A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area

Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Peer reviewe

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

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

Headcase Promotes Cell Survival and Niche Maintenance in the <i>Drosophila</i> Testis

<div><p>At the apical tip of the <i>Drosophila</i> testis, germline and somatic stem cells surround a cluster of somatic cells called the hub. Hub cells produce a self-renewal factor, Unpaired (Upd), that activates the JAK-STAT pathway in adjacent stem cells to regulate stem cell behavior. Therefore, apical hub cells are a critical component of the stem cell niche in the testis. In the course of a screen to identify factors involved in regulating hub maintenance, we identified headcase (hdc). Hub cells depleted for <i>hdc</i> undergo programmed cell death, suggesting that anti-apoptotic pathways play an important role in maintenance of the niche. Using hdc as paradigm, we describe here the first comprehensive analysis on the effects of a progressive niche reduction on the testis stem cell pool. Surprisingly, single hub cells remain capable of supporting numerous stem cells, indicating that although the size and number of niche support cells influence stem cell maintenance, the testis stem cell niche appears to be remarkably robust in the its ability to support stem cells after severe damage.</p></div

Alterations in GSCs, CySCs, and hub area during progressive hub cell loss.

<p>(<b>A, B and D left panels</b>) Testes with 7–8 hub cells (FasIII, red) from 1-day old <i>updGal4;UAS-hdcRNAi¹;Gal80^ts</i> males raised at 18°C. (<b>A’, B’ and D’, right panels</b>) Testes with 1–2 hub cells from <i>updGal4;UAS-hdcRNAi¹;Gal80^ts</i> males after 7–9 days at 29°C to induce transgene expression. (<b>A, A’</b>) GSCs were counted as Stat92E⁺ germ cells (green) contacting the hub (<b>B, B’</b>) CySCs were counted as Zfh1⁺ cells (white) within a 15 µm radius from the center of the hub. <b>C</b>) Graph representing hub cell:GSC:CySC ratio during progressive hub cell loss; N≥20 testes for each genotype/timepoint; (<b>D, D’</b>) Hub area was measured based on FasIII⁺/DAPI⁺ cells. (<b>E</b>) Graph of hub area during hub cell loss. N≥20 testes for each genotype/timepoint. Means and SD are shown. Scale bars, 20 µm.</p

Hub cell conversion to the cyst cell lineage does not happen after <i>hdc</i> loss-of-funtion in the hub.

<p>Lineage-tracing analysis using G-TRACE in (<b>A, A’</b>) controls (genotype: <i>updGal4;G-TRACE;Gal80^ts</i>) or (<b>B–C”</b>) upon loss of <i>hdc</i> function (genotypes: <i>updGal4;G-TRACE; UAS-hdcRNAi³/Gal80^ts and updGal4;UAS-hdcRNA^1;G-TRACE/Gal80^ts</i>) shows restricted expression of dsRed and GFP in hub cells. (<b>B” and C”</b>) Note no change in the levels of <i>upd</i> promoter activity (DsRed) was observed in the different categories of hub cell loss.</p

<i>hdc</i> function is required to maintain hub cells in the <i>Drosophila</i> testis.

<p>(<b>A</b>) Schematic of the male germline stem cell niche. (<b>B</b>) Hub cell quantification at 1d(blue) and 10d(red) in controls and upon reduction of <i>hdc</i>. N = 30 for each genotype/time point. Mean and SD are shown; ***P<0.001 (Kruskal–Wallis one-way analysis of variance). (<b>C</b>) Immunofluorescence image of <i>wild-type</i> (<i>wt</i>) testis. FasIII (hub, red), Vasa (germ line, green) (<b>C’</b>) Phase-contrast image of a <i>wt</i> testis. Asterisk denotes apical tip; transit-amplifying spermatogonia (black bar); spermatocytes (arrows). (<b>D and D’</b>) Reduction of <i>hdc</i> in hub cells leads to loss of hub cells and niche degeneration. Note absence of FasIII⁺ hub cells (red) and presence of large spermatocytes or mature sperm (<b>D’</b>) at the apical tip. (<b>E and E’</b>) Testis from <i>wt</i> larval (L3) male gonad showing Hdc expression in all cells at the apical tip. (<b>F and F’</b>) RNAi-mediated knock-down of <i>hdc</i> in hub cells results in loss of Hdc protein. Similar results were obtained for all RNAi lines tested. Scale bars, 20 µm. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068026#pone-0068026-t001" target="_blank">Table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068026#pone-0068026-g002" target="_blank">Figure 2</a>.</p