Significant Clusters Research Articles

Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.

Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. The development and independent validation datasets included 62,154 patients from two EDs and 73,453 patients from one ED, respectively. Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. These machine learning models were also compared against an existing clinical risk score. To support clinical actionability, clinician investigators conducted manual chart reviews of the cases identified by the model. Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. The model, which combined DICE and LR, boosted predictive performance while providing well-defined features. The model was relatively robust to sensitivity analyses regarding performance across age, race, and by varying predictor availability but less robust across diagnostic groups. Clinician examination demonstrated discrete model performance characteristics within clinical subtypes of RVA. This machine learning model demonstrated a strong predictive performance for 72- RVA. Despite the limited clinical actionability potentially due to model complexity, the rarity of the outcome, and variable relevance, the clinical examination offered guidance on further variable inclusion for enhanced predictive accuracy and actionability.

EFFECT OF SUBSTRATE AND WALKING SURFACES ON CENTRAL METATARSAL FOOT PAD WEIGHT LOADING IN MAGELLANIC PENGUINS (SPHENISCUS MAGELLANICUS) WITH AND WITHOUT PODODERMATITIS: AN EX VIVO STUDY.

Pododermatitis is common in penguins kept under human care. Substrate optimization plays an important role in prevention and treatment; however, there is limited information on biomechanical properties of commonly used substrates on penguin feet. The objectives were to test the ability of different substrates to decrease weight loading on the central metatarsal pad of penguin feet in an ex vivo model using feet with and without bumblefoot harvested from two Magellanic penguin (Spheniscus magellanicus) cadavers. Penguin feet were attached to a digital force gauge mounted onto a stand for compression testing at 2.5 and 5 kg. Forces at the central metatarsal pad were measured in triplicate using small force sensors. Tested substrates included five granular surfaces (sand, wet sand, pea gravel, wet pea gravel, and crushed ice), three compliant surfaces (short-leaf Astroturf, long-leaf Astroturf, and neoprene), and three firm surfaces (tile, rubber drainage mat, and 3M Safety-Walk Wet Area Matting). Data were analyzed using linear mixed models. There were multifaceted effects of applied pressures, substrate surfaces, and pododermatitis on central metatarsal measured pressures. In general, doubling compression forces resulted in higher measured pressures in all firm and compliant surfaces but not in granular surfaces. Firm surfaces were associated with higher recorded plantar pressures at 2.5 kg, but different significance groupings emerged at 5 kg with a high-, medium-, and low-pressure cluster of surfaces. Pododermatitis lesions resulted in significant alterations in statistical significance clustering among substrate surfaces and unique substrate behaviors. The results of this study could help in making recommendations pertaining to foot health for penguin exhibits.

Assessment of the influence of levees along Yangtze River on Oncomelania hupensis, the intermediate host of Schistosoma japonicum

BackgroundOncomelania hupensis is the exclusive intermediate host of Schistosoma japonicum in China. Snail control is an essential component of schistosomiasis elimination programme. With 70 years of continuous efforts, the range of O. hupensis had reduced significantly, but slowed down in last decades. A large number of levees against flooding were constructed along Yangtze River and its affiliated lakes in the middle and lower reaches, which influenced the hydrology and ecology in the alluvial plains. The purpose of this study was to assess the impact of levees on the distribution of O. hupensis in the middle and lower reaches of the Yangtze River.MethodsThe snail habitats were digitalised by hand-held GPS system. The years for discovery and elimination of snail habitats were extracted from historical records. The accumulated snail-infested range for each habitat was calculated on the basis of annual reports. The current distribution of O. hupensis was determined by systematic and environmental sampling. The geographical distribution of levees was obtained from satellite imagery. To assess the impact of levees, the data pertaining to O. hupensis were divided into two parts: inside and outside the Yangtze River. Joinpoint regression was utilised to divide the study time span and further characterise the regression in each period. The 5-year-period moving averages of eliminated area infested by snails were calculated for the habitats inside and outside Yangtze River. The moving routes of corresponding geographical median centres were simulated in ArcGIS. Hotspot analysis was used to determine the areas with statistical significance clustering of O. hupensis density.ResultsThree periods were identified according to Joinpoint regression both inside and outside Yangtze River. The area infested by O. hupensis increased in the first two periods. It decreased rapidly outside Yangtze River year over year after 1970, while that inside the Yangtze River did not change significantly. Furthermore, the latter was significantly higher than the former. It was observed that the present density of O. hupensis inside Yangtze River was lower than outside the Yangtze River. The median centre for eliminated ranges inside Yangtze River wavered between the east (lower reach) and the west (middle reach). In contrast, the median centre for eliminated ranges continuously moved from the east to the west.ConclusionsOur findings indicated that the levees had a considerable negative impact on the distribution of O. hupensis outside Yangtze River. Some hotspots observed in the irrigation areas need a sluice system at the inlet of branch for snail control. The major distribution of O. hupensis located in Hubei might be caused by severe waterlogging. The intensive surveillance should be implemented there. The biggest two freshwater lakes, the major endemic regions historically, were identified as cold spots. The long-term impact of Three Gorges Dam on the distribution of O. hupensis in the lakes should be monitored and evaluated.Graphical

Historical Hot Spots of Dengue and Zika Viruses to Guide Targeted Vector Control in San Juan, Puerto Rico (2010-2022).

Dengue viruses (DENV) continue to cause large outbreaks in tropical countries, while chikungunya and Zika (ZIKV) viruses have added complexity to Aedes-borne disease prevention and control efforts. Because these viruses are transmitted by the same vectors in urban areas, it is useful to understand if sequential outbreaks caused by these viruses have commonalities, such as similar seasonal and spatial patterns, that would help anticipate and perhaps prevent future outbreaks. We explored and analyzed the heterogeneity of confirmed cases of DENV (2010-2014 and 2015-2022) and ZIKV (2016-2017) during outbreaks in the San Juan metropolitan area of Puerto Rico to explore their degree of overlap and prioritize areas for Aedes aegypti control. Deidentified, georeferenced case data were aggregated into grid cells (500 × 500 m) within a geographical information system of the study area and analyzed to calculate the degree of overlap between outbreaks. Spatial autocorrelations using local indicators of spatial associations were conducted to identify significant disease case hot spots and correlations between outbreaks. We found that 75% of cases during the three transmission periods were concentrated in 25% of the total number of grid cells covering the study area. We also found significant clustering of cases during each outbreak, enabling identification of consistent disease hot spots. Our results showed 85% spatial overlap between cases of ZIKV in 2015-2017 and DENV in 2010-2014 and 97% overlap between DENV cases in 2010-2014 and 2015-2022. These results reveal urban areas at greater risk of future arbovirus outbreaks that should be prioritized for vector control.

Accounting for Greenhouse Gas Emissions in the Agricultural System of China Based on the Life Cycle Assessment Method

Agricultural systems contribute nearly one-third of global anthropogenic GHG emissions and are an important source of GHGs globally. The clarification of the GHG emission pattern from agriculture is of paramount importance in the establishment of an agricultural emission reduction mechanism and the realization of China’s dual-carbon target. Based on the life cycle assessment method (LCA), this paper comprehensively quantifies the greenhouse gas emissions from the agricultural system in China, encompassing rice, wheat, and corn cultivation as well as animal husbandry including cows, horses, donkeys, mules, camels, pigs, and sheep. The analysis covers the period 2000–2020 and examines the spatial distribution, temporal trends, and structural changes in the greenhouse gas emissions within China’s agriculture sector. The main results are as follows: (1) from 2000 to 2020, China witnessed a consistent upward trajectory in its total agricultural GHG emissions, exhibiting an average annual growth rate of 0.73%. Notably, methane (CH4) emissions emerged as the largest contributor, displaying an overall fluctuating pattern. Carbon dioxide (CO2) emissions demonstrated intermittent increases with a noteworthy annual growth rate of 3%, signifying the most rapid expansion within this context. Conversely, nitrous oxide (N2O) emissions experienced decline over the specified period. (2) GHG emissions from cultivation demonstrate an upward trajectory, primarily driven by the CH4 emissions originating from rice cultivation and CO2 resulting from straw incineration. Conversely, GHG emissions stemming from animal husbandry declined, with the primary source being CH4 emissions arising from animal enteric fermentation. Agricultural N2O emissions predominantly arise due to manure management and nitrogen fertilizer application. (3) Agricultural greenhouse gas emissions exhibit significant variations in spatial distribution, gradually concentrating towards the North China Plain, the middle and lower reaches of the Yangtze River, and the northeast. Specifically, agricultural CH4 emissions are progressively concentrated in China’s pivotal rice-growing regions, encompassing the middle and lower reaches of the Yangtze River Plain, as well as livestock breeding areas like Inner Mongolia. Agricultural CO2 emissions primarily concentrate in dryland crop production zones such as North China and Northeast China. Meanwhile, Agricultural N2O emissions predominantly occur in Inner Mongolia and the North China Plain. China’s agricultural greenhouse gas emissions in 2020 show a significant spatial clustering effect, with hotspots primarily concentrated in Shandong, Anhui, Henan, and other regions and cold spots focused in the western and southern areas. The emission patterns of agricultural GHGs are closely intertwined with farming practices, regional development levels, and national policy; hence, tailored measures for emission reduction should be formulated based on specific crop types, livestock categories, agricultural production activities, and regional development characteristics.

Spatio-temporal distribution and evolution of the Tujia traditional settlements in China.

The rapid modernization in China has aggravated the reduction of the traditional settlements and aroused concern about the protection and research. This study aims to examine the spatial-temporal variations Tujia traditional settlements in China and to delineate the driving mechanism of the settlement distribution. Previous studies have focused on the characteristics of settlements in provincial or smaller areas, providing lacked information regarding spatial distribution heterogeneity of Tujia traditional settlements in China. In this study, the spatial heterogeneity and influence factors of the distribution of traditional Tujia settlements were examined using the GIS platform and statistical methods. The results reveal that the spatial distribution of settlements exhibits clustering with the pattern of "scattered distribution in a large region, while concentrated in small areas". The settlements were generally built in low hills, gentle slopes, sunny slopes and low-relief terrain areas, with elevation, relief degree of land surface (RDLS), slope and aspect were the key factors affecting the distribution. In Song, Yuan, Ming and Qing dynasties, settlements showed significant clustering all through, though the location and number of clustering center kept changing. In this process, the history of the Tujia chieftain and the transportation and marketing lines of Sichuan salt had a profound influence on the historical evolution of the settlement.

Identifying and Marketing the Spiritual Tourism Destination: A Case Study on Gokarna, Karnataka

Purpose : India has an elusive history as a spiritual destination. The study investigates if a beach destination exists to be a contested site for ‘pilgrim tourists’ and spiritual tourists or could it be the destination which facilitates co-existence. Methodology: The research is undertaken at Gokarna, a significant religious destination in the coastal Karnataka. The study privileges quantitative techniques of descriptive statistics and Cluster Analysis to recognize statistically evident and discriminant visitor types backed by qualitative analysis and interpretation. Findings: The study presents statistically significant clusters of Religious Adherents, Health/Lifestyle seekers, New Age Spiritually Inclined Tourists and Recreational Tourists. The study establishes the elusive connect the new age tourists share with the well-established religious destinations. Implications : The study convincingly recommends that Gokarna could be promoted as a spiritual destination while being acclaimed as beach tourism destination for the new age tourists who seek philosophical and life experiences. Study assists the destination to retain its core offerings and the policy makers to devise well informed development plan to sustainably manage the place and its aura.

Spatiotemporal Patterns and Risk Factors for Newcastle Disease Virus among Chickens in a Tanzania Live Bird Market

Village poultry plays a vital role in providing essential nutrition and income for rural communities in Africa. In this context, poultry are often traded through live bird markets (LBMs), which serve as central trading hubs where producers connect with traders and consumers, facilitating the flow of poultry products along the value chain. While they serve as important trading hubs, these markets create an environment where avian pathogens, like Newcastle disease virus (NDV) and avian influenza virus, can easily emerge and spread. Improving our understanding of the epidemiology of NDV in LBMs is important for assessing disease risks and identifying factors that contribute to its persistence. Local chickens at the Mawenzi LBM in Morogoro municipality were surveyed for NDV presence, its temporal and spatial distribution, and risk factors for NDV infection. Twenty-three percent of 659 local chickens sampled over a 1-year period were positive for NDV based on PCR. Increased odds of NDV infection were identified in chickens that had been in the market for 2 or more days prior to sampling and during the period extending from August through October. Four significant spatiotemporal clusters of NDV-positive chickens encompassing 13 villages were detected between August and October 2020, illustrating geographic hotspots of infection when NDV was most prevalent. Similar to the other LBMs, this market had enclosures with high densities of birds of mixed species, limited biosecurity, and the presence of birds with observable illness. Bird traders who source the chickens from the villages, described long transit times in mixed enclosures with limited sanitation practices and without consideration of sick birds or vaccination status prior to arriving at the LBM. This study highlights the need to invest in improvements to infrastructure and biosecurity for LBMs as well as training opportunities for increasing traders’ knowledge on hygiene and sanitation practices, animal welfare, and poultry biosecurity measures.

Green Transition Assessment, Spatial Correlation, and Obstacles Identification: Evidence from Urban Governance Data of 288 Cities in China

The green transition of China’s cities is crucial for ecology civilization realization. Based on the driver–pressure–state–impact–response (DPSIR) framework, an integrated technique for order preference by similarity to ideal solution (TOPSIS) model with entropy weight, this study achieved the comprehensive assessment of the green transition of 288 province-level municipalities and prefecture-level cities in China over 18 years from 2002 to 2019, in addition to the spatial correlations and obstacles analysis. The results indicate that major cities in China have a more significant green transition value, and the eastern region is developing fast, while the northeast region is relatively slow. There was heterogeneous spatial distribution for green transition, because of the disequilibrium sustainable development of 288 cities. Green transition has a significantly positive spatial autocorrelation in the cities of China, the high–high significant clusters greatly increased, and the main locations changed from the northeast to southeast of China. Frequent obstacles were also found, including road infrastructure construction, water resources, and the green coverage of urban built-up areas. Based on these results, several policy implications were put forward, including the optimization of environmental laws and regulations, the development of green transportation infrastructure, resource conservation and the circular economy, the establishment of a green financial system, and increasing the linkage for the green transition of different cities.

The effect of public health interventions on COVID-19 incidence in Queensland, Australia: a spatial cluster analysis

Background Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements. Methods Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases. Results Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001. Conclusions Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

A case study on the early stage of Pinus nigra invasion and its impact on species composition and pattern in Pannonic sand grassland

Alien woody species are successful invaders, frequently used for afforestation in regions like semi-arid lands. Shrubs and trees create important microhabitats in arid areas. Understorey vegetation in these habitats has unique species composition and coexistence. However, the impact of solitary woody species on understorey vegetation is less understood. This study evaluated the effect of native (Juniperus communis) and invasive solitary conifers (Pinus nigra) on surrounding vegetation, where individuals were relatively isolated (referred to as solitary conifers). The field study conducted in Pannonic dry sand grassland in 2018 recorded plant and lichen species presence around six selected solitary conifers. Composition and pattern of understorey vegetation were assessed using 26 m belt transects with 520 units of 5 cm × 5 cm contiguous microquadrats. Compositional diversity (CD) and the number of realized species combinations (NRC) were calculated from the circular transects. Results showed native conifer J. communis created more complex, organized microhabitats compared to alien P. nigra. CD and NRC values were significantly higher under native conifers than invasive ones (p = 0.045 and p = 0.026, respectively). Native species also had more species with a homogeneous pattern than the alien species. Alien conifers negatively affected understorey vegetation composition and pattern: some species exhibited significant gaps and clusters of occurrences along the transects under P. nigra. Based on our study, the removal of invasive woody species is necessary to sustain habitat diversity.

Geospatial and multilevel clustering of zero-dose children in Kikwit, Democratic Republic of the Congo in 2022.

Zero-dose children remain highly vulnerable to vaccine-preventable diseases and can sustain transmission even in highly vaccinated populations. The WHO Immunization Agenda 2030 has prioritised reaching out to these children. We assessed the spatial distribution of zero-dose children together with the associated risk factors in a provincial capital in the Democratic Republic of Congo. A cross sectional survey was conducted in the city of Kikwit between September 28 and October 14, 2022. Data were collected both at household and health area level. QGIS and SATscan were used to describe and identify hotspots among zero-dose children, and a mixed effect logistic regression model was used to identify risk factors. Overall, 1,863 children aged 12-23 months were enrolled. Kikwit city had a 16.3% zero-dose prevalence, with significant variation between and within health zones. Two hotspots were identified through geospatial analysis, each spanning multiple health areas. Multilevel analysis revealed significant clustering at health area level and found six associated risk factors. These include the absence of home visits by community health workers (aOR = 1.90), living more than a kilometre from a health centre (aOR = 1.95), the mother's lack of tetanus vaccination (aOR = 3.16), and inability to name a vaccine-preventable disease (aOR = 3.20). However, secondary (aOR = 0.56) or tertiary (aOR = 0.21) education of mothers/guardians and belonging to Bunda (aOR = 0.36) or Mbala (aOR = 0.52) ethnicity reduced the risk of zero-dose. We observed a high prevalence of zero-dose children with a heterogeneous spatial distribution of epidemiological importance. Due to sub-zonal diversity, a health zone approach to reduce zero-dose immunization appears very limited. Zero-dose prevalence was related to the community health workers' home visit, to the distance of residence to a health centre and to household-level factors. Geospatial results could help in targeting priority health areas and communities for vaccination.

Exposure to Coxiella burnetii in Wild Lagomorphs in Spanish Mediterranean Ecosystems.

Coxiella burnetii is an important zoonotic pathogen of worldwide distribution that can infect a wide range of wild and domestic species. The European wild rabbit (Oryctolagus cuniculus) can play a role as a reservoir for this bacterium in certain epidemiological scenarios, but, to date, a very limited numbers of large-scale serosurveys have been conducted for this species worldwide. Although exposure in hare species has also been described, C. burnetii in Iberian hare (Lepus granatensis) has never been assessed. Here, we aimed to determine the seroprevalence and risk factors associated with C. burnetii exposure in wild lagomorphs in the Mediterranean ecosystems of southern Spain. Between the 2018/2019 and 2021/2022 hunting seasons, blood samples from 638 wild lagomorphs, including 471 wild rabbits and 167 Iberian hares, were collected from 112 hunting grounds distributed across all eight provinces of Andalusia (southern Spain). The overall apparent individual seroprevalence was 8.9% (57/638; 95% CI: 6.8-11.4). Antibodies against C. burnetii were found in 11.3% (53/471; 95% CI: 8.4-14.1) of the wild rabbits and 2.4% (4/167; 95% CI: 0.1-4.7) of the Iberian hares. Seropositive animals were detected for 16 (14.3%; 95% CI: 7.8-20.8) of the 112 hunting grounds tested and in all the hunting seasons sampled. A generalized estimating equations model showed that the geographical area (western Andalusia) and presence of sheep were risk factors potentially associated with C. burnetii exposure in wild lagomorphs. A statistically significant spatial cluster (p < 0.001) was identified in the south-west of Andalusia. Our results provide evidence of moderate, endemic and heterogeneous circulation of C. burnetii in wild lagomorph populations in Spanish Mediterranean ecosystems. Risk-based strategies for integrative surveillance programs should be implemented in these species to reduce the risk of transmission of the bacterium to sympatric species, including humans.

Contribution of innovation studies to the intellectual structure of the hospitality and tourism literature

PurposeThis study aims to identify the role of innovation research in formulating the intellectual structure of the hospitality and tourism literature by performing a bibliometric analysis.Design/methodology/approachIn total, 6,255 journal articles on innovation were gathered from Scopus and analyzed using co-citation, bibliographic coupling and thematic content analyses. The most influential articles were also carefully read to reveal a nomological network of innovation research in hospitality and tourism scholarship.FindingsCo-citation analysis reveals that there are six significant clusters in the field of innovation research. Various philosophical underpinnings might be used in different circumstances, with actor-network and Schumpeterian theory playing significant roles. A review of current works using bibliographic coupling reveals five interesting emerging research areas and makes numerous recommendations for when to conduct more studies. A review of influential articles displayed differences between the co-citation and bibliographic coupling analysis findings and produced a framework for further investigation of the knowledge field.Originality/valueThis paper is among the first integrative reviews on innovation research in hospitality and tourism by quantitatively reviewing published articles and qualitatively reviewing the content of the most influential studies.

Spatial variation and determinants of mother and newborn skin-to-skin contact care practices in Ethiopia: A spatial and multilevel mixed-effect analysis.

Skin-to-skin contact care practice is placing a naked baby on the mother's chest with no cloth separating them, in a prone position covered by a cloth or blanket. It improves the survival of newborns by preventing hypothermia, improving breastfeeding, and strengthening mother-to-child bonding. Nevertheless, it remains under-practiced in many resource-constrained settings. Therefore, the main objective of this study is to explore the spatial variation and determinants of mother and newborn skin-to-skin contact care practices in Ethiopia. The study was done using the 2016 Ethiopian Demographic and Health Survey data. A weighted sample of 10417 mothers who gave live birth before the five-year survey was extracted for the analysis. Arc GIS version 10.3 and SaTscan version 10.0.2 were used for the spatial analysis. A multilevel mixed logistic regression model was fitted to identify factors associated with skin-to-skin contact care practices of mothers and newborns. Finally, a statistically significant association was declared at a P-value of < 0.05. In this study, skin-to-skin contact care practice of mothers and newborns was non-random across Ethiopia with Moran's I: 0.48, p < 0.001. The most likely significant primary and secondary clusters were found in Addis Ababa (RR = 2.39, LLR = 116.80, p <0.001) and Dire Dewa and Harari (RR = 2.02, LLR = 110.45, p <0.001), respectively. In this study, place of delivery (AOR = 12.29, 95%CI:10.41, 14.54), rich wealth index (AOR = 1.29, 95% CI: 1.05,1.59), medium wealth index (AOR = 1.38, 95% CI:1.17, 1.68), having 1-3 antenatal care visits(AOR = 1.86,95% CI: 1.56, 2.29), having ≥4 antenatal care visits (AOR = 1.93,95% CI: 1.56, 2.39), initiating breastfeeding within the first hour (AOR = 1.75,95% CI:1.49,2.05) and media exposure (AOR = 1.20,95%CI 1.02,1.41) were factors associated with skin to skin contact care practice of mothers and newborns. This study concludes that the Skin-to-skin contact care practices of mother and newborn is not random in Ethiopia. Therefore, the implementation of essential newborn care packages should be regularly monitored and evaluated, particularly in the cold spot areas of skin-to-skin contact care practices. Besides, media advertising regarding the importance of Skin-to-skin contact care practices for mothers and newborns should be scaled up to increase the practices.

Gut Microbiota Alterations in Adolescent Idiopathic Scoliosis Are Associated with Aberrant Bone Homeostasis.

Low bone mineral density is the major prognostic factor for adolescent idiopathic scoliosis (AIS), but the underlying mechanisms remain unclear. Accumulating evidence suggests that gut microbiota (GM) have the potential to affect bone development, and the GM signatures are altered in AIS patients. However, the effect of GM alterations on aberrant bone homeostasis in AIS remains unclear. This study aims to investigate the GM profile in AIS patients with different bone mineral density (BMD) and explore the association between GM, osteopenia, and aberrant bone turnover. A total of 126 patients with AIS who received surgical treatment were retrospectively included in this study. We analyzed the composition of the GM by 16S rRNA sequencing and BMD by dual X-ray absorptiometry. Based on the BMD of the femur neck, the patients were divided into the osteopenia group (OPN) if the Z score < -1, and the normal (NOR) group if the Z score ≥ -1 SD compared to the healthy control. For the 16S rRNA sequencing, the raw reads were filtered to remove low-quality reads, and operational taxonomic units were identified with the Uparse program. Weighted UniFrac distance matrix for the beta-diversity metrics and principal coordinate analysis (PCoA) was performed, and the statistical comparisons were made with permutational multivariate analysis of variance (PERMANOVA) and analysis of similarity (ANONISM). Linear discriminant analysis effect size (LEfSe) was used to identify the enriched species in two groups. The "Random forest" was applied to determine the optimal biomarker for OPN according to the mean decrease in Gini value. The metabolic function was predicted by the Tax4Fun analysis. The Pearson correlation coefficient was used to evaluate the associations between GM species, bone turnover markers, and BMD. The serum β-CTX was increased in the OPN group (n = 67) compared to the NOR group (n = 59). Patients in OPN groups showed significantly decreased α diversity indicated by the Shannon index. Principal coordinate analysis (PCoA) analysis showed significant clustering of GM between OPN and NOR groups. At genus level, the Escherichia-Shigella and Faecalibacterium were significantly enriched in the OPN group compared to that in the NOR group (p < 0.05), whereas the abundance of Prevotella was significantly decreased (p = 0.0012). The relative abundance of Megamonas and Prevotella was positively correlated with the femur BMD. The abundance of Escherichia-Shigella was negatively correlated with femur BMD and positively correlated with serum β-CTX levels. Functional analysis revealed significant differences in starch and sucrose metabolism, pyruvate and cysteine, and methionine metabolism between NOR and OPN groups. The alterations of GM in AIS patients are correlated with osteopenia. The association between enriched species, BMD, and bone turnover markers provides novel diagnostic and therapeutic targets for the clinical management of AIS.

Effects of Whole-Plant Corn Silage on Growth Performance, Serum Biochemical Indices, and Fecal Microorganisms in Hezuo Pigs.

In this study, we investigated the effects of the dietary inclusion of different proportions of whole-plant corn silage on growth performance, serum biochemical indexes, and intestinal microorganisms in Hezuo pigs. Thirty-two two-month-old Hezuo pigs (body weight: 7.88 ± 0.81 kg) were randomly divided into four groups of eight pigs (half male, half female) each. The control (CON) group received a basal diet, while the three experimental groups were fed the basal diet, part of which had been replaced with 5%, 10%, and 15% whole-plant corn silage, respectively. The experiment lasted for 127 days, including 7 days of pre-testing and 120 days of formal testing. At the end of the experiment, blood and fecal samples were collected. Compared with the CON group, the feed-to-gain ratio was significantly lower in the 10% test group (p < 0.05), whereas the total protein, albumin, triglyceride, and glucose contents were significantly higher (p < 0.05). No significant differences in total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, creatinine, urea, aspartate aminotransferase, and alanine aminotransferase were observed among the groups (p > 0.05). The addition of whole-plant corn silage to the diet significantly increased alpha diversity in the pig gut based on 16S rRNA gene sequencing. The principal coordinate analysis results showed significant clustering of the different groups (p < 0.05). At the phylum level, the addition of whole-plant corn silage to the diet significantly decreased (p < 0.05) the relative abundance of Firmicutes and significantly increased (p < 0.05) that of Bacteroidetes. At the genus level, the relative abundance of Streptococcus significantly decreased (p < 0.05) with increasing silage supplementation levels, whereas species diversity significantly increased (p < 0.05). In conclusion, 10% is the recommended inclusion ratio for whole-plant corn silage in the diets of pigs.

Proteomics Clustering Research of ITRAQ Markers in Plasma of AIDS Patients with Different Chinese Medicine Syndromes.

We aimed to distinguish the different Chinese medicine (CM) syndromes of acquired immune deficiency syndrome (AIDS) patients at the proteomics level. We collected AIDS patients diagnosed with different CM syndromes from Weishi County, Kaifeng City, Henan Province, China, including Qi-deficiency syndrome (named QD group) and dampness-heat syndrome (named DH group). Healthy people were collected as controls from Weishi County, Kaifeng city, Henan Province, China. The plasma from three groups were labeled with ITRAQ, LC/MC was used for protein quantitative analysis. Finally, sequence search and cluster analysis were performed. Overall, 27 different proteins were found. Three proteins were up-regulated and 2 proteins down-regulated in the QD group, 11 proteins up-regulated and 13 proteins down-regulated in the DH group. Compared with DH group, there were 7 different proteins in QD group, among which 5 proteins were down-regulated and 2 proteins were up-regulated. When the target protein of DH group was up-regulated, the protein of HC group was down-regulated correspondingly. The significance analysis and clustering of protein results showed that DH group was significantly different from QD group and HC group at the protein level (P<0.05). However, the QD group could not be effectively distinguished from the HC group. AAT, PF4, C-reactive protein and c4bp may be used as potential biomarkers in DH group. Mass spectrometry based on feature selection can be used to classify different CM syndromes.

Effectively answering why questions on structural graph clustering

The pSCAN algorithm is widely acknowledged for its efficiency in structural graph clustering across diverse graph applications, frequently utilized to detect significant clusters within graphs. The results of pSCAN are constrained by two parameters: (1) the structural similarity constraint ϵ; (2) the density constraint μ. Nevertheless, determining appropriate values for these parameters proves challenging as users often lack the requisite professional knowledge. Consequently, users may inquire about the inclusion of unexpected vertices in specified clusters, posing why questions. In this paper, we aim to address these inquiries by investigating the challenge of answering why questions on structural graph clustering. The objective is to refine the initialized clustering parameters to exclude unexpected vertices from specified clusters. Initially, we introduce the crucial concept of label-vertices and delve into a unified explanation framework designed for addressing why questions on structural graph clustering. Subsequently, we propose two effective refining algorithms, namely FReEPS and FReMU, specifically tailored to modify the similarity constraint ϵ and the density constraint μ independently. Moreover, in our approach to enhancing efficiency, we introduce two baseline explanation algorithms, ReParFirstMUand ReParFirstEPS, simultaneously refining the parameters ϵ and μ to exclude unexpected vertices from specified clusters. Additionally, we explore two improved algorithms, FReParFirstMUand FReParFirstEPS, incorporating a process of pruning unnecessary parameter combinations and computations. This enhancement aims to improve the efficiency of simultaneously refining the two parameters for addressing these questions. Finally, we conducted comprehensive experiments on real social network datasets. The results illustrate that the explanation efficiency of FReEPS and FReMU surpasses that of the state-of-the-art explanation algorithms by 1-2 orders of magnitude. Moreover, FReMu exhibits faster performance than FReEPS. Furthermore, simultaneous refinement of the parameters ϵ and μ yields better-refined parameters. Additionally, FReParFirstMU and FReParFirstEPS outperform ReParFirstMU and ReParFirstEPS, respectively.

Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study

BackgroundDespite many efforts to control leprosy worldwide, it is still a significant public health problem in low- and middle-income regions. It has been endemic in China for thousands of years, and southwest China has the highest leprosy burden in the country.MethodsThis observational study was conducted with all newly detected leprosy cases in southwest China from 2010 to 2020. Data were extracted from the Leprosy Management Information System (LEPMIS) database in China. The Joinpoint model was used to determine the time trends in the study area. Spatial autocorrelation statistics was performed to understand spatial distribution of leprosy cases. Spatial scan statistics was applied to identify significant clusters with high rate.ResultsA total of 4801 newly detected leprosy cases were reported in southwest China over 11 years. The temporal trends declined stably. The new case detection rate (NCDR) dropped from 4.38/1,000,000 population in 2010 to 1.25/1,000,000 population in 2020, with an average decrease of 12.24% (95% CI: −14.0 to − 10.5; P < 0.001). Results of global spatial autocorrelation showed that leprosy cases presented clustering distribution in the study area. Most likely clusters were identified during the study period and were frequently located at Yunnan or the border areas between Yunnan and Guizhou Provinces. Secondary clusters were always located in the western counties, the border areas between Yunnan and Sichuan Provinces.ConclusionsGeographic regions characterized by clusters with high rates were considered as leprosy high-risk areas. The findings of this study could be used to design leprosy control measures and provide indications to strengthen the surveillance of high-risk areas. These areas should be prioritized in the allocation of resources.