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The small sample size of tropical cyclone (TC) genesis in the observations prevents us from fully characterizing its spatiotemporal variations. Here we take advantage of a large ensemble of 60-km-resolution atmospheric simulations to address this issue over the northwest Pacific (NWP) during 1951–2010. The variations in annual TC genesis density are explored separately on interannual and decadal time scales. The interannual variability is dominated by two leading modes. One is characterized by a dipole pattern, and its temporal evolution is closely linked to the developing ENSO. The other mode features high loadings in the central part of the basin, with out-of-phase changes near the equator and date line, and tends to occur during ENSO decay years. On decadal time scales, TC genesis density variability is primarily controlled by one mode, which exhibits an east–west dipole pattern with strong signals confined to south of 20°N and is tied to the interdecadal Pacific oscillation–like sea surface temperature anomalies. Further, we investigate the seasonal evolution of the ENSO effect on TC genesis density. The results highlight the distinct impacts of the two types of ENSO (i.e., eastern Pacific vs central Pacific) on TC genesis density in the NWP during a specific season and show the strong seasonal dependency of the TC genesis response to ENSO. Although the results from the observations are not as prominent as those from the simulations because of the small sample size, the high consistency between them demonstrates the fidelity of the model in reproducing TC statistics and variability in the observations.

 

Abstract The variability and predictability of tropical cyclone genesis frequency (TCGF) during 1973–2010 at both basinwide and sub-basin scales in the northwest Pacific are investigated using a 100-member ensemble of 60-km-resolution atmospheric simulations that are forced with observed sea surface temperatures (SSTs). The sub-basin regions include the South China Sea (SCS) and the four quadrants of the open ocean. The ensemble-mean results well reproduce the observed interannual-to-decadal variability of TCGF in the southeast (SE), northeast (NE), and northwest (NW) quadrants, but show limited skill in the SCS and the southwest (SW) quadrant. The skill in the SE and NE quadrants is responsible for the model’s ability to replicate the observed variability in basinwide TCGF. Above-normal TCGF is tied to enhanced relative SST (i.e., local SST minus tropical-mean SST) either locally or to the southeast of the corresponding regions in both the observations and ensemble mean for the SE, NE, and NW quadrants, but only in the ensemble mean for the SCS and the SW quadrant. These results demonstrate the strong SST control of TCGF in the SE, NE, and NW quadrants; both empirical and theoretical analyses suggest that ensembles of ∼10, 20, 35, and 15 members can capture the SST-forced TCGF variability in these three sub-basin regions and the entire basin, respectively. In the SW quadrant and the SCS, TCGF contains excessive noise, particularly in the observations, and thus shows low predictability. The variability and predictability of the large-scale atmospheric environment and synoptic-scale disturbances and their contributions to those of TCGF are also discussed. 

Abstract This study quantifies the contributions of tropical sea surface temperature (SST) variations during the boreal warm season to the interannual-to-decadal variability in tropical cyclone genesis frequency (TCGF) over the Northern Hemisphere ocean basins. The first seven leading modes of tropical SST variability are found to affect basinwide TCGF in one or more basins, and are related to canonical El Niño–Southern Oscillation (ENSO), global warming (GW), the Pacific meridional mode (PMM), Atlantic multidecadal oscillation (AMO), Pacific decadal oscillation (PDO), and the Atlantic meridional mode (AMM). These modes account for approximately 58%, 50%, and 56% of the variance in basinwide TCGF during 1969–2018 over the North Atlantic (NA), northeast Pacific (NEP), and northwest Pacific (NWP) Oceans, respectively. The SST effect is weak on TCGF variability in the north Indian Ocean. The SST modes dominating TCGF variability differ among the basins: ENSO, the AMO, AMM, and GW are dominant for the NA; ENSO and the AMO for the NEP; and the PMM, interannual AMO, and GW for the NWP. A specific mode may have opposite effects on TCGF in different basins, particularly between the NA and NEP. Sliding-window multiple linear regression analyses show that the SST effects on basinwide TCGF are stable in time in the NA and NWP, but have strengthened since the 1990s in the NEP. The SST effects on local TC genesis and occurrence frequency are also explored, and the underlying physical mechanisms are examined by diagnosing a genesis potential index and its components. 

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort , for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring. 

Abstract Direct implementation of metal-organic frameworks as the catalyst for CO 2 electroreduction has been challenging due to issues such as poor conductivity, stability, and limited > 2e − products. In this study, Au nanoneedles are impregnated into a cupric porphyrin-based metal-organic framework by exploiting ligand carboxylates as the Au 3+ -reducing agent, simultaneously cleaving the ligand-node linkage. Surprisingly, despite the lack of a coherent structure, the Au-inserted framework affords a superb ethylene selectivity up to 52.5% in Faradaic efficiency, ranking among the best for metal-organic frameworks reported in the literature. Through operando X-ray, infrared spectroscopies and density functional theory calculations, the enhanced ethylene selectivity is attributed to Au-activated nitrogen motifs in coordination with the Cu centers for C-C coupling at the metalloporphyrin sites. Furthermore, the Au-inserted catalyst demonstrates both improved structural and catalytic stability, ascribed to the altered charge conduction path that bypasses the incoherent framework. This study underlines the modulation of reticular metalloporphyrin structure by metal impregnation for steering the CO 2 reduction reaction pathway. 

The operation and maintenance of infrastructure components and systems can be modeled as a Markov process, partially or fully observable. Information about the current condition can be summarized by the “inner” state of a finite state controller. When a control policy is assigned, the stochastic evolution of the system is completely described by a Markov transition function. This article applies finite state Markov chain analyses to identify relevant features of the time evolution of a controlled system. We focus on assessing if some critical conditions are reachable (or if some actions will ever be taken), in identifying the probability of these critical events occurring within a time period, their expected time of occurrence, their long-term frequency, and the probability that some events occur before others. We present analytical methods based on linear algebra to address these questions, discuss their computational complexity and the structure of the solution. The analyses can be performed after a policy is selected for a Markov decision process (MDP) or a partially observable MDP. Their outcomes depend on the selected policy and examining these outcomes can provide the decision makers with deeper understanding of the consequences of following that policy, and may also suggest revising it. 

Artificial muscles based on stimuli-responsive polymers usually exhibit mechanical compliance, versatility, and high power-to-weight ratio, showing great promise to potentially replace conventional rigid motors for next-generation soft robots, wearable electronics, and biomedical devices. In particular, thermomechanical liquid crystal elastomers (LCEs) constitute artificial muscle-like actuators that can be remotely triggered for large stroke, fast response, and highly repeatable actuations. Here, we introduce a digital light processing (DLP)–based additive manufacturing approach that automatically shear aligns mesogenic oligomers, layer-by-layer, to achieve high orientational order in the photocrosslinked structures; this ordering yields high specific work capacity (63 J kg −1 ) and energy density (0.18 MJ m −3 ). We demonstrate actuators composed of these DLP printed LCEs’ applications in soft robotics, such as reversible grasping, untethered crawling, and weightlifting. Furthermore, we present an LCE self-sensing system that exploits thermally induced optical transition as an intrinsic option toward feedback control.