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Time synchronization remains as a challenging task in wireless sensor networks that face severe resource constraints. Unlike previous work's aiming at pure clock accuracy, this paper proposes On-Demand Synchronization (ODS), a design to achieve efficient clock synchronization with customized performance. By carefully modeling the error uncertainty of skew detection and its propagation over time, ODS develops a novel uncertainty-driven mechanism to adaptively adjust each clock calibration interval individually rather than traditional periodic synchronization, for minimum communication overhead while satisfying the desired accuracy. Besides, ODS provides a nice feature of predictable accuracy, allowing nodes to acquire the useful information about real-time qualities of their synchronization. We implemented ODS on the MICAz mote platform, and evaluated it through testbed experiments with 33 nodes as well as simulations obeying real world conditions. Results show that ODS is practical, flexible, and quickly adapts to varying accuracy requirements and different traffic load in the network for improved system efficiency. I. INTRODUCTIONTime synchronization is one of the most fundamental and widely employed middle-ware services in wireless sensor networks (WSN) [10]. It allows nodes in the network to have a common notion of time, either respect to a global reference or among themselves [9]. Accurate time synchronization is critical for saving communication energy [26][28], promoting localization accuracy [1][24], optimizing surveillance coverage [18][34], extending network lifetime [14][15] and improving system security [4]. However, due to extremely limited resources at each low-cost sensor node (e.g., poor clock quality, limited computation and communication capabilities, ultra tight energy budgets, etc), time synchronization remains as a challenging task in the WSN community. Previous research in this field mostly focused on how to do time synchronization for better clock accuracy (e.g., AD [33], TSS [19], RBS [20], TPSN [21], FTSP [22], VHT [5], etc). In spite of microsecond (µs) level accuracy achieved [5][7][22], they are not designed to provide a precise and convenient trade-off between service performance and energy efficiency, which complicates their deployment for energy sensitive applications with different timing requirements. In practice, systems depend on diverse synchronization qualities. For example, the bridge surveillance project [18] demands tens-of-µs clock accuracy for effective data acquisition; to detect jamming attacks in the network [4], the deviation of a couple of mil-

Background Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets ( n = 780) to evaluate its prognostic and predictive performance. Findings A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort ( n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 – 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 – 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 – 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211). Interpretation Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership. Funding The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

The application of Li-S batteries suffers from many issues, polysulfide dissolution in particular. The fresh Li metal reacts with polysulfide continuously, which aggravates irregular Li plating/stripping behavior and decomposition of organic electrolyte resulting in short cycle life and low Coulombic efficiency. Nature has provided a lot of inspiration for human to realize structural construction and functional integration, as it does to battery design. In this report, learning from the hydrophobic property of natural species, a scaly polysulfiphobic artificial interphase layer are constructed on lithium metal anodes that can repel LiPS through the functional decylphosphonate groups that are proved by in operando XPS with Ar ion sputtering. Moreover, the obtained artificial interphase layer keeps dendrite-free morphology and restrains side reactions during cycling effectively. In situ XRD measurements are employed to demonstrate the inhibiting effect for decomposition of organic electrolyte. The as-obtained LDP-Li anodes exhibit outstanding electrochemical performance with the specific capacity of ∼1000 mAh g corresponding to Coulombic efficiency of nearly 99%, which shows great promise for the application of Li-S batteries.

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Critical commentary on and a brief summary of representative F− fluorescent probes, imaging agents and nanosensors based on Si–F chemistry are provided, followed by pointing out the limitations of current systems and possible future directions.