Two-cluster Model Research Articles

Divisions within the British Parliamentary Labour Party under Keir Starmer: Results of a Cluster Analysis

This paper offers a methodologically innovative two-stage approach for studying divisions amongst parliamentary representatives. Using the Parliamentary Labour Party (PLP) as our case study, we construct a dataset of all Labour MPs elected in the 2019 general election, along with their nominations in the 2020 Labour Party leadership and deputy leadership elections and their membership of, or affiliation with, various party-aligned organisations. We then conduct a cluster analysis based on this dataset, which reveals the existence of a two-cluster model—comprised of the Mainstream (N = 162) and the Left (N = 33)—and a five-cluster model—in which the Left exists alongside the Tribune Soft Left, the Labour Friends of Palestine and the Middle East Soft Left, the Unaligned Centrists, and the Right. Finally, we test the robustness of our clusters via a canonical correspondence analysis of the language used by MPs on social media (Twitter/X) and their contributions to parliamentary debates (Hansard). We show that the MPs from different clusters do use different languages to one another in both fora. We also find that the main divide within the PLP is between the left of the party and the rest of the party, and that the deputy leader, Angela Rayner, has a broader base of support amongst the PLP than its current leader Keir Starmer.

Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning

The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. We selected 166 PLC biopsies divided into training, internal and external validation sets: 90, 29 and 47 samples, respectively. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, tiles of 256x256 pixels were extracted from the WSIs and used to train a ResNet18 neural network. The tumour/non-tumour annotations served as labels during training, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Pathological review classified the training and validation sets into hepatocellular carcinoma (HCC, 33/90, 11/29 and 26/47), intrahepatic cholangiocarcinoma (iCCA, 28/90, 9/29 and 15/47), and cHCC-CCA (29/90, 9/29 and 6/47). In the two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the two-cluster model predictions (major contingent) in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (cluster 0: 5-97%; cluster 1: 2-94%). Our method applied to PLC HES biopsy could identify specific morphological features of HCC and iCCA. Although no specific features of cHCC-CCA were recognized, assessing the proportion of HCC and iCCA tiles within a slide could facilitate the identification of cHCC-CCA. The diagnosis of primary liver cancers can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified primary liver cancers on routine-stained biopsies using a weakly supervised learning method. Our model identified specific features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Despite no specific features of cHCC-CCA being recognized, the identification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma tiles within a slide could facilitate the diagnosis of primary liver cancers, and particularly cHCC-CCA.

Datacentric Similarity Matching of Emergent Stigmergic Clustering to Fractional Factorial Vectoring: A Case for Leaner-and-Greener Wastewater Recycling

Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robustifying the effectiveness of water treatment is a complex multidimensional characteristic problem. In this study, a non-linear Taguchi-type orthogonal-array (OA) sampler is enriched with an emergent stigmergic clustering procedure to conduct the screening/optimization of multiple UF/NF aquametric performance metrics. The stochastic solver employs the Databionic swarm intelligence routine to classify the resulting multi-response dataset. Next, a cluster separation measure, the Davies–Bouldin index, is used to evaluate input and output relationships. The self-organized bionic-classifier data-partition appropriateness is matched for signatures between the emergent stigmergic clustering memberships and the OA factorial vector sequences. To illustrate the proposed methodology, recently-published multi-response multifactorial L9(34) OA-planned experiments from two interesting UF-/NF-membrane processes are examined. In the study, seven UF-membrane process characteristics and six NF-membrane process characteristics are tested (1) in relationship to four controlling factors and (2) to synchronously evaluate individual factorial curvatures. The results are compared with other ordinary clustering methods and their performances are discussed. The unsupervised robust bionic prediction reveals that the permeate flux influences both the UF-/NF-membrane process performances. For the UF process and a three-cluster model, the Davies–Bouldin index was minimized at values of 1.89 and 1.27 for the centroid and medoid centrotypes, respectively. For the NF process and a two-cluster model, the Davies–Bouldin index was minimized for both centrotypes at values close to 0.4, which was fairly close to the self-validation value. The advantage of this proposed data-centric engineering scheme relies on its emergent and self-organized clustering capability, which retraces its appropriateness to the fractional factorial rigid structure and, hence, it may become useful for screening and optimizing small-data wastewater operating conditions.

Resonance Structure of 8Be within the Two-Cluster Resonating Group Method

A microscopic two-cluster model is applied to study the elastic alpha-alpha scattering and the resonance structure of 8Be. The model is an algebraic version of the Resonating Group Method (RGM), which involves the complete set of oscillator functions to expand the wave function of a two-cluster system. The interaction of nucleons inside each cluster and the interaction between clusters are determined by the well-known semirealistic nucleon-nucleon potentials which are employed in calculations. They differ by a size of the core at small distances between nucleons and realize the strong, moderate, and weak cores. They allow us to study dependence of calculated quantities on the shape of a nucleon-nucleon potential. The detailed analysis of resonance wave functions is carried out in the oscillator, coordinate, and momentum spaces. Effects of the Pauli principle on the wave functions of the 8Be continuous spectrum states are thoroughly studied.

The “affected” pharmacist and the “business as usual” pharmacist: Exploring the experiences of pharmacists during COVID-19 through cluster analysis

BackgroundThe global coronavirus disease 2019 (COVID-19) pandemic has necessitated considerable changes in the delivery of pharmacy services, with pharmacists experiencing increasing demands and a high rate of burnout. The ability to categorize pharmacists based on their burnout risk and associated factors could be used to tailor burnout interventions. ObjectiveThis study aimed to identify subgroups (profiles) of pharmacists and use these profiles to describe interventions tailored to improve pharmacist’s well-being. MethodsA survey was disseminated to pharmacists working in Australia during April and June 2020. The survey measured demographics, burnout, and psychosocial factors associated with working during COVID-19. A two-step cluster analysis was used to categorize pharmacists based on burnout and other variables. ResultsA total of 647 survey responses contained data that were used for analysis. Participants were mostly female (75.7%) and working full time (65.2%). The final cluster analysis yielded an acceptable two-cluster model describing 2 very different pharmacist experiences, using 10 variables. Cluster 2 (representing 53.1% of participants) describes the “affected” pharmacist, who has a high degree of burnout, works in community pharmacy, experiences incivility, is less likely to report sufficient precautionary measures in their workplace, and has had an increase in workload and overtime. In contrast, cluster 1 (representing 46.9% of participants) describes the profile of a “business as usual” hospital pharmacist with the opposite experiences.Interventions focused on the “affected” pharmacist such as financial support to employ specialized staff and equitable access to personal protective equipment should be available to community pharmacists, to reduce the risk to these frontline workers. ConclusionThe use of cluster analysis has identified 2 distinct profiles of pharmacists working during COVID-19. The “affected” pharmacist warrants targeted interventions to address the high burnout experienced in this group.

Transdiagnostic inflammatory subgroups among psychiatric disorders and their relevance to role functioning: a nested case-control study of the ALSPAC cohort

Individuals with psychotic disorders and depressive disorder exhibit altered concentrations of peripheral inflammatory markers. It has been suggested that clinical trials of anti-inflammatory therapies for psychiatric disorders should stratify patients by their inflammatory profile. Hence, we investigated whether different subgroups of individuals exist across psychiatric disorders, based on their inflammatory biomarker signatures. We measured the plasma concentrations of 17 inflammatory markers and receptors in 380 participants with psychotic disorder, depressive disorder or generalised anxiety disorder and 399 controls without psychiatric symptoms from the ALSPAC cohort at age 24. We employed a semi-supervised clustering algorithm, which discriminates multiple clusters of psychiatric disorder cases from controls. The best fit was for a two-cluster model of participants with psychiatric disorders (Adjusted Rand Index (ARI) = 0.52 ± 0.01) based on the inflammatory markers. Permutation analysis indicated the stability of the clustering solution performed better than chance (ARI = 0.43 ± 0.11; p < 0.001), and the clusters explained the inflammatory marker data better than a Gaussian distribution (p = 0.021). Cluster 2 exhibited marked increases in sTNFR1/2, suPAR, sCD93 and sIL-2RA, compared to cluster 1. Participants in the cluster exhibiting higher inflammation were less likely to be in employment, education or training, indicating poorer role functioning. This study found evidence for a novel pattern of inflammatory markers specific to psychiatric disorders and strongly associated with a transdiagnostic measure of illness severity. sTNFR1/2, suPAR, sCD93 and sIL-2RA could be used to stratify clinical trials of anti-inflammatory therapies for psychiatric disorders.

Preservice teacher cluster memberships in an edtech course: A study of their TPACK development

Researchers have been examining preservice teachers’ development of TPACK in edtech courses for years. However, most empirical studies used small samples to look at preservice teachers’ TPACK development in the edtech courses. With a small dataset, it is hard to know whether there consistently exist knowledge gaps in preservice teachers’ prior knowledge and TPACK development in the edtech course. Studies with large samples are needed because they provide a large amount of empirical data that can be used as strong evidence for justifying whether the edtech course has any impact on preservice teachers’ TPACK development across all demographic and prior knowledge groups. Thus, the purpose of this study was to examine eighteen cohorts of preservice teachers’ pre-TPACK scores and their cluster assignment, as well as their post-TPACK scores in a required edtech course. A two-step cluster analysis was used based on pre-TPACK scores and demographics to identify groups of preservice teachers that share common characteristics in their pre-TPACK scores. A two-cluster model was a good fit (cluster 1 - lower pre-TPACK scores and cluster 2 - higher pre-TPACK scores). Independent-sample t-tests were run to test whether there were differences in the post-TPACK scores. Findings revealed that cluster 2 preservice teachers had higher post-TPACK scores. These results might help teacher educators make data-driven decisions to design targeted instructions for diverse preservice teacher groups. Details on potential actions for designing the edtech course and practices for developing TPACK were discussed.

Longitudinal estimation of stress-related states through bio-sensor data

Purpose The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data. Design/methodology/approach The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months. Findings The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures. Research limitations/implications Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors. Practical implications Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs. Social implications Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases. Originality/value The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

Single-nodule hepatitis B virus-associated hepatocellular carcinoma smaller than 3 cm: two phenotypes defined by cluster analysis and their association with the outcome of ablation as the first-line therapy

Objectives Hepatocellular carcinoma (HCC) is a heterogeneous disease. This study aimed to identify the heterogeneity related to the prognosis of ablation in patients with single-nodule hepatitis B virus (HBV)-associated HCC ≤3 cm. Methods A total of 359 patients with single-nodule HBV-associated HCC ≤3 cm treated with curative thermal ablation were retrospectively investigated. Hierarchical cluster analysis was applied to obtain more homogeneous patient clusters concerning demographic and physiological characteristics. Discriminant analysis was performed to identify the relatively important variables for cluster analysis. Multiple correspondence analysis (MCA) was used to clarify the relationship between clusters and categorical variables. Overall survival (OS) was compared among clusters using the Kaplan–Meier model. Results A two-cluster model was identified. Cluster 1 (n = 85) showed a higher percentage of female and older patients, higher inflammation response (higher prognostic nutritional index [PNI] and Glasgow prognostic score [GPS]), worse liver function (higher albumin–bilirubin grade and Child–Pugh grade), and relatively poorer immune status (higher neutrophil-to-lymphocyte ratio [NLR]) than cluster 2 (n = 274). NLR and GPS were the two most influential variables for cluster analysis (p < .0001). Cluster 2 had a significantly better prognosis than cluster 1. MCA revealed a clear negative correlation between inflammation status and liver function. Compared with cluster 1, the hazard ratios for OS of cluster 2 were 0.47 and 0.52 before and after adjusting for age, respectively (p < .05). Conclusions This study identified two sub-phenotypes of patients with single-nodule HBV-associated HCC ≤3 cm and their association with the outcome of thermal ablation alone as the first-line therapy. Key points Thermal ablation alone as the first-line therapy is not suitable for all patients with single-nodule hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) ≤3 cm. Patients with single-nodule HBV-associated HCC ≤3 cm can be identified as two sub-phenotypes associated with the outcome of thermal ablation alone as the first-line therapy, based on key preoperative clinical characteristics, especially inflammatory response and immune status. Patients with single-nodule HBV-associated HCC ≤3 cm characterized by late-onset disease, worse liver function, poorer immune status, and higher inflammatory response (with higher inflammatory response being the most important factor) are not suitable for thermal ablation alone as the first-line therapy. In contrast, patients with single-nodule HBV-associated HCC ≤3 cm characterized by early-onset disease, better liver function, lower inflammatory response, and good immune status (with lower inflammatory response being the most important factor) are particularly suitable for thermal ablation alone. Implications for patient care In the treatment of patients with single-nodule HBV-associated HCC ≤3 cm, thermal ablation alone as the first-line therapy should be carefully considered after recognizing the key clinical characteristics, among which inflammatory response and immune status are the two most important factors involved in clinical heterogeneity, and inflammatory response is closely related to the prognosis of thermal ablation alone as the first-line therapy for these patients.

Difficulties Experienced by Pre-Service Teachers During Pedagogical Practice: Psychometric Properties of the Questionnaire Structure

The aim of our study was to describe the psychometric properties of the structure of a questionnaire designed to assess the difficulties experienced by pre-service teachers during their pedagogical practice. The study involved 110 pre-service teachers (university students): 96 women and 14 men, with a mean age of 24,28 years (SD = 5,03). Based on our previous research, a set of 20 items was developed asking students to rate their experience on a five-point scale. The hierarchical items cluster analysis algorithm ICLUST was used to test the psychometric characteristics of the questionnaire structure. The analysis was performed according to Revelle’s guidelines. Due to the ordinal nature of items, the cluster analysis was performed on the basis of a polychoric correlation matrix. The statistical analysis was carried out using the psych package in R. The reliability of the clusters was assessed taking into account Cronbach’s alpha and Revelle’s beta indicators. Cluster fit, pattern fit, and RMSR were selected as model fit indicators. Two models were developed: the seven-cluster model and two-cluster model. The seven-cluster model consisted of the following clusters: search and selection of material (K1; α = 0,75, β = 0,75), selection of teaching methods (K10; α = 0,77, β = 0,69), classroom management (K13; α = 0,81, β = 0,72), lack of subject and pedagogical knowledge (K3; α = 0,67, β = 0,67), emotions (K4; α = 0,75, β = 0,75), time management (K12; α = 0,70, β = 0,62), and organizational difficulties (K11; α = 0,66, β = 0,60). The two-cluster model consisted of the following clusters: search and selection of material (K1; α = 0,75, β = 0,75) and general difficulties (K18; α = 0,85, β =0,75). Combining 20 items into seven clusters allowed to achieve the best psychometric characteristics of the questionnaire and to reliably assess the difficulties experienced by pre-service teachers during their pedagogical practice. The characteristics of the two-cluster model were satisfactory, and the 18-items general difficulty scale of this model can be chosen as an alternative to calculate the overall estimate of the difficulties experienced by pre-service teachers during their pedagogical practice.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals. Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the US National Heart, Lung, and Blood Institute (NHLBI) LAM registry. Prospective outcomes were associated with cluster results. Two- and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and tuberous sclerosis complex (TSC) (p=0.041). Patients in the third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model the future risk of pneumothorax was 3.3 (95% CI 1.7-5.6)-fold greater in cluster 1 than cluster 2 (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters 2 and 3 than cluster 1 (p<0.00001). In the NHLBI cohort, the incidence of death or lung transplant was much lower in clusters 2 and 3 (p=0.0045). Machine learning has identified clinically relevant clusters associated with complications and outcome. Assigning individuals to clusters could improve decision making and prognostic information for patients.

Spatial patterns in regional life expectancy trends in the European Union between 1990 and 2018

Abstract Background A substantial life expectancy gap exists at the regional level in the European Union (EU). It has consistently followed an east-west pattern since 1990. The spatial pattern in the rate of change of regional life expectancy is less well established. Understanding both patterns is necessary to guide regional cohesion and health inequalities policy in the EU. Our aim was to investigate whether there are distinct spatial patterns in regional life expectancy trends in the EU since 1990. Methods Using Eurostat's regional life expectancy database, we constructed a panel of sex-specific period life expectancy at birth (PLE) for 201 EU NUTS2 regions between 1990 and 2018. Regions with populations less than a hundred thousand and with more than ten years of missing data were excluded. We clustered the regions based on the similarity of the shapes in PLE trends for each sex using a bootstrap version of the K-means algorithm. The optimal numbers of clusters were determined based on a majority of five empirical criteria. Finally, we used a bootstrap version of the join count test to assess the degree of the spatial association of the identified clusters. Results Preliminary results of clustering on PLE trends support a two-cluster model for women (high and moderate rate of PLE improvement) and a three-cluster model for men (very high, high, and moderate rate of PLE improvement). The clusters do not correspond to EU macro-regions or the year of EU accession. The female high rate and male very high rate of PLE improvement clusters overlap in northern Portugal, northern Italy, Finland, Poland, Czechia, Austria, the Budapest region, and Slovenia. The join count tests reject a random spatial patterning of clusters. Conclusions EU regional life expectancy trends since 1990 exhibit a spatial pattern that does not correspond to EU macro-regions or recent EU accession. Analysing spatial patterns in regional lifespan disparity trends would be an interesting next step. Key messages Subnational EU life expectancy trends exhibit a spatial pattern that undermines the prevailing east-west divide narrative as eastern European regions are among the best performing since 1990. National differences in health may not reflect the experience of European citizens in their regions. Subnational and dynamic perspectives are key for future health disparities research in the EU.

Child labor, sex and mental health outcomes amongst adolescent refugees

IntroductionRefugee adolescents living in camps and settlements in low and middle-income countries are a vulnerable population who face protection and psychosocial risks. This study explores prevalence of child labor amongst adolescent refugees from South Sudan in two refugee settlements in Uganda, to understand impacts of child labor on mental health outcomes, and examines the influence of sex on these impacts. MethodsSurveys were conducted in Adjumani and Kiryandongo refugee settlements, Uganda, with 470 adolescents between 13 and 17 interviewed between December 2014–February 2015. Univariate finite mixture modeling identified a two-cluster model of child labor. Logistic regression models assessed the association of child labor and mental health. ResultsA two-cluster solution for child labor activity was determined among the 332 adolescents who self-reported engaging in any child labor (Significant child labor: n = 174, 37%; moderate child labor cluster: n = 158, 34%; no child labor cluster: n = 138, 29%. Odds of depression amongst adolescents exposed to significant vs. no child labor was 4.15 (95% CI: 2.01–8.56), in a model examining interaction of sex and child labor and controlling for socio-demographic variables. For the anxiety outcome, girls exposed to significant vs. no child labor are less likely to report higher levels of anxiety (OR: 0.29, 95% CI: 0.09–0.90). ConclusionsAdolescents living in refugee settlements in Uganda report high levels of participation in child labor. Protection of adolescents from the risks involved with child labor in refugee contexts is an important and often over-looked area of child protection in humanitarian settings.

Intensive support for adults with intellectual disability and behaviours that challenge: a survey of provision and service typologies in England.

Approximately 18% of adults with intellectual disabilities living in the community display behaviours that challenge. Intensive support teams (ISTs) have been recommended to provide high-quality responsive care aimed at avoiding unnecessary admissions and reducing lengthy in-patient stays. To identify and describe the geographical distribution and characteristics of ISTs, and to develop a typology of IST service models in England. We undertook a national cross-sectional survey of 73 ISTs. A hierarchical cluster analysis was performed based on six prespecified grouping factors (mode of referrals, size of case-load, use of outcome measures, staff composition, hours of operation and setting of service). A simplified form of thematic analysis was used to explore free-text responses. Cluster analysis identified two models of IST provision: (a) independent and (b) enhanced provision based around a community intellectual disability service. ISTs aspire to adopt person-centred care, mostly use the framework of positive behaviour support for behaviour that challenges, and report concerns about organisational and wider context issues. This is the first study to examine the delivery of intensive support to people with intellectual disability and behaviour that challenges. A two-cluster model of ISTs was found to have statistical validity and clinical utility. The clinical heterogeneity indicates that further evaluation of these service models is needed to establish their clinical and cost-effectiveness.

A 3D Wideband Two-Cluster Channel Model for Massive MIMO Vehicle-to-Vehicle Communications in Semi-Ellipsoid Environments

This paper presents a three dimensional (3D) wideband two-cluster channel model for massive multiple-input and multiple-output (MIMO) vehicle-to-vehicle (V2V) communications. In the model, we introduce multiple confocal semi-ellipsoid models to depict the distribution of clusters in roadside environments; therefore, the statistical properties for different propagation delays can be derived and investigated. Furthermore, we adopt the birth-death process to simulate the dynamic characteristics of clusters on both the array and time axes, which efficiently characterize the non-stationarity of massive MIMO communication systems. Some comparisons of the spatial cross-correlation functions (CCFs) of propagation links via the two-cluster model with and without the cluster evolution for different taps are made. In addition, we study the temporal auto-correlation functions (ACFs) of the propagation links with respect to the different moving time of the mobile transmitter (MT) and mobile receiver (MR). Simulation results of the statistical channel characteristics nicely match those of the theoretical results, which demonstrate that our model is suitable for describing real massive MIMO V2V communication environments.

Does the Excited Cluster 14N* Exist in the 15O Nucleus?

On the basis of the modified potential two-cluster model with forbidden state which effectively take the Pauli principle into account, calculations have been performed of the astrophysical S-factor of radiative p14N capture to the ground state of the 15O nucleus at proton energies up to 5 MeV in the center-of-mass system (c.m.s.) with allowance for wide resonances up to 3.4 MeV in the c.m.s. For an acceptable explanation of the available experimental data it is necessary to allow the existence of the 14N cluster in the excited state 14N* at an excitation energy of 5.69 MeV and angular momentum Jπ = 1–. It is assumed that in such a case it is possible to use the wave function of the 4D1/2 state with respect to the relative motion of the p14N* clusters. It is also shown that a description of the S-factor of p14N capture in the resonance region is possible only under the assumption that all of the low-lying resonances at 260(1/2+), 987(3/2+), 1447(1/2+), 2187(3/2+), and 3211(3/2+) keV in the c.m.s. are 4D1/2 and 4D3/2 scattering waves.

Exploring the Sensory Profiles of Children on the Autism Spectrum Using the Short Sensory Profile-2 (SSP-2)

The aim of this study was to identify sensory subtypes in children on the autism spectrum using the Short Sensory Profile-2 (SSP-2). Caregivers of children on the autism spectrum aged 4-11years (n = 271) completed the SSP-2. Analysis using Dirichlet process mixture model identified a two-cluster model which provided the best solution to subtype sensory responses. Two distinct subtypes were identified: Uniformly elevated (67%) with high scores across all quadrants and Raised avoiding and sensitivity (33%) with raised scores in the avoiding and sensitivity quadrants. There were no differences between subtypes based on chronological age and autism characteristics measured using the social communication questionnaire (total score). Based on the SSP-2, children were reported to experience differences in responses to sensory input, in particular in the area of sensitivity and avoiding.

A General 3D Non-Stationary Wideband Twin-Cluster Channel Model for 5G V2V Tunnel Communication Environments

In this paper, we propose a general three-dimensional (3D) non-stationary wideband two-cluster channel model for fifth-generation (5G) vehicle-to-vehicle (V2V) tunnel communication environments. In the proposed model, the received signal is constructed as a sum of the components with line-of-sight (LoS) propagations and the components via the two-cluster model, i.e., non-line-of-sight (NLoS) propagations; therefore, the model has the ability to sufficiently describe a variety of 5G wireless communication scenarios. In order to investigate the non-stationary properties of clusters, we introduce a birth-death algorithm to model the appearance and disappearance of clusters on both the array and time axes. The impacts of the non-stationary properties of clusters on the multiple-input and multiple-output (MIMO) channels are investigated via statistical properties, including spatial cross-correlation functions (CCFs), temporal spatial auto-correlation functions (ACFs), and Doppler power spectrum densities (PSDs). Numerical results of the proposed propagation properties fit the simulation results and prior measured results very well, which demonstrate that the proposed 3D model is able to describe the real 5G V2V communications in tunnel environments.

Phenomenological cluster-based model of Ca2+ waves and oscillations for inositol 1,4,5-trisphosphate receptor ( IP3R ) channels

Clusters of IP3 receptor channels in the membranes of the endoplasmic reticulum (ER) of many non-excitable cells release calcium ions in a cooperative manner giving rise to dynamical patterns such as Ca2+ puffs, waves, and oscillations that occur on multiple spatial and temporal scales. We introduce a minimal yet descriptive reaction-diffusion model of IP3 receptors for a saturating concentration of IP3 using a principled reduction of a detailed Markov chain description of individual channels. A dynamical systems analysis reveals the possibility of excitable, bistable and oscillatory dynamics of this model that correspond to three types of observed patterns of calcium release -- puffs, waves, and oscillations respectively. We explain the emergence of these patterns via a bifurcation analysis of a coupled two-cluster model, compute the phase diagram and quantify the speed of the waves and period of oscillations in terms of system parameters. We connect the termination of large-scale Ca2+ release events to IP3 unbinding or stochasticity.

Longitudinal trajectories of asthma exacerbations from infancy to school age.

IntroductionPrevious studies which used data-driven methodologies have reported the existence of an exacerbation-prone asthma subtype, which is independent of asthma severity. However, longitudinal patterns of asthma exacerbations during childhood have not been studied.&#x0D; Objectives and ApproachWe sought to investigate whether there are distinct longitudinal trajectories of asthma exacerbations from infancy to school-age that could facilitate better understanding of the heterogeneity of asthma syndrome.&#x0D; We used longitudinal k-means modelling (an unsupervised data-driven method), to analyse linked primary care data from 916 participants in a population-based birth cohort study (Manchester Asthma and Allergy Study), to ascertain clusters of children with similar trajectories of asthma exacerbations during childhood (n=160). We tested the validity of these clusters in relation to lung function, airway hyperreactivity and inflammation, allergic sensitisation, and the use of asthma medication.&#x0D; ResultsA two-cluster model provided the optimal solution for our data set. Based on the pattern of exacerbations from infancy to age 8 years, we assigned the clusters as: “Early-onset frequent exacerbations (FE)” (n=10) and “Infrequent exacerbations (IE)” (n=150). Shorter duration of breastfeeding was the strongest risk factor for FE (median weeks 0 (IQR: 0-1.75) vs IE, median weeks 6 (IQR: 0-20), p&lt;0.001). Children in the FE cluster were more likely to exhibit persistent wheeze (90% vs 47%, p=0.03) and have poorer lung function, more airway hyperreactivity, and more airway inflammation throughout childhood (Table 1). In a post-hoc analysis, when we compared children in the exacerbation clusters with those who have wheezed only (n=389), and those that wheezed but had no exacerbations (n=338), other early life risk factors such as atopic sensitisation (IE - RR: 3.2 (95%CI: 2.1-5.1), p&lt;0.001) (FE - RR: 10.9 (95%CI: 2.1-57.7), p=0.004), exposure to tobacco smoke at birth (FE - RR: 2.8 (95%CI: 1.3-6.3) ,p=0.02), position in sibship (IE - RR: 1.5 (95%CI: 1.0-2.3), p=0.03), and day care attendance (IE - RR:0.6 (95%CI: 0.4-0.9), p=0.01) were significantly associated with exacerbations.&#x0D; Conclusion/ImplicationsWe have identified two distinct patterns of asthma exacerbations during childhood with different outcomes, early-life risk factors, and lung function when compared to children who wheeze, but have no exacerbations. These results indicate that exacerbations represent an independent susceptibility phenotype.