Identifying Driving Factors of Atmospheric N2O5 with Machine Learning.

Dinitrogen pentoxide (N2O5) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of N2O5 formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NOx-induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of N2O5 before and during the 2022 Winter Olympics (WO) in Beijing. Higher N2O5 concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient N2O5 concentrations and showed that ozone (O3), nitrogen dioxide (NO2), and relative humidity (RH) were the most important driving factors of N2O5. Compared to the Pre-WO period, the variation in trace gases (i.e., NO2 and O3) along with the reduced N2O5 uptake coefficient was the main reason for higher N2O5 levels during the WO period. By predicting N2O5 under various control scenarios of NOx and calculating the nitrate formation potential from N2O5 uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NOx was approximately 13 ppbv, below which NOx reduction effectively reduced the level of night-time nitrate formations. These results demonstrate the capacity of machine learning to provide insights into understanding atmospheric nitrogen chemistry and highlight the necessity of more stringent emission control of NOx to mitigate haze pollution.

Fiber optic-based integrated system for in vivo multiscale pharmacokinetic monitoring.

This paper presents the development of a fiber-optic-based fluorescence detection system for multi-scale monitoring of drug distribution in living animals. The integrated system utilized dual laser sources at the wavelengths of 488 nm and 650 nm and three photomultiplier channels for multi-color fluorescence detection. The emission spectra of fluorescent substances were tracked using the time-resolved fluorescence spectroscopy module to continuously monitor their blood kinetics. The fiber bundle, consisting of 30,000 optic filaments, was designed for wide-field mesoscopic imaging of the drug's interactions within organs. The inclusion of a gradient refractive index (GRIN) lens within the setup enabled fluorescence confocal laser scanning microscopy to visualize the drug distribution at the cellular level. The system performance was verified by imaging hepatic and renal tissues in mice using cadmium telluride quantum dots (CdTe QDs) and R3. By acquiring multi-level images and real-time data, our integrated system underscores its potential as a potent tool for drug assessment, specifically within the realms of pharmacokinetic and pharmacodynamic investigations.

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

Rapidly damping hydrogels engineered through molecular friction.

Hydrogels capable of swift mechanical energy dissipation hold promise for a range of applications including impact protection, shock absorption, and enhanced damage resistance. Traditional energy absorption in such materials typically relies on viscoelastic mechanisms, involving sacrificial bond breakage, yet often suffers from prolonged recovery times. Here, we introduce a hydrogel designed for friction-based damping. This hydrogel features an internal structure that facilitates the motion of a chain walker within its network, effectively dissipating mechanical stress. The hydrogel network architecture allows for rapid restoration of its damping capacity, often within seconds, ensuring swift material recovery post-deformation. We further demonstrate that this hydrogel can significantly shield encapsulated cells from mechanical trauma under repetitive compression, owing to its proficient energy damping and rapid rebound characteristics. Therefore, this hydrogel has potential for dynamic load applications like artificial muscles and synthetic cartilage, expanding the use of hydrogel dampers in biomechanics and related areas.

Associations between autistic and comorbid somatic problems of gastrointestinal disorders, food allergy, pain, and fatigue in adults.

LAY ABSTRACT: What is already known about the topic?Autistic children frequently often have accompanying physical health problems. However, this has been much less studied in autistic men and women during adulthood.What does this article add?This is one of the first studies to investigate the associations between autistic and somatic problems in adults from the general population. Using a continuous measure of autistic symptom scores and a categorical definition of autism (referred to below as probable autism) which considered symptom severity, childhood age of onset, and functional impairment, we found that autistic problems and irritable bowel syndrome, food allergy, pain, and fatigue were associated in adults. Sex differences were present for pain and fatigue, for which the associations with autistic symptom scores were somewhat stronger in females than males. Regarding age differences, the associations with fatigue and having food allergy were more pronounced in younger adults. Conversely, older individuals had a higher risk of developing irritable bowel syndrome or experiencing pain if they met the criteria for probable autism.Implications for practice, research, or policyThere is a need for providing routine programs of screening, assessment, and treatment of autism-related somatic problems and developing evidence-based interventions for autistic individuals. These could be tailored to the needs of specific autistic populations. For example, autistic females could be given extra attention about the potential presence of pain and fatigue, younger adults about the potential presence of food allergy and fatigue, and older adults concerning the potential presence of irritable bowel syndrome and pain.

Preparation and optimization of dummy molecularly imprinted polymer-based solid-phase extraction system for selective enrichment of p-toluene sulfonate esters genotoxic impurities.

Sulfonate esters, one class of genotoxic impurities (GTIs), have gained significant attention in recent years due to their potential to cause genetic mutations and cancer. In the current study, we employed the dummy template molecular imprinting technology with a dummy template molecule replacing the target molecule to establish a pretreatment method for samples containing p-toluene sulfonate esters. Through computer simulation and ultraviolet-visible spectroscopy analysis, the optimal functional monomer acrylamide and polymerization solvent chloroform were selected. Subsequently, a dummy template molecularly imprinted polymer (DMIP) was prepared by the precipitation polymerization method, and the polymer was characterized in morphology, particle size, and composition. The results of the adsorption and enrichment study demonstrated that the DMIP has high adsorption capability (Q = 7.88 mg/g) and favorable imprinting effects (IF = 1.37); Further, it could simultaneously adsorb three p-toluene sulfonate esters. The optimal adsorption conditions were obtained by conditional optimization of solid-phase extraction (SPE). A pH 7 solution was selected as the loading condition, the methanol/1 % phosphoric acid solution (20:80, v/v) was selected as the washing solution, and acetonitrile containing 10 % acetic acid in 6 mL was selected as the elution solvent. Finally, we determined methyl p-toluene sulfonate alkyl esters, ethyl p-toluene sulfonate alkyl esters, and isopropyl p-toluene sulfonate alkyl esters in tosufloxacin toluene sulfonate and capecitabine at the 10 ppm level (relative to 1 mg/mL active pharmaceutical ingredient (API) samples) by using DMIP-based SPE coupled with HPLC. This approach facilitated the selective enrichment of p-toluene sulfonate esters GTIs from complex API samples.

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults.

The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

Understanding the Mechanochemistry of Mechano-Radicals in Self-Growth Materials by Single-Molecule Force Spectroscopy.

Recent research on mechano-radicals has provided valuable insights into self-growth and adaptive responsive materials. Typically, mechanophores must remain inert in the absence of force but respond quickly to external tension before other linkages within the polymer network. Azo compounds exhibit promising combinations of mechanical stability and force-triggered reactivity, making them widely used as mechano-radicals in force-responsive materials. However, the activation conditions and behavior of azo compounds have yet to be quantitatively explored. In this study, we investigated the mechanical strength of three azo compounds using single-molecule force spectroscopy. Our results revealed that these compounds exhibit rupture forces ranging from ~500 to 1000 pN, at a loading rate of 3×104 pN s-1. Importantly, these mechanophores demonstrate distinct kinetic properties. Their unique mechanical attributes enable azo bond scission and free radical generation before causing major polymer backbone damage of entire material during polymer network deformation. This fundamental understanding of mechanophores holds significant promise for the development of self-growth materials and their related applications.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer.

BACKGROUND AND PURPOSE: Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. MATERIAL AND METHODS: 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. RESULTS: A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. CONCLUSION: In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Immunomodulatory activity of heat-killed Lacticaseibacillus paracaseiMCC1849 based on the activation of plasmacytoid dendritic cells in the peripheral blood of healthy adults.

Probiotics are widely used in food for their health benefits to the host. Inactivated probiotics also reportedly improve the intestinal environment and immune regulation. Our previous studies showed that heat-killed Lacticaseibacillus paracasei MCC1849 (hk-MCC1849) effectively induced IL-12 production in mouse spleen cells and significantly reduced cold symptoms in clinical trial subjects. To further elucidate the mechanism of host immune regulation by hk-MCC1849, human peripheral blood mononuclear cells (PBMCs) were cocultured with hk-MCC1849. The Toll-like receptor 9 ligands CpG-ODN 2216 and hk-MCC1849 and the heat-killed Lacticaseibacillus rhamnosus ATCC53103 were used as positive and negative controls, respectively. The results showed that, compared with the control, hk-MCC1849 significantly increased the expression of the plasmacytoid dendritic cell (pDC) marker CD86 (p < .0001) and the pDC marker HLA-DR (p < .001) in PBMCs. The expression levels of the IL-12p40, IFNα, IFNα1, IFNγ, and ISG15 genes were significantly increased after coculture with hk-MCC1849 (p < .05, p < .05, p < .05, p < .05, and p < .05, respectively, vs. control). Furthermore, to confirm whether hk-MCC1849 directly interacted with pDCs, DCs were enriched with PBMCs following 24 h of coculture with hk-MCC1849. Phagocytosis of fluorescently labeled hk-MCC1849 by pDCs was observed, and there were significant increases in CD86 (p < .05) and HLA-DR (p < .0001) expression in pDCs. These results suggest that hk-MCC1849 exerts a potential immunomodulatory effect on the host through the activation of peripheral pDCs.

KBTBD4 Cancer Hotspot Mutations Drive Neomorphic Degradation of HDAC1/2 Corepressor Complexes.

Cancer mutations can create neomorphic protein-protein interactions to drive aberrant function 1 . As a substrate receptor of the CULLIN3-RBX1 E3 ubiquitin ligase complex, KBTBD4 is recurrently mutated in medulloblastoma (MB) 2 , the most common embryonal brain tumor in children, and pineoblastoma 3 . These mutations impart gain-of-function to KBTBD4 to induce aberrant degradation of the transcriptional corepressor CoREST 4 . However, their mechanism of action remains unresolved. Here, we elucidate the mechanistic basis by which KBTBD4 mutations promote CoREST degradation through engaging HDAC1/2, the direct neomorphic target of the substrate receptor. Using deep mutational scanning, we systematically map the mutational landscape of the KBTBD4 cancer hotspot, revealing distinct preferences by which insertions and substitutions can promote gain-of-function and the critical residues involved in the hotspot interaction. Cryo-electron microscopy (cryo-EM) analysis of two distinct KBTBD4 cancer mutants bound to LSD1-HDAC1-CoREST reveals that a KBTBD4 homodimer asymmetrically engages HDAC1 with two KELCH-repeat propeller domains. The interface between HDAC1 and one of the KBTBD4 propellers is stabilized by the MB mutations, which directly insert a bulky side chain into the active site pocket of HDAC1. Our structural and mutational analyses inform how this hotspot E3-neo-substrate interface can be chemically modulated. First, our results unveil a converging shape complementarity-based mechanism between gain-of-function E3 mutations and a molecular glue degrader, UM171. Second, we demonstrate that HDAC1/2 inhibitors can block the mutant KBTBD4-HDAC1 interface, the aberrant degradation of CoREST, and the growth of KBTBD4-mutant MB models. Altogether, our work reveals the structural and mechanistic basis of cancer mutation-driven neomorphic protein-protein interactions and pharmacological strategies to modulate their action for therapeutic applications.

A Comprehensive Review on Reductive Recycling of Cathode Materials of Spent Lithium-Ion Batteries.

The prosperity of the lithium-ion battery market is inevitably accompanied by the depletion of corresponding resources and the accumulation of spent batteries in a dialectical manner. Spent lithium-ion batteries are harboring the characteristics of hazardous waste and high-value resources, so efficient recycling is of great significance. The cathode material is considered as an interesting target for repurposing. Despite some important reviews give commendable emphasis to recycling technologies, there is still a dearth of exploration of recycling mechanisms. This deficiency of awareness highlights the need for further research and development in this area. This review aims to systematically review and thoroughly discuss the reduction reaction mechanism of each method regarding different cathode materials. And systematically digest the selection of reducing agent and the effect of reduction reaction on material regeneration are systematically digested, as well as the impact of the reduction reaction on the regeneration of materials. This review emphasizes the importance of balancing efficiency, economic and environmental benefits in reuse technologies. Finally, the review proposes an outlook on the opportunities and challenges facing the reuse of key materials for next-generation spent batteries aimed at promoting the green and sustainable development of lithium-ion batteries, circular economy and ecological balance.

Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning.

Recent advancements in pre-trained language-image models have ushered in a new era of visual comprehension. Leveraging the power of these models, this article tackles two issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of data biases within them; (2) the evaluation of image captions and steering of their generation process. On the one hand, by visually examining the captions generated from language-image models for an image dataset, we gain deeper insights into the visual contents, unearthing data biases that may be entrenched within the dataset. On the other hand, by depicting the association between visual features and textual captions, we expose the weaknesses of pre-trained language-image models in their captioning capability and propose an interactive interface to steer caption generation. The two parts have been coalesced into a coordinated visual analytics system, fostering the mutual enrichment of visual and textual contents. We validate the effectiveness of the system with domain practitioners through concrete case studies with large-scale image datasets.

Feeding Appropriate Nutrients during the Adult Stage to Promote the Growth and Development of Carposina sasakii Offspring.

Nutrients consumed during the adult stage are a key factor affecting the growth, development, and reproduction of insect offspring and thus could play an important role in insect population research. However, there is absence of conclusive evidence regarding the direct effects of parental (F0) nutritional status on offspring (F1) fitness in insects. Carposina sasakii Matsumura is a serious, widespread fruit-boring pest that negatively impacts orchards and the agricultural economy across East Asia. In this study, life history data of F1 directly descended from F0C. sasakii fed with seven different nutrients (water as control, 5 g·L-1 honey solution, 10 g·L-1 honey solution, 5 g·L-1 sucrose solution, 10 g·L-1 sucrose solution, 15 g·L-1 sucrose solution, and 20 g·L-1 sucrose solution) were collected under laboratory conditions. The growth and development indices, age-stage specific survival rate, age-stage specific fecundity, age-stage specific life expectancy, age-stage specific reproductive value, and population parameters of these offspring were analyzed according to the age-stage, two-sex life table theory. The results showed that the nutritional status of F0 differentially affects the growth, development, and reproduction of F1. The F1 offspring of F0 adult C. sasakii fed with 10 g·L-1 sucrose had significantly higher life table parameters than those of other treatments (intrinsic rate of increase, r = 0.0615 ± 0.0076; finite rate of increase, λ = 1.0634 ± 0.0081; net reproductive rate, R0 = 12.61 ± 3.57); thus, 10 g·L-1 sucrose was more suitable for raising C. sasakii in the laboratory than other treatments. This study not only provides clear evidence for the implications of altering F0 nutritional conditions on the fitness of F1 in insects, but also lays the foundation for the implementation of feeding technologies within the context of a well-conceived laboratory rearing strategy for C. sasakii.

Oxygen-Independent Radiodynamic Therapy: Radiation-Boosted Chemodynamics for Reprogramming the Tumor Immune Environment and Enhancing Antitumor Immune Response.

Radiodynamic therapy (RDT) has emerged as a promising modality for cancer treatment, offering notable advantages such as deep tissue penetration and radiocatalytic generation of oxygen free radicals. However, the oxygen-dependent nature of RDT imposes limitations on its efficacy in hypoxic conditions, particularly in modulating and eliminating radioresistant immune suppression cells. A novel approach involving the creation of a "super" tetrahedron polyoxometalate (POM) cluster, Fe12-POM, has been developed for radiation boosted chemodynamic catalysis to enable oxygen-independent RDT in hypoxic conditions. This nanoscale cluster comprises four P2W15 units functioning as energy antennas, while the Fe3 core serves as an electron receptor and catalytic center. Under X-ray radiation, a metal-to-metal charge transfer phenomenon occurs between P2W15 and the Fe3 core, resulting in the valence transition of Fe3+ to Fe2+ and a remarkable 139-fold increase in hydroxyl radical generation compared to Fe12-POM alone. The rapid generation of hydroxyl radicals, in combination with PD-1 therapy, induces a reprogramming of the immune environment within tumors. This reprogramming is characterized by upregulation of CD80/86, downregulation of CD163 and FAP, as well as the release of interferon-γ and tumor necrosis factor-α. Consequently, the occurrence of abscopal effects is facilitated, leading to significant regression of both local and distant tumors in mice. The development of oxygen-independent RDT represents a promising approach to address cancer recurrence and improve treatment outcomes.

Ultra-Galactocation to Sialic Acid on Tumor Cells with A Penta-Functional Dendritic Probe for Enhanced Immune-Killing.

Glycans on tumor cell surface have significant impacts in the immune-killing process. Here an ultra-galactocation to sialic acid (Sia) strategy is designed to hugely introduce galactose (Gal) to Sia and on tumor cells in vivo by using a penta-functional dendritic probe (Den@5F), which efficiently enhances the immune-killing of tumor cells. The Den@5F contains five different kinds of functional groups, including Gal, Cy5, amino, phenylboronic acid (PBA) and 4-(4-(hydroxymethyl)-2-methoxy-5-nitrophenoxy) butanoate (mNB), which can be conveniently prepared through a two-step reaction. After injecting into the tumor-bearing mouse, Den@5F can efficiently block Sia through the specific recognition between PBA and Sia on tumor cells and hugely introduce Gal through the subsequent photo-crosslinking between mNB and amino groups to multiply conjugate excessive Den@5Fs. The comprehensively blocked Sia can prevent the immune escape, and the hugely introduced Gal can promote the immune stimulation of the immune cells, which lead to an efficient enhancement of the immune-killing. The proposed strategy provides a significant and promising tool to promote the clinical immunotherapy of tumor.

Clinical significance of small nuclear ribonucleoprotein U1 subunit 70 in patients with hepatocellular carcinoma.

Background & Aims: Small nuclear ribonucleoprotein U1 subunit 70 (SNRNP70) as one of the components of the U1 small nuclear ribonucleoprotein (snRNP) is rarely reported in cancers. This study aims to estimate the application potential of SNRNP70 in hepatocellular carcinoma (HCC) clinical practice. Methods: Based on the TCGA database and cohort of HCC patients, we investigated the expression patterns and prognostic value of SNRNP70 in HCC. Then, the combination of SNRNP70 and alpha-fetoprotein (AFP) in 278 HCC cases was analyzed. Next, western blotting and immunohistochemistry were used to detect the expression of SNRNP70 in nucleus and cytoplasm. Finally, Cell Counting Kit-8 (CCK-8) and scratch wound healing assays were used to detect the effect of SNRNP70 on the proliferation and migration of HCC cells. Results: SNRNP70 was highly expressed in HCC. Its expression was increasingly high during the progression of HCC and was positively related to immune infiltration cells. Higher SNRNP70 expression indicated a poor outcome of HCC patients. In addition, nuclear SNRNP70/AFP combination could be a prognostic biomarker for overall survival and recurrence. Cell experiments confirmed that knockdown of SNRNP70 inhibited the proliferation and migration of HCC cells. Conclusion: SNRNP70 may be a new biomarker for HCC progression and HCC diagnosis as well as prognosis. SNRNP70 combined with serum AFP may indicate the prognosis and recurrence status of HCC patients after operation.