and bank notes.
Kernel Machine and Distributed Lag Models for Assessing Windows of Susceptibility to Mixtures of Time-Varying Environmental Exposures in Children's Health Studies

Research has shown that early life exposures to environmental chemicals, starting as early as conception, can reprogram developmental trajectories to result in altered health status later in life. These principles likely apply to complex mixtures as well as individual chemicals. We thus consider statistical methods to estimate the association between mixtures of multiple time-varying exposures and a future health outcome, e.g. exposure to multiple air pollutants observed weekly throughout pregnancy and birth weight. First, we illustrate how to use traditional distributed lag models, distributed lag nonlinear models, and Bayesian kernel machine regression to estimate the association between multiple time-varying exposures and a health outcome. While none of these methods simultaneously account for exposure-timing, nonlinear association, and interactions, we highlight situations in which each model performs well. Second, we propose a new method to estimate the association between multiple time-varying exposures and a health outcome. The proposed approach is, to our knowledge, the first method to simultaneously account for exposure-timing, nonlinear associations, and interactions between time-varying exposures. The proposed approach is a Bayesian kernel machine regression method that accounts for exposure timing using a functional weight component within the kernel. The weight function identifies developmental periods with increased association between exposure and a future health outcome, often referred to as a window of susceptibility. We demonstrate the proposed methods in an analysis of exposure to four ambient pollutants and birth weight in a Boston-area perinatal cohort.
Depression gets old fast: do stress and depression accelerate cell aging?

Depression has been likened to a state of “accelerated aging,” and depressed individuals have a higher incidence of various diseases of aging, such as cardiovascular and cerebrovascular diseases, metabolic syndrome, and dementia. Chronic exposure to certain interlinked biochemical pathways that mediate stress‐related depression may contribute to “accelerated aging,” cell damage, and certain comorbid medical illnesses. Biochemical mediators explored in this theoretical review include the hypothalamic–pituitary–adrenal axis (e.g., hyper‐ or hypoactivation of glucocorticoid receptors), neurosteroids, such as dehydroepiandrosterone and allopregnanolone, brain‐derived neurotrophic factor, excitotoxicity, oxidative and inflammatory stress, and disturbances of the telomere/telomerase maintenance system. A better appreciation of the role of these mediators in depressive illness could lead to refined models of depression, to a re‐conceptualization of depression as a whole body disease rather than just a “mental illness,” and to the rational development of new classes of medications to treat depression and its related medical comorbidities. Depression and Anxiety, 2010. © 2010 Wiley‐Liss, Inc.
A new method for detecting signal regions in ordered sequences of real numbers, and application to viral genomic data

We present a fast, robust and parsimonious approach to detecting signals in an ordered sequence of numbers. Our motivation is in seeking a suitable method to take a sequence of scores corresponding to properties of positions in virus genomes, and find outlying regions of low scores. Suitable statistical methods without using complex models or making many assumptions are surprisingly lacking. We resolve this by developing a method that detects regions of low score within sequences of real numbers. The method makes no assumptions a priori about the length of such a region; it gives the explicit location of the region and scores it statistically. It does not use detailed mechanistic models so the method is fast and will be useful in a wide range of applications. We present our approach in detail, and test it on simulated sequences. We show that it is robust to a wide range of signal morphologies, and that it is able to capture multiple signals in the same sequence. Finally we apply it to viral genomic data to identify regions of evolutionary conservation within influenza and rotavirus.
Exploring the Daschle Collection using Text Mining

A U.S. Senator from South Dakota donated documents that were accumulated during his service as a house representative and senator to be housed at the Bridges library at South Dakota State University. This project investigated the utility of quantitative statistical methods to explore some portions of this vast document collection. The available scanned documents and emails from constituents are analyzed using natural language processing methods including the Latent Dirichlet Allocation (LDA) model. This model identified major topics being discussed in a given collection of documents. Important events and popular issues from the Senator Daschles career are reflected in the changing topics from the model. These quantitative statistical methods provide a summary of the massive amount of text without requiring significant human effort or time and can be applied to similar collections.
Degradation of p53 by Human Alphapapillomavirus E6 Proteins Shows a Stronger Correlation with Phylogeny than Oncogenicity

Background Human Papillomavirus (HPV) E6 induced p53 degradation is thought to be an essential activity by which high-risk human Alphapapillomaviruses (alpha-HPVs) contribute to cervical cancer development. However, most of our understanding is derived from the comparison of HPV16 and HPV11. These two viruses are relatively distinct viruses, making the extrapolation of these results difficult. In the present study, we expand the tested strains (types) to include members of all known HPV species groups within the Alphapapillomavirus genus. Principal Findings We report the biochemical activity of E6 proteins from 27 HPV types representing all alpha-HPV species groups to degrade p53 in human cells. Expression of E6 from all HPV types epidemiologically classified as group 1 carcinogens significantly reduced p53 levels. However, several types not associated with cancer (e.g., HPV53, HPV70 and HPV71) were equally active in degrading p53. HPV types within species groups alpha 5, 6, 7, 9 and 11 share a most recent common ancestor (MRCA) and all contain E6 ORFs that degrade p53. A unique exception, HPV71 E6 ORF that degraded p53 was outside this clade and is one of the most prevalent HPV types infecting the cervix in a population-based study of 10,000 women. Alignment of E6 ORFs identified an amino acid site that was highly correlated with the biochemical ability to degrade p53. Alteration of this amino acid in HPV71 E6 abrogated its ability to degrade p53, while alteration of this site in HPV71-related HPV90 and HPV106 E6s enhanced their capacity to degrade p53. Conclusions These data suggest that the alpha-HPV E6 proteins' ability to degrade p53 is an evolved phenotype inherited from a most recent common ancestor of the high-risk species that does not always segregate with carcinogenicity. In addition, we identified an amino-acid residue strongly correlated with viral p53 degrading potential.
Measurements of Ion Density and Electron Temperature Around Voids in Dusty Plasmas

In dusty plasmas, dust particles are levitated by balance of forces inward to the plasmas (typically corresponding to electrostatic force) and outward from the plasmas (ion drag force) as well as gravity. The balance of the forces tends to make a region free from dust particles, so-called void at the center of the plasmas. This paper made it clear in measurements of ion density and electron temperature with a double-probe method that the void was a region involving ionization. The dust particles were distributed in the other region diffusion dominant for electrons and ions. The void is shown to be reduced by restricting the region involving ionization.
A UK clinical audit addressing the quality of prescribing of sodium valproate for bipolar disorder in women of childbearing age

Objectives To review prescribing practice concerning valproate, an established human teratogen, for the management of bipolar disorder in women of childbearing age. Design The Prescribing Observatory for Mental Health conducted a baseline clinical audit in the UK, as part of a quality improvement programme. Participants Six hundred and forty-eight clinical teams from 55 mental health Trusts submitted retrospective treatment data relating to patients with a diagnosis of bipolar disorder. Results Of the audit sample of 6705 patients, 3854 were 50 years of age or younger. Valproate was prescribed for 24% of women and 43% men in this age group, and the mean dose of valproate was lower in women (1196 mg) than in men (1391 mg). For only half of such women was there documented evidence that information had been provided on the risks for the unborn child and the need for adequate contraception. Valproate was more often used in men to treat mania and aggression, while the most common treatment targets in women were hypomania and relapse prevention. Conclusions Despite explicit recommendations in national treatment guidelines and published safety alerts and warnings regarding the use of valproate in women of childbearing age, current prescribing of this medication to such women in the context of the treatment of bipolar disorder falls short of best practice, particularly with regard to provision of information regarding the risks associated with exposure to valproate during pregnancy. While women younger than 50 years of age were less likely to be prescribed valproate than men in the same age group, and at a lower dosage, it is unclear to what extent this reflects clinicians’ concerns about teratogenicity or is driven by perceptions of the indication for valproate, and the dosage required, for the treatment of different phases of the disorder in men and women.
RAS–MAPK Reactivation Facilitates Acquired Resistance in FGFR1-Amplified Lung Cancer and Underlies a Rationale for Upfront FGFR–MEK Blockade

The FGFR kinases are promising therapeutic targets in multiple cancer types, including lung and head and neck squamous cell carcinoma, cholangiocarcinoma, and bladder cancer. Although several FGFR kinase inhibitors have entered clinical trials, single-agent clinical efficacy has been modest and resistance invariably occurs. We therefore conducted a genome-wide functional screen to characterize mechanisms of resistance to FGFR inhibition in a FGFR1-dependent lung cancer cellular model. Our screen identified known resistance drivers, such as MET, and additional novel resistance mediators including members of the neurotrophin receptor pathway (NTRK), the TAM family of tyrosine kinases (TYRO3, MERTK, AXL), and MAPK pathway, which were further validated in additional FGFR-dependent models. In an orthogonal approach, we generated a large panel of resistant clones by chronic exposure to FGFR inhibitors in FGFR1- and FGFR3-dependent cellular models and characterized gene expression profiles employing the L1000 platform. Notably, resistant clones had enrichment for NTRK and MAPK signaling pathways. Novel mediators of resistance to FGFR inhibition were found to compensate for FGFR loss in part through reactivation of MAPK pathway. Intriguingly, coinhibition of FGFR and specific receptor tyrosine kinases identified in our screen was not sufficient to suppress ERK activity or to prevent resistance to FGFR inhibition, suggesting a redundant reactivation of RAS–MAPK pathway. Dual blockade of FGFR and MEK, however, proved to be a more powerful approach in preventing resistance across diverse FGFR dependencies and may represent a therapeutic opportunity to achieve durable responses to FGFR inhibition in FGFR-dependent cancers. Mol Cancer Ther; 17(7); 1526–39. ©2018 AACR.
Temporal prediction of in-hospital falls using tensor factorisation

In-hospital fall incidence is a critical indicator of healthcare outcome. Predictive models for fall incidents could facilitate optimal resource planning and allocation for healthcare providers. In this paper, we proposed a tensor factorisation-based framework to capture the latent features for fall incidents prediction over time. Experiments with real-world data from local hospitals in Hong Kong demonstrated that the proposed method could predict the fall incidents reasonably well (with an area under the curve score around 0.9). As compared with the baseline time series models, the proposed tensor based models were able to successfully identify high-risk locations without records of fall incidents during the past few months.
SPECIES DIVERSITY PATTERNS DERIVED FROM SPECIES–AREA MODELS

Although area, species abundances, spatial distribution, and species richness have been central components of community ecology, their interrelationships are not com- pletely understood. To describe these interrelationships, we study and test three patterns regarding species richness using species-area models. The first one is the widely accepted generalization that states that the number of species monotonically increases with sampling area. The second pattern predicts the decrease in species richness with the increase of species dominance in a given area. The third one predicts that spatial aggregation of in- dividuals within species results in lower species richness in communities. These three generalizations were investigated by modeling and simulations. First, a random-placement species-area model was used to evaluate the effects of relative species abundances on species richness in a sampling area. Then, a nonrandom species-area model was derived which explicitly encompasses the spatial distributions of species; it served to evaluate the effects of heterogeneity in spatial distributions on species richness. Species-area models were numerically evaluated using parameters estimated from a tropical rain forest com- munity, and simulations were conducted to support the numerical solutions. The three patterns regarding species diversity were consistently supported by the results. A discussion ensues, describing how the three patterns can be used to interpret and predict species diversity, and how they are supported by other diversity hypotheses. The three generaliza- tions suggest that, if we want to understand species diversity, we should go and look for mechanisms that influence the abundances and spatial distributions of species. If a mech- anism can make the species abundances more even, or their spatial distributions more regular, this factor likely contributes to species coexistence.
AN ON-LINE MONITORING TECHNIQUE FOR A PIPE THINNING

A new novel technique for the pipe thinning is introduced, which is one of the major issues regarding the structural fracture of pipes in nuclear power plant. The method is to inspect a large area of a piping system at a time on an on-line basis monitoring. The basic idea came from the fact that the group velocity of an impact wave is dependent on a wall thickness of the piping. That is, if the group velocity is measured, the wall thickness can easily be estimated. To obtain a group velocity, a time-frequency analysis is utilized. This is because an arrival time difference between the flexural waves can be measured easier in time-frequency domain than a time domain. To test the performance of this technique, experiments have been performed for a plate and elbow type pipe. Results show that the proposed technique is effective for monitoring the pipe thinning at an early stage.
Internet of Hybrid Energy Harvesting Things

Internet of Things (IoT) is a perfect candidate to realize efficient observation and management for Smart City concept. This requires deployment of large number of wireless devices. However, replenishing batteries of thousands, maybe millions of devices may be hard or even impossible. In order to solve this problem, Internet of Energy Harvesting Things (IoEHT) is proposed. Although the first studies on IoEHT focused on energy harvesting (EH) as an auxiliary power provisioning method, now completely battery-free and self-sufficient systems are envisioned. Taking advantage of diverse sources that the concept of Smart City offers helps us to fully appreciate the capacity of EH. In this way, we address the primary shortcomings of IoEHT; availability, unreliability, and insufficiency by the Internet of Hybrid EH Things (IoHEHT). In this paper, we survey the various EH opportunities, propose an hybrid EH system, and discuss energy and data management issues for battery-free operation. We mathematically prove advantages of hybrid EH compared to single source harvesting as well. We also point out to hardware requirements and present the open research directions for different network layers specific to IoHEHT for Smart City concept.
Non-invasive Risk Prediction Models in Identifying Undiagnosed Type 2 Diabetes or Predicting Future Incident Cases in the Iranian Population.

BACKGROUND Iran needs pragmatic screening methods for identifying those with undiagnosed type 2 diabetes or at high risk of developing it. The aim of this study was to assess performance of three non-invasive risk prediction models, i.e. the Finnish Diabetes Risk Score (FINDRISC), the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), and the American Diabetes Association Risk Score (ADA), for identifying those with undiagnosed type 2 diabetes (prevalent type 2 diabetes at baseline without any treatment) or those who would develop type 2 diabetes within 5 years of follow-up. METHODS 3467 participants aged ≥30 years without treated type 2 diabetes in the Tehran Lipid and Glucose Study (TLGS) were included in this study. The discrimination power of models was assessed by area under the curve (AUC), their calibrations were assessed by calibration plots and Hosmer-Lemeshow test, and their net benefits were assessed by decision curves. RESULTS 430 participants had undiagnosed type 2 diabetes at baseline and 203 developed type 2 diabetes during 5 years of followup. AUSDRISK had the highest AUC (0.77) as compared to FINDRISC (0.75; P value: 0.014), and the ADA model (0.73; P value: <0.001). The original model for AUSDRISK and calibrated versions of FINDRISC and ADA models had acceptable calibration (Hosmer-Lemeshow chi-square <20) and these models were clinically useful in a wide range of risk thresholds as their net benefit was higher than no-screening scenarios. CONCLUSION The original AUSDRISK model and recalibrated models for FINDRISC and ADA are valid and effective tools for identifying those with undiagnosed or 5-year incident type 2 diabetes in Iran.
Interaction Patterns of Nurturant Support Exchanged in Online Health Social Networking

Background Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. Objective To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. Methods We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Results Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. Conclusions The computer-mediated communication format may have the greatest influence on the supportive interactions because of characteristics such as audience reach and access. Other factors include perception of community versus personal space or purpose of communication. These results lead to a need for further research.
Origin of the DAMPE 1.4 TeV peak

Recent accurate measurements of cosmic ray electron flux by the Dark Matter Particle Explorer (DAMPE) reveal a sharp peak structure near 1.4 TeV, which is difficult to explain by standard astrophysical processes. In this letter, we propose a simple model that the enhanced dark matter annihilation via the $e^+e^-$ channel and with the thermal relic annihilation cross section around the current nearest black hole (A0620-00) can satisfactorily account for the sharp peak structure. The predicted dark matter mass is $\sim 1.5-3$ TeV.