Non‐pollinating cheater wasps benefit from seasonally poor performance of the mutualistic pollinating wasps at the northern limit of the range of Ficus microcarpa

1. Species interactions in tightly bound ecological mutualisms often feature highly specialised species' roles in which competitive exclusion may preclude multi‐species coexistence. Among the 800 fig (Ficus) species, it was originally considered that each was pollinated by their own wasp (Agaonidae). However, recent investigations show that this ‘one‐to‐one’ rule often breaks down, as fig species regularly host multiple agaonids but in ways suggesting that competitive processes still mediate biodiversity outcomes. 2. A phenological survey was conducted of the fig–fig wasp pair, Ficus microcarpa and its associated pollinating wasp, alongside its sister species, the cheating wasp, in Xishuangbanna, China. 3. Reproductive output underwent extreme seasonal variation. Seed and pollinator production fell markedly during cooler, drier months, although high levels of fig production continued. However, this resource was predominantly utilised by the cheater species, which offers no pollination services. Pollinators and cheaters rarely co‐occur, suggesting that temporal coexistence is constrained by competition for access to figs. 4. The overall findings indicate periodic rearrangements of mutualism dynamics, probably resulting from a strongly seasonal environment. Sympatric co‐occurrence may result from a window of opportunity for a functionally divergent agaonid, potentially due to constraints on the main pollinator in adapting to variable year‐round conditions that prevent competitive exclusion.     

Association between FABP2 Ala54Thr polymorphisms and type 2 diabetes mellitus risk: a HuGE Review and Meta‐Analysis

Many studies have examined the association between the FABP2 (rs1799883) Ala54Thr gene polymorphism and type 2 diabetes mellitus risk (T2DM) in various populations, but their results have been inconsistent. To assess this relationship more precisely, A HuGE review and meta‐analysis were performed. The PubMed and CNKI database was searched for case‐control studies published up to April 2014. Data were extracted and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated. Ultimately, 13 studies, comprising 2020 T2DM cases and 2910 controls were included. Overall, for the Thr carriers (Ala/Thr and Thr/Thr) versus the wild‐type homozygotes (Ala/Ala), the pooled OR was 1.18 (95% CI = 1.04–1.34, P = 0.062 for heterogeneity), for Thr/Thr versus Ala/Ala the pooled OR was 1.17 (95% CI = 1.05–1.41 P = 0.087 for heterogeneity). In the stratified analysis by ethnicity, the significantly risks were found among Asians but not Caucasians. This meta‐analysis suggests that the FABP2 (rs1799883) Ala54Thr polymorphisms are associated with increased susceptibility to T2DM risk among Asians but not Caucasians.     

Isolation of lactoperoxidase, lactoferrin, α-lactalbumin, β-lactoglobulin B and β-lactoglobulin A from bovine rennet whey using ion exchange chromatography

A mild and rapid method is described for isolating various milk proteins from bovine rennet whey. β-Lactoglobulin from bovine rennet whey was easily adsorbed on and desorbed from a weak anion exchanger, diethylaminoethyl-Toyopearl. However, α-lactalbumin could not be adsorbed onto the resin. α-Lactalbumin and β-lactoglobulin from rennet whey could also be adsorbed and separated using a strong anion exchanger, quaternary aminoethyl-Toyopearl. The rennet whey was passed through a strong cation exchanger, sulphopropyl-Toyopearl, to separate lactoperoxidase and lactoferrin. α-Lactalbumin and β-lactoglobulin were adsorbed onto quaternary aminoethyl-Toyopearl. α-Lactalbumin was eluted using a linear (0–0.15 M) concentration gradient of NaCl in 0.05 M Tris–HCl buffer (pH 8.5). Subsequently, β-lactoglobulin B and β-lactoglobulin A were eluted from the column with 0.05 M Tris–HCl (pH 6.8), using a linear (0.1–0.25 M) concentration gradient of NaCl. The yields were 1260 mg α-lactalbumin, 1290 mg β-lactoglobulin B and 2280 mg β-lactoglobulin A from 1 l rennet whey.     

A cross-talk between epithelium and endothelium mediates human alveolar–capillary injury during SARS-CoV-2 infection

COVID-19, caused by SARS-CoV-2, is an acute and rapidly developing pandemic, which leads to a global health crisis. SARS-CoV-2 primarily attacks human alveoli and causes severe lung infection and damage. To better understand the molecular basis of this disease, we sought to characterize the responses of alveolar epithelium and its adjacent microvascular endothelium to viral infection under a co-culture system. SARS-CoV-2 infection caused massive virus replication and dramatic organelles remodeling in alveolar epithelial cells, alone. While, viral infection affected endothelial cells in an indirect manner, which was mediated by infected alveolar epithelium. Proteomics analysis and TEM examinations showed viral infection caused global proteomic modulations and marked ultrastructural changes in both epithelial cells and endothelial cells under the co-culture system. In particular, viral infection elicited global protein changes and structural reorganizations across many sub-cellular compartments in epithelial cells. Among the affected organelles, mitochondrion seems to be a primary target organelle. Besides, according to EM and proteomic results, we identified Daurisoline, a potent autophagy inhibitor, could inhibit virus replication effectively in host cells. Collectively, our study revealed an unrecognized cross-talk between epithelium and endothelium, which contributed to alveolar–capillary injury during SARS-CoV-2 infection. These new findings will expand our understanding of COVID-19 and may also be helpful for targeted drug development.Subject terms: Mechanisms of disease, Viral infection     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

     

Repeated pre-training sleep restriction in adolescent rats impaired spatial performance

Sleep and Biological Rhythms - Chronic sleep deprivation (SD) is an overwhelming problem in young students. Firstly, we investigated whether different levels of pre-training SD had effects on...