Current landscape of fecal microbiota transplantation in treating depression.

Depression, projected to be the predominant contributor to the global disease burden, is a complex condition with diverse symptoms including mood disturbances and cognitive impairments. Traditional treatments such as medication and psychotherapy often fall short, prompting the pursuit of alternative interventions. Recent research has highlighted the significant role of gut microbiota in mental health, influencing emotional and neural regulation. Fecal microbiota transplantation (FMT), the infusion of fecal matter from a healthy donor into the gut of a patient, emerges as a promising strategy to ameliorate depressive symptoms by restoring gut microbial balance. The microbial-gut-brain (MGB) axis represents a critical pathway through which to potentially rectify dysbiosis and modulate neuropsychiatric outcomes. Preclinical studies reveal that FMT can enhance neurochemicals and reduce inflammatory markers, thereby alleviating depressive behaviors. Moreover, FMT has shown promise in clinical settings, improving gastrointestinal symptoms and overall quality of life in patients with depression. The review highlights the role of the gut-brain axis in depression and the need for further research to validate the long-term safety and efficacy of FMT, identify specific therapeutic microbial strains, and develop targeted microbial modulation strategies. Advancing our understanding of FMT could revolutionize depression treatment, shifting the paradigm toward microbiome-targeting therapies.

Utilizing Geographical Distribution Statistical Data to Improve Zero-Shot Species Recognition.

Species recognition is a crucial part of understanding the abundance and distribution of various organisms and is important for biodiversity conservation and management. Traditional vision-based deep learning-driven species recognition requires large amounts of well-labeled, high-quality image data, the collection of which is challenging for rare and endangered species. In addition, recognition methods designed based on specific species have poor generalization ability and are difficult to adapt to new species recognition scenarios. To address these issues, zero-shot species recognition based on Contrastive Language-Image Pre-training (CLIP) has become a research hotspot. However, previous studies have primarily utilized visual descriptive information and taxonomic information of species to improve zero-shot recognition performance, and the use of geographic distribution characteristics of species to improve zero-shot recognition performance has not been explored. To fill this gap, we proposed a CLIP-driven zero-shot species recognition method that incorporates knowledge of the geographic distribution of species. First, we designed three prompts based on the species geographic distribution statistical data. Then, the latitude and longitude coordinate information attached to each image in the species dataset was converted into addresses, and they were integrated together to form the geographical distribution knowledge of each species. Finally, species recognition results were derived by calculating the similarity after acquiring features by the trained CLIP image encoder and text encoder. We conducted extensive experiments on multiple species datasets from the iNaturalist 2021 dataset, where the zero-shot recognition accuracies of mammals, mollusks, reptiles, amphibians, birds, and insects were 44.96%, 15.27%, 17.51%, 9.47%, 28.35%, and 7.03%, an improvement of 2.07%, 0.48%, 0.35%, 1.12%, 1.64%, and 0.61%, respectively, as compared to CLIP with default prompt. The experimental results show that the fusion of geographic distribution statistical data can effectively improve the performance of zero-shot species recognition, which provides a new way to utilize species domain knowledge.

Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost.

Intelligent monitoring of endangered and rare wildlife is important for biodiversity conservation. In practical monitoring, few animal data are available to train recognition algorithms. The system must, therefore, achieve high accuracy with limited resources. Simultaneously, zoologists expect the system to be able to discover unknown species to make significant discoveries. To date, none of the current algorithms have these abilities. Therefore, this paper proposed a KI-CLIP method. Firstly, by first introducing CLIP, a foundation deep learning model that has not yet been applied in animal fields, the powerful recognition capability with few training resources is exploited with an additional shallow network. Secondly, inspired by the single-image recognition abilities of zoologists, we incorporate easily accessible expert description texts to improve performance with few samples. Finally, a simple incremental learning module is designed to detect unknown species. We conducted extensive comparative experiments, ablation experiments, and case studies on 12 datasets containing real data. The results validate the effectiveness of KI-CLIP, which can be trained on multiple real scenarios in seconds, achieving in our study over 90% recognition accuracy with only 8 training samples, and over 97% with 16 training samples. In conclusion, KI-CLIP is suitable for practical animal monitoring.

An Improved Acoustic Diffusion Equation Model for Long-Channel Underground Spaces.

The acoustic diffusion equation model has been widely applied in various scenarios, but a larger prediction error exists when applied to underground spaces, showing a significantly lower characteristic of the sound pressure level in the later stage compared to field tests since underground spaces have a more closed acoustic environment. Therefore, we analyze the characteristics of underground spaces differentiating from aboveground spaces when applying the model and propose an improved model from the perspective of energy balance. The energy neglected in the calculation of the acoustic diffusion equation model is compensated in long channel underground spaces named "acoustic escape compensation". A simulation and two field experiments are conducted to verify the effectiveness of the proposed compensation strategy in long-channel underground spaces. The mean square error is used to evaluate the differences between the classical model and the improved model, which shows a numerical improvement of 1.3 in the underground field test. The results show that the improved model is more suitable for describing underground spaces. The proposed strategy provides an effective extension of the acoustic diffusion equation model to solve the problem of sound field prediction and management in underground spaces.

UAV path planning method for data collection of fixed-point equipment in complex forest environment.

In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach.

[Effects of Drip Irrigation Patterns and Biochar Addition on Soil Mineral Nitrogen and Microbial Regulation of Greenhouse].

Drip irrigation and biochar amendment could affect the nitrogen form and transformation. Creating a deep understanding of the interacting effects of drip irrigation patterns and biochar on soil mineral nitrogen, as well as the key functional genes and microbial community involved in nitrogen transformation is helpful for improving facility agricultural management, increasing water and nitrogen use efficiency, and reducing the nitrate accumulation and groundwater pollution caused by nitrogen leaching. Four treatments [surface drip irrigation (D), insert drip irrigation (ID, insert depth 15 cm), surface drip irrigation +10 t·hm-2 of biochar (DB), and insert drip irrigation +10 t·hm-2 of biochar (IDB)] were conducted in a solar greenhouse, and non-rhizospheric and rhizospheric soils of pepper plants were studied. There was no effect of drip irrigation patterns and biochar on ammonium-nitrogen in the non-rhizospheric and rhizospheric soils. Compared with surface drip irrigation, insert drip irrigation decreased the nitrate-nitrogen concentration in the non-rhizosphere soil (P<0.05), but biochar addition weakened the difference. Biochar addition decreased the nitrate-nitrogen concentration in the rhizosphere soil under the same drip irrigation patterns. In the D treatment, biochar significantly decreased the number of copies of AOA, AOB, and nirK genes in the non-rhizospheric soil, and AOA gene copies in the rhizospheric soil (P<0.05); however, there was an increase in the number of copies of AOB and nirK genes in the rhizospheric soil of the D and ID treatments (P<0.05). Based on the structural equation model (SEM), in the non-rhizospheric and rhizospheric soils, pH and electrical conductivity were the environmental factors with the greatest influence on the ammonium-nitrogen and nitrate concentrations, respectively, and the gene copy number of AOB was the biotic factor with the greatest influence on the nitrate-nitrogen concentration. Based on PICRUSt, the γ-Proteobacteria contributed mostly to ammonia monooxygenase gene (K10945) expression, whereas the α-Proteobacteria, especially the rhizobia members, contributed mostly to nitrite reductase gene (K00368) expression. Biochar addition regulated the bacterial community structure that participated in K10945 gene expression in the non-rhizospheric soil and K00368 gene expression in the rhizospheric soil (P<0.05). Overall, biochar addition contributed more to nitrate-nitrogen and microbial mineral nitrogen-transformation processes in the agricultural soil than did the drip irrigation patterns.

[Effects of soil wetting pattern on the soil water-thermal environment and cotton root water consumption under mulched drip irrigation].

Abstract: To explore the effects of soil wetting pattern on soil water-thermal environment and water consumption of cotton root under mulched drip irrigation, a field experiment with three drip intensities (1.69, 3.46 and 6.33 L · h(-1)), was carried out in Shihezi, Xinjiang Autonomous Region. The soil matric potential, soil temperature, cotton root distribution and water consumption were measured during the growing period of cotton. The results showed that the main factor influencing the soil temperature of cotton under plastic mulch was sunlight. There was no significant difference in the soil temperature and root water uptake under different treatments. The distribution of soil matrix suction in cotton root zone under plastic mulch was more homogeneous under ' wide and shallow' soil wetting pattern (W633). Under the 'wide and shallow' soil wetting pattern, the average difference of cotton root water consumption between inner row and outer row was 0.67 mm · d(-1), which was favorable to the cotton growing trimly at both inner and outer rows; for the 'narrow and deep' soil wetting pattern (W169), the same index was 0.88 mm · d(-1), which was unfavorable to cotton growing uniformly at both inner and outer rows. So, we should select the broad-shallow type soil wetting pattern in the design of drip irrigation under mulch.

[Pterional keyhole approach in surgical treatment of ruptured anterior circulation intracranial aneurysm: a report of 313 cases].

OBJECTIVE: To review the surgical modality with pterional keyhole approach in treatment of anterior circulation aneurysm. METHODS: Three hundred and thirteen patients with ruptured anterior circulation intracranial aneurysm treated surgically with pterional keyhole approach between January 2009 and June 2014 in Department of Neurosurgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, were included in the analysis. Complete occlusion rate of aneurysms and incidence of major complications including delayed cerebral ischemia and chronic hydrocephalus were documented. Surgical outcomes at 6-month follow up were assessed by modified Rankin Scale. RESULTS: Totally 348 aneurysms were treated with pterional keyhole approach, 326 aneurysms were completely clipped, 16 aneurysms were partly clipped, and 6 aneurysms were wrapped with gauze material. Among 313 patients, 15 patients (4.79%) suffered from delayed cerebral ischemia, and 10 patients (3.19%) suffered from hydrocephalus. At the 6-month follow up, the rate of good outcome was 66.77% (209/313). CONCLUSIONS: The pterional keyhole approach can be used to clip most of anterior circulation aneurysms, and it seems to have advantages over the traditional approaches with lower incidence of complications and similar outcomes.

[Influences of different kinds of water retentive agents on water use efficiency and root mor- phology of winter wheat].

The effects of 5 different kinds of water retentive agents at 2 application levels on yield, water use efficiency and root morphology of winter wheat were studied through field experiments. The results showed that there were significant differences in tiller number, flag leaf area, yield and water use efficiency (WUE) among the water retentive agent treatments of different varieties and application levels. Compared with the control, the yield increased by 1.3%-7.9%, and the WUE increased from 17.1 kg · hm⁻² · mm⁻¹ to 18.0-20.7 kg · hm⁻² · mm⁻¹ under these 5 different kinds of water retentive agent treatments. The influences of water retentive agents on average root diameter, total root length and total root surface of winter wheat all reached a significant level. The total root length increased by 3.7%-19.1% and 6.3%-27.3%, and the total root surface area increased by 6.5%-21.7% and 2.9%-18.5% in the 0-20 and 20-40 cm soil layers, respectively. The root morphology characteristics were significantly positively correlated with both yield and WUE of winter wheat. The compound water retentive agent of acrylamide/inorganic mineral had the most significant influence on the increase of WUE and the promotion of root growth of winter wheat.