nils-herrmann/clean_scirepeval huggingface dataset:
Molecular mechanisms of heavy metal hyperaccumulation and phytoremediation.

A relatively small group of hyperaccumulator plants is capable of sequestering heavy metals in their shoot tissues at high concentrations. In recent years, major scientific progress has been made in understanding the physiological mechanisms of metal uptake and transport in these plants. However, relatively little is known about the molecular bases of hyperaccumulation. In this paper, current progresses on understanding cellular/molecular mechanisms of metal tolerance/hyperaccumulation by plants are reviewed. The major processes involved in hyperaccumulation of trace metals from the soil to the shoots by hyperaccumulators include: (a) bioactivation of metals in the rhizosphere through root-microbe interaction; (b) enhanced uptake by metal transporters in the plasma membranes; (c) detoxification of metals by distributing to the apoplasts like binding to cell walls and chelation of metals in the cytoplasm with various ligands, such as phytochelatins, metallothioneins, metal-binding proteins; (d) sequestration of metals into the vacuole by tonoplast-located transporters. The growing application of molecular-genetic technologies led to the well understanding of mechanisms of heavy metal tolerance/accumulation in plants, and subsequently many transgenic plants with increased resistance and uptake of heavy metals were developed for the purpose of phytoremediation. Once the rate-limiting steps for uptake, translocation, and detoxification of metals in hyperaccumulating plants are identified, more informed construction of transgenic plants would result in improved applicability of the phytoremediation technology.
Countering terrorism through dark web analysis

Dark Web analysis is an important aspect in field of counter terrorism (CT). In the present scenario terrorist attacks are biggest problem for the mankind and whole world is under constant threat from these well-planned, sophisticated and coordinated terrorist operations. Terrorists anonymously set up various web sites embedded in the public Internet, exchanging ideology, spreading propaganda, and recruiting new members. Dark web is a hotspot where terrorists are communicating and spreading their messages. Now every country is focusing for CT. Dark web analysis can be an efficient proactive method for CT by detecting and avoiding terrorist threats/attacks. In this paper we have proposed dark web analysis model that analyzes dark web forums for CT and connecting the dots to prevent the country from terrorist attacks.
Sentiment analysis of Twitter data for predicting stock market movements

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from Twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.
You Are What You Eat : How Food Affects Your Mood

http://dujs.dartmouth.edu/2011/02/you-are-what-you-eat-how-food-affects-your-mood/ or thousands of years, people have believed that food could influence their health and wellbeing. Hippocrates, the father of modern medicine, once said: “Let your food be your medicine, and your medicine be your food” (1). In medieval times, people started to take great interest in how certain foods affected their mood and temperament. Many medical culinary textbooks of the time described the relationship between food and mood. For example, quince, dates and elderberries were used as mood enhancers, lettuce and chicory as tranquilizers, and apples, pomegranates, beef and eggs as erotic stimulants (1). The past 80 years have seen immense progress in research, primarily short-term human trials and animal studies, showing how certain foods change brain structure, chemistry, and physiology thus affecting mood and performance. These studies suggest that foods directly influencing brain neurotransmitter systems have the greatest effects on mood, at least temporarily. In turn, mood can also influence our food choices and expectations on the effects of certain foods can influence our perception.
The subduction factory : How it operates in the evolving Earth

JULY 2005, GSA TODAY ABSTRACT The subduction factory processes raw materials such as oceanic sediments and oceanic crust and manufactures magmas and continental crust as products. Aqueous fluids, which are extracted from oceanic raw materials via dehydration reactions during subduction, dissolve particular elements and overprint such elements onto the mantle wedge to generate chemically distinct arc basalt magmas. The production of calc-alkalic andesites typifies magmatism in subduction zones. One of the principal mechanisms of modern-day, calc-alkalic andesite production is thought to be mixing of two endmember magmas, a mantle-derived basaltic magma and an arc crust-derived felsic magma. This process may also have contributed greatly to continental crust formation, as the bulk continental crust possesses compositions similar to calc-alkalic andesites. If so, then the mafic melting residue after extraction of felsic melts should be removed and delaminated from the initial basaltic arc crust in order to form “andesitic” crust compositions. The waste materials from the factory, such as chemically modified oceanic materials and delaminated mafic lower crust materials, are transported down to the deep mantle and recycled as mantle plumes. The subduction factory has played a central role in the evolution of the solid Earth through creating continental crust and deep mantle geochemical reservoirs.
Why Mate Choices are not as Reciprocal as we Assume : The Role of Personality , Flirting and Physical Attractiveness

MITJA D. BACK*, LARS PENKE, STEFAN C. SCHMUKLE, KAROLINE SACHSE, PETER BORKENAU and JENS B. ASENDORPF Department of Psychology, Johannes Gutenberg-University Mainz, Germany Department of Psychology and Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, UK Department of Psychology, Westfälische Wilhelms-University Münster, Germany Department of Psychology, Martin-Luther University Halle-Wittenberg, Germany Department of Psychology, Martin-Luther University Halle-Wittenberg, Germany Department of Psychology, Humboldt University Berlin, Germany
Gut brain axis: diet microbiota interactions and implications for modulation of anxiety and depression.

The human gut microbiome is composed of an enormous number of microorganisms, generally regarded as commensal bacteria. Without this inherent microbial community, we would be unable to digest plant polysaccharides and would have trouble extracting lipids from our diet. Resident gut bacteria are an important contributor to healthy metabolism and there is significant evidence linking gut microbiota and metabolic disorders such as obesity and diabetes. In the past few years, neuroscience research has demonstrated the importance of microbiota in the development of brain systems that are vital to both stress reactivity and stress-related behaviours. Here we review recent literature that examines the impact of diet-induced changes in the microbiota on stress-related behaviours including anxiety and depression.
Magnetic fields: how is plant growth and development impacted?

This review provides detailed insight on the effects of magnetic fields on germination, growth, development, and yield of plants focusing on ex vitro growth and development and discussing the possible physiological and biochemical responses. The MFs considered in this review range from the nanoTesla (nT) to geomagnetic levels, up to very strong MFs greater than 15 Tesla (T) and also super-weak MFs (near 0 T). The theoretical bases of the action of MFs on plant growth, which are complex, are not discussed here and thus far, there is limited mathematical background about the action of MFs on plant growth. MFs can positively influence the morphogenesis of several plants which allows them to be used in practical situations. MFs have thus far been shown to modify seed germination and affect seedling growth and development in a wide range of plants, including field, fodder, and industrial crops; cereals and pseudo-cereals; grasses; herbs and medicinal plants; horticultural crops (vegetables, fruits, ornamentals); trees; and model crops. This is important since MFs may constitute a non-residual and non-toxic stimulus. In addition to presenting and summarizing the effects of MFs on plant growth and development, we also provide possible physiological and biochemical explanations for these responses including stress-related responses of plants, explanations based on dia-, para-, and ferromagnetism, oriented movements of substances, and cellular and molecular changes.
Health outcomes following treatment for 6 months with once daily tiotropium compared with twice daily salmeterol in patients with COPD.

A study was undertaken to record exacerbations and health resource use in patients with COPD during 6 months of treatment with tiotropium, salmeterol, or matching placebos.  Patients with COPD were enrolled in two 6-month randomised, placebo controlled, double blind, double dummy studies of tiotropium 18 μg once daily via HandiHaler or salmeterol 50 μg twice daily via a metered dose inhaler. The two trials were combined for analysis of heath outcomes consisting of exacerbations, health resource use, dyspnoea (assessed by the transitional dyspnoea index, TDI), health related quality of life (assessed by St George’s Respiratory Questionnaire, SGRQ), and spirometry.  1207 patients participated in the study (tiotropium 402, salmeterol 405, placebo 400). Compared with placebo, tiotropium but not salmeterol was associated with a significant delay in the time to onset of the first exacerbation. Fewer COPD exacerbations/patient year occurred in the tiotropium group (1.07) than in the placebo group (1.49, p<0.05); the salmeterol group (1.23 events/year) did not differ from placebo. The tiotropium group had 0.10 hospital admissions per patient year for COPD exacerbations compared with 0.17 for salmeterol and 0.15 for placebo (not statistically different). For all causes (respiratory and non-respiratory) tiotropium, but not salmeterol, was associated with fewer hospital admissions while both groups had fewer days in hospital than the placebo group. The number of days during which patients were unable to perform their usual daily activities was lowest in the tiotropium group (tiotropium 8.3 (0.8), salmeterol 11.1 (0.8), placebo 10.9 (0.8), p<0.05). SGRQ total score improved by 4.2 (0.7), 2.8 (0.7) and 1.5 (0.7) units during the 6 month trial for the tiotropium, salmeterol and placebo groups, respectively (p<0.01 tiotropium v placebo). Compared with placebo, TDI focal score improved in both the tiotropium group (1.1 (0.3) units, p<0.001) and the salmeterol group (0.7 (0.3) units, p<0.05). Evaluation of morning pre-dose FEV1, peak FEV1 and mean FEV1 (0–3 hours) showed that tiotropium was superior to salmeterol while both active drugs were more effective than placebo.  Exacerbations of COPD and health resource usage were positively affected by daily treatment with tiotropium. With the exception of the number of hospital days associated with all causes, salmeterol twice daily resulted in no significant changes compared with placebo. Tiotropium also improved health related quality of life, dyspnoea, and lung function in patients with COPD.
Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine

The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein-protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct text-mining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the text-mining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation.
Enterprise Architecture as Enabler of Organizational Agility - A Municipality Case Study

Organizational agility is one of the top management concerns as organizations face today increasingly changing environments. Among enterprise architecture (EA) benefits, organizational agility has been claimed as one of them, perceived as a direct or indirect benefit, for example, through business-IT alignment, another top management concern. However, even with reasonable explanations in the EA literature, there is still a lack of empirical evidence to support such claim. Our research looks for that evidence seeking to understand how the development and use of EA may contribute for organizational agility. Having one of the biggest municipalities in the country as the research setting, using a mixmethods approach, a case study was carried out to identify EA artefacts, understand EA at use and examine agility in a specific change situation. In this case, enterprise architecture was not just used but was developed and improved during the change situation to enable organizational agility.
The key performance indicators of the BIM implementation process

The question of what the best method of adopting BIM has not been answered yet. By identifying a standard method that will benchmark the different BIM adoption cases and comparing the efficiency gains in these cases, a standard benchmarking method can be developed. This paper explains the live experience of BIM adoption in a KTP (Knowledge Transfer Partnership) project, undertaken between the University of Salford and John McCall Architects practicing in the housing and regeneration fields, with a particular focus on a set of KPIs that have been developed and tested through the action research strategy in the project. Weighting of these KPI’s has been developed from an architectural business perspective. In regard to company KPIs, there are two sets of performance indicators; i) how well the adoption of the BIM concept, ii) how beneficial the adoption of BIM to the business. Both are important. But to justify a business case for BIM adoption, the business related KPIs need to be established. BIM implementation strategy and subsequent expected efficiency gains from BIM adoption may have variation depending on the needs, requirements and the envisioned scope for improvements in the business. The main characteristics of BIM implementation strategy and the subsequent efficiency gains are clarified after the detail review and analysis of current practice. This includes i) process mapping of current practice at JMA, ii) conducting soft system analysis, iii) stakeholder review and analysis and finally iv) identification of competitive advantages from BIM implementation.
Positive psychology interventions: a meta-analysis of randomized controlled studies

The use of positive psychological interventions may be considered as a complementary strategy in mental health promotion and treatment. The present article constitutes a meta-analytical study of the effectiveness of positive psychology interventions for the general public and for individuals with specific psychosocial problems.



We conducted a systematic literature search using PubMed, PsychInfo, the Cochrane register, and manual searches. Forty articles, describing 39 studies, totaling 6,139 participants, met the criteria for inclusion. The outcome measures used were subjective well-being, psychological well-being and depression. Positive psychology interventions included self-help interventions, group training and individual therapy.



The standardized mean difference was 0.34 for subjective well-being, 0.20 for psychological well-being and 0.23 for depression indicating small effects for positive psychology interventions. At follow-up from three to six months, effect sizes are small, but still significant for subjective well-being and psychological well-being, indicating that effects are fairly sustainable. Heterogeneity was rather high, due to the wide diversity of the studies included. Several variables moderated the impact on depression: Interventions were more effective if they were of longer duration, if recruitment was conducted via referral or hospital, if interventions were delivered to people with certain psychosocial problems and on an individual basis, and if the study design was of low quality. Moreover, indications for publication bias were found, and the quality of the studies varied considerably.



The results of this meta-analysis show that positive psychology interventions can be effective in the enhancement of subjective well-being and psychological well-being, as well as in helping to reduce depressive symptoms. Additional high-quality peer-reviewed studies in diverse (clinical) populations are needed to strengthen the evidence-base for positive psychology interventions.
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were “silently” patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with.
Control Techniques in AC, DC, and Hybrid AC–DC Microgrid: A Review

The microgrid concept is gaining popularity with the proliferation of distributed generation. Control techniques in the microgrid are an evolving research topic in the area of microgrids. A large volume of survey articles focuses on the control techniques of the microgrid; however, a systematic survey of the hierarchical control techniques based on different microgrid architectures is addressed very little. The hierarchy of control in microgrid comprises three layers, which are primary, secondary, and tertiary control layers. A review of the primary and secondary control strategies for the ac, dc, and hybrid ac–dc microgrid is addressed in this paper. Furthermore, it includes the highlights of the state-of-the-art control techniques and evolving trends in the microgrid research.