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Abstract Background The choroid plexus (ChP) helps maintain the homeostasis of the brain by forming the blood-CSF barrier via tight junctions (TJ) at the choroid plexus epithelial cells, and subsequently preventing neuroinflammation by restricting immune cells infiltration into the central nervous system. However, whether chronic cerebral hypoperfusion causes ChP structural damage and blood-CSF barrier impairment remains understudied. Methods The bilateral carotid stenosis (BCAS) model in adult male C57BL/6 J mice was used to induce cerebral hypoperfusion, a model for vascular contributions to cognitive impairment and dementia (VCID). BCAS-mediated changes of the blood-CSF barrier TJ proteins, apical secretory Na+-K+-Cl− cotransporter isoform 1 (NKCC1) protein and regulatory serine-threonine kinases SPAK, and brain infiltration of myeloid-derived immune cells were assessed. Results BCAS triggered dynamic changes of TJ proteins (claudin 1, claudin 5) accompanied with stimulation of SPAK-NKCC1 complex and NF-κB in the ChP epithelial cells. These changes impacted the integrity of the blood-CSF barrier, as evidenced by ChP infiltration of macrophages/microglia, neutrophils and T cells. Importantly, pharmacological blockade of SPAK with its potent inhibitor ZT1a in BCAS mice attenuated brain immune cell infiltration and improved cognitive neurological function. Conclusions BCAS causes chronic ChP blood-CSF damage and immune cell infiltration. Our study sheds light on the SPAK-NKCC1 complex as a therapeutic target in neuroinflammation.

Abstract Despite the importance of cellular senescence in human health, how damaged cells undergo senescence remains elusive. We have previously shown that promyelocytic leukemia nuclear body (PML-NBs) translocation of the ciliary FBF1 is essential for senescence induction in stressed cells. Here we discover that an early cellular event occurring in stressed cells is the transient assembly of stress-induced nucleus-to-cilium microtubule arrays (sinc-MTs). The sinc-MTs are distinguished by unusual polyglutamylation and unique polarity, with minus-ends nucleating near the nuclear envelope and plus-ends near the ciliary base. KIFC3, a minus-end-directed kinesin, is recruited to plus-ends of sinc-MTs and interacts with the centrosomal protein CENEXIN1. In damaged cells, CENEXIN1 co-translocates with FBF1 to PML-NBs. Deficiency of KIFC3 abolishes PML-NB translocation of FBF1 and CENEXIN1, as well as senescence initiation in damaged cells. Our study reveals that KIFC3-mediated nuclear transport of FBF1 along polyglutamylated sinc-MTs is a prerequisite for senescence induction in mammalian cells.

Abstract Hydrogen oxidation reaction in alkaline media is critical for alkaline fuel cells and electrochemical ammonia compressors. The slow hydrogen oxidation reaction in alkaline electrolytes requires large amounts of scarce and expensive platinum catalysts. While transition metal decoration can enhance Pt catalysts’ activity, it often reduces the electrochemical active surface area, limiting the improvement in Pt mass activity. Here, we enhance Pt catalysts’ activity without losing surface-active sites by using a Pd-Ru pair. Utilizing a mildly catalytic thermal pyrolysis approach, Pd-Ru pairs are decorated on Pt, confirmed by extended X-ray absorption fine structure and high-angle annular dark-field scanning transmission electron microscopy. Density functional theory and ab-initio molecular dynamics simulations indicate preferred Pd and Ru dopant adsorption. The Pd-Ru decorated Pt catalyst exhibits a mass-based exchange current density of 1557 ± 85 A g−1 metal for hydrogen oxidation reaction, demonstrating superior performance in an ammonia compressor.

Abstract Background In cardiac surgical procedures, patients carrying high-risk profiles are prone to encompass complicated cardiopulmonary bypass (CPB) separation. Intraoperative transesophageal echocardiography (TEE), a readily available tool, is utilized to detect cardiac structural and functional pathologies as well as to facilitate clinical management of CPB separation, especially in the episodes of hemodynamic compromise. However, the conventional TEE examination, always performed in a liberal fashion without any restriction of view acquisition, is relatively time-consuming; there appear its flaws in the context of critically severe status. We therefore developed the perioperative rescue transesophageal echocardiography (PReTEE), a simplified three-view TEE protocol consisting of midesophageal four chamber, midesophageal left ventricular long axis, and transgastric short axis. Methods This is a single-center and randomized controlled trial which will be implemented in Peking Union Medical College Hospital, Beijing, China. A total of 46 TEE scans are schemed to be performed by 6 operators participating in and randomly assigned to either the PReTEE or the conventional TEE group. This study is purposed to investigate whether the efficiency of discriminating leading causes of difficult CPB wean-off can be significantly improved via an abbreviated sequence of TEE views. The primary outcome of interest is the difference between the groups of PReTEE and the conventional TEE in the successful discrimination of etiologies in specified 120 s. Cox proportional hazards model will be further employed to calculate the outcome difference. Discussion The estimated results of this trial are oriented at verifying whether a simplified TEE exam sequence can improve the efficiency of etiologies discrimination during CPB separation in cardiac surgery. Trial registration ClinicalTrials.gov NCT05960552. Registered on 6 July 2023.

AIM: To evaluate the application of an intelligent diagnostic model for pterygium. METHODS: For intelligent diagnosis of pterygium, the attention mechanisms—SENet, ECANet, CBAM, and Self-Attention—were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University. Conventional classification models—VGG16, ResNet50, MobileNetV2, and EfficientNetB7—were trained on the same dataset for comparison. To evaluate model performance in terms of accuracy, Kappa value, test time, sensitivity, specificity, the area under curve (AUC), and visual heat map, 470 test images of the anterior segment of the pterygium were used. RESULTS: The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%, and the Kappa value of the model was 88.92%. The testing time using the model was 9ms/image in the server and 138ms/image in the local computer. The sensitivity, specificity, and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%, 100%, and 100%, respectively; using anterior segment images in the observation period were 88.30%, 95.32%, and 96.70%, respectively; and using the anterior segment images in the surgery period were 88.18%, 94.44%, and 97.30%, respectively. CONCLUSION: The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.

Abstract Microbial metabolism upholds a fundamental role in the sustainability of water ecosystems. However, how microorganisms surviving in low-concentration substrate water environments, including the existence of emerging compounds of interest, remains unclear. In this review, microbial strategies for concentrating, utilizing, and metabolizing of low concentration substrates were summarized. Microorganisms develop substrate-concentrating strategies at both the cell and aggregate levels in substrate-limited settings. Following, microbial uptake and transport of low-concentration substrates are facilitated by adjusting physiological characteristics and shifting substrate affinities. Finally, metabolic pathways, such as mixed-substrate utilization, syntrophic metabolism, dynamic response to nutrient variation, and population density-based mechanisms allow microorganisms to efficiently utilize low-concentration substrates and to adapt to challenging oligotrophic environments. All these microbial strategies will underpin devising new approaches to tackle environmental challenges and drive the sustainability of water ecosystems, particularly in managing low-concentration contaminants (i.e., micropollutants).

Aberrant expression of cyclin-dependent kinase 5 (Cdk5) has been reported in pituitary adenomas. However, the role of Cdk5 in the tumorigenesis remains unclear. We show that prenatal p25-activated Cdk5 phosphorylates minichromosome maintenance protein 2 (Mcm2), enhancing minichromosome maintenance (MCM) family proteins and driving intermediate lobe-located melanotrope-originated pituitary tumorigenesis. In a mouse model with CaMKII promoter-driven transgenic induction of p25, we observed intermediate lobe-originated pituitary adenoma producing non-functional proopiomelanocortin (POMC)-derived peptides under persistent p25 overexpression. Single-cell RNA sequencing revealed Mcm2 may play an important role during tumor progression. Subsequently, Mcm2 was identified as a potential phosphorylated substrate of Cdk5, mediating the tumorous proliferation of melanotrope cells. Silencing Cdk5 or Mcm2 suppressed cell proliferation and colony formation in the 293T cell lines. Therefore, our findings provide a new mouse model of intermediate lobe-originated pituitary adenoma induced by p25/Cdk5 and unveil a previously unappreciated role of Cdk5 and Mcm2 in pituitary adenoma tumorigenesis.

Purpose: To evaluate the relationship between the real-time changes of macular structure and visual function in rhegmatogenous retinal detachment (RRD) patients. Methods: Forty-six patients were enrolled in this retrospective study. The best corrected visual acuity (BCVA) and macular structural changes were analyzed within 3 months after silicone oil tamponade. Results: The mean final BCVA was significantly better than the preoperative BCVA (P = 0.002). The parafoveal thickness became thinner, the proportion of subretinal fluid (SRF) decreased, and the proportion of intact external limiting membrane (ELM) increased within 3 months postoperatively. The recovery stage and the integrity of ELM in the SRF (-) group were significantly faster than that in the SRF (+) group (all P < 0.05). The central foveal thickness (CFT), the inferior and temporal thickness of the parafovea, and the integrity of the ELM were significantly correlated with BCVA at each time point (all P < 0.05). Long duration of preoperative RRD, thinner CFT at 1 month postoperatively, and without integrity of ELM at 3 months postoperatively were associated with poor final BCVA recovery (R2 = 0.462). Conclusions: The macular microstructural tended to restore integrity within 3 months. The presence of SRF in macula delayed the recovery of RRD patients but did not affect the visual function.

Residual enrofloxacin (ENR) exposed in aqueous environments is challenging to the ecosphere. In this work, a layered double hydroxide CoAl-LDH was used to activate the common oxidizing agent persulfate (PS) for photodegradation of ENR, and the degradation pathways of ENR were scrutinized and elucidated. The results indicated that, under the optimal conditions obtained through orthogonal experiments, even though the degradation rate of ENR was as high as 97.72%, the removal of total organic carbon (TOC) from the system was only about 30%. Eleven probable reaction pathways were categorized, and thirty-one types of intermediates were identified in participating in the complex degradation process. The major products of ENR were P4 (C17H20FN3O3), P22 (C19H22FN3O4), P19 (C17H18FN3O3), which are mainly derived from the cleavage of the piperazine groups and quinolone rings. Density functional theory (DFT) calculations of the Fukui index for ENR revealed that the two N atoms in the piperazine ring were the core reactive sites in triggering the degradation chains, which were sensitive for electrophilic attack by the dominant radicals (•OH and SO4•-) generated from the composite PS-UV-CoAl-LDH system.

Abstract Apicomplexan parasites harbor a complex endomembrane system as well as unique secretory organelles. These complex cellular structures require an elaborate vesicle trafficking system, which includes Rab GTPases and their regulators, to assure the biogenesis and secretory of the organelles. Here we exploit the model apicomplexan organism Toxoplasma gondii that encodes a family of Rab GTPase Activating Proteins, TBC (Tre-2/Bub2/Cdc16) domain-containing proteins. Functional profiling of these proteins in tachyzoites reveals that TBC9 is the only essential regulator, which is localized to the endoplasmic reticulum (ER) in T. gondii strains. Detailed analyses demonstrate that TBC9 is required for normal distribution of proteins targeting to the ER, and the Golgi apparatus in the parasite, as well as for the normal formation of daughter inner membrane complexes (IMCs). Pull-down assays show a strong protein interaction between TBC9 and specific Rab GTPases (Rab11A, Rab11B, and Rab2), supporting the role of TBC9 in daughter IMC formation and early vesicular transport. Thus, this study identifies the only essential TBC domain-containing protein TBC9 that regulates early vesicular transport and IMC formation in T. gondii and potentially in closely related protists.

Abstract DLL3 acts as an inhibitory ligand that downregulates Notch signaling and is upregulated by ASCL1, a transcription factor prevalent in the small-cell lung cancer (SCLC) subtype SCLC-A. Currently, the therapeutic strategies targeting DLL3 are varied, including antibody-drug conjugates (ADCs), bispecific T-cell engagers (BiTEs), and chimeric antigen receptor (CAR) T-cell therapies. Although rovalpituzumab tesirine (Rova-T) showed promise in a phase II study, it failed to produce favorable results in subsequent phase III trials, leading to the cessation of its development. Conversely, DLL3-targeted BiTEs have garnered significant clinical interest. Tarlatamab, for instance, demonstrated enhanced response rates and progression-free survival compared to the standard of care in a phase II trial; its biologics license application (BLA) is currently under US Food and Drug Administration (FDA) review. Numerous ongoing phase III studies aim to further evaluate tarlatamab’s clinical efficacy, alongside the development of novel DLL3-targeted T-cell engagers, both bispecific and trispecific. CAR-T cell therapies targeting DLL3 have recently emerged and are undergoing various preclinical and early-phase clinical studies. Additionally, preclinical studies have shown promising efficacy for DLL3-targeted radiotherapy, which employs β-particle-emitting therapeutic radioisotopes conjugated to DLL3-targeting antibodies. DLL3-targeted therapies hold substantial potential for SCLC management. Future clinical trials will be crucial for comparing treatment outcomes among various approaches and exploring combination therapies to improve patient survival outcomes.

Abstract We aim to develop machine learning (ML) models for predicting the complexity and mortality of polytrauma patients using clinical features, including physician diagnoses and physiological data. We conducted a retrospective analysis of a cohort comprising 756 polytrauma patients admitted to the intensive care unit (ICU) at Pizhou People’s Hospital Trauma Center, Jiangsu, China between 2020 and 2022. Clinical parameters encompassed demographics, vital signs, laboratory values, clinical scores and physician diagnoses. The two primary outcomes considered were mortality and complexity. We developed ML models to predict polytrauma mortality or complexity using four ML algorithms, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost). We assessed the models’ performance and compared the optimal ML model against three existing trauma evaluation scores, including Injury Severity Score (ISS), Trauma Index (TI) and Glasgow Coma Scale (GCS). In addition, we identified several important clinical predictors that made contributions to the prognostic models. The XGBoost-based polytrauma mortality prediction model demonstrated a predictive ability with an accuracy of 90% and an F-score of 88%, outperforming SVM, RF and ANN models. In comparison to conventional scoring systems, the XGBoost model had substantial improvements in predicting the mortality of polytrauma patients. External validation yielded strong stability and generalization with an accuracy of up to 91% and an AUC of 82%. To predict polytrauma complexity, the XGBoost model maintained its performance over other models and scoring systems with good calibration and discrimination abilities. Feature importance analysis highlighted several clinical predictors of polytrauma complexity and mortality, such as Intracranial hematoma (ICH). Leveraging ML algorithms in polytrauma care can enhance the prognostic estimation of polytrauma patients. This approach may have potential value in the management of polytrauma patients.

Abstract Background The protozoan parasite Toxoplasma gondii encodes dozens of phosphatases, among which a plant-like phosphatase absent from mammalian genomes named PPKL, which is involved in regulating brassinosteroid signaling in Arabidopsis, was identified in the genome. Among the Apicomplexa parasites, T. gondii is an important and representative pathogen in humans and animals. PPKL was previously identified to modulate the apical integrity and morphology of the ookinetes and parasite motility and transmission in another important parasite, Plasmodium falciparum. However, the exact function of PPKL in the asexual stages of T. gondii remains unknown. Methods The plant auxin-inducible degron (AID) system was applied to dissect the phenotypes of PPKL in T. gondii. We first analyzed the phenotypes of the AID parasites at an induction time of 24 h, by staining of different organelles using their corresponding markers. These analyses were further conducted for the parasites grown in auxin for 6 and 12 h using a quantitative approach and for the type II strain ME49 of AID parasites. To further understand the phenotypes, the potential protein interactions were analyzed using a proximity biotin labeling approach. The essential role of PPKL in parasite replication was revealed. Results PPKL is localized in the apical region and nucleus and partially distributed in the cytoplasm of the parasite. The phenotyping of PPKL showed its essentiality for parasite replication and morphology. Further dissections demonstrate that PPKL is required for the maturation of daughter parasites in the mother cells, resulting in multiple nuclei in a single parasite. The phenotype of the daughter parasites and parasite morphology were observed in another type of T. gondii strain ME49. The substantial defect in parasite replication and morphology could be rescued by genetic complementation, thus supporting its essential function for PPKL in the formation of parasites. The protein interaction analysis showed the potential interaction of PPKL with diverse proteins, thus explaining the importance of PPKL in the parasite. Conclusions PPKL plays an important role in the formation of daughter parasites, revealing its subtle involvement in the proper maturation of the daughter parasites during division. Our detailed analysis also demonstrated that depletion of PPKL resulted in elongated tubulin fibers in the parasites. The important roles in the parasites are potentially attributed to the protein interaction mediated by kelch domains on the protein. Taken together, these findings contribute to our understanding of a key phosphatase involved in parasite replication, suggesting the potential of this phosphatase as a pharmaceutic target. Graphical Abstract

Abstract Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Preclinical models serve as indispensable tools in translational medicine. Specifically, patient-derived models such as patient-derived xenografts (PDX), induced pluripotent stem cells (iPSC), organoids, and recently developed technique of conditional reprogramming (CR) have been employed to reflect the host characteristics of diseases. CR technology involves co-culturing epithelial cells with irradiated Swiss-3T3-J2 mouse fibroblasts (feeder cells) in the presence of a Rho kinase (ROCK) inhibitor, Y-27632. CR technique facilitates the rapid conversion of both normal and malignant cells into a “reprogrammed stem-like” state, marked by robust in vitro proliferation. This is achieved without reliance on exogenous gene expression or viral transfection, while maintaining the genetic profile of the parental cells. So far, CR technology has been used to study biology of diseases, targeted therapies (precision medicine), regenerative medicine, and noninvasive diagnosis and surveillance. Respiratory diseases, ranking as the third leading cause of global mortality, pose a significant burden to healthcare systems worldwide. Given the substantial mortality and morbidity rates of respiratory diseases, efficient and rapid preclinical models are imperative to accurately recapitulate the diverse spectrum of respiratory conditions. In this article, we discuss the applications and future potential of CR technology in modeling various respiratory tract diseases, including lung cancer, respiratory viral infections (such as influenza and Covid-19 and etc.), asthma, cystic fibrosis, respiratory papillomatosis, and upper aerodigestive track tumors. Furthermore, we discuss the potential utility of CR in personalized medicine, regenerative medicine, and clinical translation.

The conformal mapping function from the interior of the complex plane's unit circle to the exterior of any simple closed curve on the real plane finds widespread applications, including the use of complex variable methods in elasticity studies. Our MATLAB toolbox employs numerical methods to solve such conformal mapping functions, applicable to physical domains featuring simple closed curves of arbitrary shapes, and even extending to slit-like structures. Featuring a user-friendly GUI program, the toolbox efficiently computes conformal mapping functions, streamlining the solving process.

Bathymodioline mussels dominate deep-sea methane seep and hydrothermal vent habitats and obtain nutrients and energy primarily through chemosynthetic endosymbiotic bacteria in the bacteriocytes of their gill. However, the molecular mechanisms that orchestrate mussel host–symbiont interactions remain unclear. Here, we constructed a comprehensive cell atlas of the gill in the mussel Gigantidas platifrons from the South China Sea methane seeps (1100 m depth) using single-nucleus RNA-sequencing (snRNA-seq) and whole-mount in situ hybridisation. We identified 13 types of cells, including three previously unknown ones, and uncovered unknown tissue heterogeneity. Every cell type has a designated function in supporting the gill’s structure and function, creating an optimal environment for chemosynthesis, and effectively acquiring nutrients from the endosymbiotic bacteria. Analysis of snRNA-seq of in situ transplanted mussels clearly showed the shifts in cell state in response to environmental oscillations. Our findings provide insight into the principles of host–symbiont interaction and the bivalves' environmental adaption mechanisms.

Traditionally, leopards (Panthera pardus) and snow leopards (P. uncia) occupy distinct habitats. This study explored the coexistence of snow leopards and leopards within the Gaurishankar Conservation Area in Lapchi Valley, Nepal. Both leopard species are crucial for ecosystem stability as apex predators. Camera trapping data from 2018 to 2023 confirmed twenty species of wild mammals and two species of domesticated livestock in Lapchi Valley, where snow leopards have strong spatio-temporal overlap (Δ=0.74) with leopards. Snow leopards exhibited distinct temporal patterns before and after the leopards’ arrival in the study area. Generalized Linear Mixed Effect Models (GLMM) revealed seasonal and prey-type associations with leopard presence, highlighting the snow leopards' preference for cold season and wild prey. Leopards, however, did not show a significant seasonal preference. The findings highlight Lapchi Valley's ecological richness as well as the need for comprehensive conservation strategies. The record of spatial and temporal overlap between the leopards and snow leopards provides important insights into the behavioral dynamics of apex predators and the need for targeted biodiversity conservation in the face of changing ecosystems. The study emphasizes the valley's transboundary significance, calling for collaborative efforts between Nepal and China to help promote effective conservation management. Lapchi Valley's isolation, traditional and religious importance, and recent infrastructure developments further impact the conservation challenges.

For the problems of Grey wolf optimizer (GWO) easy to fall into local optimum and lack of population diversity, this thesis raises a Grey wolf optimizer combined with an Artificial fish swarm algorithm (AFGWO). First, the search method of grey wolves is improved by combining the clustering behavior of Artificial fish swarm algorithm (AFSA) which avoids them falling into a local optimum. Second, to make the exploration and exploitation more balanced, the individual position of the worst grey wolf is updated by combining the foraging behavior of AFSA. Third, AFGWO adds quadratic interpolation and elite reverse learning to enrich population diversity. AFGWO is compared with other 10 algorithms on CEC2017 to evaluate its performance. The experimental results and four statistical analysis methods show that the proposed AFGWO based on above three improvement strategies has good solving ability and stability, and outperforms other comparative algorithms. In addition, AFGWO solves four classical engineering design problems well and demonstrates its good ability to solve realistic optimization problems. In general, AFGWO improved by the above strategy is better than GWO in performance, and is also very competitive with other intelligent algorithms.

The Pilot Free Trade Zone (PFTZ) policy represents a significant step in China’s institutional innovation and broader opening-up, crucial for fostering green and high-quality economic growth. The existing literature has conducted in-depth studies on the economic effects, energy conservation, emission reduction and green innovation of PFTZs, but the effect on green technology innovation efficiency (GTIE) remains underexplored. Institutional innovation in the PFTZs will profoundly affect regional green development, and green innovation, and will certainly affect the regional GTIE. Based on this, this paper uses the city-level data of prefecture-level cities in China from 2005 to 2020 to empirically test the impact of PFTZ establishment on GTIE. First, the Super-SBM model was applied to evaluate the GTIE across 281 Chinese cities. Then, taking the establishment of PFTZs as a quasi-natural experiment, the multi-stage differentially differential model is constructed to empirically assess PFTZ policy’s impact on GTIE. The findings indicate that PFTZs can significantly enhance the GTIE. This improvement is facilitated through four pathways: the digital economy, the high-tech industries agglomeration, innovation agglomeration, and talent agglomeration. The results offer strategies for balancing external openness with environmental protection in developing countries and furnish empirical support for creating high-quality development frameworks.

Abstract In this paper, on one hand, the time-varying characteristics of the heat source and thermal boundary conditions of the high-speed spindle system were analyzed considering the thermal-structural coupling effect. And a transient bearing temperature field prediction method combining the thermal network method and finite element method was proposed. Furthermore, the relationships between time step, calculation efficiency and calculation results were analyzed. On the other hand, a online real-time monitoring system of the transient temperature of the cylindrical roller bearing inner ring for maximum speed of 13,000 r/min was designed and implemented using fibre optic sensing technology. Comparing with the conventional static thermal analysis results, it is verified that the simulation method proposed in this paper has higher accuracy. This paper provides a new approach for analysing and testing the thermal characteristics of high-speed spindle system.

The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems.

Saponins are an important class of active components in Chinese herbal medicines (CHMs), which are present in large quantities in Ginseng Radix et Rhizoma, Notoginseng Radix et Rhizoma, Polygonati Rhizoma, etc., and have immune regulation, anti-tumor, anti-inflammatory, anti-cardiovascular disease, and hypoglycemic activities. Storage and processing are essential processes in the production process of CHMs which affect the stability of saponin components and then reduce the medicinal and economic value. Therefore, it is of great importance to investigate the effects of storage and processing conditions on the content of saponin components in CHMs. In this paper, the effects of various storage and processing factors, including temperature, pH, enzymes, meta lions, extraction methods, etc., on the saponin content of CHMs are investigated and the underlying mechanisms for the quantitative changes of saponin are summarized. These findings may provide technical guidance for the production and processing of saponin-rich CHMs.

ObjectivesTo explore the correlation between the vessel density (VD) of the retina and choroid vascular plexuses and the thicknesses of their respective retinal layers and choroid membranes in participants with severe non-proliferative diabetic retinopathy (NPDR).MethodsWe retrospectively analyzed the data of 42 eyes of 42 participants with diabetes mellitus (DM) and severe NPDR. In addition, 41 eyes of 41 healthy controls were evaluated. Measurements were taken for both groups using optical coherence tomography angiography (OCTA), including the area and perimeter of the foveal vascular zone (FAZ) and the vascular density (VD) in the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choroid capillary (CC). These measurements were compared with the retinal thickness (RT) of the inner/intermediate retinal layers and choroidal thickness (CT). The study evaluated the correlation between RT or CT and VD in the respective vascular networks, namely superficial capillary plexus (SCP), deep capillary plexus (DCP), or CC.ResultsThe inner RT and VD in all plexuses were significantly lower in the severe NPDR group than in the healthy controls. Furthermore, the FAZ area and perimeter were larger in the severe NPDR group. Inner RT was correlated with VD in the SCP group (r=0.67 and r=0.71 in the healthy control and severe NPDR groups, respectively; p

Myricanol (MY) is one of the main active components from bark of Myrica Rubra. It is demonstrated that MY rescues dexamethasone (DEX)-induced muscle dysfunction via activating silent information regulator 1 (SIRT1) and increasing adenosine 5’-monophosphate-activated protein kinase (AMPK) phosphorylation. Since SIRT1 and AMPK are widely involved in the metabolism of nutrients, we speculated that MY may exert beneficial effects on DEX-induced metabolic disorders. This study for the first time applied widely targeted metabolomics to investigate the beneficial effects of MY on glucose, lipids, and protein metabolism in DEX-induced metabolic abnormality in mice. The results showed that MY significantly reversed DEX-induced soleus and gastrocnemius muscle weight loss, muscle fiber damage, and muscle strength loss. MY alleviated DEX-induced metabolic disorders by increasing SIRT1 and glucose transporter type 4 (GLUT4) expressions. Additionally, myricanol prevented muscle cell apoptosis and atrophy by inhibiting caspase 3 cleavages and muscle ring-finger protein-1 (MuRF1) expression. Metabolomics showed that MY treatment reversed the serum content of carnitine ph-C1, palmitoleic acid, PS (16:0_17:0), PC (14:0_20:5), PE (P-18:1_16:1), Cer (t18:2/38:1(2OH)), four amino acids and their metabolites, and 16 glycerolipids in DEX mice. Kyoto encyclopedia of genes and genomes (KEGG) and metabolic set enrichment analysis (MSEA) analysis revealed that MY mainly affected metabolic pathways, glycerolipid metabolism, lipolysis, fat digestion and absorption, lipid and atherosclerosis, and cholesterol metabolism pathways through regulation of metabolites involved in glutathione, butanoate, vitamin B6, glycine, serine and threonine, arachidonic acid, and riboflavin metabolism. Collectively, MY can be used as an attractive therapeutic agent for DEX-induced metabolic abnormalities.

The apicoplast is a four-membrane plastid found in the apicomplexans, which harbors biosynthesis and organelle housekeeping activities in the matrix. However, the mechanism driving the flux of metabolites, in and out, remains unknown. Here, we used TurboID and genome engineering to identify apicoplast transporters in Toxoplasma gondii. Among the many novel transporters, we show that one pair of apicomplexan monocarboxylate transporters (AMTs) appears to have evolved from a putative host cell that engulfed a red alga. Protein depletion showed that AMT1 and AMT2 are critical for parasite growth. Metabolite analyses supported the notion that AMT1 and AMT2 are associated with biosynthesis of isoprenoids and fatty acids. However, stronger phenotypic defects were observed for AMT2, including in the inability to establish T. gondii parasite virulence in mice. This study clarifies, significantly, the mystery of apicoplast transporter composition and reveals the importance of the pair of AMTs in maintaining the apicoplast activity in apicomplexans.

Abstract Background The protozoan parasite Toxoplasma gondii encodes a dozen Rab proteins, which are parts of the small GTPase superfamily and regulate intracellular membrane trafficking. Our previous study showed that depletion of Rab1B caused severe defects regarding parasite growth and morphological structure, yet early defects of endocytic trafficking and vesicle sorting to the rhoptry in T. gondii are not expected to have a strong effect. To understand this discrepancy, we performed an integrated analysis at the level of transcriptomics and metabolomics. Methods In the study, tetracycline-inducible TATi/Ty-Rab1B parasite line treated with ATc at three different time points (0, 18 and 24 h) was used. We first observed the morphological changes caused by Rab1B depletion via transmission electron technology. Then, high-throughput transcriptome along with non-targeted metabolomics were performed to analyze the RNA expression and metabolite changes in the Rab1B-depleted parasite. The essential nature of Rab1B in the parasite was revealed by the integrated omics approach. Results Transmission electron micrographs showed a strong disorganization of endo-membranes in the Rab1B-depleted parasites. Our deep analysis of transcriptome and metabolome identified 2181 and 2374 differentially expressed genes (DEGs) and 30 and 83 differentially expressed metabolites (DEMs) at 18 and 24 h of induction in the tetracycline-inducible parasite line, respectively. These DEGs included key genes associated with crucial organelles that contain the rhoptry, microneme, endoplasmic reticulum and Golgi apparatus. The analysis of qRT-PCR verified some of the key DEGs identified by RNA-Seq, supporting that the key vesicular regulator Rab1B was involved in biogenesis of multiple parasite organelles. Functional enrichment analyses revealed pathways related to central carbon metabolisms and lipid metabolisms, such as the TCA cycle, glycerophospholipid metabolism and fatty acid biosynthesis and elongation. Further correlation analysis of the major DEMs and DEGs supported the role of Rab1B in biogenesis of fatty acids (e.g. myrisoleic acid and oleic acid) (R > 0.95 and P

The hedgehog genus Mesechinus (Erinaceidae, Eulipotyphla) is currently comprised of four species, M. dauuricus, M. hughi, M. miodon, and M. wangi. Except for M. wangi, which is found in southwestern China, the other three species are mainly distributed in northern China and adjacent Mongolia and Russia. From 2018 to 2023, we collected seven Mesechinus specimens from Anhui and Zhejiang provinces, eastern China. Here, we evaluate the taxonomic and phylogenetic status of these specimens by integrating molecular, morphometric, and karyotypic approaches. Our results indicate that the Anhui and Zhejiang specimens are distinct from the four previously recognized species and are a new species. We formally described it here as Mesechinus orientalis sp. nov. It is the only Mesechinus species occurring in eastern China and is geographically distant from all known congeners. Morphologically, the new species is most similar to M. hughi, but it is distinguishable from that species by the combination of its smaller size, shorter spines, and several cranial characteristics. Mesechinus orientalis sp. nov. is a sister to the lineage composed of M. hughi and M. wangi from which it diverged approximately 1.10 Ma.

Ceramic-coated graphite powders are considered as effective raw materials to fabricate three-dimensional continuous ceramic skeleton-reinforced graphite matrix composites which can overcome their inherent poor densification and improve their mechanical and antioxidation properties. However, the morphology and thickness regulation of ceramic coatings on graphite particles are still a great challenge. Herein, SiC-coated graphite (graphite@SiC) powders were prepared by nitriding combustion synthesis using Si and graphited mesocarbon microbead (MCMB) as raw powders with polytetrafluoroethylene (PTFE) as a promoter. The effects of the PTFE content and the Si/MCMB molar ratio on the phase composition and coating morphology were investigated. The phase transition and microstructure evolution of a combustion synthesis (CS) process were revealed by a gas-released quenching experiment. When the Si/MCMB molar ratio was 1 : 3 and the PTFE content was 10 wt%, the thickness of the SiC coating synthesized under 2 MPa N2 reached 1.14 μm. The corresponding sintered graphite@SiC composite had relative density of 99.2% and flexural strength of 231 MPa, accompanied by a significant improvement in high-temperature antioxidation properties. The as-synthesized graphite@SiC powders with good sinterability and antioxidation properties show great promise for applications in the nuclear industry and other extreme fields.

Abstract Body fluid biomarkers are very important, because they can be detected in a non-invasive or minimally invasive way. The discovery of secreted proteins in human body fluids is an essential step toward proteomic biomarker identification for human diseases. Recently, many computational methods have been proposed to predict secreted proteins and achieved some success. However, most of them are based on a manual negative dataset, which is usually biased and therefore limits the prediction performances. In this paper, we first propose a novel positive-unlabeled learning framework to predict secreted proteins in a single body fluid. The secreted protein discovery in a single body fluid is transformed into multiple binary classifications and solved via multi-task learning. Also, an effective convolutional neural network is employed to reduce the overfitting problem. After that, we then improve this framework to predict secreted proteins in multiple body fluids simultaneously. The improved framework adopts a globally shared network to further improve the prediction performances of all body fluids. The improved framework was trained and evaluated on datasets of 17 body fluids, and the average benchmarks of 17 body fluids achieved an accuracy of 89.48%, F1 score of 56.17%, and PRAUC of 58.93%. The comparative results demonstrate that the improved framework performs much better than other state-of-the-art methods in secreted protein discovery.

Abstract Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future.

Abstract Distant metastasis remains a leading cause of mortality among patients with colorectal cancer (CRC). Organotropism, referring to the propensity of metastasis to target specific organs, is a well-documented phenomenon in CRC, with the liver, lungs, and peritoneum being preferred sites. Prior to establishing premetastatic niches within host organs, CRC cells secrete substances that promote metastatic organotropism. Given the pivotal role of organotropism in CRC metastasis, a comprehensive understanding of its molecular underpinnings is crucial for biomarker-based diagnosis, innovative treatment development, and ultimately, improved patient outcomes. In this review, we focus on metabolic reprogramming, tumor-derived exosomes, the immune system, and cancer cell-organ interactions to outline the molecular mechanisms of CRC organotropic metastasis. Furthermore, we consider the prospect of targeting metastatic organotropism for CRC therapy.

Neoadjuvant therapy (NAT) for early-stage pancreatic ductal adenocarcinoma (PDA) has recently gained prominence. We investigated the clinical significance of mucin 5 AC (MUC5AC), which exists in two major glycoforms, a less-glycosylated immature isoform (IM) and a heavily glycosylated mature isoform (MM), as a biomarker in resected PDA. Immunohistochemistry was performed on 100 resected PDAs to evaluate the expression of the IM and MM of MUC5AC using their respective monoclonal antibodies, CLH2 (NBP2-44455) and 45M1 (ab3649). MUC5AC localization (cytoplasmic, apical, and extra-cellular (EC)) was determined, and the H-scores were calculated. Univariate and multivariate (MVA) Cox regression models were used to estimate progression-free survival (PFS) and overall survival (OS). Of 100 resected PDA patients, 43 received NAT, and 57 were treatment-naïve with upfront surgery (UpS). In the study population (n = 100), IM expression (H-scores for objective response vs. no response vs. UpS = 104 vs. 152 vs. 163, p = 0.01) and MM-MUC5AC detection rates (56% vs. 63% vs. 82%, p = 0.02) were significantly different. In the NAT group, MM-MUC5AC-negative patients had significantly better PFS according to the MVA (Hazard Ratio: 0.2, 95% CI: 0.059–0.766, p = 0.01). Similar results were noted in a FOLFIRINOX sub-group (n = 36). We established an association of MUC5AC expression with treatment response and outcomes.

Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

IntroductionThe study of how soil fauna interact with soil ecosystems is an important research field. At present, there has been little research on the diversity and distribution patterns of soil macroarthropods and on the relationship between soil fauna and environmental factors in Tibet.MethodsThe data collection for soil macroarthropods and soil samples was conducted using the plum blossom five-point sampling method. Subsequently, the gathered data were meticulously organized and analyzed through a blend of ecological and statistical approaches.ResultsIn total, 2880 soil macroarthropods were captured from 19 plots in the Nianchu River Basin, and the soil macroarthropod Hill numbers was at its lowest in spring. Sea buckthorn forest habitats had the highest Hill numbers. We found that Carabidae, Lycosidae, and Formicidae were always dominant species during seasonal changes in the Nianchu River Basin. Among the four different habitat types studied, Carabidae always appeared across the four studied habitat types and was one of the most significant taxa. The niche range of soil macroarthropods was wetland habitat > farmland habitat > sea buckthorn forest habitat > grassland habitat, and their community within sea buckthorn forest habitats was stable. The niche breadth of soil macroarthropods was the largest in summer, and the community was stable. Soil temperature and soil water content were the most important factors affecting the alpha diversity of soil macroarthropods, while altitude and soil temperature were the most important factors affecting their beta diversity.DiscussionIn summary, the results provide a comprehensive overview of the seasonal and habitat dynamics of soil macroarthropods in the Nianchu River Basin; it is strongly believed that the research carried out in this paper can contribute valuable information toward further research on the soil fauna diversity and ecological functions in this area and provide a strong scientific basis for the protection and sustainable development of the Nianchu River Basin ecosystem.

Neuroinflammation is a pathological event associated with many neurological disorders, including dementia and stroke. The choroid plexus (ChP) is a key structure in the ventricles of the brain that secretes cerebrospinal fluid (CSF), forms a blood-CSF barrier, and responds to disease conditions by recruiting immune cells and maintaining an immune microenvironment in the brain. Despite these critical roles, the exact structural and functional changes to the ChP over post-stroke time remain to be elucidated. We induced ischemic stroke in C57BL/6J mice via transient middle cerebral artery occlusion which led to reduction of cerebral blood flow and infarct stroke. At 1–7 days post-stroke, we detected time-dependent increase in the ChP blood-CSF barrier permeability to albumin, tight-junction damage, and dynamic changes of SPAK-NKCC1 protein complex, a key ion transport regulatory system for CSF production and clearance. A transient loss of SPAK protein complex but increased phosphorylation of the SPAK-NKCC1 complex was observed in both lateral ventricle ChPs. Most interestingly, stroke also triggered elevation of proinflammatory Lcn2 mRNA and its protein as well as infiltration of anti-inflammatory myeloid cells in ChP at day 5 post-stroke. These findings demonstrate that ischemic strokes cause significant damage to the ChP blood-CSF barrier, contributing to neuroinflammation in the subacute stage.

Abstract Wildlife is reservoir of emerging viruses. Here we identified 27 families of mammalian viruses from 1981 wild animals and 194 zoo animals collected from south China between 2015 and 2022, isolated and characterized the pathogenicity of eight viruses. Bats harbor high diversity of coronaviruses, picornaviruses and astroviruses, and a potentially novel genus of Bornaviridae. In addition to the reported SARSr-CoV-2 and HKU4-CoV-like viruses, picornavirus and respiroviruses also likely circulate between bats and pangolins. Pikas harbor a new clade of Embecovirus and a new genus of arenaviruses. Further, the potential cross-species transmission of RNA viruses (paramyxovirus and astrovirus) and DNA viruses (pseudorabies virus, porcine circovirus 2, porcine circovirus 3 and parvovirus) between wildlife and domestic animals was identified, complicating wildlife protection and the prevention and control of these diseases in domestic animals. This study provides a nuanced view of the frequency of host-jumping events, as well as assessments of zoonotic risk.

Exposure to irreparable stresses induces transient ciliogenesis, enabling communication with PML-NBs via a FBF1 pathway to trigger senescence in mammalian cells. Fbf1 ablation reduces senescence and associated health decline in mice, highlighting cilia as a promising senotherapy target.

Abstract Background With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful to better understand the pathogenesis of complex diseases. However, traditional biological experiments are expensive and time-consuming. Therefore, it is necessary to develop more efficient computational methods for exploring underlying disease-related miRNAs. Results In this paper, we present a new computational method based on positive point-wise mutual information (PPMI) and attention network to predict miRNA-disease associations (MDAs), called PATMDA. Firstly, we construct the heterogeneous MDA network and multiple similarity networks of miRNAs and diseases. Secondly, we respectively perform random walk with restart and PPMI on different similarity network views to get multi-order proximity features and then obtain high-order proximity representations of miRNAs and diseases by applying the convolutional neural network to fuse the learned proximity features. Then, we design an attention network with neural aggregation to integrate the representations of a node and its heterogeneous neighbor nodes according to the MDA network. Finally, an inner product decoder is adopted to calculate the relationship scores between miRNAs and diseases. Conclusions PATMDA achieves superior performance over the six state-of-the-art methods with the area under the receiver operating characteristic curve of 0.933 and 0.946 on the HMDD v2.0 and HMDD v3.2 datasets, respectively. The case studies further demonstrate the validity of PATMDA for discovering novel disease-associated miRNAs.

Based on the observation that live fish caudal fins exhibit softness, flexibility, and active deformation, which are essential for generating thrust, stability, and maneuverability, herein, a comprehensive study on a novel type of bionic robotic fish with an actively deformable caudal fin is presented. The article first presents the design of the robot. Next, the quasisteady model theory is used to establish the hydrodynamic force for modeling the deformable caudal fin. Furthermore, three deformation modes are studied and compared: the conventional nondeformable mode, the sine‐based mode, and the instantaneous mode. Finally, a series of extensive experiments are conducted to evaluate various performance metrics of this innovative untethered biomimetic robotic fish, including thrust, swimming speed, yaw stability, turning radius, and turning rate. The results demonstrate that the introduction of active deformation of the caudal fin significantly enhances the swimming performance in the aforementioned indices when compared to the conventional nondeformable mode. Notably, the instantaneous mode exhibits best performance in terms of thrust, swimming speed, turning radius, and turning rate, while the sine‐based mode demonstrates the best yaw stability. Consequently, this research contributes to the advancement of robotic fish design and the development of underwater biomimetic robots.

Summary: Neovascular age-related macular degeneration AMD (nAMD) is characterized by choroidal neovascularization (CNV) and could lead to irreversible blindness. However, anti-vascular endothelial growth factor (VEGF) therapy has limited efficacy. Therefore, we generated a chimpanzee adenoviral vector (AdC68-PFC) containing three genes, pigment endothelial-derived factor (PEDF), soluble fms-like tyrosine kinase-1 (sFlt-1), and soluble forms of CD59 (sCD59), to treat nAMD. The results showed that AdC68-PFC mediated a strong onset of PEDF, sFlt-1, and sCD59 expression both in vivo and in vitro. AdC68-PFC showed preventive and therapeutic effects following intravitreal (IVT) injection in the laser-induced CNV model and very low-density lipoprotein receptor-deficient (Vldlr−/−) mouse model. In vitro assessment indicated that AdC68-PFC had a strong inhibitory effect on endothelial cells. Importantly, the safety test showed no evidence of in vivo toxicity of adenovirus in murine eyes. Our findings suggest that AdC68-PFC may be a long-acting and safe gene therapy vector for future nAMD treatments.

Toxoplasma gondii acquires host cytosolic materials, yet the mechanism remains unknown. Wan et al. reveal the micropore as an essential organelle at the plasma membrane for endocytosis of host cytosolic proteins and biotin, and Golgi ceramide.

In this paper, inspired by the T cell immune process, a new meta-heuristic algorithm named T cell immune algorithm (TCIA) is proposed to better solve various complex practical problems. The core difficulty of meta-heuristic algorithms is balancing exploration and exploitation. TCIA mimics the behavior of T-cell immune process in recognizing antigens, activating cells and attacking pathogens, thus to better balance exploration and exploitation. Recognition is the process of judging the cell concentration at the current location, which is used to evaluate the fitness value of the current cell. Activation is the process by which T cells are activated and thus differentiate into multiple cells, helping the population to explore on a larger scope. Attacks are divided into random attack and directed attack. Random attack helps the population escape from local optima by randomly selecting a target cell to attack. Targeted attacks push the overall approach to the location with the highest concentration of target cells, which makes better use of the global optimal solution. The TCIA is tested on CEC2022 and compared with other 10 algorithms. Experimental results and statistical analysis show that TCIA is more effective than other 10 classical and new meta-heuristic algorithms in solving constraint function problems. TCIA is tested on three multi-objective functions, which verifies its good ability in solving multi-objective functions. Two classical engineering design problems are applied to verify the ability of TCIA to solve practical problems. The parameters of Back propagation neural network are optimized by TCIA to predict the compressive strength of concrete. The results show that the ability of TCIA to solve a variety of problems is balanced and excellent.

Abstract Acquiring a subterranean lifestyle entails a substantial shift for many aspects of terrestrial vertebrates’ biology. Although this lifestyle is associated with multiple instances of convergent evolution, the relative success of some subterranean lineages largely remains unexplained. Here, we focus on the mammalian transitions to life underground, quantifying bone microanatomy through high‐resolution X‐ray tomography. The true moles stand out in this dataset. Examination of this family's bone histology reveals that the highly fossorial moles acquired a unique phenotype involving large amounts of compacted coarse cancellous bone. This phenotype exceeds the adaptive optimum seemingly shared by several other subterranean mammals and can be traced back to some of the first known members of the family. This remarkable microanatomy was acquired early in the history of the group and evolved faster than the gross morphology innovations of true moles’ forelimb. This echoes the pattern described for other lifestyle transitions, such as the acquisition of bone mass specializations in secondarily aquatic tetrapods. Highly plastic traits—such as those pertaining to bone structure—are hence involved in the early stages of different types of lifestyle transitions.

Abstract Extracting knowledge from heterogeneous data sources is fundamental for the construction of structured biomedical knowledge graphs (BKGs), where entities and relations are represented as nodes and edges in the graphs, respectively. Previous biomedical knowledge extraction methods simply considered limited entity types and relations by using a task-specific training set, which is insufficient for large-scale BKGs development and downstream task applications in different scenarios. To alleviate this issue, we propose a joint continual learning biomedical information extraction (JCBIE) network to extract entities and relations from different biomedical information datasets. By empirically studying different joint learning and continual learning strategies, the proposed JCBIE can learn and expand different types of entities and relations from different datasets. JCBIE uses two separated encoders in joint-feature extraction, hence can effectively avoid the feature confusion problem comparing with using one hard-parameter sharing encoder. Specifically, it allows us to adopt entity augmented inputs to establish the interaction between named entity recognition and relation extraction. Finally, a novel evaluation mechanism is proposed for measuring cross-corpus generalization errors, which was ignored by traditional evaluation methods. Our empirical studies show that JCBIE achieves promising performance when continual learning strategy is adopted with multiple corpora.

Columnar rockfall occur widely in China and cause extreme damage to man-made structures and facilities. On 12 August 2004, Zengziyan W12 perilous rock collapsed and ran out a horizontal distance of 600 m. The rockfall caused airblast with a height of 150 m. In this paper, the dynamic characteristics and fragmentation evolution is studied based on the discrete element software MatDEM. The simulation of the rockfall process is achieved based on the discrete element model with real distribution of joints. The simulation is valid by comparing the simulation and video data. Through deeper development on MatDEM, the rock size evolution is analyzed, it was found that there were four significant breakage moments during the rockfall. The moments are rock compression breakage at the bottom of the rockfall, collision breakage of middle-upper rock mass with low-velocity triangle area, collision of middle and upper part of rock mass with ground, respectively. The fragmentation evolution of the pre- and post- rockfall is studied based on the fractal theory and dual parameters Weibull distribution. The results show that the proportion of fine particles increase obviously after the rockfall. This paper offers the basis of dynamic characteristics and fragmentation evolution of other rockfalls.

Abstract Background Melanoma is a malignant tumor with a high mortality rate. Some microorganisms have been shown to activate the immune system and limit cancer progression. The objective of this study is to evaluate the anti-melanoma effect of Neospora caninum, a livestock pathogen with no pathogenic activity in humans. Methods Neospora caninum tachyzoites were inoculated into a C57BL/6 mouse melanoma model by intratumoral and distal subcutaneous injections. Tumor volumes were measured, and cell death areas were visualized by hematoxylin and eosin staining and quantified. Apoptosis in cell cultures and whole tumors was detected by propidium iodide (PI) and TUNEL staining, respectively. Cytokine and tumor-associated factor levels in tumors and spleens were detected by real-time quantitative polymerase chain reaction. Infiltration of macrophages and CD8+ T cells in the tumor microenvironment (TME) were detected by immunohistochemistry with anti-CD68 and anti-CD8 antibodies, respectively. Finally, 16S rRNA sequencing of mice cecal contents was performed to evaluate the effect of N. caninum on gut microbial diversity. Results Intratumoral and distal subcutaneous injections of N. caninum resulted in significant inhibition of tumor growth (P

ABSTRACT In the apicomplexans, endocytosed cargos (e.g., hemoglobin) are trafficked to a specialized organelle for digestion. This follows a unique endocytotic process at the micropore/cytostome in these parasites. However, the mechanism underlying endocytic trafficking remains elusive, due to the repurposing of classical endocytic proteins for the biogenesis of apical organelles. To resolve this issue, we have exploited the genetic tractability of the model apicomplexan Toxoplasma gondii, which ingests host cytosolic materials (e.g., green fluorescent protein[GFP]). We determined an association between protein prenylation and endocytic trafficking, and using an alkyne-labeled click chemistry approach, the prenylated proteome was characterized. Genome editing, using clustered regularly interspaced short palindromic repaet/CRISPR-associated nuclease 9 (CRISPR/Cas9), was efficiently utilized to generate genetically modified lines for the functional screening of 23 prenylated candidates. This identified four of these proteins that regulate the trafficking of endocytosed GFP vesicles. Among these proteins, Rab1B and YKT6.1 are highly conserved but are non-classical endocytic proteins in eukaryotes. Confocal imaging analysis showed that Rab1B and Ras are substantially localized to both the trans-Golgi network and the endosome-like compartments in the parasite. Conditional knockdown of Rab1B caused a rapid defect in secretory trafficking to the rhoptry bulb, suggesting a trafficking intersection role for the key regulator Rab1B. Further experiments confirmed a critical role for protein prenylation in regulating the stability/activity of these proteins (i.e., Rab1B and YKT6.1) in the parasite. Our findings define the molecular basis of endocytic trafficking and reveal a potential intersection function of Rab1B on membrane trafficking in T. gondii. This might extend to other related protists, including the malarial parasites. IMPORTANCE The protozoan Toxoplasma gondii establishes a permissive niche, in host cells, that allows parasites to acquire large molecules such as proteins. Numerous studies have demonstrated that the parasite repurposes the classical endocytic components for secretory sorting to the apical organelles, leaving the question of endocytic transport to the lysosome-like compartment unclear. Recent studies indicated that endocytic trafficking is likely to associate with protein prenylation in malarial parasites. This information promoted us to examine this association in the model apicomplexan T. gondii and to identify the key components of the prenylated proteome that are involved. By exploiting the genetic tractability of T. gondii and a host GFP acquisition assay, we reveal four non-classical endocytic proteins that regulate the transport of endocytosed cargos (e.g., GFP) in T. gondii. Thus, we extend the principle that protein prenylation regulates endocytic trafficking and elucidate the process of non-classical endocytosis in T. gondii and potentially in other related protists.