Generalized Linear Models Research Articles

Effect of Cigarette Smoking on Postoperative Outcomes After Arthroscopic Bone Marrow Stimulation for Osteochondral Lesions of the Talus.

Bone marrow stimulation (BMS) is presently considered first-line surgical treatment for osteochondral lesions of the talus (OLTs); however, some patients still experience pain or dysfunction after surgery, and the reasons for success or failure remain somewhat unclear. This study aimed to investigate the effect of smoking on postoperative outcomes after arthroscopic BMS for OLTs. Consecutive patients with OLTs who underwent BMS between January 2017 and January 2020 were included. Smokers were defined as patients who actively consumed cigarettes before surgery and postoperatively, whereas nonsmokers were patients who never smoked. Visual analog scale (VAS), American Orthopaedic Foot & Ankle Society ankle hindfoot score (AOFAS), Karlsson-Peterson, and Tegner scores were assessed preoperatively and at follow-up. Additionally, a general linear model (GLM) was performed, followed by the interaction analysis to explore the potential influence of smoking. The study enrolled 104 patients with a mean follow-up of 30.91 ± 7.03 months, including 28 smokers and 76 nonsmokers. There were no significant differences in patient age (35.2 ± 10.0 years vs 37.6 ± 9.7 years, P = .282) or OLT area (63.7 ± 38.7 mm2 vs 52.8 ± 37.0 mm2, P = .782). Both univariate analysis and GLM revealed that smoking was associated with worse postoperative pain levels, Karlsson-Peterson, and AOFAS scores (P < .05). The interaction analysis showed a significant interaction between smoking and OLT area for postoperative Karlsson-Peterson scores (general ankle function) (P = .031). Simple main effects analysis revealed that the negative effect of smoking on Tegner score significantly increased among patients >32 years old or with OLT area>50 mm2 (P < .05). Smoking was associated with worse clinical outcomes following BMS of OLTs. As the size of OLTs increased, the difference in general ankle function between smokers and nonsmokers also increased. Furthermore, smokers who were older than 32 years or had larger OLTs were less likely to resume participation in high-level activities.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species’ potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen’s kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

Sleep quality during pregnancy and fetal growth: A prospective cohort study.

The aim of this study is to investigate the association between sleep quality during pregnancy and fetal growth. Pregnant women and their fetuses at 16-20 gestational weeks in Nantong Maternal and Child Health Hospital were recruited. Women were classified as having "good sleep quality" (Pittsburgh Sleep Quality Index score ≤ 5) and "poor sleep quality" (Pittsburgh Sleep Quality Index score > 5) according to the Pittsburgh Sleep Quality Index scores. The fetal growth was evaluated by three ultrasonographic examinations, birth weight and birth length. We used general linear model and multiple linear regression models to estimate the associations. A total of 386 pairs of mother and infant were included in the data analysis. After adjusting for gestational weight gain, anxiety and depression, fetuses in the good sleep quality group had greater abdominal circumference (p = 0.039 for 28-31+6 weeks gestation, p = 0.012 for 37-40+6 weeks gestation) and femur length (p = 0.014 for 28-31+6 weeks gestation, p = 0.041for 37-40+6 weeks gestation) at 28-31+6 weeks gestation and 37-40+6 weeks gestation, and increased femur length (p = 0.007) at 28-31+6 weeks gestation. Birth weights (p = 0.018) were positively associated with sleep quality. Poor sleep quality was associated with poor intrauterine physical development, decreased abdominal circumference and femur length, and lower birth weight after adjusting for confounding factors. Attention to the fetal growth of pregnant women with poor sleep quality has the potential to decrease the risk of adverse fetal outcomes.

Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach

Competitive analysis in digital and technology is trending in the business field. However, the field of digital and technology in the business world is vast and challenging to analyse. The purpose of this research is first to identify the success factor which represent the company performance. Second, is to identify the significant services provided by the company to their business user. Then, based on the first and second objectives, a predictive modelling is developed to produce the best solution to their business user. The research is implementing a case study from Telecommunication Company and using data science life cycle methodology. The statistical modelling that is used to develop the competitor’s analysis model is generalised linear model (GLM) which integrated with machine learning approach. Furthermore, the synthetic data set is created by using Gamma Distribution, Gaussian Distribution and Poisson Distribution due to some data from the case study is confidential. The synthetic data set is based on existing real data which are from Telecommunication Company sentiment analysis data, were used to investigate the performance of the proposed model. The machine learning technique is used to get the accuracy of the significant GLM which has been developed. The accuracy is tested by using the error rates of the machine learning technique which are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared. This research discovered that the business solution to the significant service for the business user and discovered the best statistical model to be used for the business solution. The results show that the Gumbel distribution is the best fit model for the synthetic dataset where the values of RMSE is 1.0574, MAE is 0.9168 and R-squared is 0.3994, and the significant success factor that has been identified by using the GLM is advertising success factor. The model developed can be improved with another type of data set and different sizes of data. Hence, further studies and real-world data are required for better validation.

The association between exposure to volatile organic chemicals and serum α-Klotho in USA middle to old aged population: A cross-sectional study from NHANES 2011–2016

BackgroundVolatile Organic Compounds (VOCs) constitute an omnipresent category of environmental contaminants. Numerous studies have identified associations between various VOCs and human diseases. The anti-aging protein α-Klotho has been shown to exert protective influences across a variety of disease origins and progressions. This study aims to explore the relationship between serum α-Klotho levels and exposure to VOCs in humans. MethodsThis analysis utilized data from 1672 participants aged from 40 to 79 years in 2011–2016 NHANES. Exposure to VOCs was assessed through measurements of urinary VOC metabolites (mVOCs), with 16 mVOCs selected for analysis. Multivariate generalized linear models (GLM), restricted cubic splines (RCS), weighted quantile sum (WQS) regression models, and Bayesian kernel machine regression (BKMR) models were employed to examine the connection between serum α-Klotho and individual mVOCs and mVOCs mixtures, as well as to identify the primary monomeric mVOCs responsible for these associations. ResultsOur research revealed that 8 mVOCs exhibited inverse associations with serum α-Klotho levels in GLM and RCS models. Particularly noteworthy, N-Acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA), a metabolite of acrylonitrile, emerged as the most influential mVOC in both WQS and BKMR models. Furthermore, the mVOCs mixture was found to be negatively correlated with serum α-Klotho. The detrimental effects of mVOCs on serum α-Klotho were observed to significantly diminish in individuals with elevated serum vitamin D levels. ConclusionOur study highlights a significant inverse relationship between serum α-Klotho and the mixture of mVOCs, indicating that exposure to VOCs may impact the molecular pathways of aging and related diseases by influencing α-Klotho concentrations. Remarkably, the attenuation of this association by high serum vitamin D levels implies potential therapeutic strategies. Our study underscores the importance of minimizing VOCs exposure to mitigate the adverse effects on α-Klotho. Further research is warranted to elucidate the underlying mechanisms of these relationships.

Modeling of Effectiveness of N3-Substituted Amidrazone Derivatives as Potential Agents against Gram-Positive Bacteria.

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the minimum inhibitory concentration (MIC) against Gram-positive bacteria: Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Nocardia corallina, and Mycobacterium smegmatis. For the analysis of compounds characterized by experimentally measured MIC values, we included physicochemical properties (e.g., molecular weight, number of hydrogen donors and acceptors, topological polar surface area, compound percentages of carbon, nitrogen, and oxygen, melting points, and lipophilicity) as potential predictors. The presence of R1 and R2 substituents, as well as interactions between melting temperature and R1 or R2 substituents, were also considered. The set of potential predictors also included possible biological effects (e.g., antibacterial, antituberculotic) of tested compounds calculated with the PASS (Prediction of Activity Spectra for Substances) program. Using GLMs with least absolute shrinkage and selection (LASSO), least-angle regression, and stepwise selection, statistically significant models with the optimal value of the adjusted determination coefficient and of seven fit criteria were chosen, e.g., Akaike's information criterion. The most often selected variables were as follows: molecular weight, PASS_antieczematic, PASS_anti-inflam, squared melting temperature, PASS_antitumor, and experimental lipophilicity. Additionally, relevant to the bacterial strain, the interactions between melting temperature and R1 or R2 substituents were selected, indicating that the relationship between MIC and melting temperature depends on the type of R1 or R2 substituent.

Associations of an HDL apolipoproteomic index with cardiometabolic risk factors before and after exercise training in the HERITAGE Family Study

Background and aimsPrevious studies have derived and validated an HDL apolipoproteomic score (pCAD) that predicts coronary artery disease (CAD) risk. However, the associations between pCAD and markers of cardiometabolic health in healthy adults are not known, nor are the effects of regular exercise on pCAD. MethodsA total of 641 physically inactive adults free of cardiovascular disease from the HERITAGE Family Study completed 20 weeks of exercise training. The pCAD index (range 0–100) was calculated using measurements of apolipoproteins A-I, C–I, C-II, C-III, and C-IV from ApoA-I-tagged serum (higher index = higher CAD risk). The associations between pCAD index and cardiometabolic traits at baseline and their training responses were assessed with Spearman correlation and general linear models. A Bonferroni correction of p < 8.9 × 10−04 was used to determine statistical significance. ResultsThe mean ± SD baseline pCAD index was 29 ± 32, with 106 (16.5 %) participants classified as high CAD risk. At baseline, pCAD index was positively associated with blood pressure, systemic inflammation, and body composition. HDL size, VO2max, and HDL-C were negatively associated with pCAD index at baseline. Of those classified as high CAD risk at baseline, 52 (49 %) were reclassified as normal risk after training. Following training, pCAD index changes were inversely correlated (p < 1.4 × 10−04) with changes in HDL-C, HDL size, and LDL size. ConclusionsA higher pCAD index was associated with a worse cardiometabolic profile at baseline but improved with regular exercise. The results from this study highlight the potential role of HDL apolipoproteins as therapeutic targets for lifestyle interventions, particularly in high-risk individuals.

Associations between seminal plasma metals/metalloids and semen quality: A cross-sectional study

Exposure to metals/metalloids is reported to potentially influence semen quality. While most studies have focused on single metal impacts, the link between exposure to multiple metals and semen quality has remained less explored. The study aimed to investigate the effects of both individual and mixed metal/metalloid exposure on semen quality. A total of 330 men were recruited from three reproductive centers in eastern China. Seminal plasma levels of 25 metals/metalloids and sperm parameters were determined. We used the Generalized Linear Model (GLM) and Restricted Cubic Spline (RCS) to assess the relationships between single metals/metalloids and semen quality. The weighted quantile sum (WQS) models were then applied to evaluate the combined effect of all these metals/metalloids. We observed positive associations of exposure to lithium (Li), zinc (Zn), and magnesium (Mg) with an increased risk of below reference values for progressive motility and total motility using a logistic regression model (P < 0.05). Additionally, our results also revealed a significant inverse relationship between aluminum (Al) and both sperm concentration and count, while cobalt (Co) demonstrated a positive association with sperm concentration (P < 0.05). Notably, the WQS model indicated a significant positive association between exposure to metal/metalloid mixtures and the risk of abnormal progressive motility (OR: 1.57; 95%CI: 1.10, 2.24) and abnormal total motility (OR: 1.53; 95%CI: 1.06, 2.19), with this association primarily driven by Li, Mg, and Zn. In summary, our findings indicate that exposure to metal/metalloid mixtures might have an adverse effect on semen quality.

Increased genetic risk for β-cell failure is associated with β-cell function decline in people with prediabetes.

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the influence of T2D pPS on diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin or placebo arms. Associations were tested using general linear models and Cox regression adjusted for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at one year follow-up adjusted for baseline measures (effect per pPS standard deviation (SD) -0.04, P=9.6 x 10-7; -8.45 uU/mg, P=5.6 x 10-6, respectively) and with increased diabetes incidence adjusted for BMI at nominal significance (HR 1.10 per SD, P=0.035). The liver/lipid pPS was associated with reduced one-year baseline-adjusted triglyceride levels (effect per SD -4.37, P=0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function.

Clinico-demographic factors associated with the treatment response to cognitive behavioral therapy for insomnia

BackgroundCognitive behavioral therapy for insomnia (CBT-I) is among the recommended non-pharmacological treatments for patients with insomnia. While there are multiple reports on the effects of CBT-I treatment, few studies evaluating the factors associated with the treatment response to CBT-I have been reported. The present study aimed to confirm the effects of CBT-I in patients with insomnia and to examine the clinico-demographic factors that can predict the outcomes of CBT-I in these patients.MethodsOverall, 62 patients were included in the present study. To confirm the effectiveness of CBT-I, we compared the pre- and post-CBT-I therapy values of several sleep parameters. Furthermore, to identify the clinico-demographic factors that could be predictive of the treatment response to CBT-I, we performed generalized linear model (GLM) analysis.ResultsThe values of several sleep parameters were significantly lower after treatment than at baseline. The results of the GLM analysis revealed that sex and occupation were significantly associated with the treatment response to CBT-I.ConclusionsThe present results suggest that several clinico-demographic factors should be considered in the treatment of patients with insomnia.

Emotion Processing in Peripheral Neuropathic Pain: An Observational Study.

In clinical practice, the implementation of tailored treatment is crucial for assessing the patient's emotional processing profile. Here, we investigate all three levels of analysis characterizing emotion processing, i.e., recognition, representation, and regulation, in patients with peripheral neuropathic pain (PNP). Sixty-two patients and forty-eight healthy controls underwent quantitative sensory testing, i.e., psychophysical tests to assess somatosensory functions such as perception of cold (CDT), heat-induced pain (HPT), and vibration (VDT), as well as three standardized tasks to assess emotional processing: (1) the Ekman 60-Faces Test (EK-60F) to assess recognition of basic facial emotions, (2) the Reading the Mind in the Eyes Test (RME) to assess the ability to represent the feelings of another person by observing their eyes, and (3) the 20-item Toronto Alexithymia Scale (TAS-20) to assess emotional dysregulation, i.e., alexithymia. General Linear Model analysis revealed a significant relationship between left index finger VDT z-scores in PNP patients with alexithymia. The RME correlated with VDT z-scores of the left little finger and overall score for the EK-60F. In patients with PNP, emotion processing is impaired, which emphasizes the importance of assessing these abilities appropriately in these patients. In this way, clinicians can tailor treatment to the needs of individual patients.

Effect of a Long-Term Online Home-Based Supervised Exercise Program on Physical Fitness and Adherence in Breast Cancer Patients: A Randomized Clinical Trial

The purpose of the present study was to analyze the effect of a synchronous-supervised online home-based exercise program (HBG) during 24 weeks on body composition, physical fitness and adherence compared to an exercise recommendation group (ERG) without supervision with patients undergoing breast cancer treatment. Fifty-nine female breast cancer patients (31 in HBG and 28 in the ERG) undergoing cancer treatments participated in the present randomized clinical trial. The exercise program consisted of a 60 min combined resistance and aerobic supervised exercise session (6–8 points on Borg Scale CR-10, moderate intensity), twice a week during 24 weeks. The exercise recommendation group only received general recommendations to comply with the current ACSM guidelines. Body composition and physical fitness were assessed at baseline, 12 weeks and 24 weeks of the program. Adherence to the intervention was measured according to the minutes of exercise completed per session during each week. A general linear model of two-way repeated measures showed significant improvements (p &lt; 0.05) in physical fitness that were observed in the home-based exercise group at the baseline, 12-week and 24-week assessments compared to the exercise recommendation group. Adherence was also higher in the home-based exercise group. However, no changes (p &gt; 0.05) in body composition between groups and moments were observed. In this sense, supervised home-based exercise interventions can be an interesting strategy to improve physical fitness and adherence rates in breast cancer patients undergoing treatment.

Telehealth Infrastructure, Accountable Care Organization, and Medicare Payment for Patients with Alzheimer's Disease and Related Dementia Living in Socially Vulnerable Areas.

Background: Structural social determinants of health have an accumulated negative impact on physical and mental health. Evidence is needed to understand whether emerging health information technology and innovative payment models can help address such structural social determinants for patients with complex health needs, such as Alzheimer's disease and related dementias (ADRD). Objective: This study aimed to test whether telehealth for care coordination and Accountable Care Organization (ACO) enrollment for residents in the most disadvantaged areas, particularly those with ADRD, was associated with reduced Medicare payment. Methods: The study used the merged data set of 2020 Centers for Medicare and Medicaid Services Medicare inpatient claims data, the Medicare Beneficiary Summary File, the Medicare Shared Savings Program ACO, the Center for Medicare and Medicaid Service's Social Vulnerability Index (SVI), and the American Hospital Annual Survey. Our study focused on community-dwelling Medicare fee-for-service beneficiaries aged 65 years and up. Cross-sectional analyses and generalized linear models (GLM) were implemented. Analyses were implemented from November 2023 to February 2024. Results: Medicare fee-for-service beneficiaries residing in SVI Q4 (i.e., the most vulnerable areas) reported significantly higher total Medicare costs and were least likely to be treated in hospitals that provided telehealth post-discharge services or have ACO affiliation. Meanwhile, the proportion of the population with ADRD was the highest in SVI Q4 compared with other SVI levels. The GLM regression results showed that hospital telehealth post-discharge infrastructure, patient ACO affiliation, SVI Q4, and ADRD were significantly associated with higher Medicare payments. However, coefficients of interaction terms among these factors were significantly negative. For example, the average interaction effect of telehealth post-discharge and ACO, SVI Q4, and ADRD on Medicare payment was -$1,766.2 (95% confidence interval: -$2,576.4 to -$976). Conclusions: Our results suggested that the combination of telehealth post-discharge and ACO financial incentives that promote care coordination is promising to reduce the Medicare cost burden among patients with ADRD living in socially vulnerable areas.

Efficacy of the residential care transition module: A telehealth intervention for dementia family caregivers of relatives living in residential long-term care settings.

The purpose of this study was to evaluate the efficacy of the Residential Care Transition Module, a six-session, psychosocial, and psychoeducational telehealth intervention for family caregivers of cognitively impaired relatives living in a residential long-term care setting. Eligible participants (including care recipients, regardless of time since admission) were randomized to treatment or usual care control conditions. Survey data were collected at baseline, 4 months, 8 months, and 12 months (N = 240). Primary analytic outcomes included caregiver subjective stress (a stress process mechanism) and depressive symptoms (a measure of global well-being). Secondary analytic outcomes included secondary role strains, residential care stress, caregiver sense of competence, and self-efficacy (additional mechanisms of action). General linear models tested for the main effects of the intervention at 4 months, and longitudinal mixed models examined the 12-month effects of the intervention. Post hoc analyses also examined the influence of moderators. No significant differences between the treatment and control groups for any primary analytic outcome were apparent. Caregivers in the treatment group whose relatives were admitted to residential long-term care in the prior 3 months were more likely to indicate reductions in depressive symptoms over the first 4 months of participation. Over the 12-month study period, caregivers in the treatment group who were employed reported increased self-efficacy over time. The heterogeneity of dementia care requires a broader consideration of key contextual factors that may influence the efficacy of nonpharmacological interventions. Aligning measures with the preferences, goals, and values of dementia caregivers may further demonstrate the direct benefits of interventions such as the Residential Care Transition Module. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Inverted U-Shaped Association between Serum Vitamin D and Serum Uric Acid Status in Children and Adolescents: A Large Cross-Sectional and Longitudinal Analysis.

Serum vitamin D is associated with hyperuricemia. However, previous studies have been controversial, with limited focus on children and adolescents. This study aimed to examine the cross-sectional and longitudinal associations between serum vitamin D and serum uric acid (SUA) levels in children and adolescents. The cross-sectional survey comprised 4777 participants aged 6 to 18 years, while the longitudinal survey involved 1641 participants aged 6 to 12 years, all derived from an ongoing cohort study in Shenzhen, China. Restricted cubic splines were used to visualize the dose-response relationship between vitamin D and SUA and the risk of higher SUA status. Two-segment generalized linear models (GLM) and logistic models were used to assess the association between vitamin D and SUA and higher SUA status, respectively. The longitudinal analysis used GLM. We observed an inverted U-shaped relationship between vitamin D and SUA (p-overall < 0.0001, p-nonlinear = 0.0002), as well as the risk of higher SUA status (p-overall = 0.0054, p-nonlinear = 0.0015), with the vitamin D inflection point at 24.31 and 21.29 ng/mL, respectively. A 10 ng/mL increment in 25(OH)D3 levels, when below 20.92 ng/mL, was associated with a 68% rise in the risk of higher SUA status (OR: 1.68, 95%CI: 1.07-2.66). Conversely, when 25(OH)D3 levels were above or equal to 20.92 ng/mL, a 10 ng/mL increment was associated with a 45% reduction risk of higher SUA status (OR: 0.55, 95%CI: 0.36-0.84). Longitudinal analysis indicated that the annual change of SUA was from -4.80 (β, 95%CI: -10.74, 1.13) to -9.00 (β, 95%CI: -15.03, -2.99) and then to -6.77 (β, 95%CI: -12.83, -0.71, p for trend = 0.0212) μmol/L when increasing the quartile of vitamin D3. An inverse U-shaped relationship was observed between vitamin D and SUA as well as the risk of higher SUA status. Sufficient vitamin D levels appear to play a preventative role against the age-related increase in SUA. Ensuring adequate vitamin D levels may be beneficial in improving uric acid metabolism.

Machine learning allows for large-scale habitat prediction of a wide-ranging carnivore across diverse ecoregions

ContextResource selection functions are powerful tools for predicting habitat selection of animals. Recently, machine-learning methods such as random forest have gained popularity for predicting habitat selection due to their flexibility and strong predictive performance.ObjectivesWe tested two methods for predicting continental-scale, second-order habitat selection of a wide-ranging large carnivore, the mountain lion (Puma concolor), to support continent-wide conservation management, including estimating abundance, and to predict habitat suitability for recolonizing or reintroduced animals.MethodsWe compared a generalized linear model (GLM) and a random forest model using GPS location data from 476 individuals across 20 study sites in the western USA and Canada and remotely-sensed landscape data. We internally validated models and examined their ability to correctly classify used and available points by calculating area under the receiver operating characteristics (AUC). We performed leave-one-out (LOO) out-of-sample tests of predictive strength on both models.ResultsBoth models suggested that mountain lions select for steeper slopes, areas closer to water, and with higher normalized difference vegetation index (NDVI), and against variables associated with human impact. The random forest model (AUC = 0.94) demonstrated that mountain lion habitat can be accurately predicted at continental scales, outperforming the traditional GLM model (AUC = 0.68). Our LOO validation provided similar results (x̄ = 0.93 for the random forest and x̄ = 0.65 for the GLM).ConclusionsWe found that the added flexibility of the random forest model provided deeper insights into how individual covariates impacted habitat selection across diverse ecosystems. Our LOO analyses suggested that our model can predict mountain lion habitat selection in unoccupied areas or where local data are unavailable. Our model thus provides a tool to support discussions and analyses relevant to continent-wide mountain lion conservation and management including estimating metapopulation abundance.

Evaluation of the Glymphatic System in Schizophrenia Spectrum Disorder Using Proton Magnetic Resonance Spectroscopy Measurement of Brain Macromolecule and Diffusion Tensor Image Analysis Along the Perivascular Space Index.

The glymphatic system (GS), a brain waste clearance pathway, is disrupted in various neurodegenerative and vascular diseases. As schizophrenia shares clinical characteristics with these conditions, we hypothesized GS disruptions in patients with schizophrenia spectrum disorder (SCZ-SD), reflected in increased brain macromolecule (MM) and decreased diffusion-tensor-image-analysis along the perivascular space (DTI-ALPS) index. Forty-seven healthy controls (HCs) and 103 patients with SCZ-SD were studied. Data included 135 proton magnetic resonance spectroscopy (1H-MRS) sets, 96 DTI sets, with 79 participants contributing both. MM levels were quantified in the dorsal-anterior cingulate cortex (dACC), dorsolateral prefrontal cortex, and dorsal caudate (point resolved spectroscopy, echo-time = 35ms). Diffusivities in the projection and association fibers near the lateral ventricle were measured to calculate DTI-ALPS indices. General linear models were performed, adjusting for age, sex, and smoking. Correlation analyses examined relationships with age, illness duration, and symptoms severity. MM levels were not different between patients and HCs. However, left, right, and bilateral DTI-ALPS indices were lower in patients compared with HCs (P < .001). In HCs, age was positively correlated with dACC MM and negatively correlated with left, right, and bilateral DTI-ALPS indices (P < .001). In patients, illness duration was positively correlated with dACC MM and negatively correlated with the right DTI-ALPS index (P < .05). In the entire population, dACC MM and DTI-ALPS indices showed an inverse correlation (P < .01). Our results suggest potential disruptions in the GS of patients with SCZ-SD. Improving brain's waste clearance may offer a potential therapeutic approach for patients with SCZ-SD.

Group Size: An Active Ingredient of School-Based Language Therapy.

This study explored the relation between therapy group size and language outcomes for children receiving school-based language therapy through an implementation science lens. Data for the current study were gathered as part of the Speech-Language Therapy Experiences in Public Schools study. Participants included 273 English-speaking kindergarten through second-grade children with language impairment receiving business-as-usual therapy on the caseloads of 75 speech-language pathologists. Data were collected over an academic year, including weekly therapy logs, speech-language pathology questionnaires, and pre- and post-language measures. Descriptive analysis revealed that children primarily experienced small-group therapy sessions (two to four children); however, there was considerable variability in group size. Hierarchical general linear modeling indicated that caseload size did not explain group size variability. However, the number of student cancellations was positively associated with receipt of large-group sessions. Notably, a significant negative association was found between receipt of large-group sessions (i.e., five to 10 children) and language outcomes. Children who received more than the average number of sessions in large groups (i.e., more than 5% of total sessions) experienced 0.18 SD less language gain over the academic year compared to the mean (0.54 SD). No other group size configurations (i.e., individual, small group, and extra large) yielded significant associations with language outcomes. The findings suggest that young children receiving language-based therapy in large groups make substantially fewer language gains over an academic year. These results have considerable implications for educational policy and clinical practice, which are discussed through an implementation science frame.

Associations among plasma, MRI, and amyloid PET biomarkers of Alzheimer's disease and related dementias and the impact of health-related comorbidities in a community-dwelling cohort.

We evaluated associations between plasma and neuroimaging-derived biomarkers of Alzheimer's disease and related dementias and the impact of health-related comorbidities. We examined plasma biomarkers (neurofilament light chain, glial fibrillary acidic protein, amyloid beta [Aβ] 42/40, phosphorylated tau 181) and neuroimaging measures of amyloid deposition (Aβ-positron emission tomography [PET]), total brain volume, white matter hyperintensity volume, diffusion-weighted fractional anisotropy, and neurite orientation dispersion and density imaging free water. Participants were adjudicated as cognitively unimpaired (CU; N=299), mild cognitive impairment (MCI; N=192), or dementia (DEM; N=65). Biomarkers were compared across groups stratified by diagnosis, sex, race, and APOE ε4 carrier status. General linear models examined plasma-imaging associations before and after adjusting for demographics (age, sex, race, education), APOE ε4 status, medications, diagnosis, and other factors (estimated glomerular filtration rate [eGFR], body mass index [BMI]). Plasma biomarkers differed across diagnostic groups (DEM>MCI>CU), were altered in Aβ-PET-positive individuals, and were associated with poorer brain health and kidney function. eGFR and BMI did not substantially impact associations between plasma and neuroimaging biomarkers. Plasma biomarkers differ across diagnostic groups (DEM>MCI>CU) and are altered in Aβ-PET-positive individuals. Altered plasma biomarker levels are associated with poorer brain health and kidney function. Plasma and neuroimaging biomarker associations are largely independent of comorbidities.

Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm

Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm