Time series obtained from time-dependent experiments contain rich information on kinetics and dynamics of the system under investigation. This work describes an unsupervised learning framework, along with the derivation of the necessary analytical expressions, for the analysis of Gaussian-distributed time series that exhibit discrete states. After the time series has been partitioned into segments in a model-free manner using the previously developed change-point (CP) method, this protocol starts with an agglomerative hierarchical clustering algorithm to classify the detected segments into possible states. The initial state clustering is further refined using an expectation-maximization (EM) procedure, and the number of states is determined by a Bayesian information criterion (BIC). Also introduced here is an achievement scalarization function, usually seen in artificial intelligence literature, for quantitatively assessing the performance of state determination. The statistical learning framework, which is comprised of three stages, detection of signal change, clustering, and number-of-state determination, was thoroughly characterized using simulated trajectories with random intensity segments that have no underlying kinetics, and its performance was critically evaluated. The application to experimental data is also demonstrated. The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.

A novel mechanochemical method was proposed to reconstruct quickly moisture-degraded HKUST-1. The degraded HKUST-1 can be restored within minutes. The reconstructed samples were characterized, and confirmed to have 95% surface area and 92% benzene capacity of the fresh HKUST-1. It is a simple and effective strategy for degraded MOF reconstruction.

Less evidence is available currently to reveal whether the immune system and productivity of laying hens change under long periods of ammonia exposure in hot climate. The present study was conducted to determine the effects of chronic exposure to high temperature and ammonia concentrations on health, immune response, and reproductive hormones of commercial laying hens. A total of five hundred and seventy six 20-week-old laying hens (Hy-Line Brown) were used in this study. Birds were housed in cages (4 birds per cage) and received 16-wk treatments in 6 artificial environmental chambers. Hens were allocated to 6 treatments: treatment 1 (T1, 20°C, ≤5 ppm, control group), treatment 2 (T2, 20°C, 20 ppm), treatment 3 (T3, 20°C, 45 ppm), treatment 4 (T4, 35°C, ≤5 ppm), treatment 5 (T5, 35°C, 20 ppm), and treatment 6 (T6, 35°C, 45 ppm). Blood samples were collected at 22, 26, 30, 34, and 38 wk of age and plasma IgG, IgM, IgA, corticosterone ( CORT ), total antioxidant capacity ( T-AOC ), luteinizing hormone ( LH ), estradiol ( E2 ), and follicular stimulating hormone ( FSH ) were measured. The results of this study showed that high ambient temperature and excessive ammonia increased the concentration of IgG but decreased the concentration of IgA, T-AOC, LH, FSH, and E2 of hens compared with those of the control birds. From the age of 34 wk, significantly increased concentrations of IgG were observed in hens exposed to moderate and high levels of ammonia. CORT level showed marked differences between the treatments only at the age of 26 wk. In addition, LH and E2 of hens demonstrated significant differences among the treatments in the middle and later stages of the experiment, while FSH levels of the control birds were significantly higher than the others at the age of 38 wk. Excessive ammonia in high temperature was a physiological stress factor that had a negative effect, which inhibited immune function and impacted the reproductive hormones.

Nonribosomal peptide synthetases (NRPS) incorporate assorted amino acid substrates into complex natural products. The substrate is activated via the formation of a reactive aminoacyl adenylate and is subsequently attached to the protein template via a thioester bond. The reactive nature of such intermediates, however, leads to side reactions that also break down the high-energy anhydride bond. The off-pathway kinetics or their relative weights compared to that of the on-pathway counterpart remains generally elusive. Here, we introduce multiplatform kinetics profiling to quantify the relative weights of on- and off-pathway reactions. Using the well-defined stoichiometry of thioester formation, we integrate a mass spectrometry (MS) kinetics assay, a high-performance liquid chromatography (HPLC) assay, and an ATP-pyrophosphate (PPi) exchange assay to map out a highly efficient on-pathway kinetics profile of the substrate activation and intermediate uploading (>98% relative weight) for wide-type gramicidin S synthetase A (GrsA) and a 87% rate profile for a cysteine-free GrsA mutant. Our kinetics profiling approach complements the existing enzyme-coupled byproduct-release assays, unraveling new mechanistic insights of substrate activation/channeling in NRPS enzymes.

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