Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of cell velocity distributions in populations of motile algae

SUMMARY The self-propulsion of unicellular algae in still ambient fluid is studied using a previously reported laser-based tracking method, supplemented by new tracking software. A few hundred swimming cells are observed simultaneously and the average parameters of the cells' motility are calculated. The time-dependent, two-dimensional distribution of swimming velocities is measured and the three-dimensional distribution is recovered by assuming horizontal isotropy. The mean and variance of the cell turning angle are quantified, to estimate the reorientation time and rotational diffusivity of the bottom-heavy cell. The cells' phototactic and photokinetic responses to the laser light are evaluated. The results are generally consistent both with earlier assumptions about the nature of cell swimming and quantitative measurements, appropriately adjusted. The laser-based tracking method, which makes it possible to average over a large number of motile objects, is shown to be a powerful tool for the study of microorganism motility.
The Potassium-Argon Laser Experiment (KArLE): In situ geochronology for planetary robotic missions

The Potassium (K) - Argon (Ar) Laser Experiment (KArLE) will make in situ noble-gas geochronology measurements aboard planetary robotic landers and rovers. Laser-Induced Breakdown Spectroscopy (LIBS) is used to measure the K abundance in a sample and to release its noble gases; the evolved Ar is measured by mass spectrometry (MS); and relative K content is related to absolute Ar abundance by sample mass, determined by optical measurement of the ablated volume. KArLE measures a whole-rock K-Ar age to 10% or better for rocks 2 billion years old (Ga) or older, sufficient to resolve the absolute age of many planetary samples. The LIBS-MS approach is attractive because the analytical components have been flight proven, do not require further technical development, and provide complementary measurements as well as in situ geochronology.
Accounting testing in software cost estimation: A case study of the current practice and impacts

Testing has become an integral part of most software projects. It accounts for even as high a share as 40% of the overall work effort. At the same time software projects are systematically exceeding their effort and schedule forecasts. Regardless of the overruns and big share of testing, there is very little advice for estimating testing activities. This case study research assesses the current practice of estimating testing activities, and the impact of these practices on estimation and project success. Based on the interviews with 11 stakeholders involved in two case projects and examination of project documentation, this study shows that companies easily deviate from their standard procedures, when estimating testing. This may even lead to severe estimation errors. The deviations can be explained by negative attitudes towards testing. Furthermore, this study shows that the extant literature has sparsely addressed estimation of software testing.
The effect of fire-management on invertebrates within a South African savanna.

Fire plays a key role in determining the structure and function of savanna ecosystems, without which the characteristic open grassland would be lost, along with the high biodiversity it supports. Over recent decades human development has meant that savanna systems have become heavily utilised, requiring burn management to be undertaken. As savannas are regarded as fire-prone systems, this practise had become an unquestioned tradition. Despite the wide use of burn regimes as a management tool, little is known about the effect of burning on non-focal taxa. This study looks at both the long and short-term effects of burning, in a South African game reserve, on invertebrates within a patch mosaic structure. Since invertebrates encompass a broad group of organisms, analysis was divided into three levels based upon location within the ecosystem. Changes in the abundance and diversity of invertebrates at ground level, grass level and tree level were studied. The short-term study showed that ground invertebrate abundance increased following a burn event, whereas, grass and tree invertebrates showed declines. Analysis of invertebrate abundance and diversity within six different burn ages enabled the long-term effects of a patch mosaic burn regime to be monitored. Ground invertebrate abundance was high in recently burnt patches compared to undisturbed sites. Grass invertebrates declined immediately following the burn event in the short-term study; however, abundances remained similar across the burn ages in the long-term study. In contrast to grass and ground invertebrates, the abundance of tree invertebrates increased as the level of disturbance decreased; with the highest abundance being found in the unburnt site. This study provides a general understanding of the effect burning can have on the invertebrate community; while opening new doors into a relatively untouched subject. Future research into this would be beneficial for the maintenance of the complex savanna ecosystem.
Compositional Models for Audio Processing: Uncovering the structure of sound mixtures

Many classes of data are composed as constructive combinations of parts. By constructive combination, we mean additive combination that does not result in subtraction or diminishment of any of the parts. We will refer to such data as compositional data. Typical examples include population or counts data, where the total count of a population is obtained as the sum of counts of subpopulations. To characterize such data, various mathematical models have been developed in the literature. These models, in conformance with the nature of the data, represent them as nonnegative linear combinations of parts, which themselves are also nonnegative to ensure that such a combination does not result in subtraction or diminishment. We will refer to such models as compositional models.
A Multilingual Natural-Language Interface to Regular Expressions

This report explains a natural-language interface to the formalism of XFST (Xerox Finite State Tool), which is a rich language used for specifying finite state automata and transducers. By using the interface, it is possible to give input to XFST in English and French, as well as to translate formal XFST code into these languages. It is also possible to edit XFST source files and their natural-language equivalents interactively, in parallel. The interface is based on an abstract syntax of the regular expression language and of a corresponding fragment of natural language. The relations between the different components are defined by compositional interpretation and generation functions, and by corresponding combinatory parsers. This design has been inspired by the logical grammar of Montague. The grammar-driven design makes it easy to extend and to modify the interface, and also to link it with other functionalities such as compiling and semantic reasoning. It is also easy to add new languages to the interface. Both the grammatical theory and the interface facilities based on it have been implemented in the functional programming language Haskell, which supports a declarative and modular style of programming. Some of the modules developed for the interface have other uses as well: there is a type system of regular expressions, preventing some compiler errors, a denotational semantics in terms of lazy lists, and an extension of the XFST script language by definitions of functions.
On the uplink performance of LAS-CDMA

In this paper large area synchronized (LAS)-CDMA is investigated, which exhibits a significantly better performance than the traditional random code based DS-CDMA system. Closed-form formulas are derived for characterizing the BER performance of LAS-CDMA as a function of the number of resolvable paths L/sub p/, the maximum delay difference T/sub max/ and the number of users K when communicating over a Nakagami-m fading channel. Moreover, the limited number of available LAS codes having a certain interference free window width suggests that the employment of LAS-CDMA is beneficial in a low-user-load scenario, where a near-single-user performance can be achieved without a multiuser detector.
Learning to recommend with trust and distrust relationships

With the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in the industry, traditional recommender systems suffer from several problems. First, the sparseness of the user-item matrix seriously affects the recommendation quality. Second, traditional recommender systems ignore the connections among users, which loses the opportunity to provide more accurate and personalized recommendations. In this paper, aiming at providing more realistic and accurate recommendations, we propose a factor analysis-based optimization framework to incorporate the user trust and distrust relationships into the recommender systems. The contributions of this paper are three-fold: (1) We elaborate how user distrust information can benefit the recommender systems. (2) In terms of the trust relations, distinct from previous trust-aware recommender systems which are based on some heuristics, we systematically interpret how to constrain the objective function with trust regularization. (3) The experimental results show that the distrust relations among users are as important as the trust relations. The complexity analysis shows our method scales linearly with the number of observations, while the empirical analysis on a large Epinions dataset proves that our approaches perform better than the state-of-the-art approaches.
A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

MOTIVATION Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects. RESULTS We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes. AVAILABILITY AND IMPLEMENTATION The source code repository is publicly available at https://github.com/yangzi4/iNMF. CONTACT gmichail@umich.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Compensating inaccurate annotations to train 3D facial landmark localization models

In this paper we investigate the impact of inconsistency in manual annotations when they are used to train automatic models for 3D facial landmark localization. We start by showing that it is possible to objectively measure the consistency of annotations in a database, provided that it contains replicates (i.e. repeated scans from the same person). Applying such measure to the widely used FRGC database we find that manual annotations currently available are suboptimal and can strongly impair the accuracy of automatic models learnt therefrom. To address this issue, we present a simple algorithm to automatically correct a set of annotations and show that it can help to significantly improve the accuracy of the models in terms of landmark localization errors. This improvement is observed even when errors are measured with respect to the original (not corrected) annotations. However, we also show that if errors are computed against an alternative set of manual annotations with higher consistency, the accuracy of the models constructed using the corrections from the presented algorithm tends to converge to the one achieved by building the models on the alternative, more consistent set.
Implementing the \Omega Failure Detector in the Crash-Recovery Model with partial Connectivity and/or Synchrony

Unreliable failure detectors are mechanisms providing information about process failures, that allows to solve several problems in asynchronous systems, e.g., consensus. A particular class of failure detectors, Omega, provides an eventual leader election functionality. Recently, an algorithm implementing Omega with unknown membership and weak synchrony has been proposed by Jimenez et al. In that work, a crash failure model and a system in which every process has a direct communication link with every other process are assumed. In this paper, we adapt this algorithm to the crash-recovery failure model, and show that it also works in systems with partial connectivity and/or synchrony
Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification.

BACKGROUND Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition. METHODS First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation. RESULTS The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments. CONCLUSIONS Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.
Assessment of Subclinical Atherosclerosis by Carotid Intima–Media Thickness and Epicardial Adipose Tissue Thickness in Prediabetes

Impaired fasting glucose (IFG) and impaired glucose intolerance (IGT) are predictors of cardiovascular disease (CVD). We tested the hypothesis that epicardial fat thickness (EFT) and carotid intima–media thickness (cIMT), as markers of early atherosclerosis, are increased in patients with prediabetes. We prospectively enrolled 246 patients (162 with prediabetes and 84 controls). Prediabetes was defined according to American Diabetes Association criteria, and patients were divided into 3 groups: group 1—IFG, group 2—IGT, and group 3—IFG + IGT. Both cIMT and EFT were significantly greater in patients with prediabetes compared with controls (0.81 ± 0.20 mm vs 0.68 ± 0.16 mm, P < .001 and 7.0 ± 2.0 mm vs 5.6 ± 1.6 mm, P < .001, respectively). This difference was mainly attributed to patients with IGT. Age, waist circumference, and 2-hour glucose independently predicted cIMT, while 2-hour glucose was the only independent predictor of EFT in multivariate analysis among other relevant parameters for cIMT and EFT. The cIMT and EFT (measured noninvasively) could be useful indicators of CVD risk in these patients. In order to prove this hypothesis, long-term prospective studies with greater patient numbers are required.
Crystal Structures of Beta- and Gammaretrovirus Fusion Proteins Reveal a Role for Electrostatic Stapling in Viral Entry

ABSTRACT Membrane fusion is a key step in the life cycle of all envelope viruses, but this process is energetically unfavorable; the transmembrane fusion subunit (TM) of the virion-attached glycoprotein actively catalyzes the membrane merger process. Retroviral glycoproteins are the prototypical system to study pH-independent viral entry. In this study, we determined crystal structures of extramembrane regions of the TMs from Mason-Pfizer monkey virus (MPMV) and xenotropic murine leukemia virus-related virus (XMRV) at 1.7-Å and 2.2-Å resolution, respectively. The structures are comprised of a trimer of hairpins that is characteristic of class I viral fusion proteins and now completes a structural library of retroviral fusion proteins. Our results allowed us to identify a series of intra- and interchain electrostatic interactions in the heptad repeat and chain reversal regions. Mutagenesis reveals that charge-neutralizing salt bridge mutations significantly destabilize the postfusion six-helix bundle and abrogate retroviral infection, demonstrating that electrostatic stapling of the fusion subunit is essential for viral entry. Our data indicate that salt bridges are a major stabilizing force on the MPMV and XMRV retroviral TMs and likely provide the key energetics for viral and host membrane fusion.
Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial

Introduction Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate. Methods We conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts. Results Outcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial. Conclusion The MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality. Trial registration NCT03015454.
A Study on Low-Frequency Variability in Current and Sea Level in the Lombok Strait and Adjacent Region.

Current meter data in the mid-Lombok Strait region in 1985 show that 42% of the vari­ ance in the currents is within the intraseasonal frequency band (10-100 day-1). It is about two times the variance of the tidal currents. This intraseasonal variability appeared as episodic northward flows of 20 to 80 cm/s recurring every 20 to 60 days. Our data indicate these northward flows are driven by episodes of sea-level rise south of the Lombok Strait which are controlled by large scale westerly wind events in the Southeastern Indian Ocean. These northward flow events occur almost simultaneously with either a cyclone or a typhoon generated in south or north monsoon trough, respectively. The occurrence of these northward flows and cyclone/typhoon events follow 5 20 days after the appearance of an atmospheric pressure trough system over the Eastern Indian Ocean Western Pacific region. Sea-level data from 1984-1990 in the Eastern Indian Ocean and Western Pacific region show that the intraseasonal variability is a persistent phenomena along the southern coast of the Indonesian Archipelago. The energy of this variability is concentrated at a 40-60 day period band. The magnitude of this variability suggests that the intraseasonal variability can serve as a significant modulator of the Indonesian throughflow. The surface currents in the Lombok Strait reach 150 cm/s in the mid Strait region and 300 cm/s in the sill region. The monthly mean flow varies from 20 cm/s northward into the Flores S ea to 60 cm/s southward into the Indian Ocean. Within the Lombok Strait, the geostrophic approximation computed from cross-strait sea-level differences explains most the sub-inertial along-strait flow less than 60 cm/s. The sea-level difference outside the entrances of the Strait suggests the flow through the Strait is a frictional balance with a Rayleigh friction coefficient estimated 4.6x10-5 sec-1.