Why we need companies to report on their “digital ESG”

Rapid digitalisation is often described in techno-deterministic terms. Fantastical narratives around crypto, blockchain, Web3, metaverse, etc., can go unchallenged when they are woven into a breathless thread of inevitability. Some regulators may even unwittingly encourage this view. We have paid the price in the form of lost privacy, algorithmic data manipulation, gambling in crypto exchanges, cyber breaches blamed on users, and other consequences. Yuhyun Park and Lutfey Siddiqi write that it is time to require that businesses produce an environment, social, and governance report of their digital activities

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from an RGB image. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep-learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral tradeoff, offering simple hardware requirements and potential applications of various machine-learning techniques.Comment: This paper will appear in PNAS Nexu

Inferring Loss-of-Heterozygosity from Unpaired Tumors Using High-Density Oligonucleotide SNP Arrays

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples

One- and Two-Sample Nonparametric Inference Procedures in the Presence of Dependent Censoring

Competing risks; Martingale; Simultaneous confidence interval; Sensitivity analysis; Survival analysis,

Site-Selective Pyridine C-H Alkylation with Alcohols and Thiols via Single-Electron Transfer of Frustrated Lewis Pairs

A unified strategy for the deoxygenative or desulfurative pyridylation of various alcohols and thiols has been developed through a single-electron transfer (SET) process of frustrated Lewis pairs (FLPs) derived from pyridinium salts and PtBu3. Mechanistic studies revealed that N-amidopyridinium salts serve as effective Lewis acids for the formation of FLPs with PtBu3, and the generated phosphine radical cation ionically couples with the in situ generated xanthate, eventually affording the alkyl radical through facile beta-scission under photocatalyst-free conditions. The reaction efficiency was further accelerated by visible-light irradiation. This method is conceptually appealing by using encounter complexes in FLP chemistry to promote SET, which provides a previously unrecognized opportunity for the selective heteroarylation of a diverse range of alcohols and thiols with various functional groups, even in complex settings under mild reaction conditions.11Nsciescopu