Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel, non-symbiotic isolates of Neorhizobium from a dryland agricultural soil

Semi-selective enrichment, followed by PCR screening, resulted in the successful direct isolation of fast-growing Rhizobia from a dryland agricultural soil. Over 50% of these isolates belong to the genus Neorhizobium, as concluded from partial rpoB and near-complete 16S rDNA sequence analysis. Further genotypic and genomic analysis of five representative isolates confirmed that they form a coherent group within Neorhizobium, closer to N. galegae than to the remaining Neorhizobium species, but clearly differentiated from the former, and constituting at least one new genomospecies within Neorhizobium. All the isolates lacked nod and nif symbiotic genes but contained a repABC replication/maintenance region, characteristic of rhizobial plasmids, within large contigs from their draft genome sequences. These repABC sequences were related, but not identical, to repABC sequences found in symbiotic plasmids from N. galegae, suggesting that the non-symbiotic isolates have the potential to harbor symbiotic plasmids. This is the first report of non-symbiotic members of Neorhizobium from soil.
Learning with Feature Network and Label Network Simultaneously

For many supervised learning problems, limited training samples and incomplete labels are two difficult challenges, which usually lead to degenerated performance on label prediction. To improve the generalization performance, in this paper, we propose Doubly Regularized Multi-Label learning (DRML) by exploiting feature network and label network regularization simultaneously. In more details, the proposed algorithm first constructs a feature network and a label network with marginalized linear denoising autoencoder in data feature set and label set, respectively, and then learns a robust predictor with the feature network and the label network regularization simultaneously. While DRML is a general method for multi-label learning, in the evaluations we focus on the specific application of multi-label text tagging. Extensive evaluations on three benchmark data sets demonstrate that DRML outstands with a superior performance in comparison with some existing multi-label learning methods.
‘Because the baby asks for it’: a mixed-methods study on local perceptions toward nutrition during pregnancy among marginalised migrant women along the Myanmar–Thailand border

ABSTRACT Background: Under- and over-nutrition during pregnancy are known risk factors for pregnancy complications and adverse pregnancy and infant outcomes. Understanding perceptions around nutrition in pregnancy can create culturally appropriate interventions for improved health outcomes. Objective: A mixed-methods study was performed to explore local perceptions and practices of diet and physical activity in pregnancy in a marginalised population along the Myanmar–Thailand border. Methods: From April to July 2017, a cross-sectional survey and focus group discussions were conducted with pregnant women reporting to antenatal care; in-depth interviews were conducted with senior midwives at participating organisations along the Myanmar–Thailand border. Results: A total of 388 pregnant women were interviewed at two clinic sites along the Myanmar–Thailand border. A high proportion of women had limited knowledge of and poor dietary practices. Consuming a sweetened drink in the last 24 hours as well as being a non-teenage, multigravida woman was significantly associated with high body mass index (BMI) compared to normal BMI. Qualitative analysis combined focus group discussions (n = 66) and in-depth interviews (n = 4) summarising emergent themes: common foods eaten or avoided and rationale; benefits of nutrition; perceptions of overweight and weight gain during pregnancy; barriers to a healthy diet; and sources of diet information. Conclusions: There is limited awareness about healthy diets and lifestyle in these marginalised, migrant communities along the Myanmar–Thailand border. This study suggests that simple, culturally appropriate messaging should be provided to women and communities with low health literacy to generate awareness about healthy lifestyles and their effects on pregnancy outcomes as an important element of a broader strategy to address maternal nutrition in this population. However, more studies to determine the effectiveness of a broad range of interventions in low- and middle-income countries (LMIC) are needed, especially in marginalised migrant populations.
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling

Multimodal features play a key role in wearable sensor based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a “collect fully and select wisely” principle as well as an interpretable parallel recurrent model with convolutional attentions to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the accuracy and interpretability of the proposed model based on extensive experiments. The results show that our model achieves competitive performance on two benchmarked datasets and works well in real life scenarios.
Spatiotemporal drought variability in the Mediterranean over the last 900 years

Recent Mediterranean droughts have highlighted concerns that climate change may be contributing to observed drying trends, but natural climate variability in the region is still poorly understood. We analyze 900 years (1100–2012) of Mediterranean drought variability in the Old World Drought Atlas (OWDA), a spatiotemporal tree ring reconstruction of the June‐July‐August self‐calibrating Palmer Drought Severity Index. In the Mediterranean, the OWDA is highly correlated with spring precipitation (April–June), the North Atlantic Oscillation (January–April), the Scandinavian Pattern (January–March), and the East Atlantic Pattern (April–June). Drought variability displays significant east‐west coherence across the basin on multidecadal to centennial timescales and north‐south antiphasing in the eastern Mediterranean, with a tendency for wet anomalies in the Black Sea region (e.g., Greece, Anatolia, and the Balkans) when coastal Libya, the southern Levant, and the Middle East are dry, possibly related to the North Atlantic Oscillation. Recent droughts are centered in the western Mediterranean, Greece, and the Levant. Events of similar magnitude in the western Mediterranean and Greece occur in the OWDA, but the recent 15 year drought in the Levant (1998–2012) is the driest in the record. Estimating uncertainties using a resampling approach, we conclude that there is an 89% likelihood that this drought is drier than any comparable period of the last 900 years and a 98% likelihood that it is drier than the last 500 years. These results confirm the exceptional nature of this drought relative to natural variability in recent centuries, consistent with studies that have found evidence for anthropogenically forced drying in the region.
Acquiring and characterizing plane-to-ray indirect light transport

Separation of light transport into direct and indirect paths has enabled new visualizations of light in everyday scenes. However, indirect light itself contains a variety of components from subsurface scattering to diffuse and specular interreflections, all of which contribute to complex visual appearance. In this paper, we present a new imaging technique that captures and analyzes these components of indirect light via light transport between epipolar planes of illumination and rays of received light. This plane-to-ray light transport is captured using a rectified projector-camera system where we vary the offset between projector and camera rows (implemented as synchronization delay) as well as the exposure of each camera row. The resulting delay-exposure stack of images can capture live short and long-range indirect light transport, disambiguate subsurface scattering, diffuse and specular interreflections, and distinguish materials according to their subsurface scattering properties.
Defect Clustering and Nano‐Phase Structure Characterization of Multi‐Component Rare Earth Oxide Doped Zirconia‐Yttria Thermal Barrier Coatings

Advanced oxide thermal barrier coatings have been developed by incorporating multi-component rare earth oxide dopants into zirconia-yttria to effectively promote the creation of the thermodynamically stable, immobile oxide defect clusters and/or nano-scale phases within the coating systems. The presence of these nano-sized defect clusters has found to significantly reduce the coating intrinsic thermal conductivity, improve sintering resistance, and maintain long-term high temperature stability. In this paper, the defect clusters and nano-structured phases, which were created by the addition of multi-component rare earth dopants to the plasma-sprayed and electron-beam physical vapor deposited thermal barrier coatings, were characterized by high-resolution transmission electron microscopy (TEM). The defect cluster size, distribution, crystallographic and compositional information were investigated using high-resolution TEM lattice imaging, selected area diffraction (SAD), electron energy-loss spectroscopy (EELS) and energy dispersive spectroscopy (EDS) analysis techniques. The results showed that substantial defect clusters were formed in the advanced multi-component rare earth oxide doped zirconia- yttria systems. The size of the oxide defect clusters and the cluster dopant segregation was typically ranging from 5 to 50 nm. These multi-component dopant induced defect clusters are an important factor for the coating long-term high temperature stability and excellent performance.
Distinguishing profile deviations from a part's deformation using the maximum normed residual test

Non-rigid parts, in free-state, may have a considerable different shape than their nominal model due to dimensional and geometric variations of manufacturing process, gravity loads and residual stress induced distortion. Therefore, sorting profile deviation from a part's deformation by comparing the part's nominal shape to its scanned free-state shape is a challenging task. This task is a key step in the Iterative Displacement Inspection (IDI) algorithm used for the inspection of non-rigid parts without the use of costly specialized fixtures. This paper proposes the use of the statistical maximum normed residual test to improve the aforementioned identification task. Thirty two simulated manufactured parts are studied to show that the proposed method reduces the type I and II identification error of the IDI method.
Editorial: International collaborative research in the addictions.

DOI:10.1097/YCO.0000000000000433 Over the past decade, articles published in the Addictive Disorders section of Current Opinion in Psychiatry have been centred on a theme. In this period, we have covered topics such as substance use and comorbid mental health disorders, sex considerations in substance use, emerging drugs, epidemiology of drug use and innovative treatment approaches. This year, we have selected the topic of international collaborative research in addictive disorders, with an emphasis on research collaborations fostered by the US National Institute of Drug Abuse (NIDA) and the European Union. Eight review articles are presented and as is standard practice, they focus on studies published in a 2-year publication window, in this case 2016–end2017. They are written by authors from seven countries, namely Spain (two articles), the United Kingdom, Italy, the United States, Mexico, Taiwan and Malaysia (one each). NIDA is well-recognized to be the major source of funding for research in drug abuse worldwide and for 25 years, it has run its International Program which has funded multiple initiatives, including international collaborative studies, training in research methods of international scientists through scholarships and fellowships, and has hosted an annual meeting at the College of problems in Drug Dependence meetings (CPDD) where NIDA supported scientists both in their early careers and more established researchers have presented their work to their peers. The European Union has several mechanisms for supporting research within its member countries and involving in many cases collaborating investigators from countries in other regions. The articles published in this year’s section represent those who have been supported through the NIDA International Program and those who have been funded through European Union mechanisms. The WHO also undertakes and promotes international collaborative research and an example of this is the ATLAS-SU Project which is summarized in the article by Marı́n-Navarrete and colleagues (pp. 306–314). The section begins with a review of the NIDA International Program by its longstanding Director, Dr Steven Gust, and a program contractor, Judy McCormally. The extent of the NIDA International
多参数流式细胞术检测多发性骨髓瘤患者微小残留病106例临床观察

目的 探讨多参数流式细胞术（MFC）检测微小残留病（MRD）在我国初诊多发性骨髓瘤（MM）患者中的可行性及其预后意义。 方法 回顾性分析2013年6月至2015年6月106例采用MFC进行MRD检测连续收治的初诊MM患者的临床资料。 结果 ①106例患者中48例（45.3%）MRD转为阴性，中位达MRD阴性时间为3个月。 接受自体造血干细胞移植（ASCT）患者MRD转阴率显著高于未接受ASCT患者（62.2%对36. 2%，χ2=6.536，P=0.011）。 ②48例完全缓解（CR）患者中7例（14.6%）MRD持续阳性，这7例患者中5例已疾病进展，中位无进展生存（PFS）时间为19个月，3例死亡，中位总生存（OS）时间为28个月，MRD持续阳性CR患者的PFS及OS时间均明显短于MRD转阴CR患者（P值均<0.05）。 ③中位随访38个月，MRD转阴患者的预后显著优于阳性患者，二者中位PFS时间分别为未达到和17个月（P<0.001），中位OS均未达到（P<0.001）。 根据MRD水平将患者分为四组：≥1%、0.1%~<1%、0.01%~<0.1%和阴性，结果显示随着MRD水平的降低，患者PFS及OS时间有逐渐延长趋势。 ④多因素分析显示，MRD是影响MM患者PFS[HR=0.133（95% CI 0.062~0.288），P<0.001]和OS[HR=0.156（95% CI 0.050~0.484），P=0.001]的独立预后因素。 根据MRD、细胞遗传学情况将患者分为4组，结果显示MRD转阴的高危患者预后（PFS及OS）明显好于MRD阳性的高危患者（P<0.05），而与MRD转阴的标危患者预后相近（P>0.05）。 结论 ASCT患者中MFC检测的MRD转阴率更高。 MRD是MM患者的独立预后因素，根据MRD水平可以对患者预后进行分层。 MRD转阴的细胞遗传学高危患者的预后与标危患者相近。 MFC检测MRD值得在临床推广应用。
Biological species in the viral world

Significance The biological species concept (BSC) has served as the basis for defining species for over 75 years. Members of a biological species are defined by their ability to exchange genetic material, and it was originally thought that asexual lineages were not amenable to species-level classification based on the BSC since clonal individuals are reproductively isolated from one another. In this study, we demonstrate that the rates and patterns of gene exchange in acellular organisms (viruses and bacteriophages) allow the assignment of true biological species, an essential step to organizing the tree of life. Our results show that a universal species definition, based on the BSC, can be used to define biological species in all major lifeforms. Due to their dependence on cellular organisms for metabolism and replication, viruses are typically named and assigned to species according to their genome structure and the original host that they infect. But because viruses often infect multiple hosts and the numbers of distinct lineages within a host can be vast, their delineation into species is often dictated by arbitrary sequence thresholds, which are highly inconsistent across lineages. Here we apply an approach to determine the boundaries of viral species based on the detection of gene flow within populations, thereby defining viral species according to the biological species concept (BSC). Despite the potential for gene transfer between highly divergent genomes, viruses, like the cellular organisms they infect, assort into reproductively isolated groups and can be organized into biological species. This approach revealed that BSC-defined viral species are often congruent with the taxonomic partitioning based on shared gene contents and host tropism, and that bacteriophages can similarly be classified in biological species. These results open the possibility to use a single, universal definition of species that is applicable across cellular and acellular lifeforms.
Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models

In this paper, we aim at introducing a new machine learning model, namely reconciled polynomial machine, which can provide a unified representation of existing shallow and deep machine learning models. Reconciled polynomial machine predicts the output by computing the inner product of the feature kernel function and variable reconciling function. Analysis of several concrete models, including Linear Models, FM, MVM, Perceptron, MLP and Deep Neural Networks, will be provided in this paper, which can all be reduced to the reconciled polynomial machine representations. Detailed analysis of the learning error by these models will also be illustrated in this paper based on their reduced representations from the function approximation perspective.
TENSOR NETWORK COMPLEXITY OF MULTILINEAR MAPS PER AUSTRIN

We study tensor networks as a model of arithmetic computation for evaluating multilinear maps. This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n ) time matrix multiplication, O(n logn) time discrete Fourier transform, O(n ) time 3t-clique counting and O∗(2n) time for computing the permanent of a matrix. For counting homomorphisms of a general pattern graph P into a host graph on n vertices we obtain an upper bound of O(n ) bw(P ) where bw(P ) is the branchwidth of P . This essentially matches the bound for counting cliques, and yields small improvements over previous algorithms for many choices of P . While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various linear maps, including: (a) an Ω(n ) time lower bound for counting homomorphisms from P to an n-vertex graph, matching the upper bound if ω = 2. In particular for P a v-clique this yields an Ω(nd2v/3e) time lower bound for counting v-cliques. (b) an Ω(2) time lower bound for the permanent of an n× n matrix. (c) an Ω(n) time lower bound for the trilinear map Dijk = ∑ lAijlBiklCjkl generalizing matrix multiplication, taking three n× n× n tensors A,B,C and producing an n× n× n tensor D, ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem. ar X iv :1 71 2. 09 63 0v 2 [ cs . C C ] 7 A pr 2 01 8
CALIDAD DEL AGUA SUBTERRANEA EN LA ZONA SUR DE LA CIUDAD DE MEXICO CON BASE EN MICROORGANISMOS

Con el objeto de observar la eficiencia en la aplicacion de la norma oficial mexicana NOM-127-SSA1-1994 sobre agua para uso y consumo humano, limites permisibles de calidad y tratamientos a los que debe someterse el agua para su potabilizacion, se llevo a cabo un muestreo en 5 pozos piloto de la zona geohidrologica sur de la Ciudad de Mexico. Se compararon 3 tecnicas analiticas para la determinacion de bacterias, filtrando 100 mL de agua para coliformes totales, coliformes fecales y estreptococos fecales, concentrando 5 L de agua y enriqueciendo para Escherichia coli, estreptococos fecales, Vibrio y cuenta total de bacterias heterotrofas, asi como la tecnica molecular de reaccion en cadena utilizando polimerasa para E. coli y Shigella.
Strategic Sequences of Arguments for Persuasion Using Decision Trees

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. For this, it can be advantageous forthe persuader to have a model of the persuadee. Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, these developments have not systematically harnessed established notions in decision theory for maximizing the outcome of a dialogue. To address this, we present a general framework for representing persuasion dialogues as a decision tree, and for using decision rules for selecting moves. Furthermore, we provide some empirical results showing how some well-known decision rules perform, and make observations about their general behaviour in the context of dialogues where there is uncertainty about the accuracy of the user model.
Network Learning with Local Propagation

This paper presents a locally decoupled network parameter learning with local propagation. Three elements are taken into account: (i) sets of nonlinear transforms that describe the representations at all nodes, (ii) a local objective at each node related to the corresponding local representation goal, and (iii) a local propagation model that relates the nonlinear error vectors at each node with the goal error vectors from the directly connected nodes. The modeling concepts (i), (ii) and (iii) offer several advantages, including (a) a unified learning principle for any network that is represented as a graph, (b) understanding and interpretation of the local and the global learning dynamics, (c) decoupled and parallel parameter learning, (d) a possibility for learning in infinitely long, multi-path and multi-goal networks. Numerical experiments validate the potential of the learning principle. The preliminary results show advantages in comparison to the state-of-the-art methods, w.r. t. the learning time and the network size while having comparable recognition accuracy.
Relationship between circulating VCAM-1, ICAM-1, E-selectin and MMP9 and the extent of coronary lesions

OBJECTIVES: Inflammatory molecules play a role in the development of atherosclerosis, which is the primary origin of cardiovascular disorders. However, to the best of our knowledge, no study has attempted to investigate the relationship between these circulating molecules and the prediction of cardiovascular risk. The present study aimed to investigate the relationships of vascular cell adhesion molecule-1, intercellular adhesion molecule-1, E-selectin and matrix metalloproteinase 9 serum concentrations with the extent of coronary lesions. METHODS: Seventy-four individuals who were undergoing coronary angiography for the first time for diagnostic purposes were enrolled in this study. The extent of the coronary lesion was assessed using the Friesinger Index, and subjects were classified into four groups: no lesions, minor lesions, intermediate lesions and major lesions. Serum biochemical parameters and serum concentrations of vascular cell adhesion molecule-1, intercellular adhesion molecule-1, E-selectin and matrix metalloproteinase 9 were analyzed. RESULTS: The vascular cell adhesion molecule-1 concentration was higher than 876 ng/mL in individuals with intermediate and major lesions (p<0.001 and p=0.020, respectively). Moreover, logistic regression analysis showed that these patients had an increased risk of having an intermediate lesion (p=0.007). Interestingly, all individuals with major lesions had vascular cell adhesion molecule-1 concentrations higher than 876 ng/mL. No association was found between the concentrations of the other proteins and the Friesinger Index. CONCLUSIONS: Serum vascular cell adhesion molecule-1 may be associated with the extent of coronary lesions. Moreover, it may represent an alternative to improve the cardiovascular risk classification in patients without acute coronary syndrome.