Label-free Raman imaging for screening of anti-inflammatory function food.

Natural bioactive compounds and plant constituents are considered to have a positive anti-inflammatory effect. This study aimed to establish a screening technique for anti-inflammatory function in foods based on label-free Raman imaging. A visible anti-inflammatory analysis method based on coherent anti-Stokes Raman scattering (CARS) was established with an LPS-induced RAW264.7 cell model. Dynamic changes in proteins and lipids were determined at laser pump light wavelengths of 2956 cm-1 and 2856 cm-1, respectively. The method was applied to a plant-based formula (JC) with anti-inflammatory activity. Q-TOF-MS and HPLC analyses revealed the main active constituents of JC as quercetin, kaempferol, l-glutamine, and sodium copper chlorophyllin. In in vitro and in vivo verification experiments, JC showed significant anti-inflammatory activity by regulating the TLR4/NF-κB pathway. In conclusion, this study successfully established a label-free and visible method for screening anti-inflammatory constituents in plant-based food products, which will facilitate the evaluation of functional foods.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

Sinoflavonoids NJ and NK, anti-inflammatory prenylated flavonoids from the fruits of Podophyllum hexandrum Royle.

Two new prenylated flavonoids named sinoflavonoids NJ and NK (1-2), along with ten known compounds were isolated from the fruits of Podophyllum hexandrum Royle. The chemical structures were determined through NMR spectroscopic data and MS analysis. Sinoflavonoid NJ (1) with an unusual 5,11-dioxabenzo[b]fluoren-10-one skeleton was firstly reported from Berberidaceae. The isolated flavonoids were tested with LPS-induced RAW 264.7 mouse macrophages model for their anti-inflammatory activity. Sinoflavonoid NJ (1) showed the most potent inhibition on nitric oxide production with IC50 value as 0.06 µM.

A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.

Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.

Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation.

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.

Road to entire insulation for resonances from a forced mechanical system.

The effective solution to avoid machinery damage caused by resonance has been perplexing the field of engineering as a core research direction since the resonance phenomenon was discovered by Euler in 1750. Numerous attempts have been performed to reduce the influence of resonance since the earlier of last century, by introducing a nonlinear structure or a closed-loop control system. However, the existed methodologies cannot eliminate resonance completely even extra problems were introduced inevitably, which means the technical choke-point of resonance-free remains unsolved. Here we propose a designable archetype model, which establishes a mapping between the mechanical properties and its structure. A general inverse method for structure construction is proposed based upon the required property for the system with quasi-zero stiffness of any designed finite order and the zero-stiffness properties. It is shown that an ellipse trajectory tracking of the designed model is the sufficient and necessary condition to satisfy the zero-stiffness property. Theoretical analysis shows that no resonant response happens in a zero-stiffness system to the full-band frequency excitation, or equivalently, the system can completely isolate the energy transfer between the load and environment, when the damping ratio approaches zero. Finally, an experimental rig for the prototype structure is built up according to the sufficient and necessary condition of the zero-stiffness system, for which the special dynamic behaviours are verified through experiments of frequency-sweep and random-vibration as well. Experimental results show that the prototype of the initial vibration isolation frequency of zero-stiffness system is much lower than 0.37 Hz, and the vibration attenuation of the proposed model is about 16.86 dB, 45.63 dB, and 112.37 dB at frequencies of 0.37 Hz, 1 Hz, and 10 Hz, respectively. The distinguished geometric structure of the zero-stiffness system leads to a new inspiration for the design of resonance-free in metamaterial unit and the inverse method can even adapt the design for a more targeted applications based on an arbitrary complex dynamic requirement.

Revealing the Immune Heterogeneity between Systemic Lupus Erythematosus and Rheumatoid Arthritis Based on Multi-Omics Data Analysis.

The pathogenesis of systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are greatly influenced by different immune cells. Nowadays both T-cell receptor (TCR) and B-cell receptor (BCR) sequencing technology have emerged with the maturity of NGS technology. However, both SLE and RA peripheral blood TCR or BCR repertoire sequencing remains lacking because repertoire sequencing is an expensive assay and consumes valuable tissue samples. This study used computational methods TRUST4 to construct TCR repertoire and BCR repertoire from bulk RNA-seq data of both SLE and RA patients' peripheral blood and analyzed the clonality and diversity of the immune repertoire between the two diseases. Although the functions of immune cells have been studied, the mechanism is still complicated. Differentially expressed genes in each immune cell type and cell-cell interactions between immune cell clusters have not been covered. In this work, we clustered eight immune cell subsets from original scRNA-seq data and disentangled the characteristic alterations of cell subset proportion under both SLE and RA conditions. The cell-cell communication analysis tool CellChat was also utilized to analyze the influence of MIF family and GALECTIN family cytokines, which were reported to regulate SLE and RA, respectively. Our findings correspond to previous findings that MIF increases in the serum of SLE patients. This work proved that the presence of LGALS9, PTPRC and CD44 in platelets could serve as a clinical indicator of rheumatoid arthritis. Our findings comprehensively illustrate dynamic alterations in immune cells during pathogenesis of SLE and RA. This work identified specific V genes and J genes in TCR and BCR that could be used to expand our understanding of SLE and RA. These findings provide a new insight inti the diagnosis and treatment of the two autoimmune diseases.

Guidelines for Installation of Sensors in Smart Sensing Platforms in Underground Spaces.

The purpose of this study is to propose guidelines for sensor installation in different types of underground space smart sensing platforms. Firstly, we classify the underground space, analyze the scene requirements according to the classification of underground space, and sort out the requirements for sensors in various types of underground space. Secondly, according to the requirements of underground space scenes for sensors, the types of sensors and corresponding parameters are clarified. After that, the system design and sensor installation guidelines of the underground space smart sensing platform are proposed by sorting out the data acquired by the sensor.

The mitigating role of climate smart villages to the impacts of COVID-19 pandemic in the Myanmar rural communities.

Climate smart village approach is identified as an important strategy laid out in the Myanmar Climate Smart Agriculture Strategy (MCSAS, 2016) Four climate smart villages were established in 2017 to facilitate participatory action research to develop the CSV approach as well as to generate evidence of outcomes. The CSV approach is based on the principle of community-directed research process where community-members collaborate with an external researcher to investigate community challenges and their solutions. Like other countries in 2020, the height of the COVID-19 pandemic, Myanmar implemented wide-scale national and local restrictions on mobility that impacted trade and business resulting to an economic slowdown. Rural communities dominated by smallholder agriculture in Myanmar are not spared from the negative impacts of these restrictions. This paper seeks to assess the impacts of the COVID-19 pandemic to the 4 climate smart villages in Myanmar by analyzing household survey data (N = 527) collected in 2020 during the height of economic disruptions and comparing these data to the household survey conducted during the pre-pandemic period of 2018. Our analysis indicated that overall, the effect of the pandemic to agriculture production in 2020 production season in the 4 CSVs has been minimal as evidenced by the continued agriculture production at the same levels as the pre-pandemic conditions in 2018. The effects to household food security and diet diversity has been varied. Sakta village in Chin state in the highlands have demonstrated that diversified production systems enable them to achieve food security in the pandemic year of 2020.

Extensive conformational and physical plasticity protects HER2-HER3 tumorigenic signaling.

Surface-targeting biotherapeutic agents have been successful in treating HER2-amplified cancers through immunostimulation or chemodelivery but have failed to produce effective inhibitors of constitutive HER2-HER3 signaling. We report an extensive structure-function analysis of this tumor driver, revealing complete uncoupling of intracellular signaling and tumorigenic function from regulation or constraints from their extracellular domains (ECDs). The canonical HER3 ECD conformational changes and exposure of the dimerization interface are nonessential, and the entire ECDs of HER2 and HER3 are redundant for tumorigenic signaling. Restricting the proximation of partner ECDs with bulk and steric clash through extremely disruptive receptor engineering leaves tumorigenic signaling unperturbed. This is likely due to considerable conformational flexibilities across the span of these receptor molecules and substantial undulations in the plane of the plasma membrane, none of which had been foreseen as impediments to targeting strategies. The massive overexpression of HER2 functionally and physically uncouples intracellular signaling from extracellular constraints.

Protein Tyrosine Phosphatase 1B (PTP1B): Insights into its New Implications in Tumorigenesis.

In vivo, tyrosine phosphorylation is a reversible and dynamic process governed by the opposing activities of protein tyrosine kinases and phosphatases. Defective or inappropriate operation of these proteins leads to aberrant tyrosine phosphorylation, which contributes to the development of many human diseases, including cancers. PTP1B, a non-transmembrane phosphatase, is generally considered a negative regulator of the metabolic signaling pathways and a promising drug target for type II diabetes and obesity. Recently, PTP1B is gaining considerable interest due to its important function and therapeutic potential in other diseases. An increasing number of studies have indicated that PTP1B plays a vital role in the initiation and progression of cancers and could be a target for new cancer therapies. Following recent advances in the aspects mentioned above, this review is focused on the major functions of PTP1B in different types of cancer and the underlying mechanisms behind these functions, as well as the potential pharmacological effects of PTP1B inhibitors in cancer therapy.

Structured H∞ Control for Spacecraft with Flexible Appendages.

Spacecraft with large flexible appendages are characterized by multiple system modes. They suffer from inherent low-frequency disturbances in the operating environment that consequently result in considerable interference in the operational performance of the system. It is required that the control design ensures the system's high pointing precision, and it is also necessary to suppress low-frequency resonant interference as well as take into account multiple performance criteria such as attitude stability and bandwidth constraints. Aiming at the comprehensive control problem of this kind of flexible spacecraft, we propose a control strategy using a structured H-infinity controller with low complexity that was designed to meet the multiple performance requirements, so as to reduce the project cost and implementation difficulty. According to the specific resonant mode of the system, the design strategy of adding an internal mode controller, a trap filter, and a series PID controller to the structured controller is proposed, so as to achieve the comprehensive control goals through cooperative control of multiple control modules. A spacecraft with flexible appendages (solar array) is presented as an illustrative example in which a weighted function was designed for each performance requirement of the system (namely robustness, stability, bandwidth limit, etc.), and a structured comprehensive performance matrix with multiple performance weights and decoupled outputs was constructed. A structured H-infinity controller meeting the comprehensive performance requirements is given, which provides a structured integrated control method with low complexity for large flexible systems that is convenient for engineering practice, and provides a theoretical basis and reference examples for structured H-infinity control. The simulation results show that the proposed controller gives better control performance compared with the traditional H-infinity one, and can successfully suppress the vibration of large flexible appendages at 0.12 Hz and 0.66 Hz.

Deep Learning-Enabled Label-Free On-Chip Detection and Selective Extraction of Cell Aggregate-Laden Hydrogel Microcapsules.

Microfluidic encapsulation of cells/tissues in hydrogel microcapsules has attracted tremendous attention in the burgeoning field of cell-based medicine. However, when encapsulating rare cells and tissues (e.g., pancreatic islets and ovarian follicles), the majority of the resultant hydrogel microcapsules are empty and should be excluded from the sample. Furthermore, the cell-laden hydrogel microcapsules are usually suspended in an oil phase after microfluidic generation, while the microencapsulated cells require an aqueous phase for further culture/transplantation and long-term suspension in oil may compromise the cells/tissues. Thus, real-time on-chip selective extraction of cell-laden hydrogel microcapsules from oil into aqueous phase is crucial to the further use of the microencapsulated cells/tissues. Contemporary extraction methods either require labeling of cells for their identification along with an expensive detection system or have a low extraction purity (<≈30%). Here, a deep learning-enabled approach for label-free detection and selective extraction of cell-laden microcapsules with high efficiency of detection (≈100%) and extraction (≈97%), high purity of extraction (≈90%), and high cell viability (>95%) is reported. The utilization of deep learning to dynamically analyze images in real time for label-free detection and on-chip selective extraction of cell-laden hydrogel microcapsules is unique and may be valuable to advance the emerging cell-based medicine.

Carbon nano-onion-mediated dual targeting of P-selectin and P-glycoprotein to overcome cancer drug resistance.

The transmembrane P-glycoprotein (P-gp) pumps that efflux drugs are a major mechanism of cancer drug resistance. They are also important in protecting normal tissue cells from poisonous xenobiotics and endogenous metabolites. Here, we report a fucoidan-decorated silica-carbon nano-onion (FSCNO) hybrid nanoparticle that targets tumor vasculature to specifically release P-gp inhibitor and anticancer drug into tumor cells. The tumor vasculature targeting capability of the nanoparticle is demonstrated using multiple models. Moreover, we reveal the superior light absorption property of nano-onion in the near infrared region (NIR), which enables triggered drug release from the nanoparticle at a low NIR power. The released inhibitor selectively binds to P-gp pumps and disables their function, which improves the bioavailability of anticancer drug inside the cells. Furthermore, free P-gp inhibitor significantly increases the systemic toxicity of a chemotherapy drug, which can be resolved by delivering them with FSCNO nanoparticles in combination with a short low-power NIR laser irradiation.

Author Correction: Precise targeting of POLR2A as a therapeutic strategy for human triple negative breast cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cold-Responsive Nanoparticle Enables Intracellular Delivery and Rapid Release of Trehalose for Organic-Solvent-Free Cryopreservation.

Conventional cryopreservation of mammalian cells requires the use of toxic organic solvents (e.g., dimethyl sulfoxide) as cryoprotectants. Consequently, the cryopreserved cells must undergo a tedious washing procedure to remove the organic solvents for their further applications in cell-based medicine, and many of the precious cells may be lost or killed during the procedure. Trehalose has been explored as a nontoxic alternative to traditional cryoprotectants. However, mammalian cells do not synthesize trehalose or express trehalose transporters in their membranes, and the lack of an approach for the efficient intracellular delivery of trehalose has been a major hurdle for its use in cell cryopreservation. In this study, a cold-responsive polymer (poly(N-isopropylacrylamide-co-butyl acrylate)) is utilized to synthesize nanoparticles for the encapsulation and intracellular delivery of trehalose. The trehalose-laden nanoparticles can be efficiently taken up by mammalian cells. The nanoparticles quickly and irreversibly disassemble upon cold treatment, enabling the controlled and rapid release of trehalose from the nanoparticles inside cells. The latter is confirmed by an evident increase in cell volume upon cold treatment. This rapid cold-triggered intracellular release of trehalose is crucial to developing a fast protocol to cryopreserve cells using trehalose. Cells with intracellular trehalose delivered using the nanoparticles show comparable postcryopreservation viability compared to that of cells treated with DMSO, eliminating the need for the tedious and cell-damaging washing procedure required for using the DMSO-cryopreserved cells in vivo. This cold-responsive nanoparticle may greatly facilitate the use of trehalose as a nontoxic cryoprotectant for banking cells and tissues to meet their high demand by modern cell-based medicine.