Miracle-dz/UniFAR-FTUs huggingface dataset:
Bloom of a denitrifying methanotroph, ‘Candidatus Methylomirabilis limnetica’, in a deep stratified lake

Methanotrophic bacteria represent an important biological filter regulating methane emissions into the atmosphere. Planktonic methanotrophic communities in freshwater lakes are typically dominated by aerobic gamma-proteobacteria, with a contribution from alpha-proteobacterial methanotrophs and the NC10 bacteria. The NC10 clade encompasses methanotrophs related to 'Candidatus Methylomirabilis oxyfera', which oxidize methane using a unique pathway of denitrification that tentatively produces N2 and O2 from nitric oxide (NO). Here, we describe a new species of the NC10 clade, 'Ca. Methylomirabilis limnetica', which dominated the planktonic microbial community in the anoxic depths of the deep stratified Lake Zug in two consecutive years, comprising up to 27% of the total bacterial population. Gene transcripts assigned to 'Ca. M. limnetica' constituted up to one third of all metatranscriptomic sequences in situ. The reconstructed genome encoded a complete pathway for methane oxidation, and an incomplete denitrification pathway, including two putative nitric oxide dismutase genes. The genome of 'Ca. M. limnetica' exhibited features possibly related to genome streamlining (i.e. less redundancy of key metabolic genes) and adaptation to its planktonic habitat (i.e. gas vesicle genes). We speculate that 'Ca. M. limnetica' temporarily bloomed in the lake during non-steady-state conditions suggesting a niche for NC10 bacteria in the lacustrine methane and nitrogen cycle.
Identification of regulatory elements in the AGT1 promoter of ale and lager strains of brewer's yeast

Agt1 is an interesting α‐glucoside transporter for the brewing industry, as it efficiently transports maltotriose, a sugar often remaining partly unused during beer fermentation. It has been shown that on maltose the expression level of AGT1 is much higher in ale strains than in lager strains, and that glucose represses the expression, particularly in the ale strains. In the present study the regulatory elements of the AGT1 promoter of one ale and two lager strains were identified by computational methods. Promoter regions up to 1.9 kbp upstream of the AGT1 gene were sequenced from the three brewer's yeast strains and the laboratory yeast strain CEN.PK‐1D. The promoter sequence of the laboratory strain was identical to the AGT1 promoter of strain S288c of the Saccharomyces Genome Database, whereas the promoter sequences of the industrial strains diverged markedly from the S288c strain. The AGT1 promoter regions of the ale and lager strains were for the most part identical to each other, except for one 22 bp deletion and two 94 and 95 bp insertions in the ale strain. Computational analyses of promoter elements revealed that the promoter sequences contained several Mig1‐ and MAL‐activator binding sites, as was expected. However, some of the Mig1 and MAL‐activator binding sites were located on the two insertions of the ale strain, and thus offered a plausible explanation for the different expression pattern of the AGT1 gene in the ale strains. Accordingly, functional analysis of A60 ale and A15 lager strain AGT1 promoters fused to GFP (encoding the green fluorescent protein) showed a significant difference in the ability of these two promoters to drive GFP expression. Under the control of the AGT1 promoter of the ale strain the emergence of GFP was strongly induced by maltose, whereas only a low level of GFP was detected with the construct carrying the AGT1 promoter of the lager strain. Thus, the extra MAL‐activator binding element, present in the AGT1 promoter of the ale strain, appears to be necessary to reach a high level of induction by maltose. Both AGT1 promoters were repressed by glucose but their derepression was different, possibly due to a distinct distribution of Mig1 elements in these two promoters. Copyright © 2011 John Wiley & Sons, Ltd.
Nanophotonic particle simulation and inverse design using artificial neural networks

New deep learning techniques may hold the key to solving intractable photonics problems. We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.
Association Between NR3C1 Gene Polymorphisms and Toxicity Induced by Glucocorticoids Therapy in Saudi Children with Acute Lymphoblastic Leukemia

Background: Glucocorticoids (GCs) are key hormones used for the treatment of acute lymphoblastic leukemia (ALL) in children, but their cytotoxic effects are not well defined. The aim of this study was to evaluate the association between polymorphisms in NR3C1 encoding for protein involved in the GCs metabolism and its role in the development of ALL and the toxicity outcome, in terms of liver toxicity, glucose abnormality and infections, in ALL Saudi children. Methods: The following polymorphisms BCII rs41423247, ER22/23 EK rs6189 and rs6190 and N363S rs6195 in NR3C1 were analyzed in 70 children with ALL treated according to the ALL 2000 study protocol in comparison to 60 control subjects. Treatment toxicities and their association with genotypes were evaluated according to Common Toxicity Criteria (NCI-CTC). Results: This study demonstrated that the NR3C1 did not contribute to the development of childhood ALL. Homozygous ER22/23EK polymorphism was not found in both ALL patients and in control group whereas the heterozygous polymorphism was only observed in the control group (6.66%). The toxicology data in this study showed a significant difference between ALL patients carrying N363S polymorphism and wild type (40% and 6.51% respectively, P= 0.009) and a high-risk factor in the toxicity of glucose abnormality (OR=10.167; 1.302-79.339). BCII shows increased risk factors towards the liver toxicity (OR=2.667; 0.526-7.330) as well as the glucose abnormality (OR=7.5; 1.039-54.116). Conclusion: This study suggested that the polymorphisms in NR3C1 were not associated with the development of ALL in children. N363S polymorphism was sensitive to glucocorticoids and it may contribute to the glucose abnormality for these patients.
Enantiomeric distribution of key volatile components in Citrus essential oils

Citrus as many other plants present characteristic distribution of some enantiomers, thus it is often possible to use this parameter for identification, characterization, genuineness, and pharmacological activity assessment. In particular, it is possible to reveal adulteration of different nature, such as addition of synthetic compounds, or natural components of different botanical origin, with drastic changes in the biological and olfactory properties. This study is focused on the evaluation of the enantiomeric excesses of numerous samples of different Citrus species: C. deliciosa Ten. , C. limon (L.) Burm. , C. bergamia, C. aurantifolia (Christm.) Swing. , C. latifolia Tan. , C. sinensis (L.) Osbeck, and C. aurantium L. The enantiomeric distribution is determined by direct esGC and, depending on the complexity of the essential oil, by MDGC with a chiral column in the second dimension. The research is focused on the determination of fourteen chiral components which present specific distribution in the essential oils investigated. Particular attention is given to the trend of the enantiomeric distribution during the productive season, so to identify useful parameters for quality assessment also in consideration of the wide range of variability often reported in literature. The components investigated were the following: α-thujene, α-pinene, camphene, β-pinene, sabinene, α-phellandrene, β-phellandrene, limonene, linalool, camphor, citronellal, linalyl acetate, terpinen-4-ol, α-terpineol. The use of MDGC allowed the separation of the enantiomers of camphor and citronellal, otherwise not separated by conventional esGC; however for the separation of the enantiomers of α-pinene it was preferable to use conventional esGC. The MDGC system allowed to determine the enantiomeric distribution of camphene, α- and β-phellandrene in lime essential oil for the first time. The results are discussed in function of seasonal variation and, when possible, in function of the extraction technology, with particular regards to lime oils.
Swarm based autonomous landmine detecting robots

Recently it has been found out that, at least 60 countries in the world are contaminated by landmines. These landmines have become a growing concern, due to their threat to lives of human and cattle. There are more than 50–100 million landmines still not diffused in more than 50 countries like Egypt, Angola, Iran, Afghanistan, Cambodia, etc. Though several countries have started taking some important measures like funding the demining projects, training people with demining techniques, etc., landmines have become a major threat to the mortals. There are many detection techniques, but most of these involve humans, which in turn creates a great threat to their lives and limbs. The autonomous swarm based landmine detecting robots proposed in this paper involves a swarm of robots that could autonomously detect landmines and share the location to the control station. The algorithm and the theoretical concepts are shaped into a working model. This paper gives the reader a clear picture about how these robots help in demining.
Music Data Organization Using Heuristic Hierarchical Agglomerative Co-Clustering

In Data mining, Information Retrieval (IR) is the study of significant recent interest. In IR the Music Information Retrieval (MIR) is unitary of the challenging problems. At MIR, the utilization of Tags, Styles and Mood labels (T/S/M) can be extracted from some music websites. A typical problem is how to understand the relationship between the T/S/M. Co-clustering is the combination of two different types of data simultaneously. Hierarchical Co-clustering (HCC) extracts the acoustic features of music to find the similarity among the artist. In this paper, we systematically analyze the application of Heuristic Hierarchical Agglomerative Co-clustering (HHACC) method for music data organization. We demonstrate that this HHACC method achieves better performance than other clustering methods.
Regime Change Detection Using Directional Change Indicators in the Foreign Exchange Market to Chart Brexit

A regime change is a significant change in the collective trading behaviour in a financial market. Being able to detect the occurrence of regime change could lead to a better understanding and monitoring of financial markets. In this paper, a novel method is proposed to detect regime change, which makes use of a data-driven approach, that of directional change (DC). Compared to the conventional approach of using time series analysis, DC is an alternative approach to sample price movement. As variables observed under time series do not apply to DC, our first contributions is the identification of a new relevant indicator for regime change detection. Our second contribution is the comparison of both the DC approach and time series analysis, their ability to achieve regime change detection. The ability of both approaches in regime change detection is examined over a period of market uncertainty, that of Brexit. The results demonstrate that the DC approach is as effective as the time-series approach in detecting regime changes. Moreover, the DC approach is encouraging because some market regime changes are detected under DC, but are not found under time series. That means they support each other in the detection of regime change, and can also provide extra information to complement each other. Together, regime changes detected under both DC and time series provide a better insight into the market, which market participants and regulators could benefit from.
Anonymous Walk Embeddings

The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.
Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts

Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high number of OOV words poses a difficulty for word-based neural models. Meanwhile, there is plenty of evidence to the effectiveness of character-based neural models in mitigating this OOV problem. We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts. Our experiments show that (1) character models outperform word embedding-only models by up to 4 F1 points, (2) character models perform better in OOV cases with an improvement of as high as 15 F1 points, and (3) character models are robust against a very high OOV rate.
Japanese Predicate Conjugation for Neural Machine Translation

Neural machine translation (NMT) has a drawback in that can generate only high-frequency words owing to the computational costs of the softmax function in the output layer. In Japanese-English NMT, Japanese predicate conjugation causes an increase in vocabulary size. For example, one verb can have as many as 19 surface varieties. In this research, we focus on predicate conjugation for compressing the vocabulary size in Japanese. The vocabulary list is filled with the various forms of verbs. We propose methods using predicate conjugation information without discarding linguistic information. The proposed methods can generate low-frequency words and deal with unknown words. Two methods were considered to introduce conjugation information: the first considers it as a token (conjugation token) and the second considers it as an embedded vector (conjugation feature). The results using these methods demonstrate that the vocabulary size can be compressed by approximately 86.1% (Tanaka corpus) and the NMT models can output the words not in the training data set. Furthermore, BLEU scores improved by 0.91 points in Japanese-to-English translation, and 0.32 points in English-to-Japanese translation with ASPEC.
Evolution of Profit Persistence in the USA: Evidence from Three Periods

In the present study we analyze and compare profit persistence during the periods 1950–66, 1967–83 and 1984–99 in the USA. While most of the previous studies performed persistence analysis on survivors only, the present set-up allows for companies to enter and exit the analyzed sample, giving a more comprehensive depiction of the US economy during this half of the century. The results point towards a constant increase of competition after the opening of the US economy to international competition in the 1960–80s. Key determinants of profit persistence seem to be the firm's and industry size, industry growth, and more recently risk, advertising and exports.
Manganese-porphyrin-enhanced MRI for the detection of cancer cells: A quantitative in vitro investigation with multiple clinical subtypes of breast cancer

Magnetic resonance imaging (MRI) contrast agents (CAs) are chemical compounds that can enhance image contrast on T1- or T2- weighted MR image. We have previously demonstrated the potential of MnCl2, a manganese-based CA, in cellular imaging of breast cancer using T1-weighted MRI. In this work, we examined the potential of another class of manganese-based CAs, manganese porphyrins (MnPs), for sensitive cellular detection of multiple clinical subtypes of breast cancer using quantitative MRI. Using a clinical 3.0-T MRI scanner, the relaxivities of two MnPs, MnTPPS4 and MnTPPS3NH2, and conventional Gd-DTPA (control) were measured in ultrapure water and their T1 contrast enhancement patterns were characterized in multiple clinical subtypes of breast cancer. The toxicity of the three CAs was evaluated in vitro. Compared to Gd-DTPA, both MnTPPS3NH2 and MnTPPS4 enabled a more sensitive multi-subtype detection of four breast cell lines at doses that posed no cytotoxic effects, with MnTPPS3NH2 producing the greatest positive enhancement. The superior T1 enhancement capabilities of MnPs over Gd-DTPA are statistically significant and are likely due to their greater cellular uptake and relaxivities. The results demonstrate that multiple clinical subtypes of breast cancer can be imaged on a 3.0-T MRI scanner using MnPs as T1 cellular CAs.
In Vivo Molecular Characterization of Abdominal Aortic Aneurysms Using Fibrin‐Specific Magnetic Resonance Imaging

Background The incidence of abdominal aortic aneurysms (AAAs) will significantly increase during the next decade. Novel biomarkers, besides diameter, are needed for a better characterization of aneurysms and the estimation of the risk of rupture. Fibrin is a key protein in the formation of focal hematoma associated with the dissection of the aortic wall and the development of larger thrombi during the progression of AAAs. This study evaluated the potential of a fibrin‐specific magnetic resonance (MR) probe for the in vivo characterization of the different stages of AAAs. Methods and Results AAAs spontaneously developed in ApoE−/− mice following the infusion of angiotensin‐II (Ang‐II, 1 μg/kg−1·per minute). An established fibrin‐specific molecular MR probe (EP2104R, 10 μmol/kg−1) was administered after 1 to 4 weeks following Ang‐II infusion (n=8 per group). All imaging experiments were performed on a clinical 3T Achieva MR system with a microscopy coil (Philips Healthcare, Netherlands). The development of AAA‐associated fibrin‐rich hematoma and thrombi was assessed. The high signal generated by the fibrin probe enabled high‐resolution MR imaging for an accurate assessment and quantification of the relative fibrin composition of focal hematoma and thrombi. Contrast‐to‐noise‐ratios (CNRs) and R1‐relaxation rates following the administration of the fibrin probe were in good agreement with ex vivo immunohistomorphometry (R 2=0.83 and 0.85) and gadolinium concentrations determined by inductively coupled plasma mass spectroscopy (R 2=0.78 and 0.72). Conclusions The fibrin‐specific molecular MR probe allowed the delineation and quantification of changes in fibrin content in early and advanced AAAs. Fibrin MRI could provide a novel in vivo biomarker to improve the risk stratification of patients with aortic aneurysms.
A Seven-Year Retrospective Study on the Surveillance of Hepatitis B in Laos

Objective Lao PDR is one of the most highly endemic countries for hepatitis B in Asia and the second country for liver cancer incidence. Therefore, the follow-up of infected individuals through predictive serological markers is of utmost importance to monitor the progression of the pathology and take the decision on treatment. Methods A retrospective-descriptive cohort study was conducted on 3,857 HBV-infected patients. Information about infection status (viral load, VL), liver function (aminotransferases), and treatments was recorded. Results M/F sex ratio was 1.77 for a median age of 37. Patients under 37 displayed higher VL than older ones and men had higher VL than women. Initial VL ranged from <50 IU/mL to 2.5 1013 IU/mL. Median aminotransferase values were 45.5 U/L for ALAT and 44 U/L for ASAT, ranging from <8 to >2,000 U/L. Men had higher aminotransferase than women. Globally 20% of patients received treatment (mainly immunostimulant and reverse-transcriptase inhibitors); 11% had high levels of VL and liver enzymes, but only 2% of them were treated. Conclusion Public health decisions should be taken urgently to rationalise vaccination and provide fair access to early diagnosis and treatment; otherwise the burden of HBV-associated diseases will be overwhelming for Laos in the near future.
Reward Constrained Policy Optimization

Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior. While constraints may solve this issue, there is no closed form solution for general constraints. In this work we present a novel multi-timescale approach for constrained policy optimization, called `Reward Constrained Policy Optimization' (RCPO), which uses an alternative penalty signal to guide the policy towards a constraint satisfying one. We prove the convergence of our approach and provide empirical evidence of its ability to train constraint satisfying policies.
Knowledge, Attitude and Practices on Substance Use Disorders by University Students: A Review of Literature

Background: This is a systematic review of literature describing the knowledge of substance use disorders (SUDs) by university students and determines their attitude and practices towards SUDs. It compares university students in different countries.Methods: The author conducted a systematic review of selected studies on SUDs conducted among university students and published between 2014 and 2017. An extensive search of literature was performed from 13th to 17th November, 2017 using Ovid interface to search MEDLINE, ScienceDirect, RESEARCH-Open Access for indexed journals in the subject area. The study author conducted this systematic review on the subject from journals availed online.Results: Findings of nine articles were included in this review. The included articles assessed university students at various academic years and programmes. The SUDs included alcohol use disorder, nicotine dependence, legal, illegal drugs dependence and pharmaceutical drug dependence.Conclusions: The findings from the reviewed studies indicate awareness by university students on substance use disorders. However, there is need for enhanced awareness and education programmes for university students on SUDs aimed at producing a positive impact on their practices, attitudes and knowledge on this subject.