Global patterns of species richness of the holarctic alpine herb Saxifraga: The role of temperature and habitat heterogeneity

Postponed access: the file will be available after 2022-08-03The effects of contemporary climate, habitat heterogeneity and long-term climate change on species richness are well studied for woody plants in forest ecosystems, but poorly understood for herbaceous plants, especially in alpine–arctic ecosystems. Here, we aim to test if the previously proposed hypothesis based on the richness–environment relationship could explain the variation in richness patterns of the typical alpine–arctic herbaceous genus Saxifraga. Using a newly compiled distribution database of 437 Saxifraga species, we estimated the species richness patterns for all species, narrow- and wide-ranged species. We used generalized linear models and simultaneous autoregressive models to evaluate the effects of contemporary climate, habitat heterogeneity and historical climate on species richness patterns. Partial regressions were used to determine the independent and shared effects of different variables. Four widely used models were tested to identify their predictive power in explaining patterns of species richness. We found that temperature was negatively correlated with the richness patterns of all and wide-ranged species, and that was the most important environmental factor, indicating a strong conservatism of its ancestral temperate niche. Habitat heterogeneity and long-term climate change were the best predictors of the spatial variation of narrow-ranged species richness. Overall, the combined model containing five predictors can explain ca. 40%–50% of the variation in species richness. We further argued that additional evolutionary and biogeographical processes might have also played an essential role in shaping the Saxifraga diversity patterns and should be considered in future studies.acceptedVersio

Background and roles: myosin in autoimmune diseases

The myosin superfamily is a group of molecular motors. Autoimmune diseases are characterized by dysregulation or deficiency of the immune tolerance mechanism, resulting in an immune response to the human body itself. The link between myosin and autoimmune diseases is much more complex than scientists had hoped. Myosin itself immunization can induce experimental autoimmune diseases of animals, and myosins were abnormally expressed in a number of autoimmune diseases. Additionally, myosin takes part in the pathological process of multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, autoimmune myocarditis, myositis, hemopathy, inclusion body diseases, etc. However, research on myosin and its involvement in the occurrence and development of diseases is still in its infancy, and the underlying pathological mechanisms are not well understood. We can reasonably predict that myosin might play a role in new treatments of autoimmune diseases

Selective Catalytic Dehydrogenative Oxidation of Bio-Polyols to Lactic Acid

The global demand for lactic acid (LA) is increasing due to its successful application as monomer for the manufacture of bioplastics. Although N-heterocyclic carbene (NHC) iridium complexes are promising molecular catalysts for LA synthesis, their instabilities have hindered their utilization especially in commercial applications. Here, we report that a porous self-supported NHC-iridium coordination polymer can efficiently prevent the clusterization of corresponding NHC-Ir molecules and can function as a solid molecular recyclable catalyst for dehydrogenation of bio-polyols to form LA with excellent activity (97 %) and selectivity (>99 %). A turnover number of up to 5700 could be achieved in a single batch, due to the synergistic participation of the Ba2+ and hydroxide ions, as well as the blockage of unwanted pathways by adding methanol. Our findings demonstrate a potential route for the industrial production of LA from cheap and abundant bio-polyols, including sorbitol

Prospects for the sustainability of social-ecological systems (SES) on the Mongolian plateau: five critical issues

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Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Preliminary Investigation of the Mechanical Anisotropy of the Normal Human Corneal Stroma

Purpose. To investigate the anisotropic characteristics of the normal human corneal stroma using fresh corneal tissue. Methods. Sixty-four corneal specimens extracted from stromal lenticules were included in this study. The specimens were cut in the temporal-nasal (horizontal) or superior-inferior (vertical) direction. Strip specimens were subjected to uniaxial tensile testing. The tensile properties of the specimens were measured and compared in the two directions. Results. The low-strain tangent modulus was statistically significantly greater in the vertical direction than in the horizontal direction (1.32 ± 0.50 MPa vs 1.17 ± 0.43 MPa; P=0.035), as was the high-strain tangent modulus (51.26 ± 8.23 MPa vs 43.59 ± 7.96 MPa; P≤0.001). The elastic modulus in the vertical direction was also higher than that in horizontal direction at stresses of 0.01, 0.02, and 0.03 MPa, but not statistically significant; so, P=0.338, 0.373, and 0.417, respectively. Conclusions. The biomechanical behavior in normal human corneal stroma tissue is slightly stiffer in the vertical direction than in the horizontal direction. This information may aid our understanding of the biomechanical properties of the cornea and related diseases