nils-herrmann/clean_scirepeval huggingface dataset:
Genes involved in Listeria monocytogenes biofilm formation at a simulated food processing plant temperature of 15 degC.
Venue: International journal of food microbiology

Listeria monocytogenes is a pathogenic foodborne bacterium whose persistence in food processing environments is in part attributed to its biofilm formation. Most biofilm studies have been carried out at 30 37 degC rather than at temperatures found in the food processing plants (i.e. 10 20 degC) The objective of the present study was to mine for novel genes that contribute to L. monocytogenes biofilm formation at 15 degC using the random insertional mutagenesis approach. A library of 11,024 L. monocytogenes 568 (serotype 1/2a) Himar1 insertional mutants was created. Mutants with reduced or enhanced biofilm formation at 15 degC were detected in microtiter plate assays with crystal violet and safranin staining. Fourteen mutants expressed enhanced biofilm phenotypes, and harbored transposon insertions in genes encoding cell wall biosynthesis, motility, metabolism, stress response, and cell surface associated proteins. Deficient mutants (n=5) contained interruptions in genes related to peptidoglycan, teichoic acid, or lipoproteins. Enhanced mutants produced significantly (p<0.05) higher cell densities in biofilm formed on stainless steel (SS) coupons at 15 degC (48 h) than deficient mutants, which were also more sensitive to benzalkonium chloride. All biofilm deficient mutants and four enhanced mutants in the microtiter plate assay (flaA, cheR, lmo2563 and lmo2488) formed no biofilm in a peg lid assay (Calgary biofilm device) while insertions in lmo1224 and lmo0543 led to excess biofilm in all assays. Two enhanced biofilm formers were more resistant to enzymatic removal with DNase, proteinase K or pectinase than the parent strain. Scanning electron microscopy of individual biofilms made by five mutants and the parent on SS surfaces showed formation of heterogeneous biofilm with dense zones by immotile mutants, while deficient mutants exhibited sparse growth. In conclusion, interruptions of 9 genes not previously linked to biofilm formation in L. monocytogenes (lmo2572, lmo2488 (uvrA) lmo1224, lmo0434 (inlB) lmo0263 (inlH) lmo0543, lmo0057 (EsaA) lmo2563, lmo0453) caused enhanced biofilm formation in the bacterium at 15 degC. The remaining mutants harbored interruptions in 10 genetic loci previously associated with biofilm formation at higher temperatures, indicating some temperature driven differences in the formation of biofilm by L. monocytogenes.
Surface modification of zein colloidal particles with sodium caseinate to stabilize oil in water pickering emulsion

Zein colloidal nanoparticles can adsorb at the oil water interface to form Pickering emulsion. However, zein Pickering emulsion is usually not stable due to the poor wettability of zein colloidal nanoparticles. The objective of this study was to modify the surface of zein nanoparticles using sodium caseinate (NaCas) and assess the properties of zein/NaCas nanocomplexes and the resultant oil in water Pickering emulsions. One percent (w/w) of zein/NaCas colloidal nanocomplexes were formed, with the zein:NaCas ratios (w/w) ranging from 10:1 to 10:4 at pH 3 by an ultrasound treatment. The zeta potential of the zein/NaCas nanocomplexes showed altered surface charges, indicating that NaCas adsorbed on the surface of the zein colloidal nanoparticles. Three phase contact angle measurements suggested that the original zein colloidal nanoparticles were preferentially wetted in water. The incorporation of 0.1% 0.2% (w/w) NaCas significantly enhanced its wettability in the oil, and intermediate wettability was achieved at a zein:NaCas ratio of 10:3. Confocal laser scanning microscope (CLSM) images showed that the incorporation of NaCas improved the interfacial coverage of the Pickering emulsions. When the zein:NaCas ratio ranged from 10:1 to 10:3, the interface was composed of zein/NaCas nanocomplexes. At a zein:NaCas ratio of 10:4, NaCas can competitively adsorbed to the interface and formed a hybrid interfacial structure. The Zein/NaCas nanocomplexes stabilized the Pickering emulsions and exhibited greater centrifugal stability than plain zein emulsions at most pHs and ionic strengths. The underlying mechanisms of the improved emulsion stability are discussed in this paper. This study explored a novel approach to stabilizing Pickering emulsions via the surface modification method using a food grade protein.
Question Answering via Integer Programming over Semi Structured Knowledge
Venue: IJCAI

Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non trivial inference and knowledge that goes beyond factoid retrieval. Yet, most systems for this task are based on relatively shallow Information Retrieval (IR) and statistical correlation techniques operating on large unstructured corpora. We propose a structured inference system for this task, formulated as an Integer Linear Program (ILP) that answers natural language questions using a semi structured knowledge base derived from text, including questions requiring multi step inference and a combination of multiple facts. On a dataset of real, unseen science questions, our system significantly outperforms +14% the best previous attempt at structured reasoning for this task, which used Markov Logic Networks (MLNs) It also improves upon a previous ILP formulation by 17.7% When combined with unstructured inference methods, the ILP system significantly boosts overall performance +10% Finally, we show our approach is substantially more robust to a simple answer perturbation compared to statistical correlation methods.
Meta Learning with Selective Data Augmentation for Medical Entity Recognition
Venue: Int. J. Comput. Linguistics Appl.

With the increasing number of annotated corpora for supervised Named Entity Recognition, it becomes interesting to study the combination and augmentation of these corpora for the same annotation task. In this paper, we particularly study the combination of heterogeneous corpora for Medical Entity Recognition by using a meta learning classifier that combines the results of individual Conditional Random Fields (CRFs) models trained on different corpora. We propose selective data augmentation approaches and compare them with several meta learning algorithms and baselines. We evaluate our approach using four sub classifiers trained on four heterogeneous corpora. We show that despite the high disagreements between the individual models on the four test corpora, our selective data augmentation approach improves performance on all test corpora and outperforms the combination of all training corpora.
Functions and Failures of Transnational Activism: Discourses of Children's Resistance and Repression in Global Anti Apartheid Networks

Throughout the later twentieth century, Britain's Anti Apartheid Movement (AAM) sat at the heart of a transnational advocacy network. From the mid 1980s, the increased repression of child and youth activists in South Africa became a particular topic of concern for the AAM, which launched a campaign to investigate and globally disseminate information about the brutality inflicted against black children. This network, in both its formal and informal forms, helped to garner essential international support for young activists detained by the apartheid regime. Yet, as children and youth's own stories were collected and rewritten for global audiences, they were abstracted from their political and cultural contexts, simplified into neat binaries of right and wrong, and framed according particular areas of moral global concern. While this strategy earned the AAM unprecedented media and governmental attention, it simultaneously denied children's resilience and political agency. This case study thus reveals the asymmetrical functioning of many transnational advocacy networks, which despite their good intentions, tend to privilege the connected over the disconnected. Exploring the AAM through a network approach reveals a web of connections among multiple individuals, groups and geographical sites, which complicate spatial conceptions of empire and postcolonial humanitarianism. Such an approach to global history exposes not just how metropole and periphery were mutually constituted, but also how they were misconstituted through uneven exchanges or overlooked connections.
Microbial nitrogen removal pathways in integrated vertical flow constructed wetland systems.
Venue: Bioresource technology

Microbial nitrogen (N) removal pathways in planted (Canna indica L. and unplanted integrated vertical flow constructed wetland systems (IVCWs) were investigated. Results of, molecular biological and isotope pairing experiments showed that nitrifying, anammox, and denitrifying bacteria were distributed in both down flow and up flow columns of the IVCWs. Further, the N transforming bacteria in the planted IVCWs were significantly higher than that in the unplanted ones (p<0.05) Moreover, the potential nitrification, anammox, and denitrification rates were highest (18.90, 11.75, and 7.84nmolNg( 1)h( 1) respectively) in the down flow column of the planted IVCWs. Significant correlations between these potential rates and the absolute abundance of N transformation genes further confirmed the existence of simultaneous nitrification, anammox, and denitrification (SNAD) processes in the IVCWs. The anammox process was the major N removal pathway (55.6 60.0% in the IVCWs. The results will further our understanding of the microbial N removal mechanisms in IVCWs.
Performance of a vertical subsurface flow constructed wetland under different operational conditions

The performance of a vertical subsurface flow constructed wetland (VSSFCW) for sewage effluent treatment was studied in an eight month experiment under different operational conditions including: vegetation (the presence or absence of common reeds "Phragmites australis" media type (gravel or vermiculite) and mode of sewage feeding (continuous or batch) Plants had a significant effect (P
Microbial population dynamics in response to bioaugmentation in a constructed wetland system under 10degC.
Venue: Bioresource technology

Compound microbial inocula were enriched and applied to a pilot scale constructed wetland system to investigate their bioaugmentation effect on nitrogen removal under cold temperature (10degC) The results showed a 10% higher removal efficiency of ammonia and total nitrogen compared to a control (unbioaugmented) group. The microbial community structures before and after the bioaugmentation were analyzed through high throughput sequencing using Miseq Illumina platform. A variation of species richness and community equitability was observed in both systems. It is demonstrated that, based on the response of both the performance and microbial community, bioaugmentation using compound microbial inocula can fine tune the bacterial population and enhance the nitrogen removal efficiency of a constructed wetland system.
Use of biochar to enhance constructed wetland performance in wastewater reclamation

Constructed wetlands are established efficient technologies and provide sustainable solution for wastewater treatment. Similarly, biochar, which is an organic material, produced by means of pyrolysis, offers simple and low cost techniques to treat water and reduce carbon footprint. Combining both of these technologies can greatly augment the efficiency of the system. The objective of this study was to evaluate the efficiency of constructed wetlands by using biochar as media. Horizontal wetland beds with dimension (1 m x 0.33 m x 0.3 m) were prepared using gravels and biochar, and cultivated with the Canna species. Synthetic wastewater was passed through these beds with average flow rate of 1.2 x 10 7 m^3/sec achieving a retention time of three days. Pollutant removal performance was compared between the controlled and experimental wetland beds. This study reveals that the wetland with biochar were more efficient as compared to the wetland with gravels alone with average removal rate of 91.3% COD, 58.3% TN, 58.3% NH3, 92% NO3 N, 79.5% TP, and 67.7% PO4.
Attention Based Bidirectional Long Short Term Memory Networks for Relation Classification
Venue: ACL

Relation classification is an important semantic processing task in the field of natural language processing (NLP) State ofthe art systems still rely on lexical resources such as WordNet or NLP systems like dependency parser and named entity recognizers (NER) to get high level features. Another challenge is that important information can appear at any position in the sentence. To tackle these problems, we propose Attention Based Bidirectional Long Short Term Memory Networks(AttBLSTM) to capture the most important semantic information in a sentence. The experimental results on the SemEval 2010 relation classification task show that our method outperforms most of the existing methods, with only word vectors.
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Venue: ICML

Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN) a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the inputs and the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN can be trained end to end and obtains state of the art results on several types of tasks and datasets: question answering (Facebook's bAbI dataset) text classification for sentiment analysis (Stanford Sentiment Treebank) and sequence modeling for part of speech tagging (WSJ PTB) The training for these different tasks relies exclusively on trained word vector representations and input question answer triplets.
Key Value Memory Networks for Directly Reading Documents
Venue: EMNLP

Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method,  Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state of the art results on the existing WikiQA benchmark.
Vascular Complications of Cancer Chemotherapy
Venue: The Canadian journal of cardiology

Development of new anticancer drugs has resulted in improved mortality rates and 5 year survival rates in patients with cancer. However, many of the modern chemotherapies are associated with cardiovascular toxicities that increase cardiovascular risk in cancer patients, including hypertension, thrombosis, heart failure, cardiomyopathy, and arrhythmias. These limitations restrict treatment options and might negatively affect the management of cancer. The cardiotoxic effects of older chemotherapeutic drugs such as alkylating agents, antimetabolites, and anticancer antibiotics have been known for a while. The newer agents, such as the antiangiogenic drugs that inhibit vascular endothelial growth factor signalling are also associated with cardiovascular pathology, especially hypertension, thromboembolism, myocardial infarction, and proteinuria. Exact mechanisms by which vascular endothelial growth factor inhibitors cause these complications are unclear but impaired endothelial function, vascular and renal damage, oxidative stress, and thrombosis might be important. With increasing use of modern chemotherapies and prolonged survival of cancer patients, the incidence of cardiovascular disease in this patient population will continue to increase. Accordingly, careful assessment and management of cardiovascular risk factors in cancer patients by oncologists and cardiologists working together is essential for optimal care so that prolonged cancer survival is not at the expense of increased cardiovascular events.
Learning Convolutional Neural Networks for Graphs
Venue: ICML

Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous node and edge attributes. Analogous to image based convolutional networks that operate on locally connected regions of the input, we present a general approach to extracting locally connected regions from graphs. Using established benchmark data sets, we demonstrate that the learned feature representations are competitive with state of the art graph kernels and that their computation is highly efficient.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Venue: NIPS

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low dimensional regular grids, where image, video and speech are represented, to high dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs.
An Approach Based on HPLC Fingerprint and Chemometrics to Quality Consistency Evaluation of Matricaria chamomilla L. Commercial Samples
Venue: Front. Plant Sci.

Chamomile has been used as an herbal medication since ancient times and is still popular because it contains various bioactive phytochemicals that could provide therapeutic effects. In this study, a simple and reliable HPLC method was developed to evaluate the quality consistency of nineteen chamomile samples through establishing a chromatographic fingerprint, quantification of phenolic compounds and determination of antioxidant activity. For fingerprint analysis, 12 peaks were selected as the common peaks to evaluate the similarities of commercial samples of chamomile obtained from different manufacturers. A similarity analysis was performed to assess the similarity/dissimilarity of chamomile samples where values varied from 0.868 to 0.990 what indicating that samples from different manufacturers were consistent. Additionally, simultaneous quantification of five phenolic acids (gallic, caffeic, syringic, p coumaric, ferulic) and four flavonoids (rutin, myricetin, quercetin and keampferol) was performed to interpret the quality consistency. In quantitative analysis, the nine individual phenolic compounds showed good regression (r 0.9975) Inter and intra day precisions for all analyzed compounds expressed as relative standard deviation (CV) ranged from 0.05% to 3.12% Since flavonoids and other polyphenols are commonly recognized as natural antioxidants, the antioxidant activity of chamomile samples was evaluated using 1,1 diphenyl 2 picrylhydrazyl (DPPH) radical scavenging activity and ferric reducing/antioxidant power (FRAP) assay. Correlation analysis was used to assess the relationship between antioxidant activity and phenolic composition, and multivariate analysis (PCA and HCA) were applied to distinguish chamomile samples. Results shown in the study indicate high similarity of chamomile samples among them, widely spread in the market and commonly used by people as infusions or teas, as well as that there were no statistically significant differences among them, which in turn is a proof of high quality of commercially available samples of chamomile. The study indicated that the combination of chromatographic fingerprint and quantitative analysis can be readily utilized as a quality consistency method for chamomile and related medicinal preparations. Moreover, the applied strategy seems to be the most promising for the assessment of the investigated plant material.
The effectiveness of person centred planning for people with intellectual disabilities: A systematic review.
Venue: Research in developmental disabilities

To evaluate the effectiveness of Person Centred Planning (PCP) on outcomes for individuals with intellectual disabilities (ID) across the age range.  The electronic databases PsycInfo, Embase, CINHAL, PubMed, Web of Science, Scopus and Medline were searched for studies evaluating the impact of PCP on people with ID, published between 1990 and 2014; these were supplemented by manual searches of reference lists. Studies were considered irrespective of methodology, sample size and publication source, if outcomes reflected the impact of PCP on individuals with ID.  Seven quantitative, five qualitative and four mixed methods studies were included in the review. The overall quality of the evidence was low but suggestive that PCP may have a positive, yet moderate, impact on some outcomes for individuals with ID, particularly community participation, participation in activities and daily choice making. For other outcomes such as employment the findings were inconsistent.  The evidence supporting the effectiveness of PCP is limited and does not demonstrate that PCP can achieve radical transformations in the lives of people with ID. Clearer descriptions of PCP and its components are needed. Small scale successful demonstrations of effectiveness exist, but its clinical, cost effectiveness and wider implementation must be investigated in large scale studies.
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