Optical Nonlinearity Enabled Super-Resolved Multiplexing Microscopy.

Optical multiplexing for nanoscale object recognition is of great significance within the intricate domains of biology, medicine, anti-counterfeiting, and microscopic imaging. Traditionally, the multiplexing dimensions of nanoscopy are limited to emission intensity, color, lifetime, and polarization. Here, a novel dimension, optical nonlinearity, is proposed for super-resolved multiplexing microscopy. This optical nonlinearity is attributable to the energy transitions between multiple energy levels of the doped lanthanide ions in upconversion nanoparticles (UCNPs), resulting in unique optical fingerprints for UCNPs with different compositions. A vortex beam is applied to transport the optical nonlinearity onto the imaging point-spread function (PSF), creating a robust super-resolved multiplexing imaging strategy for differentiating UCNPs with distinctive optical nonlinearities. The composition information of the nanoparticles can be retrieved with variations of the corresponding PSF in the obtained image. Four channels multiplexing super-resolved imaging with a single scanning, applying emission color and nonlinearity of two orthogonal imaging dimensions with a spatial resolution higher than 150 nm (1/6.5λ), are demonstrated. This work provides a new and orthogonal dimension - optical nonlinearity - to existing multiplexing dimensions, which shows great potential in bioimaging, anti-counterfeiting, microarray assays, deep tissue multiplexing detection, and high-density data storage

Advances in multiplexed photoelectrochemical sensors for multiple components

Multiplexed photoelectrochemical (M-PEC) sensors are transforming the landscape of analytical detection by offering unprecedented sensitivity and the ability to simultaneously detect multiple targets—ranging from biomolecules and small organic compounds to metal ions. These sensors represent a significant leap forward in key sectors such as biomedical diagnostics, environmental monitoring, and food safety, overcoming limitations of traditional single-signal PEC sensors, which often struggle with interference and selectivity in complex samples. Recent innovations in materials, such as quantum dots, metal–organic frameworks, and nanocomposites, have driven improvements in light-harvesting efficiency, signal amplification, and target specificity. Furthermore, advanced multiplexing strategies—such as wavelength-resolved, potential-resolved, spatial-resolved, and multi-mode sensing—have empowered these sensors to achieve enhanced performance in detecting multiple analytes with minimal crosstalk. Despite impressive progress, challenges remain, particularly in improving long-term stability, scalability, and real-world applicability. This review discusses cutting-edge advancements in M-PEC sensor detection strategies and the applications of M-PEC sensors for detecting multiple targets, while offering perspectives on future directions, including the push toward miniaturization, high-throughput screening, and ultra-sensitive trace-level detection, setting the stage for widespread practical implementation across various applications

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Clioquinol and pyrrolidine dithiocarbamate complex with copper to form proteasome inhibitors and apoptosis inducers in human breast cancer cells

INTRODUCTION: A physiological feature of many tumor tissues and cells is the tendency to accumulate high concentrations of copper. While the precise role of copper in tumors is cryptic, copper, but not other trace metals, is required for angiogenesis. We have recently reported that organic copper-containing compounds, including 8-hydroxyquinoline-copper(II) and 5,7-dichloro-8-hydroxyquinoline-copper(II), comprise a novel class of proteasome inhibitors and tumor cell apoptosis inducers. In the current study, we investigate whether clioquinol (CQ), an analog of 8-hydroxyquinoline and an Alzheimer's disease drug, and pyrrolidine dithiocarbamate (PDTC), a known copper-binding compound and antioxidant, can interact with copper to form cancer-specific proteasome inhibitors and apoptosis inducers in human breast cancer cells. Tetrathiomolybdate (TM), a strong copper chelator currently being tested in clinical trials, is used as a comparison. METHODS: Breast cell lines, normal, immortalized MCF-10A, premalignant MCF10AT1K.cl2, and malignant MCF10DCIS.com and MDA-MB-231, were treated with CQ or PDTC with or without prior interaction with copper, followed by measurement of proteasome inhibition and cell death. Inhibition of the proteasome was determined by levels of the proteasomal chymotrypsin-like activity and ubiquitinated proteins in protein extracts of the treated cells. Apoptotic cell death was measured by morphological changes, Hoechst staining, and poly(ADP-ribose) polymerase cleavage. RESULTS: When in complex with copper, both CQ and PDTC, but not TM, can inhibit the proteasome chymotrypsin-like activity, block proliferation, and induce apoptotic cell death preferentially in breast cancer cells, less in premalignant breast cells, but are non-toxic to normal/non-transformed breast cells at the concentrations tested. In contrast, CQ, PDTC, TM or copper alone had no effects on any of the cells. Breast premalignant or cancer cells that contain copper at concentrations similar to those found in patients, when treated with just CQ or PDTC alone, but not TM, undergo proteasome inhibition and apoptosis. CONCLUSION: The feature of breast cancer cells and tissues to accumulate copper can be used as a targeting method for anticancer therapy through treatment with novel compounds such as CQ and PDTC that become active proteasome inhibitors and breast cancer cell killers in the presence of copper

Measurement of the branching fraction for ψ(3686)→ωK+K-

With 1.06 × 108 ψ(3686) events collected with the BESIII detector, the branching fraction of ψ (3686) → ωK+K- is measured to be (1.54 ± 0.04 ± 0.11) × 10-4. This is the most precise result to date, due to the largest ψ(3686) sample, improved signal reconstruction efficiency, good simulation of the detector performance, and a more accurate knowledge of the continuum contribution. Using the branching fraction of J/ψ → ωK+K-, the ratio β(ψ(3868) → ωK+K-)/β(J/ψ → ωK+K-) is determined to be (18.4 ± 3.7)%. This constitutes a significantly improved test of the 12% rule, with the uncertainty now dominated by the J/ψ branching fraction

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe