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Chitosanases can catalyze the release of chitooligosaccharides which have a number of medical applications. Therefore, Chitosanases are good candidates for large-scale enzymatic synthesis due to their favorable thermostability properties and high catalytic efficiency. To further improve the thermostability of a chitosanase from Bacillus sp. TS, which has a half-life of 5.32 min, we mutated specific serine residues that we identified as potentially relevant through structure comparison with thermophilic CelA from Clostridium thermocellum . Out of a total of 15 mutants, three, namely S265G, S276A, and S347G, show higher thermostability. Their half-lives at 60 °C were calculated as 34.57 min, 36.79 min and 7.2 min. The K m values of S265G, S276A and S347G mutants show substrate binding ability comparable to that of the wild-type enzyme, while the S265G mutant displays a significant decrease of enzymatic activities. Additionally, we studied the synergistic effects of combined mutations, observing that all double mutants and the triple mutant are more stable than the wild-type enzyme and single mutants. Finally, we investigated the mechanisms which might give a reasonable explanation for the improved thermostability via comparative analysis of the resulting 3D structures.

Background Ramie degumming is often carried out at high temperatures; therefore, thermostable alkaline pectate lyase (PL) is beneficial for ramie degumming for industrial applications. Thermostable PLs are usually obtained by exploring new enzymes or reconstructing existing enzyme by rational design. Here, we improved the thermostability of an alkaline pectate lyase (PelN) from Paenibacillus sp. 0602 with rational design and structure-based engineering. Results From 26 mutants, two mutants of G241A and G241V showed a higher thermostability compared with the wild-type PL. The mutant K93I showed increasing specific activity at 45 °C. Subsequently, we obtained combinational mutations (K93I/G241A) and found that their thermostability and specific activity improved simultaneously. The K93I/G241A mutant showed a half-life time of 15.9 min longer at 60 °C and a melting temperature of 1.6 °C higher than those of the wild PL. The optimum temperature decreased remarkably from 67.5 °C to 60 °C, accompanied by a 57% decrease in Km compared with the Km value of the wild-type strain. Finally, we found that the intramolecular interaction in PelN was the source in the improvements of molecular properties by comparing the model structures. Rational design of PelN was performed by stabilizing the α-helices with high conservation and increasing the stability of the overall structure of the protein. Two engineering strategies were applied by decreasing the mutation energy calculated by Discovery Studio and predicting the free energy in the process of protein folding by the PoPMuSiC algorithm. Conclusions The results demonstrated that the K93I/G241A mutant was more suitable for industrial production than the wild-type enzyme. Furthermore, the two forementioned strategies could be extended to reveal engineering of other kinds of industrial enzymes.

The success of deep learning (DL) relies heavily on training data from which DL models encapsulate information. Consequently, the development and deployment of DL models expose data to potential privacy breaches, which are particularly critical in data-sensitive contexts like medicine. We propose a new technique named DiffGuard that generates realistic and diverse synthetic medical images with annotations, even indistinguishable for experts, to replace real data for DL model training, which cuts off their direct connection and enhances privacy safety. We demonstrate that DiffGuard enhances privacy safety with much less data leakage and better resistance against privacy attacks on data and model. It also improves the accuracy and generalizability of DL models for segmentation and classification of mediastinal neoplasms in multi-center evaluation. We expect that our solution would enlighten the road to privacy-preserving DL for precision medicine, promote data and model sharing, and inspire more innovation on artificial-intelligence-generated-content technologies for medicine.

Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one’s health status, but few studies have revealed that the eye’s features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals.

The chitosanase gene from a Bacillus sp. strain isolated from soil in East China was cloned and expressed in Escherichia coli. The gene had 1224 nucleotides and encoded a mature protein of 407 amino acid residues. The optimum pH and temperature of the purified recombinant chitosanase were 5.0 and 60 °C, respectively, and the enzyme was stable below 40 °C. The Kₘ, Vₘₐₓ, and specific activity of the enzyme were 1.19 mg mL–¹, 674.71 μmol min–¹at 50 °C, and 555.3 U mg–¹, respectively. Mn²⁺was an activator of the recombinant chitosanase, while Co²⁺was an inhibitor. Hg²⁺and Cu²⁺inhibited the enzyme at 1 mM. The highest level of enzyme activity (186 U mL–¹) was achieved in culture medium using high cell-density cultivation in a 7-L fermenter. The main products of chitosan hydrolyzed by recombinant chitosanase were (GlcN)₃–₆. The chitosanases was successfully secreted to the culture media through the widely used SecB-dependent type II pathway in E. coli. The high yield of the extracellular overexpression, relevant thermostability, and effective hydrolysis of commercial grade chitosan showed that this recombinant enzyme had a great potential for industrial applications.

Biotechnological applications of microbial pectate lyases (Pels) in plant fiber processing are promising, eco-friendly substitutes for conventional chemical degumming processes. However, to potentiate the enzymes’ use for industrial applications, resolving the molecular structure to elucidate catalytic mechanisms becomes necessary. In this manuscript, we report the high resolution (1.45 Å) crystal structure of pectate lyase (pelN) from Paenibacillus sp. 0602 in apo form. Through sequence alignment and structural superposition with other members of the polysaccharide lyase (PL) family 1 (PL1), we determined that pelN shares the characteristic right-handed β-helix and is structurally similar to other members of the PL1 family, while exhibiting key differences in terms of catalytic and substrate binding residues. Then, based on information from structure alignments with other PLs, we engineered a novel pelN. Our rational design yielded a pelN mutant with a temperature for enzymatic activity optimally shifted from 67.5 to 60 °C. Most importantly, this pelN mutant displayed both higher specific activity and ramie fiber degumming ability when compared with the wild-type enzyme. Altogether, our rational design method shows great potential for industrial applications. Moreover, we expect the reported high-resolution crystal structure to provide a solid foundation for future rational, structure-based engineering of genetically enhanced pelNs.

The chitosanase from Bacillus sp. TS (CsnTS) is an enzyme belonging to the glycoside hydrolase family 8. The sequence of CsnTS shares 98 % identity with the chitosanase from Bacillus sp. K17. Crystallography analysis and site-direct mutagenesis of the chitosanase from Bacillus sp. K17 identified the important residues involved in the catalytic interaction and substrate binding. However, despite progress in understanding the catalytic mechanism of the chitosanase from the family GH8, the functional roles of some residues that are highly conserved throughout this family have not been fully elucidated. This study focused on one of these residues, i.e., the aspartic acid residue at position 318. We found that apart from asparagine, mutation of Asp318 resulted in significant loss of enzyme activity. In-depth investigations showed that mutation of this residue not only impaired enzymatic activity but also affected substrate binding. Taken together, our results showed that Asp318 plays an important role in CsnTS activity.

In geological engineering and related fields, accurately and quickly identifying lithology and assessing rock strength are crucial for ensuring structural safety and optimizing design. Traditional rock strength assessment methods mainly rely on field sampling and laboratory tests, such as uniaxial compressive strength (UCS) tests and velocity tests. Although these methods provide relatively accurate rock strength data, they are complex, time-consuming, and unable to reflect real-time changes in field conditions. Therefore, this study proposes a new method based on artificial intelligence and neural networks to improve the efficiency and accuracy of rock strength assessments. This research utilizes a Transformer + UNet hybrid model for lithology identification and an optimized ResNet-18 model for determining rock weathering degrees, thereby correcting the strength of the tunnel face surrounding rock. Experimental results show that the Transformer + UNet hybrid model achieves an accuracy of 95.57% in lithology identification tasks, while the optimized ResNet model achieves an accuracy of 96.13% in rock weathering degree determination. Additionally, the average relative error in tunnel face strength detection results is only 9.33%, validating the feasibility and effectiveness of this method in practical engineering applications. The multi-model neural network system developed in this study significantly enhances prediction accuracy and efficiency, providing robust scientific decision support for tunnel construction, thereby improving construction safety and economy.

Scrap tire rubber and nylon fiber are waste materials that could potentially be recycled and used to improve the mechanical properties of asphalt pavement. The objective of this research was to investigate the properties of scrap tire rubber and nylon fiber (R-F) modified warm mix asphalt mixture (WMA). The high-temperature performance was estimated by the Hamburg wheel-tracking testing (HWTT) device. The low-temperature cracking performance was evaluated by the disk-shaped compact tension (DCT) test and the indirect tensile strength (IDT) test. The stress and strain relationship was assessed by the dynamic modulus test at various temperatures and frequencies. The extracted asphalt binder was evaluated by the dynamic shear rheometer (DSR). Pavement distresses were predicted by pavement mechanistic-empirical (M-E) analysis. The test results showed that: (1) The R-F modified WMA had better high-temperature rutting performance. The dynamic modulus of conventional hot mix asphalt mixture (HMA) was 21.8%~103% lower than R-F modified WMA at high temperatures. The wheel passes and stripping point of R-F modified WMA were 2.17 and 5.8 times higher than those of conventional HMA, respectively. Moreover, the R-F modified warm mix asphalt had a higher rutting index than the original asphalt. (2) R-F modified WMA had better cracking resistance at a low temperature. The failure energy of the R-F modified WMA was 24.3% higher than the conventional HMA, and the fracture energy of the R-F modified WMA was 7.7% higher than the conventional HMA. (3) The pavement distress prediction results showed the same trend compared with the laboratory testing performance in that the R-F modified WMA helped to improve the IRI, AC cracking, and rutting performance compared with the conventional HMA. In summary, R-F modified WMA can be applied in pavement construction.

To improve the prediction accuracy for the stratum settlement induced by tunnels and obtain a theoretical method for determining the influence radius of settlement, a new empirical formula for the surface settlement curve is proposed in this study, and the slice method is introduced into the calculation of the influence radius of settlement. It is assumed that the disturbance boundary of the tunnel is a circular arc surface, the disturbed soil is divided into multiple slices, and the sliding force and resistance on the circular arc are obtained through a stress analysis of the slices. An arc with the same values of the sliding force and resistance can be determined as the actual disturbance boundary of the overlying strata as formed by the subsurface tunneling. On this basis, the influence radius of settlement and maximum settlement at different depths can be determined, and the settlement curve can be depicted by substituting the influence radius and maximum settlement into the expression of the settlement curve. The rationality of the proposed method is verified based on four sets of measured data. The surface settlement curves and the settlements at different depths on the center line of the tunnel obtained by the new method are generally consistent with the measured data.