Osteopontin is a proximal effector of leptin-mediated non-alcoholic steatohepatitis (NASH) fibrosis

INTRODUCTION: Liver fibrosis develops when hepatic stellate cells (HSC) are activated into collagen-producing myofibroblasts. In non-alcoholic steatohepatitis (NASH), the adipokine leptin is upregulated, and promotes liver fibrosis by directly activating HSC via the hedgehog pathway. We reported that hedgehog-regulated osteopontin (OPN) plays a key role in promoting liver fibrosis. Herein, we evaluated if OPN mediates leptin-profibrogenic effects in NASH.METHODS: Leptin-deficient (ob/ob) and wild-type (WT) mice were fed control or methionine-choline deficient (MCD) diet. Liver tissues were assessed by Sirius-red, OPN and αSMA IHC, and qRT-PCR for fibrogenic genes. In vitro, HSC with stable OPN (or control) knockdown were treated with recombinant (r)leptin and OPN-neutralizing or sham-aptamers. HSC response to OPN loss was assessed by wound healing assay. OPN-aptamers were also added to precision-cut liver slices (PCLS), and administered to MCD-fed WT (leptin-intact) mice to determine if OPN neutralization abrogated fibrogenesis.RESULTS: MCD-fed WT mice developed NASH-fibrosis, upregulated OPN, and accumulated αSMA+ cells. Conversely, MCD-fed ob/ob mice developed less fibrosis and accumulated fewer αSMA+ and OPN+ cells. In vitro, leptin-treated HSC upregulated OPN, αSMA, collagen 1α1 and TGFβ mRNA by nearly 3-fold, but this effect was blunted by OPN loss. Inhibition of PI3K and transduction of dominant negative-Akt abrogated leptin-mediated OPN induction, while constitutive active-Akt upregulated OPN. Finally, OPN neutralization reduced leptin-mediated fibrogenesis in both PCLS and MCD-fed mice.CONCLUSION: OPN overexpression in NASH enhances leptin-mediated fibrogenesis via PI3K/Akt. OPN neutralization significantly reduces NASH fibrosis, reinforcing the potential utility of targeting OPN in the treatment of patients with advanced NASH.</p

Development and reproductive performance of Hereford heifers of different frame sizes up to mating at 14-15 months of age

ABSTRACT Body development and reproductive performance of a hundred forty-two 14 to 15-month-old heifers, classified at weaning according to frame size as small, medium, and large, were evaluated. The parameters evaluated were: body weight, hip height, body condition score, weight gain, ovarian activity, and pregnancy rate. At weaning, body weight and hip height were significantly different among frame scores, (small – 133.0 kg, 92.2 cm; medium – 158.5 kg, 96.6 cm; and large – 185.2 kg; 100.2 cm). After weaning, heifers grazed together on natural pastures during the autumn and on ryegrass (Lolium multiflorum La.) during the winter and spring. Frame score differences remained until the beginning of the breeding season (BS), starting on average at 14 months of age. Weight gain between weaning and the beginning of BS was not different among frame scores (0.740 kg/day, on average). Body weights at the beginning of the BS were significantly different, of 255.7 kg (53.3% of the mature weight) for small heifers, 285.0 kg (59.4%) for medium heifers, and 307.6 kg (64.1%) for large heifers. Ovarian activity at the beginning of the BS was not different among the three groups. The average weight gain values during the BS of 0.492, 0.472, and 0.421 kg/day for small, medium, and large heifers, respectively, were significantly different. Pregnancy rates were not different among groups (small, 71.4%; medium, 76.4%; and large, 76.5%). Frame score did not influence the reproductive performance of heifers, but the small and medium heifers conceived 29 and 20 days earlier, respectively, than the large heifers

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Computational modeling of 2D frictional contact problems based on the use of coupling finite elements and combined contact/friction damage constitutive model

This work proposes a new approach to describe 2D frictional contact problems based on the use of coupling finite elements (CFEs) and a combined contact/friction damage constitutive model. This technique is able to establish the interaction between non-matching meshes, representing different subdomains (bodies) of a problem, which share common boundaries. The formulation is developed within a framework considering elastic bodies, small deformations and static loading conditions. Initially, the bodies are discretized in finite elements in a totally independent way. Then, CFEs are inserted to enforce the contact constraints in the context of penalty methods. As the CFEs share nodes from both sides of the non-matching meshes, an appropriate constitutive model can be easily defined to describe the interaction between the bodies on the contact interface based on the concept of a relative displacement. The proposed constitutive model is able to describe the frictional slippage and separation between the bodies. This constitutive model is integrated using an implicit–explicit scheme to avoid numerical convergence problems. The insertion of CFEs does not introduce any additional degree of freedom to the problem and these elements are introduced into the standard finite element mesh in a pre-processing stage to avoid pre-processing tasks during the analysis. The same framework is applied to deal with overlapping and non-overlapping meshes and its implementation in conventional FE programs is quite simple without drastic modifications. Five numerical analyses are performed and the results demonstrated that the strategy developed is able to represent the interaction between bodies, coherently and accurately.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São Paulo State University - UNESP, Av. Eng. Luiz Edmundo C. Coube 14-01University of São Paulo - USP Department of Structural and Geotechnical Engineering, Av. Prof. Luciano Gualberto, Trav. do Biênio n. 380São Paulo State University - UNESP, Av. Eng. Luiz Edmundo C. Coube 14-01CNPq: 310223/2020-2CNPq: 310401/2019-