A simple model for the anomalous intrinsic viscosity of dendrimers

The intrinsic viscosity of dendrimers in solution shows several anomalous behaviors that have hitherto not been explained within the existing theoretical frameworks of either Zimm or Rouse. Here we propose a simple two-zone model based on the radial segmental density profile of the dendrimers and combine a non-draining core with a free-draining outer region description, to arrive at a simple formula that captures most of the main features in the intrinsic viscosity data obtained in experiments

Direct in-vitro assay of resistant starch in phosphorylated cross-linked starch

Direct assay of resistant starch (RS) in food and feed is accomplished by (i) removal of lipid, protein, and digestible starch to obtain insoluble dietary fiber, and (ii) dissolution of the resistant starch in the insoluble fiber followed by its quantification with specific enzymes. Phosphorylated cross-linked (CL) RS resists dissolution and therefore has not been assayed directly. The objective of this study was to develop a method to solubilize the RS fraction in phosphorylated (0.4% phosphorus) CL wheat starch (RS4) after its incubation with α-amylase and amyloglucosidase for 16 h at 37 °C as directed by the RS assay AOAC Method 2002.02. The residue was hydrolyzed and solubilized by conducting two back-to-back incubations with thermostable α-amylase for 30 min at 100 °C and pH 5.0, cooling to 50 °C, then incubating quickly with amyloglucosidase at 50 °C for 1 h at pH 5.0. Importantly, the cooling process after α-amylase incubation was done by placing the mixture in a water bath at 50 °C. The degree of hydrolysis of the CL phosphorylated wheat starch was determined as d-glucose using high-performance anion-exchange chromatography with pulsed amperometric detection (99.0%), glucose-oxidase/peroxidase (95.3%), and phenol-sulfuric acid determination of total carbohydrate (105.2%). Based on those findings, we propose a direct determination of RS in foods containing phosphorylated CL wheat starch

Research on surface movement and deformation characteristics of loess gully landform in Northern Shaanxi

In order to study the surface movement and deformation characteristics of the collapsible loess gully landform in the northern Shaanxi mining area in the middle reaches of the Yellow River Basin, the N1212 working face in the loess gully area of the Ningtiaota Mine has been systematically monitored for surface subsidence to analyze the high-intensity mining conditions Deformation characteristics of the ground surface subsidence, determine the maximum surface subsidence speed and the maximum subsidence speed lag angle, surface movement time and dynamic surface movement parameters. The results of the study show that the discontinuous deformation and destruction of the surface in high-strength coal mining in the collapsible loess layer in northern Shaanxi are severe, and the loess surface is easily affected by the combined effects of movement and deformation and topographic conditions, resulting in uneven settlement. Under high-strength mining conditions, the surface movement and deformation are severely developed , The maximum surface subsidence value is 5255 mm, the maximum horizontal movement value is 2680 mm, the maximum subsidence speed is 187.4 mm/d, the maximum subsidence coefficient of single coal seam mining is 0.63, the maximum subsidence coefficient of oblique repeated mining is 0.84, the active period is about 55 d, and the period of subsidence is about 55 d. The amount accounts for 97% of the total subsidence, the maximum lagging distance of the down-town velocity is 74 m, and the maximum lagging angle of the sinking velocity is 67°. The above results verify that in the high-intensity mining of shallow coal seams, the surface subsidence is proportional to the geological mining factors when the ground subsidence is severe, the activity period is short, and the mining is repeated. The surface deformation of high-intensity mining in the valley terrain has the characteristics of fast speed, large collapse and heavy damage

Investigating Dynamic Molecular Events in Melanoma Cell Nucleus During Photodynamic Therapy by SERS

Photodynamic therapy (PDT) involves the uptake of photosensitizers by cancer cells and the irradiation of a light with a specific wavelength to trigger a series of photochemical reactions based on the generation of reactive oxygen, leading to cancer cell death. PDT has been widely used in various fields of biomedicine. However, the molecular events of the cancer cell nucleus during the PDT process are still unclear. In this work, a nuclear-targeted gold nanorod Raman nanoprobe combined with surface-enhanced Raman scattering spectroscopy (SERS) was exploited to investigate the dynamic intranuclear molecular changes of B16 cells (a murine melanoma cell line) treated with a photosensitizer (Chlorin e6) and the specific light (650 nm). The SERS spectra of the cell nucleus during the PDT treatment were recorded in situ and the spectroscopic analysis of the dynamics of the nucleus uncovered two main events in the therapeutic process: the protein degradation and the DNA fragmentation. We expect that these findings are of vital significance in having a better understanding of the PDT mechanism acting on the cancer cell nucleus and can further help us to design and develop more effective therapeutic platforms and methods

3D Bioprinting tissue analogs: Current development and translational implications

Three-dimensional (3D) bioprinting is a promising and rapidly evolving technology in the field of additive manufacturing. It enables the fabrication of living cellular constructs with complex architectures that are suitable for various biomedical applications, such as tissue engineering, disease modeling, drug screening, and precision regenerative medicine. The ultimate goal of bioprinting is to produce stable, anatomically-shaped, human-scale functional organs or tissue substitutes that can be implanted. Although various bioprinting techniques have emerged to develop customized tissue-engineering substitutes over the past decade, several challenges remain in fabricating volumetric tissue constructs with complex shapes and sizes and translating the printed products into clinical practice. Thus, it is crucial to develop a successful strategy for translating research outputs into clinical practice to address the current organ and tissue crises and improve patients' quality of life. This review article discusses the challenges of the existing bioprinting processes in preparing clinically relevant tissue substitutes. It further reviews various strategies and technical feasibility to overcome the challenges that limit the fabrication of volumetric biological constructs and their translational implications. Additionally, the article highlights exciting technological advances in the 3D bioprinting of anatomically shaped tissue substitutes and suggests future research and development directions. This review aims to provide readers with insight into the state-of-the-art 3D bioprinting techniques as powerful tools in engineering functional tissues and organs

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality