Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and genetic analyses reveal the protein SepF as a new membrane anchor for the Z ring

Significance A key step in bacterial cell division is the polymerization of FtsZ at midcell into a ring-like structure. This so-called Z ring forms a scaffold for the other cell division proteins. FtsA anchors the Z ring to the cell membrane; however, many bacterial species do not have FtsA. Here, we show that the conserved protein SepF, which forms large protein rings, also functions as a membrane anchor for the Z ring. We determined the molecular structure of the FtsZ-binding and ring-forming domain of SepF and show that the membrane-binding domain is located at the very beginning of the protein. These results explain why FtsA and SepF can fulfill similar functions in bacterial cell division. A key step in bacterial cell division is the polymerization of the tubulin homolog FtsZ at midcell. FtsZ polymers are anchored to the cell membrane by FtsA and are required for the assembly of all other cell division proteins. In Gram-positive and cyanobacteria, FtsZ filaments are aligned by the protein SepF, which in vitro polymerizes into large rings that bundle FtsZ filaments. Here we describe the crystal structure of the only globular domain of SepF, located within the C-terminal region. Two-hybrid data revealed that this domain comprises the FtsZ binding site, and EM analyses showed that it is sufficient for ring formation, which is explained by the filaments in the crystals of SepF. Site-directed mutagenesis, gel filtration, and analytical ultracentrifugation indicated that dimers form the basic units of SepF filaments. High-resolution structured illumination microscopy suggested that SepF is membrane associated, and it turned out that purified SepF not only binds to lipid membranes, but also recruits FtsZ. Further genetic and biochemical analyses showed that an amphipathic helix at the N terminus functions as the membrane-binding domain, making SepF a unique membrane anchor for the FtsZ ring. This clarifies why Bacillus subtilis grows without FtsA or the putative membrane anchor EzrA and why bacteria lacking FtsA contain SepF homologs. Both FtsA and SepF use an amphipathic helix for membrane binding. These helices prefer positively curved membranes due to relaxed lipid density; therefore this type of membrane anchor may assist in keeping the Z ring positioned at the strongly curved leading edge of the developing septum.
Evaluation of the modern visual SLAM methods

Simultaneous Localization and Mapping (SLAM) is a challenging task in robotics. Researchers work hard on it, so several novel SLAM algorithms as well as enhancements for the known ones are published every year. We have selected recent (2013-mid. 2015) approaches that in theory can be run on mobile robot and evaluated it. This paper gives brief intuitive description of ORB-SLAM, LSD-SLAM, L-SLAM and OpenRatSLAM algorithms, then compares the algorithms theoretically (based on given description) and evaluates them with TUM RGB-D benchmark.
Examining the Positive Cognitive Triad: A Link Between Resilience and Well-Being

Extant literature links higher levels of resilience to overall well-being; however, the underlying mechanisms explaining this relation are unclear. Replicating and extending the study of Mak, Ng, and Wong, the present study investigated the “positive cognitive triad” of hope, world-view, and self-esteem as a possible mediator between resilience and well-being for the first time in an American sample. Participants (n = 198) completed online surveys of self-esteem, hope, view of the world, and resilience. Consistent with expectations, the triad was a significant mediator of the relation between resilience and well-being. These findings underscore the importance of developing interventions targeting the positive cognitive triad and examining the triad within the context of mental illness. Limitations and areas for future research are discussed.
Using continental observations in global atmospheric inversions of CO2: North American carbon sources and sinks

We evaluate North American carbon fluxes using a monthly global Bayesian synthesis inversion that includes wellcalibrated carbon dioxide concentrations measured at continental flux towers. We employ the NASA Parametrized Chemistry Tracer Model (PCTM) for atmospheric transport and a TransCom-style inversion with subcontinental resolution. We subsample carbon dioxide time series at four North American flux tower sites for mid-day hours to ensure sampling of a deep, well-mixed atmospheric boundary layer. The addition of these flux tower sites to a global network reduces North America mean annual flux uncertainty for 2001–2003 by 20% to 0.4 Pg C yr-1 compared to a network without the tower sites. North American flux is estimated to be a net sink of 1.2 ± 0.4 Pg C yr-1 which is within the uncertainty bounds of the result without the towers. Uncertainty reduction is found to be local to the regions within North America where the flux towers are located, and including the towers reduces covariances between regions within NorthAmerica. Mid-day carbon dioxide observations from flux towers provide a viable means of increasing continental observation density and reducing the uncertainty of regional carbon flux estimates in atmospheric inversions.
A Restaurant Recommender System Based on User Preference and Location in Mobile Environment

Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. In this paper, we propose a restaurant recommender system in mobile environment. This recommender system adopts a user preference model by using the features of user's visited restaurants, and also utilizes the location information of user and restaurants to dynamically generate the recommendation results. Baidu map cloud service is used to implement the proposed recommender system. The result of a case study shows that the proposed restaurant recommender system can effectively utilize user's preference and the location information to recommend the personalized and suitable restaurants for different users.
Neural Representation Learning in Linguistic Structured Prediction

Advances in neural network architectures and training algorithms have demonstrated the effectiveness of representation learning in natural language processing. This thesis argues for the importance of modeling discrete structure in language, even when learning continuous representations. We propose that explicit structure representations and learned distributed representations can be efficiently combined for improved performance over (i) traditional approaches to structure and (ii) uninformed neural networks that ignore all but surface sequential structure. We demonstrate, on three distinct problems, how assumptions about structure can be integrated naturally into neural representation learners for NLP problems, without sacrificing computational efficiency. First, we propose segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments and show that, compared to models that do not explicitly represent segments such as BIO tagging schemes and connectionist temporal classification (CTC), SRNNs obtain substantially higher accuracies on tasks including phone recognition and handwriting recognition. Second, we propose dynamic recurrent acyclic graphical neural networks (DRAGNN), a modular neural architecture that generalizes the encoder/decoder concept to include explicit linguistic structures. Linguistic structures guide the building process of the neural networks by following the transitions and encoding the (partial) structures constructed those transitions explicitly into the hidden layer activations. We show that our framework is significantly more accurate and efficient than sequence-to-sequence with attention for syntactic dependency parsing and yields more accurate multi-task learning for extractive summarization tasks. Third, we propose to use discrete stochastic attention to model the alignment structures explicitly in the neural sequence-to-sequence translation model. We regularize the posterior distributions of the latent alignment decisions using the posteriors computed from models that make stronger independence assumptions but that have the same latent variables. We show that our posterior regularization scheme leads to substantially improved generalization. Since the posterior regularization objective can be generally expensive to compute, we propose several approximations based on importance sampling and find that they are either as good as or better than the exact objective in terms of held-out generalization. The techniques proposed in this thesis automatically learn structurally informed representations of the inputs. Linguistically motivated inductive biases help learning better representations in the neural models and these representations and components can be better integrated with other end-to-end deep learning systems within and beyond NLP.
Life Cycle Human Capital Investment : New Evidence on an Old Issue *

Tests of the life cycle human capital investment theory using the implied restrictions on the covariance structure of earnings have not been favorable to the theory. The theory implies that earnings follow individual specific growth paths, which has been modeled as a random coefficient on experience, or “random growth. ” The random growth model implies an individual effect in earnings changes. An alternative model for the covariance structure of earnings is the “random walk” model which implies that there are not individual-specific earnings growth paths, and there is not an individual effect in earnings changes. MaCurdy (1982) and Abowd and Card (1989) showed that there is not an individual effect in earnings changes, and therefore the random walk model provides a better fit to the covariance structure. Baker (1997) has revisited this issue and finds regression evidence in favor of random growth, but also reports conflicting statistical tests rejecting random growth. In this paper, we examine the implications of the life cycle human capital investment theory for earnings changes and find that the random growth specification does not capture the decline in the variance of earnings changes that results from the transversality condition necessary to solve for the optimal investment path. We develop and estimate a model for the covariance structure that allows for increased curvature for the wage profile for workers with a higher rate of growth. In earnings changes, the model allows for a declining variance in individual rates over the life cycle. We fit the model using the Panel Study of Income Dynamics and find significant variation among individuals in growth profiles that decline with experience. We also estimate correlations in earnings profiles within families and find that brothers have similar earnings growth paths, suggesting that the variation in earnings changes is systematic and not random. Finally, we estimate the model on regression residuals from replications of the data used by MaCurdy and by Abowd and Card and obtain results similar to those using our data. These results are supportive for the existence of individual specific growth paths for earnings, and therefore provide evidence in favor of the life cycle human capital investment model.
Auditory Emotion Word Primes Influence Emotional Face Categorization in Children and Adults, but Not Vice Versa

In order to assess how the perception of audible speech and facial expressions influence one another for the perception of emotions, and how this influence might change over the course of development, we conducted two cross-modal priming experiments with three age groups of children (6-, 9-, and 12-years old), as well as college-aged adults. In Experiment 1, 74 children and 24 adult participants were tasked with categorizing photographs of emotional faces as positive or negative as quickly as possible after being primed with emotion words presented via audio in valence-congruent and valence-incongruent trials. In Experiment 2, 67 children and 24 adult participants carried out a similar categorization task, but with faces acting as visual primes, and emotion words acting as auditory targets. The results of Experiment 1 showed that participants made more errors when categorizing positive faces primed by negative words versus positive words, and that 6-year-old children are particularly sensitive to positive word primes, giving faster correct responses regardless of target valence. Meanwhile, the results of Experiment 2 did not show any congruency effects for priming by facial expressions. Thus, audible emotion words seem to exert an influence on the emotional categorization of faces, while faces do not seem to influence the categorization of emotion words in a significant way.
Epilepsy and Autoimmunity in Pediatric Patients

Abstract Our aim was to determine the presence and possible role of autoantibodies in epileptic patients with an undetermined etiology. Eighty epilepsy patients, who were referred to the Pediatric Neurology Department at Ankara University between November 2011 and April 2012, were enrolled in the study. Antinuclear antibodies (ANA), anticardiolipin IgG, antiphospholipid, antithyroid peroxidase, paraneoplastic, glutamic acid decarboxylase (GAD), and N-methyl-d-aspartate (NMDA) receptor antibodies were studied in our university laboratory. In addition, NMDA receptor (NMDAR), voltage-gated potassium channel (VGKC)-complex, leucine-rich, glioma inactivated 1 (LGI1) and contactin-associated protein 2 (CASPR2) antibodies were studied at the Oxford University Immunology Laboratory. The study included 35 girls (44%) and 45 boys (56%) with a mean symptom age of 8.6 ± 4.53 years. ANA was detected in 15 (18.8%), antiphospholipid Ab in 3 (3.75%), anticardiolipin Ab in 1 (1.25%), and antithyroid peroxidase in 3 (3.75%) epileptic patients. In addition, anti-GAD Ab was detected in 7 (8.75%), anti-Yo Ab in 3 (3.75%), and anti-Ma2 in 3 (3.75%) epileptic patients. Anti-VGKC was positive in 13 (16.25%) epileptic patients. We performed a pioneer study to investigate the association between autoimmunity and pediatric epilepsy and we conclude that autoimmunity should be considered in epileptic patients with an undetermined etiology.
Well-being and conservation: diversity and change in visions of a good life among the Maasai of northern Tanzania

The Simanjiro plains, east of Tarangire National Park in Northern Tanzania are a key dispersal area for wildlife, and are of vital importance to Maasai pastoral livelihoods, which are rapidly diversifying. Diversification is coupled with fragmentation of the rangelands as agriculture expands and multiple actors compete for land. These changes reflect transformations occurring across pastoral rangelands, and pose the broader challenge of reconciling conservation and development objectives. We propose that qualitative research using a three-dimensional human well-being framework, encompassing material, relational, and subjective aspects, can inform locally legitimate and socially just conservation. Through semistructured group interviews across four villages, we explore well-being conceptions of Maasai men and women in Simanjiro. In particular we focus on the value of understanding social complexity for conservation research in the form of (i) the heterogeneity of conceptions of well-being across gender, age groups, and villages; (ii) temporal dynamics in notions and experiences. Material assets, namely land (for grazing and agriculture) and livestock are important to people but are intertwined with other aspects of well-being. Subjective well-being is centered on concerns about future security, especially with regards to land. Autonomy and social unity (relational dimension) are key priorities. We reflect on the implications for conservation at the study site, and more broadly on how well-being can better be incorporated into policy and practice that takes social justice seriously. The diversity we find in well-being priorities and experiences shows the importance of taking a disaggregated approach that conceptualizes benefits and burdens across a range of locally important well-being components ensuring the priorities of the most marginalized groups are represented.
Botnet traffic detection using hidden Markov models

We develop a novel approach to detect botnet traffic using hidden Markov models (HMMs). Botnets are becoming a major source of spam, distributed denial-of-service attacks (DDoS) and other cybercrime [21]. Analogs to botnets are likely to become threats to supervisory control and data acquisition (SCADA) and smart grid systems [19]. Malware that spies on and subverts SCADA systems or their SCADA analogs has been found recently [4]. More sabotage and cyberattacks are expected on the energy infrastructure [2], [3]. Attack detection on energy infrastructure is important. In this work, we detect node corruption by the largest botnet in the wild, Zeus. In Zeus, bots are controlled by attackers under a centralized command and control (C&C) infrastructure. We infer hidden Markov models from Zeus botnet traffic timing data. Inter-packet timings are due to botnet behaviors and C&C communication patterns among bots are usually similar. Inferred HMMs detect the botnet communication traffic. Experiment results on real-world traffic data show that this approach accurately differentiates between botnet traffic and normal traffic.
A cross-site qualitative study of physician order entry.

OBJECTIVE To describe the perceptions of diverse professionals involved in computerized physician order entry (POE) at sites where POE has been successfully implemented and to identify differences between teaching and nonteaching hospitals. DESIGN A multidisciplinary team used observation, focus groups, and interviews with clinical, administrative, and information technology staff to gather data at three sites. Field notes and transcripts were coded using an inductive approach to identify patterns and themes in the data. MEASUREMENTS Patterns and themes concerning perceptions of POE were identified. RESULTS Four high-level themes were identified: (1) organizational issues such as collaboration, pride, culture, power, politics, and control; (2) clinical and professional issues involving adaptation to local practices, preferences, and policies; (3) technical/implementation issues, including usability, time, training and support; and (4) issues related to the organization of information and knowledge, such as system rigidity and integration. Relevant differences between teaching and nonteaching hospitals include extent of collaboration, staff longevity, and organizational missions. CONCLUSION An organizational culture characterized by collaboration and trust and an ongoing process that includes active clinician engagement in adaptation of the technology were important elements in successful implementation of physician order entry at the institutions that we studied.
Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement

Researchers and users are well aware of the difficulties related to finding an appropriate configuration of the axes mapping attributes in multidimensional visualization techniques, particularly in visualizations that show a large number of attributes simultaneously. We address this problem with a simple strategy that offers both dimension ordering and dimension reduction. Dimension ordering is based on attribute similarity heuristics, and the basic rationale is extended to support dimension reduction. We discuss the performance of our algorithms and present some results of their application to several data sets. The algorithms improve the capability of visualization techniques to segregate clusters present in the data and reduce the visual clutter aggravated by arbitrary distributions of the axes
Anti‐GM2 ganglioside antibodies are a biomarker for acute canine polyradiculoneuritis

Acute canine polyradiculoneuritis (ACP) is considered to be the canine equivalent of the human peripheral nerve disorder Guillain‐Barré syndrome (GBS); an aetiological relationship, however, remains to be demonstrated. In GBS, anti‐glycolipid antibodies (Abs) are considered as important disease mediators. To address the possibility of common Ab biomarkers, the sera of 25 ACP dogs, 19 non‐neurological, and 15 epileptic control dogs were screened for IgG Abs to 10 glycolipids and their 1 : 1 heteromeric complexes using combinatorial glycoarrays. Anti‐GM2 ganglioside Abs were detected in 14/25 ACP dogs, and anti‐GA1 Abs in one further dog. All controls except for one were negative for anti‐glycolipid Abs. In this cohort of cases and controls, the glycoarray screen reached a diagnostic sensitivity of 60% and a specificity of 97%; a lower sensitivity (32%) was reported using a conventional glycolipid ELISA. To address the possible pathogenic role for anti‐GM2 Abs in ACP, we identified GM2 in canine sciatic nerve by both mass spectrometry and thin layer chromatography overlay. In immunohistological studies, GM2 was localized predominantly to the abaxonal Schwann cell membrane. The presence of anti‐GM2 Abs in ACP suggests that it may share a similar pathophysiology with GBS, for which it could thus be considered a naturally occurring animal model.
Tensors for Data Mining and Data Fusion

Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner’s point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today’s big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.
A Variance Minimization Criterion to Active Learning on Graphs

We consider the problem of active learning over the vertices in a graph, without feature representation. Our study is based on the common graph smoothness assumption, which is formulated in a Gaussian random field model. We analyze the probability distribution over the unlabeled vertices conditioned on the label information, which is a multivariate normal with the mean being the harmonic solution over the field. Then we select the nodes to label such that the total variance of the distribution on the unlabeled data, as well as the expected prediction error, is minimized. In this way, the classifier we obtain is theoretically more robust. Compared with existing methods, our algorithm has the advantage of selecting data in a batch offline mode with solid theoretical support. We show improved performance over existing label selection criteria on several real world data sets.