Novel Human Milk Fat Substitutes Based on Medium- and Long-Chain Triacylglycerol Regulate Thermogenesis, Lipid Metabolism, and Gut Microbiota Diversity in C57BL/6J Mice.

Human milk is naturally rich in medium- and long-chain triacylglycerols (MLCT), accounting for approximately 30% of the total fat. However, infant formula fat is prepared using a physical blend of vegetable oils, which rarely contains MLCT, similar to human milk. The differences in MLCT between human milk and infant formulas may cause different lipid metabolisms and physiological effects on infants, which are unknown. This study aimed to analyze the metabolic characteristics of formula lipid containing novel human milk fat substitutes based on MLCT (FL-MLCT) and compare their effects with those of the physical blend of vegetable oils (FL-PB) on lipid metabolism and gut microbiota in mice. Compared with the FL-PB group, the FL-MLCT group showed increased energy expenditure, decreased serum triacylglycerol level, and significantly lower aspartate aminotransferase level, epididymal and perirenal fat weight, and adipocyte size. Moreover, the abundances of Firmicutes/Bacteroidota, Actinobacteriota, and Desulfovibrionaceae were significantly decreased in the FL-MLCT group. Novel human milk fat substitutes MLCT could inhibit visceral fat accumulation, improve liver function, and modulate the mice gut microbiota composition, which may contribute to controlling obesity.

Tunable VO2 cavity enables multispectral manipulation from visible to microwave frequencies.

Optical materials capable of dynamically manipulating electromagnetic waves are an emerging field in memories, optical modulators, and thermal management. Recently, their multispectral design preliminarily attracts much attention, aiming to enhance their efficiency and integration of functionalities. However, the multispectral manipulation based on these materials is challenging due to their ubiquitous wavelength dependence restricting their capacity to narrow wavelengths. In this article, we cascade multiple tunable optical cavities with selective-transparent layers, enabling a universal approach to overcoming wavelength dependence and establishing a multispectral platform with highly integrated functions. Based on it, we demonstrate the multispectral (ranging from 400 nm to 3 cm), fast response speed (0.9 s), and reversible manipulation based on a typical phase change material, vanadium dioxide. Our platform involves tandem VO2-based Fabry-Pérot (F-P) cavities enabling the customization of optical responses at target bands independently. It can achieve broadband color-changing capacity in the visible region (a shift of ~60 nm in resonant wavelength) and is capable of freely switching between three typical optical models (transmittance, reflectance, and absorptance) in the infrared to microwave regions with drastic amplitude tunability exceeding 0.7. This work represents a state-of-art advance in multispectral optics and material science, providing a critical approach for expanding the multispectral manipulation ability of optical systems.

A novel graphene/triolein complex-based lubricant for improving high temperature water-based drilling fluid.

Drilling engineering plays a pivotal role in the exploration and extraction of subsurface resources. It heavily depends on drilling fluid, which serves various essential functions including cooling the drill bit, removing drilled cuttings, maintaining formation pressure equilibrium, stabilizing the wellbore, transmitting hydraulic pressure, and safeguarding oil and gas reservoirs. Nonetheless, drilling fluid encounters multiple obstacles such as leakage control, waste fluid management, prevention of wellbore collapse, avoidance of hole enlargement, and environmental preservation. In order to surmount these challenges, the introduction of lubricants into the drilling fluid yields a multitude of advantages, encompassing equipment safeguarding, enhanced drilling efficiency, preservation of wellbore integrity, and bolstered drilling safety. These factors hold crucial significance in ensuring the triumph of drilling operations. This paper presents the introduction of a new lubricant derived from triolein. Following the preparation of graphene and triolein, they were incorporated into the drilling fluid system. A set of tests was subsequently conducted after aging at 240 °C for 16 hours. To assess the impact of the lubricant on the drilling fluid, an examination of rheological and filtration properties was conducted. Additionally, investigations into the friction coefficient, adhesion coefficient, and extreme pressure lubricity were carried out to evaluate the lubricating performance of the drilling fluid. Adding lubricants at a temperature of 240 degrees Celsius has successfully controlled the adhesion coefficient of the drilling fluid to below 0.2, reaching a minimum of 0.055, resulting in a reduction rate of over 70%. This indicates that the lubricant performs well at high temperatures, effectively reducing friction and enhancing drilling speed.

TGF-ß score based on silico analysis can robustly predict prognosis and immunological characteristics in lower-grade glioma: the evidence from multicenter studies.

INTRODUCTION: Nowadays, mounting evidence shows that variations in TGF-ß signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-ß antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-ß signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigen-presenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-ß related genes in LGG is still unknown. METHODS: We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation. RESULTS: In addition, previous publications were explored for the 223 regulators of the TGF-ß signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-ß score was calculated. Multiple statistical approaches verified the predictive value of the TGF-ß score for the training cohort and two external validation cohorts. Considering the importance of the TGF-ß signaling pathway in immune regulation, we evaluated the prediction of the TGF-ß score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-ß score had the best predictive efficiency (C-index =0.812). CONCLUSION: In conclusion, our findings suggest that TGF-ß signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

Automated object recognition in high-resolution optical remote sensing imagery.

This paper reports the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, as well as summarize the challenges, champion solutions, and future directions.

Comparison of lipid structure and composition in human or cow's milk with different fat globules by homogenization.

Homogenization is used in human milk to add supplements for premature infants and in cow's milk to make it more uniform and stable for commercial purposes. However, it may destroy the milk fat globule (MFG) structure and composition, affecting its functional characteristics. This study aims to compare human and cow's milk with particle size ranges of 4-6 µm (large-sized), 1-2 µm (medium-sized), and 0.3-0.5 µm (small-sized) before and after homogenization at different pressure levels. CLSM and SDS-PAGE were used to perform the structural characterization. The lipid compositions were analyzed using GC and LC-MS. The results showed that homogenization obviously changed the MFG structure and its lipid composition. After homogenization, more caseins and whey proteins were adsorbed on both the human and cow's milk fat globule interface, while the proteins observed in human milk were dispersed. This could be attributed to the different types and contents of proteins initially. The influence of homogenization on milk phospholipids was higher than triacylglycerols and fatty acids, which was highly correlated with their initial distributions in MFGs. These results provide new information about the interfacial composition of human and cow's milk fat globules upon homogenization and establish the scientific basis for homogenization application in human and cow's milk to help explore their potential functions.

Multi-Modal Remote Sensing Image Segmentation with Intuition-Inspired Hypergraph Modeling.

Multi-modal remote sensing (RS) image segmentation aims to comprehensively utilize multiple RS modalities to assign pixel-level semantics to the studied scenes, which can provide a new perspective for global city understanding. Multi-modal segmentation inevitably encounters the challenge of modeling intra- and inter-modal relationships, i.e., object diversity and modal gaps. However, the previous methods are usually designed for a single RS modality, limited by the noisy collection environment and poor discrimination information. Neuropsychology and neuroanatomy confirm that the human brain performs the guiding perception and integrative cognition of multi-modal semantics through intuitive reasoning. Therefore, establishing a semantic understanding framework inspired by intuition to realize multi-modal RS segmentation becomes the main motivation of this work. Drived by the superiority of hypergraphs in modeling high-order relationships, we propose an intuition-inspired hypergraph network (I2HN) for multi-modal RS segmentation. Specifically, we present a hypergraph parser to imitate guiding perception to learn intra-modal object-wise relationships. It parses the input modality into irregular hypergraphs to mine semantic clues and generate robust mono-modal representations. In addition, we also design a hypergraph matcher to dynamically update the hypergraph structure from the explicit correspondence of visual concepts, similar to integrative cognition, to improve cross-modal compatibility when fusing multi-modal features. Extensive experiments on two multi-modal RS datasets show that the proposed I2HN outperforms the state-of-the-art models, achieving F1/mIoU accuracy 91.4%/82.9% on the ISPRS Vaihingen dataset, and 92.1%/84.2% on the MSAW dataset. The complete algorithm and benchmark results will be available online.

LINC01234 Sponging of the miR-513a-5p/AOX1 Axis is Upregulated in Osteoporosis and Regulates Osteogenic Differentiation of Bone Marrow Mesenchymal Stem Cells.

Non-coding RNAs, including long-chain non-coding RNA (lncRNA) and micro-RNA (miRNA), have been implicated in osteoporosis (OP) progression by regulating osteoblast-dependent bone metabolism. Herein, we investigated whether LINC01234, miR-513a-5p, and AOX1 regulate osteogenic differentiation and proliferation of human bone marrow mesenchymal stem cells (hMSCs). The expression of LINC01234, miR-513a-5p, and AOX1 was monitored using RT-qPCR or western blotting. Cell proliferation was assessed using a CCK8 assay. Alkaline phosphatase activity (ALP) and alizarin red dye staining were performed to determine osteogenic differentiation. Furthermore, the expression of osteoblast differentiation markers, such as ALP, BMP1 (bone morphogenetic protein 1), osteocalcin (OCN), and osteopontin (OPN), was determined by RT-qPCR. Luciferase reporter and RNA immunoprecipitation (RIP) assays were performed to verify the interplay between miR-513a-5p and LINC01234 or AOX1. Compared with the plasma of healthy controls, LINC01234 and AOX1 were highly expressed in the plasma of patients with OP, whereas miR-513a-5p showed low expression. In contrast, LINC01234 and AOX1 expression displayed a gradual decrease in induced differentiated hMSCs, while miR-513a-5p expression was upregulated with induction time. The predicted binding sites between miR-513a-5p and LINC01234 or AOX1 were verified by luciferase reporter and RIP assays. LINC01234 silencing induced osteogenic differentiation and proliferation in vitro, and miR-513a-5p silencing blunted osteogenic differentiation and proliferation modulated by LINC01234. AOX1 silencing caused by miR-513a-5p enhances osteogenic proliferation and differentiation. LINC01234 sponging of the miR-513a-5p/AOX1 axis impeded the osteogenic differentiation of hMSCs, favoring OP progression.

Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery.

With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.

Full Geometric Control of Hidden Color Information in Diffraction Gratings under Angled White Light Illumination.

Under white light illumination, gratings produce an angular distribution of wavelengths dependent on the diffraction order and geometric parameters. However, previous studies of gratings are limited to at least one geometric parameter (height, periodicity, orientation, angle of incidence) kept constant. Here, we vary all geometric parameters in the gratings using a versatile nanofabrication technique, two-photon polymerization lithography, to encode hidden color information through two design approaches. The first approach hides color information by decoupling the effects of grating height and periodicity under normal and oblique incidence. The second approach hides multiple sets of color information by arranging gratings in sectors around semicircular pixels. Different images are revealed with negligible crosstalk under oblique incidence and varying sample rotation angles. Our analysis shows that an angular separation of ≥10° between adjacent sectors is required to suppress crosstalk. This work has potential applications in information storage and security watermarks.

Medium- and long-chain triacylglycerols and di-unsaturated fatty acyl-palmitoyl-glycerols in Chinese human milk: Association with region during the lactation.

The triacylglycerols (TAGs) of medium- and long-chain triacylglycerols (MLCT) and di-unsaturated fatty acyl-palmitoyl-glycerols (UPU) in human milk provide better nutritional effects, and should be prioritized as crucial focuses on neonatal nutrition research. However, little has been done on the influences of the lactation stage and regional diversity on MLCT and UPU. In this study, we collected 204 human milk samples during colostrum, 1st and 4th month from the north (Baotou), central (Beijing), east (Jinan), southwest (Kunming), southeast (Shenzhen), and northwest (Xining) regions of China. There were 122 species of TAGs detected with UPLC-Q-TOF-MS, including 60 kinds of MLCT and 15 kinds of UPU. The MLCT and UPU type TAGs in human milk were ~27 and ~38%, respectively. The sum content of MLCT and UPU in human milk was stable. Compared to the regional diversity, lactation stages showed more obvious influences on MLCT and UPU composition. Moreover, a summary of TAG studies indicated that Chinese human milk showed a higher ratio of O-P-L to O-P-O than in western countries.

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer.

MOTIVATION: Accurate ADMET (an abbreviation for 'absorption, distribution, metabolism, excretion and toxicity') predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customized to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks and self-supervised tasks. RESULTS: Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customized ADMET endpoints, meeting various demands of drug research and development requirements. AVAILABILITY AND IMPLEMENTATION: H-ADMET is freely accessible at https://paddlehelix.baidu.com/app/drug/admet/train. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multicolor Light Mixing in Optofluidic Concave Interfaces for Anticounterfeiting with Deep Learning Authentication.

Anticounterfeiting technology has received tremendous interest for its significance in daily necessities, medical industry, and high-end products. Confidential tags based on photoluminescence are one of the most widely used approaches for their vivid visualization and high throughput. However, the complexity of confidential tags is generally limited to the accessibility of inks and their spatial location; generating an infinite combination of emission colors is therefore a challenging task. Here, we demonstrate a concept to create complex color light mixing in a confined space formed by microscale optofluidic concave interfaces. Infinite color combination and capacity were generated through chaotic behavior of light mixing and interaction in an ininkjet-printed skydome structure. Through the chaotic mixing of emission intensity, wavelength, and light propagation trajectories, the visionary patterns serve as a highly unclonable label. Finally, a deep learning-based machine vision system was built for the authentication process. The developed anticounterfeiting system may provide inspiration for utilizing space color mixing in optical security and communication applications.

Efficient and Accurate Computational Model of Neuron with Spike Frequency Adaptation.

Simplified models of neurons are widely used in computational investigations of large networks. One of the most important performance metrics of simplified models is their accuracy in reproducing action potential (spike) timing. In this article, we developed a simple, computationally efficient neuron model by modifying the adaptive exponential integrate and fire (AdEx) model [1] with sigmoid afterhyperpolarization current (Sigmoid AHP). Our model can precisely match the spike times and spike frequency adaptation of cortical pyramidal neurons. The accuracy was similar to a more complex two compartment biophysically realistic model of the same neurons. This work provides a simplified neuronal model with improved spike timing accuracy for use in modeling of large neural networks.Clinical Relevance- Accurate and computationally efficient single neuron model will enable large network modeling of brain regions involved in neurological and psychiatric disorders and may lead to a better understanding of the disorder mechanisms.

Understanding the Impact of Neural Variations and Random Connections on Inference.

Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.

Floating solid-state thin films with dynamic structural colour.

Thin-film architectures are a staple in a wide range of technologies, such as semiconductor devices, optical coatings, magnetic recording, solar cells and batteries. Despite the industrial success of thin-film technology, mostly due to the easy fabrication and low cost, a fundamental drawback remains: it is challenging to alter the features of the film once fabricated. Here we report a methodology to modify the thickness and sequence of the innermost solid-state thin-film layers. We start with a thin-film stack of amorphous iron oxide and silver. By applying a suitable voltage bias and then reversing it, we can float the silver layer above or below the oxide layer by virtue of the migration of silver atoms. Scanning transmission electron microscopy reveals various sequences and thicknesses of the silver and oxide layers achieved with different experimental conditions. As a proof-of-principle, we show a dynamic change of structural colours of the stack derived from this process. Our results may offer opportunities to dynamically reconfigure thin-film-based functional nanodevices in situ.

Handheld robotic needle holder training: slower but better.

BACKGROUND: Handheld robotic laparoscopic instruments fill the gap between robotic and conventional instruments, combining the advantages of degrees of freedom and low price. The difficulty and value in learning these new instruments require detailed investigation. METHODS: Forty novice surgeons with no laparoscopic experience were randomly assigned to two groups: conventional instrument group (Group Conv) and robotic instrument group (Group Rob). The same training protocol was used in both groups: after viewing a standard operation film, laparoscopic suture training was administered using the corresponding instruments. After each training period, surgeons were tested using a force-sensing test platform. Maximum force (MF) and impulse (IMP) of operators through each ring were recorded. Learning curves based on MF and IMP for both instruments were compared. Institutional review board approval is not needed for this study. RESULTS: MF and IMP of both groups decreased with increased training time; the learning curve of Group Conv decreased faster than that of Group Rob. When training time reached 13 h, the MF of Group Rob was significantly lower than that of Group Conv (P < 0.05), while IMP showed no significant difference between the two groups. CONCLUSIONS: Effective training reduces operator MF and IMP, possibly decreasing damage to tissues with both conventional and handheld robotic needle holders. Group Rob took longer to reach a plateau, but subsequently had lower suture tension than did Group Conv. MF is more sensitive than IMP for measuring performance progress.

MicroRNA-503-5p improves carotid artery stenosis by inhibiting the proliferation of vascular smooth muscle cells.

Carotid artery stenosis (CAS) is a common arteriosclerotic vascular disease affected by vascular smooth muscle cells (VSMCs). The aim of the present study was to investigate the expression and diagnostic value of microRNA (miR)-503-5p in asymptomatic patients with CAS and to further explore the effect of miR-503-5p on VSMC proliferation. The levels of miR-503-5p in the serum of 62 asymptomatic patients with CAS and 60 healthy controls were detected by reverse transcription-quantitative PCR. The association between miR-503-5p and the clinical characteristics of the patients was analyzed using the χ2 test. A receiver operating characteristic curve was drawn to evaluate the diagnostic value of miR-503-5p to distinguish asymptomatic patients with CAS from healthy controls. Finally, miR-503-5p inhibitors and mimics were transfected into VSMCs in vitro to detect the effect of miR-503-5p on the proliferation ability through Cell Counting Kit-8 assays. The serum levels of miR-503-5p in asymptomatic patients with CAS were significantly reduced as compared with those in healthy individuals. The expression levels of miR-503-5p were significantly associated with diabetes and arterial stenosis. Furthermore, the area under the ROC curve was 0.817, the specificity was 79.03% and the sensitivity was 83.30%, which proved that miR-503-5p had a high diagnostic accuracy in patients with CAS. Finally, the in vitro proliferation assay indicated that overexpression of miR-503-5p significantly inhibited the proliferation of VSMCs. In conclusion, miR-503-5p is a potential diagnostic biomarker for asymptomatic CAS and overexpression of miR-503-5p may inhibit the proliferation of VSMCs and improve CAS.

Identification of a novel series of azabenzimidazole-derived inhibitors of spleen tyrosine kinase.

Spleen Tyrosine Kinase (SYK) is a well-studied enzyme with therapeutic applications in oncology and autoimmune diseases. We identified an azabenzimidazole (ABI) series of SYK inhibitors by mining activity data of 86,000 compounds from legacy biochemical assays with SYK and other homologous kinases as target enzymes. A structure-based design and hybridization approach was then used to improve the potency and kinase selectivity of the hits. Lead compound 23 from this novel ABI series has a SYK IC50 = 0.21 nM in a biochemical assay and inhibits growth of SUDHL-4 cells at a GI50 = 210 nM.