Unveiling Latent Structure of Venture Capital Syndication Networks

Venture capital (VC) is a form of private equity financing provided by VC institutions to startups with high growth potential due to innovative technology or novel business models but also high risks. To against uncertainties and benefit from mutual complementarity and sharing resources and information, making joint-investments with other VC institutions on the same startup are pervasive, which forms an ever-growing complex syndication network. Attaining objective classifications of VC institutions and revealing the latent structure of joint-investment behaviors between them can deepen our understanding of the VC industry and boost the healthy development of the market and economy. In this work, we devise an iterative Loubar method based on the Lorenz curve to make objective classification of VC institutions automatically, which does not require setting arbitrary thresholds and the number of categories. We further reveal distinct investment behaviors across categories, where the top-ranked group enters more industries and investment stages with a better performance. Through network embedding of joint investment relations, we unveil the existence of possible territories of top-ranked VC institutions, and the hidden structure of relations between VC institutions.     

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

This paper proposes an improved human-body-segmentation algorithm with attention-based feature fusion and a refined corner-based feature-point design with sub-pixel stereo matching for the anthropometric system. In the human-body-segmentation algorithm, four CBAMs are embedded in the four middle convolution layers of the backbone network (ResNet101) of PSPNet to achieve better feature fusion in space and channels, so as to improve accuracy. The common convolution in the residual blocks of ResNet101 is substituted by group convolution to reduce model parameters and computational cost, thereby optimizing efficiency. For the stereo-matching scheme, a corner-based feature point is designed to obtain the feature-point coordinates at sub-pixel level, so that precision is refined. A regional constraint is applied according to the characteristic of the checkerboard corner points, thereby reducing complexity. Experimental results demonstrated that the anthropometric system with the proposed CBAM-based human-body-segmentation algorithm and corner-based stereo-matching scheme can significantly outperform the state-of-the-art system in accuracy. It can also meet the national standards GB/T 2664-2017, GA 258-2009 and GB/T 2665-2017; and the textile industry standards FZ/T 73029-2019, FZ/T 73017-2014, FZ/T 73059-2017 and FZ/T 73022-2019.     

Statistical Degree Distribution Design for Using Fountain Codes to Control the Peak-To-Average Power Ratio in an OFDM System

Utilizing fountain codes to control the peak-to-average power ratio (PAPR) is a classic scheme in Orthogonal Frequency Division Multiplexing (OFDM) wireless communication systems. However, because the robust soliton distribution (RSD) produces large-degree values, the decoding performance is severely reduced. In this paper, we design statistical degree distribution (SD) under a scenario that utilizes fountain codes to control the PAPR. The probability of the PAPR produced is combined with RSD to design PRSD, which enhances the smaller degree value produced. Subsequently, a particle swarm optimization (PSO) algorithm is used to search the optimal degree value between the binary exponential distribution (BED) and PRSD distribution according to the minimum average degree principle. Simulation results demonstrate that the proposed method outperforms other relevant degree distributions in the same controlled PAPR threshold, and the average degree value and decoding efficiency are remarkably improved.     

Design,Synthesis and Bioactivity of Novel Low Bee-Toxicity Compounds Based on Flupyrimin

Neonicotinoids are important insecticides for controlling aphids in agriculture. Growing research suggested that neonicotinoid insecticides are a key factor causing the decline of global pollinator insects, such as bees. Flupyrimin (FLP) is a novel nicotinic insecticide with unique biological properties and no cross-resistance, and is safe for pollinators. Using FLP as the lead compound, a series of novel compounds were designed and synthesized by replacing the amide fragment with a sulfonamideone. Their structures were confirmed by ¹H NMR, ¹³C NMR and HRMS spectra. Bioassay results showed that compound 2j had good insecticidal activity against Aphis glycines with an LC₅₀ value of 20.93 mg/L. Meanwhile, compound 2j showed significantly lower acute oral and contact toxicity to Apis mellifera. In addition, compound 2j interacted well with the protein in insect acetylcholine binding protein (AChBP). The molecular docking on honeybee nicotinic acetylcholine receptor (nAChR) indicated that the sulfonamide group of compound 2j did not form a hydrogen bond with Arg173 of the β subunit, which conforms to the reported low bee-toxicity conformation. In general, target compound 2j can be regarded as a bee-friendly insecticide candidate.     

H-Bonding Room Temperature Phosphorescence Materials via Facile Preparation for Water-Stimulated Photoluminescent Ink

Pure organic room-temperature phosphorescence (RTP) materials built upon noncovalent interactions have attracted much attention because of their high efficiency, long lifetime, and stimulus-responsive behavior. However, there are limited reports of noncovalent RTP materials because of the lack of specific design principles and clear mechanisms. Here, we report on a noncovalent material prepared via facile grinding that can emit fluorescence and RTP emission differing from their components’ photoluminescent behavior. Exciplex can be formed during the preparation process to act as the minimum emission unit. We found that H-bonds in the RTP system provide restriction to nonradiative transition but also enhance energy transformation and energy level degeneracy in the system. Moreover, water-stimulated photoluminescent ink is produced from the materials to achieve double-encryption application with good resolution.     

Integrating a Luminescent Porous Aromatic Framework into Indicator Papers for Facile,Rapid, and Selective Detection of Nitro Compounds

Porous aromatic framework materials with high stability, sensitivity, and selectivity have great potential to provide new sensors for optoelectronic/fluorescent probe devices. In this work, a luminescent porous aromatic framework material (LNU-23) was synthesized via the palladium-catalyzed Suzuki cross-coupling reaction of tetrabromopyrene and 1,2-bisphenyldiborate pinacol ester. The resulting PAF solid exhibited strong fluorescence emission with a quantum yield of 18.31%, showing excellent light and heat stability. Because the lowest unoccupied molecular orbital (LUMO) of LNU-23 was higher than that of the nitro compounds, there was an energy transfer from the excited LNU-23 to the analyte, leading to the selective fluorescence quenching with a limit of detection (LOD) ≈ 1.47 × 10⁻⁵ M. After integrating the luminescent PAF powder on the paper by a simple dipping method, the indicator papers revealed a fast fluorescence response to gaseous nitrobenzene within 10 s, which shows great potential in outdoor fluorescence detection of nitro compounds.     

Inhibitory Effects of Myriocin on Non-Enzymatic Glycation of Bovine Serum Albumin

Advanced glycation end products (AGEs) are the compounds produced by non-enzymatic glycation of proteins, which are involved in diabetic-related complications. To investigate the potential anti-glycation activity of Myriocin (Myr), a fungal metabolite of Cordyceps, the effect of Myr on the formation of AGEs resulted from the glycation of bovine serum albumin (BSA) and the interaction between Myr and BSA were studied by multiple spectroscopic techniques and computational simulations. We found that Myr inhibited the formation of AGEs at the end stage of glycation reaction and exhibited strong anti-fibrillation activity. Spectroscopic analysis revealed that Myr quenched the fluorescence of BSA in a static process, with the possible formation of a complex (approximate molar ratio of 1:1). The binding between BSA and Myr mainly depended on van der Waals interaction, hydrophobic interactions and hydrogen bond. The synchronous fluorescence and UV-visible (UV-vis) spectra results indicated that the conformation of BSA altered in the presence of Myr. The fluorescent probe displacement experiments and molecular docking suggested that Myr primarily bound to binding site 1 (subdomain IIA) of BSA. These findings demonstrate that Myr is a potential anti-glycation agent and provide a theoretical basis for the further functional research of Myr in the prevention and treatment of AGEs-related diseases.     

High-Throughput Preparation and Machine Learning Screening of a Blue-Phase Liquid Crystal Based on Inkjet Printing

Blue-phase liquid crystal (BPLC) is considered as the next-generation liquid crystal display material, but its practical application is seriously affected by a narrow temperature range and a long research period. In this paper, we used inkjet printing technology to prepare BPLC materials with high throughput, and try to use machine vision technology to test BPLC with high throughput. The “standard curve method” for establishing each printing channel and the “vector matching method” for searching the chromaticity value of the minimum distance were proposed to improve the accuracy of inkjet printing BPLC materials. For a large number of sample-phase images, we propose a machine learning method to identify the liquid crystal phase. In this paper, for the first time, the high-throughput preparation and high-throughput detection of 1080 BPLC samples with five common components by a comprehensive experimental method has been successfully realized. The results are helpful to improve the research efficiency of blue-phase materials and provide a theoretical basis and practical guidance for rapid screening of multi-component BPLC materials.     

The Biosynthesis Related Enzyme,Structure Diversity and Bioactivity Abundance of Indole-Diterpenes: A Review

Indole diterpenes are a large class of secondary metabolites produced by fungi, possessing a cyclic diterpenoid backbone and an indole moiety. Novel structures and important biological activity have made indole diterpenes one of the focuses of synthetic chemists. Although the discovery, identification, structural diversity, biological activity and especially structure–activity relationship of indole diterpenes have been reported in some papers in recent years, they are absent of a systematic and comprehensive analysis, and there is no elucidation of enzymes related to this kind of natural product. Therefore, it is necessary to summarize the relevant reports to provide new perspectives for the following research. In this review, for the first time, the function of related synthases and the structure–activity relationship of indole diterpenes are expounded, and the recent research advances of them are emphasized.     

Non-Destructive Detection of Moldy Walnuts Based on Hyperspectral Imaging Technology

Walnuts with their shells are a popular agricultural product in China. However, mildew from growth can sometimes be processed into foods. It is difficult to visually determine which walnuts have mildew without breaking the shells. A non-destructive method for detecting walnuts with mildew was studied by combining spectral data with image information. A total of 120 “Lüling” walnuts with shells were used for the mildew experiment. The characteristics of the spectral data from six surfaces of all samples were collected in the range of 370–1042 nm on days 0, 15, and 30. The spectrum was pretreated using SNV, and the feature bands were extracted using PCA and modeled using a support vector machine (SVM). The results show that the overall classification accuracy was 93%, with an of accuracy of 100% for INEN walnuts (normal internally and externally). The accuracy for IMEM walnuts (mildew internally and externally) reached 87.29%. There was an accuracy of 78.6% for IMEN walnuts (mildew internally and normal externally). The non-destructive detection of mildewed walnuts can be undertaken using hyperspectral imaging technology, which provides a new technique for exploring the mechanisms of walnuts with mildew.