A New High-Performance Porous Carbon-Coated Mn3O4/Na2CO3 Cathode for Suppressing Mn 2+ Dissolution in Aqueous Zinc Ion Batteries.

Manganous-manganic oxide (Mn3O4), akin to other manganese-based oxides, faces several critical challenges such as substantial capacity fading and limited rate performance due to its inferior electrical conductivity, in addition to the inevitable dissociation of Mn 2+ . To address these issues, we introduce for the first time a novel carbon-coated Mn3O4/Na2CO3 (Mn3O4/Na2CO3/C) composite material. Comprehensive characterizations indicate that Na2CO3 effectively curtails Mn 2+ dissolution, enhances carbon encapsulation throughout charging/discharging cycles, and exposes additional active sites on the Mn3O4/Na2CO3/C composite. Electrochemical assessments confirm that the Mn3O4/Na2CO3/C-2 cathode exhibits exceptional electrochemical performance, outperforming other cathodes in the ZnSO4 system. Moreover, the Mn3O4/Na2CO3/C-2 cathode delivers a high specific capacity of ~550 mAh g -1 at 0.1 A g -1 and maintains a significant capacity of ~230 mAh g -1 after 360 cycles at 1.0 A g -1 within the 2.0 M ZnSO4+0.2 M MnSO4 electrolyte system, demonstrating its potential as a high-performance cathode material for aqueous zinc-ion batteries.

Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences.

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Transdermal delivery of elastin peptide assisted by betaine-based deep eutectic solvent to ameliorate skin photoaging.

The unique amino acid composition of elastin peptide (EP) makes it an excellent resource to obtain antioxidant peptides. It exhibits high elastase inhibitory activity with the potential to resist skin aging and is currently used in a many cosmetic products. However, the inherent low permeability of the skin limits its ability to penetrate the skin. To address this issue, a deep eutectic solvent (SAB) with excellent bioactivity was synthesized from betaine and succinic acid and used as a permeation enhancer to improve the absorption and utilization of EP in this paper. The results showed that low SAB concentrations significantly increased the transdermal delivery of EP. The 3D epidermal skin model (EpiKutis®) demonstrated that SAB/EP induced the synthesis of hyaluronic acid (HA) and filaggrin (FLG), accelerated skin barrier repair, and reduced water loss. Additionally, the zebrafish embryonic model showed that SAB/EP could reduce melanin secretion, decrease melanin deposition, and have an ameliorative effect on skin photoaging. Cellular experiments proved that SAB/EP can stimulate human skin fibroblasts to secrete procollagen I and elastin, improving skin elasticity and anti-wrinkle. The combination of EP and DES is a new attempt that is expected to be used as a safe and effective anti-wrinkle cosmetic material.

A bibliometric analysis of endoplasmic reticulum stress and atherosclerosis.

The mechanisms underlying the occurrence and development of atherosclerosis (AS) are diverse, among which endoplasmic reticulum stress (ERS) is an important mechanism that should not be overlooked. However, up to now, there has been no bibliometric study on the relationship between ERS and AS. To understand the research progress in ERS and AS, this paper conducted a statistical analysis of publications in this field using bibliometrics. A total of 1,035 records were retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, and the R package "bibliometric" were used to analyze the spatiotemporal distribution, countries, authors, institutions, journals, references, and keywords of the literature, and to present the basic information of this field through visualized maps, as well as determine the collaboration relationships among researchers in this field. This field has gradually developed and stabilized over the past 20 years. The current research hotspots in this field mainly include the relationship between ERS and AS-related cells, the mechanisms by which ERS promotes AS, related diseases, and associated cytokines, etc. Vascular calcification, endothelial dysfunction, NLRP3 inflammasome, and heart failure represent the frontier research in this field and are becoming new research hotspots. It is hoped that this study will provide new insights for research and clinical work in the field of ERS and AS.

RA-Net: reverse attention for generalizing residual learning.

Since residual learning was proposed, identity mapping has been widely utilized in various neural networks. The method enables information transfer without any attenuation, which plays a significant role in training deeper networks. However, interference with unhindered transmission also affects the network's performance. Accordingly, we propose a generalized residual learning architecture called reverse attention (RA), which applies high-level semantic features to supervise low-level information in the identity mapping branch. It means that higher semantic features selectively transmit low-level information to deeper layers. In addition, we propose a Modified Global Response Normalization(M-GRN) to implement reverse attention. RA-Net is derived by embedding M-GRN in the residual learning framework. The experiments show that the RA-Net brings significant improvements over residual networks on typical computer vision tasks. For classification on ImageNet-1K, compared with resnet101, RA-Net improves the Top-1 accuracy by 1.7% with comparable parameters and computational cost. For COCO detection, on Faster R-CNN, reverse attention improves box AP by 1.9%. Meanwhile, reverse attention improves UpperNet's mIoU by 0.7% on ADE20K segmentation.

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.

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.

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.

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.