nils-herrmann/clean_scirepeval huggingface dataset:
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Venue: Nature Biotechnology
Year: 2013

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community's functional capabilities. Here we describe PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
Valoracion de los regimenes de trabajo y descanso. Caso de estudio: Varadero (Cuba) (Valuation of the Work and Rest Systems. Case Study: Varadero (Cuba)
Year: 2018

<b>Spanish Abstract:/b> Los regimenes de trabajo y descanso (rtd) son un factor necesario para alcanzar altos niveles de productividad y bienestar laboral, de ahi la importancia de su estudio. El objetivo de esta investigacion fue valorar los rtd de los trabajadores de una empresa hotelera en el polo turistico de Varadero. El metodo de investigacion fue de induccion cientifica, analitico y sintetico. El estudio considero las areas de recepcion, camareras de piso y meseros. Los instrumentos de investigacion empleados fueron la encuesta de valoracion de los elementos del descanso, la Prueba de Yoshitake, la fotografia individual y la estimacion del tiempo de descanso segun la Organizacion Internacional del Trabajo (oi t) El analisis de los datos, siguiendo los instrumentos estadisticos, permitio el calculo del tiempo total de descanso: 45, 40 y 30 minutos para camareras, meseros y recepcion, respectivamente. Un analisis cualitativo y cuantitativo de los efectos negativos para los trabajadores permitio la propuesta de los ajustes para su distribucion en la jornada laboral de los grupos de trabajo estudiados en la empresa hotelera.<br><br><b>English Abstract:/b> The study of Work and Rest Regimes (WRR) are a necessary factor to achieve high levels of productivity and work well being. The objective of this research was to assess the rtd of workers in a hotel company in the tourist pole of Varadero. The research method was scientific induction, analytical and synthetic. The study considered the jobs: reception, floor maids and waiters. The research tools used were: the assessment survey of the elements of rest, the Yoshitake test, the individual photograph, estimation of the Rest Time according to the International Labor Organization (ILO) The analysis of the data, following the statistical tools, allowed the calculation of the total rest time: 45, 40 and 30 minutes for waitresses, waiters and reception respectively. A qualitative and quantitative analysis of the negative effects for the workers allowed the proposal of the adjustments for the distribution in the working day to the work groups studied in the hotel company.
General purpose curvilinear object detection with fuzzy mathematical morphology
Venue: Appl. Soft Comput.
Year: 2017

Display Omitted We present two general purpose algorithms for curvilinear object detection.The first one is based on the fuzzy morphological hit or miss transform.The second one uses the fuzzy top hat transform.We compare them with other state of the art general purpose methods.We show the strengths and potential of our algorithms across varied research grounded imagery. Curvilinear object detection is the common denominator of several applications. Some illustrative examples are road detection from aerial or satellite images, human airways from volumetric 3D scans or vascular structures in eye fundus images. In this work, we propose two general purpose curvilinear object detectors that may serve as building blocks for application specific systems. To do so, we employ fuzzy mathematical morphology operators due to their robustness with respect to uncertainty and noise, and the trade off they offer between expressive power and computational requirements. The extraction of linear features is based on, respectively, the fuzzy hit or miss transform and the fuzzy top hat transform. They can be customized depending on the width of the objects of interest. We compare these two approaches with other state of the art, general purpose curvilinear object detectors to highlight their strengths and shortcomings. Both detectors succeed at localizing the objects of interest in different greyscale images.
Unsupervised Pseudo Labeling for Extractive Summarization on Electronic Health Records
Venue: ArXiv
Year: 2018

Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs) However, the training of a supervised model requires disease specific medical background and is thus very expensive. We studied how to utilize the intrinsic correlation between multiple EHRs to generate pseudo labels and train a supervised model with no external annotation. Experiments on real patient data validate that our model is effective in summarizing crucial disease specific information for patients.
A generalized Spatio Temporal Threshold Clustering method for identification of extreme event patterns
Year: 2021

Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large scale well connected spatial patterns to smaller scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.
The Effectiveness of Full body EOS Compared With Conventional Chest X ray in Preoperative Evaluation of the Chest for Patients Undergoing Spine Operations: A Preliminary Study
Venue: Spine
Year: 2018

A prospective radiographic comparative study.  The aim of this study was to compare full body EOS with conventional chest X ray (CXR) for use in the preoperative evaluation of the chest in patients undergoing spine operations.  The full body EOS reproduces an image of the chest similar to a routine CXR. The potential for the former replacing the latter is plausible. This is especially applicable in spine patients who would routinely have a preoperative full body EOS performed.  A radiographic comparative study of 266 patients was conducted at a single tertiary center from January 2013 to July 2016. Each patient had EOS and CXR done in random order <2 weeks apart. Two radiologists reported the image findings using a checklist. A third radiologist was consulted in cases of discrepancy. Interobserver agreement was calculated using Gwet AC1 and a comparison between EOS and CXR findings was analyzed using paired Chi squared test. Multivariate analysis was performed to identify predictors for abnormal radiological findings. The institutional ethics committee approved this prospective study and waiver of informed consent was obtained.  There were 84 males (31.6% and 182 females (68.4% The mean age was 38.9 years (SD 25.0 years) High interobserver agreement was found for EOS and CXR (Gwet AC1 0.993 and 0.988, respectively) There were no significant differences between both imaging modalities. Rare diagnoses precluded comparison of certain conditions. Age >18 years [odds ratio (OR) 7.69; P 0.009] and American Society of Anesthesiologists physical status 3 (OR 6.64; P 0.018) were independent predictors of abnormal radiological findings.  EOS is not inferior to, and may be used to replace CXR in preoperative radiological screening of thoracic conditions especially in low risk patients 18 years old and patients with ASA <3. Preoperative assessment should never rely on a single modality. High risk patients should be sent for a thorough work up before spine surgery.
Quality Control of Procollagen in Cells.
Venue: Annual review of biochemistry
Year: 2021

Collagen is the most abundant protein in mammals. A unique feature of collagen is its triple helical structure formed by the Gly Xaa Yaa repeats. Three single chains of procollagen make a trimer, and the triple helical structure is then folded in the endoplasmic reticulum (ER) This unique structure is essential for collagen's functions in vivo, including imparting bone strength, allowing signal transduction, and forming basement membranes. The triple helical structure of procollagen is stabilized by posttranslational modifications and intermolecular interactions, but collagen is labile even at normal body temperature. Heat shock protein 47 (Hsp47) is a collagen specific molecular chaperone residing in the ER that plays a pivotal role in collagen biosynthesis and quality control of procollagen in the ER. Mutations that affect the triple helical structure or result in loss of Hsp47 activity cause the destabilization of procollagen, which is then degraded by autophagy. In this review, we present the current state of the field regarding quality control of procollagen. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http:/www.annualreviews.org/page/journal/pubdates for revised estimates.
Trusted execution environment with Intel SGX
Year: 2020

Trusted execution environments (TEE) are emerging as a new form of computing on today's hardware. TEEs ensure that code outside of the TEE, including the operating system and hypervisor, cannot compromise the execution integrity and confidentiality of programs run inside the TEE. Based on hardware rooted trust, TEEs additionally allow to prove the integrity of such execution even to remote third parties (remote attestation) By using TEEs that protect not only against software attackers but also hardware attackers, even the cloud provider is moved out of the trust domain. By leveraging such hardware based TEEs, there is an alternate approach on building secure multiparty computation toolkits. One such TEE is from Intel Software Guard Extension (SGX) technology available in current hardware. This chapter describes the abstractions and properties offered by TEEs, explains the realization of the TEE abstraction in Intel SGX, explores the deployment of SGX in the Cloud to realize secure multiparty applications, and finishes with an Outlook on challenges and opportunities ahead.
Composable Deep Reinforcement Learning for Robotic Manipulation
Venue: 2018 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2018

Model free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q learning can be applied to real world robotic manipulation. The application of this method to real world manipulation is facilitated by two important features of soft Q learning. First, soft Q learning can learn multimodal exploration strategies by learning policies represented by expressive energy based models. Second, we show that policies learned with soft Q learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q learning is substantially more sample efficient than prior model free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real world tasks.
Step Zero: Determinants and Implications of Reliance on the Qualitative Goodwill Impairment Assessment
Year: 2019

I examine the use of qualitative versus quantitative impairment testing methods and their differential implications for future impairments and goodwill valuation. Using hand collected data, I find that the likelihood of reliance on a qualitative assessment is decreasing in both the complexity of goodwill and the risk of an underlying goodwill impairment. I also find that the likelihood of large future goodwill impairments is lower on average for firm years in which the firm relies on a qualitative assessment. Finally, I investigate investor perceptions of this choice and find evidence that investors place a premium on goodwill when firms rely on a qualitative assessment versus a quantitative test. This finding suggests that investors perceive a firm's goodwill impairment testing method choice as reflective of management's private information. Overall, results suggest that managers are not using the discretion in ASU 2011 08 opportunistically, which was a concern in implementing this new standard.
Automatic Detection and Classification of Audio Events for Road Surveillance Applications
Venue: Sensors
Year: 2018

This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time domain, frequency domain, and joint time frequency features extracted from a class of quadratic time frequency distributions (QTFDs) to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.
Missense Mutations in the Unfoldase ClpC1 of the Caseinolytic Protease Complex Are Associated with Pyrazinamide Resistance in Mycobacterium tuberculosis
Venue: Antimicrobial Agents and Chemotherapy
Year: 2016

Previously, we showed that mutations in Mycobacterium tuberculosispanD, involved in coenzyme A biosynthesis, cause resistance against pyrazinoic acid, the bioactive component of the prodrug pyrazinamide. To identify additional resistance mechanisms, we isolated mutants resistant against pyrazinoic acid and subjected panD wild type strains to whole genome sequencing. Eight of the nine resistant strains harbored missense mutations in the unfoldase ClpC1 associated with the caseinolytic protease complex.
Flower color influences insect visitation in alpine New Zealand.
Venue: Ecology
Year: 2010

Despite a long standing belief that insect pollinators can select for certain flower colors, there are few experimental demonstrations that free flying insects choose between natural flowers based on color. We investigated responses of insect visitors to experimental manipulations of flower color in the New Zealand alpine. Native syrphid flies (Allograpta and Platycheirus) and solitary bees (Hylaeus and Leioproctus) showed distinct preferences for visiting certain flower species. These responses were determined, in part, by flower color, as insects also responded to experimental manipulations of visible petal color in 7 out of 11 tests with different combinations of flower species and insect type. When preferences were detected, syrphid flies chose yellow over white petals regardless of flower species, whereas Hylaeus chose white over yellow Ourisia glandulosa. In some cases, the strength and direction of color preference depended on the context of other floral traits, in which case the response usually favored the familiar, normal combination of traits. Syrphid flies also visited in response to floral morphological traits but did not show preference based on UV reflectance. The unusually high preponderance of white flowers in the New Zealand alpine is not explained by complete generalization of flower color choice. Instead, the insect visitors show preferences based on color, including colors other than white, along with other floral traits. Furthermore, they can respond in complex ways to combinations of floral cues, suggesting that traits may act in nonadditive ways in determining pollinator visitation.
Interactions between Type of Instruction and Type of Language Feature: A Meta Analysis.
Year: 2010

A meta analysis was conducted to investigate the effects of explicit and implicit instruction on the acquisition of simple and complex grammatical features in English. The target features in the 41 studies contributing to the meta analysis were categorized as simple or complex based on the number of criteria applied to arrive at the correct target form (Hulstijn de Graaff, 1994) The instructional treatments were classified as explicit or implicit following Norris and Ortega (2000) The results indicate larger effect sizes for explicit over implicit instruction for simple and complex features. The findings also suggest that explicit instruction positively contributes to learners' controlled knowledge and spontaneous use of complex and simple forms.
Response of fungal composition and diversity to simulated nitrogen deposition and manipulation of precipitation in soils of an Inner Mongolia desert steppe of northern China
Venue: Canadian Journal of Soil Science
Year: 2017

Nitrogen (N) deposition and precipitation changes can strongly influence soil microbial properties in arid and semiarid regions. Here, we examined these effects on soil samples from the Inner Mongolia desert steppe of northern China after 7 yr of consecutive simulated N deposition by adding NH4NO3 and manipulation of precipitation, using a dilution plate method, PCR analysis, and 18S rRNA sequencing. The experimental treatments were as follows: control (CK) N addition +N) N and water addition +N+W) and N addition plus water reduction +N W) In this study, 14 genera and 32 fungal species were isolated, and Penicillium was determined to be the dominant fungal group. Treatment +N W significantly increased (by 94.8% the number of cultivable fungi as compared with CK. Compared with the CK community, fungal communities exposed to the three treatments, especially +N+W and +N W, showed shifts in the relative abundances of cultivable fungi. Treatment +N W significantly enhanced species richness compared with +N at the 0 2 cm soil depth. However, N addition and manipulation of precipitation did not influence species richness, the Shannon Weiner index, or evenness at the 0 30 cm soil depth. This study can provide insight into how fungal composition and diversity respond to climate change scenarios.
Genome wide expression profiling, in vivo DNA binding analysis, and probabilistic motif prediction reveal novel Abf1 target genes during fermentation, respiration, and sporulation in yeast.
Venue: Molecular biology of the cell
Year: 2008

The autonomously replicating sequence binding factor 1 (Abf1) was initially identified as an essential DNA replication factor and later shown to be a component of the regulatory network controlling mitotic and meiotic cell cycle progression in budding yeast. The protein is thought to exert its functions via specific interaction with its target site as part of distinct protein complexes, but its roles during mitotic growth and meiotic development are only partially understood. Here, we report a comprehensive approach aiming at the identification of direct Abf1 target genes expressed during fermentation, respiration, and sporulation. Computational prediction of the protein's target sites was integrated with a genome wide DNA binding assay in growing and sporulating cells. The resulting data were combined with the output of expression profiling studies using wild type versus temperature sensitive alleles. This work identified 434 protein coding loci as being transcriptionally dependent on Abf1. More than 60% of their putative promoter regions contained a computationally predicted Abf1 binding site and/or were bound by Abf1 in vivo, identifying them as direct targets. The present study revealed numerous loci previously unknown to be under Abf1 control, and it yielded evidence for the protein's variable DNA binding pattern during mitotic growth and meiotic development.