Global synthesis of the classifications, distributions, benefits and issues of terracing

For thousands of years, humans have created different types of terraces in different sloping conditions, meant to mitigate flood risks, reduce soil erosion and conserve water. These anthropogenic landscapes can be found in tropical and subtropical rainforests, deserts, and arid and semiarid mountains across the globe. Despite the long history, the roles of and the mechanisms by which terracing improves ecosystem services (ESs) remain poorly understood. Using literature synthesis and quantitative analysis, the worldwide types, distributions, major benefits and issues of terracing are presented in this review. A key terracing indicator, defined as the ratio of different ESs under terraced and non-terraced slopes (δ), was used to quantify the role of terracing in providing ESs. Our results indicated that ESs provided by terracingwas generally positive because themean values of δ were mostly greater than one. The most prominent role of terracing was found in erosion control (11.46 ± 2.34), followed by runoff reduction (2.60 ± 1.79), biomass accumulation (1.94 ± 0.59), soil water recharge (1.20±0.23), and nutrient enhancement (1.20±0.48). Terracing, to a lesser extent, could also enhance the survival rates of plant seedlings, promote ecosystem restoration, and increase crop yields.While slopes experiencing severe human disturbance (e.g., overgrazing and deforestation) can generally become more stable after terracing, negative effects of terracing may occur in poorly-designed or poorly-managed terraces. Among the reasons are the lack of environmental legislation, changes in traditional concepts and lifestyles of local people, as well as price decreases for agricultural products. All of these can accelerate terrace abandonment and degradation. In light of these findings, possible solutions regarding socio-economic changes and techniques to improve already degraded terraces are discussed

Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images

PurposeStudying perivascular spaces (PVSs) is important for understanding the pathogenesis and pathological changes of neurological disorders. Although some methods for automated segmentation of PVSs have been proposed, most of them were based on 7T MR images that were majorly acquired in healthy young people. Notably, 7T MR imaging is rarely used in clinical practice. Herein, we propose a deep-learning-based method that enables automatic segmentation of PVSs on T2-weighted 3T MR images.MethodTwenty patients with Parkinson’s disease (age range, 42–79 years) participated in this study. Specifically, we introduced a multi-scale supervised dense nested attention network designed to segment the PVSs. This model fosters progressive interactions between high-level and low-level features. Simultaneously, it utilizes multi-scale foreground content for deep supervision, aiding in refining segmentation results at various levels.ResultOur method achieved the best segmentation results compared with the four other deep-learning-based methods, achieving a dice similarity coefficient (DSC) of 0.702. The results of the visual count of the PVSs in our model correlated extremely well with the expert scoring results on the T2-weighted images (basal ganglia: rs = 0.845, P < 0.001; rs = 0.868, P < 0.001; centrum semiovale: rs = 0.845, P < 0.001; rs = 0.823, P < 0.001 for raters 1 and 2, respectively). Experimental results show that the proposed method performs well in the segmentation of PVSs.ConclusionThe proposed method can accurately segment PVSs; it will facilitate practical clinical applications and is expected to replace the method of visual counting directly on T1-weighted images or T2-weighted images

Assessment of iris volume in glaucoma patients with type 2 diabetes mellitus by AS-OCT

AIM: To examine the change of iris volume measured by CASIA2 anterior segment optical coherence tomography (AS-OCT) in glaucoma patients with or without type 2 diabetes mellitus (T2DM) and explore if there is a correlation between hemoglobin A1c (HbA1c) level and iris volume. METHODS: In a cross-sectional study, 72 patients (115 eyes) were divided into two groups: primary open angle glaucoma (POAG) group (55 eyes) and primary angle-closure glaucoma (PACG) group (60 eyes). Patients in each group were separately classified into patients with or without T2DM. Iris volume and glycosylated HbA1c level were measured and analyzed. RESULTS: In the PACG group, diabetic patients' iris volume was significantly lower than those of non-diabetics (P=0.02), and there was a significant correlation between iris volume and HbA1c level in the PACG group (r=-0.26, P=0.04). However, diabetic POAG patients' iris volume was noticeably higher than those of non-diabetics (P=0.01), and there was a significant correlation between HbA1c level and iris volume (r=0.32, P=0.02). CONCLUSION: Diabetes mellitus impact iris volume size, as seen by increased iris volume in the POAG group and decreased iris volume in the PACG group. In addition, iris volume is significantly correlated with HbA1c level in glaucoma patients. These findings imply that T2DM may compromise iris ultrastructure in glaucoma patients

Gut microbiota orchestrates skeletal muscle development and metabolism in germ-free and SPF pigs

The gut microbiota, as a crucial symbiotic microbial community in the host, participates in regulating the host’s metabolism, immunity, and tissue development. Skeletal muscle is a key tissue for movement and energy metabolism in the body, with its development and function regulated by multiple factors; however, the molecular mechanisms by which the gut microbiota influences skeletal muscle remain unclear. This study utilized germ-free (GF) and specific pathogen-free (SPF) pig models, combined with multiple analytical approaches, to systematically investigate the effects of gut microbiota absence on skeletal muscle development, muscle fiber typing, and metabolism. The study found that skeletal muscle development in GF pigs was impaired, with significant changes in muscle fiber diameter and the proportion of type I muscle fibers, with the forelimb extensor digitorum lateralis being the most significantly affected. Metabolic analysis revealed that short-chain fatty acid (SCFA) levels in the muscles of GF pigs were reduced, while amino acid and organic acid levels were elevated, suggesting that the gut microbiota regulates muscle energy metabolism. RNA-seq analysis revealed that the expression levels of protein-coding genes (PCGs) and LncRNAs in the muscles of GF pigs were generally reduced, with LncRNAs exhibiting more pronounced dynamic changes. Differentially expressed genes were enriched in muscle development and immune pathways, with significant changes in the expression patterns of HOX and Homeobox family genes, myokines, and myosin heavy chain (MYH) subtypes. WGCNA analysis identified 16 core genes associated with muscle nutrient metabolism and nine core genes related to muscle fiber phenotypes. Cis-acting LncRNA target gene prediction identified 40 differentially expressed LncRNAs and their regulated 29 PCGs, which are primarily involved in skeletal muscle development and immune responses, suggesting that LncRNAs may influence muscle homeostasis by regulating adjacent genes. In summary, the absence of gut microbiota disrupts skeletal muscle morphogenesis, metabolic characteristics, and transcriptional regulatory networks, with LncRNAs potentially mediating the regulation of muscle-specific genes in this process. This study elucidates the interaction mechanisms between the gut microbiota and skeletal muscle, providing a theoretical foundation and data support for further exploration of the microbiota-muscle axis in pathophysiological contexts

Treatment of depressive disorders in primary care - protocol of a multiple treatment systematic review of randomized controlled trials

Background: Several systematic reviews have summarized the evidence for specific treatments of primary care patients suffering from depression. However, it is not possible to answer the question how the available treatment options compare with each other as review methods differ. We aim to systematically review and compare the available evidence for the effectiveness of pharmacological, psychological, and combined treatments for patients with depressive disorders in primary care. Methods/Design: To be included, studies have to be randomized trials comparing antidepressant medication (tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), hypericum extracts, other agents) and/or psychological therapies (e.g. interpersonal psychotherapy, cognitive therapy, behavioural therapy, short dynamically-oriented psychotherapy) with another active therapy, placebo or sham intervention, routine care or no treatment in primary care patients in the acute phase of a depressive episode. Main outcome measure is response after completion of acute phase treatment. Eligible studies will be identified from available systematic reviews, from searches in electronic databases (Medline, Embase and Central), trial registers, and citation tracking. Two reviewers will independently extract study data and assess the risk of bias using the Cochrane Collaboration's corresponding tool. Meta-analyses (random effects model, inverse variance weighting) will be performed for direct comparisons of single interventions and for groups of similar interventions (e.g. SSRIs vs. tricyclics) and defined time-windows (up to 3 months and above). If possible, a global analysis of the relative effectiveness of treatments will be estimated from all available direct and indirect evidence that is present in a network of treatments and comparisons. Discussion: Practitioners do not only want to know whether there is evidence that a specific treatment is more effective than placebo, but also how the treatment options compare to each other. Therefore, we believe that a multiple treatment systematic review of primary-care based randomized controlled trials on the most important therapies against depression is timely

Anthroposophic therapy for chronic depression: a four-year prospective cohort study

BACKGROUND: Depressive disorders are common, cause considerable disability, and do not always respond to standard therapy (psychotherapy, antidepressants). Anthroposophic treatment for depression differs from ordinary treatment in the use of artistic and physical therapies and special medication. We studied clinical outcomes of anthroposophic therapy for depression. METHODS: 97 outpatients from 42 medical practices in Germany participated in a prospective cohort study. Patients were aged 20–69 years and were referred to anthroposophic therapies (art, eurythmy movement exercises, or rhythmical massage) or started physician-provided anthroposophic therapy (counselling, medication) for depression: depressed mood, at least two of six further depressive symptoms, minimum duration six months, Center for Epidemiological Studies Depression Scale, German version (CES-D, range 0–60 points) of at least 24 points. Outcomes were CES-D (primary outcome) and SF-36 after 3, 6, 12, 18, 24, and 48 months. Data were collected from July 1998 to March 2005. RESULTS: Median number of art/eurythmy/massage sessions was 14 (interquartile range 12–22), median therapy duration was 137 (91–212) days. All outcomes improved significantly between baseline and all subsequent follow-ups. Improvements from baseline to 12 months were: CES-D from mean (standard deviation) 34.77 (8.21) to 19.55 (13.12) (p < 0.001), SF-36 Mental Component Summary from 26.11 (7.98) to 39.15 (12.08) (p < 0.001), and SF-36 Physical Component Summary from 43.78 (9.46) to 48.79 (9.00) (p < 0.001). All these improvements were maintained until last follow-up. At 12-month follow-up and later, 52%–56% of evaluable patients (35%–42% of all patients) were improved by at least 50% of baseline CES-D scores. CES-D improved similarly in patients not using antidepressants or psychotherapy during the first six study months (55% of patients). CONCLUSION: In outpatients with chronic depression, anthroposophic therapies were followed by long-term clinical improvement. Although the pre-post design of the present study does not allow for conclusions about comparative effectiveness, study findings suggest that the anthroposophic approach, with its recourse to non-verbal and artistic exercising therapies can be useful for patients motivated for such therapies

Identifying the uneven distribution of health and education services in China using open geospatial data

Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes. Health and education are critical domains of the Unites Nations’ Sustainable Development Goals (SDGs), yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies. This means that such studies lack accessibility metrics for large-scale quantitative evaluations. To address this deficiency, we evaluated the accessibility of health and education services in mainland China in 2021 using point-of-interest data, OpenStreetMap road data, land cover data, and WorldPop spatial demographic data. The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h. On the basis of the road network and land cover information, the overall average time costs of reaching hospital and school were 20 and 22 min, respectively. In terms of population coverage, 94.7% and 92.5% of the population in China has a time cost of less than 1 h in obtaining hospital and school services, respectively. Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions, with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas. Furthermore, the cumulative time cost incurred by the bottom 20% of counties (by GDP) from access to hospital and school services reached approximately 80% of the national total. Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties. The accessibility metrics proposed in this study are highly related to SDGs 3 and 4, and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes. The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions

Alumina Nano-Wires and Nano-Belts Fabricated by an Effective Chemical Etching of PAA Template

A tilted cathode was adopted to change the distribution of the electric field during the second anodization of the two-step method for fabricating nanoporous anodic alumina (NanoPAA). A variety of controllable patterns of NanoPAA templates were firstly prepared. After that, the functional nanostructures of alumina such as nanowires and nanobelts were accomplished by chemically etching the NanoPAA templates with phosphoric acid. In addition, the formation mechanisms of alumina nanopores and diverse low dimensional nanostructures influenced by the modified electric field were also discussed from the surface morphologies imaged by the field emission scanning electron microscopy technology. This chemical etching method was taken for an alternative approach for quickly fabricating low dimensional alumina nanostructures with high production and cost-effective.</jats:p

Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features

As individuals spend most of their time indoors, determining whether a mobile device is located indoors or outdoors – and identifying the specific building it is in – is essential for enabling building-level location-based services and fine-grained human activity analysis. However, existing indoor positioning techniques often rely on dedicated infrastructure or dense signal fingerprinting, limiting their scalability across diverse urban environments. To address this, we propose a lightweight, data-driven framework for building-level mobile device location recognition that integrates indoor/outdoor (I/O) classification and building matching using limited sensor data. A random forest model is trained on a structured, scene-diverse sample library to classify I/O status based on multi-sensor features. For devices identified as indoors, building identification is performed using a Bayesian inference model that incorporates prior knowledge derived from anonymous crowdsourced data, leveraging spatial heterogeneity in sensor feature distributions across candidate buildings. Experiments conducted in three Chinese cities demonstrated that I/O classification achieved over 90% accuracy, and building matching based on crowdsourced data achieved at least 70% precision using only satellite or Wi-Fi features. Our approach requires no infrastructure deployment or extensive labeled data, offering a scalable and practical solution for building-level location inference across large and heterogeneous regions