Line-Based 6-DoF Object Pose Estimation and Tracking With an Event Camera.

Pose estimation and tracking of objects is a fundamental application in 3D vision. Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur, which enables them to address challenging high dynamic range scenes or high-speed motion. These features make event cameras an ideal complement over standard cameras for object pose estimation. In this work, we propose a line-based robust pose estimation and tracking method for planar or non-planar objects using an event camera. Firstly, we extract object lines directly from events, then provide an initial pose using a globally-optimal Branch-and-Bound approach, where 2D-3D line correspondences are not known in advance. Subsequently, we utilize event-line matching to establish correspondences between 2D events and 3D models. Furthermore, object poses are refined and continuously tracked by minimizing event-line distances. Events are assigned different weights based on these distances, employing robust estimation algorithms. To evaluate the precision of the proposed methods in object pose estimation and tracking, we have devised and established an event-based moving object dataset. Compared against state-of-the-art methods, the robustness and accuracy of our methods have been validated both on synthetic experiments and the proposed dataset. The source code is available at https://github.com/Zibin6/LOPET.

Motion measurements of explosive shock waves based on an event camera.

Shock wave measurement is vital in assessing explosive power and designing warheads. To obtain satisfactory observation data of explosive shock waves, it is preferable for optical sensors to possess high-dynamic range and high-time resolution capabilities. In this paper, the event camera is first employed to observe explosive shock waves, leveraging its high dynamic range and low latency. A comprehensive procedure is devised to measure the motion parameters of shock waves accurately. Firstly, the plane lines-based calibration method is proposed to compute the calibration parameters of the event camera, which utilizes the edge-sensitive characteristic of the event camera. Then, the fitted ellipse parameters of the shock wave are estimated based on the concise event data, which are gained by utilizing the characteristics of the event triggering and shock waves' morphology. Finally, the geometric relationship between the ellipse parameters and the radius of the shock wave is derived, and the motion parameters of the shock wave are estimated. To verify the performance of our method, we compare our measurement results in the TNT explosion test with the pressure sensor results and empirical formula prediction. The relative measurement error compared to pressure sensors is the lowest at 0.33% and the highest at 7.58%. The experimental results verify the rationality and effectiveness of our methods.

Enhancing Vault Prediction and ICL Sizing Through Advanced Machine Learning Models.

PURPOSE: To use artificial intelligence (AI) technology to accurately predict vault and Implantable Collamer Lens (ICL) size. METHODS: The methodology focused on enhancing predictive capabilities through the fusion of machine-learning algorithms. Specifically, AdaBoost, Random Forest, Decision Tree, Support Vector Regression, LightGBM, and XGBoost were integrated into a majority-vote model. The performance of each model was evaluated using appropriate metrics such as accuracy, precision, F1-score, and area under the curve (AUC). RESULTS: The majority-vote model exhibited the highest performance among the classification models, with an accuracy of 81.9% area under the curve (AUC) of 0.807. Notably, LightGBM (accuracy = 0.788, AUC = 0.803) and XGBoost (ACC = 0.790, AUC = 0.801) demonstrated competitive results. For the ICL size prediction, the Random Forest model achieved an impressive accuracy of 85.3% (AUC = 0.973), whereas XG-Boost (accuracy = 0.834, AUC = 0.961) and LightGBM (accuracy = 0.816, AUC = 0.961) maintained their compatibility. CONCLUSIONS: This study highlights the potential of diverse machine learning algorithms to enhance postoperative vault and ICL size prediction, ultimately contributing to the safety of ICL implantation procedures. Furthermore, the introduction of the novel majority-vote model demonstrates its capability to combine the advantages of multiple models, yielding superior accuracy. Importantly, this study will empower ophthalmologists to use a precise tool for vault prediction, facilitating informed ICL size selection in clinical practice. [J Refract Surg. 2024;40(3):e126-e132.].

An expedited model for identifying potential patients with periodic leg movements.

Periodic leg movements during sleep (PLMS) may have crucial consequences in adults. This study aimed to identify baseline characteristics, symptoms, or questionnaires that could help to identify sleep-disordered breathing patients with significant PLMS. Patients aged 20-80 years who underwent polysomnography for assessing sleep disturbance were included. Various factors such as sex, age, body measurements, symptoms, apnea-hypopnea index (AHI), and sleep quality scales were analysed to determine the presence of PLMS. The study included 1480 patients with a mean age of 46.4 ± 13.4 years, among whom 110 (7.4%) had significant PLMS with a PLM index of 15 or higher. There were no significant differences observed in terms of sex or BMI between patients with and without significant PLMS. However, the odds ratios (OR) for PLMS were 4.33, 4.41, and 4.23 in patients who were aged over 50 years, had insomnia, or had an ESS score of less than 10, respectively. Notably, the OR increased up to 67.89 times in patients who presented with all three risk factors. Our analysis identified significant risk factors for PLMS: age over 50, self-reported insomnia, and lower daytime sleepiness levels. These findings aid in identifying potential PLMS patients, facilitating confirmatory examinations and managing associated comorbidities.

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. CITATION: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

C. elegans spermatocyte divisions show a weak spindle checkpoint response.

Male meiotic division exhibits two consecutive chromosome separation events without apparent pausing. Several studies have shown that spermatocyte divisions are not stringently regulated as in mitotic cells. In this study, we investigated the role of the canonical spindle assembly (SAC) pathway in Caenorhabditis elegans spermatogenesis. We found the intensity of chromosome-associated outer kinetochore protein BUB-1 and SAC effector MDF-1 oscillates between the two divisions. However, the SAC target securin is degraded during the first division and remains undetectable for the second division. Inhibition of proteasome-dependent protein degradation did not affect the progression of the second division but stopped the first division at metaphase. Perturbation of spindle integrity did not affect the duration of meiosis II, and only slightly lengthened meiosis I. Our results demonstrate that male meiosis II is independent of SAC regulation, and male meiosis I exhibits only weak checkpoint response.

Predictability comparison of sizing parameters for postoperative vault after implantable Collamer lens implantation.

PURPOSE: This study aims to assess the accuracy of three parameters (white-to-white distance [WTW], angle-to-angle [ATA], and sulcus-to-sulcus [STS]) in predicting postoperative vault and to formulate an optimized predictive model. METHODS: In this retrospective study, a cohort of 465 patients (comprising 769 eyes) who underwent the implantation of the V4c implantable Collamer lens with a central port (ICL) for myopia correction was examined. Least absolute shrinkage and selection operator (LASSO) regression and classification models were used to predict postoperative vault. The influences of WTW, ATA, and STS on predicting the postoperative vault and ICL size were analyzed and compared. RESULTS: The dataset was randomly divided into training (80%) and test (20%) sets, with no significant differences observed between them. The screened variables included only seven variables which conferred the largest signal in the model, namely, lens thickness (LT, estimated coefficients for logistic least absolute shrinkage of -0.20), STS (-0.04), size (0.08), flat K (-0.006), anterior chamber depth (0.15), spherical error (-0.006), and cylindrical error (-0.0008). The optimal prediction model depended on STS (R2=0.419, RMSE=0.139), whereas the least effective prediction model relied on WTW (R2=0.395, RMSE=0.142). In the classified prediction models of the vault, classification prediction of the vault based on STS exhibited superior accuracy compared to ATA or WTW. CONCLUSIONS: This study compared the capabilities of WTW, ATA, and STS in predicting postoperative vault, demonstrating that STS exhibits a stronger correlation than the other two parameters.

Deep-computer-generated holography with temporal-focusing and a digital propagation matrix for rapid 3D multiphoton stimulation.

Deep learning-based computer-generated holography (DeepCGH) has the ability to generate three-dimensional multiphoton stimulation nearly 1,000 times faster than conventional CGH approaches such as the Gerchberg-Saxton (GS) iterative algorithm. However, existing DeepCGH methods cannot achieve axial confinement at the several-micron scale. Moreover, they suffer from an extended inference time as the number of stimulation locations at different depths (i.e., the number of input layers in the neural network) increases. Accordingly, this study proposes an unsupervised U-Net DeepCGH model enhanced with temporal focusing (TF), which currently achieves an axial resolution of around 5 µm. The proposed model employs a digital propagation matrix (DPM) in the data preprocessing stage, which enables stimulation at arbitrary depth locations and reduces the computation time by more than 35%. Through physical constraint learning using an improved loss function related to the TF excitation efficiency, the axial resolution and excitation intensity of the proposed TF-DeepCGH with DPM rival that of the optimal GS with TF method but with a greatly increased computational efficiency.

Interior-Confined Vacancy in Potassium Manganese Hexacyanoferrate for Ultra-Stable Potassium-Ion Batteries.

Metal hexacyanoferrates (HCFs) are viewed as promising cathode materials for potassium-ion batteries (PIBs) because of their high theoretical capacities and redox potentials. However, the development of an HCF cathode with high cycling stability and voltage retention is still impeded by the unavoidable Fe(CN)6 vacancies (VFeCN) and H2O in the materials. Here, a repair method is proposed that significantly reduces the VFeCN content in potassium manganese hexacyanoferrate (KMHCF) enabled by the reducibility of sodium citrate and removal of ligand H2O at high temperature (KMHCF-H). The KMHCF-H obtained at 90 °C contains only 2% VFeCN, and the VFeCN is concentrated in the lattice interior. Such an integrated Fe-CN-Mn surface structure of the KMHCF-H cathode with repaired surface VFeCN allows preferential decomposition of potassium bis(fluorosulfonyl)imide (KFSI) in the electrolyte, which constitutes a dense anion-dominated cathode electrolyte interphase (CEI) , inhibiting effectively Mn dissolution into the electrolyte. Consequently, the KMHCF-H cathode exhibits excellent cycling performance for both half-cell (95.2 % at 0.2 Ag-1 after 2000 cycles) and full-cell (99.4 % at 0.1 Ag-1 after 200 cycles). This thermal repair method enables scalable preparation of KMHCF with a low content of vacancies, holding substantial promise for practical applications of PIBs.

Recent Advances in Rechargeable Metal-CO2 Batteries with Nonaqueous Electrolytes.

This review article discusses the recent advances in rechargeable metal-CO2 batteries (MCBs), which include the Li, Na, K, Mg, and Al-based rechargeable CO2 batteries, mainly with nonaqueous electrolytes. MCBs capture CO2 during discharge by the CO2 reduction reaction and release it during charging by the CO2 evolution reaction. MCBs are recognized as one of the most sophisticated artificial modes for CO2 fixation by electrical energy generation. However, extensive research and substantial developments are required before MCBs appear as reliable, sustainable, and safe energy storage systems. The rechargeable MCBs suffer from the hindrances like huge charging-discharging overpotential and poor cyclability due to the incomplete decomposition and piling of the insulating and chemically stable compounds, mainly carbonates. Efficient cathode catalysts and a suitable architectural design of the cathode catalysts are essential to address this issue. Besides, electrolytes also play a vital role in safety, ionic transportation, stable solid-electrolyte interphase formation, gas dissolution, leakage, corrosion, operational voltage window, etc. The highly electrochemically active metals like Li, Na, and K anodes severely suffer from parasitic reactions and dendrite formation. Recent research works on the aforementioned secondary MCBs have been categorically reviewed here, portraying the latest findings on the key aspects governing secondary MCB performances.

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Low-Coordinated Zn-N2 Sites as Bidirectional Atomic Catalysis for Room-Temperature Na-S Batteries.

The rational design of advanced catalysts for sodium-sulfur (Na-S) batteries is important but remains challenging due to the limited understanding of sulfur catalytic mechanisms. Here, we propose an efficient sulfur host consisting of atomic low-coordinated Zn-N2 sites dispersed on N-rich microporous graphene (Zn-N2@NG), which realizes state-of-the-art sodium-storage performance with a high sulfur content of 66 wt %, high-rate capability (467 mA h g-1 at 5 A g-1), and long cycling stability for 6500 cycles with an ultralow capacity decay rate of 0.0062% per cycle. Ex situ methods combined with theoretical calculations demonstrate the superior bidirectional catalysis of Zn-N2 sites on sulfur conversion (S8 ↔ Na2S). Furthermore, in situ transmission electron microscopy was applied to visualize the microscopic S redox evolution under the catalysis of Zn-N2 sites without liquid electrolytes. During the sodiation process, both surface S nanoparticles and S molecules in the mircopores of Zn-N2@NG quickly convert into Na2S nanograins. During the following desodiation process, only a small part of the above Na2S can be oxidized into Na2Sx. These results reveal that, without liquid electrolytes, Na2S is difficult to be decomposed even with the assistance of Zn-N2 sites. This conclusion emphasizes the critical role of liquid electrolytes in the catalytic oxidation of Na2S, which was usually ignored by previous works.

Air pollution exacerbates mild obstructive sleep apnea by disrupting nocturnal changes in lower-limb body composition: a cross-sectional study conducted in urban northern Taiwan.

BACKGROUND: Few studies have explored the role of body composition linking air pollution to obstructive sleep apnea (OSA). OBJECTIVE: To estimate the effects of air pollution on body composition and OSA, and that of body composition on OSA. METHODS: This study included 3550 individuals. A spatiotemporal model estimated personal exposure. Nocturnal changes in body composition were assessed through bioelectric impedance analysis. OSA was diagnosed using polysomnography. A generalized linear model was used to evaluate the absolute nocturnal changes in body composition associated with an interquartile range (IQR) increase in pollutants. A generalized logistic model was used to estimate odds ratios (ORs) of mild-OSA compared to non-OSA. Association between body composition and apnea-hypopnea index (AHI) was investigated through partial least squares (PLS) regression. RESULTS: Nocturnal changes in lower-limb body composition were associated with NO2 and PM2.5 in all patients. In participants with AHI <15, both short- and long-term NO2 exposures affected body composition and mild-OSA, while PM2.5 was not associated with either outcome. In a PLS model incorporating eight NO2-associated lower-limb parameters, the variable importance projection scores (VIP) of left leg impedance (LLIMP), predicted muscle mass (LLPMM), fat-free mass (LLFFM), and right leg impedance (RLIMP) exceeded 1; the corresponding coefficients ranked in the top four for AHI prediction. The adjusted OR (mild vs. non-OSA) was 1.67 (95 % CI: 1.36-2.03) associated with an IQR increase in prediction value estimated from body compositions. Notably, the two-pollutant model investigating the effects of pollutants on body compositions revealed associations of four parameters (LLIMP, LLPMM, LLFFM, and RLIMP) with NO2 in all lags, which indicates their indispensability in the association between NO2 and AHI. CONCLUSIONS: NO2 exacerbates mild-OSA by disrupting nocturnal changes in lower-limb body composition of patients with AHI <15. PM2.5 was associated with nocturnal changes in lower-limb body composition but not with mild-OSA.

Tissue-resident, extravascular Ly6c- monocytes are critical for inflammation in the synovium.

Monocytes are abundant immune cells that infiltrate inflamed organs. However, the majority of monocyte studies focus on circulating cells, rather than those in tissue. Here, we identify and characterize an intravascular synovial monocyte population resembling circulating non-classical monocytes and an extravascular tissue-resident monocyte-lineage cell (TR-MC) population distinct in surface marker and transcriptional profile from circulating monocytes, dendritic cells, and tissue macrophages that are conserved in rheumatoid arthritis (RA) patients. TR-MCs are independent of NR4A1 and CCR2, long lived, and embryonically derived. TR-MCs undergo increased proliferation and reverse diapedesis dependent on LFA1 in response to arthrogenic stimuli and are required for the development of RA-like disease. Moreover, pathways that are activated in TR-MCs at the peak of arthritis overlap with those that are downregulated in LFA1-/- TR-MCs. These findings show a facet of mononuclear cell biology that could be imperative to understanding tissue-resident myeloid cell function in RA.

Higher Particulate Matter Deposition in Alveolar Region Could Accelerate Body Fat Accumulation in Obstructive Sleep Apnea.

We conducted a cross-sectional study to investigate associations of particulate matter (PM) of less than 2.5 µm in aerodynamic diameter (PM2.5) and PM deposition with nocturnal changes in body composition in obstructive sleep apnea (OSA) patients. A bioelectric impedance analysis was used to measure the pre- and postsleep body composition of 185 OSA patients. Annual exposure to PM2.5 was estimated by the hybrid kriging/land-use regression model. A multiple-path particle dosimetry model was employed to estimate PM deposition in lung regions. We observed that an increase in the interquartile range (IQR) (1 µg/m3) of PM2.5 was associated with a 20.1% increase in right arm fat percentage and a 0.012 kg increase in right arm fat mass in OSA (p < 0.05). We observed that a 1 µg/m3 increase in PM deposition in lung regions (i.e., total lung region, head and nasal region, tracheobronchial region, and alveolar region) was associated with increases in changes of fat percentage and fat mass of the right arm (ß coefficient) (p < 0.05). The ß coefficients decreased as follows: alveolar region > head and nasal region > tracheobronchial region > total lung region (p < 0.05). Our findings demonstrated that an increase in PM deposition in lung regions, especially in the alveolar region, could be associated with nocturnal changes in the fat percentage and fat mass of the right arm. PM deposition in the alveolar region could accelerate the body fat accumulation in OSA.

Continuous Positive Airway Pressure Reduces Plasma Neurochemical Levels in Patients with OSA: A Pilot Study.

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aß42), and the product of the two (Aß42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAß42: 0.58 pg/mL; ΔAß42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aß42 decreased considerably in the CPAP group (ΔT-Tau: -2.22 pg/mL; ΔAß42 × T-Tau: -44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea-hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

MICROGLIA AND INFILTRATING T-CELLS ADOPT LONG-TERM, AGE-SPECIFIC, TRANSCRIPTIONAL CHANGES AFTER TRAUMATIC BRAIN INJURY IN MICE.

ABSTRACT: Aged traumatic brain injury (TBI) patients suffer increased mortality and long-term neurocognitive and neuropsychiatric morbidity compared with younger patients. Microglia, the resident innate immune cells of the brain, are complicit in both. We hypothesized that aged microglia would fail to return to a homeostatic state after TBI and adopt a long-term injury-associated state within aged brains compared with young brains after TBI. Young and aged male C57BL/6 mice underwent TBI via controlled cortical impact versus sham injury and were sacrificed 4 months post-TBI. We used single-cell RNA sequencing to examine age-associated cellular responses after TBI. Brains were harvested, and CD45+ cells were isolated via fluorescence-activated cell sorting. cDNA libraries were prepared using the 10x Genomics Chromium Single Cell 3' Reagent Kit, followed by sequencing on a HiSeq 4,000 instrument and computational analyses. Post-injury, aged mice demonstrated a disparate microglial gene signature and an increase in infiltrating T cells compared with young adult mice. Notably, aged mice post-injury had a subpopulation of age-specific, immune-inflammatory microglia resembling the gene profile of neurodegenerative disease-associated microglia with enriched pathways involved in leukocyte recruitment and brain-derived neurotrophic factor signaling. Meanwhile, post-injury, aged mice demonstrated heterogeneous T-cell infiltration with gene profiles corresponding to CD8 effector memory, CD8 naive-like, CD8 early active T cells, and Th1 cells with enriched pathways, such as macromolecule synthesis. Taken together, our data showed that the aged brain had an age-specific gene signature change in both T-cell infiltrates and microglia, which may contribute to its increased vulnerability to TBI and the long-term sequelae of TBI.

Battery Performance Amelioration by Introducing a Conducive Mixed Electrolyte in Rechargeable Mg-O2 Batteries.

With magnesium being a cost-effective anode metal compared to the other conventional Li-based anodes in the energy market, it could be a capable source of energy storage. However, Mg-O2 batteries have struggled its way to overcome the poor cycling stability and sluggish reaction kinetics. Therefore, Ru metallic nanoparticles on carbon nanotubes (CNTs) were introduced as a cathode for Mg-O2 batteries, which are known for their inherent electronic properties, large surface area, and increased crystallinity to favor remarkable oxygen reduction reactions and oxygen evolution reactions (ORR and OER). Also, we deployed a first-of-its-kind, conducive mixed electrolyte (CME) (2 M Mg(NO3)2:1 M Mg(TFSI)2/diglyme). Hence, this synergistic incorporation of CME-based Ru/CNT Mg-O2 batteries could unleash long cycle life with low overpotential, excellent reversibility, and high ionic conductivity and also reduces the intrinsic corrosion behavior of Mg anodes. Correspondingly, this novel amalgamation of CME with Ru/CNT cathode has displayed superior cyclic stability of 65 cycles and a maximum discharge potential of 25 793 mAh g-1 with a small overvoltage plateau of 1.4 V, noticeably subjugating the findings of conventional single electrolyte (CSE) (1 M Mg(TFSI)2/diglyme). This CME-based Ru/CNT Mg-O2 battery design could have a significant outcome as a future battery technology.

MAGNET: A web-based application for gene set enrichment analysis using macrophage data sets.

Characterization of gene lists obtained from high-throughput genomic experiments is an essential task to uncover the underlying biological insights. A common strategy is to perform enrichment analyses that utilize standardized biological annotations, such as GO and KEGG pathways, which attempt to encompass all domains of biology. However, this approach provides generalized, static results that may fail to capture subtleties associated with research questions within a specific domain. Thus, there is a need for an application that can provide precise, relevant results by leveraging the latest research. We have therefore developed an interactive web application, Macrophage Annotation of Gene Network Enrichment Tool (MAGNET), for performing enrichment analyses on gene sets that are specifically relevant to macrophages. Using the hypergeometric distribution, MAGNET assesses the significance of overlapping genes with annotations that were curated from published manuscripts and data repositories. We implemented numerous features that enhance utility and user-friendliness, such as the simultaneous testing of multiple gene sets, different visualization options, option to upload custom datasets, and downloadable outputs. Here, we use three example studies compared against our current database of ten publications on mouse macrophages to demonstrate that MAGNET provides relevant and unique results that complement conventional enrichment analysis tools. Although specific to macrophage datasets, we envision MAGNET will catalyze developments of similar applications in other domains of interest. MAGNET can be freely accessed at the URL https://magnet-winterlab.herokuapp.com. Website implemented in Python and PostgreSQL, with all major browsers supported. The source code is available at https://github.com/sychen9584/MAGNET.