Stratified films offer high performance and multifunctionality, yet achieving fully stratified films remains a challenge. The layer-by-layer method, involving the sequential deposition of each layer, has been commonly utilized for stratified film fabrication. However, this approach is time-consuming, labor-intensive, and prone to leaving defects within the film. Alternatively, the self-stratification process exploiting a drying binary colloidal mixture is intensively developed recently, but it relies on strict operating conditions, typically yielding a heterogeneous interlayer. In this study, an active interfacial stratification process for creating completely stratified nanoparticle (NP) films is introduced. The technique leverages NPs with varying interfacial activity at the air-water interface. With the help of depletion pressure, the lateral compression of NP mixtures at the interface induces individual desorption of less interfacial active NPs into the subphase, while more interfacial active NPs remain at the interface. This simple compression leads to nearly perfect stratified NP films with controllability, universality, and scalability. Combined with a solvent annealing process, the active stratification process enables the fabrication of stratified films comprising a polymeric layer atop a NP layer. This work provides insightful implications for designing drug encapsulation and controlled release, as well as manufacturing transparent and flexible electrodes.

Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high-dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large-sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite-sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survival time.

BACKGROUND: In Huntington\'s disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.
OBJECTIVE: To improve stratification of Huntington\'s disease individuals for clinical trials.
METHODS: We used data from 451 gene positive individuals with Huntington\'s disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.
RESULTS: The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).
CONCLUSIONS: This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Adverse life course events associated with unemployment can negatively affect individuals\' future labor market prospects. Unauthorized status, and subsequent unauthorized employment, may operate similarly, marring immigrants\' labor market prospects even after they change legal status. However, it is unclear how and why any durable disadvantage associated with prior unauthorized status operates differently by gender. This is an important shortcoming given that legal status and gender overlap to influence both migration and stratification. Using longitudinal data from a nationally-representative sample of lawful permanent residents, we find durable disadvantage associated with prior exposure to unauthorized status, especially among women. Men with prior exposure to unauthorized status experience persistent occupational disadvantage over time relative to men who were never unauthorized. However, women with exposure to unauthorized status experience widening occupational disadvantage over time relative to women who were never unauthorized. Human capital and legal processes help to explain this pattern.

One of the most hostile marine habitats on Earth is the surface of the South Pacific Gyre (SPG), characterized by high solar radiation, extreme nutrient depletion and low productivity. During the SO-245 \"UltraPac\" cruise through the center of the ultra-oligotrophic SPG, the marine alphaproteobacterial group AEGEAN169 was detected by fluorescence in situ hybridization at relative abundances up to 6% of the total microbial community in the uppermost water layer, with two distinct populations (Candidatus Nemonibacter and Ca. Indicimonas). The high frequency of dividing cells combined with high transcript levels, suggest that both clades may be highly metabolically active. Comparative metagenomic and metatranscriptomic analyses of AEGEAN169 revealed that they encoded subtle but distinct metabolic adaptions to this extreme environment in comparison to their competitors SAR11, SAR86, SAR116, and Prochlorococcus. Both AEGEAN169 clades had the highest percentage of transporters per predicted proteins (9.5 and 10.6%, respectively). In particular, the high expression of ABC transporters in combination with proteorhodopsins and the catabolic pathways detected, suggest a potential scavenging lifestyle for both AEGEAN169 clades. Although both AEGEAN169 clades may share the genomic potential to utilize phosphonates as a phosphorus source, they differ in their metabolic pathways for carbon and nitrogen. Ca. Nemonibacter potentially use glycine-betaine, whereas Ca. Indicimonas may catabolize urea, creatine, and fucose. In conclusion, the different potential metabolic strategies of both clades suggest that both are well adapted to thrive resource-limited conditions and compete well with other dominant microbial clades in the uppermost layers of SPG surface waters.

Microbial communities, which include prokaryotes and protists, play an important role in aquatic ecosystems and influence ecological processes. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composition and track spatio-temporal dynamics in both marine and freshwater environments. While marine ecosystems have been extensively studied, there is a notable research gap in understanding eukaryotic microbial communities in temperate lakes. Our study addresses this gap by investigating the free-living bacteria and small protist communities in Lake Roś (Poland), a dimictic temperate lake. Metabarcoding analysis revealed that both the bacterial and protist communities exhibit distinct seasonal patterns that are not necessarily shaped by dominant taxa. Furthermore, machine learning and statistical methods identified crucial amplicon sequence variants (ASVs) specific to each season. In addition, we identified a distinct community in the anoxic hypolimnion. We have also shown that the key factors shaping the composition of analysed community are temperature, oxygen, and silicon concentration. Understanding these community structures and the underlying factors is important in the context of climate change potentially impacting mixing patterns and leading to prolonged stratification.

Danjiangkou Reservoir is a critical water source for the South-to-North Water Diversion Project, which harbors a diverse bacterioplankton community with varying depths, and the understanding of its nitrogen and phosphorus cycle and associated driving factors remains limited. In this study, we selected five ecological sites within Danjiangkou Reservoir and conducted metagenomics analysis to investigate the vertical distribution of bacterioplankton communities in the surface, middle, and bottom layers. Furthermore, we analyzed and predicted the function of nitrogen and phosphorus cycles, along with their driving factors. Our findings revealed the dominance of Proteobacteria, Actinobacteria, and Planctomycetes in the Danjiangkou Reservoir. Significant differences were observed in the structure of bacterioplankton communities across different depths, with temperature （T）, oxidation-reduction potential （ORP）, dissolved oxygen （DO）, and Chla identified as primary factors influencing the bacterioplankton composition. Analysis of nitrogen cycle functional genes identified 39 genes, including gltB, glnA, gltD, gdhA, NRT, etc., which were involved in seven main pathways, encompassing nitrogen fixation, nitrification, denitrification, and dissimilatory nitrate reduction. Phosphorus cycle function gene analysis identified 54 genes, including pstS, ppx-gppA, glpQ, ppk1, etc., primarily participating in six main pathways, including organic P mineralization, inorganic P solubilization, and regulatory. Cluster analysis indicated that different depths were significant factors influencing the composition and abundance of nitrogen and phosphorus cycle functional genes. The composition and abundance of nitrogen and phosphorus cycle functional genes in the surface and bottom layers differed and were generally higher than those in the middle layer. Deinococcus, Hydrogenophaga, Limnohabitans, Clavibacter, and others were identified as key species involved in the nitrogen and phosphorus cycle. Additionally, we found significant correlations between nitrogen and phosphorus cycle functional genes and environmental factors such as DO, pH, T, total dissolved solids （TDS）, electrical conductivity （EC）, and Chla. Furthermore, the content of these environmental factors exhibited depth-related changes in the Danjiangkou Reservoir, resulting in a distinct vertical distribution pattern of bacterioplankton nitrogen and phosphorus cycle functional genes. Overall, this study sheds light on the composition, function, and influencing factors of bacterioplankton communities across different layers of Danjiangkou Reservoir, offering valuable insights for the ecological function and diversity protection of bacterioplankton in this crucial reservoir ecosystem.

The deep-water area in the central Bohai Sea (BS) serves as a spawning ground and nursery for fish, shrimp, and crabs. Frequent hypoxia will affect the ecological environment in the central BS. Data from an on-site investigation of the central BS in the spring and summer of 2022 were used to analyze the relevant factors generating the occurrence of hypoxia in the central BS through the eutrophication index E, apparent oxygen consumption (AOU), and Spearman correlation. The hypoxia area was largely distributed in the study area\'s deep water section, and stratification was the main cause of hypoxia at the bottom. Organic matter mineralization, degradation, and biological respiration further exacerbated the hypoxia. In the summer of 2022, temperature stratification was the dominant factor influencing hypoxia.

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online.

UNASSIGNED: For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.
UNASSIGNED: We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.
UNASSIGNED: Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.
UNASSIGNED: The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.