Bacterial and fungal community structures in Hulun Lake are regulated by both stochastic processes and environmental factors.

Microorganisms are a crucial component of lake ecosystems and significant contributors to biogeochemical cycles. However, the understanding of how primary microorganism groups (e.g., bacteria and fungi) are distributed and constructed within different lake habitats is lacking. We investigated the bacterial and fungal communities of Hulun Lake using high-throughput sequencing techniques targeting 16S rRNA and Internal Transcribed Spacer 2 genes, including a range of ecological and statistical methodologies. Our findings reveal that environmental factors have high spatial and temporal variability. The composition and community structures vary significantly depending on differences in habitats. Variance partitioning analysis showed that environmental and geographical factors accounted for <20% of the community variation. Canonical correlation analysis showed that among the environmental factors, temperature, pH, and dissolved oxygen had strong control over microbial communities. However, the microbial communities (bacterial and fungal) were primarily controlled by the dispersal limitations of stochastic processes. This study offers fresh perspectives regarding the maintenance mechanism of bacterial and fungal biodiversity in lake ecosystems, especially regarding the responses of microbial communities under identical environmental stress.IMPORTANCELake ecosystems are an important part of the freshwater ecosystem. Lake microorganisms play an important role in material circulation and energy flow owing to their unique enzymatic and metabolic capacity. In this study, we observed that bacterial and fungal communities varied widely in the water and sediments of Hulun Lake. The primary factor affecting their formation was identified as dispersal limitation during stochastic processes. Environmental and geographical factors accounted for <20% of the variation in bacterial and fungal communities, with pH, temperature, and dissolved oxygen being important environmental factors. Our findings provide new insights into the responses of bacteria and fungi to the environment, shed light on the ecological processes of community building, and deepen our understanding of lake ecosystems. The results of this study provide a reference for lake management and conservation, particularly with respect to monitoring and understanding microbial communities in response to environmental changes.

Tuning the magnetic properties of double transition-metal carbide CoMC (M = Ti, V, Cr, Mn, Fe, Ni) monolayers.

The intrinsic ferromagnetism of two-dimensional transition metal carbide Co2C is remarkable. However, its practical application in spintronic devices is encumbered by a low Curie temperature (TC). To surmount this constraint, double transition-metal carbide CoMC (M = Ti, V, Cr, Mn, Fe, Ni) monolayers are constructed with the aim of improving the magnetic properties and Curie temperature of Co2C. The magnetic properties of CoMC monolayers are comprehensively investigated by first-principles calculations and the effects of hole doping and biaxial strain on the magnetic properties of CoMC (M = V, Cr, Mn) monolayers are also studied. The ground states of CoTiC, CoMnC and CoNiC monolayers all favor ferromagnetic ordering, whereas the CoVC and CoCrC monolayers favor antiferromagnetic ordering and the CoFeC monolayer is non-magnetic. Excitedly, the CoMnC monolayer displays a high total magnetic moment of 4.024µB and a TC of 1366 K. Moreover, the control of hole doping can effectively improve the TC of CoVC, CoCrC, and CoMnC monolayers to 680, 1317, 3044 K, respectively. Finally, applying the in-plain biaxial strain, the CoVC monolayer can be transformed into a ferromagnetic semiconductor under a tensile strain of 6%. The TC values of CoVC, CoCrC, and CoMnC monolayers are tuned by biaxial strain to 440, 1334 and 2390 K, respectively. Their TC above room temperature demonstrates that these monolayers have potential applications in spintronic devices. These theoretical investigations provide valuable insights into guiding experimental synthesis endeavors.

METTL16 promotes liver cancer stem cell self-renewal via controlling ribosome biogenesis and mRNA translation.

BACKGROUND: While liver cancer stem cells (CSCs) play a crucial role in hepatocellular carcinoma (HCC) initiation, progression, recurrence, and treatment resistance, the mechanism underlying liver CSC self-renewal remains elusive. We aim to characterize the role of Methyltransferase 16 (METTL16), a recently identified RNA N6-methyladenosine (m6A) methyltransferase, in HCC development/maintenance, CSC stemness, as well as normal hepatogenesis. METHODS: Liver-specific Mettl16 conditional KO (cKO) mice were generated to assess its role in HCC pathogenesis and normal hepatogenesis. Hydrodynamic tail-vein injection (HDTVi)-induced de novo hepatocarcinogenesis and xenograft models were utilized to determine the role of METTL16 in HCC initiation and progression. A limiting dilution assay was utilized to evaluate CSC frequency. Functionally essential targets were revealed via integrative analysis of multi-omics data, including RNA-seq, RNA immunoprecipitation (RIP)-seq, and ribosome profiling. RESULTS: METTL16 is highly expressed in liver CSCs and its depletion dramatically decreased CSC frequency in vitro and in vivo. Mettl16 KO significantly attenuated HCC initiation and progression, yet only slightly influenced normal hepatogenesis. Mechanistic studies, including high-throughput sequencing, unveiled METTL16 as a key regulator of ribosomal RNA (rRNA) maturation and mRNA translation and identified eukaryotic translation initiation factor 3 subunit a (eIF3a) transcript as a bona-fide target of METTL16 in HCC. In addition, the functionally essential regions of METTL16 were revealed by CRISPR gene tiling scan, which will pave the way for the development of potential inhibitor(s). CONCLUSIONS: Our findings highlight the crucial oncogenic role of METTL16 in promoting HCC pathogenesis and enhancing liver CSC self-renewal through augmenting mRNA translation efficiency.

Revealing the Marine Cycles of Volatile Sulfur Compounds and Their Biogeochemical Controls: A Case of the Western North Pacific.

Volatile sulfur compounds, such as dimethyl sulfide (DMS), carbonyl sulfide (OCS), and carbon disulfide (CS2), have significant implications for both atmospheric chemistry and climate change. Despite the crucial role of oceans in regulating their atmospheric budgets, our comprehension of their cycles in seawater remains insufficient. To address this gap, a field investigation was conducted in the western North Pacific to clarify the sources, sinks, and biogeochemical controls of these gases in two different marine environments, including relatively eutrophic Kuroshio-Oyashio extension (KOE) and oligotrophic North Pacific subtropical gyre. Our findings revealed higher concentrations of these gases in both seawater and the atmosphere in the KOE compared to the subtropical gyre. In the KOE, nutrient-rich upwelling stimulated rapid DMS biological production, while reduced seawater temperatures hindered the removal of OCS and CS2, leading to their accumulation. Furthermore, we have quantitatively evaluated the relative contribution of each pathway to the source and sink of DMS, OCS, and CS2 within the mixed layer and identified vertical exchange as a potential sink in most cases, transporting substantial amounts of these gases from the mixed layer to deeper waters. This research advances our understanding of sulfur gas source-sink dynamics in seawater, contributing to the assessment of their marine emissions and atmospheric budgets.

Development and Validation of a Prognostic Nomogram for Patients with AFP and DCP Double-Negative Hepatocellular Carcinoma After Local Ablation.

Purpose: Although alpha-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP) have a certain predictive ability for the prognosis of hepatocellular carcinoma (HCC), there are still some cases of aggressive recurrence among patients with AFP and DCP double-negative HCC (DNHC) after local ablation. However, prediction models to forecast the prognosis of DNHC patients are still lacking. Thus, this retrospective study aims to explore the prognostic factors in DNHC patients and develop a nomogram to predict recurrence. Patients and methods: 493 DNHC patients who underwent the local ablation at Beijing You'an Hospital between January 1, 2014, and December 31, 2022, were enrolled. A part that was admitted from January 1, 2014, to December 31, 2018, was designated to the training cohort (n = 307); others from January 1, 2019, to December 31, 2022, were allocated to the validation cohort (n = 186). Lasso regression and Cox regression were employed with the aim of screening risk factors and developing the nomogram. The nomogram outcome was assessed by discrimination, calibration, and decision curve analysis (DCA). Results: Independent prognostic factors selected by Lasso-Cox analysis included age, tumor size, tumor number, and gamma-glutamyl transferase. The area under the receiver operating characteristic (ROC) curves (AUCs) of the training and validation groups (0.738, 0.742, 0.836, and 0.758, 0.821) exhibited the excellent predicted outcome of the nomogram. Calibration plots and DCA plots suggest desirable calibration performance and clinical utility. Patients were stratified into three risk groups by means of the nomogram: low-risk, intermediate-risk, and high-risk, respectively. There exists an obvious distinction in recurrence-free survival (RFS) among three groups (p<0.0001). Conclusion: In conclusion, we established and validated a nomogram for DNHC patients who received local ablation. The nomogram showed excellent predictive power for the recurrence of HCC and could contribute to guiding clinical decisions.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.

Introduction: This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS). Methods: The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results: The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients. Conclusions: The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.

An Automatic Needle Puncture Path-Planning Method for Thermal Ablation of Lung Tumors.

Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient's CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians' workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment.

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs.

Purpose: The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy. Methods and Materials: The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment. Expert segmentations were used as the independent standard for the prostate, seminal vesicles, bladder, rectum, and femoral heads for all 115 data sets. In addition, 3 independent radiation oncologists contoured the prostate, seminal vesicles, and rectum for a subset of patients such that the interobserver variability could be quantified. Consensus contours were then generated from these independent contours using a simultaneous truth and performance level estimation approach, and the deviation of Deep LOGISMOS + JEI contours to the consensus contours was evaluated and compared with the interobserver variability. Results: The absolute accuracy of Deep LOGISMOS + JEI generated contours was evaluated using median absolute surface-to-surface distance which ranged from a minimum of 0.20 mm for the bladder to a maximum of 0.93 mm for the prostate compared with the independent standard across all data sets. The median relative surface-to-surface distance was less than 0.17 mm for all organs, indicating that the Deep LOGISMOS + JEI algorithm did not exhibit a systematic under- or oversegmentation. Interobserver variability testing yielded a mean absolute surface-to-surface distance of 0.93, 1.04, and 0.81 mm for the prostate, seminal vesicles, and rectum, respectively. In comparison, the deviation of Deep LOGISMOS + JEI from consensus simultaneous truth and performance level estimation contours was 0.57, 0.64, and 0.55 mm for the same organs. On average, the Deep LOGISMOS algorithm took less than 26 seconds for contour segmentation. Conclusions: Deep LOGISMOS + JEI segmentation efficiently generated clinically acceptable prostate and normal tissue contours, potentially limiting the need for time intensive manual contouring with each fraction.

Prognostic significance of globulin/low-density lipoprotein ratio in patients with hepatocellular carcinoma after local ablative therapy: a retrospective cohort study.

Background: Low-density lipoprotein (LDL) and globulin (GLOB) have been found to be predictors for some malignant tumors, but their predictive value in hepatocellular carcinoma (HCC) has hardly been elucidated. This study assessed the prognostic significance of GLOB to LDL ratio (GLR) in HCC patients before ablation. Methods: This study analyzed 312 early-stage HCC patients who were hospitalized and underwent ablative treatment in Beijing You'an Hospital, Capital Medical University, from 1 January 2014 to 1 January 2019. The primary endpoint was the recurrence-free survival (RFS), calculated from treatment initiation to cancer recurrence, whereas the overall survival (OS) was measured from treatment initiation to death or last follow-up. Cox regression analysis was used to assess the GLR independently associated with recurrence and survival. OS and RFS were calculated by Kaplan-Meier analysis and compared between groups using the log rank test. The optimal cut-off value and prognostic role of GLR and other markers were evaluated via the receiver operating characteristic (ROC) curves and the Youden index. Results: Univariate and multivariate analysis found that the tumor number, tumor size, and GLR were independent risk factors of relapse, whereas etiology, tumor number, tumor size, fibrinogen (Fib), and GLR were related to OS. We classified the patients into groups with high and low levels of GLR based on the optimal cut-off value of GLR identified by ROC curve. The cumulative 1-, 3-, and 5-year RFS rates in the low GLR group were 76.4%, 53.8%, and 43.4% respectively, whereas those in the high GLR group were 71%, 31%, and 22%, respectively (P<0.001). In terms of OS, the low GLR group showed a 1-, 3-, and 5-year OS of 99.5%, 92.0%, and 80.2% respectively, and 98%, 73%, and 63% respectively for the high GLR group (P<0.001). Finally, patients were stratified by GLR and tumor size. The outcomes revealed that patients in group A (GLR <16.54 and tumor size ≤30 mm) showed better prognosis than those in group B (GLR ≥16.54 and tumor size ≤30 mm or GLR <16.54 and tumor size >30 mm) and group C (GLR ≥16.54 and tumor size >30 mm) (P<0.001). Conclusions: Preoperative GLR ratio has predictive value for patients with HCC who have undergone complete ablation.

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation.

RATIONALE AND OBJECTIVES: Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem. MATERIALS AND METHODS: T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development. Subsequent external validation was performed on 59 cases (27 GB and 32 BM) from another institution. Four different mask-sequence combinations were evaluated using area under the curve (AUC), precision, recall and F1-scores. Diagnostic performance of a neuroradiologist and a general radiologist, both without and with the model output available, was also assessed. RESULTS: 3D-model using the T1-CE tumor mask (TM) showed the highest performance [AUC 0.93 (95% CI 0.858-0.995)] on the external test set, followed closely by the model using T1-CE TM and FLAIR mask of peri-tumoral region (PTR) [AUC of 0.91 (95% CI 0.834-0.986)]. Models using T2WI masks showed robust performance on the internal dataset but lower performance on the external set. Both neuroradiologist and general radiologist showed improved performance with model output provided [AUC increased from 0.89 to 0.968 (p = 0.06) and from 0.78 to 0.965 (p = 0.007) respectively], the latter being statistically significant. CONCLUSION: 3D-CNNs showed robust performance for differentiating GB from BMs, with T1-CE TM, either alone or combined with FLAIR-PTR masks. Availability of model output significantly improved the accuracy of the general radiologist.

Analysis of the Complete Mitochondrial Genome of Pteronura brasiliensis and Lontra canadensis.

P. brasiliensis and L. canadensis are two otter species, which successfully occupied semi-aquatic habitats and diverged from other Mustelidae. Herein, the full-length mitochondrial genome sequences were constructed for these two otter species for the first time. Comparative mitochondrial genome, selection pressure, and phylogenetic independent contrasts (PICs) analyses were conducted to determine the structure and evolutionary characteristics of their mitochondrial genomes. Phylogenetic analyses were also conducted to confirm these two otter species' phylogenetic position. The results demonstrated that the mitochondrial genome structure of P. brasiliensis and L. canadensis were consistent across Mustelidae. However, selection pressure analyses demonstrated that the evolutionary rates of mitochondrial genome protein-coding genes (PCGs) ND1, ND4, and ND4L were higher in otters than in terrestrial Mustelidae, whereas the evolutionary rates of ND2, ND6, and COX1 were lower in otters. Additionally, PIC analysis demonstrated that the evolutionary rates of ND2, ND4, and ND4L markedly correlated with a niche type. Phylogenetic analysis showed that P. brasiliensis is situated at the base of the evolutionary tree of otters, and then L. canadensis diverged from it. This study suggests a divergent evolutionary pattern of Mustelidae mitochondrial genome PCGs, prompting the otters' adaptation to semi-aquatic habitats.

Decrease of Cellular Communication Network Factor 1 (CCN1) Attenuates PTZ-Kindled Epilepsy in Mice.

To investigate the molecular mechanism of communication network factor 1 (CCN1) regulating pentylenetetrazol (PTZ)-induced epileptogenesis, deepen the understanding of epilepsy seizure pathogenesis, and provide new drug action targets for its clinical prevention and treatment. Differentially expressed genes (DEGs) on microarrays GSE47516 and GSE88992 were analyzed online using GEO2R. Pathway enrichment and protein-protein interaction network (PPI) analysis of DEGs were carried out using Metascape. Brain tissue samples of severe traumatic brain injury patients (named Healthy group) and refractory epilepsy patients (named Epilepsy group) were obtained and analyzed by qRT-PCR and immunohistochemistry (IHC) staining. A PTZ-induced epilepsy mouse model was established and verified. Morphological changes of neurons in mouse brain tissue were detected using hematoxylin and eosin (HE) staining. qRT-PCR was conducted to detect the mRNA expressions of apoptosis-associated proteins Bax, Caspase-3 and bcl2. TUNEL staining was performed to detect brain neuron apoptosis. The levels of myocardial enzymology, GSH, MDA and ROS in blood of mouse were detected by biochemical assay. CCN1 expression was increased in epilepsy brain tissue samples. CCN1 decreasing effectively prolongs seizure incubation period and decreases seizure duration. Silencing of CCN1 also reduces neuronal damage and apoptosis, decreases mRNA and protein expression of proapoptotic proteins Bax and Caspase-3, increases mRNA expression of antiapoptotic protein Bcl2. Moreover, decrease of CCN1 decreases myocardial enzymatic indexes CK and CK-MB levels, reduces myocardial tissue hemorrhage, and relieves oxidative stress response in hippocampal and myocardial tissue. CCN1 expression is increased in epileptic samples. CCN1 decreasing protects brain tissue by attenuating oxidative stress and inhibiting neuronal apoptosis triggered by PTZ injection, which probably by regulating Nrf2/HO-1 pathway.

Deep learning framework for forecasting en route airspace emissions considering temporal-spatial correlation.

The air transport system is currently in a rapid development stage, accurate forecasting emissions is critical for identifying and mitigating its environmental impact. Accurate forecasting depends not only on temporal features from historical air traffic data but also on the influence of spatial factors. This paper proposes a deep learning-based forecasting framework for en route airspace emissions. It combines three-channel networks: a graph convolutional network, a gated recurrent unit, and the attention mechanism, in order to extract the spatial, temporal, and global temporal dynamics trends, respectively. The model is evaluated with real-world datasets, and the experimental results outperform existing state-of-the-art benchmarks on different evaluation metrics and forecasting horizons in complex airspace networks. Our method provides an alternative for forecasting air traffic emissions using publicly available traffic flow data. Furthermore, we propose an extension index that can be taken as an early warning indicator for stakeholders to monitor air traffic emissions.

Development and validation of a nomogram to predict the recurrence of hepatocellular carcinoma patients with dynamic changes in AFP undergoing locoregional treatments.

Background: Serum alpha-fetoprotein (AFP) is an important clinical indicator for screening, diagnosis, and prognosis of primary hepatocellular carcinoma (HCC). Our team's previous study showed that patients with negative AFP at baseline and positive AFP at relapse had a worse prognosis (N-P). Therefore, the aim of our study was to develop and validate a nomogram for this group of patients. Methods: A total of 513 patients with HCC who received locoregional treatments at Beijing You'an Hospital, Capital Medical University, from January 2012 to December 2019 were prospectively enrolled. Patients admitted from 2012 to 2015 were assigned to the training cohort (n = 335), while 2016 to 2019 were in the validation cohort (n =183). The clinical and pathological features of patients were collected, and independent risk factors were identified using univariate and multivariate Cox regression analysis as a basis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Results: The content of the nomogram includes gender, tumor number, tumor size, lymphocyte, direct bilirubin (DBIL), gamma-glutamyl transferase (GGT), and prealbumin. The C-index (0.717 and 0.752) and 1-, 3-, and 5-year AUCs (0.721, 0.825, 0.845, and 0.740, 0.868, 0.837) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classify of patients with dynamic changes in AFP into three groups according to the risk of recurrence: low risk, intermediate risk, and high risk. There was a statistically significant difference in RFS between the three groups in the training and validation cohorts (P<0.001). Conclusion: The nomogram developed and validated in this study had good predictive power for patients with dynamic changes in AFP.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

PURPOSE: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL). METHODOLOGY: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC. RESULTS: Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]). CONCLUSION: Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

Genetic diversity and structure of mongolian gazelle (Procapra gutturosa) populations in fragmented habitats.

BACKGROUND: The Mongolian gazelle (Procapra gutturosa) population has shown a considerable range of contractions and local extinctions over the last century, owing to habitat fragmentation and poaching. A thorough understanding of the genetic diversity and structure of Mongolian gazelle populations in fragmented habitats is critical for planning effective conservation strategies. RESULT: In this study, we used eight microsatellite loci and mitochondrial cytochrome b (Cytb) to compare the levels of genetic diversity and genetic structure of Mongolian gazelle populations in the Hulun Lake National Nature Reserve (HLH) with those in the China-Mongolia border area (BJ). The results showed that the nucleotide diversity and observed heterozygosity of the HLH population were lower than those of the BJ population. Moreover, the HLH and BJ populations showed genetic differentiation. We concluded that the HLH population had lower genetic diversity and a distinct genetic structure compared with the BJ population. CONCLUSION: The genetic diversity of fragmented Mongolian gazelle populations, can be improved by protecting these populations while reinforcing their gene exchange with other populations. For example, attempts can be made to introduce new individuals with higher genetic diversity from other populations to reduce inbreeding.

Segmentation quality assessment by automated detection of erroneous surface regions in medical images.

Despite the advancement in deep learning-based semantic segmentation methods, which have achieved accuracy levels of field experts in many computer vision applications, the same general approaches may frequently fail in 3D medical image segmentation due to complex tissue structures, noisy acquisition, disease-related pathologies, as well as the lack of sufficiently large datasets with associated annotations. For expeditious diagnosis and quantitative image analysis in large-scale clinical trials, there is a compelling need to predict segmentation quality without ground truth. In this paper, we propose a deep learning framework to locate erroneous regions on the boundary surfaces of segmented objects for quality control and assessment of segmentation. A Convolutional Neural Network (CNN) is explored to learn the boundary related image features of multi-objects that can be used to identify location-specific inaccurate segmentation. The predicted error locations can facilitate efficient user interaction for interactive image segmentation (IIS). We evaluated the proposed method on two data sets: Osteoarthritis Initiative (OAI) 3D knee MRI and 3D calf muscle MRI. The average sensitivity scores of 0.95 and 0.92, and the average positive predictive values of 0.78 and 0.91 were achieved, respectively, for erroneous surface region detection of knee cartilage segmentation and calf muscle segmentation. Our experiment demonstrated promising performance of the proposed method for segmentation quality assessment by automated detection of erroneous surface regions in medical images.

Protocol for modulating the noradrenergic pathway from locus coeruleus to heart to prevent sudden unexpected death in epilepsy in mouse models.

The locus coeruleus (LC) and noradrenergic neurotransmission are involved in the regulation of sudden unexpected death in epilepsy (SUDEP). Here, we present a protocol for modulating the noradrenergic pathway from LC to heart to prevent SUDEP in acoustic and pentylenetetrazole-induced DBA/1 mouse models of SUDEP. We describe steps for constructing SUDEP models, calcium signal recording, and electrocardiogram monitoring. We then detail measurement of tyrosine hydroxylase content and activity, ß1 and p-ß1-AR content, and destruction of LCNE neurons. For complete details on the use and execution of this protocol, please refer to Lian et al.1.

The States of Different 5-HT Receptors Located in the Dorsal Raphe Nucleus Are Crucial for Regulating the Awakening During General Anesthesia.

General anesthesia is widely used in various clinical practices due to its ability to cause loss of consciousness. However, the exact mechanism of anesthesia-induced unconsciousness remains unclear. It is generally thought that arousal-related brain nuclei are involved. 5-Hydroxytryptamine (5-HT) is closely associated with sleep arousal. Here, we explore the role of the 5-HT system in anesthetic awakening through pharmacological interventions and optogenetic techniques. Our data showed that exogenous administration of 5-hydroxytryptophan (5-HTP) and optogenetic activation of 5-HT neurons in the dorsal raphe nucleus (DR) could significantly shorten the emergence time of sevoflurane anesthesia in mice, suggesting that regulation of the 5-HT system using both endogenous and exogenous approaches could mediate delayed emergence. In addition, we first discovered that the different 5-HT receptors located in the DR, known as 5-HT autoreceptors, are essential for the regulation of general anesthetic awakening, with 5-HT1A and 5-HT2A/C receptors playing a regulatory role. These results can provide a reliable theoretical basis as well as potential targets for clinical intervention to prevent delayed emergence and some postoperative risks.