Han Wang

Background: Depression-like phenotypes in animal models often show graded behavioral variation rather than uniform group-wise changes, yet treatment effects are still commonly evaluated using categorical comparisons. In particular, it remains difficult to determine whether treated animals retain model-like behavioral features or shift toward a more control-like state. Here, we developed a continuous behavioral phenotyping framework based on multidimensional open-field-test (OFT) features to quantify model-like behavioral states and treatment-associated behavioral shifts. Methods: In the present dataset, untreated CUMS- and LPS-induced mice jointly defined a shared model-like behavioral state characterized by reduced locomotor activity and altered spatial exploration. By integrating principal component analysis with k-nearest-neighbor-based behavioral inference, we derived two complementary indices: Δd, representing the relative distance of an animal from the model-like versus control-like behavioral centroids, and p_depression, a kNN-derived score representing similarity to the untreated model-like reference state. These indices were used to position individual animals along a spectrum between control-like and model-like behavioral states. Results: Higher p_depression and lower Δd indicated OFT phenotypes more similar to untreated model mice. Using this framework, NSC-treated animals showed treatment-associated shifts away from model-like behavioral patterns and toward more control-like states, while preserving substantial inter-individual variability. Behavioral scores also showed group-level correspondence with neuroinflammatory markers, suggesting preliminary biological relevance. Because the primary model-specific biological comparisons were based on a limited subgroup size (n = 4 per group), the observed NSC-associated treatment effects should be regarded as exploratory and require confirmation in larger, prospectively powered cohorts. Conclusions: Together, these findings support continuous OFT-based behavioral phenotyping as a promising exploratory approach for evaluating treatment-associated behavioral change and phenotypic heterogeneity in preclinical depression research.

Autism spectrum disorder (ASD) involves complex genetic–environmental interactions. Prenatal valproic acid (VPA) exposure, a known environmental risk factor, induces ASD-like phenotypes in rodents, although the mechanisms linking gut microbiota dysbiosis to neurobehavioral deficits remain unclear. Evidence suggests gut–brain axis dysregulation via altered microbial diversity and reduced short-chain fatty acid (SCFA)-producing taxa contributes to ASD pathogenesis. This study investigated whether prenatal VPA exposure drives ASD-like behaviors through gut dysbiosis and SCFA-producer depletion (e.g., Clostridia, Lachnospiraceae), exploring neuroinflammation and oxidative stress as mechanisms. An ASD rat model was established by maternal VPA injection during specific gestational days. Behavioral tests assessed anxiety, sociability, repetitive behaviors, and cognition. Gut microbiota composition (16S rRNA sequencing), cytokine levels (ELISA), oxidative stress markers (biochemical assays), and microglial activation (Iba1 immunofluorescence) were analyzed. VPA-exposed offspring showed ASD-like behaviors accompanied by neurodevelopmental toxicity, manifesting as social deficits, repetitive grooming, and impaired memory. Concurrently, gut analysis revealed reduced alpha diversity and depleted SCFA-producers (e.g., Clostridia, Lachnospiraceae), alongside increased Bacteroidia and Enterobacteriaceae. Neuroinflammation (elevated IL-1β, IL-6, TNF-α, microglial activation) and oxidative stress (reduced GSH, SOD; elevated MDA, NO) were evident. Multivariate analyses linked dysbiosis to behavioral impairments. Prenatal VPA exposure induces gut microbiota dysbiosis, potentially exacerbating neuroinflammation and oxidative stress to drive ASD-like phenotypes. This establishes a mechanistic link between prenatal insults, gut–brain axis disruption, and neurodevelopmental abnormalities, highlighting microbial modulation and SCFA supplementation as potential ASD therapeutics. Furthermore, integrating behavioral, microbial, and molecular analyses advances understanding of gut–brain interactions in ASD and identifies microbiota–metabolite pathways as targets for neurodevelopmental disorders.

Drying involves the evaporation of moisture, accompanied by simultaneous heat transfer, mass transfer, and momentum transfer. While the diffusion law is considered an applicable model for explaining the drying phenomenon, the actual drying process cannot be accurately predicted using an analytical solution with a constant diffusion coefficient. Energy efficiency in the drying process is low due to an insufficient understanding of the mechanisms governing moisture migration from solids to air. The development of drying theory has stalled due to an unsolvable discrepancy between experimental results and analytical results. This study analyzes the effect of the binding energy of moisture in paddy rice on the diffusion coefficient. The theoretical relationship between water activity and drying rate in paddy rice was investigated, and the drying process was successfully explained by analyzing free energy transfer and transition theory. The mechanism of rice grain drying was described using a new theoretical solution for the drying process. These results provide new insights into the development of a scientific evaluation standard for assessing the efficiency of actual drying processes.

Water scarcity poses a significant challenge for people living in arid areas. Despite the effectiveness of many bioinspired surfaces in promoting vapor condensation, their water-harvesting efficiency is insufficient. This is often exacerbated by overheating, which decreases the performance in terms of the micro-droplet concentration and movement on surfaces. In this study, we used a spotted amphiphilic surface to enhance the surfaces’ water-harvesting efficiency while maintaining their heat emissivity. Through hydrophilic particle screening and hydrophobic groove modifying, the coalescence and sliding characteristics of droplets on the amphiphilic surfaces were improved. The incorporation of boron nitride (BN) nanoparticles further enhanced the surfaces’ ability to harvest energy from condensation. To evaluate the water-harvesting performance of these amphiphilic surfaces, we utilized a real-time recording water-harvesting platform to identify microscopic weight changes on the surfaces. Our findings indicated that the inclusion of glass particles in hydrophobic grooves, combined with 1.0 wt.% BN nanoparticles, enhanced the water-harvesting efficiency of the amphiphilic surfaces by more than 20%.

Chickpeas hold significant nutritional and cultural importance, being a rich source of protein, fiber, and essential vitamins and minerals. They are a staple ingredient in various cuisines worldwide. Peeling chickpeas is considered a crucial pre-consumption operation due to the undesirability of peels for some uses. This study aimed to design, test, and evaluate a small chickpea seed peeling machine. The peeling prototype was designed in accordance with the chickpeas’ measured properties; the seeds’ moisture content was determined to be 6.96% (d.b.). The prototype was examined under four different levels of drum revolving speeds (100, 200, 300, and 400 rpm), and three different numbers of brush peeling rows. The prototype was tested with rotors of four, eight, and twelve rows of brushes. The evaluation of the chickpea peeling machine encompassed several parameters, including the machine’s throughput (kg/h), energy consumption (kW), broken seeds percentage (%), unpeeled seeds percentage (%), and peeling efficiency (%). The obtained results revealed that the peeling machine throughput (kg/h) exhibited an upward trend with increases in the rotation speed of the peeling drum. Meanwhile, the throughput decreased as the number of peeling brushes installed on the roller increased. The highest recorded productivity of 71.29 kg/h was achieved under the operational condition of 400 rpm and four peeling brush rows. At the same time, the peeling efficiency increased with the increase in both of peeling drum rotational speed and number of peeling brush rows. The highest peeling efficiency (97.2%) was recorded at the rotational speed of 400 rpm and twelve peeling brush rows. On the other hand, the lowest peeling efficiency (92.85%) was recorded at the lowest drum rotational speed (100 rpm) and number of peeling brush rows (4 rows). In the optimal operational condition, the machines achieved a throughput of 71.29 kg/h, resulting in a peeling cost of 0.001 USD per kilogram. This small-scale chickpea peeling machine is a suitable selection for small and medium producers.

Solar energy has become an energy source for a wide range of uses, including in solar cookers, due to its availability, cleanliness, environmental friendliness, and sustainability. In this study, an indirect solar cooker was investigated by measuring its thermal performance using a Graphene Oxide (GO) nanofluid. Water, GO (250 ppm)-water, and GO (500 ppm)-water were used as heat transfer fluids. The experimental set-up consisted of the cooking part and a solar collector, which are the two essential elements in indirect solar cookers. The cooking part was a wooden box solar cooker, and the parabolic trough solar collector was a polished stainless steel structure. The solar cooker was assessed using the stagnation test and load test as well as energy and exergy measurements. According to the test results, the averaged F₁ was 0.1 for the base fluid water, 0.11 for GO (250 ppm)-water, and 0.13 for GO (500 ppm)-water. In addition, using a GO nanofluid instead of water in the solar cooker, the system’s thermal performance, energy, and exergy efficiency were improved. The use of the GO (250 ppm)-water and GO (500 ppm)-water nanofluids instead of water in the system improved the overall energy efficiency of the system by 3.3 and 4.2%. Moreover, using GO (500 ppm)-water allowed for achieving superior performance.

Improving the performance of the threshing process is of utmost importance in enhancing the quality of sunflower seeds and minimizing power consumption in sunflower production. In this study, we developed a modified sunflower threshing machine by incorporating two types of threshing rotors, namely the angled rasp bar rotor and the tine bar rotor, as compared to the round bar rotor. The performance of these rotors was evaluated under various rotational speeds (150, 200, 250, and 300 rpm) and concave clearances (10, 15, and 20 mm). The evaluation parameters included machine throughput, the specific energy of threshing, the percentage of damaged seeds, the percentage of unthreshed seeds, and threshing efficiency. The results indicate that the specific energy decreased with an increase in rotor speed and a decrease in concave clearance, with the tine bar rotor exhibiting the lowest values. Threshing efficiency showed an increasing trend with higher rotor speeds and reduced concave clearance. The modifications made to the rotor design resulted in an enhanced threshing efficiency, with an improvement from 96.30% to 97.93% achieved at a rotor revolving speed of 300 rpm and a concave clearance of 10 mm. Moreover, the specific energy consumption reduced from 9.65 kW·h/ton to 5.09 kW·h/ton under the same operational conditions. These findings highlight the efficacy of the novel rotor design modifications in optimizing the performance of the stationary sunflower threshing machine, leading to improved efficiency and reduced energy consumption in sunflower seed threshing operations. Given its performance characteristics, this machine exhibits potential suitability for sunflower farms of small to medium scale.