UNC-13L, UNC-13S, and Tomosyn form a protein code for fast and slow neurotransmitter release in Caenorhabditis elegans

Synaptic transmission consists of fast and slow components of neurotransmitter release. Here we show that these components are mediated by distinct exocytic proteins. The Caenorhabditis elegans unc-13 gene is required for SV exocytosis, and encodes long and short isoforms (UNC-13L and S). Fast release was mediated by UNC-13L, whereas slow release required both UNC-13 proteins and was inhibited by Tomosyn. The spatial location of each protein correlated with its effect. Proteins adjacent to the dense projection mediated fast release, while those controlling slow release were more distal or diffuse. Two UNC-13L domains accelerated release. C2A, which binds RIM (a protein associated with calcium channels), anchored UNC-13 at active zones and shortened the latency of release. A calmodulin binding site accelerated release but had little effect on UNC-13’s spatial localization. These results suggest that UNC-13L, UNC-13S, and Tomosyn form a molecular code that dictates the timing of neurotransmitter release. DOI: http://dx.doi.org/10.7554/eLife.00967.00

Ozone and haze pollution weakens net primary productivity in China

Atmospheric pollutants have both beneficial and detrimental effects on carbon uptake by land ecosystems. Surface ozone (O3) damages leaf photosynthesis by oxidizing plant cells, while aerosols promote carbon uptake by increasing diffuse radiation and exert additional influences through concomitant perturbations to meteorology and hydrology. China is currently the world’s largest emitter of both carbon dioxide and short-lived air pollutants. The land ecosystems of China are estimated to provide a carbon sink, but it remains unclear whether air pollution acts to inhibit or promote carbon uptake. Here, we employ Earth system modeling and multiple measurement datasets to assess the separate and combined effects of anthropogenic O3 and aerosol pollution on net primary productivity (NPP) in China. In the present day, O3 reduces annual NPP by 0.6 Pg C (14 %) with a range from 0.4 Pg C (low O3 sensitivity) to 0.8 Pg C (high O3 sensitivity). In contrast, aerosol direct effects increase NPP by 0.2 Pg C (5 %) through the combination of diffuse radiation fertilization, reduced canopy temperatures, and reduced evaporation leading to higher soil moisture. Consequently, the net effects of O3 and aerosols decrease NPP by 0.4 Pg C (9 %) with a range from 0.2 Pg C (low O3 sensitivity) to 0.6 Pg C (high O3 sensitivity). However, precipitation inhibition from combined aerosol direct and indirect effects reduces annual NPP by 0.2 Pg C (4 %), leading to a net air pollution suppression of 0.8 Pg C (16 %) with a range from 0.6 Pg C (low O3 sensitivity) to 1.0 Pg C (high O3 sensitivity). Our results reveal strong dampening effects of air pollution on the land carbon uptake in China today. Following the current legislation emission scenario, this suppression will be further increased by the year 2030, mainly due to a continuing increase in surface O3. However, the maximum technically feasible reduction scenario could drastically relieve the current level of NPP damage by 70 % in 2030, offering protection of this critical ecosystem service and the mitigation of long-term global warming

A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming

Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard. This problem is solved either by iterative algorithms, which suffer from slow convergence, or more recently by using deep learning tools, which require time-consuming pre-training process. In this paper, we propose a low-complexity meta-learning based gradient descent algorithm. A meta network with lightweight architecture is applied to learn an adaptive gradient descent update rule to directly optimize the beamformer. This lightweight network is trained during the iterative optimization process, which we refer to as \emph{training while solving}, which removes both the training process and the data-dependency of existing deep learning based solutions.Extensive simulations show that the proposed method achieves superior WSR performance compared to existing learning-based approaches as well as the conventional WMMSE algorithm, while enjoying much lower computational load

A New Method for the Determination of Nucleic Acid Using an Eu3+– nicotinic Acid Complex as a Resonance Light Scattering Probe

This study found that in Tris-HCl buffer, the resonance light scattering (RLS) intensity of the Eu3+-nicotinic acid system can be greatly enhanced by nucleic acids and the enhanced intensity is proportional to the concentration of nucleic acid in the range of 7×10-8-1×10-5 g∙mL-1 for fsDNA, and its detection limit is 2×10-8 g∙mL-1. Based on this, a new method for the determination of nucleic acids is proposed. Synthetic and actual samples are determined satisfactorily. The interaction mechanism is also studied. It is thought that nucleic acid can bind with the Eu3+-nicotinic acid complex through electrostatic attraction and thus form a large Eu3+-nicotinic acid-nucleic acid complex

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease pro- gression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from dig- ital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to under- stand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.ER

Sounds of COVID-19: exploring realistic performance of audio-based digital testing.

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65-0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85-0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment

Label-free quantitative proteomic analysis of molting-related proteins of Trichinella spiralis intestinal infective larvae

International audienceAbstractMolting is a key step for body-size expansion and environmental adaptation of parasitic nematodes, and it is extremely important for Trichinella spiralis growth and development, but the molting mechanism is not fully understood. In this work, label-free LC–MS/MS was used to determine the proteome differences between T. spiralis muscle larvae (ML) at the encapsulated stage and intestinal infective larvae (IIL) at the molting stage. The results showed that a total of 2885 T. spiralis proteins were identified, 323 of which were differentially expressed. These proteins were involved in cuticle structural elements, regulation of cuticle synthesis, remodeling and degradation, and hormonal regulation of molting. These differential proteins were also involved in diverse intracellular pathways, such as fatty acid biosynthesis, arachidonic acid metabolism, and mucin type O-glycan biosynthesis. qPCR results showed that five T. spiralis genes (cuticle collagen 14, putative DOMON domain-containing protein, glutamine synthetase, cathepsin F and NADP-dependent isocitrate dehydrogenase) had significantly higher transcriptional levels in 10 h IIL than ML (P < 0.05), which were similar to their protein expression levels, suggesting that they might be T. spiralis molting-related genes. Identification and characterization of T. spiralis molting-related proteins will be helpful for developing vaccines and new drugs against the early enteral stage of T. spiralis

Molecular phylogeny and macroevolution of Chaitophorinae aphids (Insecta: Hemiptera: Aphididae)

Chaitophorinae is a predominantly Northern Hemisphere aphid subfamily characterized by numerous setae on the body. Two constituent tribes are associated with different host plants, with Chaitophorini feeding on deciduous trees and shrubs and Siphini colonizing grasses. Based on data from multiple genes (COI, COII, Cytb and EF-1α), geographical distribution and host association, this study investigated the phylogeny and macroevolution of Chaitophorinae using phylogenetic reconstruction, molecular dating, model-based ancestral area and character estimations and diversification rate calculation. Our results support the monophyly of Chaitophorinae and two tribes, indicate that Sipha and the two largest genera Chaitophorus and Periphyllus are not monophyletic, and suggest a need for a change in the taxonomic status of Lambersaphis, which was nested within Chaitophorus in the phylogenetic tree. We recovered an origin of Chaitophorinae on Acer plants from eastern Asia during the Late Cretaceous to early Palaeocene, followed by multiple dispersals into other areas that were responsible for its contemporary distribution. The origins of Siphini and Chaitophorus + Lambersaphis coincided with colonizations of novel host plants. An increase in diversification rate occurred within Chaitophorus in the Miocene and was associated with range expansion and switching onto new host plants, highlighting the roles of dispersal and host shift in aphid diversification