Mapping effective connectivity by virtually perturbing a surrogate brain

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in human due to being invasive and limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in the rest of regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of EC on a brain-wide scale. Our validation of NPI, using models with established EC, shows its superiority over Granger Causality and Dynamic Causal Modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Applications on a disease-specific dataset highlight the potential of using personalized EC as biomarkers for neurological diseases. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and can facilitate neuroscience research and clinical applications

Laminated Monolithic Perovskite/Silicon Tandem Photovoltaics

Perovskite/silicon tandem photovoltaics have attracted enormous attention in science and technology over recent years. In order to improve the performance and stability of the technology, new materials and processes need to be investigated. However, the established sequential layer deposition methods severely limit the choice of materials and accessible device architectures. In response, a novel lamination process that increases the degree of freedom in processing the top perovskite solar cell (PSC) is proposed. The very first prototypes of laminated monolithic perovskite/silicon tandem solar cells with stable power output efficiencies of up to 20.0% are presented. Moreover, laminated single-junction PSCs are on par with standard sequential layer deposition processed devices in the same architecture. The numerous advantages of the lamination process are highlighted, in particular the opportunities to engineer the perovskite morphology, which leads to a reduction of non-radiative recombination losses and and an enhancement in open-circuit voltage (Voc). Laminated PSCs exhibit improved stability by retaining their initial efficiency after 1-year aging and show good thermal stability under prolonged illumination at 80 °C. This lamination approach enables the research of new architectures for perovskite-based photovoltaics and paves a new route for processing monolithic tandem solar cells even with a scalable lamination process

PIE-seq: identifying RNA-binding protein targets by dual RNA-deaminase editing and sequencing

Abstract RNA-binding proteins (RBPs) are essential for gene regulation, but it remains a challenge to identify their RNA targets across cell types. Here we present PIE-Seq to investigate Protein-RNA Interaction with dual-deaminase Editing and Sequencing by conjugating C-to-U and A-to-I base editors to RBPs. We benchmark PIE-Seq and demonstrate its sensitivity in single cells, its application in the developing brain, and its scalability with 25 human RBPs. Bulk PIE-Seq identifies canonical binding features for RBPs such as PUM2 and NOVA1, and nominates additional target genes for most tested RBPs such as SRSF1 and TDP-43/TARDBP. Homologous RBPs frequently edit similar sequences and gene sets in PIE-Seq while different RBP families show distinct targets. Single-cell PIE-PUM2 uncovers comparable targets to bulk samples and applying PIE-PUM2 to the developing mouse neocortex identifies neural-progenitor- and neuron-specific target genes such as App. In summary, PIE-Seq provides an orthogonal approach and resource to uncover RBP targets in mice and human cells

NEPE Propellant Mesoscopic Modeling and Damage Mechanism Study Based on Inversion Algorithm

To accurately characterize the mesoscopic properties of NEPE (Nitrate Ester Plasticized Polyether) propellant, the mechanical contraction method was used to construct a representative volume element (RVE) model. Based on this model, the macroscopic mechanical response of NEPE propellant at a strain rate of 0.0047575 s−1 was simulated and calculated, and the parameters of the cohesive zone model (CZM) were inversely optimized using the Hooke–Jeeves algorithm by comparing the simulation results with the results of the uniaxial tensile test of NEPE propellants. Additionally, the macroscopic mechanical behavior of NEPE composite solid propellants at strain rates of 0.00023776 s−1 and 0.023776 s−1 was also predicted. The mesoscopic damage evolution process of NEPE propellants was investigated by the established model. The study results indicate that the predicted curves are relatively consistent with the basic features and change trends of the test curves. Therefore, the established model can effectively simulate the mesoscopic damage process of NEPE composite solid propellants and their macroscopic mechanical properties

A High-Accuracy Stochastic FIR Filter with Adaptive Scaling Algorithm and Antithetic Variables Method

Digital filter is an important fundamental component in digital signal processing (DSP) systems. Among the digital filters, the finite impulse response (FIR) filter is one of the most commonly used schemes. As a low-complexity hardware implementation technique, stochastic computing has been applied to overcome the huge hardware cost problem of high-order FIR filters. However, the stochastic FIR filter (SFIR) scheme suffers from long processing latency and accuracy degradation. In this paper, the bit stream representation noise is theoretically analyzed, and an adaptive scaling algorithm (ASA) is proposed to improve the accuracy of SFIR with the same bit stream length. Furthermore, a novel antithetic variables method is proposed to further improve the accuracy. According to the simulation results on a 64-tap FIR filter, the ASA and AV methods gain 17 dB and 6 dB on the signal-to-noise ratio (SNR), respectively. The hardware implementation results are also presented in this paper, which illustrates that the proposed ASA-AV-SFIR filter increases 4.6 times hardware efficiency with respect to the existing SFIR schemes

Insights into the Fungal Community and Functional Roles of Pepper Rhizosphere Soil under Plastic Shed Cultivation

The rhizosphere fungal community is essential for determining plant health and improving crop productivity. The fungal community structure and functional roles in the plastic shed soils were explored using high throughput sequencing and FUNGuild in this study. The fungal community structures shifted between the rhizosphere and non-rhizosphere soils. The greatest abundance variation was observed for the rare fungal members with relative abundances <0.1%. In the rhizosphere soil of pepper, the abundance of the genera Purpureocillium, Metacorgyceps, Arthrobotrys, Cephalotheca, and Scedosporium increased significantly, among which, Purpureocillium, Arthrobotrys and Metacorgyceps exhibited biocontrol characteristics. Co-occurrence network analysis revealed different interactions of fungal communities in the rhizosphere and non-rhizosphere soils, both of which were dominated by low abundance members. More positive correlation was identified among the rare members, the fungal pathotroph functions and phthalate acid ester in the rhizosphere soil. This study highlights the important niche of the rare fungal members in soil microbial ecology under plastic shed cultivation

Fine Mapping and Identification of BnaC06.FtsH1, a Lethal Gene That Regulates the PSII Repair Cycle in Brassica napus

Photosystem II (PSII) is an important component of the chloroplast. The PSII repair cycle is crucial for the relief of photoinhibition and may be advantageous when improving stress resistance and photosynthetic efficiency. Lethal genes are widely used in the efficiency detection and method improvement of gene editing. In the present study, we identified the naturally occurring lethal mutant 7-521Y with etiolated cotyledons in Brassica napus, controlled by double-recessive genes (named cyd1 and cyd2). By combining whole-genome resequencing and map-based cloning, CYD1 was fine-mapped to a 29 kb genomic region using 15,167 etiolated individuals. Through cosegregation analysis and functional verification of the transgene, BnaC06.FtsH1 was determined to be the target gene; it encodes an filamentation temperature sensitive protein H 1 (FtsH1) hydrolase that degrades damaged PSII D1 in Arabidopsis thaliana. The expression of BnaC06.FtsH1 was high in the cotyledons, leaves, and flowers of B. napus, and localized in the chloroplasts. In addition, the expression of EngA (upstream regulation gene of FtsH) increased and D1 decreased in 7-521Y. Double mutants of FtsH1 and FtsH5 were lethal in A. thaliana. Through phylogenetic analysis, the loss of FtsH5 was identified in Brassica, and the remaining FtsH1 was required for PSII repair cycle. CYD2 may be a homologous gene of FtsH1 on chromosome A07 of B. napus. Our study provides new insights into lethal mutants, the findings may help improve the efficiency of the PSII repair cycle and biomass accumulation in oilseed rape