Generalized models for estimating cerebral lateralisation of young children using functional transcranial Doppler ultrasound.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35-48) introduced generalised model-based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child-friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4- to 7-year-old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period-of-interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates.

Treatment of congenital disorders of glycosylation: An overview.

While the identification and diagnosis of congenital disorders of glycosylation (CDG) have rapidly progressed, the available treatment options are still quite limited. Mostly, we are only able to manage the disease symptoms rather than to address the underlying cause. However, recent years have brought about remarkable advances in treatment approaches for some CDG. Innovative therapies, targeting both the root cause and resulting manifestations, have transitioned from the research stage to practical application. The present paper aims to provide a detailed overview of these exciting developments and the rising concepts that are used to treat these ultra-rare diseases.

Plastic ingestion by odontocetes from the Western South Atlantic: A particular concern to a threatened species.

The ingestion of debris by marine fauna is a growing threat to biodiversity. This study aimed to evaluate and characterize litter ingestion by odontocetes from the Western South Atlantic. Between 2018 and 2022, 154 stomachs from six species were collected from stranded individuals and incidental captures. Stomach contents were analyzed with the naked eye and items of anthropic origin found were counted and physically/chemically characterized. Generalized Linear Models were used to evaluate the influence of biological factors on the presence/absence of litter in stomachs, and for Pontoporia blainvillei only, the influence of these factors on the number of ingested items was also tested; additionally, a temporal analysis of ingestion was done for this species (1994-2022). A total of 156 items, mainly macro-sized plastics made of polypropylene, were found in 52 stomachs of four species: Tursiops spp. (FO% = 3.3%), Steno bredanensis (10.0%), Delphinus delphis (28.6%) and P. blainvillei (47.5%). The presence/absence of litter was explained only by species (χ2 = 28.29 and p < 0.001). For P. blainvillei, a threatened species in the region, the number of items was positively influenced by individual size (χ2 = 6.01 and p = 0.01) and sex (χ2 = 7.93 and p = 0.005). There was an increase in plastic ingestion by this species over the years (χ2 = 121.6 and p < 0.001) and it was estimated that 75% of P. blainvillei stomachs will contain plastic by 2040. The ingestion of litter by odontocetes from the Western South Atlantic was confirmed and the potential risks posed by this type of pollution were evidenced, especially since these species also face other anthropic pressures. These results further demonstrate the increasing threat of litter in the ocean and highlight the importance of circularity of plastics and proper waste management.

Deeply-Learned Generalized Linear Models with Missing Data.

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to various supervised learning problems. However, the greater prevalence and complexity of missing data in such datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, dlglm, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of the Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data. Supplementary materials for this article are available online.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM.

BACKGROUND: Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. METHODS: We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivity Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. RESULTS: In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. CONCLUSIONS: The applicability of our algorithms depends on the ℓ 2 sensitivity Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy.

LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. RESULTS: LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories. AVAILABILITY AND IMPLEMENTATION: The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/. SUPPLEMENTARY INFORMATION: Available at Bioinformatics online.

Evaluation of Generative Language Models in Personalizing Medical Information: Instrument Validation Study.

BACKGROUND: Although uncertainties exist regarding implementation, artificial intelligence-driven generative language models (GLMs) have enormous potential in medicine. Deployment of GLMs could improve patient comprehension of clinical texts and improve low health literacy. OBJECTIVE: The goal of this study is to evaluate the potential of ChatGPT-3.5 and GPT-4 to tailor the complexity of medical information to patient-specific input education level, which is crucial if it is to serve as a tool in addressing low health literacy. METHODS: Input templates related to 2 prevalent chronic diseases-type II diabetes and hypertension-were designed. Each clinical vignette was adjusted for hypothetical patient education levels to evaluate output personalization. To assess the success of a GLM (GPT-3.5 and GPT-4) in tailoring output writing, the readability of pre- and posttransformation outputs were quantified using the Flesch reading ease score (FKRE) and the Flesch-Kincaid grade level (FKGL). RESULTS: Responses (n=80) were generated using GPT-3.5 and GPT-4 across 2 clinical vignettes. For GPT-3.5, FKRE means were 57.75 (SD 4.75), 51.28 (SD 5.14), 32.28 (SD 4.52), and 28.31 (SD 5.22) for 6th grade, 8th grade, high school, and bachelor's, respectively; FKGL mean scores were 9.08 (SD 0.90), 10.27 (SD 1.06), 13.4 (SD 0.80), and 13.74 (SD 1.18). GPT-3.5 only aligned with the prespecified education levels at the bachelor's degree. Conversely, GPT-4's FKRE mean scores were 74.54 (SD 2.6), 71.25 (SD 4.96), 47.61 (SD 6.13), and 13.71 (SD 5.77), with FKGL mean scores of 6.3 (SD 0.73), 6.7 (SD 1.11), 11.09 (SD 1.26), and 17.03 (SD 1.11) for the same respective education levels. GPT-4 met the target readability for all groups except the 6th-grade FKRE average. Both GLMs produced outputs with statistically significant differences (P<.001; 8th grade P<.001; high school P<.001; bachelors P=.003; FKGL: 6th grade P=.001; 8th grade P<.001; high school P<.001; bachelors P<.001) between mean FKRE and FKGL across input education levels. CONCLUSIONS: GLMs can change the structure and readability of medical text outputs according to input-specified education. However, GLMs categorize input education designation into 3 broad tiers of output readability: easy (6th and 8th grade), medium (high school), and difficult (bachelor's degree). This is the first result to suggest that there are broader boundaries in the success of GLMs in output text simplification. Future research must establish how GLMs can reliably personalize medical texts to prespecified education levels to enable a broader impact on health care literacy.

Predator in proximity: how does a large carnivore respond to anthropogenic pressures at fine-scales? Implications for interface area management.

Background: Driven by habitat loss and fragmentation, large carnivores are increasingly navigating human-dominated landscapes, where their activity is restricted and their behaviour altered. This movement, however, raises significant concerns and costs for people living nearby. While intricately linked, studies often isolate human and carnivore impacts, hindering effective management efforts. Hence, in this study, we brought these two into a common framework, focusing on an interface area between the critical tiger habitat and the human-dominated multiple-use buffer area of a central Indian protected area. Methods: We employed a fine-scale camera trap survey complemented by GPS-collar movement data to understand spatio-temporal activity patterns and adjustments of tigers in response to anthropogenic pressures. We used an occupancy framework to evaluate space use, Bayesian circular GLMs to model temporal activity, and home range and step length analyses to assess the movement patterns of tigers. Further, we used predation-risk models to understand conflict patterns as a function of tiger presence and other habitat variables. Results: Despite disturbance, a high proportion of the sampled area was occupied by 17 unique tigers (ψ = 0.76; CI [0.73-0.92]). The distance to villages (ß ± SE = 0.63 ± 0.21) and the relative abundance of large-bodied wild prey (ß ± SE = 0.72 ± 0.37) emerged as key predictors of tiger space use probability, indicating a preference for wild prey by tigers, while human influences constrained their habitat utilisation. Distance to villages was also identified as the most significant predictor of the tigers' temporal activity (µ ± σ = 3.03 ± 0.06 rad) that exhibited higher nocturnality near villages. A total of 11% of tiger home ranges were within village boundaries, accompanied by faster movement in these areas (displacement 40-82% higher). Livestock depredation probability by tigers increased with proximity to villages (P = 0.002) and highway (P = 0.003). Although tiger space use probability (P = 0.056) and wild prey abundance (P = 0.134) were non-significant at the 0.05 threshold, their presence in the best-fit predation-risk model suggests their contextual relevance for understanding conflict risk. The results highlight the importance of appropriately managing livestock near human infrastructures to effectively mitigate conflict. Conclusions: Shared space of carnivores and humans requires dynamic site-specific actions grounded in evidence-based decision-making. This study emphasises the importance of concurrently addressing the intricate interactions between humans and large carnivores, particularly the latter's behavioural adaptations and role in conflict dynamics. Such an integrated approach is essential to unravel cause-effect relationships and promote effective interface management in human-dominated landscapes.

In vitro treatment with liposome-encapsulated Mannose-1-phosphate restores N-glycosylation in PMM2-CDG patient-derived fibroblasts.

PMM2-CDG is the most common congenital disorder of glycosylation (CDG). Patients with this disease often carry compound heterozygous mutations of the gene encoding the phosphomannomutase 2 (PMM2) enzyme. PMM2 converts mannose-6-phosphate (M6P) to mannose-1-phosphate (M1P), which is a critical upstream metabolite for proper protein N-glycosylation. Therapeutic options for PMM2-CDG patients are limited to management of the disease symptoms, as no drug is currently approved to treat this disease. GLM101 is a M1P-loaded liposomal formulation being developed as a candidate drug to treat PMM2-CDG. This report describes the effect of GLM101 treatment on protein N-glycosylation of PMM2-CDG patient-derived fibroblasts. This treatment normalized intracellular GDP-mannose, increased the relative glycoprotein mannosylation content and TNFα-induced ICAM-1 expression. Moreover, glycomics profiling revealed that GLM101 treatment of PMM2-CDG fibroblasts resulted in normalization of most high mannose glycans and partial correction of multiple complex and hybrid glycans. In vivo characterization of GLM101 revealed its favorable pharmacokinetics, liver-targeted biodistribution, and tolerability profile with achieved systemic concentrations significantly greater than its effective in vitro potency. Taken as a whole, the results described in this report support further exploration of GLM101's safety, tolerability, and efficacy in PMM2-CDG patients.

A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.

Motivation: High-throughput sequencing technology facilitates the quantitative analysis of microbial communities, improving the capacity to investigate the associations between the human microbiome and diseases. Our primary motivating application is to explore the association between gut microbes and obesity. The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis. Results: We propose a GLM-based zero-inflated generalized Poisson factor analysis (GZIGPFA) model to analyze microbiome data with complex characteristics. The GZIGPFA model is based on a zero-inflated generalized Poisson (ZIGP) distribution for modeling microbiome count data. A link function between the generalized Poisson rate and the probability of excess zeros is established within the generalized linear model (GLM) framework. The latent parameters of the GZIGPFA model constitute a low-rank matrix comprising a low-dimensional score matrix and a loading matrix. An alternating maximum likelihood algorithm is employed to estimate the unknown parameters, and cross-validation is utilized to determine the rank of the model in this study. The proposed GZIGPFA model demonstrates superior performance and advantages through comprehensive simulation studies and real data applications.

Liposome-encapsulated mannose-1-phosphate therapy improves global N-glycosylation in different congenital disorders of glycosylation.

Phosphomannomutase 2 (PMM2) converts mannose-6-phospahate to mannose-1-phosphate; the substrate for GDP-mannose, a building block of the glycosylation biosynthetic pathway. Pathogenic variants in the PMM2 gene have been shown to be associated with protein hypoglycosylation causing PMM2-congenital disorder of glycosylation (PMM2-CDG). While mannose supplementation improves glycosylation in vitro, but not in vivo, we hypothesized that liposomal delivery of mannose-1-phosphate could increase the stability and delivery of the activated sugar to enter the targeted compartments of cells. Thus, we studied the effect of liposome-encapsulated mannose-1-P (GLM101) on global protein glycosylation and on the cellular proteome in skin fibroblasts from individuals with PMM2-CDG, as well as in individuals with two N-glycosylation defects early in the pathway, namely ALG2-CDG and ALG11-CDG. We leveraged multiplexed proteomics and N-glycoproteomics in fibroblasts derived from different individuals with various pathogenic variants in PMM2, ALG2 and ALG11 genes. Proteomics data revealed a moderate but significant change in the abundance of some of the proteins in all CDG fibroblasts upon GLM101 treatment. On the other hand, N-glycoproteomics revealed the GLM101 treatment enhanced the expression levels of several high-mannose and complex/hybrid glycopeptides from numerous cellular proteins in individuals with defects in PMM2 and ALG2 genes. Both PMM2-CDG and ALG2-CDG exhibited several-fold increase in glycopeptides bearing Man6 and higher glycans and a decrease in Man5 and smaller glycan moieties, suggesting that GLM101 helps in the formation of mature glycoforms. These changes in protein glycosylation were observed in all individuals irrespective of their genetic variants. ALG11-CDG fibroblasts also showed increase in high mannose glycopeptides upon treatment; however, the improvement was not as dramatic as the other two CDG. Overall, our findings suggest that treatment with GLM101 overcomes the genetic block in the glycosylation pathway and can be used as a potential therapy for CDG with enzymatic defects in early steps in protein N-glycosylation.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal.

PREMISE: Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. METHODS: Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. RESULTS: Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. CONCLUSIONS: Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Exponential family measurement error models for single-cell CRISPR screens.

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens-"thresholded regression"-exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights.

Associations between the number of siblings, parent-child relationship and positive youth development of adolescents in mainland China: A cross-sectional study.

BACKGROUND: Positive development plays an important role in youth when dealing with stressful circumstances. According to the resource dilution theory, adolescents with or without siblings may receive different levels of emotional and material resources from their parents. The purpose of this study is to examine the association between the positive development of adolescents in China today with their family characteristics such as the number of siblings. METHODS: A total of 2072 junior high and senior high school students (13 to 18 years old) in Chengdu, Sichuan, China, were investigated by cluster sampling. The Chinese Positive Youth Development scales (CPYDs) were used to measure positive youth development. The generalized linear model was used to explore the relationships among the number of siblings, parent-child relationships and positive youth development. RESULTS: Adolescents from only-child families had better performance on positive development (H = 21.87, P < 0.001) and better relationships with parents (H = 15.1, P < 0.05). The positive development of male and female adolescents does not significantly differ in families with different numbers of siblings. The generalized linear model showed that a positive parent-child relationship is positively correlated with adolescent positive development (P < 0.05). CONCLUSION: Positive youth development is not only associated with the number of siblings but also other modifiable familial factors. The positive relationship between parents and adolescents is of great practical value in daily life to improve youth development, and this might be the real lesson the resource dilution theory tells.

Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach.

Forest biomass plays a crucial role in the global carbon cycle as a significant contributor derived from both soil and trees. This study focuses on investigating tree carbon stock (TCS) and estimating aboveground biomass (AGB) based on elevation within the Srivilliputhur Wildlife Sanctuary forest, while also exploring the various factors that influence their contribution. Utilizing a non-destructive approach for carbon estimation, we found that the total tree biomass in this region ranged from 220.9 Mg/ha (in Z6) to 720.6 Mg/ha (Z2), while tree carbon stock ranged from 103.8 to 338.7 Mg/ha. While Kruskal-Wallis tests did not reveal a significant relationship (p = 0.09) between TCS and elevation, linear regression showed a weak correlation (R2 = 0.002, p < 0.05) with elevation. To delve deeper into the factors influencing TCS and biomass distribution, we employed a random forest (RF) machine learning algorithm, demonstrating that stand structural attributes, such as basal area (BA), diameter at breast height (DBH), and density, held a more prominent role than climatic variables, including temperature, precipitation, and slope. Generalized linear models (GLM) were also utilized, confirming that BA, mean DBH, and elevation significantly influenced AGB (p ≤ 0.001), with species richness, precipitation, and temperature having lower significance (p ≤ 0.01) comparatively. Overall, the RF model exhibited superior performance (R2 = 0.92, RMSE = 0.12) in terms of root mean square error (RMSE) compared to GLM (R2 = 0.88, RMSE = 0.35). These findings shed light on the intricate dynamics of biomass distribution and the importance of both stand structural and climatic factors in shaping forest ecosystems.

The Correlation between Metal Mixed Exposure and Lung Function in Different Ages of the Population.

Herein, we explored the overall association between metal mixtures and lung functions in populations of varying ages and the relationship among the associated components. The 2007-2012 National Health and Nutrition Examination Survey data of 4382 American participants was analyzed, and generalized linear, elastic net, quantile g-computation, and Bayesian kernel machine regression models were used to evaluate the relationship between exposure to the metal mixture and lung function at various ages. The results of barium exposure at distinct stages revealed that children and adolescents exhibited greater lung function changes than those in adults and the elderly. Additionally, compared with children and adolescents, cadmium- and arsenic-containing metabolites contributed to nonconductive lung function changes in adults and the elderly exposed to metal mixtures. The results showed that the effects of exposure to metal mixtures on lung function in children and adolescents were predominantly caused by lead and barium. Altogether, children and adolescents were found to be more susceptible to metal-exposure-mediated lung function changes than adults and the elderly.

Staged operation in the surgical treatment of granulomatous lobular mastitis.

Background: Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast condition characterized by an unclear etiology and an undefined therapeutic approach. Surgical intervention is considered an alternative modality for managing GLM. Staged operation is the predominant and characteristic surgical approach in the treatment of GLM in our center; therefore, we evaluated the efficacy of staged operative techniques in this cohort study. Methods: We retrospectively reviewed 212 patients with GLM who underwent staged operation between August 2020 and July 2022 in the inpatient department of our institute. Their clinical history information, clinic complaints, treatment details, surgical outcomes, follow-up results, and scores on the satisfaction questionnaire were analyzed. The patients were called for follow-up and consultation with a deadline of August 2023. Results: The median follow-up time was 27 months (range, 14-37 months). In total, 212 patients were treated with three different staged procedures according to the individual assessment and patient willingness, including 168 patients who underwent one-stage debridement operation and two-stage suture operation (DO + SO), 25 patients who underwent one-stage debridement operation without suture (DO), and 19 patients who underwent one-stage debridement and simultaneous suture operation (DSO). The median recovery time was 29 days (range, 14-60 days). A minority of patients developed postoperative complications, including effusion (1.89%), flap ischemia (0.94%), areola-nipple ischemia (0.94%) and sinus tract formation (2.36%). The scores of the satisfaction questionnaire were 43.10±3.09, and 186 patients (87.74%) gave high scores for postoperative breast appearance. Only 5 of 212 patients (2.36%) developed recurrence. Conclusions: Staged operation performed in our institute is an effective and safe surgical therapy in patients with GLM, yielding a short recovery time, low recurrence and good cosmetic results.

Impact of oil price volatility and economic policy uncertainty on business investment - Insights from the energy sector.

This research employs a worldwide sample of 4017 energy sector companies from 1996 to 2022 to examine the effects of economic policy uncertainty (EPU) and oil price uncertainty (OPU) on corporate investment in oil/energy sector and this study analyze how market instability and international economic disasters shape the connection between OPU and business assets. GLM regression with firms-years fixed effects and firm-based clustering indicate that both OPU and EPU had a detrimental influence on corporate investment in energy sector. Generalized linear models provide a universal method for addressing various response modeling issues. It is also revealed that oil-producing nations experience OPU and EPU's negative effects more severely than oil-consuming nations. This paper also demonstrates that the link between corporate investment, OPU and EPU is influenced by nations that produce oil, market volatility, and global financial crises. Strong evidence was found supporting the notion that OPU and EPU had a statistically significant detrimental impact on business assets. The findings of the paper are consistent under a variety of robustness tests and show that the association between OPU and EPU and business assets still holds. The results have significant bearing on the asset strategies that company managers and governments should adopt in light of the volatility of oil prices and EPU and this study provide valuable insights for policymakers who are focused on achieving energy transition, enhancing energy security, and meeting environmental goals such as reducing greenhouse gas emissions.

Climatic, oceanic, freshwater, and local environmental drivers of New Zealand estuarine macroinvertebrates.

Understanding the responses of organisms to different environmental drivers is critical for improving ecosystem management and conservation. Estuarine ecosystems are under pressure from multiple anthropogenic stressors (e.g. increasing sediment and nutrient loads, pollution, climate change) that are affecting the functions and services these ecosystems provide. Here, we used long-term estuarine benthic invertebrate monitoring data (∼30 year time-series) to evaluate the responses of macrobenthic invertebrate communities and indicator species to climatic, oceanic, freshwater, and local environmental drivers in New Zealand estuaries. We aimed to improve our ability to predict ecosystem change and understand the effects of multiple environment drivers on benthic communities. Our analyses showed that the abundance and richness of macrobenthic fauna and four indicator taxa (bivalves known to have differing tolerances to sediment mud content: Austrovenus stutchburyi, Macomona liliana, Theora lubrica, and Arthritica bifurca) responded to unique combinations of multiple environmental drivers across sites and times. Macrobenthic responses were highly mixed (i.e., positive and negative) and site-dependent. We also show that responses of macrobenthic fauna were lagged and most strongly related to climatic and oceanic drivers. The way the macrobenthos responded has implications for predicting and understanding the ecological consequences of a rapidly changing environment and how we conserve and manage coastal ecosystems.

Estimating soil N2O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands.

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N2O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N2O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N2O emissions (N2Om, kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N2O drivers. Significantly different N2Om were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N2O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N2O emissions reported based on area (N2Om), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N2O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N2Om using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.