OBJECTIVE: This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder).
METHODS: The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance.
RESULTS: We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.
CONCLUSIONS: These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.

OBJECTIVE: This research was conducted in order to assess the 1-week aid needs determined by the health professional who voluntarily served in the WhatsApp communication network during the earthquakes in Kahramanmaraş, Turkey.
METHODS: This descriptive retrospective epidemiological study focuses on the 1-week messaging activities of the WhatsApp group created by volunteers after the earthquake in Turkey. During the 7-day period included in the research, a total of 5813 messages were sent. 3472 of these messages were not included in the research for various reasons, and a total of 2341 messages were used for the research.
RESULTS: In all 7 days, it was seen that most of the messages were sent via text message and that voice messages were very few. When the distribution of the needs demanded by the earthquake victims for 7 days was examined, it was seen that there was a significant increase in the needs on the fourth day after the earthquake, and that the highest increase was the need for tents.
CONCLUSIONS: While the demands for rescue services increased in the first 2 days, it was determined that the demands for water especially, dry food, and tents increased from the third day onwards. It is suggested that a professional online infrastructure system should be created to enable the transfer of instant scene and need information that can be activated in such disasters.

Addiction medicine is a dynamic field that encompasses clinical practice and research in the context of societal, economic, and cultural factors at the local, national, regional, and global levels. This field has evolved profoundly during the past decades in terms of scopes and activities with the contribution of addiction medicine scientists and professionals globally. The dynamic nature of drug addiction at the global level has resulted in a crucial need for developing an international collaborative network of addiction societies, treatment programs and experts to monitor emerging national, regional, and global concerns. This protocol paper presents methodological details of running longitudinal surveys at national, regional, and global levels through the Global Expert Network of the International Society of Addiction Medicine (ISAM-GEN). The initial formation of the network with a recruitment phase and a round of snowball sampling provided 354 experts from 78 countries across the globe. In addition, 43 national/regional addiction societies/associations are also included in the database. The surveys will be developed by global experts in addiction medicine on treatment services, service coverage, co-occurring disorders, treatment standards and barriers, emerging addictions and/or dynamic changes in treatment needs worldwide. Survey participants in categories of (1) addiction societies/associations, (2) addiction treatment programs, (3) addiction experts/clinicians and (4) related stakeholders will respond to these global longitudinal surveys. The results will be analyzed and cross-examined with available data and peer-reviewed for publication.

BACKGROUND: Lichens represent not only the mutualism of fungal and photosynthetic partners but also are composed of microbial consortium harboring diverse fungi known as endolichenic fungi. While endolichenic fungi are known to exert a remarkable influence on lichen ecology through their crucial roles in nutrient cycling, bioprospecting and biodiversity, the enigmatic community structures of these fungal inhabitants remain shrouded in mystery, awaiting further exploration and discovery. To address knowledge gap, we conducted metabarcoding on two lichens using 18S gene amplification, Dirinara applanta and Parmotrema tinctorum, and compared their microbial communities to those found in the pine bark to which the lichens were attached. Our hypothesis was that the endolichenic communities would exhibit distinct diversity patterns, community structures, network structures, and specialist composition compared to the surrounding epiphytic community.
RESULTS: Our investigation has shed light on the clear demarcation between the endolichenic and epiphytic fungal communities, as they exhibit markedly different characteristics that set them apart from each other. This research demonstrated that the endolichenic communities are less diverse as compared to the epiphytic communities. Through community similarity analysis, we observed that two endolichenic communities are more similar to each other in terms of community composition than with the adjacent epiphytic communities. Moreover, we unveiled a striking contrast in the network structures between the endolichenic and epiphytic communities, as the former displayed a more modular and less nested features that is evocative of a potent host-filtration mechanism.
CONCLUSIONS: Through our investigation, we have discovered that lichens harbor less intricate and interconnected fungal communities compared to the neighboring epiphytic environment. These observations provide valuable insights into the metagenomic architecture of lichens and offer a tantalizing glimpse into the unique mycobiome.

Building networks is an essential part of health promotion. However, network analysis remains relatively unexplored in this field. This study introduces a new technique that maps thematic agendas and geographical locations of health promotion actors.
This case study used elements of quantitative and qualitative methods to analyse network data. We used empirical data from two networks in Bavaria, a federal state of Germany.
We identified a total of 55 actors in the first network and 64 actors in the second. We categorized the thematic agenda of actors according to their main field of work: \"healthy childhood development,\" \"healthy middle age phase,\" \"healthy ageing,\" \"health equity in all phases of life.\" One network showed a significant surplus of actors that focus on \"healthy ageing.\" We combined and analysed data from both networks collectively. Two districts with no health promotion actors within their geographical borders were identified. To put geographical gaps into context, data about deprivation and age was included.
Results identified geographical areas with high need for support from health promotion actors. Through comparison of our results with existing literature, we derived potential network strategies for further successful networking. This study adds a new perspective to characterize health promotion networks by mapping them thematically and geographically. The concept can be used to give health promotion organisations relevant insight into network structures. This can improve decision-making processes concerning partnership strategy and finally lead to a positive health impact. Hence, our findings encourage further development of this technique and other networking methods in the field of health equity and health promotion.

Correlating aroma expression with volatile compounds has long been an ambition in researches of flavor chemistry. To propose a reliable methodology to depict wine aroma, 76 oak barrel-aged dry red wines were investigated through the combination of machine learning algorithm and multivariate analysis. Aromatic characteristic was evaluated by quantitative descriptive analysis (QDA), while non- or oak derived volatiles were detected by HS-SPME-GC-MS and targeted SPE-GC-QqQ-MS/MS, respectively. Results showed that variable importance for projection values (VIPs) from partial least-squares regression (PLSR) and mean decrease accuracy (MDA) from random forest were efficient parameters for feature selection. The correlating accuracy of the optimal PLSR model to predict intensities of different aroma characteristics through selected volatile compounds could achieve 0.754 to 0.943, representing potential application to manage wine aroma by chemical assay in winemaking. From the perspective of mathematical modeling in the real wine matrix, the network analysis between aroma characteristics and key volatile compounds indicated that the expression of oak aroma was not only directly contributed by volatiles derived from oak wood, but also influenced by ethyl esters, including ethyl acetate, ethyl butanoate, ethyl hexanoate, ethyl decanoate, and ethyl nonanoate.

Humans and water are closely connected in large river basins and form social-ecological systems (SESs). However, cross-scale effect in SESs make it difficult to identify the key forces driving human-water connections at the community scale when ignoring the landscape context. Focusing on the incongruous human-water relationships in the lower reaches of the Yellow River, we built local resident perception-based networks linking the agricultural subsystem, environmental subsystem, and cultural subsystem by distributing farmer household questionnaires and extracted 13 indicators from 7 kinds of network metrics to indicate human-water connections. We applied analysis of variance (ANOVA), random forest (RF) and multilevel linear model (MLM) methods to identify the driving forces of perception-based human-water connections among 20 factors at both the community and landscape scales. The results showed that the perception-based network indicators were mainly directly influenced by community-level driving factors, especially the accessibility of information, such as the frequency of going out, the frequency of accessing the Yellow River channel, and the information source for the national policy on the Yellow River. The influences of community-level driving factors on network indicators were affected by landscape-level driving factors, e.g., the nighttime light, population density, gross domestic product and proportion of artificial land, thus indicating indirect influences from the landscape context. These analyses and findings can enrich the methods by which social, ecological and hydrological elements are structurally linked in sociohydrologic research and highlight the cross-scale effect of the landscape context on human-water systems at the community level.

Previously, urban planning approaches have tended to convert local agglomeration into network connections to advance urban development. However, is this successful experience learned from developed counties appropriate for developing countries? Scholars hold different opinions on this debate. To answer this question, we need to examine the effects of urban agglomeration in developing countries with a quantitative method. In this paper, we introduced a method of examining network connections from a geospatial perspective to explore the practice and spatial consequences of regional integration using a new concept of \"coupling distance\" based on metal valence bond theory. Then we applied this method to conduct an empirical case study of the urban agglomeration in the middle reaches of the Yangtze River region in China. We found that: (1) the real integration scale of the investigated urban areas was less than one-fourth the planned area, as most of interactions between cities are local, although we see the positive facilitation of urban networks on cross-provincial integration. (2) In terms of spatial consequences, the study area demonstrated phenomena of \"agglomeration shadows\", \"enclaves\" and \"inverse integration\". Specifically, these \"agglomeration shadows\" were all in their province\'s geometric centers, which seemed to have suffered a \"central position curse\". (3) Both \"enclaves\" and \"inverse integration\" call for a readjustment of government-led regional integration planning. Differently, the former has a positive attitude towards integration while the latter holds the opposite attitude. This study hopes to provide operationalizing methods and guidelines for planners and decision makers in the field of regional integration planning.

The ability of disaster response, preparedness, and mitigation efforts to assess the loss of physical accessibility to health facilities and to identify impacted populations is key in reducing the humanitarian consequences of disasters. Recent studies use either network- or raster-based approaches to measure accessibility in respect to travel time. Our analysis compares a raster- and a network- based approach that both build on open data with respect to their ability to assess the loss of accessibility due to a severe flood event. As our analysis uses open access data, the approach should be transferable to other flood-prone sites to support decision-makers in the preparation of disaster mitigation and preparedness plans.
Our study is based on the flood events following Cyclone Idai in Mozambique in 2019 and uses both raster- and network-based approaches to compare accessibility to health sites under normal conditions to the aftermath of the cyclone to assess the loss of accessibility. Part of the assessment is a modified centrality indicator, which identifies the specific use of the road network for the population to reach health facilities.
Results for the raster- and the network-based approaches differed by about 300,000 inhabitants (~ 800,000 to ~ 500,000) losing accessibility to healthcare sites. The discrepancy was related to the incomplete mapping of road networks and affected the network-based approach to a higher degree. The modified centrality indicator allowed us to identify road segments that were most likely to suffer from flooding and to highlight potential backup roads in disaster settings.
The different results obtained between the raster- and network-based methods indicate the importance of data quality assessments in addition to accessibility assessments as well as the importance of fostering mapping campaigns in large parts of the Global South. Data quality is therefore a key parameter when deciding which method is best suited for local conditions. Another important aspect is the required spatial resolution of the results. Identification of critical segments of the road network provides essential information to prepare for potential disasters.

Background: Dry Needling (DN) has been demonstrated to be effective in improving sensorimotor function and spasticity in patients with chronic stroke. Electroencephalogram (EEG) has been used to analyze if DN has effects on the central nervous system of patients with stroke. There are no studies on how DN works in patients with chronic stroke based on EEG analysis using complex networks. Objective: The aim of this study was to assess how DN works when it is applied in a patient with stroke, using the graph theory. Methods: One session of DN was applied to the spastic brachialis muscle of a 62-year-old man with right hemiplegia after stroke. EEG was used to analyze the effects of DN following metrics that measure the topological configuration: 1) network density, 2) clustering coefficient, 3) average shortest path length, 4) betweenness centrality, and 5) small-worldness. Measurements were taken before and during DN. Results: An improvement of the brain activity was observed in this patient with stroke after the application of DN, which led to variations of local parameters of the brain network in the delta, theta and alpha bands, and inclined towards those of the healthy control bands. Conclusions: This case study showed the positive effects of DN on brain network of a patient with chronic stroke.