Trajectory Analysis Research Articles

A greater negative impact of future climate change on vegetation in Central Asia: Evidence from trajectory/pattern analysis

In the context of global warming, vegetation changes exhibit various patterns, yet previous studies have focused primarily on monotonic changes, often overlooking the complexity and diversity of multiple change processes. Therefore, it is crucial to further explore vegetation dynamics and diverse change trajectories in this region under future climate scenarios to obtain a more comprehensive understanding of local ecosystem evolution. In this study, we established an integrated machine learning prediction framework and a vegetation change trajectory recognition framework to predict the dynamics of vegetation in Central Asia under future climate change scenarios and identify its change trajectories, thus revealing the potential impacts of future climate change on vegetation in the region. The findings suggest that various future climate scenarios will negatively affect most vegetation in Central Asia, with vegetation change intensity increasing with increasing emission trajectories. Analyses of different time scales and trend variations consistently revealed more pronounced downward trends. Vegetation change trajectory analysis revealed that most vegetation has undergone nonlinear and dramatic changes, with negative changes outnumbering positive changes and curve changes outnumbering abrupt changes. Under the highest emission scenario (SSP5-8.5), the abrupt vegetation changes and curve changes are 1.7 times and 1.3 times greater, respectively, than those under the SSP1-2.6 scenario. When transitioning from lower emission pathways (SSP1-2.6, SSP2-4.5) to higher emission pathways (SSP3-7.0, SSP5-8.5), the vegetation change trajectories shift from neutral and negative curve changes to abrupt negative changes. Across climate scenarios, the key climate factors influencing vegetation changes are mostly evapotranspiration and soil moisture, with temperature and relative humidity exerting relatively minor effects. Our study reveals the negative response of vegetation in Central Asia to climate change from the perspective of vegetation dynamics and change trajectories, providing a scientific basis for the development of effective ecological protection and climate adaptation strategies.

Subphenotypes of platelet count trajectories in sepsis from multi-center ICU data

Although thrombocytopenia on admission to the ICU is associated with increased in-hospital mortality in septic patients, the role of longitudinally measured platelet counts, which are dynamically changing, is unclear. We aimed to identify patterns of dynamic platelet count trajectories and evaluate their association with outcomes and thrombocytopenia in septic patients. We tested the longitudinal platelet trajectory patterns of sepsis patients within the first four days of ICU admission in the MIMIC-IV database and their association with 28-day mortality, and independently validated our findings in the eICU-CRD database. Statistical methods used included multivariate regression, propensity score analysis, doubly robust estimation, gradient boosting model, and inverse probability weighting to ensure the robustness of our findings. A total of 22,866 septic patients were included in our study. The trajectory analysis categorizes patients into ascending (AS), stable (ST), or descending (DS) patterns. The risk of 28-day mortality was increased in the DS patients (OR = 2.464, 95%CI 1.895–3.203, p < 0.001) and ST patients (OR = 1.302, 95%CI 1.067–1.589, p = 0.009) compared to AS patients. The AS patients had lower ICU length of stay (2.36 vs. 4.32, p < 0.001) and 28-day maximum SOFA scores (5.00 vs. 6.00, p < 0.001) than the DS patients, but had more ventilator-free days within 28 days than the DS group (26.00 vs. 24.00, p < 0.001). The mediating effect of thrombocytopenia was significant (p < 0.001 for the average causal mediation effect (ACME)). Longitudinal platelet trajectory was associated with risk-adjusted 28-day mortality among patients with sepsis and was proportionally mediated through thrombocytopenia.

Medullary thyroid cancer: single-cell transcriptome and tumor evolution

BackgroundMedullary thyroid cancer (MTC) is a rare neuroendocrine tumor that originates from the parafollicular C cells of thyroid gland. Understanding the fundamental pathophysiology of MTC is essential for clinical management. Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for identifying distinct cell types, offering a new biological foundation for comprehending the MTC ecosystem and developing precise treatment.MethodsFormalin fixed and paraffin-embedded (FFPE) samples of primary and adjacent non-cancerous tissues of three MTC cases were collected, and single-cell transcriptome data of MTC were obtained by using scRNA-seq technology. Annotated cell subpopulations were categorized and functionally enriched by principal component analysis, differential gene expression, and cell clustering analysis, to explore the biological process of tumor evolution that may be involved in each cell subpopulation. The copy number variation (CNV) profile was used to distinguish the malignancy of parafollicular thyroid cells, and the evolutionary trajectories of normal cells and tumor cells were revealed by the proposed time series analysis. The highly expressed genes in each cell subpopulation were analyzed by the FindAllMarker function of Seurat software, and verified by immunohistochemistry and fluorescence in situ hybridization. The prognostic value of specific cell subtypes was validated using large-scale public datasets.ResultsA total of 32,544 cells were obtained from the MTC tissue samples and 11,751 cells from the adjacent non-cancerous samples, which were classified into 7 heterogenous subpopulations by using R package of Seurat module. Copy number variations (CNVs) were significantly higher in tumor tissues than in adjacent non-tumor samples, predominantly enriched in subtypes C2 and C4. In addition, the pseudo-time for trajectory analysis suggested that the evolution of MTC tumor cells might begin with the C2 subtype, then transition to the early cancer subgroup C3, and further differentiate into four major malignant cell subpopulations C0, C1, C5 and C6. Survival analysis of a thyroid cancer cohort using the TCGA dataset revealed that high expression of genes linked to the C0 subcluster was correlated with poorer overall survival compared to low expression. Immunohistochemical staining showed that MAP3K4 was highly expressed in MTC tissues compared to adjacent non-cancerous tissues. Fluorescence in situ hybridization also confirmed the amplification of these two genes in MTC samples.ConclusionsBy conducting scRNA-seq on FFPE samples, we mapped the single-cell transcriptome of MTC, uncovering the tumor heterogeneity and unique biological features of each cellular subpopulation. The biological roles of identified tumor cell subpopulations such as C0 and C3 subtypes of parafollicular cells suggested the potential to discover new therapeutic targets and biomarkers for MTC, providing valuable insights for future translational and clinical research.

UQCRB and LBH are correlated with Gleason score progression in prostate cancer: Spatial transcriptomics and experimental validation

Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.

Meningeal solitary fibrous tumor cell states phenocopy cerebral vascular development and homeostasis.

Meningeal solitary fibrous tumors (SFTs) are rare mesenchymal neoplasms that are associated with local recurrence and hematogenous metastasis. The cell states and spatial transcriptomic architecture underlying the unique clinical behavior of meningeal SFTs are unknown. Single-cell (n=4), spatial (n=8), and bulk RNA sequencing (n=22) was used to define the cell states and spatial transcriptomic architecture of meningeal SFTs across histological grades and in patient-matched pairs of primary/recurrent or intracranial/metastatic samples. Immunofluorescence, immunohistochemistry, and comparison of single-cell types to meningiomas, or to cerebral vascular development or homeostasis, were used for validation. Here we show meningeal SFTs are comprised of regionally distinct gene expression programs that resemble cerebral vascular development or homeostasis. Single-cell trajectory analysis and pseudotemporal ordering of single-cells suggest that meningeal SFT cell fate decisions are dynamic and interchangeable. Cell-cell communication analyses demonstrate receptor-ligand interactions throughout the meningeal SFT microenvironment, particularly between SFT cells, endothelia, and immature neurons. Direct comparison of single-cell transcriptomes from meningeal SFTs versus meningiomas shows that SFT cells are enriched in expression of endothelial markers while meningiomas cells are enriched in expression of mural cells markers. Meningeal SFT spatial transcriptomes show regionally distinct intratumor heterogeneity in cell states, gene expression programs, and cell-cell interactions across WHO histological grades and in patient-matched pairs of primary/recurrent or intracranial/metastatic samples. These results shed light on pathways underlying meningeal SFT biology in comparison to other central nervous system tumors and provide a framework for integrating single-cell, spatial, and bulk RNA sequencing data across human cancers and normal tissues.

Exploring third-order KdV–SIdV families: Analytical solutions, conservation properties, and phase plane trajectories

This study focuses on the Scale-Invariant (SIdV) families of third-order equations, which are among the few known equations sharing the same solitary wave solution of the KdV equation in the form of sech2. The primary objective is to generalize existing or discover new third-order KdV–SIdV families that feature the analytical sech2 solution of the KdV equation. The study aims to identify all admissible advecting velocity functions ensuring that the KdV–SIdV families accommodate every asymptotically decaying KdV traveling wave with the same wave speed, independent of the advecting velocity. Furthermore, the investigation seeks to delineate their corresponding conserved properties, thereby enhancing our understanding of these intriguing nonlinear partial differential equations. It is shown that the discovered KdV–SIdV families, while sharing the same solitary wave solution, exhibit differing conservation properties. Additionally, a qualitative analysis is conducted using phase plane trajectories to examine the traveling wave solutions and their characteristics for a particular SIdV equation. The emphasis lies in comprehensively exploring potential bifurcations and phase portraits of the vector fields governed by the corresponding system in the parametric space. Through the analysis of distinct phase trajectories across various regions, we derive a new diverse range of solitonic wave solutions, including bell/anti-bell solitary waves, peakon waves, singular waves, and periodic singular waves.

Trajectory & maneuver design of the NEA Scout solar sail mission

The Near-Earth Asteroid Scout (NEA Scout) mission aimed to deliver a 6U CubeSat to a slow flyby of a Near Earth Asteroid using a solar sail as the primary propulsion system. NEA Scout was launched as a secondary payload aboard Artemis I, on November 16, 2022, but communication with the spacecraft was not achieved. This paper describes the target asteroid selection and the innovative trajectory and maneuver design for the NEA Scout mission, including cold gas maneuvers, multiple lunar flybys, solar sail thrusting, and design margins for solar sailing. The resulting trajectory design tools, solutions, and analyses are potentially applicable to future solar sail missions.

Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver

Sequential lytic cycles driven by cascading transcriptional waves underlie pathogenesis in the apicomplexan parasite Toxoplasma gondii. This parasite’s unique division by internal budding, short cell cycle, and jumbled up classically defined cell cycle stages have restrained in-depth transcriptional program analysis. Here, unbiased transcriptome and chromatin accessibility maps throughout the lytic cell cycle are established at the single-cell level. Correlated pseudo-timeline assemblies of expression and chromatin profiles maps transcriptional versus chromatin level transition points promoting the cell division cycle. Sequential clustering analysis identifies functionally related gene groups promoting cell cycle progression. Promoter DNA motif mapping reveals patterns of combinatorial regulation. Pseudo-time trajectory analysis reveals transcriptional bursts at different cell cycle points. The dominant burst in G1 is driven largely by transcription factor AP2XII-8, which engages a conserved DNA motif, and promotes the expression of 44 ribosomal proteins encoding regulon. Overall, the study provides integrated, multi-level insights into apicomplexan transcriptional regulation.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

A single-cell atlas of the miracidium larva of Schistosoma mansoni reveals cell types, developmental pathways, and tissue architecture.

Schistosoma mansoni is a parasitic flatworm that causes the major neglected tropical disease schistosomiasis. The miracidium is the first larval stage of the life cycle. It swims and infects a freshwater snail, transforms into a mother sporocyst, where its stem cells generate daughter sporocysts that give rise to human-infective cercariae larvae. To understand the miracidium at cellular and molecular levels, we created a whole-body atlas of its ~365 cells. Single-cell RNA sequencing identified 19 transcriptionally distinct cell clusters. In situ hybridisation of tissue-specific genes revealed that 93% of the cells in the larva are somatic (57% neural, 19% muscle, 13% epidermal or tegument, 2% parenchyma, and 2% protonephridia) and 7% are stem. Whereas neurons represent the most diverse somatic cell types, trajectory analysis of the two main stem cell populations indicates that one of them is the origin of the tegument lineage and the other likely contains pluripotent cells. Furthermore, unlike the somatic cells, each of these stem populations shows sex-biased transcriptional signatures suggesting a cell-type-specific gene dosage compensation for sex chromosome-linked loci. The miracidium represents a simple developmental stage with which to gain a fundamental understanding of the molecular biology and spatial architecture of schistosome cells.

A single-cell atlas of the miracidium larva of Schistosoma mansoni reveals cell types, developmental pathways, and tissue architecture

Schistosoma mansoni is a parasitic flatworm that causes the major neglected tropical disease schistosomiasis. The miracidium is the first larval stage of the life cycle. It swims and infects a freshwater snail, transforms into a mother sporocyst, where its stem cells generate daughter sporocysts that give rise to human-infective cercariae larvae. To understand the miracidium at cellular and molecular levels, we created a whole-body atlas of its ~365 cells. Single-cell RNA sequencing identified 19 transcriptionally distinct cell clusters. In situ hybridisation of tissue-specific genes revealed that 93% of the cells in the larva are somatic (57% neural, 19% muscle, 13% epidermal or tegument, 2% parenchyma, and 2% protonephridia) and 7% are stem. Whereas neurons represent the most diverse somatic cell types, trajectory analysis of the two main stem cell populations indicates that one of them is the origin of the tegument lineage and the other likely contains pluripotent cells. Furthermore, unlike the somatic cells, each of these stem populations shows sex-biased transcriptional signatures suggesting a cell-type-specific gene dosage compensation for sex chromosome-linked loci. The miracidium represents a simple developmental stage with which to gain a fundamental understanding of the molecular biology and spatial architecture of schistosome cells.

Gene Misexpression in a Smoc2+ve/Sox2-Low Population in Juvenile Prop1-Mutant Pituitary Gland.

Mutations in the pituitary-specific transcription factor Prophet of Pit-1 (PROP1) are the most common genetic etiology of combined pituitary hormone deficiency (CPHD). CPHD is associated with short stature, attributable to growth hormone deficiency and/or thyroid-stimulating hormone deficiency, as well as hypothyroidism and infertility. Pathogenic lesions impair pituitary development and differentiation of endocrine cells. We performed single-cell RNA sequencing of pituitary cells from a wild-type and a Prop1-mutant P4 female mouse to elucidate population-specific differential gene expression. We observed a Smoc2+ve population that expressed low Sox2, which trajectory analyses suggest are a transitional cell state as stem cells differentiate into endocrine cells. We also detected ectopic expression of Sox21 in these cells in the Prop1df/df mutant. Prop1-mutant mice are known to overexpress Pou3f4, which we now show to be also enriched in this Smoc2+ve population. We sought to elucidate the role of Pou3f4 during pituitary development and to determine the contributions of Pou3f4 upregulation to pituitary disease by utilizing double-mutant mice lacking both Prop1 and Pou3f4. However, our data showed that Pou3f4 is not required for normal pituitary development and function. Double mutants further demonstrated that the upregulation of Pou3f4 was not causative for the overexpression of Sox21. These data indicate loss of Pou3f4 is not a potential cause of CPHD, and further studies may investigate the functional consequence of upregulation of Pou3f4 and Sox21, if any, in the novel Smoc2+ve cell population.

Determination of Hydrogen Bonds in GROMACS: A New Implementation to Overcome Memory Limitation.

This work introduces a new faster implementation of the hydrogen bond network (complex arrangement of hydrogen bonds between or within molecules) search algorithm in biomacromolecules and their environment. Existing implementation of such an algorithm in GROMACS [Abraham et al. GROMACS 2024.2 Manual. 2024.] has limitations in the analysis of large structures and trajectories. The new implementation, in the form of a native GROMACS trajectory analysis module, allows for quick analysis of molecular dynamics trajectories without restrictions, thus overcoming the limitations of the original algorithm. The application of the developed method enabled the acquisition and analysis of hydrogen bond networks in the studied defensin-like protein Pentadiplandra brazzeana, as well as the study of hydrogen bond occupancies between the protein's residues and water molecules. The data obtained using the new implementation coincided with the experimental data.

Knowledge Graph of Low-Carbon Technologies in the Energy Sector and Cost Evolution Based on LDA2Vec: A Case Study in China

Climate change has attracted global attention, highlighting the critical role of low-carbon technologies in addressing environmental challenges. Due to the multidisciplinary nature, complexity, and diversity of research content on low-carbon technologies, a comprehensive overview is still limited. This paper uses bibliometrics analysis to discuss the research status and hotspots of low-carbon technology from a macro-perspective. The LDA2Vec topic recognition model is adopted to identify key technical terms, and CiteSpace software 6.3.1 Advanced Edition is used to conduct in-depth analysis of the development trajectory of low-carbon technology. After checking the frequency of the relevant keywords, four key techniques were identified. In order to further analyze the research results, the learning curve theory is used to predict the cost development trend of key low-carbon technologies. The results show that: (i) low-carbon technologies play a key role in the energy sector and have a potential impact on policy making, and the cost of related technologies will be significantly reduced in the next few years. (ii) Global low-carbon technologies have entered an important period of development, but remaining challenges need to be addressed by optimizing technological performance. (iii) It is very important to strengthen the research on hydrogen production technology and photovoltaic power generation technology; the cost reduction in hydrogen production technology is still significant and there is room for further optimization. (iv) To effectively address the high costs and technical barriers associated with emerging low-carbon technologies, increased funding for research and development is critical.

Single-cell RNA-sequencing Analysis Reveals the Promoting Role of the Hedgehog Pathway in Epithelial Cells during Cervical Cancer Progression.

This study explored the role of the Hedgehog pathway in epithelial cells during cervical cancer [CC] progression, providing new insights for improving current CC treatment. Abnormal activation of the Hedgehog signaling pathway is associated with the malignant transformation of CC epithelial cells. Single-cell atlas of CC and the role of Hedgehog pathway in epithelial cells during CC progression remain to be explored. To comprehensively analyze the mechanism of Hedgehog pathway activation in CC epithelial cells and its impact on tumor progression by applying single-cell RNA sequencing [scRNA-seq] analysis. The scRNA-seq data were acquired from the Gene Expression Omnibus [GEO] database and then processed with the Seurat package. FindNeighbors and Find- Clusters functions were applied to cluster the cells. The CellMarker database was used for subgroup annotation. Differentially expressed genes [DEGs] in each cell subgroup were filtered by FindAllMarkers function. Biological function analysis for the gene set of interest was performed using Clusterprofiler package. AUCell function was employed to calculate the score of the Hedgehog pathway. The differentiation trajectory in epithelial cell subtypes was generated by performing Pseudotime analysis. Finally, protein-protein network [PPI] was used to investigate the interactions between the Hedgehog pathway and other pathways enriched in the gene set of interest. A total of 9 major cell subpopulations were classified and the proportion of epithelial cells was the highest in CC samples. Further analysis revealed that the Hedgehog pathway was abnormally activated in STYK1+ and TP73+ epithelial cell subtypes. Pseudo-time trajectory analysis showed that the differentiation trajectory of STYK1+ epithelial cells gradually transformed into defense-to-virus cells or into proliferation cells, while TP73+ epithelial cells eventually differentiated into two branches of response to estrogen and virus-induced proliferation. PPI analysis showed that the Hedgehog pathway was involved in the proliferation and viral process of epithelial cells in CC. The current study comprehensively analyzed the features of CC samples and differentiation trajectories of epithelial cell subtypes, as well as the role of the Hedgehog pathway in epithelial cells during CC progression. More importantly, effective target genes were discovered for the molecular diagnosis and precise treatment of CC.

Vertical distribution of ozone in spring based on two high tower observations over the Pearl River Delta, China

This study investigates the vertical distribution characteristics of ozone (O3) in the Pearl River Delta (PRD) region during spring, utilising observational data from two tall towers in urban and suburban areas of the PRD, i.e., the 600 m high Canton Tower and the 356 m high Shenzhen Meteorology Gradient Tower (SZMGT), between 2018 and 2020. The observations indicate that the peak times of O3 concentrations differ under polluted and clean conditions, with polluted conditions showing 1–3 h later peaks compared with clean conditions. The diurnal variation in the O3 concentration is more pronounced at the SZMGT than at the Canton Tower. The O3 vertical gradients differ between the two towers, with the Canton Tower showing larger daytime gradients and the SZMGT showing larger nighttime gradients under polluted conditions. The vertical O3 distribution patterns are categorised into three types: Polluted, Moderate, and Good levels. The O3 concentration initially rises with altitude and then decreases under polluted conditions. Elevated O3 is observed in the lower planetary boundary layer (PBL), approximately between 110 and 210 m in height, where the concentration is 1.1–1.4 times higher than the surface level. Both the Moderate and Good levels occur under clean conditions, in which the O3 of the Canton Tower increases with altitude, whereas the O3 of the SZMGT first increases and then decreases with altitude. The correlation between O3 and the meteorological conditions (temperature, relative humidity, wind direction, and wind speed) decreases with increasing altitude. The cluster analysis of backward trajectories identifies three main transport paths affecting the O3 levels: northeasterly continental, southerly oceanic, and easterly coastal paths. Combining the observations from O3 lidar in Guangzhou and Shenzhen and two additional cities within the PRD region, Foshan and Zhongshan, the analysis of a typical pollution case indicates that different cities have different local chemical generation and regional transport contributions to O3 pollution. A conceptual model reflecting the horizontal transport paths and vertical structures of O3 pollution in the spring over the PRD is proposed, which enhances our understanding of the O3 vertical distribution in different cities and contributes to the analysis of the O3 pollution mechanism in the PRD agglomeration.

Pax3/7 gene function in Oikopleura dioica supports a neuroepithelial-like origin for its house-making Fol territory

Larvacean tunicates feature a spectacular innovation not seen in other animals - the trunk oikoplastic epithelium (OE). This epithelium produces a house, a large and complex extracellular structure used for filtering and concentrating food particles. Previously we identified several homeobox transcription factor genes expressed during early OE patterning. Among these are two Pax3/7 copies that we named pax37A and pax37B. The vertebrate homologs, PAX3 and PAX7 are involved in developmental processes related to neural crest and muscles. In the ascidian tunicate Ciona intestinalis, Pax3/7 plays a role in the development of cells deriving from the neural plate border, including trunk epidermal sensory neurons and tail nerve cord neurons, as well as in the neural tube closure. Here we have investigated the roles of Oikopleura dioica pax37A and pax37B in the development of the OE, by using CRISPR-Cas9 mutant lines and analyzing scRNA-seq data from wild-type animals. We found that pax37B but not pax37A is essential for the differentiation of cell fields that produce the food concentrating filter of the house: the anterior Fol, giant Fol and Nasse cells. Trajectory analysis supported a neuroepithelial-like or a preplacodal ectoderm transcriptional signature in these cells. We propose that the highly specialized secretory epithelial cells of the Fol region either maintained or evolved neuroepithelial features. This is supported by a fragmented gene regulatory network involved in their development that also operates in ascidian epidermal neurons.

Reduction of sawing forces in bone cutting: Innovative oscillating saw mechanism based on trajectory analysis

Oscillating bone sawing is a critical procedure in orthopedic surgery. However, conventional oscillating saw mechanisms often result in excessive sawing forces, which are detrimental to implant fixation and postoperative patient recovery. Therefore, there is an urgent need to design a new oscillating saw mechanism to reduce sawing forces during surgery, including avoiding ineffective impact forces on bone cutting and preventing ploughing forces caused by negative rake angle contact with the workpiece. In this study, an innovative oscillating sawing mechanism is proposed to effectively inhibit the generation and accumulation of impact forces, avoid negative rake angle contact with the workpiece. Oscillating sawing experiments under various cutting conditions demonstrated that the proposed mechanism significantly reduces cutting forces and prevents defects due to crack propagation of the bone and saw teeth damage. The proposed design offers an effective mechanism to achieve small and stable sawing forces in bone sawing surgery, and it inspires tailored oscillating saw techniques for specific machining needs, such as thin deep groove cutting.

Effect of growth trajectories in communication skills on juvenile recidivism

AbstractPurposeIncreasing numbers of children with behaviour problems in school are recognized as having language and communication difficulties. However, communication limitations have not been assessed adequately as a risk factor for juvenile recidivism. The aim of this study was to examine how changes in communication skills impacted subsequent engagement in crime among youth.MethodsFour waves of longitudinal data of youth who were placed in juvenile justice residential facilities were used for the study. Group‐based trajectory and multivariate regression analyses were used to investigate how the growth trajectories of communication skills impacted recidivism.ResultsGroup‐based trajectory analysis revealed heterogeneous growth in communication skills among residential youth. More interestingly, the scale of growth predicted recidivism.ConclusionsThis study extends an understanding of juvenile aggression and criminal offences by underlining communication limitation issues as a focal predictor of recidivism. Sufficient resources are needed for the development, evaluation and implementation of intervention programmes to help youth overcome limitations in communication.

Inflammatory Biomarkers and Risk of Psychiatric Disorders

Individuals with psychiatric disorders have been reported to have elevated levels of inflammatory biomarkers, and prospective evidence is limited regarding the association between inflammatory biomarkers and subsequent psychiatric disorders risk. To assess the associations between inflammation biomarkers and subsequent psychiatric disorders risk. This was a prospective cohort study including individuals from the Swedish Apolipoprotein Mortality Risk (AMORIS) cohort, with no prior psychiatric diagnoses and having a measurement of at least 1 inflammatory biomarker. Data from the UK Biobank were used for validation. Longitudinal trajectories of studied biomarkers were visualized before diagnosis of psychiatric disorders in the AMORIS cohort via a nested case-control study. In addition, genetic correlation and mendelian randomization (MR) analyses were conducted to determine the genetic overlap and causality of the studied associations using publicly available GWAS summary statistics. Inflammatory biomarkers, eg, leukocytes, haptoglobin, immunoglobulin G (IgG), C-reactive protein (CRP), platelets, or albumin. Any psychiatric disorder or specific psychiatric disorder (ie, depression, anxiety, and stress-related disorders) was identified through the International Statistical Classification of Diseases, Eighth, Ninth, and Tenth Revision codes. Among the 585 279 individuals (mean [SD] age, 45.5 [14.9] years; 306 784 male [52.4%]) in the AMORIS cohort, individuals with a higher than median level of leukocytes (hazard ratio [HR], 1.11; 95% CI, 1.09-1.14), haptoglobin (HR, 1.13; 95% CI, 1.12-1.14), or CRP (HR, 1.02; 95% CI, 1.00-1.04) had an elevated associated risk of any psychiatric disorders. In contrast, we found an inverse association for IgG level (HR, 0.92; 95% CI, 0.89-0.94). The estimates were comparable for depression, anxiety, and stress-related disorders, specifically, and these results were largely validated in the UK Biobank (n = 485 620). Analyses of trajectories revealed that individuals with psychiatric disorders had higher levels of leukocytes and haptoglobin and a lower level of IgG than their controls up to 30 years before the diagnosis. The MR analysis suggested a possible causal relationship between leukocytes and depression. In this cohort study, inflammatory biomarkers including leukocytes, haptoglobin, CRP, and IgG were associated with a subsequent risk of psychiatric disorders, and thus might be used for high-risk population identification. The possible causal link between leukocytes and depression supports the crucial role of inflammation in the development of psychiatric disorders.