In ovo serial skeletal muscle diffusion tractography of the developing chick embryo using DTI: feasibility and correlation with histology

Abstract Background Magnetic resonance imaging is a noninvasive method of evaluating embryonic development. Diffusion tensor imaging (DTI), based on the directional diffusivity of water molecules, is an established method of evaluating tissue structure. Yet embryonic motion degrades the in vivo acquisition of long-duration DTI. We used a dual-cooling technique to avoid motion artifact and aimed to investigate whether DTI can be used to monitor chick embryonic skeletal muscle development in ovo, and to investigate the correlation between quantitative DTI parameters fractional anisotropy (FA) and fiber length and quantitative histologic parameters fiber area percentage (FiberArea%) and limb length. Results From 84 normally developing chick embryos, 5 were randomly chosen each day from incubation days 5 to 18 and scanned using 3.0 Tesla magnetic resonance imaging. A dual-cooling technique is used before and during imaging. Eggs were cracked for making histological specimen after imaging. 3 eggs were serially imaged from days 5 to 18. We show that skeletal muscle fibers can be tracked in hind limb in DTI beginning with incubation day 8. Our data shows a good positive correlation between quantitative DTI and histologic parameters (FA vs FiberArea%: r= 0.943, p\u3c0.0001; Fiber_length vs Limb_length: r=0.974, p\u3c0.0001). The result of tracked fibers in DTI during incubation corresponds to the development of chick embryonic skeletal muscle as reported in the literature. Conclusion Diffusion tensor imaging can provide a noninvasive means of evaluating skeletal muscle development in ovo

Theoretical Insight into the Spectral Characteristics of Fe(II)-Based Complexes for Dye-Sensitized Solar Cells—Part I: Polypyridyl Ancillary Ligands

The design of light-absorbent dyes with cheaper, safer, and more sustainable materials is one of the key issues for the future development of dye-sensitized solar cells (DSSCs). We report herein a theoretical investigation on a series of polypyridyl Fe(II)-based complexes of FeL2(SCN)2, [FeL3]2+, [FeL′(SCN)3]-, [FeL′2]2+, and FeL′′(SCN)2 (L = 2,2′-bipyridyl-4,4′-dicarboxylic acid, L′ = 2,2′,2″-terpyridyl-4,4′,4″-tricarboxylic acid, L″ = 4,4‴-dimethyl-2,2′ : 6′,2″ :6″,2‴-quaterpyridyl-4′,4″-biscarboxylic acid) by density functional theory (DFT) and time-dependent DFT (TD-DFT). Molecular geometries, electronic structures, and optical absorption spectra are predicted in both the gas phase and methyl cyanide (MeCN) solution. Our results show that polypyridyl Fe(II)-based complexes display multitransition characters of Fe → polypyridine metal-to-ligand charge transfer and ligand-to-ligand charge transfer in the range of 350–800 nm. Structural optimizations by choosing different polypyridyl ancillary ligands lead to alterations of the molecular orbital energies, oscillator strength, and spectral response range. Compared with Ru(II) sensitizers, Fe(II)-based complexes show similar characteristics and improving trend of optical absorption spectra along with the introduction of different polypyridyl ancillary ligands

Genomic and Proteomic Analyses of the Fungus Arthrobotrys oligospora Provide Insights into Nematode-Trap Formation

Nematode-trapping fungi are “carnivorous” and attack their hosts using specialized trapping devices. The morphological development of these traps is the key indicator of their switch from saprophytic to predacious lifestyles. Here, the genome of the nematode-trapping fungus Arthrobotrys oligospora Fres. (ATCC24927) was reported. The genome contains 40.07 Mb assembled sequence with 11,479 predicted genes. Comparative analysis showed that A. oligospora shared many more genes with pathogenic fungi than with non-pathogenic fungi. Specifically, compared to several sequenced ascomycete fungi, the A. oligospora genome has a larger number of pathogenicity-related genes in the subtilisin, cellulase, cellobiohydrolase, and pectinesterase gene families. Searching against the pathogen-host interaction gene database identified 398 homologous genes involved in pathogenicity in other fungi. The analysis of repetitive sequences provided evidence for repeat-induced point mutations in A. oligospora. Proteomic and quantitative PCR (qPCR) analyses revealed that 90 genes were significantly up-regulated at the early stage of trap-formation by nematode extracts and most of these genes were involved in translation, amino acid metabolism, carbohydrate metabolism, cell wall and membrane biogenesis. Based on the combined genomic, proteomic and qPCR data, a model for the formation of nematode trapping device in this fungus was proposed. In this model, multiple fungal signal transduction pathways are activated by its nematode prey to further regulate downstream genes associated with diverse cellular processes such as energy metabolism, biosynthesis of the cell wall and adhesive proteins, cell division, glycerol accumulation and peroxisome biogenesis. This study will facilitate the identification of pathogenicity-related genes and provide a broad foundation for understanding the molecular and evolutionary mechanisms underlying fungi-nematodes interactions

Mass spectrometric kinetic method for molecular recognition: From *chiral analysis to isomeric quantification

Mass spectrometric kinetic method for chiral analysis has many attractive features (it is fast, simple and accurate, there is no requirement for isotopic labeling, it is tolerant to impurities, and a commercial instrument can be used) and it allows rapid determination of very low enantiomeric excess, a challenging issue faced by the pharmaceutical industry. Recently, it has been successfully extended to rapid and accurate chiral and isomeric quantification, including ternary mixture analysis of optical and positional isomers as well as binary and ternary mixture analysis of different amino acids. The technique uses electrospray ionization or sonic spray ionization to generate the trimeric cluster ions followed by collision-induced dissociation (CID) in commercial ion trap and quadrupole/time-of-flight mass spectrometers. The dissociation kinetics is examined by the kinetic method, a sensitive linear free energy method of treating mass spectrometric results and converting them to thermochemical data. Chiral analysis of amino acids, α-hydroxy acids, antiviral nucleoside agent and some chiral drugs, as well as isomeric quantification of positional isomeric peptides, is demonstrated. The fixed-ligand kinetic method has been developed to improve accuracy in chiral and isomeric analysis by controlling and simplifying the dissociation kinetics. The key innovation is to replace one of the reference ligands with a fixed ligand (Lfixed) So that it is not lost during CID. By changing the properties (size, functionality, and chirality) of the fixed ligands, the metal-ligand and ligand-ligand interactions can be optimized to maximize chiral recognition. The generality of this approach is explored using antibiotics, peptides, and sugars. Further, multiple single-point calibration curves could be constructed using the fixed-ligand quotient ratio method by reversing the chirality of the fixed ligands and the reference ligands. This provides a solution when only one analyte has known optical purity. The fixed-ligand kinetic method allows one to accurately determine less than 2% enantiomeric excess

A Wideband Cryogenic Readout Amplifier with Temperature-Insensitive Gain for SNSPD

This paper presents a temperature-insensitive wideband cryogenic amplifier for superconducting nanowire single-photon detectors (SNSPD). With a proposed folded diode-connected transistor load to realize a good device-tracking feature, the theoretical derivations the simulations and test results prove that the amplifier-gain cell has a stable gain performance over a wide temperature range, solving the issues of a lack of the accurate cryogenic device models. The amplifier achieves a gain of 26 dB from 100 kHz to 1 GHz at 4.2 K, consuming only 1.8 mW from a 1.8 V supply. With a 0.13-μm SiGe BiCMOS process, the chip area is 0.5 mm²

STrans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Pavement Crack Detection

Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model the long-range dependencies between pixels and easily lose edge detail information in complex scenes. Moreover, irregular crack shapes also make the detection task challenging. To address these issues, an automatic pavement crack detection architecture named STrans-YOLOX is proposed. Specifically, the architecture first exploits the CNN backbone to extract feature information, preserving the local modeling ability of the CNN. Then, Swin Transformer is introduced to enhance the long-range dependencies through a self-attention mechanism by supplying each pixel with global features. A new global attention guidance module (GAGM) is used to ensure effective information propagation in the feature pyramid network (FPN) by using high-level semantic information to guide the low-level spatial information, thereby enhancing the multi-class and multi-scale features of cracks. During the post-processing stage, we utilize α-IoU-NMS to achieve the accurate suppression of the detection boxes in the case of occlusion and overlapping objects by introducing an adjustable power parameter. The experiments demonstrate that the proposed STrans-YOLOX achieves 63.37% mAP and surpasses the state-of-the-art models on the challenging pavement crack dataset

An Enhanced Lightweight Network for Road Damage Detection Based on Deep Learning

Achieving accurate and efficient detection of road damage in complex scenes has always been a challenging task. In this paper, an enhanced lightweight network, E-EfficientDet, is proposed. Firstly, a feature extraction enhancement module (FEEM) is designed to increase the receptive field and improve the feature expression capability of the network, which can extract richer multi-scale feature information. Secondly, to promote the reuse of feature information between different layers in the network and take full advantage of multi-scale context information, four pyramid modules with different structures are designed based on the idea of semi-dense connection, among which the bidirectional feature pyramid network with longitudinal connection (LC-BiFPN) is more suitable for road damage detection. Finally, to meet the road damage detection tasks under different hardware resource constraints, the E-EfficientDet-D0~D2 networks are proposed in this paper based on the compound scaling strategy. Experimental results show that the detection accuracy of E-EfficientDet-D0 improves by 2.41% compared with the original EfficientDet-D0 on the publicly available road damage dataset and outperforms other networks such as YOLOv5s, YOLOv7-tiny, YOLOv4-tiny, Faster R-CNN, and SSD. Meanwhile, the detection speed of EfficientDet-D0 can reach 27.0 FPS, which meets the demand for real-time detection, and the model size is only 32.31 MB, which is suitable for deployment in mobile devices such as unmanned inspection carts, UAVs, and smartphones. In addition, the detection accuracy of E-EfficientDet-D2 can reach 57.51%, which is 4.39% higher than E-EfficientDet-D0, and the model size is 61.78 MB, which is suitable for practical application scenarios that require higher detection accuracy and better hardware performance