Estimating the structures at high or low quantiles has become an important subject and attracted increasing attention across numerous fields. However, due to data sparsity at tails, it usually is a challenging task to obtain reliable estimation, especially for high-dimensional data. This paper suggests a flexible parametric structure to tails, and this enables us to conduct the estimation at quantile levels with rich observations and then to extrapolate the fitted structures to far tails. The proposed model depends on some quantile indices and hence is called the quantile index regression. Moreover, the composite quantile regression method is employed to obtain non-crossing quantile estimators, and this paper further establishes their theoretical properties, including asymptotic normality for the case with low-dimensional covariates and non-asymptotic error bounds for that with high-dimensional covariates. Simulation studies and an empirical example are presented to illustrate the usefulness of the new model.
Biochar-poly(lactic acid) (PLA) composites are emerging as waste-derived biocomposites that integrate biomass valorization, biodegradable polymer development, and circular bioeconomy strategies. This review critically synthesizes how biochar feedstock, pyrolysis temperature, ash content, inorganic composition, surface chemistry, particle size, filler loading, and processing route influence the thermal, mechanical, degradability, and functional performance of PLA-based composites. Current evidence shows that optimized biochar incorporation can improve stiffness, tensile or flexural modulus, crystallization behaviour, impact resistance, dimensional stability, and composting-driven degradation. These benefits are mainly linked to biochar's carbon-rich structure, porous morphology, nucleating ability, surface functionality, and interfacial interactions with PLA. However, performance gains are not universal. Excessive loading or poor dispersion can reduce tensile strength, elongation at break, thermal stability, melt flow, and processability because of particle agglomeration, weak filler-matrix adhesion, moisture sensitivity, pore blockage, and processing-induced PLA chain scission. Particular attention is given to ash and inorganic residues, including alkali and alkaline-earth metals, carbonates, phosphates, silicates, and metal oxides, which may either promote crystallization and char formation or catalyze PLA degradation depending on their speciation, concentration, and dispersion. The review compares solvent casting, melt mixing, extrusion, compression and injection molding, filament production, and additive manufacturing, highlighting their advantages and processing constraints. Application opportunities in packaging, agriculture, water treatment, construction-related materials, biomedical systems, and 3D printing are discussed alongside food-contact safety, migration, durability, biocompatibility, regulatory, and end-of-life considerations. Wider adoption requires feedstock standardization, ash chemistry control, improved interfacial design, application-specific validation, and life-cycle assessment.
This paper introduces BinGFI, a novel, fully automated computational method for conducting statistical inference in binary response models that does not rely on Markov chain Monte Carlo or explicit mathematical integration. BinGFI is based on generalized fiducial inference (GFI) and extends the AutoGFI framework (Du et al., 2025) originally developed for additive Gaussian noise models to Bernoulli noise. This paper also develops a regularized extension, BinGFI-R, which incorporates convex penalties and a de-biasing step for improved inference accuracy. BinGFI-R can be used in situations where regularization is needed, such as in high-dimensional settings. The flexibility of BinGFI and BinGFI-R enables their application to a wide range of binary models, including classical logistic regression, covariate-assisted ranking estimation, and the Rasch model for item response theory. Through extensive simulations and comparisons with existing inference methods, we demonstrate that BinGFI and BinGFI-R achieve competitive or superior performance in terms of estimation accuracy, coverage rates, and interval widths.
The therapeutic potential of oligonucleotides (oligos) is limited by insufficient delivery to extrahepatic tissues. In vitro assays often fail to accurately predict in vivo behavior, while testing each oligo candidate in animals remains inherently low throughput. Here, we conceive a barcoded oligonucleotide system (BOLT), a platform that enables high-throughput in vivo evaluations of small-molecule ligands and identifies tissue-specific oligo delivery. BOLT integrates rational design of oligo barcodes, modular conjugation chemistry, and next-generation sequencing (NGS)-based quantification, allowing simultaneous evaluation of many chemically diverse ligand-oligo conjugates within a single animal. Notably, this platform is applicable in both mice and nonhuman primates (NHPs). Using BOLT, we discovered ligands with tropism for tissues such as the brain, lung, and muscle. Collectively, these results indicate that the BOLT platform can accelerate the discovery of tissue-targeting ligands for broad oligo therapeutics.
