Variability in soybean yield responses to elevated atmospheric CO2: Insights from non-structural carbohydrate remobilisation during seed filling.

The increasing atmospheric CO2 concentration (e[CO2]) has mixed effects on soybean most varieties' yield. This study elucidated the effect of e[CO2] on soybean yield and the underlying mechanisms related to photosynthetic capacity, non-structural carbohydrate (NSC) accumulation, and remobilisation. Four soybean cultivars were cultivated in open-top chambers at two CO2 levels. Photosynthesis rates were determined from R2 to R6. Plants were sampled at R5 and R8 to determine carbohydrate concentrations. There were significant variations in yield responses among the soybean cultivars under e[CO2], from no change in DS1 to a 22% increase in SN14. DS1 and SN14 had the smallest and largest increase, respectively, in daily carbon assimilation capacity. Under e[CO2], DS1, MF5, and XHJ had an increase in Ci, at which point the transition from Rubisco-limited to ribulose-1,5-bisphosphate regeneration-limited photosynthesis occurred, in contrast with SN14. Thus, the cultivars might have distinct mechanisms that enhance photosynthesis under e[CO2] conditions. A positive correlation was between daily carbon assimilation response to e[CO2] and soybean yield, emphasising the importance of enhanced photosynthate accumulation before the R5 stage in determining yield response to e[CO2]. E[CO2] significantly influenced NSC accumulation in vegetative organs at R5, with variation among cultivars. There was enhanced NSC remobilisation during seed filling, indicating cultivar-specific responses to the remobilisation of sucrose and soluble sugars, excluding sucrose and starch. A positive correlation was between leaf and stem NSC remobilisation and yield response to e[CO2], emphasising the role of genetic differences in carbohydrate remobilisation mechanisms in determining soybean yield variation under elevated CO2 levels.

Minimally-invasive implantable device enhances brain cancer suppression.

Current brain tumor treatments are limited by the skull and BBB, leading to poor prognosis and short survival for glioma patients. We introduce a novel minimally-invasive brain tumor suppression (MIBTS) device combining personalized intracranial electric field therapy with in-situ chemotherapeutic coating. The core of our MIBTS technique is a wireless-ultrasound-powered, chip-sized, lightweight device with all functional circuits encapsulated in a small but efficient "Swiss-roll" structure, guaranteeing enhanced energy conversion while requiring tiny implantation windows ( ~ 3 × 5 mm), which favors broad consumers acceptance and easy-to-use of the device. Compared with existing technologies, competitive advantages in terms of tumor suppressive efficacy and therapeutic resolution were noticed, with maximum ~80% higher suppression effect than first-line chemotherapy and 50-70% higher than the most advanced tumor treating field technology. In addition, patient-personalized therapy strategies could be tuned from the MIBTS without increasing size or adding circuits on the integrated chip, ensuring the optimal therapeutic effect and avoid tumor resistance. These groundbreaking achievements of MIBTS offer new hope for controlling tumor recurrence and extending patient survival.

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.

BACKGROUND: Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS: We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS: Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.

Inferring single-cell copy number profiles through cross-cell segmentation of read counts.

BACKGROUND: Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS: By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS: Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

Toxic metal contamination effects mediated by hotspot intensity of soil enzymes and microbial community structure.

Metal contamination from mine waste is a widespread threat to soil health. Understanding of the effects of toxic metals from mine waste on the spatial patterning of rhizosphere enzymes and the rhizosphere microbiome remains elusive. Using zymography and high-throughput sequencing, we conducted a mesocosm experiment with mine-contaminated soil, to compare the effects of different concentrations of toxic metals on exoenzyme kinetics, microbial communities, and maize growth. The negative effects of toxic metals exerted their effects largely on enzymatic hotspots in the rhizosphere zone, affecting both resistance and the area of hotspots. This study thus revealed the key importance of such hotspots in overall changes in soil enzymatic activity under metal toxicity. Statistical and functional guild analysis suggested that these enzymatic changes and associated microbial community changes were involved in the inhibition of maize growth. Keystone species of bacteria displayed negative correlations with toxic metals and positive correlations with the activity of enzymatic hotspots, suggesting a potential role. This study contributes to an emerging paradigm, that changes both in the activity of soil enzymes and soil biota - whether due to substrate addition or in this case toxicity - are largely confined to enzymatic hotspot areas.

Ternary-Metal Sn-Pb-Zn Perovskite to Reconstruct Top Surface for Efficient and Stable Less-Pb Perovskite Solar Cells.

Though Sn-Pb alloyed perovskite solar cells (PSCs) achieved great progress, there is a dilemma to further increase Sn for less-Pb requirement. High Sn ratio (>70%) perovskite exhibits nonstoichiometric Sn:Pb:I at film surface to aggravate Sn2+ oxidation and interface energy mismatch. Here, ternary metal alloyed (FASnI3 )0.7 (MAPb1- x Znx I3 )0.3 (x = 0-3%) is constructed for Pb% < 30% perovskite. Zn with smaller ionic size and stronger ionic interaction than Sn/Pb assists forming high-quality perovskite film with ZnI6 4- enriched at surface to balance Sn:Pb:I ratio. Differing from uniform bulk doping, surface-rich Zn with lower lying orbits pushes down the energy band of perovskite and adjusts the interface energy for efficient charge transfer. The alloyed PSC realizes efficiency of 19.4% at AM1.5 (one of the highest values reported for Pb% < 30% PSCs). Moreover, stronger bonding of Zn─I and Sn─I contributes to better durability of ternary perovskite than binary perovskite. This work highlights a novel alloy method for efficient and stable less-Pb PSCs.

Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics.

The classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching, transcription, translation, degradation and dilution. While this model is easily simulated, its analytical solution remains an unsolved problem. Here we modify this model to explicitly include cell-cycle dynamics and then derive an exact solution for the time-dependent joint distribution of mRNA and protein numbers. We show large differences between this model and the classical model which captures cell-cycle effects implicitly via effective first-order dilution reactions. In particular we find that the Fano factor of protein numbers calculated from a population snapshot measurement are underestimated by the classical model whereas the correlation between mRNA and protein can be either over- or underestimated, depending on the timescales of mRNA degradation and promoter switching relative to the mean cell-cycle duration time.

Paenibacillus polygoni sp. nov., an endophytic bacterium isolated from Polygonum lapathifolium L. in wetland.

A Gram-stain-positive, aerobic, rod-shaped, non-motile, yellowish and glossy strain, C31T, was isolated from a wetland plant Polygonum lapathifolium L. located south of Poyang Lake, Jiangxi Province, PR China. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain C31T showed similarity values of lower than 97.0 % to other type species belonging to the genus Paenibacillus. The genomic average nucleotide identity values between strain C31T and its reference type species ranged from 68.9-70.9 % and the digital DNA-DNA hybridization values were lower than 27.8 %. The genomic DNA G+C content of strain C31T was 41.9 mol%. The optimal growth temperature, pH and NaCl concentration were 37 °C, pH 7 and 0.5 %, respectively. The major cellular fatty acids (>5.0 %) of strain C31T were anteiso-C15 : 0 (73.7 %), anteiso-C17 : 0 (8.4 %) and iso-C15 : 0 (5.2 %). The polar lipids of strain C31T were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and unidentified phospholipids. The respiratory quinone was MK-7. Based on these phylogenetic and phenotypic characterizations, strain C31T represents a novel species within the genus Paenibacillus. Therefore, the proposed name for this newly identified species is Paenibacillus polygoni sp. nov. and the type strain is C31T (=CCTCC AB 2022349T=KCTC 43565T).

A multi-level analysis on the causes of train-pedestrian collisions in Southwest China 2011-2020.

Collisions between trains and pedestrians are the primary cause of railway casualties. However, there remains a lack of comprehensive understanding regarding the underlying causes of this phenomenon. This study employs a multi-level approach to investigate the factors associated with the occurrence and severity of train-pedestrian collisions. The investigation is based on 2160 independent cases that occurred in southwest China from 2011 to 2020. Multiple contributing factors related to the victim, train, track, and socio-economic status of the surrounding district were examined, utilizing information from various sources. At the county level, several risk factors were identified in predicting the occurrence rate. These factors include higher population density and a greater number of normal-speed stations. However, the presence of high-speed train stations did not exhibit any significant impact. Additionally, the study found that regulations pertaining to protective fences were highly effective in reducing the occurrence rate. Regarding the prediction of collision severity, certain factors were found to increase the death rate. These factors include young men as victims, engaging in lying down or crossing behaviors, higher train speeds, gentle downhill slopes, lower education levels, and a higher proportion of the labor force. These findings emphasize the necessity of adopting a comprehensive perspective when examining the causes of train-pedestrian collisions. Furthermore, it underscores the significance of considering the notable differences between rapidly developing countries such as China and developed countries. Based on our findings, we also provide corresponding policy suggestions.

Conversion of steppe to cropland increases spatial heterogeneity of soil functional genes.

The microbiome function responses to land use change are important for the long-term prediction and management of soil ecological functions under human influence. However, it has remains uncertain how the biogeographic patterns of soil functional composition change when transitioning from natural steppe soils (NS) to agricultural soils (AS). We collected soil samples from adjacent pairs of AS and NS across 900 km of Mollisol areas in northeast China, and the soil functional composition was characterized using shotgun sequencing. AS had higher functional alpha-diversity indices with respect to KO trait richness and a higher Shannon index than NS. The distance-decay slopes of functional gene composition were steeper in AS than in NS along both spatial and environmental gradients. Land-use conversion from steppe to farmland diversified functional gene profiles both locally and spatially; it increased the abundances of functional genes related to labile carbon, but decreased those related to recalcitrant substrate mobilization (e.g., lignin), P cycling, and S cycling. The composition of gene functional traits was strongly driven by stochastic processes, while the degree of stochasticity was higher in NS than in AS, as revealed by the neutral community model and normalized stochasticity ratio analysis. Alpha-diversity of core functional genes was strongly related to multi-nutrient cycling in AS, suggesting a key relationship to soil fertility. The results of this study challenge the paradigm that the conversion of natural to agricultural habitat will homogenize soil properties and biology while reducing local and regional gene functional diversity.

Ultrasensitive FET biosensor chip based on self-assembled organic nanoporous membrane for femtomolar detection of Amyloid-ß.

Early diagnosis of Alzheimer's disease (AD) is critical for preventing disease progression, however, the diagnosis of AD remains challenging for most patients due to limitations of current sensing technologies. A common pathological feature found in AD-affected brains is the accumulation of Amyloid-ß (Aß) polypeptides, which lead to neurofibrillary tangles and neuroinflammatory plaques. Here, we developed a portable ultrasensitive FET biosensor chip based on a self-assembled nanoporous membrane for ultrasensitive detection of Aß protein in complex environments. The microscale semiconductor channel was covered with a self-assembled organic nanoporous membrane modified by antibody molecules to pick up and amplify the Aß protein signal. The nanoporous structure helps protect the sensitive channel from non-target proteins and improves its stability since no chemical functionalization process involved, largely reduces background noise of the sensing platform. When a bio-gated target is captured, the doping state of the polymer bulk could be tuned and amplified the strength of the weak signal, achieving ultrasensitive detecting performance (enabling the device to detect target protein less than 1 fg/ml in 1 µl sample). Moreover, the device simplifies the circuit connection by integrating all the connections on a 2 cm × 2 cm chip, avoiding expensive and complex manufacturing processes, and makes it usable for portable prognosis. We believe that this ultrasensitive, portable, low-cost Aß sensor chip shows the great potential in the early diagnosis of AD and large-scale population screening applications.

Dynamical Transition of Operator Size Growth in Quantum Systems Embedded in an Environment.

In closed generic many-body systems, unitary evolution disperses local quantum information into highly nonlocal objects, resulting in thermalization. Such a process is called information scrambling, whose swiftness is quantified by the operator size growth. However, the impact of couplings to the environment on the process of information scrambling remains unexplored for quantum systems embedded within an environment. Here we predict a dynamical transition in quantum systems with all-to-all interactions accompanied by an environment, which separates two phases. In the dissipative phase, information scrambling halts as the operator size decays with time, while in the scrambling phase, dispersion of information persists, and the operator size grows and saturates to an O(N) value in the long-time limit with N the number of degrees of freedom of the systems. The transition is driven by the competition between the system's intrinsic and environment propelled scramblings and the environment-induced dissipation. Our prediction is derived from a general argument based on epidemiological models and demonstrated analytically via solvable Brownian Sachdev-Ye-Kitaev models. We provide further evidence which suggests that the transition is generic to quantum chaotic systems when coupled to an environment. Our study sheds light on the fundamental behavior of quantum systems in the presence of an environment.

Stimulation of primed carbon under climate change corresponds with phosphorus mineralization in the rhizosphere of soybean.

Elevated CO2 and temperature likely alter photosynthetic carbon inputs to soils, which may stimulate soil microbial activity to accelerate the decomposition of soil organic carbon (SOC), liberating more phosphorus (P) into the soil solution. However, this hypothesis on the association of SOC decomposition and P transformation in the plant rhizosphere requires robust soil biochemical evidence, which is critical to nutrient management for the mitigation of soil quality against climate change. This study investigated the microbial functional genes relevant to P mineralization together with priming processes of SOC in the rhizosphere of soybean grown under climate change. Soybean plants were grown under elevated CO2 (eCO2, 700 ppm) combined with warming (+ 2 °C above ambient temperature) in open-top chambers. Photosynthetic carbon flow in the plant-soil continuum was traced with 13CO2 labeling. The eCO2 plus warming treatment increased the primed carbon (C) by 43 % but decreased the NaHCO3-extratable organic P by 33 %. Furthermore, NaHCO3-Po was negatively correlated with phosphatase activity and microbial biomass C. Elevated CO2 increased the abundances of C degradation genes, such as abfA and ManB, and P mineralization genes, such as gcd, phoC and phnK. The results suggested that increased photosynthetic carbon inputs to the rhizosphere of plants under eCO2 plus warming stimulated the microbial population and metabolic functions of both SOC and organic P mineralization. There is a positive relationship between the rhizosphere priming effect and P mineralization. The response of microorganisms to plant-C flow is decisive for coupled C and P cycles, which are likely accelerated under climate change.

The use of car safety seats for children in China: A questionnaire survey based on the theory of planned behavior.

OBJECTIVE: The purpose of this study was to analyze the psychological characteristics underlying Chinese parents' behaviors in using child car seats and to understand their decision-making processes. Based on the theory of planned behavior (TPB), three extended variables of perceived accident severity, perceived benefits, and perceived barriers were introduced. From the perspective of social psychology, the psychological factors that influence parents' use of child car seats and their interrelationships were explored. METHODS: A questionnaire was designed to collect data, including information on demographic characteristics, basic components of the TPB, and relevant extension variables. Using on online survey, 585 valid questionnaires were collected. Structural equation modeling was used to calibrate the data, and multiple group analysis was performed on the demographic variables. RESULTS: The extended TPB can effectively explain and predict parents' behaviors when using children's car seats. The results of the model show that parents' positive attitudes toward child safety seats (CSSs), others' recognition of their own use and perceptual control of the use of CSSs increase their willingness to use CSSs. Parents' willingness to use has a positive impact on the use of CSSs. Additionally, for the three extended variables introduced, perceived benefit significantly promoted parental intention and behavior to use CSS for children; perceived barriers significantly reduced parental use of CSS; and perceived accident severity had no significant effect on parental use of CSS. CONCLUSIONS: This study established the validity of the extended TPB model in predicting parents' behaviors in using car seats for their children. In addition, the current findings may provide a theoretical basis for policy development to promote CSS use.

Constructing the behavioral sequence of the takeover process-TOR, behavior characteristics and phases division: A real vehicle experiment.

Autonomous driving will still use human-machine co-driving to handle complex situations for a long term, which requires the driver to control the vehicle and avoid hazards by executing appropriate behavioral sequences after takeover prompts. Previous studies focused on the division of static behavioral indicators and major phases in the initial phase of takeover, while lacking the construction of behavioral sequences based on the dynamic changes of behavioral characteristics during the takeover process. This study divides the takeover process in a detailed manner and investigates the impact of audio types on the behavioral sequence at each phase. 20 professional drivers performed the NDRT in autonomous driving mode on real roads, and after receiving audio prompts, they took over the vehicle and performed hazard avoidance maneuvers. The results show that the behavioral characteristics could construct the behavioral sequence of different phases, with the dynamic characteristics of the takeover operation change. In addition, different types of audio prompts will affect the timing of the takeover operation and its driving performance. Choosing different audio prompts or combinations can help improve the effect of taking over the vehicle. This study helps to provide guidance on the design of human-machine interaction for behavior optimization at different phases, so that guiding the driver to take over the vehicle safely and effectively.

Tapered whisker reservoir computing for real-time terrain identification-based navigation.

This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain.

rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations.

Intra-tumor heterogeneity (ITH) is one of the major confounding factors that result in cancer relapse, and deciphering ITH is essential for personalized therapy. Single-cell DNA sequencing (scDNA-seq) now enables profiling of single-cell copy number alterations (CNAs) and thus aids in high-resolution inference of ITH. Here, we introduce an integrated framework called rcCAE to accurately infer cell subpopulations and single-cell CNAs from scDNA-seq data. A convolutional autoencoder (CAE) is employed in rcCAE to learn latent representation of the cells as well as distill copy number information from noisy read counts data. This unsupervised representation learning via the CAE model makes it convenient to accurately cluster cells over the low-dimensional latent space, and detect single-cell CNAs from enhanced read counts data. Extensive performance evaluations on simulated datasets show that rcCAE outperforms the existing CNA calling methods, and is highly effective in inferring clonal architecture. Furthermore, evaluations of rcCAE on two real datasets demonstrate that it is able to provide a more refined clonal structure, of which some details are lost in clonal inference based on integer copy numbers.