Map Clustering Research Articles

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

Genetic Diversity, Analysis of Some Agro-Morphological and Quality Traits, and Utilization of Plant Resources of Coriander (Coriandrum sativum) Supported with Cluster and Multivariate Analyses

In this study, 119 different coriander genotypes (38 different countries), including 114 genotypes and five cultivars, were undertaken to characterize the genotypes based on phenotypic, morphological, yield, and International Union for the Protection of New Varieties of Plants (UPOV) properties, along with some chemical properties. The yield components were between 1.34 and 21.49 g for thousand-grain weight, 0.02–9.58 g/plant for fruit yield, 0.01–50.78 g/plant for biological yield, and 8.48–73.36% for harvest index. Similarly, the results of this study revealed significant variations in essential oil (0.05–1.86%v/w) and fixed oil content (10.22–34.03%v/w). The main components of the essential oil were determined as linalool (3.13–45.70%v/v), p-cymene (0.10–15.77%v/v), ɣ-terpinene (0.04–13.80%v/v), while the fixed oil main acids were determined as petroselinic (24.47–87.70%v/v), palmitic (7.13–23.04%v/v), elaidic (1.55–47.44%v/v), and behenic acids (3.17–12.56%v/v). The cluster, heat map, correlation, and principal coordinate (PCA) analyses were conducted to determine the genetic diversity and relationship among the genotypes based on the examined properties. The cluster and heat map analyses showed differences in the same origin genotypes. Petroselinic acid was the major contributing factor for PCA. As a result of this study, Ames 13900 and Ames 18595 genotypes had high values for fruit yield, fixed oil content, and essential oil content.

The Business Angel, Being Both Skilled and Decent

In this review, we explore the scientific landscape regarding Business Angels (BAs), a person who decides to offer their skills to improve the community where they live, distinguishing the different ways in which it is possible to contribute. This systematic review starts with a precise definition of what a BA is in terms of goals, strategies, and intervention area, which could be in social, environmental, technological, or educational areas, differentiating a BA from other kinds of stakeholders, revealing some precise influences due to different cultures or entrepreneurial ecosystems. We are talking about a person who is authoritative not only in terms of competence, knowledge, and skills but who is also noteworthy in terms of moral stature and accountability. We used three databases, SCOPUS, WOS, and EBSCO, using VosViewer to create cluster mapping to differentiate the various areas of research on this topic. We revealed three clusters, namely Business Angel, entrepreneurship, and venture capital, that clarify different facets of the construct. BA activities are very different from conventional strategies and protocols. BAs can operate in innovative areas, bring their own technologies to improve capabilities, handle projects related to sustainability and innovation, and even address social functions and social cohesion.

Distribution and determinants of early marriage and motherhood: a multilevel and geospatial analysis of 707 districts in India

BackgroundEarly marriage and motherhood have long been prevalent in India, with 44.5% of women aged 20–24 reporting marriage before 18 in NFHS 3 (2005–2006), dropping to 26.8% in NFHS 4 (2015–2016). Early motherhood has also seen a significant decline, with first births by age 18 decreasing from 34.3% in NFHS I to 8.2% in NFHS V. Despite these improvements, significant regional disparities persist due to social, normative, and legal factors. This study investigates the spatial heterogeneity of early marriage and motherhood across Indian districts, offering a multilevel analysis that reveals critical local variations often obscured at broader levels. Understanding these patterns is crucial for targeted policy interventions and addressing the root causes of early marriage and motherhood.MethodsUtilizing data from the fifth round of National Family Health Survey, this study employs multilevel logistic regression and geospatial analysis to assess the determinants and spatial distribution of early marriage and early motherhood among ever-married women in India. The analysis incorporates individual, household, and community-level variables, complemented by spatial analysis techniques, including Empirical Bayes Bivariate Moran’s I values and LISA cluster maps, to identify regional patterns and hotspots.ResultsThis study revealed that educational attainment emerged as a critical determinant, with uneducated women significantly more likely to marry early. Socioeconomic factors, such as poverty and limited mass media exposure, also heightened the risk of early marriage and motherhood. Caste and religion were significantly associated with these events, with marginalized groups facing higher prevalence. Spatial analysis revealed significant geographic disparities, with central and eastern regions showing higher concentrations of early marriage and motherhood. District-level characteristics and the influence of neighboring districts were also significant, highlighting the importance of localized interventions.ConclusionsThe findings underscore the critical role of education, economic empowerment, and media literacy in mitigating early marriage and motherhood risks. The study calls for multi-sectoral interventions in geographical hotspots to break the cycle of early family formation and promote reproductive health. Policies enhancing educational opportunities, addressing economic disadvantages, and considering district-specific factors are essential. Comprehensive strategies are necessary to empower women, foster reproductive health, and address the multifaceted nature of early marriage and motherhood in India.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Through a comprehensive search in PubMed and Scopus databases, we reviewed 5258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps. No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis in the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3"transcranial magnetic stimulation therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Single-cell transcriptomic atlas of enteroendocrine cells along the murine gastrointestinal tract.

Enteroendocrine cells (EECs) produce over 20 gut hormones which contribute to intestinal physiology, nutrient metabolism and the regulation of food intake. The objective of this study was to generate a comprehensive transcriptomic map of mouse EECs from the stomach to the rectum. EECs were purified by flow-cytometry from the stomach, upper small intestine, lower small intestine, caecum and large intestine of NeuroD1-Cre mice, and analysed by single cell RNA sequencing. Regional datasets were analysed bioinformatically and combined into a large cluster map. Findings were validated by L-cell calcium imaging and measurements of CCK secretion in vitro. 20,006 EECs across the full gastrointestinal tract could be subdivided based on their full transcriptome into 10 major clusters, each exhibiting a different pattern of gut hormone expression. EECs from the stomach were largely distinct from those found more distally, even when expressing the same hormone. Cell clustering was also observed when performed only using genes related to GPCR cell signalling, revealing GPCRs predominating in different EEC populations. Mc4r was expressed in 55% of Cck-expressing cells in the upper small intestine, where MC4R agonism was found to stimulate CCK release in primary cultures. Many individual EECs expressed more than one hormone as well as machinery for activation by multiple nutrients, which was supported by the finding that the majority of L-cells exhibited calcium responses to multiple stimuli. This comprehensive transcriptomic map of mouse EECs reveals patterns of GPCR and hormone co-expression that should be helpful in predicting the effects of nutritional and pharmacological stimuli on EECs from different regions of the gut. The finding that MC4R agonism stimulates CCK secretion adds to our understanding of the melanocortin system.

Spatial inequality in sub-national human development index: A case study of West Bengal districts

Human Development and its regional disparity is an area of topical interest. In this paper, we consider space as an instrumental variable in understanding this disparity and explore spatial interdependence in human development across districts of West Bengal using alternate indicators of human development. This study uses both the standard United Nations Development Programme (UNDP) technique and an alternative methodology to construct a sub-national human development index. Spatial interdependence is measured using Moran's I coefficient and Local Indicators of Spatial Autocorrelation (LISA) cluster maps. Spatial regression is used to evaluate the spatial association between the Human Development Index as the dependent variable and a few independent variables. A high degree of clustering in all three dimensions of human development is observed among the southern districts of West Bengal. The pattern of cluster maps indicates the presence of both the growth pole process and the metropole-periphery process in the state. Spatial regression results indicate that the share of socially backward class population and urbanization rate are important variables affecting regional human development patterns. The findings of this study suggest policies targeted to promote social inclusion and urbanization linked with better access to economic opportunities in the lagging areas can help reduce regional imbalances.

Regime-Dependent Characteristics and Predictability of Cold-Season Precipitation Events in the St. Lawrence River Valley

Abstract The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C. Significance Statement Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.

Current trends and hotspots of etiology of auditory neuropathy in the past 30 years: A bibliometric analysis

ObjectiveTo make an overview of global research trends in the etiology of auditory neuropathy (AN) over the past 30 years using a bibliometric approach. MethodsBibliometric analyses were conducted by GraphPad Prism 9.0, Citespace 6.2.R2, and an online analysis platform to analyze and visualize publications related to etiology of AN from the Web of Science Core Collection database (WoSCC) database from 1996 to 2022. Additionally, genetic factors in human AN were analyzed. ResultsIn total, 604 original articles and reviews related to the etiology of AN from the WoSCC were included for bibliometric analysis. The results showed that annual publications and trend on etiology of AN increased linearly from 2000. Among them, the United States and China published nearly 400 records (40.32%). From the 604 records, a total of 752 keywords and 10 clustered network maps were extracted by Citespace, and ‘mutations’ was among the top 10 keywords. Analysis of genetic factors found that more than 30 genes were related to AN, and the latest burst occurred in 2022. ConclusionThe bibliometric analysis mapped the global research trends and analyzed hotspots for future. The results indicated that the annual publications increased linearly from 2000. Notably, there was a burst in genetic factors in 2022, which identified that genetic factor would remain a focus of future research.

Determining user implementation needs for pharmacist-prescribed contraception using concept mapping: A participatory, multiple stakeholder approach

BackgroundAs of October 2024, 29 states and the District of Columbia allow community pharmacists to prescribe hormonal contraceptives. Pharmacists have reported many challenges and barriers to service implementation. MethodsConcept mapping was used to gather insights from diverse stakeholders to identify resources to facilitate implementation of pharmacist-prescribed contraceptive services. Stakeholders included end-users (pharmacy managers, staff pharmacists) and decision-makers (pharmacy owners/executives). Participants' ideas for resources were collected via multi-stakeholder focus groups. Participants then rated each idea (i.e. statement) in terms of importance and feasibility (1 = not at all important/feasible, 4 = extremely important/feasible) and sorted ideas into groups based on similarity via an online survey. Sorting data were analyzed using multidimensional scaling and hierarchical cluster analysis to develop a cluster map. Descriptive statistics were used to analyze rating data to develop a go-zone map, where statements rated above average in terms of both importance and feasibility are plotted in the “go-zone” and are considered the most actionable ideas. ResultsParticipants (focus groups n = 19, survey n = 48) generated 46 unique statements which created four clusters via multidimensional scaling: 1) implementation guidance for practice setup, 2) marketing resources, 3) billing and payment resources, and 4) appointment management. The majority of the statements (13 of 15) considered the most actionable were from the implementation guidance and appointment management clusters. ConclusionsStakeholders seek guidance on implementation, marketing, billing and payment, and appointment management. To increase the ability of community pharmacies to meet the contraceptive needs of patients, resources should be dedicated to creating tools and solutions to address these identified needs.

Self-supervised learning of Vision Transformers for digital soil mapping using visual data

In arid environments, prospecting cultivable land is challenging due to harsh climatic conditions and vast, hard-to-access areas. However, the soil is often bare, with little vegetation cover, making it easy to observe from above. Hence, remote sensing can drastically reduce costs to explore these areas. For the past few years, deep learning has extended remote sensing analysis, first with Convolutional Neural Networks (CNNs), then with Vision Transformers (ViTs). The main drawback of deep learning methods is their reliance on large calibration datasets, as data collection is a cumbersome and costly task, particularly in drylands. However, recent studies demonstrate that ViTs can be trained in a self-supervised manner to take advantage of large amounts of unlabelled data to pre-train models. These backbone models can then be finetuned to learn a supervised regression model with few labelled data.In our study, we trained ViTs in a self-supervised way with a 9500 km2 satellite image of dry-lands in Saudi Arabia with a spatial resolution of 1.5 m per pixel. The resulting models were used to extract features describing the bare soil and predict soil attributes (pH H2O, pH KCl, Si composition). Using only RGB data, we can accurately predict these soil properties and achieve, for instance, an RMSE of 0.40 ± 0.03 when predicting alkaline soil pH. We also assess the effectiveness of adding additional covariates, such as elevation. The pretrained models can as well be used as visual features extractors. These features can be used to automatically generate a clustered map of an area or as input of random forests models, providing a versatile way to generate maps with limited labelled data and input variables.

Autocorrelation Spatial Based on Specific Nutritional Interventions Achievement with Stunting Cases in Toddlers at Kendari City Using Local Indicator of Spatial Autocorrelation (LISA) Method

Background:Stunting is a priority target both globally and in Indonesia. There are 10 provinces in Indonesia that are the main focus of the stunting reduction program, one of which is Southeast Sulawesi Province. Kendari City, located in Southeast Sulawesi, has experienced an increase in stunting incidence over the past three years. However, progress in reducing stunting in Kendari City has not been evenly distributed across its regions and sub-regions, with significant disparities in stunting rates between different sub-districts. Objective:To determine the spatial autocorrelation based on the achievement of specific nutritional interventions for toddlers and the incidence of stunting in Kendari City using the Local Indicator of Spatial Autocorrelation (LISA). Method:This quantitative study used the Local Indicator of Spatial Autocorrelation (LISA) method. The data on stunting incidence consisted of the number of stunting cases among toddlers in 2023 across 11 sub-districts in Kendari City. The sub-districts analyzed were Abeli, Baruga, Kadia, Kambu, Kendari, West Kendari, Mandonga, Nambo, Poasia, Puuwatu, and Wua-Wua. The study was conducted from November 2023 to May 2024 in Kendari City. A local autocorrelation test with LISA was performed to determine the spatial relationships among the sub-districts based on the research variables, with results displayed in the form of Moran's scatterplot, cluster map, and significance map. Results:The results of Moran's local bivariate test (LISA) indicated that the majority of sub-districts, particularly Kambu, exhibited significant positive autocorrelation with neighboring sub-districts and fell into the cold-spot category. This indicates that the number of specific nutritional intervention programs for toddlers and the cases of stunting in toddlers in 2023 were low in Kambu and its surrounding sub-districts, which also had similarly low values. Conclusion:There is spatial autocorrelation among the sub-districts in Kendari City. Although the cases of stunting in the Kambu sub-district are low, the achievement of intervention programs should remain optimal, as cases still exist in the area. Additionally, since Kambu has a spatial correlation with its neighboring areas, the government should target these areas for appropriate interventions to accelerate stunting reduction, particularly in Kendari City. Keywords:Spatial Autocorrelation; LISA; Specific Nutrition Interventions; Stunting Toddlers

How to optimize the CAR-T Cell therapy process? A group concept mapping analysis of preconditions for a frictionless process from a German multistakeholder perspective.

Treatment with chimeric antigen receptor T (CAR-T) cells involves a large number of interdisciplinary stakeholders and is associated with complex processes ranging from patient-specific production to follow-up care. Due to the complexity, maximum process optimization is required in order to avoid efficiency losses. This study aimed at systematically determining the preconditions for a frictionless flow of the CAR-T process by surveying the stakeholders involved. A Group Concept Mapping (GCM) analysis, a mixed-methods participatory research, was conducted. CAR-T experts from different professional backgrounds went through three steps: 1) Brainstorming relevant aspects (statements) for a frictionless process, 2) Sorting the collected statements based on their similarity, and 3) Rating the importance and feasibility of each statement. A cluster map reflecting the overarching topics was derived, and mean ratings per statement and cluster were calculated. Overall, 20 CAR-T experts participated. A total of 80 statements were collected, resulting in a map of the following 10 clusters (mean importance/feasibility): Information for patients and physicians (4.16/3.77), Supportive network (4.03/3.53), Eligibility of patients (4.41/3.63), Evidence, transparency and communication (4.01/3.33), Paperwork (4.1/2.52), Interface with pharmaceutical manufacturer (4.03/2.85), Reimbursement (4.29/2.31), Quality Management (4.17/3.18), Infrastructure of CAR-T clinics (4.1/2.93), and Patient-oriented processes (4.46/3.32). The 80 statements underlined the complex and manifold nature of the CAR-T treatment process. Our results reflect the first step in overcoming hurdles: identifying potential hurdles and required preconditions. Decision-makers and stakeholders can use the results to derive strategies and measures to further promote a frictionless process.

Abstract B089: A community-based participatory approach to developing a salon-based HPV self-collection intervention

Abstract Background: Black women in the US experience higher rates of cervical cancer mortality due to delayed diagnosis from lower screening rates. Contributing to this disparity, especially for those who are 40-64 years old, are provider bias, costs, and limited access. Recent FDA approval of HPV self-collection and current innovative research efforts for clinical and simulated home- based HPV self-collection bring promise of increasing access. However, like other innovations, equitable dissemination of HPV self-collection will be challenging. Our project seeks to distribute HPV self-collection kits in unconventional settings like hair salons. We conducted a community-based participatory study using concept mapping to gather diverse insights to design a salon-based HPV self-collection intervention. Methods: We employed GroupWisdom and REDCap online platforms to engage with our participants. Participants were members of the Black Community Advisory Council of Tucson (Black CACTus). Concept mapping steps involved: 1) Brainstorming statements from a prompt, 2) Sorting statements into piles according to meaning by each participant, 3) Rating the importance and feasibility of statements, 4) Reviewing the cluster map, and 5) Reviewing a draft/proposed intervention. Results: Black CACTus members (n=10) included hairstylists (n=3), salon clients (n=4), and healthcare providers (n=3). All participants identified as Black women, with two also identifying as Latina and White. Participants were 23-53 years old. Brainstorming yielded 39 statements. A six-cluster map was generated using the sorting data. Cost-related factors like "free" or "covered by insurance," as well as convenience, were rated as highly important by all participants. Development of clear communication and education about screening benefits, test results, and timely delivery of results were regarded as the most feasible. Conclusions: To equitably increase cervical cancer screening we must look beyond traditional clinical settings to trusted community locales like hair salons. Including the voice of the community and cultural and contextual factors is vital when tailoring cancer screening initiatives, particularly for Black women. Our study used concept mapping, in a collaborative, iterative process with vested community members to identify intervention elements and implementation strategies that have the potential to increase access to and uptake of cervical cancer screening among Black women. Citation Format: Kelly N.B. Palmer, Namoonga M. Mantina, Adebola Adegboyega, Itunu O. Sokale, Kathleen Pryor, Azaria Suero-Davis, Trevon Brooks, Jennifer Hatcher. A community-based participatory approach to developing a salon-based HPV self-collection intervention [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B089.

Evolutionary Trends in Carbon Market Risk Research

The carbon emissions trading market is an essential tool for addressing climate change. The carbon emissions trading market has a relatively short history, and the research and management of risks in this market require further development. This paper takes as its research object 1272 pieces of English literature studies published by international scholars and featured on the Web of Science between 2002 and 2024. It uses CiteSpace software to categorize changes in the trends related to carbon market risk research based on time, space, and keyword clustering mapping. The results reveal the following: (1) In terms of the timeline, the risk evolution of the international carbon market is divided into an embryonic period (2002–2007), a developmental period (2008–2018), and a prosperous period (2019–2024); (2) from the perspective of spatial distribution, carbon market risk research institutions are multipolar, with China, the United States, and the United Kingdom, among other countries, issuing more studies on the topic; these studies mainly emerge from universities and research institutions; and (3) in terms of research hotspots, they revolve around four disciplinary issues, namely, primary research related to carbon market risk, carbon market risk categories, carbon market risk measurement, and response programs.

A conserved cell-type gradient across the human mediodorsal and paraventricular thalamus.

The mediodorsal thalamus (MD) and adjacent midline nuclei are important for cognition and mental illness, but their cellular composition is not well defined. Using single-nucleus and spatial transcriptomics, we identified a conserved excitatory neuron gradient, with distinct spatial mapping of individual clusters. One end of the gradient was expanded in human MD compared to mice, which may be related to the expansion of granular prefrontal cortex in hominids. Moreover, neurons preferentially mapping onto the parvocellular division MD were associated with genetic risk for schizophrenia and bipolar disorder. Midbrain-derived inhibitory interneurons were enriched in human MD and implicated in genetic risk for major depressive disorder.

Multi Autonomous Underwater Vehicle (AUV) Distributed Collaborative Search Method Based on a Fuzzy Clustering Map and Policy Iteration

Collaborative search with multiple Autonomous Underwater Vehicles (AUVs) significantly enhances search efficiency compared to the use of a single AUV, facilitating the rapid completion of extensive search tasks. However, challenges arise in underwater environments characterized by weak communication and dynamic complexities. In large marine areas, the limited endurance of a single AUV makes it impossible to cover the entire area, necessitating a collaborative approach using multiple AUVs. Addressing the limited prior information available in uncertain marine environments, this paper proposes a map-construction method using fuzzy clustering based on regional importance. Furthermore, a collaborative search method for large marine areas has been designed using a policy-iteration-based reinforcement learning algorithm. Through continuous sensing and interaction during the marine search process, multiple AUVs constantly update the map of regional importance and iteratively optimize the collaborative search strategy to achieve higher search gains. Simulation results confirm the effective utilization of limited information in uncertain environments and demonstrate enhanced search gains in collaborative scenarios.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Through a comprehensive search in PubMed and Scopus databases, we reviewed 5,258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of consistent antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation (TMS) therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps. No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis across the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3" TMS therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

A Review of Research in the Field of Low Carbon Logistics based on the Cite-Space Tool

In order to gain a deeper understanding of the research dynamics and future trends in the field of low carbon logistics at home and abroad, Cite-Space visual analysis tool is used to construct a knowledge map to reveal the key information on the current research status, hot topic research, and development frontiers in the field of low carbon logistics.Through co-author mapping, keyword co-occurrence mapping and keyword clustering mapping, low-carbon logistics can be analyzed in multiple dimensions, and the literature can be classified into macro, meso and micro to sort out and summarize the policies, regionalization and technologies in the field of low-carbon logistics, so as to provide a basis for future research.

Adult perceptions of mental health access barriers facing youth in rural Washington State: A group concept mapping study.

To explore adult community members' perspectives concerning barriers to mental health care that confront rural-dwelling youth. Group concept mapping, a participatory community-engaged research method, was used. Adult community members brainstormed and sorted statements describing barriers rural youth encounter in accessing mental health services. Point and cluster maps were created to visualize conceptual similarities between statements. Statements were rated according to their commonness and importance (1: low commonness/importance, 5: high commonness/importance). Thirty-five adults sorted and/or rated 71 barriers facing rural youth in accessing mental health services. Seven conceptual clusters were identified: system-level barriers, knowledge and communication, youth concerns, parent/guardian concerns, parent/guardian barriers, costs and convenience, and school-level barriers. Within youth concerns, community members also identified a subcluster focused on stigma. Common and important statements related to limited after-school programs and community mental health support initiatives. Group concept mapping methodology provides structure for conceptualizing challenges facing rural youth in accessing mental health services. Policies should be informed by rural community concerns and priorities. After-school and support programs may align with mental health needs identified by rural communities.