Miracle-dz/UniFAR-FTUs huggingface dataset:
Software requirements for the analysis and interpretation of native ion mobility mass spectrometry data.

The last few years have seen a dramatic increase in applications of native mass and ion mobility spectrometry, especially for the study of proteins and protein complexes. This increase has been catalysed by the availability of commercial instrumentation capable of carrying out such analyses. As in most fields, however, the software to process the data generated from new instrumentation lags behind. Recently, a number of research groups have started addressing this by developing software, but further improvements are still required in order to realise the full potential of the datasets generated. In this perspective, we describe practical aspects as well as challenges in processing native mass spectrometry (MS) and ion mobility-MS datasets, and provide a brief overview of currently available tools. We then set out our vision of future developments that would bring the community together and lead to the development of a common platform to expedite future computational developments, provide standardised processing approaches and serve as a location for the deposition of data for this emerging field. This perspective has been written by members of the European Cooperation in Science and Technology Action on Native MS and Related Methods for Structural Biology (EU COST Action BM1403) as an introduction to the software tools available in this area. It is intended to serve as an overview for newcomers and to stimulate discussions in the community on further developments in this field, rather than being an in-depth review. Our complementary perspective (also in this issue) focuses on computational approaches used in this field.
Circular RNA in gastric cancer

Abstract Gastric cancer (GC) is a common malignancy and is the third leading cause of cancer-related death. At present, there is no simple and effective screening method for early-stage GC, and the treatment results and prognosis are poor. With the continuous improvement of molecular biology techniques, research on circular RNA (circRNA) has gradually expanded over time. Much data supports the role of circRNA in tumorigenesis. Moreover, due to its structural specificity and biological stability, circRNA is anticipated to be a potential biomarker for tumor diagnosis. Studies have confirmed that circRNA can participate in the proliferation, invasion, metastasis, and apoptosis of GC. These findings will lead to novel directions for the diagnosis and treatment of GC. This article reviews the structure and function of circRNA, summarizes the current studies on circRNA, and discusses the potential diagnostic value of circRNA in GC.
Nivicolous Trichiales from the austral Andes: unexpected diversity including two new species

ABSTRACT Nivicolous myxomycetes are a group of amoebozoan protists dependent on long-lasting snow cover worldwide. Recent fine-scale analysis of species diversity from the austral Andes revealed high intraspecific variability of most taxa, suggesting independent evolutionary processes and significant differences in species compositions between the Northern (NH) and Southern (SH) Hemispheres. The present study is the second part of this analysis based on representatives of Trichiales. A total of 173 South American collections were studied based on morphological and molecular data, and 15 taxa have been identified. Two of them, Hemitrichia crassifila and Perichaena patagonica, are proposed as new species confirmed by a phylogeny of Trichiales. However, their affinity to the genera in which they are proposed are not confirmed due to polyphyletic character of all genera of Trichiales. Four species, Dianema subretisporum, Trichia contorta var. karstenii, T. nivicola, and T. sordida, are reported for the first time from the Southern Hemisphere. One species, T. alpina, is new for Argentina. Additionally, we provide the first record of Perichaena megaspora from Chile. Specimen frequency and species diversity of Trichiales found at nivicolous localities in the austral Andes are unexpectedly high, exceeding those of Stemonitidales, the most numerous group in the Northern Hemisphere, where Trichiales play a marginal role. By contrast, Trichiales appear the main component of nivicolous assemblages in the Andes. Results of the present work, together with the earlier analysis of Stemonitidales, indicate that the Andes constitute an exceptionally important evolutionary hot spot for nivicolous myxomycetes characterized by an outstanding species diversity.
ASGR1 and Its Enigmatic Relative, CLEC10A

The large family of C-type lectin (CLEC) receptors comprises carbohydrate-binding proteins that require Ca2+ to bind a ligand. The prototypic receptor is the asialoglycoprotein receptor-1 (ASGR1, CLEC4H1) that is expressed primarily by hepatocytes. The early work on ASGR1, which is highly specific for N-acetylgalactosamine (GalNAc), established the foundation for understanding the overall function of CLEC receptors. Cells of the immune system generally express more than one CLEC receptor that serve diverse functions such as pathogen-recognition, initiation of cellular signaling, cellular adhesion, glycoprotein turnover, inflammation and immune responses. The receptor CLEC10A (C-type lectin domain family 10 member A, CD301; also called the macrophage galactose-type lectin, MGL) contains a carbohydrate-recognition domain (CRD) that is homologous to the CRD of ASGR1, and thus, is also specific for GalNAc. CLEC10A is most highly expressed on immature DCs, monocyte-derived DCs, and alternatively activated macrophages (subtype M2a) as well as oocytes and progenitor cells at several stages of embryonic development. This receptor is involved in initiation of TH1, TH2, and TH17 immune responses and induction of tolerance in naïve T cells. Ligand-mediated endocytosis of CLEC receptors initiates a Ca2+ signal that interestingly has different outcomes depending on ligand properties, concentration, and frequency of administration. This review summarizes studies that have been carried out on these receptors.
Real-Life Use of Levodopa/Carbidopa Intestinal Gel in Parkinson's Disease According to Analysis of Pump Data.

BACKGROUND Levodopa/carbidopa intestinal gel (LCIG) infusion is an efficacious treatment of motor and non-motor fluctuations in people with Parkinson's disease (PD). Real-life use of the treatment is not previously studied. OBJECTIVE The aims of the study were to explore the use of LCIG and to determine how extra doses of LCIG are used in daily life. METHODS Twenty-five PD patients with ongoing LCIG therapy were consecutively included. Pump data was retrieved from 30 days on average, by means of software, extracting the most recent pump events. RESULTS The daily duration of infusion was 15 hours on average, in 18 patients, whereas the remaining 7 patients used 24-hour infusion. Morning doses ranged from 38-190 mg levodopa, for patients who utilized this function. Median number of daily extra doses was 2.5 (range: 0-10.6) and median size of the extra dose was 24 mg (0-80 mg) levodopa. Median total daily levodopa intake with LCIG was 1201 mg (range: 417-2322 mg). CONCLUSION Retrieving pump data is possible and may be important for evaluating the at-home use of LCIG, to optimize the therapy. Adherence to treatment should be monitored, which is not technically difficult, at least in device-aided treatments for PD.
Gut Microbiota and Dietary Intake of Normal-Weight and Overweight Filipino Children

Diet and body mass index (BMI) have been shown to affect the gut microbiota of children, but studies are largely performed in developed countries. Here, we conducted a cross-sectional investigation on the differences in the bacterial gut microbiota between normal-weight and overweight urban Filipino children, and determined the relationship between their energy, macronutrient and dietary fiber intakes, and their gut microbiota composition and diversity. Forty-three children (normal-weight, n = 32; overweight, n = 11) participated in the study. Energy and fiber intakes were collected using a semi-quantitative Food Frequency Questionnaire (FFQ). The gut microbiota was profiled using 16S rRNA gene amplicon sequencing of the V3–V4 region. The diet of the children was a mixture of traditional and Western patterns. There were no significant differences in energy, macronutrients and energy-adjusted fiber intakes between the normal-weight and overweight groups, but there were significantly more children meeting the recommended fiber intake in the overweight group. Alpha and beta bacterial diversities did not significantly differ between weight groups. Relative abundance of Bifidobacterium, Turicibacter and Clostridiaceae 1 were higher in the normal-weight than overweight children, and Lachnospira was higher in overweight children.
Identification of novel Rab46 effector proteins

Rab46 is a novel Ca2+- sensing Rab GTPase shown to have important functions in endothelial and immune cells. The presence of functional Ca2+- binding, coiled-coil and Rab domains suggest that Rab46 will be important for coupling rapid responses to signalling in many cell types. The molecular mechanisms underlying Rab46 function are currently unknown. Here we provide the first resource for studying Rab46 interacting proteins. Using liquid chromatography mass spectrometry (LC-MS/MS) to identify affinity purified proteins that bind to constitutively active GFP-Rab46 or inactive GFP-Rab46 expressed in endothelial cells, we have revealed 922 possible interacting proteins. Further comparative analyses between nucleotide-locked Rab46 proteins enriched this dataset to confidently identify 29 effector proteins. Importantly, through biochemical and imaging approaches we have validated two potential effector proteins; dynein and the Na2+/ K+ ATPase subunit alpha 1 (ATP1α1). Hence, our use of affinity purification and LC-MS/MS to identify Rab46 neighbouring proteins provides a valuable resource for detecting Rab46 effector proteins and analysing Rab46 functions.
Mammalian deltavirus without hepadnavirus coinfection in the neotropical rodent Proechimys semispinosus

Significance Hepatitis delta virus (HDV) aggravates hepatitis B virus (HBV) infection of liver cells. Although the viruses are evolutionarily unrelated, HDV depends on HBV because it requires the HBV envelope protein for its transmission. HDV is only described in humans, which has triggered diverse hypotheses regarding its evolution and origins. Here we show that spiny rats (Proechimys semispinosus) carry a counterpart to HDV that surprisingly does not cause hepatitis and is not linked to HBV. The rodent deltavirus finding alone, but also taken together with the recent deltavirus findings in snakes and other vertebrates and invertebrates, suggests that a deltavirus precursor may have infected mammals before it acquired dependence on HBV as seen in humans. Hepatitis delta virus (HDV) is a human hepatitis-causing RNA virus, unrelated to any other taxonomic group of RNA viruses. Its occurrence as a satellite virus of hepatitis B virus (HBV) is a singular case in animal virology for which no consensus evolutionary explanation exists. Here we present a mammalian deltavirus that does not occur in humans, identified in the neotropical rodent species Proechimys semispinosus. The rodent deltavirus is highly distinct, showing a common ancestor with a recently described deltavirus in snakes. Reverse genetics based on a tandem minus-strand complementary DNA genome copy under the control of a cytomegalovirus (CMV) promoter confirms autonomous genome replication in transfected cells, with initiation of replication from the upstream genome copy. In contrast to HDV, a large delta antigen is not expressed and the farnesylation motif critical for HBV interaction is absent from a genome region that might correspond to a hypothetical rodent large delta antigen. Correspondingly, there is no evidence for coinfection with an HBV-related hepadnavirus based on virus detection and serology in any deltavirus-positive animal. No other coinfecting viruses were detected by RNA sequencing studies of 120 wild-caught animals that could serve as a potential helper virus. The presence of virus in blood and pronounced detection in reproductively active males suggest horizontal transmission linked to competitive behavior. Our study establishes a nonhuman, mammalian deltavirus that occurs as a horizontally transmitted infection, is potentially cleared by immune response, is not focused in the liver, and possibly does not require helper virus coinfection.
Searching for the Molecular Pathways Regulating Bone Mineral Density in the Proteome and RNA Interference Era

Osteoporosis is a polygenic disorder associated with low bone mineral density and deterioration of bone microarchitecture with increased chance of bone fractures. Although bone matrix mineralization and osteoporosis are closely related, the mineralization of bone matrix is almost a forgotten aspect in osteoporosis research. The complex processes of bone matrix mineralization and bone remodeling are tightly regulated by several transcription factors and signal transduction pathways. However, signal transduction pathways occurring at a protein level that depends not only on mRNA transcriptional regulation but also on a multitude of translational and posttranslational controls. Furthermore, proteomics allow a discerning view of complex molecular pathways, provides an efficient method to determine protein candidates, and elucidates signal transduction pathways that regulate bone mineral density and accelerates the discovery of osteoporosis causative genes. RNA interference is a powerful tool for rapid analysis of gene functions. Therefore, strategies to combine proteomics with RNA interference and transgenic RNAi would greatly improve the efficiency of gene discovery and divulge the molecular pathways involved in osteoporosis pathophysiology. In this review, current methods employed to identify genes involved in osteoporosis, which include linkage analysis, candidate gene association studies, genome wide association studies, transcriptome microarray, and proteomics are evaluated, and a new strategy is proposed.
Genetic Divergence between Gopher Rockfish (Sebastes carnatus) and Black and Yellow Rockfish (Sebastes chrysomelas)

Abstract The genus Sebastes contains over 110 species, an unusually high level of species diversity for marine fishes. Many of the 70 species that occupy the northeast Pacific Ocean coexist during parts or all of their life cycle. Although allopatric speciation explains much of this diversity, the presence of many closely related species within the same locations suggests that other isolating mechanisms may play a role. Sebastes carnatus and Sebastes chrysomelas are a sympatrically distributed pair of sister species having no morphologically distinguishing characteristics other than color: S. carnatus has pinkish spots on a brown background, and S. chrysomelas has yellow spots on a black background. In this study, seven nuclear microsatellite DNA loci were employed to assess relatedness of 111 S. carnatus and 91 S. chrysomelas sampled from three locations spanning the species' range. Analysis of the seven microsatellite loci provided evidence of genetic divergence between color morphs (FST = 0.046, P < 0.0001). Furthermore, magnitude of genetic divergence between the color morphs was consistent among geographic locations. Divergence between S. carnatus and S. chrysomelas was low relative to that detected among other species within the genus, suggesting that the two morphs represent reproductively isolated incipient species.
Performance as a Constraint: An Improved Wisdom of Crowds Using Performance Regularization

Quality assurance is one of the most important problems in crowdsourcing and human computation, and it has been extensively studied from various aspects. Typical approaches for quality assurance include unsupervised approaches such as introducing task redundancy (i.e., asking the same question to multiple workers and aggregating their answers) and supervised approaches such as using worker performance on past tasks or injecting qualification questions into tasks in order to estimate the worker performance. In this paper, we propose to utilize the worker performance as a global constraint for inferring the true answers. The existing semi-supervised approaches do not consider such use of qualification questions. We also propose to utilize the constraint as a regularizer combined with existing statistical aggregation methods. The experiments using heterogeneous multiple-choice questions demonstrate that the performance constraint not only has the power to estimate the ground truths when used by itself, but also boosts the existing aggregation methods when used as a regularizer.
A Game Theoretic Approach For Core Resilience

K-cores are maximal induced subgraphs where all vertices have degree at least k. These dense patterns have applications in community detection, network visualization and protein function prediction. However, k-cores can be quite unstable to network modifications, which motivates the question: How resilient is the k-core structure of a network, such as the Web or Facebook, to edge deletions? We investigate this question from an algorithmic perspective. More specifically, we study the problem of computing a small set of edges for which the removal minimizes the k-core structure of a network. This paper provides a comprehensive characterization of the hardness of the k-core minimization problem (KCM), including innaproximability and parameterized complexity. Motivated by these challenges, we propose a novel algorithm inspired by Shapley value---a cooperative game-theoretic concept--- that is able to leverage the strong interdependencies in the effects of edge removals in the search space. We efficiently approximate Shapley values using a randomized algorithm with probabilistic guarantees. Our experiments, show that the proposed algorithm outperforms competing solutions in terms of k-core minimization while being able to handle large graphs. Moreover, we illustrate how KCM can be applied in the analysis of the k-core resilience of networks.
Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros. , and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively. See here for our codes and demo videos.
How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context

Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic parsing in context. We present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. We evaluate 13 context modeling methods on two large complex cross-domain datasets, and our best model achieves state-of-the-art performances on both datasets with significant improvements. Furthermore, we summarize the most frequent contextual phenomena, with a fine-grained analysis on representative models, which may shed light on potential research directions. Our code is available at https://github.com/microsoft/ContextualSP.
Two-goal Local Search and Inference Rules for Minimum Dominating Set

Minimum dominating set (MinDS) is a canonical NP-hard combinatorial optimization problem with applications. For large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure with inference rules. Our algorithm named FastDS is evaluated on 4 standard benchmarks and 3 massive graphs benchmarks. FastDS obtains the best performance for almost all benchmarks, and obtains better solutions than state-of-the-art algorithms on massive graphs.
A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data

Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.
ASSESSING ENVIRONMENTAL IMPACTS: EFFECTS OF SPATIAL AND TEMPORAL VARIABILITY AT LIKELY IMPACT SCALES

Ecological and environmental variability in natural ecosystems precludes the widespread use of simplistic design and analysis tools to detect the effects of human activities on natural ecosystems. BACI (Before–After and Control–Impact) sampling and analysis is often recommended to overcome the problem of inferring that changes are due to an impact rather than natural variability. Using five different BACI design and analysis techniques (from nested ANOVA to time-series intervention) on data with spatial, temporal, and sampling variability at scales relevant to impact assessments, we investigated how BACI designs cope with interactions between spatial and temporal variability and whether natural variability is best treated as random. A number of simulated impacts of different sizes and types were imposed on long-term observations of six macrofaunal species and three diversity indices. A major problem in detecting the impacts was the differences in variability between sites, although the statistical detecti.