Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Bacteroides 16S rRNA Gene TaqMan-Based Real-Time PCR Assays for Estimation of Total, Human, and Bovine Fecal Pollution in Water

ABSTRACT Bacteroides species are promising indicators for differentiating livestock and human fecal contamination in water because of their high concentration in feces and potential host specificity. In this study, a real-time PCR assay was designed to target Bacteroides species (AllBac) present in human, cattle, and equine feces. Direct PCR amplification (without DNA extraction) using the AllBac assay was tested on feces diluted in water. Fecal concentrations and threshold cycle were linearly correlated, indicating that the AllBac assay can be used to estimate the total amount of fecal contamination in water. Real-time PCR assays were also designed for bovine-associated (BoBac) and human-associated (HuBac) Bacteroides 16S rRNA genes. Assay specificities were tested using human, bovine, swine, canine, and equine fecal samples. The BoBac assay was specific for bovine fecal samples (100% true-positive identification; 0% false-positive identification). The HuBac assay had a 100% true-positive identification, but it also had a 32% false-positive rate with potential for cross-amplification with swine feces. The assays were tested using creek water samples from three different watersheds. Creek water did not inhibit PCR, and results from the AllBac assay were correlated with those from Escherichia coli concentrations (r2 = 0.85). The percentage of feces attributable to bovine and human sources was determined for each sample by comparing the values obtained from the BoBac and HuBac assays with that from the AllBac assay. These results suggest that real-time PCR assays without DNA extraction can be used to quantify fecal concentrations and provide preliminary fecal source identification in watersheds.
Improved SAR Models - Exploiting the Target-Ligand Relationships

Small organic molecules, by binding to different proteins, can be used to modulate (inhibit/activate) their functions for therapeutic purposes and to elucidate the molecular mechanisms underlying biological processes. Over the decades structure-activity-relationship (SAR) models have been developed to quantify the bioactivity relationship of a chemical compound interacting with a target protein, with advances focussing on the chemical compound representation and the statistical learning methods. We have developed approaches to improve the performance of SAR models using compound activity information from different targets. The methods developed in the study aim to determine the candidacy of a target to help another target in improving the performance of its SAR model by providing supplemental activity information. Having identified a helping target we also develop methods to identify a subset of compounds that would result in improving the sensitivity of the SAR model. Identification of helping targets as well as helping compounds is performed using various nearest neighbor approaches using similarity measures derived from the targets as well as active compounds. We also developed methods that involve use of cross-training a series of SVM-based models for identifying the helping set of targets. Our experimental results show that our methods show statistically significant results and incorporate the
Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising

The t-SVD based Tensor Robust Principal Component Analysis (TRPCA) decomposes low rank multi-linear signal corrupted by gross errors into low multi-rank and sparse component by simultaneously minimizing tensor nuclear norm and l 1 norm. But if the multi-rank of the signal is considerably large and/or large amount of noise is present, the performance of TRPCA deteriorates. To overcome this problem, this paper proposes a new efficient iterative reweighted tensor decomposition scheme based on t-SVD which significantly improves tensor multi-rank in TRPCA. Further, the sparse component of the tensor is also recovered by reweighted l 1 norm which enhances the accuracy of decomposition. The effectiveness of the proposed method is established by applying it to the video denoising problem and the experimental results reveal that the proposed algorithm outperforms its counterparts.
Analyse statique par interprétation abstraite

Abstract interpretation was introduced as a generic framework, which allows to formalize, derive and prove static analyses, which are based on conservative approximation, that is, which are able to establish a fixed property for a subset of the programs which actually satisfy it. First, we provide a step by step description of the main steps in the design of such analyses, from the choice of a model for the programs to study, to the formalization of the analysis algorithm, including the choice of a set of predicates to use for the analysis. Then, we present a panel of recent applications of these techniques to various areas of computer science. MOTS-CLES : Interpretation Abstraite, Analyse Statique, Methodes Formelles
Follow‐up routines in gynecological cancer – time for a change?

Objective. To determine current practice of follow‐up of Norwegian gynecological cancer patients, and to review available randomized controlled trials (RCTs) in gynecologic, breast and colorectal cancer patients in order to discuss whether such studies are feasible in gynecological cancer patients. Design. A combined questionnaire study and a systematic review of RCTs in follow‐up of gynecological, breast, and colorectal cancers. Population. Gynecological, breast, and colorectal cancer patients. Methods. A questionnaire regarding follow‐up routines was mailed to 31 gynecological departments in Norway. A systematic search on MEDLINE, EMBASE, and the Cochrane Library databases was conducted to identify RCTs in follow‐up of breast, colorectal, and gynecological cancers. Results. The questionnaire study showed that the number of controls varied from eight to 16 during the first five years’ post‐treatment. Routine investigations such as chest X‐ray and cytology were frequently used in endometrial and cervical cancer. All departments used CA‐125 in follow‐up of ovarian cancer patients. Reviewing the literature, 19 RCTs of varying methodological quality were identified for colorectal and breast cancers, and none for gynecologic cancer. Different follow‐up models were compared, and most studies concluded that there were no significant differences in the detection of recurrence, overall survival, and quality of life between the studied groups. Conclusions. Follow‐up routines after gynecological cancer vary in Norway. The optimal approach is unknown and RCTs comparing follow‐up protocols are missing. Studies of breast and colorectal cancer patients show that studies on follow‐up strategies are feasible but sufficient sample size and observation time are important.
Abnormality diagnosis of transformer winding by frequency response analysis (FRA) using circuit model

A circuit model of a real transformer is established to reproduce the transfer functions of the transformer. This model is based on the configuration and dimensions of the real transformer. Using this circuit model, the sensitivities of the circuit elements to the resonant frequencies are analyzed. In frequency response analysis (FRA), this sensitivity analysis is helpful in the diagnosis of abnormalities by extracting the circuit elements having relatively high sensitivity to the shift of the resonant frequency. Furthermore, the ability to detect abnormalities in a transformer winding is discussed quantitatively by relating the shift of the resonant frequency and the degree of abnormality via variations of circuit elements on the basis of electromagnetic theory. The accumulation of such data for transformers with different classes of rated voltage and capacity, connections of phases, winding configurations, and so will contribute construction of diagnosis criteria for transformer windings by FRA.
Revenue maximization by viral marketing: A social network host's perspective

We study the novel problem of revenue maximization of a social network host that sells viral marketing campaigns to multiple competing campaigners. Each client campaigner informs the social network host about her target users in the network, as well as how much money she is willing to pay to the host if one of her target users buys her product. The social network host, in turn, assigns a set of seed users to each of her client campaigners. The seed set for a campaigner is a limited number of users to whom the campaigner provides free samples, discounted price etc. with the expectation that these seed users will buy her product, and would also be able to influence many of her target users in the network towards buying her product. Because of various product-adoption costs, it is very unlikely that an average user will purchase more than one of the competing products. Therefore, from the host's perspective, it is important to assign seed users to client campaigners in such a way that the seed assignment guarantees the maximum aggregated revenue for the host considering all her client campaigners. We formulate our problem by following two well-established influence cascading models: the independent cascade model and the linear threshold model. While our problem using both these models is NP-hard, and neither monotonic, nor sub-modular; we develop approximated algorithms with theoretical performance guarantees. However, as our approximated algorithms often incur higher running times, we also design efficient heuristic methods that empirically perform as good as our approximated algorithms. Our detailed experimental evaluation attests that the proposed techniques are effective and scalable over real-world datasets.
Knowledge Discovery for Continuous Financial Assurance Using Multiple Types of Digital Information

This paper investigates some extensions to what has emerged as classic continuous (financial) assurance (CFA). First, this paper suggests broadening the base of information that is used in such systems to include emerging social media. Second, because of the new source of data, the methodologies also need to be expanded to include knowledge discovery approaches to facilitate analysis of new and existing information. Third, I suggest that continuous financial assurance begin to integrate with other business efforts at monitoring and knowledge discovery, for example, “business process management. ” Ultimately, an integrated system could gain from information and discoveries in these other monitoring and knowledge discovery efforts. Finally, this paper suggests that additional proactive knowledge discovery be used in addition to classic continuous financial assurance data to facilitate additional discovery.
A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites

Given a set of known binding sites for a specific transcription factor, it is possible to build a model of the transcription factor binding site, usually called a motif model, and use this model to search for other sites that bind the same transcription factor. Typically, this search is performed using a position-specific scoring matrix (PSSM), also known as a position weight matrix. In this paper we analyze a set of eukaryotic transcription factor binding sites and show that there is extensive clustering of similar k-mers in eukaryotic motifs, owing to both functional and evolutionary constraints. The apparent limitations of probabilistic models in representing complex nucleotide dependencies lead us to a graph-based representation of motifs. When deciding whether a candidate k-mer is part of a motif or not, we base our decision not on how well the k-mer conforms to a model of the motif as a whole, but how similar it is to specific, known k-mers in the motif. We elucidate the reasons why we expect graph-based methods to perform well on motif data. Our MotifScan algorithm shows greatly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs.
Stochastic Models for Performance Analysis of Database Recovery Control

In this paper we develop three analytical models for a comprehensive analysis of database recovery. These models, based on semi-Markov stochastic analysis and queueing networks, not only capture the details of modern recovery mechanisms, but take the complex stochastic behavior of the system into account. Furthermore, we use multiple performance measures to analyze different recovery mechanisms, the impact of environment characteristics and the effect of tunable system parameters, thus offering database designers and administrators a better understanding of the recovery system to be designed or managed. A special case of database recovery that has been studied by previous researchers is analyzed in detail; numerical experiments offer evidence of the effectiveness of our approach. The models developed in this paper, however, are applicable to much more general systems and environments. >
IQ Imbalance in Multiuser Systems: Channel Estimation and Compensation

In this paper, we consider the uplink of a single-cell multi-user single-input multiple-output (MU-SIMO) system with in-phase and quadrature-phase imbalance (IQI). In particular, we investigate the effect of receive (RX) IQI on the performance of MU-SIMO systems with large antenna arrays employing maximum-ratio combining receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that the higher the value of signal-to-noise ratio, the higher the impact of IQI on the spectral efficiency (SE). Moreover, a novel pilot-based joint estimator of the augmented multiple-input multiple-output (MIMO) channel matrix and IQI coefficients is described, and then, a low-complexity IQI compensation scheme is proposed, which is based on the IQI coefficients' estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic SE assuming transmission over Rayleigh fading channels with large-scale fading. Furthermore, we investigate how many mobile stations should be scheduled in massive MIMO systems with IQI and show that the highest SE loss occurs at the optimal operating point. Finally, by deriving asymptotic power scaling laws and proving that the SE loss due to IQI is asymptotically independent of the number of BS antennas, we show that massive MIMO is resilient to the effect of RX IQI.
Fibroblast growth factor 10 haploinsufficiency causes chronic obstructive pulmonary disease

Background Genetic factors influencing lung function may predispose to chronic obstructive pulmonary disease (COPD). The fibroblast growth factor 10 (FGF10) signalling pathway is critical for lung development and lung epithelial renewal. The hypothesis behind this study was that constitutive FGF10 insufficiency may lead to pulmonary disorder. Therefore investigation of the pulmonary functions of patients heterozygous for loss of function mutations in the FGF10 gene was performed. Methods The spirometric measures of lung function from patients and non-carrier siblings were compared and both groups were related to matched reference data for normal human lung function. Results The patients show a significant decrease in lung function parameters when compared to control values. The average FEV1/IVC quota (FEV1%) for the patients is 0.65 (80% of predicted) and reversibility test using Terbutalin resulted in a 3.7% increase in FEV1. Patients with FGF10 haploinsufficiency have lung function parameters indicating COPD. A modest response to Terbutalin confirms an irreversible obstructive lung disease. Conclusion These findings support the idea that genetic variants affecting the FGF10 signalling pathway are important determinants of lung function that may ultimately contribute to COPD. Specifically, the results show that FGF10 haploinsufficiency affects lung function measures providing a model for a dosage sensitive effect of FGF10 in the development of COPD.
Phenotypic Diversity Arises from Secondary Signal Loss in the Elaborate Visual Displays of Toucans and Barbets

Complexity and diversity are fundamental characteristics of life, but the relationship between the two remains murky. For example, both gaining and losing complexity can support diversity—so how exactly does complexity influence the emergence of unique phenotypes? Here we address this question by examining how complexity underlies the diversity of elaborate visual displays in the avian clade Ramphastides (toucans and barbets). These species communicate in part by using body movement and colorful ornaments on the tail. We find that sexual size dimorphism predicts the evolution of one specific signal, the tail-cock gesture, implying that tail cocking is more likely to evolve under stronger sexual selection. We also discover process-level constraints on the evolution of complexity: signals are gained along a strict order of operations, where the tail-cock gesture arises before other colors and gestures. Yet virtually any signal can be lost at any time. As a result, many extant phenotypes were more likely to arise through loss of complexity, highlighting the importance of secondary signal loss to phenotypic diversity. Collectively, our results demonstrate how sexual selection catalyzes the evolution of complex phenotypes, which indirectly support diversity by allowing different traits to be modified or lost in the future.
Modeling the ratios of SKKS and SKS amplitudes with ultra‐low velocity zones at the core‐mantle boundary

Between 105–115° degrees, the SKS waveform is complicated by the formation of SPdKS, a wave that has segments of P diffraction along the core mantle boundary. While previous studies have primarily focused on the move‐out of SPdKS from SKS, we analyze the concomitant reduction of the SKS amplitude. Long‐period SKKS/SKS amplitude ratios present a coherent global pattern. SKKS/SKS is relatively large in North and South American recordings of deep Tonga‐Fiji earthquakes but PREM‐like in European recordings of earthquakes in South America and North American recordings of earthquakes in Indonesia. Modeling of SKKS/SKS indicate that Ultra‐Low Velocity Zones (ULVZs), layers at the base of the mantle with a thickness of about 10–20 km and a shear velocity reduction between 20–30%, are required to explain high SKKS/SKS ratios and the early move‐out of SPdKS.
A Tighter Bayesian CramÉR-rao Bound

It has been shown lately that any "standard" Bayesian lower bound (BLB) on the mean squared error (MSE) of the Weiss-Weinstein family (WWF) admits a "tighter" form which upper bounds the "standard" form. Applied to the Bayesian Cramér-Rao bound (BCRB), this result suggests to redefine the concept of efficient estimator relatively to the tighter form of the BCRB, an update supported by a noteworthy example. This paper lays the foundation to revisit some Bayesian estimation problems where the BCRB is not tight in the asymptotic region.
A sequential approach using genetic and morphological analyses to test species status: the case of United States federally endangered Agalinis acuta (Orobanchaceae).

PREMISE OF THE STUDY Given that inaccurate taxonomy can have negative consequences for species of conservation concern and result in erroneous conclusions regarding macroecological patterns, efficient methods for resolving taxonomic uncertainty are essential. The primary objective of this study was to assess the evolutionary distinctiveness of the federally endangered plant species Agalinis acuta (Orobanchaceae) to ensure it represents a distinct taxon warranting protection under the United States Endangered Species Act. METHODS We describe and implement a sequential approach that begins with the most restrictive criteria of genealogical exclusivity within which we first conducted a phylogenetic analysis based on six chloroplast DNA loci assayed from multiple representatives of five putative species. Because of the possibility that incomplete lineage sorting is responsible for the lack of genealogical exclusivity among A. acuta individuals, we then conducted intensive population level analyses based on 21 microsatellite loci and 61 morphological traits. KEY RESULTS The distinctiveness of A. acuta from Agalinis decemloba and Agalinis tenella was not supported under the genealogical species concept. The results from the analyses of microsatellite loci and morphological characters evaluated under alternative species concepts also did not support the distinctiveness of A. acuta from A. decemloba . CONCLUSIONS Through this successive approach, we found insufficient evidence to support the evolutionary distinctiveness of the listed taxon A. acuta . We recommend that it be synonymized under A. decemloba and also conclude that the taxon that would now include A. acuta is deserving of protection under the Endangered Species Act.
Prediction of Drowsy Driving Using EEG and Facial Expression by Machine Learning

Careless driving is the most common cause of traffic accidents. Being in a drowsy state is a cause of careless driving, which can lead to a serious accident. Therefore, in this study, we focus on predicting drowsy driving. Studies on the prediction of drowsy driving focus on the prediction aspect only . However, users have various demands, like not wanting to wear a device while driving, and it is necessary to consider such demands when we introduce the prediction system. Hence, our purpose is to predict drowsy driving that can respond to a user’s demand(s) by combining two approaches of electroencephalogram (EEG ) and facial expressions. Our method is divided into three parts by type of data (facial expressions, EEG, and both), and the users can select the one suitable for their demands. We acquire data with a depth camera and an electroencephalograph and make a machine-learning model to predict drowsy driving. As a result, it is possible to correctly predict drowsy driving in the order of facial expression < EEG < and both combined. Our framework may be applicable to data other than EEG and facial expressions.
Method for making a lignin component, a lignin component and its use and a product

The invention relates to a method for making a lignin component from lignin material by an acid treatment, the method comprising: forming a lignin component of lignin material by treating the lignin material in at least one acid treatment stage; and optimizing process conditions by means of a combination of process parameters comprising retention time, temperature and pH level at the acid treatment stage so that the retention time is over 6 hours during at least one acid treatment stage. Further, the invention relates to corresponding lignin components, a use of the lignin component and a product.