Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification

With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a sub-sample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multi-band light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are overall consistent, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.Comment: 13 pages, 6 figures, submitted to Ap

Building Random, Fair, and Verifiable Games on Blockchain. Raffle smart contract designs on Sui Network

Randomness plays a pivotal role in modern online gaming, but disputes have arisen over the accuracy of stated winning chances, resulting in legal issues and financial setbacks for gaming companies. Fortunately, blockchain-based games offer a solution to the transparency and fairness issue regarding randomness. Furthermore, emerging blockchain technology like Sui Network enhances the efficiency of smart contracts by eliminating traditional web3 barriers, such as inefficiencies and expensive transaction fees. This unlocks the potential for extensive decentralized gaming applications. This paper aims to provide insights into designing a fair, verifiable, and efficient smart contract game on blockchain by the example of building raffles on the Sui Network. We explore efficient methods for implementing randomness on smart contracts, including DRAND committee-based decentralized random beacons and single private-key-based verifiable random functions (VRF). Then, progress from basic to comprehensive smart contract design. We addressed limitations in developing blockchain games in general, such as data input and storage space constraints. We propose corresponding solutions, encompassing the utilization of Object Tables, Delegate Object Creation, and Zero-Knowledge Proofs (ZKP) to optimize storage and input efficiency. After testing our designs, we found that the transaction fees for DRAND beacons and private-key-based VRFs are similar. Moreover, Object Tables incur higher overall transaction fees, while the ZKP setup fee is cheap but becomes very expensive during the verification process. Moreover, we identified suitable designs for different application scenarios by comparing the pros and cons of different smart contract implementations. Our findings provide valuable guidance for future researchers and developers in building random, fair, and verifiable games with smart contracts

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Active galactic nuclei (AGN) are believed to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The variability of an AGN's brightness over time can reveal important information about the physical properties of the underlying black hole. The temporal variability is believed to follow a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). With upcoming wide-field surveys set to observe 100 million AGN in multiple bandpass filters, there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are well-suited for modeling AGN time series data, as they can explicitly capture the underlying stochastic dynamics. In this work, we modify latent SDEs to jointly reconstruct the unobserved portions of multivariate AGN light curves and infer their physical properties such as the black hole mass. Our model is trained on a realistic physics-based simulation of ten-year AGN light curves, and we demonstrate its ability to fit AGN light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multi-output Gaussian process regression baseline.Comment: 10 pages, 5 figures, accepted at the ICLR 2023 Workshop on Physics for Machine Learnin

PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation (TPD). The computational design of protein-based binders presents unique opportunities to access undruggable targets, but have often relied on stable 3D structures or predictions for effective binder generation. Recently, we have leveraged the expressive latent spaces of protein language models (pLMs) for the prioritization of peptide binders from sequence alone, which we have then fused to E3 ubiquitin ligase domains, creating a CRISPR-analogous TPD system for target proteins. However, our methods rely on training discriminator models for ranking heuristically or unconditionally-derived guide peptides for their target binding capability. In this work, we introduce PepMLM, a purely target sequence-conditioned de novo generator of linear peptide binders. By employing a novel masking strategy that uniquely positions cognate peptide sequences at the terminus of target protein sequences, PepMLM tasks the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon previously-validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of target substrates in cellular models. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream programmable proteome editing applications

An open label dose finding study of allopurinol to target defined reduction in urate levels in hemodialysis patients

No abstract available

Epidemiology of Extended-Spectrum Beta-Lactamase and Carbapenemase-Producing Enterobacterales in the Greater Mekong Subregion: A Systematic-Review and Meta-Analysis of Risk Factors Associated With Extended-Spectrum Beta-Lactamase and Carbapenemase Isolation.

BACKGROUND: Despite the rapid spread of extended-spectrum beta-lactamase (ESBL) producing-Enterobacterales (ESBL-E) and carbapenemase-producing Enterobacterales (CPE), little is known about the extent of their prevalence in the Greater Mekong Subregion (GMS). In this systematic review, we aimed to determine the epidemiology of ESBL-E and CPE in clinically significant Enterobacterales: Escherichia coli and Klebsiella pneumoniae from the GMS (comprising of Cambodia, Laos, Myanmar, Thailand, Vietnam and Yunnan province and Guangxi Zhuang region of China). METHODS: Following a list of search terms adapted to subject headings, we systematically searched databases: Medline, EMBASE, Scopus and Web of Science for articles published on and before October 20th, 2020. The search string consisted of the bacterial names, methods involved in detecting drug-resistance phenotype and genotype, GMS countries, and ESBL and carbapenemase detection as the outcomes. Meta-analyses of the association between the isolation of ESBL from human clinical and non-clinical specimens were performed using the "METAN" function in STATA 14. RESULTS: One hundred and thirty-nine studies were included from a total of 1,513 identified studies. Despite the heterogeneity in study methods, analyzing the prevalence proportions on log-linear model scale for ESBL producing-E. coli showed a trend that increased by 13.2% (95%CI: 6.1-20.2) in clinical blood specimens, 8.1% (95%CI: 1.7-14.4) in all clinical specimens and 17.7% (95%CI: 4.9-30.4) increase in carriage specimens. Under the log-linear model assumption, no significant trend over time was found for ESBL producing K. pneumoniae and ESBL-E specimens. CPE was reported in clinical studies and carriage studies past 2010, however a trend could not be determined because of the small dataset. Twelve studies were included in the meta-analysis of risk factors associated with isolation of ESBL. Recent antibiotic exposure was the most studied variable and showed a significant positive association with ESBL-E isolation (pooled OR: 2.9, 95%CI: 2.3-3.8) followed by chronic kidney disease (pooled OR: 4.7, 95%CI: 1.8-11.9), and other co-morbidities (pooled OR: 1.6, 95%CI: 1.2-2.9). CONCLUSION: Data from GMS is heterogeneous with significant data-gaps, especially in community settings from Laos, Myanmar, Cambodia and Yunnan and Guangxi provinces of China. Collaborative work standardizing the methodology of studies will aid in better monitoring, surveillance and evaluation of interventions across the GMS

EZH2 modifies sunitinib resistance in renal cell carcinoma by kinome reprogramming

Acquired and intrinsic resistance to receptor tyrosine kinase inhibitors (RTKi) represent a major hurdle in improving the management of clear cell renal cell carcinoma (ccRCC). Recent reports suggest that drug resistance is driven by tumor adaptation via epigenetic mechanisms that activate alternative survival pathways. The histone methyl transferase EZH2 is frequently altered in many cancers including ccRCC. To evaluate its role in ccRCC resistance to RTKi, we established and characterized a spontaneously metastatic, patient-derived xenograft (PDX) model that is intrinsically resistant to the RTKI sunitinib but not to the VEGF therapeutic antibody bevacizumab. Sunitinib maintained its anti-angiogenic and anti-metastatic activity but lost its direct anti-tumor effects due to kinome reprogramming, which resulted in suppression of pro- apoptotic and cell cycle regulatory target genes. Modulating EZH2 expression or activity suppressed phosphorylation of certain RTK, restoring the anti-tumor effects of sunitnib in models of acquired or intrinsically resistant ccRCC. Overall, our results highlight EZH2 as a rational target for therapeutic intervention in sunitinib-resistant ccRCC as well as a predictive marker for RTKi response in this disease.This research was funded by Roswell Park Cancer Institute’s Cancer Center Support Grant from National Cancer Institute, NIH P30CA016056 (RP) and a generous donation by Richard and Deidre Turner (RP). This investigation was conducted in-part in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 RR020128-01 from the National Center for Research Resources, National Institutes of Health

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes