Constraining first-order phase transitions with curvature perturbations

We investigate the curvature perturbations induced by the randomness of the quantum tunneling process during cosmological first-order phase transitions (PTs) and for the first time ultilize curvature perturbations to constrain the PT parameters. We find that the observations of the cosmic microwave background spectrum distortion and the ultracompact minihalo abundance can give strict constraints on the PTs below 100GeV, especially for the low-scale PTs and the weak PTs. The current constraint on the PT parameters is largely extended by the results in this work.Comment: 5 pages, 3 figure

A new sulfur bioconversion process development for energy- and space-efficient secondary wastewater treatment

Harvesting organic matter from wastewater is widely applied to maximize energy recovery; however, it limits the applicability of secondary treatment for acceptable effluent discharge into surface water bodies. To turn this bottleneck issue into an opportunity, this study developed oxygen-induced thiosulfatE production duRing sulfATe reductiOn (EARTO) to provide an efficient electron donor for wastewater treatment. Typical pretreated wastewater was synthesized with chemical oxygen demand of 110 mg/L, sulfate of 50 mg S/L, and varying dissolved oxygen (DO) and was fed into a moving-bed biofilm reactor (MBBR). The MBBR was operated continuously with a short hydraulic retention time of 40 min for 349 days. The formation rate of thiosulfate reached 0.12-0.18 g S/(m2.d) with a high produced thiosulfate-S/TdS-S ratio of 38-73% when influent DO was 2.7-3.6 mg/L. The sludge yield was 0.23-0.29 gVSS/gCOD, much lower than it was in conventional activated sludge processes. Then, batch tests and metabolism analysis were conducted to confirm the oxygen effect on thiosulfate formation, characterize the roles of sulfate and microbial activities, and explore the mechanism of oxygen-induced thiosulfate formation in ERATO. Results examined that oxygen supply promoted the thiosulfate-Sproduced/TdS-Sproduced ratio from 4% to 24-26%, demonstrated that sulfate and microbial activities were critical for thiosulfate production, and indicated that oxygen induces thiosulfate formation through two pathways: 1) direct sulfide oxidation, and 2) indirect sulfide oxidation, sulfide is first oxidized to S0 (dominant) which then reacts with sulfite derived from oxygen-regulated biological sulfate reduction. The proposed compact ERATO process, featuring high thiosulfate production and low sludge production, supports space- and energy-efficient secondary wastewater treatment.Comment: Written by Chu-Kuan Jiang; edited by Yang-Fan Deng, Hongxiao Guo, Guang-Hao Chen, Di Wu; Corresponding authors: Guang-Hao Chen, Di Wu; Last author (team leader): Guang-Hao Che

All the wiser: Fake news intervention using user reading preferences

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan-Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with outstanding 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care