A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It is conducive to archaeological excavation and reveals the progress of human social civilization. Xiangyang City is the focus of this paper. We selected eight geographical variables as the influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile curvature, and distance from water. With them, we randomly obtained 260 non-site points at the ratio of 1:1 between site points and non-site points based on the 260 excavated archaeological sites and constructed a sample set of geospatial data and the archaeological based on logistic regression (LR). Using 10-fold cross-validation, we trained and tested the model to select the best samples. Thus, the quantitative relationship between the archaeological sites and geographical variables was established. As a result, the Area Under the Curve (AUC) of the LR model is 0.797 and its accuracy is 0.897 in the study. A geographical detector unveils that the three influencing variables of Distance from water, elevation and Plan Curvature top the chart. The archaeological under LR is highly stable and accurate. The geographical variables constitute crucial variables in the archaeological

Machine learning and 3D bioprinting

With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more powerful, machine learning (ML) is introduced to optimize the relevant processes, applied materials, and mechanical/biological performances. The objectives of this work were to collate, analyze, categorize, and summarize published articles and papers pertaining to ML applications in bioprinting and their impact on bioprinted constructs, as well as the directions of potential development. From the available references, both traditional ML and deep learning (DL) have been applied to optimize the printing process, structural parameters, material properties, and biological/mechanical performance of bioprinted constructs. The former uses features extracted from image or numerical data as inputs in prediction model building, and the latter uses the image directly for segmentation or classification model building. All of these studies present advanced bioprinting with a stable and reliable printing process, desirable fiber/droplet diameter, and precise layer stacking, and also enhance the bioprinted constructs with better design and cell performance. The current challenges and outlooks in developing process-material-performance models are highlighted, which may pave the way for revolutionizing bioprinting technologies and bioprinted construct design.Published versionThis work was financially supported by Xi’an Jiaotong-Liverpool University’s Key Program Special Fund under Grant KSF-E-37

Primers and probe design and precision assessment of the real time RT-PCR assay in Coxsackievirus A10 and enterovirus detection

This data article contains data related to the research article entitled “Rapid detection of enterovirus and Coxsackievirus A10 by a TaqMan based duplex one-step real time RT-PCR assay” (Chen at al., 2017) [1]. Primers and probe sequence design are among the most critical factors in real-time polymerase chain reaction (PCR) assay optimization. Linearity, sensitivity, specificity and precision are the crucial criteria which are used to evaluate the performance of a new method. This data article report the primers and probe design and precision assessment of the new assay. VP1 gene of Coxsackievirus A10 (CV-A10) and 5′-NCR of different enterovirus (EV) serotypes were retrieved from GenBank database and aligned. The intra- and inter-assay variation were assessed using high, medium and low concentration of control plasmid DNA and viral RNA samples

A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion

With widely availed clinically used radionuclides, Cerenkov luminescence imaging (CLI) has become a potential tool in the field of optical molecular imaging. However, the impulse noises introduced by high-energy gamma rays that are generated during the decay of radionuclide reduce the image quality significantly, which affects the accuracy of quantitative analysis, as well as the three-dimensional reconstruction. In this work, a novel denoising framework based on fuzzy clustering and curvature-driven diffusion (CDD) is proposed to remove this kind of impulse noises. To improve the accuracy, the Fuzzy Local Information C-Means algorithm, where spatial information is evolved, is used. We evaluate the performance of the proposed framework systematically with a series of experiments, and the corresponding results demonstrate a better denoising effect than those from the commonly used median filter method. We hope this work may provide a useful data pre-processing tool for CLI and its following studies

Quantitative Determination of Ethylene Using a Smartphone-Based Optical Fiber Sensor (SOFS) Coupled with Pyrene-Tagged Grubbs Catalyst

For rapid and portable detection of ethylene in commercial fruit ripening storage rooms, we designed a smartphone-based optical fiber sensor (SOFS), which is composed of a 15 mW 365 nm laser for fluorescence signal excitation and a bifurcated fiber system for signal flow direction from probe to smartphone. Paired with a pyrene-tagged Grubbs catalyst (PYG) probe, our SOFS showed a wide linearity range up to 350 ppm with a detection limit of 0.6 ppm. The common gases in the warehouse had no significant interference with the results. The device is portable (18 cm × 8 cm × 6 cm) with an inbuilt power supply and replaceable optical fiber sensor tip. The images are processed with a dedicated smartphone application for RGB analysis and ethylene concentration. The device was applied in detection of ethylene generated from apples, avocados, and bananas. The linear correlation data showed agreement with data generated from a fluorometer. The SOFS provides a rapid, compact, cost-effective solution for determination of the fruit ethylene concentration dynamic during ripening for better fruit harvest timing and postharvest management to minimize wastage

Inhibition of co-occurring weeds and young sugarcane seedling growth by perennial sugarcane root extract

Abstract Allelopathy is a process whereby a plant directly or indirectly promotes or inhibits growth of surrounding plants. Perennial sugarcane root extracts from various years significantly inhibited Bidens pilosa, Digitaria sanguinalis, sugarcane stem seedlings, and sugarcane tissue-cultured seedlings (P < 0.05), with maximum respective allelopathies of − 0.60, − 0.62, − 0.20, and − 0.29. Allelopathy increased with increasing concentrations for the same-year root extract, and inhibitory effects of the neutral, acidic, and alkaline components of perennial sugarcane root extract from different years were significantly stronger than those of the control for sugarcane stem seedlings (P < 0.05). The results suggest that allelopathic effects of perennial sugarcane root extract vary yearly, acids, esters and phenols could be a main reason for the allelopathic autotoxicity of sugarcane ratoons and depend on the type and content of allelochemicals present, and that allelopathy is influenced by other environmental factors within the rhizosphere such as the presence of old perennial sugarcane roots. This may be a crucial factor contributing to the decline of perennial sugarcane root health

Zein Increases the Cytoaffinity and Biodegradability of Scaffolds 3D-Printed with Zein and Poly(ε-caprolactone) Composite Ink

Electrohydrodynamic printing (EHDP) has attracted extensive interests as a powerful technology to fabricate micro- to nano-scale fibrous scaffolds in a custom-tailored manner for biomedical applications. A few synthetic biopolymer inks are applicable to this EHDP technology, but the fabricated scaffolds suffered from low mechanical strength, biocompatibility, and biodegradability. In this study, a series of poly­(ε-caprolactone) (PCL)/zein composite inks were developed and their printability was examined on a solution-based EHDP system for scaffold fabrication. Multilayer grid scaffolds were manufactured by PCL, PCL/zein-10, and PCL/zein-20 inks, respectively and characterized. The mechanical strength of scaffolds printed by PCL/zein composite inks was remarkably enhanced in terms of Young’s modulus and yield stress. The enzyme-accelerated in vitro degradation study demonstrated that zein-containing scaffolds exhibited dose-responsive improvement on the degradation rate as evidenced by surface morphological change of fibers. Moreover, the biocompatibility of PCL/zein scaffolds, tested on mice embryonic fibroblast (NIH/3T3) and human nonsmall lung cancer cell (H1299), manifested better cell affinity. Our findings suggest that scaffolds fabricated by the solution-based EHDP with PCL/zein composite inks can significantly improve Young’s modulus, yield stress, biocompatibility, and biodegradability and have potential applications in drug delivery systems, 3D cell culture modeling, or tissue engineering