Exploring the competitive dynamic enzyme allocation scheme through enzyme cost minimization.

Enzyme allocation (or synthesis) is a crucial microbial trait that mediates soil biogeochemical cycles and their responses to climate change. However, few microbial ecological models address this trait, particularly concerning multiple enzyme functional groups that regulate complex biogeochemical processes. Here, we aim to fill this gap by developing a COmpetitive Dynamic Enzyme ALlocation (CODEAL) scheme for six enzyme groups that act as indicators of inorganic nitrogen (N) transformations in the Microbial-ENzyme Decomposition (MEND) model. This allocation scheme employs time-variant allocation coefficients for each enzyme group, fostering mutual competition among the multiple groups. We show that the principle of enzyme cost minimization is achieved by using the substrate's saturation level as the factor for enzyme allocation, resulting in an enzyme-efficient pathway with minimal enzyme cost per unit metabolic flux. It suggests that the relative substrate availability affects the trade-off between enzyme production and metabolic flux. Our research has the potential to give insights into the nuanced dynamics of the N cycle and inspire the evolving landscape of enzyme-mediated biogeochemical processes in microbial ecological modeling, which is gaining increasing attention.

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Noncrystalline Carbon Anodes for Advanced Sodium-Ion Storage.

Developing an anode with excellent rate performance, long-cycle stability, high coulombic efficiency, and high specific capacity is one of the key research directions of sodium-ion batteries. Among all the anode materials, noncrystalline carbon (NCC) has great possibilities according to its supreme performance and low cost, but with the complexity and variability of the structure. With the in-depth study of the sodium storage behaviors of NCC in recent years, three modes of interlayer intercalation, clustering into micropores, and adsorption are reported and summarized. Although the storage mechanism has gradually become more evident, the complex behavior of the ions at different voltage regions, especially in the low-voltage (plateau) region, still remains controversial. It is essential to understand further the relationship between ions and NCC structure during energy storage processes. Based on the summary of previous works, this article has reviewed the storage mechanism of sodium ions in NCC and evaluated the structure-behavior relationship between sodium-ion storage and the carbon structure.

Cost-effectiveness of Staple Line Reinforcement in Laparoscopic Sleeve Gastrectomy.

OBJECTIVE: To perform a cost-effectiveness analysis of staple-line reinforcement in laparoscopic sleeve gastrectomy. SUMMARY OF BACKGROUND DATA: Exponential increases in surgical costs have underscored the critical need for evidence-based methods to determine the relative value of surgical devices. One such device is staple-line reinforcement, thought to decrease bleeding rates in laparoscopic sleeve gastrectomy. METHODS: Two intervention arms were modeled, staple-line reinforcement and standard nonreinforced stapling. Bleed and leak rates and 30-day treatment costs were obtained from national and state registries. Quality-adjusted life-year (QALY) values were drawn from previous literature. Device prices were drawn from institutional data. A final incremental cost-effectiveness ratio was calculated, and one-way and probabilistic sensitivity analyses were performed. RESULTS: A total of 346,530 patient records from 2012 to 2018 were included. Complication rates for the reinforced and standard cohorts were 0.05% for major bleed in both cohorts ( P = 0.8841); 0.45% compared with 0.59% for minor bleed ( P < 0.0001); and 0.24% compared with 0.26% for leak ( P = 0.4812). Median cost for a major bleed was $5552 ($3287, $16,817) and $2406 ($1861, $3484) for a minor bleed. Median leak cost was $9897 ($4589, $21,619) and median cost for patients who did not experience a bleed, leak, or other serious complication was $1908 ($1712, $2739). Mean incremental cost of reinforced stapling compared with standard was $819.60/surgery. Net QALY gain with reinforced stapling compared with standard was 0.00002. The resultant incremental cost-effectiveness ratio was $40,553,000/QALY. One-way and probabilistic sensitivity analyses failed to produce a value below $150,000/QALY. CONCLUSIONS: Compared with standard stapling, reinforced stapling reduces minor postoperative bleeding but not major bleeding or leaks and is not cost-effective if routinely used in laparoscopic sleeve gastrectomy.

Identification of TUL01101: A Novel Potent and Selective JAK1 Inhibitor for the Treatment of Rheumatoid Arthritis.

Janus kinase 1 (JAK1) is a potential target for the treatment of rheumatoid arthritis (RA). In this study, the introduction of a spiro ring with a difluoro-substituted cyclopropionamide resulted in the identification of TUL01101 (compound 36) based on a triazolo[1,5-a]pyridine core of filgotinib. It showed excellent potency on JAK1 with an IC50 value of 3 nM and exhibited more than 12-fold selectivity for JAK2 and TYK2. Whole blood assay also demonstrated the high activity and selectivity (37-fold for JAK2). At the same time, TUL01101 also demonstrated excellent metabolic stability and pharmacokinetics (PK) profiles were assayed in three species (mouse, rat, and dog). Moreover, it has been validated for effective activity in the treatment of RA both in collagen-induced arthritis (CIA) and adjuvant-induced arthritis (AIA) models, with low dose and low toxicity. Now, TUL01101 has progressed into phase I clinical trials.

Effectively enhancing the enantioseparation ability of ß-cyclodextrin derivatives by de novo design and molecular modeling.

Rational engineering of native ß-CD as an ideal chiral selector for a definite analyte in capillary electrophoresis represents a challenge in separation science. Herein, a rational and systematic strategy that combines the de novo design and molecular modeling is firstly described to expedite the manipulation and selection of effective selector for enantioseparation in capillary electrophoresis. Using ß-adrenoreceptor agonists as model analytes, we demonstrate how this strategy efficiently improves the enantiorecognition in chiral discrimination sites of inclusion complexes. The evolved ß-CD derivative could be utilized as a chiral receptor to achieve the effective enantioseparation (Rs > 1.5) of racemic ß-adrenoreceptor agonists. We highlight a novel strategy for efficiently and rapidly manipulating native CD based on the characteristics of analyte so as to gain an excellent chiral selector.