Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural characterization of product ions by electrospray ionization and quadrupole time-of-flight mass spectrometry to support regulatory analysis of veterinary drug residues in foods. Part 2: Benzimidazoles, nitromidazoles, phenothiazines, and mectins.

RATIONALE Analysis for identification and quantification of regulated veterinary drug residues in foods is usually achieved by liquid chromatography coupled to tandem mass spectrometry (LC/MS/MS). The instrumental method requires the selection of characteristic ions, but structural elucidation is seldom performed to help ensure accuracy. This study is a continuation of previous work to characterize selected product ions in support of regulatory monitoring programs. METHODS The tandem mass spectra of 28 veterinary drugs from a previously published LC/MS/MS method were acquired with a high-resolution quadrupole time-of-flight (Q-TOF) mass spectrometer using electrospray ionization (ESI) in positive mode. The TOF analyzer was calibrated to achieve a mass accuracy error <5 ppm for the MS and MS/MS modes, and samples were infused for data acquisition. RESULTS The high mass accuracy achieved in Q-TOF allowed elucidation of the formulae of the product ions previously selected for qualitative identification. Rational interpretation of results was made and compared with the published literature, and the structure for the MS/MS product ions of four classes of regulated drugs (mectins, benzimidazoles, nitroimidazoles, and phenothiazines), totaling 28 compounds, were examined leading to the report of new structures or confirmation of published structures using low-resolution MS. CONCLUSIONS Structural characterization of the product ions selected for identification and quantification of veterinary drug residues is important information for regulatory monitoring programs in defense of regulatory enforcement actions. This study has allowed structural elucidation of 84 MS/MS product ions previously selected for the LC/MS/MS analysis of 28 drug analytes.
Risk of Nondherence to Diabetes Medications Among Medicare Advantage Enrollees: Development of a Validated Risk Prediction Tool

BACKGROUND: Low adherence to oral antidiabetic drugs (OADs) in the Medicare population can greatly reduce Centers for Medicare & Medicaid Services (CMS) star ratings for managed care organizations (MCOs). OBJECTIVE: To develop and validate a risk assessment tool (Prescription Medication Adherence Prediction Tool for Diabetes Medications [RxAPT-D]) to predict nonadherence to OADs using Medicare claims data. METHODS: In this retrospective observational study, claims data for members enrolled in a Medicare Advantage Prescription Drug (MA-PD) program in Houston, Texas, were used. Data from 2012 (baseline period) were used to identify key variables to predict adherence in 2013 (follow-up period). Members aged 65 years and older with a diabetes diagnosis, at least 1 prescription for OADs (biguanides, sulfonylureas, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, or meglitinides), and continuously enrolled for both years were included in the study. Patients with insulin prescriptions were excluded from the cohort. The study outcome, nonadherence in 2013, was defined as proportion of days covered (PDC) < 80%. Multivariable logistic models using 200 bootstrap replications (with replacement) identified factors associated with nonadherence. The final model was tested for discrimination and calibration statistics and internally validated using 10-fold cross-validation. Using weighted beta coefficients of the predictors, the RxAPT-D was created to stratify nonadherence risk and was tested for sensitivity, specificity, positive prediction value, and negative prediction value. The predictive ability of the tool was compared with that of past PDC values using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. RESULTS: Data from 7,028 MA-PD members were used for tool development. Seven predictors (age, total OAD refills, total OAD classes filled, days supply of last filled OAD, pill burden, coverage of last filled OAD, and past adherence) statistically significant in ≥ 50% of the bootstrapped samples were identified from the logistic models. The final model demonstrated good discrimination (c-statistics = 0.75) and calibration (Hosmer-Lemeshow goodness-of-fit P < 0.05) statistics, with good internal validity (area under the curve = 0.73). The RxAPT-D demonstrated adequate sensitivity statistics: sensitivity = 0.73, specificity = 0.63, positive prediction value = 0.74, and negative prediction value = 0.62. Compared with use of past adherence measures, the RxAPT-D had higher prediction ability, relative IDI = 2.09, and user defined NRI = 0.16 with 24% events correctly reclassified. CONCLUSIONS: The RxAPT is an effective tool to identify patients who are likely to become nonadherent to OADs in the follow-up year. Pharmacists in MCOs can use this tool to identify patients expected to be nonadherent to OADs and develop targeted intervention programs to assist in improving MCO CMS star ratings.
Features selection from high-dimensional web data using clustering analysis

The features selection methodologies have become an important field of the data preprocessing techniques. These methods are applied to reduced the dimension of the attributes of different datasets to simplify their analysis. Some of the classical techniques used are wrapper approaches, heuristic functions and filters. The main problem of these approaches is that they usually are black box and computationally expensive algorithms. This work presents a new straightforward strategy to reduce the dimension of the attributes. This new methodology cares about the variables distribution and has been oriented to clustering analysis. It provides an easier human interpretation of the attributes selection strategy and the resulting clusters. Finally, this new approach has been experimentally tested using the FIFA World Cup web dataset, a well-known social-based statistical data with a high number of variables, to show how the features selection strategy find the most relevant variables.
Recycling Lingware in a Multilingual MT System

We describe two methods relevant to multi-lingual machine translation systems, which can be used to port linguistic data (grammars, lexicons and transfer rules) between systems used for processing related languages. The methods are fully implemented within the Spoken Language Translator system, and were used to create versions of the system for two new language pairs using only a month of expert effort.
Performance Analysis of an Acoustic Relative Positioning System

In this work, the performance of an acoustic relative positioning system is studied, by a Monte-Carlo analysis, with the aim of determining the obtained accuracy and precision. The system has been designed in order to be used in the relative positioning estimation of mobile objects which are not restricted to a particular environment and cannot depend on any external infrastructure. In the proposed system, the spatial relations among objects are computed by only using acoustic emissions with the aim of measuring the round-trip-time-of-flight of the acoustic signals emitted by every object. Then, using the data collected in the system, object positions are computed using the multidimensional scaling technique, and a closer solution is achieved by least-square algorithms. In this performance analysis, a Gaussian distribution of the errors has been considered. The obtained results show that a sub-centimeter precision and accuracy can be obtained considering a centimeter error magnitude in the distance measurements with the proposed ranging mechanism.
Distributing the Stochastic Gradient Sampler for Large-Scale LDA

Learning large-scale Latent Dirichlet Allocation (LDA) models is beneficial for many applications that involve large collections of documents.Recent work has been focusing on developing distributed algorithms in the batch setting, while leaving stochastic methods behind, which can effectively explore statistical redundancy in big data and thereby are complementary to distributed computing.The distributed stochastic gradient Langevin dynamics (DSGLD) represents one attempt to combine stochastic sampling and distributed computing, but it suffers from drawbacks such as excessive communications and sensitivity to partitioning of datasets across nodes. DSGLD is typically limited to learn small models that have about 103 topics and $10^3$ vocabulary size. In this paper, we present embarrassingly parallel SGLD (EPSGLD), a novel distributed stochastic gradient sampling method for topic models. Our sampler is built upon a divide-and-conquer architecture which enables us to produce robust and asymptotically exact samples with less communication overhead than DSGLD. We further propose several techniques to reduce the overhead in I/O and memory usage. Experiments on Wikipedia and ClueWeb12 documents demonstrate that, EPSGLD can scale up to large models with 1010 parameters (i.e., 105 topics, 105 vocabulary size), four orders of magnitude larger than DSGLD, and converge faster.
Global Inference Using Integer Linear Programming

This report is a supplemental document of some of our papers [5, 3, 4]. It gives a simple but complete step-by-step case study, which demonstrates how we apply integer linear programming to solve a global inference problem in natural language processing. This framework first transforms an optimization problem into an integer linear program. The program can then be solved using existing numerical packages. The goal here is to provide readers an easy-to-follow example to model their own problems in this framework. There are two main parts in this report. Sec. 2 describe a problem of labeling entities and relations simultaneously as our inference task. It then discusses the constraints among the labels and shows how the objective function and constraints are transformed to an integer linear program. Although transforming the constraints to their linear forms is not difficult in this entity and relation example, sometimes it can be tricky, especially when more variables are involved. Therefore, we discuss how to handle different types of constraints in Sec. 3.
Phases of fluid and electrolyte homeostasis in the extremely low birth weight infant.

OBJECTIVE We had shown previously that preterm infants undergo three phases of fluid and electrolyte homeostasis; prediuretic, diuretic, and postdiuretic. The objectives of the present study were: (1) to determine whether infants even more immature and infants cared for under thermal environmental conditions different from those previously studied also undergo these three phases; and (2) to relate these phases to changes in renal function. METHODS Consecutive, timed urine collections were made during the first 5 days of life in 32 infants with birth weights of 1000 g or less. Infants were cared for in radiant warmers for 24 hours and then transferred to nonhumidified incubators. Diuresis was defined as urine flow rate (V) of 3 mL or more/kg per hour and weight loss of 0.8 g or more/kg per hour. The physiologic relationships among water and sodium balance, insensible water loss, arterial blood pressure, and renal function were made during the three phases. RESULTS Twenty-eight (87%) of the 32 infants underwent the three homeostatic phases. The median ages of onset and cessation of diuresis were 25 and 96 hours, respectively. There was no correlation between onset of diuresis and change of thermal environment. During the prediuretic phase, V averaged 1.6 mL/kg per hour, and 17 of 28 infants had at least one collection period in which V was less than 1 mL/kg per hour; urinary sodium excretion was 0.1 mEq/kg per hour; the glomerular filtration rate (GFR) was 0.22 mL/kg per hour; fractional excretion of sodium (FENa) was 6.2%; and urine osmolality was dilute (221 mOsm/kg). During the diuretic phase, V and sodium excretion more than tripled; GFR and FENa doubled; and there was no change in urine osmolality. During postdiuresis, V and Na excretion decreased to values intermediate between the prediuretic and diuretic phases, and FENa fell to prediuretic levels, but there was no change in GFR or urine osmolality. There was poor correlation between blood pressure and GFR. Insensible water loss was high and variable during all phases, exceeding 190 mL/kg per day in the smallest infants. CONCLUSIONS Extremely low birth weight infants manifest three phases of fluid and electrolyte homeostasis, as do more mature infants, independent of thermal environment. Diuresis and natriuresis are the result of abrupt increases in GFR and FENa. We speculate that this may be the result of expansion of the neonatal extracellular space as fetal lung fluid is reabsorbed.
A numerical study of the suitability of rigid inclusion ground reinforcement beneath caisson quay walls

The objective of this study was to determine whether rigid inclusions are suitable for reinforcement of the foundation of a caisson quay wall functioning as a container terminal. Apart from their brittle behaviour under lateral loading, rigid inclusions are well suited to the large uniform loads and stringent post-construction deflection tolerances associated with container terminal structures. Their inherent strength and stiffness means they have certain advantages over other stiffening columns commonly used for ground reinforcement in port expansion projects. Their mechanical properties allow construction to unrestricted heights at any construction rate and, in theory, RIs can be applied to all soil types. Additionally the locations of many ports coincide with rivers, deltas and estuaries which are associated with poor soil conditions often requiring ground improvement. Their suitability is of practical significance to port planners and engineers who are faced with the challenge of providing satisfactory foundation performance that is cost effective. The addition of RI ground reinforcement for this structural application would allow for greater flexibility in meeting these challenges. The literature review for this study was broad in its scope with emphasis placed on describing the mechanics of the problem, analysis methods and suitable installation methods for execution in the marine environment. One of the key outcomes of the literature review was identifying the problem of lateral loading due to “free-field” lateral ground movements. In light of this, suitable strategies for limiting and accommodating lateral loading of the RIs were proposed. A numerical study of the proposed ground improvement scheme was undertaken using the 3D finite element method. The key model outputs were caisson deflections and RI forces, moments and stresses, for the various simulated construction phases up to operational conditions. The model results were assessed in terms of the key foundation performance criteria which were related to STS crane rail tolerances and limiting tensile stresses in the RIs. This study found that for a firm clay subsoil condition the proposed RI ground reinforcement scheme met the foundation performance criteria for this structural application provided (i) strategies to limit lateral loading were implemented and (ii) the RIs were reinforced over the length where they were not fully compressed. While this study provided insights into the behaviour of RIs
Mitochondrial mutations in human cancer: Curation of translation

ABSTRACT As a genetic disease, cancer is caused by the activation of oncogenes and the inhibition of tumor suppressor genes via genetic and epigenetic mechanisms. Given the important role of energy metabolism in tumors, we analyzed the cancer-derived mutations occurring in the DNA of the mitochondrion. Mutations in the mitochondrial DNA (mtDNA) compared to nuclear DNA are 62% decreased relative to the coding length per chromosome. We find that the majority of these mutations affects highly conserved nucleotides – significantly exceeding the conservation of the mtDNA – and are devoid of single nucleotide polymorphisms (SNPs). Surprisingly, the leading resources for tumor genetics information universally use the standard genetic code for translation of nucleotide into amino acid sequences in their online resources. However, the nuclear and mitochondrial genetic codes differ for four codons and the usage of incomplete STOP codons. Hence, we analyze and curate the consequences for all mutations in the mtDNA and comprehensively reclassify missense, nonsense and synonymous mutations accordingly. In total, 10% of the mutations are incorrectly translated leading to significant changes in the distribution of mutation types with tripling of nonsense and 69% loss of nonstop extension mutations. Lastly, we provide a curated dataset of coding and non-coding mitochondrial mutations in cancer merged, standardized, duplicate-free and aggregated from two databases as a resource including orthogonal data on their high conservation and SNPs. This study generally highlights the need to universally regard the important differences between the standard and mitochondrial genetic code in life science research.
A Cryptanalysis on a Generalized Threshold Cryptosystem

In 1987, Desmedt first proposed a group oriented cryptosystem. During the past decade, group oriented cryptosystem has become one of the important cryptographical primitives. Laih and Harn proposed a generalized threshold cryptosystem with two models in 1991. In their system, each group is only assigned public key. A ciphertext can be decoded by utilizing all the secret keys owned by the users of any access instance. In this paper we will show that Laih-Harn’s generalized threshold cryptosystem can not withstand a cooperative attack. Their cryptosystem is insecure.
Automatic Seizure Detection in ECoG by Differential Operator and Windowed Variance

Differential operator has long been used in image and signal processing with great success to detect significant changes. In this paper we show that differentiation can enhance certain features of brain electrophysiological signals, contaminated with noise, artifacts, and acquisition defects, leading to efficient detection of those changes. Windowed variance method has been very successful in detecting seizure onset in the brain electrophysiological signals. In this paper we have combined these two powerful methods under the name of differential windowed variance (DWV) algorithm to automatically detect seizure onsets in almost real time, in continuous ECoG (depth-EEG) signals of epileptic patients. The main advantages of the method are simplicity of implementation and speed. We have tested the algorithm on 369 h of nonseizure ECoG as well as 59 h of seizure ECoG of 15 epileptic patients. It detected all but six seizures (91.525% accuracy) with an average delay of 9.2 s after the onset with a maximum false detection of three in 24 h of nonseizure data. Eight novel empirical measures have been introduced to avoid false detections. To ascertain the reliability of the detection method a novel methodology, called quasi-ROC (qROC) curve analysis has been introduced. DWV has been compared with a difference filter based sharp transient (ST) detection algorithm.
Dynamic simulation of pneumatic muscle actuator in Matlab/Simulink environment

The pneumatic actuator with artificial muscles in antagonistic connection was designed and realized as result of several research projects solved at the authors' workplace. Three simulation models of this actuator using different muscle models were created in Matlab/Simulink environment for obtaining of some actuator dynamic characteristics. The position of the actuator arm can be obtained from subsystem of gear nonlinearity in simulation models. In the paper there are presented two approaches to the modeling of gear mechanism: by measured and approximated static characteristic of the actuator and by mathematical description of gear using equation of motion for rotation movement. The dynamic responses of the actuator arm position obtained by simulation of the designed models are also presented in the paper.
Efficient QoS management for QoS-aware web service composition

In this paper, we propose an efficient QoS management approach for QoS-aware web service composition. In the approach, we classify web services according to theirs similarity and then design a QoS tree to manage the QoS the classified web services. Besides, by querying the managed QoS, we propose a QoS-aware web service composition via a particle swarm optimisation algorithm to perform fast web service composition. Experimental results based on two kinds of dataset show our proposed approach outperforms other schemes in terms of query cost, computation time and optimality.
Towards dictionary learning from images with non Gaussian noise

We address the problem of image dictionary learning from noisy images with non Gaussian noise. This problem is difficult. As a first step, we consider the extreme sparse code given by vector quantization, i.e. each pixel is finally associated to 1 single atom. For Gaussian noise, the natural solution is K-means clustering using the sum of the squares of differences between gray levels as the dissimilarity measure between patches. For non Gaussian noises (Poisson, Gamma,.), a new measure of dissimilarity between noisy patches is necessary. We study the use of the generalized likelihood ratios (GLR) recently introduced by Deledalle et al. in [1] to compare non Gaussian noisy patches. We propose a K-medoids algorithm generalizing the usual Linde-Buzo-Gray K-means using the GLR based dissimilarity measure. We obtain a vector quantization which provides a dictionary that can be very large and redundant. We illustrate our approach by dictionaries learnt from images featuring non Gaussian noise, and present preliminary denoising results.
Sierra: a power-proportional, distributed storage system

We present the design, implementation, and evaluation of Sierra: a power-proportional, distributed storage system. I/O workloads in data centers show significant diurnal variation, with peak and trough periods. Sierra powers down storage servers during the troughs. The challenge is to ensure that data is available for reads and writes at all times, including power-down periods. Consistency and fault-tolerance of the data, as well as good performance, must also be maintained. Sierra achieves all these through a set of techniques including power-aware layout, predictive gear scheduling, and a replicated shortterm versioned store. Replaying live server traces from a large e-mail service (Hotmail) shows power savings of at least 23%, and analysis of load from a small enterprise shows that power savings of up to 60% are possible.
Incremental Support Vector Machine Classification

Using a recently introduced proximal support vector machine classifier [4], a very fast and simple incremental support vector machine (SVM) classifier is proposed which is capable of modifying an existing linear classifier by both retiring old data and adding new data. A very important feature of the proposed single-pass algorithm , which allows it to handle massive datasets, is that huge blocks of data, say of the order of millions of points, can be stored in blocks of size (n + 1), where n is the usually small (typically less than 100) dimensional input space in which the data resides. To demonstrate the effectiveness of the algorithm we classify a dataset of 1 billion points in 10-dimensional input space into two classes in less than 2.5 hours on a 400 MHz Pentium II processor.