Prodigiosin: unveiling the crimson wonder – a comprehensive journey from diverse bioactivity to synthesis and yield enhancement

Prodigiosin (PG) is a red tripyrrole pigment from the prodiginine family that has attracted widespread attention due to its excellent biological activities, including anticancer, antibacterial and anti-algal activities. The synthesis and production of PG is of particular significance, as it has the potential to be utilized in a number of applications, including those pertaining to clinical drug development, food safety, and environmental management. This paper provides a systematic review of recent research on PG, covering aspects like chemical structure, bioactivity, biosynthesis, gene composition and regulation, and optimization of production conditions, with a particular focus on the biosynthesis and regulation of PG in Serratia marcescens. This provides a solid theoretical basis for the drug development and production of PG, and is expected to promote the further development of PG in medicine and other applications

The Early Cretaceous extensional deformation in the southeastern Beishan Range, central Asia: Constrains from 2D seismic reflection profile interpretation and apatite fission track thermochronology

Objective  The Beishan Range occupies a key position in Central Asia. This study aims to deepen the understanding of the timing, intracontinental deformation processes, and their dynamic mechanisms in the Late Mesozoic on the southern margin of the Central Asian Orogenic Belt (CAOB).   Methods  We conducted detailed analyses of the Early Cretaceous extensional and earlier compressional structures in the southeastern Beishan Range through field geological observations, interpretation of 2D reflection seismic profiles, and apatite fission track thermochronology.   Conclusion  Field observations show that Lower–Middle Jurassic strata have been strongly deformed by numerous thrusts and folds. 2D seismic reflection profiles reveal two NE- to NEE-striking normal faults. The Suosuojing fault is a SE-dipping low-angle listric normal fault, while the Wudaoming fault is a NW-dipping high-angle normal fault. These normal faults cut through the early-formed fold-thrust system, indicating a transition from contraction to extension. The Suosuojing and Wudaoming faults, respectively, define the northwestern and southeastern boundaries of the Early Cretaceous Zongkouzi basin. The Zongkouzi basin exhibits a graben geometry, with Lower Cretaceous strata displaying typical growth-strata relationships, suggesting that the normal faults were active during the late Early Cretaceous. Thermal history modeling results from apatite fission track data indicate that the footwall of the Suosuojing fault experienced rapid cooling between 132 and 110 Ma. This rapid cooling phase was closely related to the footwall exhumation during the normal slip of the Suosuojing fault. We argue that the Late Mesozoic intracontinental contraction–extension transition in the southeastern Beishan Range likely occurred between ~133 Ma and ~129 Ma in the late Early Cretaceous. The collapse of the thickened crust and coupled mantle upwelling triggered the Early Cretaceous extensional deformation in the southern CAOB

Design and Construction of a Prototype Solenoid Coil for MICE Coupling Magnets

A superconducting coupling solenoid mounted around four conventional RF cavities, which produces up to 2.6 T central magnetic field to keep the muons within the cavities, is to be used for the Muon Ionization Cooling Experiment (MICE). The coupling coil made from copper matrix NbTi conductors is the largest of three types of magnets in MICE both in terms of 1.5 m inner diameter and about 13MJ stored magnetic energy at full operation current of 210A. The stress induced inside the coil assembly during cool down and magnet charging is relatively high. In order to validate the design method and develop the coil winding technique with inside-wound SC splices required for the coupling coil, a prototype coil made from the same conductor and with the same diameter and thickness but only one-fourth long as the coupling coil was designed and fabricated by ICST. The prototype coil was designed to be charged to strain conditions that are equivalent or greater than would be encountered in the coupling coil. This paper presents detailed design of the prototype coil as well as developed coil winding skills. The analyses on stress in the coil assembly and quench process were carried out

Catalysis for CO₂ Hydrogenation—What We Have Learned/Should Learn from the Hydrogenation of Syngas to Methanol

This short review provides an in-depth analysis of the achievements and further developments of the catalytic hydrogenation of carbon dioxide (CO2) to methanol from those that are worth learning about based on the transformation of syngas into methanol. We begin by exploring the environmental and energy-related implications of utilizing CO2 as a feedstock for methanol production by emphasizing its potential to mitigate greenhouse gas emissions and facilitate renewable energy integration. Then, different catalytic formulations focusing on precious metals, copper-based catalysts, and metal oxides are summarized, and insights into their advantages and limitations in the aspects of catalytic activity, selectivity, and stability are discussed. Precious metal catalysts, such as platinum and iridium, exhibit high activity but are cost-prohibitive, while copper-based catalysts present a promising and cost-effective alternative. Metal oxides are considered for their unique properties in CO2 activation. Mechanistic insights into reaction pathways are explored, with a particular emphasis on copper-based catalysts. Moreover, the complex steps involved in CO2 hydrogenation to methanol are discussed to shed light on the key intermediates and active sites responsible for catalysis, which is crucial for catalyst design and optimization. Finally, we stress the importance of ongoing research and development efforts to enhance catalyst efficiency, mechanistic comprehension, and process optimization. This review serves as a valuable resource for researchers, engineers, and policymakers working toward a more sustainable and carbon-neutral energy future. By harnessing CO2 as a carbon feedstock for methanol synthesis, we have the potential to address environmental concerns and advance the utilization of renewable energy sources, further contributing to the transition to a cleaner and more sustainable energy landscape

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors

Objectives. To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients’ clinical characteristics, and identify the most essential features for the classification of bone tumors. Materials and Methods. In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves. Results. The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P<.001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement. Conclusions. This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting

Image_1_Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network.tiff

PurposeTo develop a cascaded deep learning model trained with apparent diffusion coefficient (ADC) and T2-weighted imaging (T2WI) for fully automated detection and localization of clinically significant prostate cancer (csPCa).MethodsThis retrospective study included 347 consecutive patients (235 csPCa, 112 non-csPCa) with high-quality prostate MRI data, which were randomly selected for training, validation, and testing. The ground truth was obtained using manual csPCa lesion segmentation, according to pathological results. The proposed cascaded model based on Res-UNet takes prostate MR images (T2WI+ADC or only ADC) as inputs and automatically segments the whole prostate gland, the anatomic zones, and the csPCa region step by step. The performance of the models was evaluated and compared with PI-RADS (version 2.1) assessment using sensitivity, specificity, accuracy, and Dice similarity coefficient (DSC) in the held-out test set.ResultsIn the test set, the per-lesion sensitivity of the biparametric (ADC + T2WI) model, ADC model, and PI-RADS assessment were 95.5% (84/88), 94.3% (83/88), and 94.3% (83/88) respectively (all p > 0.05). Additionally, the mean DSC based on the csPCa lesions were 0.64 ± 0.24 and 0.66 ± 0.23 for the biparametric model and ADC model, respectively. The sensitivity, specificity, and accuracy of the biparametric model were 95.6% (108/113), 91.5% (665/727), and 92.0% (773/840) based on sextant, and were 98.6% (68/69), 64.8% (46/71), and 81.4% (114/140) based on patients. The biparametric model had a similar performance to PI-RADS assessment (p > 0.05) and had higher specificity than the ADC model (86.8% [631/727], pConclusionThe cascaded deep learning model trained with ADC and T2WI achieves good performance for automated csPCa detection and localization.</p

Progress on Design and Construction of a MuCool Coupling Solenoid Magnet

The MuCool program undertaken by the US Neutrino Factory and Muon Collider Collaboration is to study the behavior of muon ionization cooling channel components. A single superconducting coupling solenoid magnet is necessary to pursue the research and development work on the performance of high gradient, large size RF cavities immersed in magnetic field, which is one of the main challenges in the practical realization of ionization cooling of muons. The MuCool coupling magnet is to be built using commercial copper based niobium titanium conductors and cooled by two cryo-coolers with each cooling capacity of 1.5 W at 4.2 K. The solenoid magnet will be powered by using a single 300A power supply through a single pair of binary leads that are designed to carry a maximum current of 210A. The magnet is to be passively protected by cold diodes and resistors across sections of the coil and by quench back from the 6061 Al mandrel in order to lower the quench voltage and the hot spot temperature. The magnet is currently under construction. This paper presents the updated design and fabrication progress on the MuCool coupling magnet