The mixture of probabilistic regression models is one of the most common techniques to incorporate the information of covariates into learning of the population heterogeneity. Despite its flexibility, unreliable estimates can occur due to multicollinearity among covariates. In this paper, we develop Liu-type shrinkage methods through an unsupervised learning approach to estimate the model coefficients in the presence of multicollinearity. We evaluate the performance of our proposed methods via classification and stochastic versions of the expectation-maximization algorithm. We show using numerical simulations that the proposed methods outperform their Ridge and maximum likelihood counterparts. Finally, we apply our methods to analyze the bone mineral data of women aged 50 and older.

UNASSIGNED: Ghana\'s quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana.
UNASSIGNED: The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors that contribute to this menace at the facility level. Cause-specific deaths were obtained to determine the leading causes of neonatal deaths within health facilities in the two municipal assemblies.
UNASSIGNED: The study established that the leading causes of neonatal mortality in these districts are birth asphyxia (46%), premature birth (33%), neonatal sepsis (11%) and neonatal jaundice (7%). The NBH showed that neonatal mortality in hospital facilities depend on the number of incubators, monitoring equipment, hand washing facilities, CPAPb machines, radiant warmers, physiotherapy machines, midwives, paediatric doctors and paediatric nurses in the hospital facility.
UNASSIGNED: Early management of neonatal sepsis, birth asphyxia, premature birth and neonatal infections is required to reduce neonatal deaths. The government and all stakeholders in the health sector should provide all hospital facilities with the essential equipment and the medical human resources necessary to eradicate the menace. This will make the realization of Sustainable Development Goal three, which calls for healthy lives and well-being for all, a reality.

OBJECTIVE: Is exposure to environmental chemicals associated with modifications of placental morphology and function?
CONCLUSIONS: Phthalates, a class of ubiquitous chemicals, showed an association with altered placental weight, placental vascular resistance (PVR), and placental efficiency.
BACKGROUND: Only a few epidemiological studies have assessed the effects of phenols and phthalates on placental health. Their results were affected by exposure measurement errors linked to the rapid excretion of these compounds and the reliance on a limited number of spot urine samples to assess exposure.
UNASSIGNED: A prospective mother-child cohort, with improved exposure assessment for non-persistent chemicals, recruited participants between 2014 and 2017. Sample size ranged between 355 (placental parameters measured at birth: placental weight and placental-to-fetal weight ratio (PFR): a proxy for placental efficiency) and 426 (placental parameters measured during pregnancy: placental thickness and vascular resistance).
METHODS: Phenols (four parabens, two bisphenols, triclosan, and benzophenone-3), 13 phthalate metabolites, and two non-phthalate plasticizer metabolites were measured in within-subject pools of repeated urine samples collected during the second and third trimesters of pregnancy (median = 21 samples/trimester/woman). Placental thickness and PVR were measured during pregnancy. The placenta was weighed at birth and the PFR was computed. Both adjusted linear regression and Bayesian Kernel Machine Regression were used to evaluate associations between phenols and phthalates (alone or as a mixture) and placental parameters. Effect modification by child sex was also investigated.
RESULTS: Several phthalate metabolites were negatively associated with placental outcomes. Monobenzyl phthalate (MBzP) concentrations, during the second and third trimesters of pregnancy, were associated with a decrease in both placental weight at birth (β = -20.1 g [95% CI: -37.8; -2.5] and β = -17.4 g [95% CI: -33.2; -1.6], for second and third trimester, respectively) and PFR (β = -0.5 [95% CI: -1, -0.1] and β = -0.5 [95% CI: -0.9, -0.1], for the second and third trimester, respectively). Additionally, MBzP was negatively associated with PVR during the third trimester (β= -0.9 [95% CI: -1.8; 0.1]). Mono-n-butyl phthalate (MnBP), was negatively associated with PVR in both trimesters (β = -1.3, 95% CI: [-2.3, -0.2], and β = -1.2, 95% CI: [-2.4, -0.03], for the second and third trimester, respectively). After stratification for child sex, Σ diisononyl phthalate (DiNP) (either second or third-trimester exposures, depending on the outcomes considered) was associated with decreased PVR in the third trimester, as well as decreased placental weight and PFR in males. No associations were observed for phenol biomarkers.
CONCLUSIONS: False positives cannot be ruled out. Therefore, chemicals that were associated with multiple outcomes (MnBP and DiNP) or reported in existing literature as associated with placental outcomes (MBzP) should be considered as the main results.
CONCLUSIONS: Our results are consistent with in vitro studies showing that phthalates target peroxisome proliferator-activated receptor γ, in the family of nuclear receptors involved in key placental development processes such as trophoblast proliferation, migration, and invasion. In addition to placental weight at birth, we studied placental parameters during pregnancy, which could provide a broader view of how environmental chemicals affect maternal-fetal exchanges over the course of pregnancy. Our findings contribute to the increasing evidence indicating adverse impacts of phthalate exposure on placental health.
BACKGROUND: This work was supported by the French Research Agency-ANR (MEMORI project ANR-21-CE34-0022). The SEPAGES cohort was supported by the European Research Council (N°311765-E-DOHaD), the European Community\'s Seventh Framework Programme (FP7/2007-206-N°308333-892 HELIX), the European Union\'s Horizon 2020 research and innovation programme (N° 874583 ATHLETE Project, N°825712 OBERON Project), the French Research Agency-ANR (PAPER project ANR-12-PDOC-0029-01, SHALCOH project ANR-14-CE21-0007, ANR-15-IDEX-02 and ANR-15-IDEX5, GUMME project ANR-18-CE36-005, ETAPE project ANR-18-CE36-0005-EDeN project ANR-19-CE36-0003-01), the French Agency for Food, Environmental and Occupational Health & Safety-ANSES (CNAP project EST-2016-121, PENDORE project EST-2016-121, HyPAxE project EST-2019/1/039, PENDALIRE project EST-2022-169), the Plan Cancer (Canc\'Air project), the French Cancer Research Foundation Association de Recherche sur le Cancer-ARC, the French Endowment Fund AGIR for chronic diseases-APMC (projects PRENAPAR, LCI-FOT, DysCard), the French Endowment Fund for Respiratory Health, the French Fund-Fondation de France (CLIMATHES-00081169, SEPAGES 5-00099903, ELEMENTUM-00124527). N.J. was supported by a doctoral fellowship from the University Grenoble Alpes. V.M. was supported by a Sara Borrell postdoctoral research contract (CD22/00176), granted by Instituto de Salud Carlos III (Spain) and NextGenerationEU funds. The authors declare no conflict of interest.
BACKGROUND: ClinicalTrials.gov NCT02852499.

BACKGROUND: Overweight and obesity are among the leading chronic diseases worldwide. Environmental phenols have been renowned as endocrine disruptors that contribute to weight changes; however, the effects of exposure to mixed phenols on obesity are not well established.
METHODS: Using data from adults in National Health and Nutrition Examination Survey, this study examined the individual and combined effects of four phenols on obesity. A combination of traditional logistic regression and two mixed models (weighted quantile sum (WQS) regression and Bayesian kernel-machine regression (BKMR)) were used together to assess the role of phenols in the development of obesity. The potential mediation of cholesterol on these effects was analyzed through a parallel mediation model.
RESULTS: The results demonstrated that solitary phenols except triclosan were inversely associated with obesity (P-value < 0.05). The WQS index was also negatively correlated with general obesity (β: 0.770, 95% CI: 0.644-0.919, P-value = 0.004) and abdominal obesity (β: 0.781, 95% CI: 0.658-0.928, P-value = 0.004). Consistently, the BKMR model demonstrated the significant joint negative effects of phenols on obesity. The parallel mediation analysis revealed that high-density lipoprotein mediated the effects of all four single phenols on obesity, whereas low-density lipoprotein only mediated the association between benzophenol-3 and obesity. Moreover, Cholesterol acts as a mediator of the association between mixed phenols and obesity. Exposure to single and mixed phenols significantly and negatively correlated with obesity. Cholesterol mediated the association of single and mixed environmental phenols with obesity.
CONCLUSIONS: Assessing the potential public health risks of mixed phenols helps to incorporate this information into practical health advice and guidance.

We introduce a mathematical model based on mixture theory intended to describe the tumor-immune system interactions within the tumor microenvironment. The equations account for the geometry of the tumor expansion, and the displacement of the immune cells, driven by diffusion and chemotactic mechanisms. They also take into account the constraints in terms of nutrient and oxygen supply. The numerical investigations analyze the impact of the different modeling assumptions and parameters. Depending on the parameters, the model can reproduce elimination, equilibrium or escape phases and it identifies a critical role of oxygen/nutrient supply in shaping the tumor growth. In addition, antitumor immune cells are key factors in controlling tumor growth, maintaining an equilibrium while protumor cells favor escape and tumor expansion.

UNASSIGNED: Poor comprehenders are traditionally identified as having below-average reading comprehension, average-range word reading, and a discrepancy between the two. While oral language tends to be low in poor comprehenders, reading is a complex trait and heterogeneity may go undetected by group-level comparisons.
UNASSIGNED: We took a preregistered data-driven approach to identify poor comprehenders and examine whether multiple distinct cognitive profiles underlie their difficulties. Latent mixture modelling identified reading profiles in 6846 children from the Avon Longitudinal Study of Parents and Children, based on reading and listening comprehension assessments at 8-9 years. A second mixture model examined variation in the cognitive profiles of weak comprehenders, using measures of reading, language, working memory, nonverbal ability, and inattention.
UNASSIGNED: A poor comprehender profile was not identified by the preregistered model. However, by additionally controlling for overall ability, a 6-class model emerged that incorporated a profile with relatively weak comprehension (N = 947, 13.83%). Most of these children had weak reading comprehension in the context of good passage reading, accompanied by weaknesses in vocabulary and nonverbal ability. A small subgroup showed more severe comprehension difficulties in the context of additional cognitive impairments.
UNASSIGNED: Isolated impairments in specific components of reading are rare, yet a data-driven approach can be used to identify children with relatively weak comprehension. Vocabulary and nonverbal ability were most consistently weak within this group, with broader cognitive difficulties also apparent for a subset of children. These findings suggest that poor comprehension is best characterised along a continuum, and considered in light of multiple risks that influence severity.

When Bayesian latent class analysis is used for diagnostic test data in the absence of a gold standard test, it is common to assume that any unknown test sensitivities and specificities are constant across different populations. Indeed this assumption is often necessary for model identifiability. However there are a number of practical situations, depending on the type of test and the nature of the disease, where this assumption may not be true. We present a case study of using a microscopic agglutination test to diagnose leptospiroris infection in beef cattle, which strongly suggests that sensitivity in particular varies among herds. We develop and fit an alternative model in which sensitivity is related to within-herd prevalence, and discuss the statistical and epidemiological implications.

Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing Mycobacterium tuberculosis.

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate the associations of interest. Common record linkage algorithms only rely on similarities between linking variables that appear in all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage error, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage method to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method can improve the linking process, and can result in accurate inferences. We apply the method to link Meals on Wheels recipients to Medicare enrollment records.

Establishing the relationship between a limited number of samples and segmented objects in diverse scenarios is the primary challenge in few-shot segmentation. However, many previous works overlooked the crucial support-query set interaction and the deeper information that needs to be explored. This oversight can lead to model failure when confronted with complex scenarios, such as ambiguous boundaries. To solve this problem, a duplex network that utilizes the suppression and focus concept is proposed to effectively suppress the background and focus on the foreground. Our network includes dynamic convolution to enhance the support-query interaction and a prototype match structure to fully extract information from support and query. The proposed model is called dynamic prototype mixture convolutional networks (DPMC). To minimize the impact of redundant information, we have incorporated a hybrid attentional module called double-layer attention augmented convolutional module (DAAConv) into DPMC. This module enables the network to concentrate more on foreground information. Our experiments on PASCAL-5i and COCO-20i datasets suggested that DPMC and DAAConv outperform traditional prototype-based methods by up to 5-8% on average.