Soil microbial subcommunity assembly mechanisms are highly variable and intimately linked to their ecological and functional traits.

Revealing the mechanisms underlying soil microbial community assembly is a fundamental objective in molecular ecology. However, despite increasing body of research on overall microbial community assembly mechanisms, our understanding of subcommunity assembly mechanisms for different prokaryotic and fungal taxa remains limited. Here, soils were collected from more than 100 sites across southwestern China. Based on amplicon high-throughput sequencing and iCAMP analysis, we determined the subcommunity assembly mechanisms for various microbial taxa. The results showed that dispersal limitation and homogenous selection were the primary drivers of soil microbial community assembly in this region. However, the subcommunity assembly mechanisms of different soil microbial taxa were highly variable. For instance, the contribution of homogenous selection to Crenarchaeota subcommunity assembly was 70%, but it was only around 10% for the subcommunity assembly of Actinomycetes, Gemmatimonadetes and Planctomycetes. The assembly of subcommunities including microbial taxa with higher occurrence frequencies, average relative abundance and network degrees, as well as wider niches tended to be more influenced by homogenizing dispersal and drift, but less affected by heterogeneous selection and dispersal limitation. The subcommunity assembly mechanisms also varied substantially among different functional guilds. Notably, the subcommunity assembly of diazotrophs, nitrifiers, saprotrophs and some pathogens were predominantly controlled by homogenous selection, while that of denitrifiers and fungal pathogens were mainly affected by stochastic processes such as drift. These findings provide novel insights into understanding soil microbial diversity maintenance mechanisms, and the analysis pipeline holds significant value for future research.

Parabolic cell for low-background Raman analysis of gas samples.

A compact Raman system constructed by a parabolic sample cell and an imaging spectrograph, which has good capability for enhancing a Raman signal and compressing a continuous background, has been put forward. In the Raman spectra of ambient air acquired by this system, the signal level of N2 was enhanced up to 14 times compared with free space, and the related signal to background ratio was increased nearly to 96. With an integration time of 10 s, the rotational fine structure of O2 and N2 were clearly recognized. Besides, a standard analytic gas mixture consisting of H2, CO2, and CO was also tested, and the 3σ LODs of 68 ppm for H2, 54 ppm for CO2 and 116 ppm for CO were obtained.

Efficient and Accurate Computational Model of Neuron with Spike Frequency Adaptation.

Simplified models of neurons are widely used in computational investigations of large networks. One of the most important performance metrics of simplified models is their accuracy in reproducing action potential (spike) timing. In this article, we developed a simple, computationally efficient neuron model by modifying the adaptive exponential integrate and fire (AdEx) model [1] with sigmoid afterhyperpolarization current (Sigmoid AHP). Our model can precisely match the spike times and spike frequency adaptation of cortical pyramidal neurons. The accuracy was similar to a more complex two compartment biophysically realistic model of the same neurons. This work provides a simplified neuronal model with improved spike timing accuracy for use in modeling of large neural networks.Clinical Relevance- Accurate and computationally efficient single neuron model will enable large network modeling of brain regions involved in neurological and psychiatric disorders and may lead to a better understanding of the disorder mechanisms.

Understanding the Impact of Neural Variations and Random Connections on Inference.

Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.