The transcriptional processes associated with final seed protein content are not fully characterized in soybean, particularly in progeny derived from crosses with wild soybean. To address this knowledge gap, high-protein (HP) and low-protein (LP) BC1F3 progeny were compared at 25, 35, and 45 days after flowering (D25, D35, and D45) using seed-composition measurements, RNA sequencing (RNA-seq), differential expression analysis, temporal expression clustering, functional enrichment, and weighted gene co-expression network analysis (WGCNA). The HP and LP phenotypes became most distinct late in seed development: at D45, the protein content was 9.0% higher in HP, whereas the oil content was 7.3% lower. The number of differentially expressed genes (DEGs) between HP and LP increased from 3386 at D25 to 7269 at D45, and 18,394 genes showed significant Group × Time interactions. Six temporal clusters revealed stage-dependent expression patterns enriched for cell division, photosynthesis, reserve-related functions, translation, protein processing, and redox-associated processes. WGCNA identified pink and salmon modules positively correlated with protein content and a brown module negatively correlated with protein content. Integration of stage-specific DEGs, temporal interactions, and network metrics prioritized candidates including Glyma.11G063900.1, Glyma.06G030400.1, Glyma.07G038500.2, and Glyma.03G065500.2. Modules correlated with oil content were interpreted primarily as developmental modules because the oil content was nearly collinear with the developmental stage. These results provide a stage-resolved framework for prioritizing candidate genes for functional validation and breeding aimed at improving soybean seed protein content.
Mechanized harvesting of bulk tea in unstructured hilly tea gardens requires accurate dynamic profiling of irregular canopies at a constant cutting depth to avoid harvesting coarse and old leaves, missing tender shoots, and reducing yield and quality. This task is complicated by high-frequency vibrations from crawler travel; perception disturbances caused by impacts with hard, thick branches; and actuator saturation and control chattering when abrupt chassis-posture changes occur under motor torque constraints. To address these challenges, this study proposes a cooperative control architecture that integrates spatiotemporal perception reconstruction with adaptive anti-windup control. First, a global inverse-kinematics mapping model that fuses an inertial measurement unit (IMU) with an absolute encoder is developed. The model decouples chassis motion from local terrain measurements and establishes a unified spatial reference for constant-depth actuator profiling. Second, a spatiotemporal perception reconstruction mechanism is designed for composite multifrequency noise. In the amplitude domain, a robust outlier-rejection kernel adaptively suppresses non-Gaussian transient pulses; in the temporal domain, environmental background-noise estimation and abrupt-change activation dynamically adjust the filtering bandwidth and reduce phase lag in absolute elevation reconstruction during terrain steps. Finally, a nonsingular fast terminal sliding-mode control (NFTSMC) law incorporating active feedforward and anti-windup compensation is developed to address the servo motor torque limit. The law uses chassis pitch angular velocity for feedforward compensation and combines a continuous boundary layer with an auxiliary dynamic system to correct the error manifold, thereby reducing integral windup and high-frequency servo chattering caused by torque saturation. Comparative simulations show that, under extreme multifrequency composite conditions, the global root-mean-square (RMS) tracking error is 41.1% and 35.8% lower than those of Kalman filter-proportional-integral-derivative control (KF-PID) and standard moving-horizon-estimation sliding-mode control (SMHE-SMC), respectively. Field evaluations across 30 measurement areas showed that under the Challenging Scenario (combined slope, speed, and density variations), the proposed architecture maintained mean missed-plucking and loss rates of 0.46% and 0.43%, respectively. ANOVA confirmed a significant main effect of the control strategy on the missed-plucking rate (p < 0.001, ηp2>0.9). Additionally, a structured component-wise ablation study was conducted to evaluate the specific contributions of the core modules. The results indicated that the omission of mechanisms such as inverse-kinematics mapping or anti-windup dynamics notably degraded the overall tracking accuracy by inducing uncompensated spatial drift and saturation-induced errors. These results indicate the potential of the proposed architecture for closed-loop profiling control in intelligent agricultural machinery.
Procymidone is a dicarboximide fungicide; its persistence and the formation of the toxic metabolite 3,5-dichloroaniline (3,5-DCA) pose environmental risks. A microbial strategy was developed to enhance the degradation of procymidone and its intermediates via complementary bacterial metabolism. A procymidone-degrading bacterium, Prescottella equi strain YW-05, was previously isolated from agricultural soil with long-term procymidone application. Strain YW-05 degraded 86% of procymidone (10 mg L−1) within 7 days in mineral medium, exhibiting a half-life of 2.6 days. However, the strain showed a limited ability to degrade the intermediate metabolite 3,5-DCA. Pseudomonas putida strains were screened to degrade dichloroaniline isomers, and Pseudomonas putida KACC 10270T exhibited the highest degradation activity toward 3,5-DCA. Co-inoculation with strain YW-05 and P. putida KACC 10270T enhanced 3,5-DCA degradation, reaching 90.5% removal within 14 days and 92.8% within 28 days. In soil microcosms, co-inoculation accelerated the early removal of 3,5-DCA, with 80.3% removed within 3 days compared with 52.3% in the uninoculated control, and reduced the half-life to 1.4 days. Metabolite analysis using LC-MS/MS revealed 4-chlorocatechol and cis,cis-muconic acid and suggested procymidone degradation proceeded through hydrolytic cleavage, followed by dechlorination and aromatic ring cleavage. These findings highlight the use of complementary microbial consortia as an effective strategy for detoxifying pesticide residues and their toxic intermediates in agricultural soils.
Soil tillage is energy-intensive, and the cutting resistance of soil-engaging tools is its largest single loss. Ultrasonic vibration-assisted cutting (UVAC) can reduce this resistance, but quantitative studies have concentrated on longitudinal vibration; transverse vibration, which loads the soil by cyclic lateral squeeze and release, remains largely unexplored. A calibrated discrete element model of a transverse UVAC tool–soil system is developed to determine how amplitude, frequency and cutting speed govern drag reduction and to identify the contact-level origin of the effect. Bench tests at 20 kHz supplement the simulated design space with an additional operating condition and confirm the predicted forces at eight settings—five vibration conditions and three baselines—with a mean relative error of 8.7% and the baselines within 6%; one calibrated parameter set is used throughout. Over 30–70 µm, 25–45 kHz and 0.03–0.07 m s−1, amplitude dominates, raising the drag reduction from 44.10% to 68.18%, whereas frequency acts mainly through its quadratic term and its interaction with amplitude, and cutting speed enters the same coupling. Box–Behnken optimization places the optimum at 70 µm, 25 kHz and 0.07 m s−1, with a DEM-verified reduction of 69.25% against a predicted 69.53%. Contact analysis shows that the vibration shifts load from normal compression to tangential shear and converts bond failure from concentrated rupture into progressive degradation. Amplitude is therefore the primary design variable for transverse UVAC in sandy loam at 9% moisture, with frequency and speed modulating its expression.
Dairy microbial ecosystems cannot be adequately described by total microbial counts, pathogen detection, or taxonomic profiling alone. Milk and dairy products contain interconnected microbial and genetic layers—the microbiome, resistome, mobilome, and phageome—shaped by animal-associated reservoirs, farm and processing environments, starter cultures, technological operations, and storage. This structured critical narrative review integrates these four components within a common microbial–genetic framework and examines how dairy processing reconfigures their interactions. Particular attention is given to cooling, heat treatment, fermentation, ripening, packaging, and storage and to the distinction between molecular detection and biological relevance. Across the reviewed evidence, the significance of microorganisms, antimicrobial resistance genes, mobile genetic elements, and phage-associated sequences depends on viability, functional activity, host assignment, genomic context, transfer potential, persistence, and exposure. Shotgun and long-read metagenomics, metatranscriptomics, metaproteomics, metaviromics, and viability-resolved methods provide increasingly detailed information, but interpretation remains constrained by methodological heterogeneity and limited experimental validation. Future progress requires standardized workflows, longitudinal sampling, stronger ARG–MGE–host resolution, validated phage–host interactions, and studies linking microbial identity and genetic mobility with technological outcomes and dairy safety.
