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To achieve rapid and accurate identification of valuable wood products, an analytical method was developed by combining electric soldering iron cauterization with soft ionization by chemical reaction in transfer-mass spectrometry (SICRIT-MS). SICRIT is a flow-through dielectric barrier discharge ionization technique pioneered by Zenobi et al. in 2016. The electric soldering iron cauterization-SICRIT-MS method requires no sample pretreatment, easy operation and a single analysis in less than 5 s, meeting the demands of rapid analysis. Operating parameters for the soldering iron and SICRIT ion source were optimized to achieve maximum total ion current intensity under soldering iron temperature of 450 ℃, ion source AC voltage amplitude of 2 000 V, and sample transfer line temperature of 150 ℃. With the optimized parameters, the SICRIT-MS method was applied to analyze valuable wood samples, including 29 certified standard wood samples and 6 online-purchased real samples, resulting in a dataset of 210 sets of mass spectral fingerprint data. Based on the mass spectral fingerprint data acquired under positive ion mode, a predictive model was trained using the random forest algorithm. The random forest model underwent optimization for the number of decision trees, max feature algorithm, and feature selection criteria, was evaluated through out-of-bag and 10-fold cross-validation. The results showed the error rates of out-of-bag and 10-fold cross-validation are 4.76% and 4.74%, respectively. The established random forest model can accurately distinguish wood samples from the genera Dalbergia, Guibourtia, and Pterocarpus with a classification accuracy of larger than 95%. The importance of features in distinguishing the three wood genera was investigated through binary classification modeling, revealing features 269.1, 270.1, 255.1, 159.0, 182.1, 102.1 and 83.1 as crucial in classification. These features may correspond to characteristic compounds in different wood species or differences in the content of the same compound across species. The predictive model was successfully applied to rapid identification of genera in valuable wood products sold online. Three purchased Guibourtia samples are confirmed as authentic, while the other three are not identified as the claimed genera. This method provides a scientific basis and experimental reference for authenticity identification and quality evaluation.

IntroductionThe visual stimulus-specific responses in the primary visual cortex (V1) undergo plastic changes after associative learning. During the learning process, neuronal ensembles, defined as groups of coactive neurons, are well known to be related to learning and memory. However, it remains unclear what effect learning has on ensembles, and which neuronal subgroups within those ensembles play a key role in associative learning.MethodsWe used two-photon calcium imaging in mice to record the activity of V1 neurons before and after fear conditioning associated with a visual cue (blue light). We first defined neuronal ensembles by thresholding their functional connectivity in response to blue (conditioned) or green (control) light. We defined neurons that existed both before and after conditioning as stable neurons. Neurons which were recruited after conditioning were defined as new neurons. The graph theory-based analysis was performed to quantify the changes in connectivity within ensembles after conditioning.ResultsA significant enhancement in the connectivity strength (the average correlation with other neurons) was observed in the blue ensembles after conditioning. We found that stable neurons within the blue ensembles showed a significantly smaller clustering coefficient (the value represented the degree of interconnectedness among a node's neighbors) after conditioning than they were before conditioning. Additionally, new neurons within the blue ensembles had a larger clustering coefficient, similar relative degree (the value represented the number of functional connections between neurons) and connectivity strength compared to stable neurons in the same ensembles.DiscussionOverall, our results demonstrated that the plastic changes caused by conditioning occurred in subgroups of neurons in the ensembles. Moreover, new neurons from conditioned ensembles may play a crucial role in memory formation, as they exhibited not only similar connection competence in relative degree and connectivity strength as stable neurons, but also showed a significantly larger clustering coefficient compared to the stable neurons within the same ensembles after conditioning.

Background: The treatment of bone defects remains a clinical challenge. The effect of negative pressure wound therapy (NPWT) on osteogenesis in bone defects has been recognized; however, bone marrow fluid dynamics under negative pressure (NP) remain unknown. In this study, we aimed to examine the marrow fluid mechanics within trabeculae by computational fluid dynamics (CFD), and to verify osteogenic gene expression, osteogenic differentiation to investigate the osteogenic depth under NP.Methods: The human femoral head is scanned using micro-CT to segment the volume of interest (VOI) trabeculae. The VOI trabeculae CFD model simulating the bone marrow cavity is developed by combining the Hypermesh and ANSYS software. The effect of trabecular anisotropy is investigated, and bone regeneration effects are simulated under NP scales of −80, −120, −160, and −200 mmHg. The working distance (WD) is proposed to describe the suction depth of the NP. Finally, gene sequence analysis, cytological experiments including bone mesenchymal stem cells (BMSCs) proliferation and osteogenic differentiation are conducted after the BMSCs are cultured under the same NP scale.Results: The pressure, shear stress on trabeculae, and marrow fluid velocity decrease exponentially with an increase in WD. The hydromechanics of fluid at any WD inside the marrow cavity can be theoretically quantified. The NP scale significantly affects the fluid properties, especially those fluid close to the NP source; however, the effect of the NP scale become marginal as WD deepens. Anisotropy of trabecular structure coupled with the anisotropic hydrodynamic behavior of bone marrow; An NP of −120 mmHg demonstrates the majority of bone formation-related genes, as well as the most effective proliferation and osteogenic differentiation of BMSCs compared to the other NP scales.Conclusion: An NP of −120 mmHg may have the optimal activated ability to promote osteogenesis, but the effective WD may be limited to a certain depth. These findings help improve the understanding of fluid mechanisms behind NPWT in treating bone defects.

Stroke-associated pneumonia (SAP) is a common cause of disability or death. Although the researches on SAP have been relatively mature, the method that can predict SAP with great accuracy has not yet been determined. It is necessary to discover new predictors to construct a more accurate predictive model for SAP. We continuously collected 2,366 patients with acute ischemic stroke, and then divided them into the SAP group and non-SAP group. Data were recorded at admission. Through univariate analyses and multivariate regression analyses of the data, the new predictive factors and the predictive model of SAP were determined. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were used to measure their predictive accuracy. Of the 2,366 patients, 459 were diagnosed with SAP. International normalized ratio (INR) (odds ratio = 37.981; 95% confidence interval, 7.487–192.665; P < 0.001), age and dysphagia were independent risk factors of SAP. However, walking ability within 48 h of admission (WA) (odds ratio = 0.395; 95% confidence interval, 0.287–0.543; P < 0.001) was a protective factor of SAP. Different predictors and the predictive model all could predict SAP (P < 0.001). The predictive power of the model (AUC: 0.851) which included age, homocysteine, INR, history of chronic obstructive pulmonary disease (COPD), dysphagia, and WA was greater than that of age (AUC: 0.738) and INR (AUC: 0.685). Finally, we found that a higher INR and no WA could predict SAP in patients with acute ischemic stroke. In addition, we designed a simple and practical predictive model for SAP, which showed relatively good accuracy. These findings might help identify high-risk patients with SAP and provide a reference for the timely use of preventive antibiotics.

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and "left-behind" and "single-parenting" children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system. [ABSTRACT FROM AUTHOR]