Structural and functional insights into yeast Tbf1 as an atypical telomeric repeat-binding factor.

Telomeric repeat-binding factor 1 (Tbf1) has a similar architecture as the TRF family of telomeric proteins and plays important roles in both telomere homeostasis and ribosome regulation. However, the molecular basis of why Tbf1 has such different functions compared to other TRFs remains unclear. Here, we present the crystal structures of the TRF homology (TRFH) and Myb-L domains from Schizosaccharomyces pombe Tbf1 (spTbf1). TRFH-mediated homodimerization is essential for spTbf1 stability. Importantly, spTbf1TRFH lacks the conserved docking motif for interactions with telomeric proteins, explaining why spTbf1 does not participate in the assembly of the shelterin complex. Finally, structural and biochemical analyses demonstrate that TRFH and Myb-L domains as well as the loop region of spTbf1 coordinate to recognize S. pombe telomeric double-stranded DNA. Overall, our findings provide structural and functional insights into how fungi Tbf1 acts as an atypical telomeric repeat-binding factor, which helps to understand the evolution of TRFH-containing telomeric proteins.

Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals.

A novel method is proposed based on the improved YOLOV5 and feeding functional area proposals to identify the feeding behaviors of nursery piglets in a complex light and different posture environment. The method consists of three steps: first, the corner coordinates of the feeding functional area were set up by using the shape characteristics of the trough proposals and the ratio of the corner point to the image width and height to separate the irregular feeding area; second, a transformer module model was introduced based on YOLOV5 for highly accurate head detection; and third, the feeding behavior was recognized and counted by calculating the proportion of the head in the located feeding area. The pig head dataset was constructed, including 5040 training sets with 54,670 piglet head boxes, and 1200 test sets, and 25,330 piglet head boxes. The improved model achieves a 5.8% increase in the mAP and a 4.7% increase in the F1 score compared with the YOLOV5s model. The model is also applied to analyze the feeding pattern of group-housed nursery pigs in 24 h continuous monitoring and finds that nursing pigs have different feeding rhythms for the day and night, with peak feeding periods at 7:00-9:00 and 15:00-17:00 and decreased feeding periods at 12:00-14:00 and 0:00-6:00. The model provides a solution for identifying and quantifying pig feeding behaviors and offers a data basis for adjusting the farm feeding scheme.

[Enzymatic properties and degradation characterization of a bis(2-hydroxyethyl) terephthalate hydrolase from Saccharothrix sp.]

The discovery of new enzymes for poly(ethylene terephthalate) (PET) degradation has been a hot topic of research globally. Bis-(2-hydroxyethyl) terephthalate (BHET) is an intermediate compound in the degradation of PET and competes with PET for the substrate binding site of the PET-degrading enzyme, thereby inhibiting further degradation of PET. Discovery of new BHET degradation enzymes may contribute to improving the degradation efficiency of PET. In this paper, we discovered a hydrolase gene sle (ID: CP064192.1, 5085270-5086049) from Saccharothrix luteola, which can hydrolyze BHET into mono-(2-hydroxyethyl) terephthalate (MHET) and terephthalic acid (TPA). BHET hydrolase (Sle) was heterologously expressed in Escherichia coli using a recombinant plasmid, and the highest protein expression was achieved at a final concentration of 0.4 mmol/L of isopropyl-ß-d-thiogalactoside (IPTG), an induction duration of 12 h and an induction temperature of 20 ℃. The recombinant Sle was purified by nickel affinity chromatography, anion exchange chromatography, and gel filtration chromatography, and its enzymatic properties were also characterized. The optimum temperature and pH of Sle were 35 ℃ and 8.0, and more than 80% of the enzyme activity could be maintained in the range of 25-35 ℃ and pH 7.0-9.0 and Co2+ could improve the enzyme activity. Sle belongs to the dienelactone hydrolase (DLH) superfamily and possesses the typical catalytic triad of the family, and the predicted catalytic sites are S129, D175, and H207. Finally, the enzyme was identified as a BHET degrading enzyme by high performance liquid chromatography (HPLC). This study provides a new enzyme resource for the efficient enzymatic degradation of PET plastics.

The predictive effect of different machine learning algorithms for pressure injuries in hospitalized patients: A network meta-analyses.

Background: Pressure injury has always been a focus and difficulty of nursing. With the development of nursing informatization, a large amount of structured and unstructured data has been generated, and it is difficult for traditional methods to utilize these data. With the intersection of artificial intelligence and nursing, it has become a new trend to apply machine learning algorithms to build pressure injury prediction models to manage pressure injuries. However, there is no evidence on the effectiveness of the method and which of a large number of algorithms for machine learning is more applicable to pressure injuries. Objective: This review aims to systematically synthesize existing evidence to determine the effectiveness of applying machine learning algorithms for pressure injury management, to further evaluate and compare pressure injury prediction models constructed by numerous machine learning algorithms, and to derive evidence for the best algorithms for predicting and managing pressure injuries. Design: Systematic review and network meta-analysis. Methods: A systematic electronic search was conducted in the EBSCO, Embase, PubMed, and Web of Science databases. We included all retrospective diagnostic accuracy trials and prospective diagnostic accuracy trials constructing a predictive model by machine learning for pressure injuries up to December 2021. Two review authors independently selected relevant studies and extracted data using the Cochrane handbook for systematic reviews of diagnostic test accuracy. The network meta-analysis was conducted using statistical software R and STATA. The certainty of the evidence was rated using the QUADAS-2 tool. Result: Twenty-five clinical diagnostic trials with a total of 237397 participants were identified in this review. The results of our study revealed that pressure injury machine learning models can effectively predict these injuries. Combining the algorithms separately yields the main results: decision trees (sensitivity: 0.66, 95% CI: 0.42 to 0.84, specificity: 0.90, 95% CI: 0.78 to 0.96, diagnostic odds ratio [DOR]: 18, 95% CI: 7 to 49, AUC: 0.88, 95% CI: 0.85 to 0.91), logistic regression (sensitivity: 0.71, 95% CI: 0.60 to 0.80, specificity: 0.83, 95% CI: 0.75 to 0.89, DOR: 12, 95% CI: 9 to 17, AUC: 0.84, 95% CI: 0.81 to 0.87), neural networks (sensitivity: 0.73, 95% CI: 0.55 to 0.86, specificity: 0.78, 95% CI: 0.65 to 0.87, DOR: 9, 95% CI: 5 to 19, AUC: 0.82, 95% CI: 0.79 to 0.85), random forests (sensitivity: 0.72, 95% CI: 0.26 to 0.95, specificity: 0.96, 95% CI: 0.80 to 0.99, DOR: 56, 95% CI: 3 to 1258, AUC: 0.95, 95% CI: 0.93 to 0.97), support vector machines (sensitivity: 0.81, 95% CI: 0.69 to 0.90, specificity: 0.81, 95% CI: 0.59 to 0.93, DOR: 19, 95% CI: 6 to 54, AUC: 0.88, 95% CI: 0.85 to 0.90). According to the analysis of ROC and AUC values, random forest is the best algorithm for the prediction model of pressure injury. Conclusions: This review revealed that machine learning algorithms are generally effective in predicting pressure injuries, and after data merging, the random forest algorithm is the best algorithm for pressure injury prediction. Further well-designed diagnostic controlled trials are recommended to strengthen the current evidence. Registration number PROSPERO: CRD42021276993.

Basis and current state of computed tomography perfusion imaging: a review.

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Numerical Analysis on Heat Characteristics of the Ventilation Basket for Fresh Tea Leaves.

Plastic baskets are commonly used as containers for fresh tea leaves during storage and transport after harvest. Nevertheless, there are significant challenges in controlling the core temperature of the basket since fresh tea leaves still maintain a certain degree of respiration after being harvested, with extremely high temperatures being the major factor for the color change of fresh tea leaves. A numerical model was developed to improve the temperature control of the plastic basket, by which the influence of different structural parameters on the core temperature in the plastic baskets with fresh tea leaves was analyzed. The accuracy of the model in predicting airflow and temperature distributions was validated against experimental data. The maximum RMSE was 1.158 °C and the maximum MRE was 5.410% between the simulated and test temperature value. The maximum deviation between the simulated velocity and test velocity was 0.11 m/s, the maximum RE was 29.05% and the maximum SD was 0.024. The results show that a plastic basket with a ventilation duct efficiently decreased the temperature of the fresh tea leaves and significantly affected the heat transfer between the fresh tea leaves and the ambient air compared to the plastic basket without a ventilation duct. Furthermore, the effect on the heat transfer was further expanded by the use of a plastic basket with a ventilation duct when the plastic baskets were stacked. The maximum temperature differences were 0.52 and 0.40 according to the stacked and single-layer products, respectively. The ambient temperature and the bulk density of the fresh tea leaves have a significant influence on the core temperature.

Structural insights into Pot1-ssDNA, Pot1-Tpz1 and Tpz1-Ccq1 Interactions within fission yeast shelterin complex.

The conserved shelterin complex caps chromosome ends to protect telomeres and regulate telomere replication. In fission yeast Schizosaccharomyces pombe, shelterin consists of telomeric single- and double-stranded DNA-binding modules Pot1-Tpz1 and Taz1-Rap1 connected by Poz1, and a specific component Ccq1. While individual structures of the two DNA-binding OB folds of Pot1 (Pot1OB1-GGTTAC and Pot1OB2-GGTTACGGT) are available, structural insight into recognition of telomeric repeats with spacers by the complete DNA-binding domain (Pot1DBD) remains an open question. Moreover, structural information about the Tpz1-Ccq1 interaction requires to be revealed for understanding how the specific component Ccq1 of S. pombe shelterin is recruited to telomeres to function as an interacting hub. Here, we report the crystal structures of Pot1DBD-single-stranded-DNA, Pot1372-555-Tpz1185-212 and Tpz1425-470-Ccq1123-439 complexes and propose an integrated model depicting the assembly mechanism of the shelterin complex at telomeres. The structure of Pot1DBD-DNA unveils how Pot1 recognizes S. pombe degenerate telomeric sequences. Our analyses of Tpz1-Ccq1 reveal structural basis for the essential role of the Tpz1-Ccq1 interaction in telomere recruitment of Ccq1 that is required for telomere maintenance and telomeric heterochromatin formation. Overall, our findings provide valuable structural information regarding interactions within fission yeast shelterin complex at 3' ss telomeric overhang.

Reshaping the binding channel of a novel GH113 family ß-mannanase from Paenibacillus cineris (PcMan113) for enhanced activity.

Endo-ß-mannanases are important enzymes for degrading lignocellulosic biomass to generate mannan, which has significant health effects as a prebiotic that promotes the development of gut microbiota. Here, a novel endo-ß-mannanase belonging to glycoside hydrolase (GH) family 113 from Paenibacillus cineris (PcMan113) was cloned, expressed and characterized, as one of only a few reported GH113 family ß-mannanases. Compared to other functionally and structurally characterized GH113 mannanases, recombinant PcMan113 showed a broader substrate spectrum and a better performance. Based on a structural homology model, the highly active mutant PcMT3 (F110E/N246Y) was obtained, with 4.60- and 5.53-fold increases of enzyme activity (towards KG) and catalytic efficiency (kcat/Km, against M5) compared with the WT enzyme, respectively. Furthermore, molecular dynamics (MD) simulations were conducted to precisely explore the differences of catalytic activity between WT and PcMT3, which revealed that PcMT3 has a less flexible conformation, as well as an enlarged substrate-binding channel with decreased steric hindrance and increased binding energy in substrate recognition. In conclusion, we obtained a highly active variant of PcMan113 with potential for commercial application in the manufacture of manno-oligosaccharides.

Posture Detection of Individual Pigs Based on Lightweight Convolution Neural Networks and Efficient Channel-Wise Attention.

In this paper, a lightweight channel-wise attention model is proposed for the real-time detection of five representative pig postures: standing, lying on the belly, lying on the side, sitting, and mounting. An optimized compressed block with symmetrical structure is proposed based on model structure and parameter statistics, and the efficient channel attention modules are considered as a channel-wise mechanism to improve the model architecture.The results show that the algorithm's average precision in detecting standing, lying on the belly, lying on the side, sitting, and mounting is 97.7%, 95.2%, 95.7%, 87.5%, and 84.1%, respectively, and the speed of inference is around 63 ms (CPU = i7, RAM = 8G) per postures image. Compared with state-of-the-art models (ResNet50, Darknet53, CSPDarknet53, MobileNetV3-Large, and MobileNetV3-Small), the proposed model has fewer model parameters and lower computation complexity. The statistical results of the postures (with continuous 24 h monitoring) show that some pigs will eat in the early morning, and the peak of the pig's feeding appears after the input of new feed, which reflects the health of the pig herd for farmers.

Structural and Molecular Dynamics Analysis of Plant Serotonin N-Acetyltransferase Reveal an Acid/Base-Assisted Catalysis in Melatonin Biosynthesis.

Serotonin N-acetyltransferase (SNAT) is the key rate-limiting enzyme in melatonin biosynthesis. It mediates melatonin biosynthesis in plants by using serotonin and 5-methoxytryptamine (5-MT), but little is known of its underlying mechanisms. Herein, we present a detailed reaction mechanism of a SNAT from Oryza sativa through combined structural and molecular dynamics (MD) analysis. We report the crystal structures of plant SNAT in the apo and binary/ternary complex forms with acetyl-CoA (AcCoA), serotonin, and 5-MT. OsSNAT exhibits a unique enzymatically active dimeric fold not found in the known structures of arylalkylamine N-acetyltransferase (AANAT) family. The key residues W188, D189, D226, N220, and Y233 located around the active pocket are important in catalysis, confirmed by site-directed mutagenesis. Combined with MD simulations, we hypothesize a novel plausible catalytic mechanism in which D226 and Y233 function as catalytic base and acid during the acetyl-transfer reaction.