Distribution Of Clusters Research Articles

Gsdme Promotes PAD4 Activation and DNA Externalization from Apoptotic Neutrophils

Introduction Neutrophil extracellular traps (NETs) neutralize bacterial and fungal pathogens but can also promote thrombosis, autoimmunity, and sterile inflammation. The presence of citrullinated histones, generated by the peptidylarginine deiminase 4 (PAD4), is synonymous with NETosis and is considered independent of apoptosis. However, non-apoptotic cell death signaling pathways mediated by MLKL and GSDMD can also engage PAD4 in neutrophils to trigger NET formation. Additionally, while PAD4 is hypothesized to neutralize charge on histones to facilitate chromatin decondensation, a genetic deficiency in Padi4 fails to prevent chromatin decondensation but does prevent NET formation following MLKL activation. Together, these data suggest more complex roles for PAD4 in the extrusion of nuclear DNA. Here we explored the role of GSDME - another pore-forming protein activated by apoptotic caspases - in controlling PAD4 activation, DNA externalization, and NET formation in apoptotic neutrophils. Methods Cell viability was quantified by flow cytometry and live-cell imaging using Hoechst, Cell Tracker Green, Annexin V, and propidium iodide. Histone citrullination was studied at the population level by immunoblot, at the single cell level by lattice SIM super-resolution microscopy, and at the molecular level by an in-situ ChIP-Seq methodology CUT&Tag. Calcium signaling linked to PAD4 activation was monitored by flow cytometry. Ultrastructural changes occurring in the absence of PAD4 and GSDME were investigated by transmission electron microscopy and super-resolution microscopy. Results Intrinsic and extrinsic apoptosis promote GSDME-dependent calcium mobilization and membrane permeabilization, leading to histone H3 citrullination (H3Cit), nuclear DNA extrusion, and cytoplast formation. H3Cit distribution on chromatin is developmentally controlled by PAD4 in neutrophils, with H3Cit found concentrated at promoter regions during neutrophil development. Lattice SIM super-resolution microscopy revealed clustered distribution of H3Cit in the nuclei of neutrophils, reflecting a unique spatial organization and coordination of multiple promoter elements in neutrophils. Following apoptotic stimulation, H3Cit redistributes in a coordinated process from promoter to intergenic and intronic regions. Loss of GSDME prevents nuclear and plasma membrane disruption of apoptotic neutrophils, but also blocks calcium signaling that activates PAD4, ultimately preventing NET formation. During apoptosis in GSDME-deficient neutrophils, early-apoptotic cellular changes to the chromatin and cytoplasmic granules are prolonged, generating highly atypical cellular states that fail to extrude nuclear DNA. Conclusion Apoptotic signaling engages PAD4 in neutrophils, establishing a cellular state that is primed for NETosis, but that occurs only upon membrane disruption by GSDME, thereby redefining the end-of-life for neutrophils.

Epidemiology of Antimicrobial Resistance Genes in Staphylococcus aureus Isolates from a Public Database from a One Health Perspective-Sample Origin and Geographical Distribution of Isolates.

Staphylococcus aureus are commensal bacteria that are found in food, water, and a variety of settings in addition to being present on the skin and mucosae of both humans and animals. They are regarded as a significant pathogen as well, with a high morbidity that can cause a variety of illnesses. The Centers for Disease Control and Prevention (CDC) has listed them among the most virulent and resistant to antibiotics bacterial pathogens, along with Escherichia coli, Staphylococcus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterococcus faecalis, and Enterococcus faecium. Additionally, S. aureus is a part of the global threat posed by the existence of antimicrobial resistance (AMR). Using 26,430 S. aureus isolates from a global public database (NPDIB; NCBI Pathogen Detection Isolate Browser), epidemiological research was conducted. The results corroborate the evidence of notable variations in isolate distribution and ARG (Antimicrobial Resistance Gene) clusters between isolate sources and geographic origins. Furthermore, a link between the isolates from human and animal populations is suggested by the ARG cluster patterns. This result and the widespread dissemination of the pathogens among animal and human populations highlight how crucial it is to learn more about the epidemiology of these antibiotic-resistance-related infections using a One Health approach.

Профили семейной жизнеспособности и жизнестойкость представителей российских и белорусских семей

<p>The article describes the results of cross-cultural research on the resilience and experience of family relations of Russian and Belarusian families. Comparative studies of the resilience of families of different ethnicities are both of teh theoretical and practical interest. The purpose of the study is to analyze the resilience and experience of family relations’ profiles of Russian and Belarusian families’ representatives. Representatives of 399 Russian and 404 Belarusian families took part in the study. We used the Russian-language version of the Family Resilience Assessment Scale, the questionnaires «Family Emotional Communications», «Experience of Close Relationships», and the projective technique of K. Adams «Space of Trees and Light». There are three clusters on indicators of family resilience, family emotional communications and experience of close relationships («anxious-critical», «viable», «emotionally restrained»). We give a description of the family resilience of the representatives from different clusters, the study revealed that there were no significant differences in the distribution of clusters between representatives of Russian and Belarusian families. At the same time, the study revealed differences in individual vitality of Russian and Belarusian families. Using narrative approach to historical memory, we analyzed the correlation of factors of individual vitality. We have outlined prospects for further research on this issue.</p>

Performance Improvement Using ICIC for UAV-Assisted Public Safety Networks with Clustered Users during Emergency

The application of drones, also known as unmanned aerial vehicles deployed as unmanned aerial base stations (UABSs), has received extensive interest for public safety communications (PSC) to fill the coverage gaps and establish ubiquitous connectivity. In this article, we design a PSC LTE-Advanced air–ground-based HetNet (AG-HetNet) that is a scenario representation of a geographical area during and after a disaster. As part of the AG-HetNet infrastructure, we have UABSs and ground user equipment (GUE) flocking together in clusters at safe places or evacuation shelters. AG-HetNet uses cell range expansion (CRE), intercell interference coordination (ICIC), and 3D beamforming techniques to ensure ubiquitous connectivity. Through system-level simulations and using a brute-force technique, we evaluate the performance of the AG-HetNet in terms of fifth-percentile spectral efficiency (5pSE) and coverage probability. We compare system-wide 5pSE and coverage probability when UABSs are deployed on a hexagonal grid and for different clustering distributions of GUEs. The results show that reduced power subframes (FeICIC) defined in 3GPP Release-11 can provide practical gains in 5pSE and coverage probability than the 3GPP Release-10 with almost blank subframes (eICIC).

An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping

Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learning techniques has been extensively examined. However, to date, the impact of data sampling strategies has received limited attention. Random sampling is widely favored because of its ease of use. It treats flood-related data as tabular and excludes their spatial dimensions. Flood occurrence is typically uneven over space. Therefore, non-flood sampling should not be completely random. To represent the impact of the spatial dimension, this study proposed a new sampling approach based on spatial dependence, called inverse-occurrence sampling. It selects more non-flood data in low-risk areas than in high-risk areas. The new sampling approach was compared with random and stratified sampling, using six machine learning techniques in two urban areas in Guangzhou, China, with distinct flood mechanisms, that is, Tianhe (flood density 1.5/km2, clustered distribution, average slope 9.02°, downtown district) and Panyu (flood density 0.15/km2, random distribution, average slope 4.55°, suburban district). Learning techniques include support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs), CNN-SVM, and CNN-RF. The main findings of this study were as follows: (1) Sampling approaches had a greater impact on model performance than learning techniques in terms of area under the receiver operating characteristic curve (AUC). The AUC variations caused by learning techniques ranged from 0.04 to 0.09. Meanwhile, the AUC variations caused by sampling approaches were between 0.15 and 0.22, all larger than 0.1. (2) The new sampling approach outperformed that of the other two sampling approaches for high average AUC values and small AUC variations. The outperformance is robust in regard to multiple learning techniques and different flooding mechanisms. AUCs in the inverse group had a narrower range (0.14–0.18 in Tianhe and 0.35–0.39 in Panyu) than in the random group (0.24–0.28 in Tianhe and 0.43–0.53 in Panyu) and the stratified group (0.23–0.30 in Tianhe and 0.42–0.48 in Panyu). (3) The most accurate learning technique for AUC was CNN-RF, followed by SVM, CNN-SVM, RF, CNN, and ANN. (4) ANN- and CNN-based models tended to produce polarized patterns in flood susceptibility maps, contradicting the ascending order of flood density with increasing susceptibility levels. Flood density outliers tended to appear in the models derived using RF and CNN-RF. Finally, the newly proposed sampling approach is suggested to be applied to flood susceptibility mapping to reflect the impact of spatial dependence.

Epidemiological characteristics and temporal-spatial clustering analysis on human brucellosis in Jiangsu Province, 2006–2021

The marked increase in the incidence rate of brucellosis is a serious public health concern in Jiangsu Province. However, its temporal and spatial distribution has not been studied in depth. The main purpose of this study is to depict the demographic, temporal and spatial distribution patterns and clustering characteristics of brucellosis cases in Jiangsu Province, China, from 2006 to 2021 to develop and implement effective scientific prevention and control strategies. Data for human brucellosis cases in Jiangsu Province from 2006 to 2021 were obtained from the Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS). Spatial autocorrelation analysis and temporal-spatial scan statistics were used to identify potential changes in the spatial and temporal distributions of human brucellosis in Jiangsu Province. During the years 2006–2021, 1347 brucellosis cases were reported in Jiangsu Province, with an average annual incidence rate of 0.1036 per 100,000 individuals. Middle-aged and elderly individuals (aged 40–69 years) were the main infected populations, accounting for 69.72% (939/1347) of all reported cases. The incidence of brucellosis in Jiangsu showed a long-term increasing trend and displayed pronounced seasonal variations, with the peak occurring between April and June annually. The incidence gradually expanded from the northern and southern areas to the central areas between 2006 and 2021. Global spatial autocorrelation analysis demonstrated a positive correlation in the incidence of brucellosis between 2008 and 2012–2021. Temporal-spatial clustering analysis showed that the primary cluster was detected in the northern, highly endemic regions of Jiangsu, and the three secondary clusters were in areas where there had been outbreaks of brucellosis. Human brucellosis remains a serious public health issue in Jiangsu Province. Northern and southern Jiangsu regions, with high rates of brucellosis, may require special plans and measures to monitor and control the disease. Additionally, the capacity to respond to outbreaks in high-incidence areas should be improved to prevent further brucellosis outbreaks.

Increasing Vulnerability of Village Heritage: Evidence from 123 Villages in Aba Prefecture, Sichuan, China

As the threat of global risks has increased, the study of village heritage has begun to move away from the interpretation of traditional values or the presentation of historical wisdom to focus on the vulnerability of villages. Taking Chinese traditional villages (a type of vernacular heritage) as the target, this study clarifies the connotation of village vulnerability and its generation pattern. Drawing on the framework of “exposure-sensitivity-adaptive capacity”, a set of vulnerability evaluation index systems integrating the characteristics of village heritage is proposed. By utilizing vulnerability index and obstacle degree models, we analyze the spatial differentiation and evolutionary characteristics of vulnerability in 123 traditional villages within Aba Prefecture, Sichuan Province, southwestern China, while also exploring the main factors influencing vulnerability evolution at different spatial scales. The results reveal an “east high, west low” spatial pattern and a clustered distribution of vulnerability in traditional villages across the region. From 2012 to 2019, the vulnerability levels fluctuated and intensified, with decreasing individual differences. The evolutionary characteristics of exposure, sensitivity, and adaptive capacity also displayed significant variations. Persistent and stable influences on village vulnerability were identified from factors such as land use scale, population density, gross domestic product, and land fragmentation. Based on these findings, strategic recommendations for village classification, protection, and development are proposed.

Morbidity trend and space-time clusters of COVID-19 occurrence in children and adolescents

ObjectiveTo analyze the morbidity trend and space-time distribution clusters of confirmed COVID-19 cases in children and adolescents. MethodAn ecological study of COVID-19 cases confirmed in the Information System from 2020 to 2022 in the age group from 0 to 19 years old, residents in Mato Grosso municipalities, Brazilian Midwest region. A trend analysis of the monthly morbidity rate of cases/100,000 inhabitants was used, following Prais-Winsten's regression. A space-time distribution of the Bayesian incidence rate per 100,000 inhabitants was performed, in addition to a space-time scan to identify high-risk clusters. ResultsOf all 79,592 COVID-19 cases studied, 51.6% were in females and 44.21% in people aged 15–19 years old. The mean monthly rate was 265.87 cases per 100,000 inhabitants, with a stationary trend in the period analyzed (Monthly Percentage Variation [MPV]) = 12.15; CI95%[MPV]: -0.73;26.70). The morbidity rate due to COVID-19 was higher in the female gender (283.14/100,000 inhabitants) and in the age group from 15 to 19 years old (485.90/100,000 inhabitants). An increasing trend was observed with a greater monthly time variation of 14.42% (CI95%[MPV]: 1.28;29.28)] among those aged from 10 to 14 years old. The primary cluster, which was also the one with the highest Relative Risk (RR = 5,16, p-value = 0.001), included 19 municipalities located in the North health macro-region. ConclusionThe findings indicated a monthly stationary trend in the study population, an increase in the age group from 10 to 14 years old, and areas at a higher risk for the disease in the North health macro-region of the state.

Impact of microtubule detyrosination on subcellular distribution of Nav1.5 in mdx cardiomyocytes

Abstract Background Subcellular trafficking and distribution of the cardiac sodium channel Nav1.5 in cardiomyocytes is dependent on the microtubule network as well as Nav1.5 interacting proteins. Duchenne muscular dystrophy (DMD) is associated with loss of dystrophin, a Nav1.5-interacting protein localized at the lateral membrane of cardiomyocytes, leading to sodium current (INa) reduction in this microdomain. Cardiomyocytes of DMD (mdx) mice also display increased microtubule detyrosination, but its impact on INa and Nav1.5 distribution is unclear. Purpose To investigate the effect of reducing microtubule detyrosination by the compound parthenolide (PTL) on INa and subcellular Nav1.5 distribution in mdx cardiomyocytes (CMs). Methods and Results Isolated ventricular CMs from wild type (WT) and mdx (DMD) mice were incubated with either 10 µM PTL or DMSO for 3-5 hours. INa properties were assessed using the patch-clamp technique, confocal microscopy was used to investigate microtubule detyrosination, and stochastic optical reconstruction microscopy (STORM) was employed to assess Nav1.5 cluster density and size, as well as Nav1.5 cluster distribution (crest vs groove) at the cardiomyocyte lateral membrane. Compared to WT. mdx CMs displayed increased levels of detyrosinated tubulin and decreased whole-cell INa. Incubation with PTL decreased the fraction of detyrosinated tubulin and significantly increased whole-cell INa magnitude in mdx CMs, but had no effect in WT CMs. No effect of PTL on INa gating properties were observed, indicating that it increased INa by enhancing Nav1.5 membrane trafficking. Indeed, STORM analysis showed that PTL increased Nav1.5 cluster density at both the lateral membrane and intercalated disc region in mdx CMs while it had no effect on WT CMs. Furthermore, Nav1.5-α-actinin cluster distance analysis showed that PTL increased Nav1.5 clusters specifically at the crest of the lateral membrane in mdx CMs. Conclusions In mdx CMs, reduction of MT detyrosination rescues INa density and increases Nav1.5 cluster density both at the intercalated disc and lateral membrane. Hence, in addition to loss of the Nav1.5-interacting protein dystrophin, MT remodelling contributes to INa reduction in DMD. The PTL-induced increase in Nav1.5 at the crest of the lateral membrane of mdx CMs is likely due to a dystrophin-independent mechanism since dystrophin is normally absent from this microdomain. Overall, our findings identify MT detyrosination as a potential therapeutic target for modulating INa and subcellular Nav1.5 distribution during pathophysiological conditions.

Smart container port development: recent technologies and research advances

Abstract Smart port construction projects have gradually emerged across the world in recent years, owing to the rapid development of Artificial Intelligence, Big Data, Cloud, and the Internet of Things (IoT). However, for the advantages and limitations of smart port project construction technology and construction methods, people temporarily lack a systematic understanding and summary. In this article, by checking the number of publications, geographical distribution and keyword cluster distribution, the research status and technical progress of the development of smart container ports are comprehensively reviewed from three aspects: port data acquisition technology, facilities and equipment, and intelligent decision-making. The research conclusions can provide references for the development of smart container ports and guide the future development of smart container ports.

Cluster distributions for dynamically defined point processes

The emergence of clustering of rare events for chaotic dynamics was first observed as a consequence of periodicity, i.e., by considering target sets that shrink to a periodic point, one was able to create fast returns to these target sets which were responsible for the appearance of a bulk of high observations of observable functions that were maximised at the periodic point. This meant that the Rare Events Point Processes, counting the number of entrances in these target sets, converge to a compound Poisson process, with a geometric multiplicity distribution ruling the cluster sizes. In Azevedo et al. (2016), a new mechanism to create clustering of rare events was introduced by considering observable functions maximised at a finite number of points that were linked by belonging to the same orbit. We make a deep study of the potential of this mechanism to produce different multiplicity distributions. Namely, we show that with the right choice of a system and observable, one can obtain any given finitely supported cluster size distribution. We also study the impact of symmetry and other properties of the systems on the possible clustering size distributions, which are also classified for the case of periodic maximal orbits.

Exploring the correlation between dark matter, intracluster light, and globular cluster distribution in SMACS0723

We present a free-form model of SMACS0723, the first cluster observed with JWST. This model does not make any strong assumptions on the distribution of mass (mostly made up of dark matter) in the cluster and we use it to study the possible correlation between dark matter with the intracluster light and distribution of globular clusters (GCs). To explore the uncertainty in mass modeling, we derived three lens models based on spectroscopically confirmed systems and new candidate systems with redshifts predicted by the lens model derived from the spectroscopic systems. We find that beyond the radius of influence for the brightest cluster galaxy (BCG), the total mass does not trace the intracluster light (ICL), implying the need for a dark component (dark matter). Two loop-like structures observed in the intracluster light do not have any obvious correspondence with the total mass (of mostly dark matter) distribution. The radial profiles of the ICL and the distribution of GCs are similar to each other, but they are steeper than the profile of the lens model. More specifically, we find that the total mass is shallower by 1 dex in log scale than both ICL and GC profiles. This is in excellent agreement with current N-body simulations of cold dark matter.

Learning Spatio-Temporal Semantics and Cluster Relation for Zero-Shot Action Recognition

Zero-shot Action Recognition (ZSAR) aims at bridging the video→class relation with only labeled training data of seen classes while generalizing the model to alleviate the heterogeneity of unseen actions. Most existing methods have comprehensively represented videos and action classes, however, the semantic gap and the hubness problem between them remain crucial challenges that are under-explored. In this paper, we propose an effective method to tackle the above issues. Specifically, to narrow the semantic gap, we end-to-end generate a spatio-temporal semantics for each video, which provides essential textual information to refine the video representation. Furthermore, we propose a compactness-separability loss that optimizes the intra- and inter-class relations in a unified formula and quantitatively constrains cluster distribution, thus effectively diminishing the impact of the hubness problem. Extensive experiments on UCF101, HMDB51, and Olympic Sports datasets prove the effectiveness of the proposed approach and demonstrate our approach outperforms the state-of-the-art methods.

Understanding the concept of diversity, abundance, and distribution of marine invertebrates through practicum students of the Biology Education, Pattimura University

The application of marine biology courses cannot be separated from field practicum activities which are integrated with the main scientific pattern of Pattimura University, namely build nobleh marine, meaning that the marine biology lecture process is a goal of developing human resources. One of the competencies of marine biology courses which emphasizes is that students must understand the life of various types of marine invertebrates that live in each coastal water ecosystem and must also master how to observe and research various types of marine invertebrate life found in each zone of coast (intertidal zone and subtidal). The results of the findings of various types of marine invertebrates were carried out by diversity analysis, the results were 2.0 <H'=2.363836 ≤ 3.0, meaning the level of diversity was medium. The relative density of invertebrates on various substrates includes sand 25%, density on sandy gravel substrates 9.9%, muddy substrates 11.4%, coral substrates 15%. The distribution patterns of various types of invertebrates can also be explained according to the results of analyzes including (1) clustered distribution patterns occur in Crustacea (Emita sp, Harpiosquilla raphidae, Ocypoda cursor), Gastropoda (Nerita polita, Conus sponalis), Bivalvia (Venerupis corrugate), (2) The distribution pattern is uniformly occurs in invertebrates of Bivalvia (Anadara broughtonii, Mytilus trossulus, Tridacna rosewateri, Pinctada radiate), (Diadema setosum, Holothuria scabra), Gasropoda (Strombus decorus). Proving the truth of the concept or theory of various types of invertebrates in biology courses the truth of the sea has been tested through field practicums in the coastal waters of Salahutu District, Ambon Island

A new step forward in realistic cluster lens mass modelling: analysis of Hubble Frontier Field Cluster Abell S1063 from joint lensing, X-ray, and galaxy kinematics data

ABSTRACT We present a new method to simultaneously and self-consistently model the mass distribution of galaxy clusters that combines constraints from strong lensing features, X-ray emission, and galaxy kinematics measurements. We are able to successfully decompose clusters into their collisionless and collisional mass components thanks to the X-ray surface brightness, as well as use the dynamics of cluster members, to obtain more accurate masses exploiting the fundamental plane of elliptical galaxies. Knowledge from all observables is included through a consistent Bayesian approach in the likelihood or in physically motivated priors. We apply this method to the galaxy cluster Abell S1063 and produce a mass model that we publicly release with this paper. The resulting mass distribution presents different ellipticities for the intra-cluster gas and the other large-scale mass components as well as deviation from elliptical symmetry in the main halo. We assess the ability of our method to recover the masses of the different elements of the cluster using a mock cluster based on a simplified version of our Abell S1063 model. Thanks to the wealth of mutliwavelength information provided by the mass model and the detected X-ray emission, we also found evidence for an ongoing merger event with gas sloshing from a smaller infalling structure into the main cluster. In agreement with previous findings, the total mass, gas profile, and gas mass fraction are all consistent with small deviations from the hydrostatic equilibrium. This new mass model for Abell S1063 is publicly available, as the lenstool extension used to construct it.

Knowledge-aware progressive clustering for social image

Social image data refer to the annotated image with tags in social media, in which the tags are always labeled by users. Integrating the visual and textual information of social image can obtain accurate and comprehensive feature and improve clustering performance. However, the heterogeneous gap between tags and images makes it difficult to reasonably organize the social images. In addition, the tags are often sparse and incomplete due to personal preference and cognition differences of users. To solve these problems, we propose a novel knowledge-aware progressive clustering (KAPC) method, which employs human knowledge to guide the cross-modal clustering of social images. Firstly, we design a dual-similarity semantic expansion strategy to complement the sparse tags with human knowledge, which constructs a more complete semantic similarity matrix for tags through knowledge graphs. Secondly, we define an objective function based on information theory to bridge the heterogeneous gap, which align inter-modal cluster distribution to explore the correlation between visual and textual information. Finally, a progressive iteration method is designed to make the two modalities guide each other and obtain better performance of social image clustering. Extensive experiments on four social image datasets verify the effectiveness of the proposed KAPC method.

Study of Gastropod Community Structure After Development of Tourism Activities on the Senjoyo River

It is thought that the development of the Senjoyo River as a tourist attraction in Semarang Regency will cause changes to the natural ecosystem in the area, one of which is changing the structure of the existing gastropod community. This research is a quantitative analysis research using a survey method that aims to examine the structure of the gastropod community after the development of tourism activities on the Senjoyo River. Gastropod samples were taken from 10 different observation stations along the Senjoyo River, which are located at 7˚22'23"S and 110˚31'37"E. Observation station locations were determined by the purposive random sampling method, based on tourism activity density. The result showed types of gastropods that were found in the Senjoyo River namely, Sulcospira testudinaria, Tarebia granifera, Brotia costula, Thiara scraba, and Melanoides tuberculata with clustered distribution patterns (Id>1). Statistical analysis showed that location affected gastropod abundance (Asymp. Sig of 0.043), with the highest abundance found at station seven (45,11 individuals/m2). So station seven, with sandy and muddy substrate conditions, moderate current speed (0.257 m/s), and rarely found human activity, is the most suitable location to support the survival of gastropods in the Senjoyo River. Furthermore, the gastropod community structure indicates environmental changes at observation stations nine and ten, the locations where the most activities and construction of tourism facilities were found. That was indicated by the low diversity (H’= 0.00-0.01) and evenness (E = 0.00) index of gastropods, and there is dominance by Sulcospira testudinaria (C= 0.98-1.00).

Joint unsupervised contrastive learning and robust GMM for text clustering

Text clustering aims to organize a vast collection of documents into meaningful and coherent clusters, thereby facilitating the extraction of valuable insights. While current frameworks for text clustering try to minimize the anisotropy of pre-trained language models through contrastive learning of text embeddings, the approach of treating in-batch samples as negatives is suboptimal. The K-means algorithm offers a way to sample both hard negatives and false negatives. However, relying solely on a single measure of semantic similarity between distributions and using coarse-grained weighting for negative pairs may potentially limit performance. Furthermore, considering the very similar distribution in text clusters due to rich semantics, the Mahalanobis distance-based Gaussian Mixture Model (GMM) is prone to falling into local optima due to one Gaussian model, having a smaller weight, may gradually merging into another during the parameter evaluation by the EM algorithm. To tackle these challenges, we propose a model named JourTC: Joint unsupervised contrastive learning and robust GMM for Text Clustering. In the contrastive learning phase, hard negatives, potential false negatives, and their corresponding global similarity-aware weights are determined through posterior probabilities derived from a Robust GMM (RGMM). This RGMM utilizes the entropy of each individual Gaussian model as a metric and adaptively adjusts the posterior probabilities of samples based on the Gaussian models with both maximum and minimum entropy to diminish the influence of low-entropy Gaussian models. Extensive experiments have shown that JourTC can be seamlessly integrated into existing text clustering frameworks, leading to a notable improvement in accuracy. Our code is publicly available.11http://github.com/nickhcx/JourTC.

Epidemiological Investigation of Yak (Bos grunniens) Fascioliasis in the Pastoral Area of Qinghai–Tibet Plateau, China

The objective of this investigation was to understand the epidemiology of fascioliasis in yaks in the alpine pastoral areas of the Qinghai–Tibet Plateau, China. The prevalence of Fasciola hepatica infection was estimated by examining eggs in the feces of yaks and by autopsy after the slaughter. Yaks were sampled from a total of 16 representative counties in Qinghai province, and risk factors were assessed based on regional and age characteristics. Fecal samples were obtained from 1542 yaks aged 0–1 (<1 year old), 1–2 (≥1 year old and <3 years old), and over 3 years (≥3 years old). In addition, 242 yaks over 3 years old who had not undergone fecal examinations were randomly selected for autopsy. A total of 267 fecal samples were positive for Fasciola spp. eggs. The average infection rate was 17.32% (0–60.61%), and the average infection intensity was 51.9 eggs per gram (epg) of feces, with intensities ranging from 18 to 112 epg. In Maduo, Dari, Zhiduo, Chengduo, and Datong counties, the Fasciola spp. eggs infection rate was zero. Fasciola spp. adult flukes were detected in 66 out of 242 yaks at autopsy, with a total infection rate of 27.27% and an average infection intensity of 21.2 (adult worms), with intensities ranging from 3 to 46 worms. Logistic regression model analysis showed that age was a significant risk factor for yak infection with Fasciola spp. In addition, the risk varied between regions: Haiyan, Gangcha, Duran, and Wulan were all high-risk areas for yak infection with Fasciola spp. The spatial distribution of the Fasciola spp. infection rate in each region showed a very weak negative correlation (Moran’s I = −0.062), Duran formed a spatial distribution of high–low clusters with surrounding areas, and Datong formed a low–high clustering distribution characteristic with the surrounding areas. This investigation revealed that the infection rate of Fasciola spp. in yaks was higher on the Qinghai–Tibet Plateau. Increasing age was a risk factor for infection with Fasciola spp.; different regions also have a different risk of Fasciola spp. infection. Only two regions showed clustering characteristics in the spatial distribution of infection rates. These findings extend the epidemiological information on Fasciola spp. infection in yaks and provide baseline data for the execution of control measures against Fasciola spp. infection.

Estimation of biomolecule amount by analyzing magnetic nanoparticle cluster distributions from alternating current magnetization spectra for magnetic biosensing

The hydrodynamic size distribution of magnetic nanoparticle (MNP) clusters is crucial for determining the amounts of biomolecules by analyzing the distributions of the Brownian relaxation time in the corresponding alternating current (AC) magnetization spectra. This study proposes a method for estimating the distribution of MNP clusters from recorded AC magnetization spectra using an experimental-based model. The proposed model is used to form a matrix equation that comprises the distribution and AC magnetization spectra of MNP clusters, and the MNP–biomolecule cluster distribution is obtained by solving an inverse problem. The results show that the variations in the MNP clusters distribution estimated from the model correspond to those measured by dynamic light scattering. The MNP cluster distributions estimated using the experimental model were used to estimate streptavidin concentrations in the range of 0.19–3.38 μM. The estimated values agree accurately with the true values, demonstrating the feasibility of the proposed method for biosensing.