nils-herrmann/clean_scirepeval huggingface dataset:
Streptomyces rochei A 1 induces resistance and defense related responses against Botryosphaeria dothidea in apple fruit during storage

Apple ring rot caused by Botryosphaeria dothidea is an important disease in most apple growing regions in China. It results in severe losses during fruit storage. Induced resistance against plant pathogens is a promising alternative to traditional control strategies. In the present study, a potential biocontrol agent, Streptomyces rochei A 1, was evaluated for its induction of resistance against B. dothidea in postharvest apple fruit, and the defense mechanisms involved were investigated. The results indicated that S. rochei A 1 treatment could effectively induce the resistance of apple fruit to ring rot caused by B. dothidea The lesion area and disease incidence were 65.4% and 27.1% lower than those of the control fruit after storage at 25 degC for 7 d. S. rochei A 1 treatment significantly increased the activities of peroxidase (POD) superoxide dismutase (SOD) catalase (CAT) and phenylalanine ammonia lyase (PAL) and highly inhibited lipid peroxidation. b 1,3 glucanase and chitinase activities and H 2 O 2 generation were also markedly promoted by the biocontrol agent. Moreover, the expression levels of three pathogenesis related proteins genes PR 8 GLU and CHI were enhanced. The most significant increase was observed in apple fruit pretreated with S. rochei A 1 and inoculated with B. dothidea Therefore, our results suggest that S. rochei A 1 treatment induces resistance against B. dothidea in apple fruit by stimulating a series of defense responses.
Feeling Good Makes Us Stronger: How Team Resilience Mediates the Effect of Positive Emotions on Team Performance

This study investigated the relationship between collective positive emotions at work and team resilience, expanding on the Broaden and Build theory of Fredrickson (;  2001) at the collective (i.e. work teams) level of analysis. Through the aggregate scores of 1,076 employees (61 men) grouped into 216 teams and belonging to 40 companies, five collective positive emotions were evaluated (i.e. enthusiasm, optimism, satisfaction, comfort, and relaxation) as well as team resilience. Additionally, ratings of the 216 supervisors of the teams were used to assess team performance (i.e. in and extra role performance) Structural equation modeling at the team level of analysis indicated that team resilience mediates the relationship between collective positive emotions and team performance, both in and extra role. The results highlight the importance of developing collective positive emotions to help teams to foster team resilience and improve their performance. The article concludes with practical strategies aimed at developing collective positive emotions, together with limitations and suggestions for future research.
Hate Speech and Covert Discrimination on Social Media: Monitoring the Facebook Pages of Extreme Right Political Parties in Spain

This study considers the ways that overt hate speech and covert discriminatory practices circulate on Facebook despite its official policy that prohibits hate speech. We argue that hate speech and discriminatory practices are not only explained by users' motivations and actions, but are also formed by a network of ties between the platform's policy, its technological affordances, and the communicative acts of its users. Our argument is supported with longitudinal multimodal content and network analyses of data extracted from official Facebook pages of seven extreme right political parties in Spain between 2009 and 2013. We found that the Spanish extreme right political parties primarily implicate discrimination, which is then taken up by their followers who use overt hate speech in the comment space.
Calibrating the Local and Platoon Dynamics of Car Following Models on the Reconstructed NGSIM Data

The NGSIM trajectory data are used to calibrate two car following models the IDM and the FVDM. We used the I80 dataset which has already been reconstructed to eliminate outliers, non physical data, and internal and platoon inconsistencies contained in the original data. We extract from the data leader follower pairs and platoons of up to five consecutive vehicles thereby eliminating all trajectories that are too short or contain lane changes. Four error measures based on speed and gap deviations are considered. Furthermore, we apply three calibration methods: local or direct calibration, global calibration, and platoon calibration. The last approach means that a platoon of several vehicles following a data driven leader is simulated and compared to the observed dynamics.
Towards Data Driven Process Control in Manufacturing Car Body Parts
Venue: 2016 International Conference on Computational Science and Computational Intelligence (CSCI)

The manufacturing process of car body parts is a complex industrial process where many machine parameters and material measurements are involved in establishing the quality of the final product. Data driven models have shown great advantages in helping decision makers to optimize this kind of complex processes where good physical models are hard to build. In this paper a framework for on line process monitoring and predictive modeling is proposed to optimize a car body part production process. Anomaly detection plays an important role in this framework as it can provide an early alert for operators on the production line using a complex set of machine parameters and material properties. In this paper an anomaly detection algorithm, Gloss, that is successfully implemented as the first module in the process, is introduced. Gloss finds local outliers in high dimensional mixed data sets using a relative density measure that takes the global neighborhood into account while searching for outliers in subspaces of the data. An overview of the application and implementation of the algorithm in the car body part press shop is presented.
Sub cortical brain structure segmentation using F CNN'S
Venue: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

In this paper we propose a deep learning approach for segmenting sub cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state of the art Fully Convolutional Neural Network (F CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF) whose topology corresponds to a volumetric grid. Alpha expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest based priors, as well as state of art segmentation algorithms, and show promising results on two different brain MRI datasets.
Visibility detection approach to road scene foggy images
Venue: KSII Trans. Internet Inf. Syst.

A cause of vehicle accidents is the reduced visibility due to bad weather conditions such as fog. Therefore, an onboard vision system should take visibility detection into account. In this paper, we propose a simple and effective approach for measuring the visibility distance using a single camera placed onboard a moving vehicle. The proposed algorithm is controlled by a few parameters and mainly includes camera parameter estimation, region of interest (ROI) estimation and visibility computation. Thanks to the ROI extraction, the position of the inflection point may be measured in practice. Thus, combined with the estimated camera parameters, the visibility distance of the input foggy image can be computed with a single camera and just the presence of road and sky in the scene. To assess the accuracy of the proposed approach, a reference target based visibility detection method is also introduced. The comparative study and quantitative evaluation show that the proposed method can obtain good visibility detection results with relatively fast speed.
Enhancement in Foggy Road Scene Videos Using RSWHE and Gamma Correction
Venue: 2016 International Conference on Micro Electronics and Telecommunication Engineering (ICMETE)

Video enhancement is important for video security surveillance system because the videos and images of outdoor street scenes when captured in severe climatic conditions such as fog, dust storms, mist gets degraded. Drivers much of the time turn on the headlights of their vehicles and streetlights are frequently lit which decrease the visibility and leads to colour shift problems. Due to improper visibility it is difficult for drivers to identity the targets. The aim is to achieve haze free images/videos in order to detect the road conditions properly and increase visibility to reduce the road accidents. Various techniques are used for enhancement of videos/images such as Dark channel prior, Histogram equalization, Brightness preserving Bi histogram equalization, Recursively Separated and Weighted Histogram Equalization with Gamma Correction. In the proposed work, RSWHE is combined with gamma correction to improve the visibility of road scene foggy videos and images. Better results are obtained with the proposed method.
Vehicle detection and road scene segmentation using deep learning

There has been significant progress in applying deep learning techniques to computer vision problems for perception scenarios, specifically for autonomous driving. This thesis explores many of these techniques and presents a detailed analysis of how deep learning tools can be used to perform computer vision for self driving vehicles. It is an attempt to replicate concurrent research work, while discussing the advantages and disadvantages of different approaches. The thesis also combines different methods and components, and presents a custom vehicle detection approach. The approach is based on generating a spatial grid of classifications, and then regressing bounding boxes for pixels with a high object confidence score. The custom detection approach was tested on the KITTI object detection benchmark, and was able to successfully detect objects of varying scale, lighting conditions and orientation. Additionally, an analysis of semantic segmentation techniques that use deep learning is presented, and a few hand picked approaches from literature are evaluated and compared. One of the approaches is replicated and tested on the Cityscapes benchmark for pixel level semantic segmentation. Detailed discussion and insights into future work are presented, which could be interesting for both academia and industry, especially in the area of deep learning and autonomous systems.
Stealing Machine Learning Models via Prediction APIs
Venue: USENIX Security Symposium

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML as a service "predictive analytics" systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay per query basis. The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e. "steal" the model. Unlike in classical learning theory settings, ML as a service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures.
Prevalence of probable mental disorders and help seeking behaviors among veteran and non veteran community college students.
Venue: General hospital psychiatry

Millions of disadvantaged youth and returning veterans are enrolled in community colleges. Our objective was to determine the prevalence of mental disorders and help seeking behaviors among community college students.  Veterans (n=211) and non veterans (n=554) were recruited from 11 community colleges and administered screeners for depression (PHQ 9) generalized anxiety (GAD 7) posttraumatic stress disorder (PC PTSD) non lethal self injury, suicide ideation and suicide intent. The survey also asked about the perceived need for, barriers to and utilization of services. Regression analysis was used to compare prevalence between non veterans and veterans adjusting for non modifiable factors (age, gender and race/ethnicity)  A large proportion of student veterans and non veterans screened positive and unadjusted bivariate comparisons indicated that student veterans had a significantly higher prevalence of positive depression screens (33.1% versus 19.5% P<.01) positive PTSD screens (25.7% versus 12.6% P<.01) and suicide ideation (19.2% versus 10.6% P=.01) Adjusting for age, gender and race/ethnicity, veterans were significantly more likely than non veterans to screen positive for depression (OR=2.10, P=.01) and suicide ideation (OR=2.31, P=.03) Student veterans had significantly higher odds of perceiving a need for treatment than non veterans (OR=1.93, P=.02) but were more likely to perceive stigma (beta=0.28, P=.02) Despite greater need among veterans, there were no significant differences between veterans and non veterans in use of psychotropic medications, although veterans were more likely to receive psychotherapy (OR=2.35, P=.046) CONCLUSIONS Findings highlight the substantial gap between the prevalence of probable mental health disorders and treatment seeking among community college students. Interventions are needed to link community college students to services, especially for student veterans.
Gender Differences in the Relationships Among Major Depressive Disorder, Heavy Alcohol Use, and Mental Health Treatment Engagement Among College Students.
Venue: Journal of studies on alcohol and drugs

Although major depressive disorder (MDD) and heavy episodic drinking (HED, 4+/5+ drinks in a single sitting for women/men) are common among young adults in college, the relationship between the two remains unclear. This study examined the association between MDD and HED in this population, the effect of gender on this association, and whether comorbid MDD and heavy alcohol use are associated with higher rates of mental health treatment engagement.  The study comprised 61,561 (65.3% female) undergraduate students who answered an online survey on depression, alcohol use, and treatment engagement in the past year. Hierarchical linear regressions examined the association between MDD and alcohol use (HED and peak blood alcohol concentration [pBAC] and whether gender moderated these associations. Logistic regressions were then conducted to examine the influence of MDD, heavy alcohol use, and gender on treatment engagement.  Students with MDD reported more frequent HED and higher pBAC than did students without MDD; this was especially true for female students. Rates of treatment engagement were higher among women than men, among students with MDD than students without MDD, and among female students with HED than women without HED.  The presence of an association between MDD and heavy alcohol use suggests the need for systematic screenings of both conditions. Low rates of treatment engagement in college students with MDD and heavy alcohol use calls for the development of strategies to engage this high risk group in treatment.
Key factors affecting the immunoreactivity of roasted and boiled peanuts: Temperature and water

Processing time (5 35 min) and temperature (105 degC 150 degC) during thermal processing may affect the structure and immunoreactivity of peanut allergen protein. In the present study, we aimed to find a correlation between heating mode and characteristics of peanut protein, and optimize the heating condition to reduce the allergenicity. Roasted peanut protein under the condition of extremely high temperature (over 130 degC) resulted in high allergenicity, accompanied with reduced concentration and IgE binding capacity of Ara h 1 and Ara h 3, but increased Ara h 2/6 IgE binding capacity. In addition, the structure of roasted protein under the condition of 130 degC for 20 min was the loosest with higher surface hydrophobicity index, more b fold and the irregular coil, and less a helix. In contrast, the allergenicity of boiled peanut proteins was decreased as the concentration and IgE binding capacities of all allergen were reduced. Because of the water medium, all bonding energy in the boiling group was lower than that in the roasting group. Hydrophobic interactions and hydrogen bonds were more prominent on the boiling samples. The finding indicates mild roasted temperature or boiling for 10 min can effectively reduce the peanut allergenicity.
Precursors and factors affecting formation of haloacetonitriles and chloropicrin during chlor(am)ination of nitrogenous organic compounds in drinking water.
Venue: Journal of hazardous materials

This study investigated the precursors and factors affecting formation of haloacetonitriles (HANs) and chloropicrin (TCNM) during chlorination/chloramination of eight amino acids in the effluent water of V type clarifying filtration from a drinking water treatment plant. The yields of trichloroacetonitrile (TCAN) dichloroacetonitrile (DCAN) and TCNM were higher during chlorination than during chloramination. Tyrosine and tryptophan produced the greatest amount of DCAN and also generated a small amount of TCAN during chlorination process. Besides, the yields of DCAN were higher than TCNM during chlorination/chloramination. Contact time, Cl2:org N molar ratios, pH, temperature and bromide ion affected nitrogenous disinfection by products (N DBPs) formation during chlorination of tryptophan in different degrees. TCAN, DCAN and TCNM formation showed the increasing and then decreasing with prolonged contact time. Higher Cl2:org N molar ratios improved N DBPs formation within a certain range. The pH affected N DBPs formation differently. HANs increased with increasing pH from 5 to 6 and decreased with increasing pH from 6 to 9, while TCNM increased with increasing pH from 5 to 9. Higher temperatures enhanced TCNM formation, but reduced the formation of TCAN and DCAN. The presence of bromide ions improved the yields of HANs and TCNM and shifted N DBPs to more brominated ones.
Meteorological factors affecting the sudden decline in Lake Urmia's water level
Venue: Theoretical and Applied Climatology

Lake Urmia, in northwest Iran, is the second most saline lake in the world. During the past two decades, the level of water has markedly decreased. In this paper, climate of the lake region is investigated by using data from four meteorological stations near the lake. The data include climatic parameters such as temperature, precipitation, humidity, wind speed, sunshine hours, number of rain days, and evaporation. Climate around the lake is examined by way of climate classification in the periods before and after the reduction in water level. Rainfall in the lake catchment is also evaluated using both gauge and satellite data. The results show a significant decreasing trend in mean annual precipitation and wind speed and an increasing trend in annual average temperature and sunshine hours at the four stations. Precipitation and wind speed have decreased by 37 mm and 2.7 m/s, respectively, and the mean annual temperature and sunshine hours have increased by 1.4 degC and 41.6 days, respectively, over these six decades. Only the climate of the Tabriz region is seen to have significantly changed, going from semiarid to arid. Gauge records and satellite data show a large scale decreasing trend in rainfall since 1995. The correlation between rainfall and year to year changes in lake level is 0.69 over the period 1965 to 2010. The relationship is particularly strong from the early 1990s to 2005. This suggests that precipitation has played an important role in the documented decline of the lake.
New potential humic acid stationary phase toward drug components: Development of a chemometric assisted RP HPLC method for the determination of paracetamol and caffeine in tablet formulations.
Venue: Journal of separation science

A new humic acid based stationary phase has been used, for the first time, to achieve the separation and quantification of paracetamol and caffeine in pharmaceutical preparations under reversed phase high performance liquid chromatography conditions. Central composite design was applied as a powerful tool to optimize the most dominant parameters that influence the resolution of reversed phase high performance liquid chromatography, that is, mobile phase composition (acetonitrile percentage in water) flow rate, and column temperature. The optimum conditions were obtained as 21.69% 1.5 mL/min, and 15degC, respectively, with the aid of a second order quadratic model and desirability function. Under the optimum conditions, the peaks could be baseline separated within 10 min. For the developed reversed phase high performance liquid chromatography method, the linearity was investigated in the concentration ranges of 2 160 mg/mL (R(2) 0.999) for paracetamol and 2 9.9 mg/mL (R(2) 0.991) for caffeine. Mean recoveries for paracetamol and caffeine were 95.90 and 95.68% respectively. The limits of detection and quantification were 4.1 x 10( 4) and 1.3 x 10( 3) mg/mL for paracetamol and 1.6 x 10( 4) and 5.0 x 10( 4) mg/mL for caffeine. The results showed that the new humic acid based stationary phase is very suitable for the separation of paracetamol and caffeine in pharmaceutical preparations and, thus it can be used effectively in the pharmaceutical industry.
Social learning strategies modify the effect of network structure on group performance
Venue: Nature communications

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.