Application of propofol-remifentanil intravenous general anesthesia combined with regional block in pediatric ophthalmic surgery.

OBJECTIVE: The aim of this study is to observe the anesthetic effect and safety of intravenous anesthesia without muscle relaxant with propofol-remifentanil combined with regional block under laryngeal mask airway in pediatric ophthalmologic surgery. METHODS: A total of 90 undergoing ophthalmic surgery were anesthetized with general anesthesia using the laryngeal mask airway without muscle relaxant. They were randomly divided into two groups: 45 children who received propofol-remifentanil intravenous anesthesia combined with regional block (LG group), and 45 children who received total intravenous anesthesia (G group). The peri-operative circulatory indicators, awakening time after general anesthesia, postoperative analgesic effect and the incidence of anesthesia-related adverse events were respectively compared between the two groups. RESULTS: All the children successfully underwent the surgical procedure. The awakening time after general anesthesia and removal time of laryngeal mask were significantly shorter in the LG group than in the G group (P < 0.05). There was no statistically significant difference in the heart rates in the perioperative period between the two groups (P > 0.05). There was no statistically significant difference in the incidence of intraoperative physical response, respiratory depression, postoperative nausea and vomiting (PONV) and emergence agitation (EA) between the two groups (P > 0.05). The pain score at the postoperative hour 2 was lower in the LG group than in the G group (P < 0.05). CONCLUSION: Propofol-remifentanil intravenous anesthesia combined with long-acting local anesthetic regional block anesthesia, combined with laryngeal mask ventilation technology without muscle relaxants, can be safely used in pediatric eye surgery to achieve rapid and smooth recovery from general anesthesia and better postoperative analgesia. This anesthesia scheme can improve the comfort and safety of children in perioperative period, and has a certain clinical popularization value.

Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association.

In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43- ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32- ν1- PO43- ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.

Use of Bilateral Rectus Sheath Blocks in Open Umbilical Hernia Repair.

BACKGROUND: In recent years, the rectus sheath block (RSB) has become increasingly prevalent in laparoscopic surgery. However, there is currently no definitive research on its use in the open repair of umbilical hernias with cirrhotic ascites. OBJECTIVE: In this study, we assessed the safety and clinical efficacy of ultrasound-guided (US-guided) bilateral RSBs in open umbilical hernia repair for patients diagnosed with cirrhotic ascites. STUDY DESIGN: Seventy-two patients diagnosed with umbilical hernias that presented with cirrhotic ascites and who were admitted to our hospital were randomly divided into 2 groups. These categories were labeled the RSB group (Group R) and the local infiltration group (Group L); we used US-guided RSBs in Group R and local infiltration in Group L. SETTING: The clinical outcomes of the patients in each group were compared to one another. Heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were recorded at various time points in both groups. METHODS: Measurements of the patients' outcomes were taken before anesthesia (T0), at the beginning of surgery (T1), at the time of the separation of the hernia sac (T2), at the end of surgery (T3), 6 hours postoperatively (T4), and 24 hours postoperatively (T5). On the Visual Analog Scale (VAS), pain scores at rest (T1-T3) and during activity (T4-T5) were recorded, as were the incidence of perioperative remedial analgesia and adverse effects. RESULTS: Compared to T0, both groups' HR was significantly higher at T1-T3 (P < 0.05). The SBP and DBP were also significantly higher (P < 0.05). At T1-T3, the HR of Group R was significantly slower than that of Group L (P < 0.05), and at T4-T5, the VAS score for activity in Group R was significantly lower than that of Group L (P < 0.05). Group R had a significantly lower incidence of intraoperative remedial analgesia and postoperative nausea and vomiting than did Group L (P < 0.05). Neither group required postoperative remedial analgesia, and no patient experienced adverse reactions during the perioperative period. LIMITATIONS: This study has limitations in its sample size, lack of blood ammonia levels, and absence of data on patient satisfaction, necessitating future studies to address these issues. CONCLUSION: US-guided RSBs are an efficient method of anesthesia for open umbilical hernia repair in patients diagnosed with cirrhosis. This technique not only provides precise anesthesia and appropriate analgesia but also results in a low incidence of postoperative nausea and vomiting.

Use of Raman spectroscopy to study rat lung tissues for distinguishing asphyxia from sudden cardiac death.

Determining asphyxia as the cause of death is crucial but is based on an exclusive strategy because it lacks sensitive and specific morphological characteristics in forensic practice. In some cases where the deceased has underlying heart disease, differentiation between asphyxia and sudden cardiac death (SCD) as the primary cause of death can be challenging. Herein, Raman spectroscopy was employed to detect pulmonary biochemical differences to discriminate asphyxia from SCD in rat models. Thirty-two rats were used to build asphyxia and SCD models, with lung samples collected immediately or 24 h after death. Twenty Raman spectra were collected for each lung sample, and 640 spectra were obtained for further data preprocessing and analysis. The results showed that different biochemical alterations existed in the lung tissues of the rats that died from asphyxia and SCD and could be used to distinguish between the two causes of death. Moreover, we screened and used 8 of the 11 main differential spectral features that maintained their significant differences at 24 h after death to successfully determine the cause of death, even with decomposition and autolysis. Eventually, seven prevalent machine learning classification algorithms were employed to establish classification models, among which the support vector machine exhibited the best performance, with an area under the curve value of 0.9851 in external validation. This study shows the promise of Raman spectroscopy combined with machine learning algorithms to investigate differential biochemical alterations originating from different deaths to aid determining the cause of death in forensic practice.

Combined ATAC-seq, RNA-seq, and GWAS analysis reveals glycogen metabolism regulatory network in Jinjiang oyster ( Crassostrea ariakensis).

Glycogen serves as the principal energy reserve for metabolic processes in aquatic shellfish and substantially contributes to the flavor and quality of oysters. The Jinjiang oyster ( Crassostrea ariakensis) is an economically and ecologically important species in China. In the present study, RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) were performed to investigate gene expression and chromatin accessibility variations in oysters with different glycogen contents. Analysis identified 9 483 differentially expressed genes (DEGs) and 7 215 genes with significantly differential chromatin accessibility (DCAGs) were obtained, with an overlap of 2 600 genes between them. Notably, a significant proportion of these genes were enriched in pathways related to glycogen metabolism, including "Glycogen metabolic process" and "Starch and sucrose metabolism". In addition, genome-wide association study (GWAS) identified 526 single nucleotide polymorphism (SNP) loci associated with glycogen content. These loci corresponded to 241 genes, 63 of which were categorized as both DEGs and DCAGs. This study enriches basic research data and provides insights into the molecular mechanisms underlying the regulation of glycogen metabolism in C. ariakensis.

Front-line employees' perceived COVID-19 event strength and emotional labor in the service industry: A moderated mediation model.

COVID-19 pandemic has brought enormous challenges to employees worldwide, and thus, it is important to understand whether, how, and when perceived COVID-19 event strength can influence employees' work-related outcomes. Drawing on event system theory and affective events theory, this study examined the effect of perceived COVID-19 event strength on front-line service employees' emotional labor, namely, surface acting and deep acting, through the mediating role of anxiety. In addition, it explored job insecurity as a moderator in the relationship between perceived COVID-19 event strength and anxiety. This study analyzed two-wave data (N = 191) collected from front-line employees in the service industry and found that anxiety mediated the relationship between perceived COVID-19 event strength and surface acting but not deep acting, and that job insecurity moderated the relationship between perceived COVID-19 event strength and anxiety and the indirect effect of perceived COVID-19 event strength on surface acting but not on deep acting via anxiety. Implications and directions for future research are also discussed.

Prediction of distinct populations of innate lymphoid cells by transcriptional profiles.

Innate lymphoid cells (ILCs) are a unique type of lymphocyte that differ from adaptive lymphocytes in that they lack antigen receptors, which primarily reside in tissues and are closely associated with fibers. Despite their plasticity and heterogeneity, identifying ILCs in peripheral blood can be difficult due to their small numbers. Accurately and rapidly identifying ILCs is critical for studying homeostasis and inflammation. To address this challenge, we collect single-cell RNA-seq data from 647 patients, including 26,087 transcripts. Background screening, Lasso analysis, and principal component analysis (PCA) are used to select features. Finally, we employ a deep neural network to classify lymphocytes. Our method achieved the highest accuracy compared to other approaches. Furthermore, we identified four genes that play a vital role in lymphocyte development. Adding these gene transcripts into model, we were able to increase the model's AUC. In summary, our study demonstrates the effectiveness of using single-cell transcriptomic analysis combined with machine learning techniques to accurately identify congenital lymphoid cells and advance our understanding of their development and function in the body.

Comparative transcriptome and antioxidant biomarker response reveal molecular mechanisms to cope with zinc ion exposure in the unicellular eukaryote Paramecium.

The development of industry has resulted in excessive environmental zinc exposure which has caused various health problems in a wide range of organisms including humans. The mechanisms by which aquatic microorganisms respond to environmental zinc stress are still poorly understood. Paramecium, a well-known ciliated protozoan and a popular cell model in heavy metal stress response studies, was chosen as the test unicellular eukaryotic organism in the present research. In this work, Paramecium cf. multimicronucleatum cells were exposed in different levels of zinc ion (0.1 and 1.0 mg/L) for different periods of exposure (1 and 4 days), and then analyzed population growth, transcriptomic profiles and physiological changes in antioxidant enzymes to explore the toxicity and detoxification mechanisms during the zinc stress response. Results demonstrated that long-term zinc exposure could have restrained population growth in ciliates, however, the response mechanism to zinc exposure in ciliates is likely to show a dosage-dependent and time-dependent manner. The differentially expressed genes (DEGs) were identified the characters by high-throughput sequencing, which remarkably enriched in the phagosome, indicating that the phagosome pathway might mediate the uptake of zinc, while the pathways of ABC transporters and Na+/K+-transporting ATPase contributed to the efflux transport of excessive zinc ions and the maintenance of osmotic balance, respectively. The accumulation of zinc ions triggered a series of adverse effects, including damage to DNA and proteins, disturbance of mitochondrial function, and oxidative stress. In addition, we found that gene expression changed significantly for metal ion binding, energy metabolism, and oxidation-reduction processes. RT-qPCR of ten genes involved in important biological functions further validated the results of the transcriptome analysis. We also continuously monitored changes in activity of four antioxidant enzymes (SOD, CAT, POD and GSH-PX), all of which peaked on day 4 in cells subjected to zinc stress. Collectively, our results indicate that excessive environmental zinc exposure initially causes damage to cellular structure and function and then initiates detoxification mechanisms to maintain homeostasis in P. cf. multimicronucleatum cells.

Single-cell transcriptome reveals cell division-regulated hub genes in the unicellular eukaryote Paramecium.

The transition from growth to division during the cell cycle encompasses numerous conserved processes such as large-scale DNA replication and protein synthesis. In ciliate cells, asexual cell division is accompanied by additional cellular changes including amitotic nuclear division, extensive ciliogenesis, and trichocyst replication. However, the molecular mechanisms underlying these processes remain elusive. In this study, we present single-cell gene expression profiles of Paramecium cf. multimicronucleatum cells undergoing cell division. Our results reveal that the most up-regulated genes in dividing cells compared to growing cells are associated with 1) cell cycle signaling pathways including transcription, DNA replication, chromosome segregation and protein degradation; 2) microtubule proteins and tubulin glycylases which are essential for ciliogenesis, nuclei separation and structural differentiation signaling; and 3) trichocyst matrix proteins involved in trichocyst synthesis and reproduction. Furthermore, weighted gene co-expression network analysis identified hub genes that may play crucial roles during cell division. Our findings provide insights into cell cycle regulators, microtubules and trichocyst matrix proteins that may exert influence on this process in ciliates.

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study.

Background: Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors. Objective: The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies. Methods: For this cross-sectional study, 733 patients with hypertension (aged 30-85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram. Results: Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers. Conclusion: The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.

Dissecting the microbial community structure of internal organs during the early postmortem period in a murine corpse model.

BACKGROUND: Microorganisms distribute and proliferate both inside and outside the body, which are the main mediators of decomposition after death. However, limited information is available on the postmortem microbiota changes of extraintestinal body sites in the early decomposition stage of mammalian corpses. RESULTS: This study investigated microbial composition variations among different organs and the relationship between microbial communities and time since death over 1 day of decomposition in male C57BL/6 J mice by 16S rRNA sequencing. During 1 day of decomposition, Agrobacterium, Prevotella, Bacillus, and Turicibacter were regarded as time-relevant genera in internal organs at different timepoints. Pathways associated with lipid, amino acid, carbohydrate and terpenoid and polyketide metabolism were significantly enriched at 8 h than that at 0.5 or 4 h. The microbiome compositions and postmortem metabolic pathways differed by time since death, and more importantly, these alterations were organ specific. CONCLUSION: The dominant microbes differed by organ, while they tended toward similarity as decomposition progressed. The observed thanatomicrobiome variation by body site provides new knowledge into decomposition ecology and forensic microbiology. Additionally, the microbes detected at 0.5 h in internal organs may inform a new direction for organ transplantation.

A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal.

Sentiment analysis aims to mine polarity features in the text, which can empower intelligent terminals to recognize opinions and further enhance interaction capabilities with customers. Considerable progress has been made using recurrent neural networks or pre-trained models to learn semantic representations. However, recently published models with complex structures require increasing computational resources to reach state-of-the-art (SOTA) performance. It is still a significant challenge to deploy these models to run on micro-intelligent terminals with limited computing power and memory. This paper proposes a lightweight and efficient framework based on hybrid multi-grained embedding on sentiment analysis (MC-GGRU). The gated recurrent unit model is designed to incorporate a global attention structure that allows contextual representations to be learned from unstructured text using word tokens. In addition, a multi-grained feature layer can further enrich sentence representation features with implicit semantics from characters. Through hybrid multi-grained representation, MC-GGRU achieves high inference performance with a shallow structure. The experimental results of five public datasets show that our method achieves SOTA for sentiment classification with a trade-off between accuracy and speed.

Accurate identification of traumatic lung injury (TLI) by ATR-FTIR spectroscopy combined with chemometrics.

Traumatic lung injury (TLI), which is a common mechanical injury, is receiving increasing attention because of its serious hazards. In forensic practices, accurately identifying TLI is of great importance for investigations and case trials. The main goal of this research was to identify TLI utilizing attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics. The macroscopic appearance of lung tissue showed that identifying TLI in lung tissue at the decomposition stage is not feasible by only visualization, and significant pulmonary hypostasis was observed in the lungs regardless of whether the lung tissue was injured. Average spectra and principal component analysis (PCA) suggested that the biochemical difference between injured lung tissue samples from the TLI group and noninjured lung tissue samples from the negative control group was mainly attributed to the different structures and contents of proteins. Partial least squares discriminant analysis (PLS-DA) was then utilized to identify TLI with an accuracy of 96.4% and 98.6% based on the training set and the test set, respectively. Next, we focused on samples that were misclassified in the model and proposed that the misclassification could be caused by the pulmonary hypostasis effect. Therefore, two additional PCA and PLS-DA models were created to identify the pulmonary hypostatic areas between the TLI group and the negative control group and the nonpulmonary hypostatic areas between the TLI group and the negative control group. The PCA results indicated that the biochemical difference between the two groups was still associated with proteins, and the two PLS-DA models achieved 100% accuracy based on both the training and test sets. This result indicated that when pulmonary hypostasis was considered and the lung tissue was divided into pulmonary hypostatic areas and nonpulmonary hypostatic areas for separate comparisons, TLI identification was achieved with a greater accuracy than that obtained when the two areas were combined. This research confirms that the combined application of ATR-FTIR spectroscopy and chemometrics can be utilized to accurately identify TLI.

BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference.

The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.

Distinguishing Asphyxia from Sudden Cardiac Death as the Cause of Death from the Lung Tissues of Rats and Humans Using Fourier Transform Infrared Spectroscopy.

The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.

An Emerging Strategy for Muscle Evanescent Trauma Discrimination by Spectroscopy and Chemometrics.

Trauma is one of the most common conditions in the biomedical field. It is important to identify it quickly and accurately. However, when evanescent trauma occurs, it presents a great challenge to professionals. There are few reports on the establishment of a rapid and accurate trauma identification and prediction model. In this study, Fourier transform infrared spectroscopy (FTIR) and microscopic spectroscopy (micro-IR) combined with chemometrics were used to establish prediction models for the rapid identification of muscle trauma in humans and rats. The results of the average spectrum, principal component analysis (PCA) and loading maps showed that the differences between the rat muscle trauma group and the rat control group were mainly related to biological macromolecules, such as proteins, nucleic acids and carbohydrates. The differences between the human muscle trauma group and the human control group were mainly related to proteins, polysaccharides, phospholipids and phosphates. Then, a partial least squares discriminant analysis (PLS-DA) was used to evaluate the classification ability of the training and test datasets. The classification accuracies were 99.10% and 93.69%, respectively. Moreover, a trauma classification and recognition model of human muscle tissue was constructed, and a good classification effect was obtained. The classification accuracies were 99.52% and 91.95%. In conclusion, spectroscopy and stoichiometry have the advantages of being rapid, accurate and objective and of having high resolution and a strong recognition ability, and they are emerging strategies for the identification of evanescent trauma. In addition, the combination of spectroscopy and stoichiometry has great potential in the application of medicine and criminal law under practical conditions.

Chronological Age Estimation of Male Occipital Bone Based on FTIR and Raman Microspectroscopy.

Age-related changes in bone tissue have always been an important part of bone research, and age estimation is also of great significance in forensic work. In our study, FTIR and Raman microspectroscopy were combined to explore the structural and chronological age-related changes in the occipital bones of 40 male donors. The FTIR micro-ATR mode not only achieves the comparison of FTIR and Raman efficiency but also provides a new pattern for the joint detection of FTIR and Raman in hard tissue. Statistical analysis and PCA results revealed that the structure had little effect on the FTIR and Raman results. The FTIR and Raman mineral/matrix ratio, carbonate/phosphate ratio, crystallinity, and collagen maturity of the whole showed an increasing trend during maturation, and a significant correlation was found between FTIR and Raman by comparing four outcomes. Furthermore, the results indicated that the cutoff point of the change in the relative proportion of organic matrix and inorganic minerals in males was between 19 and 35 years old, and the changes in the relative proportion of organic matrix and inorganic minerals may play a key role in age estimation. Ultimately, we established age estimation regression models. The FTIR GA-PLS regression model has the best performance and is more suitable for our experiment (RMSECV = 10.405, RMSEP = 9.2654, R 2CV = 0.814, and R 2Pred = 0.828). Overall, FTIR and Raman combined with chemometrics are an ideal method to estimate chronological age based on age-dependent component changes in male occipital bones. Our experiment provides a proof of concept and potential experimental method for chronological age estimation.

Impact of Demographic Characteristics on Workplace Friendship: A Meta-Analytic Review.

Although demographic characteristics are the most analyzed variables in the literature on workplace friendship, their effects on its formation and development remain unclear, owing to conflicting results. To draw generalizable conclusions, this study employed meta-analytical techniques to examine the relationships between employees' demographic characteristics and their workplace friendship, and investigated whether collectivism and year of data collection moderated this relationship. Seventy-seven empirical pieces of literature and 219 correlations (N = 34,856) were included in the final meta-analysis. Our findings revealed that male employees acquired more workplace friendships than female employees, and that organizational tenure, hierarchical position, and education were positively related to workplace friendships. However, employees' age and marital status had no significant effect on workplace friendships. Furthermore, the results of the moderating effects analysis suggested that, as collectivism diminished and data collection was delayed, the impact of certain demographic characteristics on workplace friendships became weaker.

Removing miscellaneous heavy metals by all-in-one ion exchange-nanofiltration membrane.

The composition of wastewater containing heavy metal mixtures is often complex and poses a serious threat to human and environmental health. Effective removal of a variety of heavy metal ions with a single technology is challenging, and the conventional split integrated technologies require multi-step processing and a massive footprint. For the first time, we achieve hierarchically integrating ion exchange and nanofiltration into all-in-one "iNF" membranes. The iNF membrane has a hierarchical structure with an interfacial polymerization layer and an ion exchange layer, which can achieve highly efficient indiscriminate heavy metal ion removal, overcoming the defect that traditional nanofiltration membranes can only remove single metal cations or oxyanions. The ion exchange layer can remove heavy metal ions through sulfonic acid groups and quaternary amine groups. In addition, the ion exchange layer can be regenerated by electro-deionization, which is meaningful for sustainable membrane usage. This facile, scalable, and compact integrated process shows outstanding potential and universal applicability in complex wastewater treatment.