A Part-to-Whole Circular Cell Explorer.

Spatial transcriptomics methods capture cellular measurements such as gene expression and cell types at specific locations in a cell, helping provide a localized picture of tissue health. Traditional visualization techniques superimpose the tissue image with pie charts for the cell distribution. We design an interactive visual analysis system that addresses perceptual problems in the state of the art, while adding filtering, drilling, and clustering analysis capabilities. Our approach can help researchers gain deeper insights into the molecular mechanisms underlying complex biological processes within tissues.

Natural-source payloads used in the conjugated drugs architecture for cancer therapy: Recent advances and future directions.

Drug conjugates are obtained from tumor-located vectors connected to cytotoxic agents via linkers, which are designed to deliver hyper-toxic payloads directly to targeted cancer cells. These drug conjugates include antibody-drug conjugates (ADCs), peptide-drug conjugates (PDCs), small molecule-drug conjugates (SMDCs), nucleic acid aptamer-drug conjugates (ApDCs), and virus-like drug conjugate (VDCs), which show great therapeutic value in the clinic. Drug conjugates consist of a targeting carrier, a linker, and a payload. Payloads are key therapy components. Cytotoxic molecules and their derivatives derived from natural products are commonly used in the payload portion of conjugates. The ideal payload should have sufficient toxicity, stability, coupling sites, and the ability to be released under specific conditions to kill tumor cells. Microtubule protein inhibitors, DNA damage agents, and RNA inhibitors are common cytotoxic molecules. Among these conjugates, cytotoxic molecules of natural origin are summarized based on their mechanism of action, conformational relationships, and the discovery of new derivatives. This paper also mentions some cytotoxic molecules that have the potential to be payloads. It also summarizes the latest technologies and novel conjugates developed in recent years to overcome the shortcomings of ADCs, PDCs, SMDCs, ApDCs, and VDCs. In addition, this paper summarizes the clinical trials conducted on conjugates of these cytotoxic molecules over the last five years. It provides a reference for designing and developing safer and more efficient conjugates.

Modular access to furo[3,2-c]chromen-4-ones via Yb(OTf)3-catalyzed [3 + 2] annulation of 4-hydroxycoumarins with ß-nitroalkenes.

A facile and efficient strategy for modular access to furo[3,2-c]chromen-4-ones using 4-hydroxycoumarin and ß-nitroalkenes via Lewis acid-catalyzed formal [3 + 2] annulation protocol is described. This reaction proceeds via cascade Michael addition/nucleophilic addition/elimination in the presence of Yb(OTf)3, which involves the formation of two new σ (C-C and C-O) bonds for the construction of a novel furan ring in a single operation. This protocol affords a variety of functional groups, thereby providing a practical and efficient method for the fabrication of a furo[3,2-c]chromen-4-one framework.

Emodin improves the cardiac function in the rats with chronic heart failure through regulation of the miR-26b-5p/PTEN pathway.

Introduction: Chronic heart failure (CHF) is a leading cause of deaths induced by cardiovascular disease. This study aimed to investigate the protective effects of emodin in CHF rats and explore the related mechanisms. Material and methods: A total of 56 Wistar rats were used to construct CHF model using the coronary artery ligation. The effects of emodin on cardiac function and inflammation were analyzed in the CHF rats. Expression of miR-26b-5p in the CHF model before and after emodin treatment was estimated by quantitative real-time polymerase chain reaction. The effects of miR-26b-5p on cardiac function and inflammation were also assessed, and its target gene was predicted and confirmed in rat cardiomyocyte H9c2. Results: Emodin treatment could significant improve the cardiac function and inflammation evidenced by the increased increased ejection fraction (EF), fractional shortening (FS), left ventricular systolic pressure (LVSP) and maximum of the first differentiation of left ventricular pressure (+LV dP/dtmax) and decreased atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), left ventricular end diastolic pressure (LVEDP), interleukin (IL)-6, tumor necrosis factor α (TNF-α) levels. Expression of miR-26b-5p was downregulated in the CHF rats (CHF 0.442 ±0.131 vs. Sham 1.044 ±0.160), and this suppressive effect was rescued by emodin (Emodin 0.902 ±0.132 vs. CHF 0.442 ±0.131). The overexpression of miR-26b-5p in CHF rats led to improved cardiac function and inflammatory response. In addition, the emodin-induced increased EF, FS, LVSP and +LV dP/dtmax and decreased ANP, BNP, LVEDP, IL-6 and TNF-α were all abrogated by the knockdown of miR-26b-5p. The target prediction results revealed that PTEN was a target gene of miR-26b-5p in H9c2 cells. Conclusions: All the results indicated that emodin serves a protective role in CHF via regulation of the miR-26b-5p/PTEN pathway. Emodin may be an effective therapeutic agent for CHF treatment.

Design optimization of groundwater circulation well based on numerical simulation and machine learning.

The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi'an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.

Speech-Driven Personalized Gesture Synthetics: Harnessing Automatic Fuzzy Feature Inference.

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture (DiTs-based). The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model's superior performance to the current state-of-the-art approaches. Persona-Gestor improves the system's usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology.

Sorafenib inhibits ossification of the posterior longitudinal ligament by blocking LOXL2-mediated vascularization.

Ossification of the Posterior Longitudinal Ligament (OPLL) is a degenerative hyperostosis disease characterized by the transformation of the soft and elastic vertebral ligament into bone, resulting in limited spinal mobility and nerve compression. Employing both bulk and single-cell RNA sequencing, we elucidate the molecular characteristics, cellular components, and their evolution during the OPLL process at a single-cell resolution, and validate these findings in clinical samples. This study also uncovers the capability of ligament stem cells to exhibit endothelial cell-like phenotypes in vitro and in vivo. Notably, our study identifies LOXL2 as a key regulator in this process. Through gain-and loss-of-function studies, we elucidate the role of LOXL2 in the endothelial-like differentiation of ligament cells. It acts via the HIF1A pathway, promoting the secretion of downstream VEGFA and PDGF-BB. This function is not related to the enzymatic activity of LOXL2. Furthermore, we identify sorafenib, a broad-spectrum tyrosine kinase inhibitor, as an effective suppressor of LOXL2-mediated vascular morphogenesis. By disrupting the coupling between vascularization and osteogenesis, sorafenib demonstrates significant inhibition of OPLL progression in both BMP-induced and enpp1 deficiency-induced animal models while having no discernible effect on normal bone mass. These findings underscore the potential of sorafenib as a therapeutic intervention for OPLL.

The role of SUMO specific peptidase 3 in secondary inflammation of ischemic stroke in mice.

Ischemic stroke is the main cause of death and disability, and microglia play a crucial role in the pathophysiology of hypoxic ischemic brain injury. We found that SENP3 is highly expressed in the early stages of ischemic stroke in both in vivo and in vitro mouse models, and may be related to the deSUMOylation of the key kinase MKK7 in the TLR4/p-JNK signaling pathway. Knocking down SENP3 can inhibit the deSUMOylation of MKK7, thereby inhibiting the activation of the TLR4/p-JNK signaling pathway in an in vitro stroke model. Proteomic analysis showed that SENP3 undergoes phosphorylation at the T429 site after ischemic stroke. Computer simulation predictions show a significant enhancement of the interaction between pT429-SENP3 and MKK7, which has been confirmed through experiments on the interaction of biological macromolecules (SPR). The mitochondrial metabolic abnormalities caused by energy abnormalities in the early stages of stroke provide a good explanation for the phosphorylation of SENP3. Therefore, we used the mitochondrial complex inhibitor TTFA to reverse demonstrate that the phosphorylation of SENP3 comes from the large amount of adenosine triphosphate produced by mitochondrial abnormal metabolism caused by early oxygen glucose deficiency. Finally, proteomic analysis indicates that a significant amount of oxidative phosphorylation does occur in the early stages of stroke. In summary, targeted regulation of SENP3 phosphorylation to affect the deSUMOylation of MKK7 may inhibit secondary inflammation in ischemic stroke.

Concentrations, Sources and Health Risk Assessment of Polycyclic Aromatic Hydrocarbons in Chinese Herbal Medicines.

The determination and evaluation of 16 polycyclic aromatic hydrocarbons (PAHs) in seven Chinese herbal medicines (CHMs) were conducted through a rapid and straightforward extraction and purification method, coupled with GC-MS. A sample-based solid-phase extraction (SPE) pretreatment technique, incorporating isotopic internal standards, was employed for detecting various medicinal parts of CHMs. The assay exhibited linearity within the range of 5 to 500 ng/mL, with linear coefficients (R2) for PAHs exceeding 0.999. The recoveries of spiked standards ranged from 63.37% to 133.12%, with relative standard deviations (RSDs) ranging from 0.75% to 14.54%. The total PAH content varied from 176.906 to 1414.087 µg/kg. Among the 16 PAHs, phenanthrene (Phe) was consistently detected at the highest levels (47.045-168.640 µg/kg). Characteristic ratio analysis indicated that oil, coal, and biomass combustion were the primary sources of PAHs in CHMs. The health risk associated with CHMs was assessed using the lifetime carcinogenic risk approach, revealing potential health risks from the consumption of honeysuckle, while the health risks of consuming Lycium chinense berries were deemed negligible. For the other five CHMs (glycyrrhizae, Coix lacryma, ginseng, lotus seed, seed of Sterculia lychnophora), the health risk from consumption fell within acceptable ranges. Furthermore, sensitivity analyses utilizing Monte Carlo exposure assessment methods identified PAH levels in CHMs as health risk sensitizers. It is crucial to recognize that the consumption of herbal medicines is not a continuous process but entails potential health risks. Hence, the monitoring and risk assessment of PAH residues in CHMs demand careful attention.

Preparation of polypeptide-metal complexes-coated Hosenkoside A and its inhibitory effect in cervical cancer.

We reported the anti-cervical cancer effect of proprietary saponin content from seeds of Impatiens balsamina L., Hosenkoside A. Our study found that Hosenkoside A significantly promotes cell apoptosis and cell cycle arrest after administration, exhibiting anti-tumor effects. Then the transcriptome sequencing results after administration showed that Hosenkoside A had a significant inhibitory effect on Histone deacetylase 3 (HDAC3). After sufficient administration time, the inhibition of HDAC3 expression level leads to a significant decrease in lysine acetylation at histone 3 sites 4 and 9, blocking the activation of Signal transducer and activator of transcription 3 (STAT3) and achieving anti-tumor effects. In addition, we encapsulated Hosenkoside A into polypeptide metal complexes (PMC) to form slow-release spheres. This material breaks down in the tumor environment, not only does it solve the problem of low drug solubility, but it also achieves targeted sustained-release drug delivery. Under the same concentration of stimulation, the PMC complex group showed better anti-tumor effects in both in vitro and in vivo experiments.

3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers.

Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.

Carcinogenic and non-carcinogenic health risk assessment of organic compounds and heavy metals in electronic cigarettes.

E-cigarettes are now very popular in the world. Compared to traditional cigarettes, e-cigarettes are often considered safer and healthier. However, their safety remains controversial and requires further research and regulation. In this study, we aimed to understand the possible hazards to humans of four compounds (formaldehyde, acetaldehyde, acrolein, and acetone) and seven heavy metals (arsenic, cadmium, manganese, lead, copper, nickel, and chromium) contained in e-cigarette liquids and aerosols and perform a health risk assessment. We searched PubMed, CNKI, and other databases for relevant literature to obtain data on organic compounds and heavy metals in e-cigarette liquids and aerosols, and conducted acute, chronic, and carcinogenic risk assessments of various chemicals by different exposure routes. This study showed that exposure to four organic compounds and seven heavy metals in e-cigarette aerosols and e-liquids can cause varying levels of health risks in humans through different routes, with the inhalation route posing a higher overall risk than dermal exposure and oral intake. Various chemicals at high exposure doses can produce health risks beyond the acceptable range. E-cigarette designers must improve their products by changing the composition of the e-liquid and controlling the power of the device to reduce the health effects on humans.

Unveiling the secrets of non-coding RNA-encoded peptides in plants: A comprehensive review of mining methods and research progress.

Non-coding RNAs (ncRNAs) are not conventionally involved in protein encoding. However, recent findings indicate that ncRNAs possess the capacity to code for proteins or peptides. These ncRNA-encoded peptides (ncPEPs) are vital for diverse plant life processes and exhibit significant potential value. Despite their importance, research on plant ncPEPs is limited, with only a few studies conducted and less information on the underlying mechanisms, and the field remains in its nascent stage. This manuscript provides a comprehensive overview of ncPEPs mining methods in plants, focusing on prediction, identification, and functional analysis. We discuss the strengths and weaknesses of various techniques, identify future research directions in the ncPEPs domain, and elucidate the biological functions and agricultural application prospects of plant ncPEPs. By highlighting the immense potential and research value of ncPEPs, we aim to lay a solid foundation for more in-depth studies in plant science.

Metal Oxide-Supported Metal Catalysts for Electrocatalytic Oxygen Reduction Reaction: Characterization Methods, Modulation Strategies, and Recent Progress.

The sluggish kinetics of the oxygen reduction reaction (ORR) with complex multielectron transfer steps significantly limits the large-scale application of electrochemical energy devices, including metal-air batteries and fuel cells. Recent years witnessed the development of metal oxide-supported metal catalysts (MOSMCs), covering single atoms, clusters, and nanoparticles. As alternatives to conventional carbon-dispersed metal catalysts, MOSMCs are gaining increasing interest due to their unique electronic configuration and potentially high corrosion resistance. By engineering the metal oxide substrate, supported metal, and their interactions, MOSMCs can be facilely modulated. Significant progress has been made in advancing MOSMCs for ORR, and their further development warrants advanced characterization methods to better understand MOSMCs and precise modulation strategies to boost their functionalities. In this regard, a comprehensive review of MOSMCs for ORR is still lacking despite this fast-developing field. To eliminate this gap, advanced characterization methods are introduced for clarifying MOSMCs experimentally and theoretically, discuss critical methods of boosting their intrinsic activities and number of active sites, and systematically overview the status of MOSMCs based on different metal oxide substrates for ORR. By conveying methods, research status, critical challenges, and perspectives, this review will rationally promote the design of MOSMCs for electrochemical energy devices.

Identification of small open reading frames in plant lncRNA using class-imbalance learning.

Recently, small open reading frames (sORFs) in long noncoding RNA (lncRNA) have been demonstrated to encode small peptides that can help study the mechanisms of growth and development in organisms. Since machine learning-based computational methods are less costly compared with biological experiments, they can be used to identify sORFs and provide a basis for biological experiments. However, few computational methods and data resources have been exploited for identifying sORFs in plant lncRNA. Besides, machine learning models produce underperforming classifiers when faced with a class-imbalance problem. In this study, an alternative method called SMOTE based on weighted cosine distance (WCDSMOTE) which enables interaction with feature selection is put forward to synthesize minority class samples and weighted edited nearest neighbor (WENN) is applied to clean up majority class samples, thus, hybrid sampling WCDSMOTE-ENN is proposed to deal with imbalanced datasets with the multi-angle feature. A heterogeneous classifier ensemble is introduced to complete the classification task. Therefore, a novel computational method that is based on class-imbalance learning to identify the sORFs with coding potential in plant lncRNA (sORFplnc) is presented. Experimental results manifest that sORFplnc outperforms existing computational methods in identifying sORFs with coding potential. We anticipate that the proposed work can be a reference for relevant research and contribute to agriculture and biomedicine.

Influence of different grinding degrees of fly ash on properties and reaction degrees of geopolymers.

This study reports the preparation of geopolymers with a mechanical performance similar to that of cement at room temperature by ground fly ash mixed with a small amount of cement. The grinding time of fly ash raw materials was 0,20,40 and 60 min, respectively. The influence of the grinding degree of the fly ash on the properties and the reaction degree of the geopolymer were investigated by XRD, SEM, EDS, and mercury compression tests. The reaction degree of the fly ash geopolymer was quantified by the selective dissolution method. Increasing the grinding degree of fly ash significantly increased the compressive strength of the geopolymer and the density of the microstructure of materials also increased. Furthermore, porosity and the average pore size decreased and the proportion of small holes in the pores gradually increased. The calculation results were in coincidence with the compressive strength test and the micro-performance test of the material, thus indicating that the selective dissolution method can reflect the influence of the grinding degree on the reaction degree of the geopolymer. Furthermore, the reaction degree of the geopolymer increased as the grinding degree of the fly ash increased. However, the growth rate of the reaction degree for the geopolymer slowed down when the fly ash was ground for more than 40 min.

Boosting the performance and durability of heterogeneous electrodes for solid oxide electrochemical cells utilizing a data-driven powder-to-power framework.

Solid oxide electrochemical cells (SOCs) hold potential as a critical component in the future landscape of renewable energy storage and conversion systems. However, the commercialization of SOCs still requires further breakthroughs in new material development and engineering designs to achieve high performance and durability. In this study, a data-driven powder-to-power framework has been presented, fully digitizing the morphology evolution of heterogeneous electrodes from fabrication to long-term operation. This framework enables accurate performance prediction over the full life cycle. The intrinsic correlation between microstructural parameters and electrode durability is elucidated through parameter analysis. Rational control of the ion-conducting phase volume fraction can effectively suppress Ni coarsening and mitigate the excessive ohmic loss caused by Ni migration. The initial and degraded electrode performances are attributed to the interplay of multiple parameters. A practical optimization strategy to enhance the initial performance and durability of the electrode is proposed through the construction of the surrogate model and the application of the optimization algorithm. The optimal electrode parameters are determined to accommodate various maximum operation time requirements. By implementing the data-driven powder-to-power framework, it is possible to reduce the degradation rate of Ni-based electrodes from 2.132% to 0.703% kh-1 with a required maximum operation time of over 50,000 h.

Tracking neural activity from the same cells during the entire adult life of mice.

Stably recording the electrical activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this timescale. Here, we introduce a method to precisely implant electronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the year-long implantation. Rigorous statistical analysis, visual stimulus-dependent measurement and unbiased, machine-learning-based analysis demonstrated that single-unit action potentials have been recorded from the same neurons of behaving mice in a very long-term stable manner. Leveraging this stable structure, we demonstrated that the same neurons can be recorded over the entire adult life of the mouse, revealing the aging-associated evolution of single-neuron activities.

Gain-of-function variants in the ion channel gene TRPM3 underlie a spectrum of neurodevelopmental disorders.

TRPM3 is a temperature- and neurosteroid-sensitive plasma membrane cation channel expressed in a variety of neuronal and non-neuronal cells. Recently, rare de novo variants in TRPM3 were identified in individuals with developmental and epileptic encephalopathy, but the link between TRPM3 activity and neuronal disease remains poorly understood. We previously reported that two disease-associated variants in TRPM3 lead to a gain of channel function . Here, we report a further 10 patients carrying one of seven additional heterozygous TRPM3 missense variants. These patients present with a broad spectrum of neurodevelopmental symptoms, including global developmental delay, intellectual disability, epilepsy, musculo-skeletal anomalies, and altered pain perception. We describe a cerebellar phenotype with ataxia or severe hypotonia, nystagmus, and cerebellar atrophy in more than half of the patients. All disease-associated variants exhibited a robust gain-of-function phenotype, characterized by increased basal activity leading to cellular calcium overload and by enhanced responses to the neurosteroid ligand pregnenolone sulfate when co-expressed with wild-type TRPM3 in mammalian cells. The antiseizure medication primidone, a known TRPM3 antagonist, reduced the increased basal activity of all mutant channels. These findings establish gain-of-function of TRPM3 as the cause of a spectrum of autosomal dominant neurodevelopmental disorders with frequent cerebellar involvement in humans and provide support for the evaluation of TRPM3 antagonists as a potential therapy.