Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure, Organization, and Expression of the Alpha Prolamin Multigenic Family Bring New Insights into the Evolutionary Relationships among Grasses

Prolamins are the major seed storage proteins of grasses. In maize and related species, prolamins are classified into α‐, β‐, γ‐, and δ‐subclasses by their solubility properties. α‐prolamins are encoded by multigene families and have a secondary structure that consists of tandem α‐helix repeats. Maize has two α‐prolamin subclasses, namely the 19 and 22 kDa subclasses that contain nine and 10 α‐helix repeats, respectively. Here, we present an evolutionary study based on the structure, organization, and expression of α‐prolamins in maize, sugarcane, sorghum, and coix. True 22 kDa subclasses containing 10 repeats are conserved in all four species, but true 19 kDa subclasses containing nine repeats are found only in maize and sugarcane. We discovered a 19 kDa‐like α‐coixin that, as in sorghum, is encoded by few genes. These data suggest that a 19 kDa progenitor present in the ancestor common to maize, coix, sorghum, and sugarcane was preserved at low copy number in coix and sorghum, while amplified into multigene family architecture in maize and sugarcane. The expression profiling of α‐prolamins, verified by two‐dimensional gels, showed highly conserved multispot composition for the 19 kDa α‐prolamins in maize and sugarcane. Coix and sorghum did not present true 19 kDa α‐prolamin spots. Our data show remarkable similarity between maize and sugarcane 19 kDa α‐prolamins regarding both gene structure and expression. Since the multigene architecture of 19 kDa α‐canein appeared after sugarcane diverged from sorghum, our data suggest that maize and sugarcane might have acquired the multigene family encoding these storage proteins from a common ancestor.
Spatially inconsistent direct and indirect effects of herbivory on floral traits and pollination success in a tropical shrub

Investigations on plant–animal interactions have traditionally focused on single interactions at a time (e.g. herbivory, pollination), yet plant fi tness is generally infl uenced in complex ways by several interactions operating concurrently, and very little is known on the degree of spatial consistency of the direct and indirect eff ects that link diff erent interactions. Th is paper evaluates experimentally whether direct and indirect eff ects of herbivory on male and female fl ower size and pollination success of the monoecious tropical shrub Cnidoscolus aconitifolius (Euphorbiaceae) remain consistent at three distant regions in Yucatan (southeastern Mexico). Plants were subjected to diff erent levels of leaf defoliation, and treatment eff ects on fl oral traits (corolla area, corolla tube length, pollen production), and male and female components of pollination success (percent pollen removal, number of pollen tubes) were subsequently measured to evaluate the indirect eff ect of herbivory on plant reproductive success via pollination. Defoliation had signifi cant direct eff ects on fl oral traits, but its indirect eff ects diff ered between the male and female components of pollination success. Moreover, the relationships between defoliation, fl oral traits and male and female pollination success varied spatially (i.e. between regions), although they were frequently more spatially-consistent for male success than for female success. Results from this study stress the importance of explicitly testing for spatial variation in direct and indirect eff ects arising from plant-animal interactions.
Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI Data

When analyzing brain tumors, two tasks are intrinsically linked, spatial localization, and physiological characterization of the lesioned tissues. Automated data-driven solutions exist, based on image segmentation techniques or physiological parameters analysis, but for each task separately, the other being performedmanually or with user tuning operations. In this paper, the availability of quantitative magnetic resonance (MR) parameters is combined with advancedmultivariate statistical tools to design a fully automated method that jointly performs both localization and characterization. Non trivial interactions between relevant physiologicalparameters are capturedthanks to recent generalized Student distributions that provide a larger variety of distributional shapes compared to the more standard Gaussian distributions. Probabilisticmixtures of the former distributions are then consideredto account for the different tissue types and potential heterogeneity of lesions. Discriminative multivariate features are extracted from this mixture modeling and turned into individual lesion signatures. The signatures are subsequently pooled together to build a statistical fingerprintmodel of the different lesion types that captures lesion characteristics while accounting for inter-subject variability. The potential of this generic procedure is demonstrated on a data set of 53 rats, with 36 rats bearing 4 different brain tumors, for which 5 quantitative MR parameters were acquired.
Chemical composition and allelopathic activity of Parthenium hysterophorus and Ambrosia polystachya weeds essential oils.

The volatile constituents of the essential oils from leaves from the weed plants Parthenium hysterophorus and Ambrosia polystachya were identified and quantified by GLC-MS and GLC. Allelopathic activities were determined by methods that evaluated the volatile effects and the direct application of these essential oils on the seed germination and seedling vigor of lettuce. We identified 27 compounds in the essential oil from P. hysterophorus, and the main constituents were germacrene-D (35.9%), trans-β-ocimene (8.5%) and β-myrcene (7.6%). In the essential oil from A. polystachya, 40 constituents were identified and the principal compounds were germacrene-D (29.3%), trans-β-ocimene (13.6%) and β-caryophyllene (9.8%). In both methods, the essential oil from A. polystachya presented a greater potential for reducing seed germination and seedling vigor in lettuce than the essential oil from P. hysterophorus, and this activity might be attributed to its higher content of monoterpenes.
The Extra-Pathway Interactome of the TCA Cycle: Expected and Unexpected Metabolic Interactions1[OPEN]

The extra-pathway interaction network of the Arabidopsis TCA cycle was generated by affinity purification proteomics and partly confirmed by bimolecular fluorescence complementation, yeast two-hybrid, and mutant analysis. The plant tricarboxylic acid (TCA) cycle provides essential precursors for respiration, amino acid biosynthesis, and general nitrogen metabolism; moreover, it is closely involved in biotic stress responses and cellular redox homeostasis. To further understand the in vivo function of the TCA cycle enzymes, we combined affinity purification with proteomics to generate a comprehensive extra-pathway protein-protein interaction network of the plant TCA cycle. We identified 125 extra-pathway interactions in Arabidopsis (Arabidopsis thaliana) mostly related to the mitochondrial electron transport complex/ATP synthesis and amino acid metabolism but also to proteins associated with redox stress. We chose three high-scoring and two low-scoring interactions for complementary bimolecular fluorescence complementation and yeast two-hybrid assays, which highlighted the reliability of our approach, supported the intimate involvement of TCA cycle enzymes within many biological processes, and reflected metabolic changes reported previously for the corresponding mutant lines. To analyze the function of a subset of these interactions, we selected two mutants of mitochondrial glutaredoxin S15 and Amidase, which have not yet been analyzed with respect to their TCA cycle function, and performed metabolite profiling and flux analysis. Consistent with their interactions identified in this study, TCA cycle metabolites and the relative TCA flux of the two mutants were altered significantly.
A Reformulated Correlated Trait–Correlated Method Model for Multitrait–Multimethod Data Effectively Increases Convergence and Admissibility Rates

The correlated trait–correlated method (CTCM) model for the analysis of multitrait–multimethod (MTMM) data is known to suffer convergence and admissibility (C&A) problems. We describe a little known and seldom applied reparameterized version of this model (CTCM-R) based on Rindskopf’s reparameterization of the simpler confirmatory factor analysis model. In a Monte Carlo study, we compare the CTCM, CTCM-R, and the correlated trait–correlated uniqueness (CTCU) models in terms of C&A, model fit, and parameter estimation bias. The CTCM-R model largely avoided C&A problems associated with the more traditional CTCM model, producing C&A solutions nearly as often as the CTCU model, but also avoiding parameter estimation biases known to plague the CTCU model. As such, the CTCM-R model is an attractive alternative for the analysis of MTMM data.
The Mediational Role of Coping Strategies in the Relationship Between Self-Esteem and Risk of Internet Addiction

The aim of the present study is to explore, through a mediation model, the relationship among self-esteem, coping strategies, and the risk of Internet addiction in a sample of 300 Italian university students. We submitted the data to a descriptive, mediational comparison between variables (t-test), and correlational statistical analyses. The results confirmed the effect of self-esteem on the risk of Internet addiction. However, we found that the introduction of coping strategies as a mediator gives rise to partial mediation. A low level of self-esteem is a predictor of avoidance-oriented coping that, in turn, affects the risk of Internet addiction.
Optical couplers for sharply bended nanowires: Sensitivity to coupler nanoparticles

We consider optical couplers that consists of optimal arrangements of nanoparticles to improve the transmission abilities of nanowire systems with sharp bends. Previously, it was shown that absence/existence of nanoparticles in a given grid can be optimized such that the power transmission can significantly be increased without smoothing the bend. In this contribution, we present the sensitivity of coupler performances to the particle geometries. Numerical results demonstrate a high dependency of coupler characteristics to particle types due to strong plasmonic interactions at short distances. These findings further support the need for case-dependent optimization.
CARL: Content-Aware Representation Learning for Heterogeneous Networks

Heterogeneous networks not only present a challenge of heterogeneity in the types of nodes and relations, but also the attributes and content associated with the nodes. While recent works have looked at representation learning on homogeneous and heterogeneous networks, there is no work that has collectively addressed the following challenges: (a) the heterogeneous structural information of the network consisting of multiple types of nodes and relations; (b) the unstructured semantic content (e.g., text) associated with nodes; and (c) online updates due to incoming new nodes in growing network. We address these challenges by developing a Content-Aware Representation Learning model (CARL). CARL performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Furthermore, an additional online update module is proposed for efficiently learning representations of incoming nodes. Extensive experiments demonstrate that CARL outperforms state-of-the-art baselines in various heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation and relevance search. We also demonstrate the effectiveness of the CARL's online update module through a category visualization study.
Chronic low-grade peripheral inflammation is associated with ultra resistant schizophrenia. Results from the FACE-SZ cohort. SHORT COMMUNICATION Running title: peripheral inflammation is associated with ultraresistant schizophrenia

Introduction. A high rate of patients with schizophrenia (SZ) does not sufficiently respond to antipsychotic medication, which is associated with relapses and poor outcomes. Chronic peripheral inflammation has been repeatedly associated with schizophrenia risk and particularly to poor responders to treatment as usual with cognitive impairment in SZ subjects. The objective of present study was to confirm if ultra resistance to treatment in schizophrenia (UTRS) was associated to chronic peripheral inflammation in a non-selected sample of community-dwelling outpatients with schizophrenia. Methods. Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical assessment, including recording of current treatment. Current psychotic symptomatology was evaluated by the Positive and Negative Syndrome scale for Schizophrenia (PANSS). UTRS was defined by current clozapine treatment+ PANSS total score≥70. Functioning was evaluated by the Global Assessment of Functioning scale. High sensitivity CRP (hs-CRP) was measured for each participant as a proxy to define peripheral low-grade inflammation. Results. 609 stabilized community-dwelling SZ subjects (mean age=32.5 years, 73.6% male gender) have been included. 60(9.9%) patients were classified in the UTRS group. In multivariate analyses, UTRS has been associated independently with
Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling

Connected component identification is a fundamental problem in graph analytics, serving as a basis for subsequent computations in a wide range of applications. To determine connectivity, several parallel algorithms, whose complexity is proportional to the number of edges or graph diameter, have been proposed. However, an optimal algorithm may extract graph components by working proportionally to the number of vertices, which can be orders of magnitude lower than the number of edges. We propose Afforest: an extension of the Shiloach-Vishkin connected components algorithm that approaches optimal work efficiency by processing subgraphs in each iteration. We prove the convergence of the algorithm, analyze its work efficiency characteristics, and provide further techniques to speed up processing graphs containing a huge component. Designed with modern parallel architectures in mind, we show that the algorithm exhibits higher memory locality than existing methods. Using both synthetic and real-world graphs, we demonstrate that Afforest achieves speedups of up to 67x over the state-of-the-art on multi-core CPUs (Broadwell, POWER8) and up to 23x on GPUs (Pascal).
Antisense oligonucleotides in neurological disorders

The introduction of genetics revolutionized the field of neurodegenerative and neuromuscular diseases and has provided considerable insight into the underlying pathomechanisms. Nevertheless, effective treatment options have been limited. This changed recently when antisense oligonucleotides (ASOs) could be translated from in vitro and experimental animal studies into clinical practice. In 2016, two ASOs were approved by the United States US Food and Drug Administration (FDA) and demonstrated remarkable efficacy in Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA). ASOs are synthetic single-stranded strings of nucleic acids. They selectively bind to specific premessenger ribonucleic acid (pre-mRNA)/mRNA sequences and alter protein synthesis by several mechanisms of action. Thus, apart from gene replacement, ASOs may therefore provide the most direct therapeutic strategy for influencing gene expression. In this review, we shall discuss basic mechanisms of ASO action, the role of chemical modifications needed to improve the pharmacodynamic and pharmacokinetic properties of ASOs, and we shall then focus on several ASOs developed for the treatment of neurodegenerative and neuromuscular disorders, including SMA, DMD, myotonic dystrophies, Huntington’s disease, amyotrophic lateral sclerosis and Alzheimer’s disease.
XMSS: eXtended Merkle Signature Scheme

This note describes the eXtended Merkle Signature Scheme (XMSS), a hash-based digital signature system. It follows existing descriptions in scientific literature. The note specifies the WOTS+ one-time signature scheme, a single-tree (XMSS) and a multi-tree variant (XMSS^MT) of XMSS. Both variants use WOTS+ as a main building block. XMSS provides cryptographic digital signatures without relying on the conjectured hardness of mathematical problems. Instead, it is proven that it only relies on the properties of cryptographic hash functions. XMSS provides strong security guarantees and is even secure when the collision resistance of the underlying hash function is broken. It is suitable for compact implementations, relatively simple to implement, and naturally resists side-channel attacks. Unlike most other signature systems, hash-based signatures can withstand so far known attacks using quantum computers.
Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns

The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.
Peritumoral endothelial indoleamine 2, 3-dioxygenase expression is an early independent marker of disease relapse in colorectal cancer and is influenced by DNA mismatch repair profile

Targeting immune checkpoint molecules has become a major new strategy in the treatment of several cancers. Indoleamine 2,3-dioxygenase (IDO)-inhibitors are a potential next-generation immunotherapy, currently investigated in multiple phase I-III trials. IDO is an intracellular immunosuppressive enzyme and its expression/activity has been associated with worse prognosis in several cancers. The aim of this study was to investigate the expression pattern of IDO in colorectal cancer (CRC). In a cohort of 94 CRC patients, primary tumors (PTs) with corresponding tumor-draining lymph nodes (TDLNs, n = 93) and extranodal/distant metastases (n = 27) were retrospectively analyzed by immunohistochemical staining for IDO, CD8 and Foxp3. 45 MSS and 37 MSI-H tumors were selected to compare IDO expression, as these tumors are considered to have different immunogenicity. A highly consistent expression pattern of IDO was observed in the PT, TDLNs and metastases, indicating that immune resistance may be determined very early in the disease course. IDO was expressed both by tumoral cells and host endothelial cells and these expressions were highly correlated (p < 0.001). IDO expression was observed more frequently in the MSI-H subset compared with the MSS subset (43% vs 22% for tumoral expression (p = 0.042) and 38% vs 16% for endothelial expression (p = 0.021)). Endothelial IDO expression was demonstrated to be a negative prognostic marker for recurrence free survival independent of disease stage and DNA mismatch repair (MMR) status (HR 20.67, 95% CI: 3.05–139.94; p = 0.002). These findings indicate that endothelial IDO expression in primary CRC, in addition to the MMR profile, may be helpful in disease stratification.
Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics

Video copy-move forgery detection is one of the hot topics in multimedia forensics to protect digital videos from malicious use. Several approaches have been presented through analyzing the side effect caused by copy–move operation. However, based on multiple similarity calculations or unstable image features, a few can well balance the detection efficiency, robustness, and applicability. In this paper, we propose a novel approach to detect frame copy–move forgeries in consideration of the three requirements. A coarse-to-fine detection strategy based on optical flow (OF) and stable parameters is designed. Specifically, coarse detection analyzes OF sum consistency to find suspected tampered points. Fine detection is then conducted for precise location of forgery, including duplicated frame pairs matching based on OF correlation and validation checks to further reduce the false detections. Experimental evaluation on three public video data sets shows that the proposed approach is effective and efficient in detecting both unsmooth manipulation and common smooth forgery and also with high robustness to regular attacks, including additive noise, filtering, and compression.
Dispersion Analysis of Deep-Subwavelength-Decorated Metallic Surface Using Field-Network Joint Solution

Due to the abilities of remarkable field confinements and enhancements, plasmonic surfaces supporting spoof surface plasmon polaritons (SPPs) are important artificial structures with many potential applications. In this paper, we propose analytical derivation to the dispersion formulation of metallic surface with complicated subwavelength decorations using a field-network joint solution, which offers a new scheme to obtain the dispersion relations of plasmonic surfaces. Based on the proposed method, we could predict not only the dispersion relationship between the frequency and wavenumber but also the approximate near-field distribution on the plasmonic surface rapidly and accurately. As an example, we analyze a series of complex-decorated metallic plasmonic surfaces, with comparisons to the full-wave simulation and experiment. The analytical, numerical, and experimental results show that these plasmonic surfaces support electromagnetic surface modes that are similar to the SPPs at the optical frequency. Meanwhile, the calculated dispersion curve and field form have great matching to the simulation and measured results.