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Abstract Studies suggest that amyotrophic lateral sclerosis (ALS) compromises the integrity of white matter fiber tracts, primarily affecting motor fibers. However, it remains uncertain whether the integrity of these fibers influences the risk of ALS. We performed bidirectional two-sample Mendelian randomization (MR) and multivariable MR analyses to evaluate the associative relationships between the integrity of fiber tracts [including the corticospinal tract (CST) and corpus callosum (CC)] and the risk of ALS. Genetic instrumental variables for specific fiber tracts were obtained from published genome-wide association studies (GWASs), including 33,292 European individuals from five diffusion magnetic resonance imaging (dMRI) datasets. Summary-level GWAS data for ALS were derived from 27,205 ALS patients and 110,881 controls. The MR results suggested that an increase in the first principal component (PC1) of fractional anisotropy (FA) in the genu of the CC (GCC) was correlated with an increased risk of ALS (P FDR = 0.001, odds ratio = 1.363, 95% confidence interval 1.178–1.577). Although other neuroimaging phenotypes [mean diffusivity in the CST, radial diffusivity (RD) in the CST, FA in the GCC, PC1 in the body of the CC (BCC), PC1 in the CST, and RD in the GCC] did not pass correction, they were also considered to have suggestive associations with the risk of ALS. No evidence revealed that ALS caused changes in the integrity of fiber tracts. In summary, the results of this study provide genetic support for the potential association between the integrity of specific fiber tracts and the risk of ALS. Greater fiber integrity in the GCC and BCC may be a risk factor for ALS, while greater fiber integrity in the CST may have a protective effect on ALS. This study provides insights into ALS development.

Abstract To non‐destructively resolve and diagnose the degradation mechanisms of lithium‐ion batteries (LIBs), it is necessary to cross‐scale decouple complex kinetic processes through the distribution of relaxation times (DRT). However, LIBs with low interfacial impedance render DRT unreliable without data processing and closed‐loop validation. This study proposes a hierarchical analytical framework to enhance timescale resolution and reduce uncertainty, including interfacial impedance reconstruction and multi‐dimensional DRT analysis. Interfacial impedance is reconstructed by eliminating simulated inductive and diffusive impedance based on a high‐fidelity frequency‐domain model. Multi‐dimensional DRT decouples solid electrolyte interphase (SEI) and charge transfer (CT) processes by the reversibility of electrochemical reactions with state of charge (SOC) to characterize electrode kinetic evolution driven by SOC and temperature through timescales and peak area. The findings reveal that reconstructed impedance improves the accuracy of identified time constants by ≈20%. Cross‐scale DRT results reveal that SOCs below 10% at 25 °C effectively distinguish electrode kinetics due to the high correlation between cathodic CT and SOC. Kinetic metrics characterize that anodic SEI or CT are different control steps limiting the low‐temperature performance of different cells. This work underscores the potential of the proposed framework for non‐destructive diagnostics of kinetic evolution.

Accurate monitoring of forest aboveground biomass (AGB) is vital for sustainable forest management. Generally, the AGB is estimated by combining satellite images and field measurements. Nevertheless, field plots are inaccessible in complex forest areas. Due to the limitations related to either optical or SAR data alone, combining the two data types is indispensable. Here, we explored the LiDAR-derived biomass as training/testing samples for the combination of yearly time-series of Sentinel-1 and Sentinel-2 images to estimate AGB in north-eastern Conghua, Guangdong province, China. The AGB reference map derived from airborne LiDAR data and field plots could provide more samples for satellite-based AGB estimation with a limited amount of sampling plots. We designed four groups of experiments based on diverse Sentinel-1 and Sentinel-2 variables, including backscatter, backscatter indices, texture features, spectral bands, vegetation indices, and biophysical features. Four different prediction methods (support vector regression, multi-layer perceptron neural network, K-nearest neighbour, and random forest (RF)) were separately used to estimate AGB. Results showed that the RF model achieved the best accuracy for AGB mapping. All Sentinel-1 and Sentinel-2 experiment (R2: 0.72, RMSEr: 17.65%) performed better than the monthly complementary experiment (R2: 0.66, RMSEr: 19.01%). Additionally, the arid period images were observed to be sensitive for estimating AGB. The most contributing Sentinels predictors were determined via a sequential forward selection. Consequently, the proposed methodology has great potential for low-cost, large-scale, and high-precision AGB estimation.

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests.

Hyaluronic acid (HA) is a well-known functional marine polysaccharide. The utilization and derivative development of HA are of great interest. Hyaluronan lyase has wide application prospects in the production of HA oligosaccharides and lower molecular weight HA. In this study, a strain of Enterobacter asburiae CGJ001 with high hyaluronan lyase activity was screened from industrial wastewater. This strain exhibited an impressive enzyme activity of 40,576 U/mL after being incubated for 14 h. Whole genome sequencing analysis revealed that E. asburiae CGJ001 contained a cluster of genes involved in HA degradation, transport, and metabolism. A newly identified enzyme responsible for glycosaminoglycan degradation was designated as HylEP0006. A strain of E. coli BL21(DE3)/pET-22b(+)-hylEP0006 was successfully constructed. HylEP0006 exhibited optimal degradation at 40 °C and pH 7.0, showing a high activity of 950,168.3 U/mg. HylEP0006 showed specific activity against HA. The minimum degradation fragment of HylEP0006 was hyaluronan tetrasaccharides, and HylEP0006 could efficiently degrade HA into unsaturated disaccharides (HA2), with HA2 as the final product. These characteristics indicate that HylEP0006 has a potential application prospect for the extraction and utilization of hyaluronic acid.

Abstract Background Current evidence of the association between a single nucleotide polymorphism (SNP) in ADAMTS14 (rs4747096) and osteoarthritis (OA) is controversial. This study aimed to determine whether the ADAMTS14 SNP is closely related to OA risk. Methods An electronic search of for the association between the rs4747096 polymorphisms and OA was performed using four online databases (updated on September 10, 2022). The association between susceptibility to OA and rs4747096 polymorphism was evaluated in four genetic models: the allele (mutation [A] vs. wild type [G]), additive (AA vs. GG and AG vs. GG), recessive (AA vs. AG + GG), and dominant (AA + AG vs. GG). This meta-analysis was performed in the R software, and effects were assessed using odds ratios (ORs) and 95% confidence intervals (CI). Results Four studies (707 cases in the case group and 859 cases in the control group) were included. The results of the meta-analysis showed that, except in the recessive genetic model, there was a significant correlation between OA risk and the rs4747096 polymorphism using the allele (OR [95% CI] = 1.48 [1.26–1.73], P

Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

Stroke and dementia have been linked to the appearance of white matter hyperintensities (WMHs). Meanwhile, diffusion tensor imaging (DTI) might capture the microstructural change in white matter early. Specific dietary interventions may help to reduce the risk of WMHs. However, research on the relationship between specific nutrients and white matter changes is still lacking. We aimed to investigate the causal effects of essential nutrients (amino acids, fatty acids, mineral elements, and vitamins) on WMHs and DTI measures, including fraction anisotropy (FA) and mean diffusivity (MD), by a Mendelian randomization analysis. We selected single nucleotide polymorphisms (SNPs) associated with each nutrient as instrumental variables to assess the causal effects of nutrient-related exposures on WMHs, FA, and MD. The outcome was from a recently published large-scale European Genome Wide Association Studies pooled dataset, including WMHs (N = 18,381), FA (N = 17,663), and MD (N = 17,467) data. We used the inverse variance weighting (IVW) method as the primary method, and sensitivity analyses were conducted using the simple median, weighted median, and MR-Egger methods. Genetically predicted serum calcium level was positively associated with WMHs risk, with an 8.1% increase in WMHs risk per standard deviation unit increase in calcium concentration (OR = 1.081, 95% CI = 1.006–1.161, p = 0.035). The plasma linoleic acid level was negatively associated with FA (OR = 0.776, 95% CI = 0.616–0.978, p = 0.032). Our study demonstrated that genetically predicted calcium was a potential risk factor for WMHs, and linoleic acid may be negatively associated with FA, providing evidence for interventions from the perspective of gene-environment interactions.

The treatment of oil-based drilling cuttings (OBDCs) with high oil content is difficult. In this study, a tertiary treatment of ultrasonication–flotation–advanced oxidation for treating OBDCs with a high oil content of 20.10 wt% was proposed for the first time. All stages of the treatment processes were optimised. The recommended parameters for ultrasonication at room temperature were a mass ratio of OBDCs to the degreaser of 1:8, an ultrasonication power of 600 W and treatment time of 30 min. After the ultrasonication treatment, the oil content of the OBDCs decreased from 20.10 wt% to 5.00 wt%. Flotation was performed at room temperature with a mass ratio of OBDCs to the degreaser of 1:10, a stirring speed of 400 rpm, an aeration head aperture of 3 μm and airflow rate of 400 mL/min under N2 injection for 60 min. After the flotation treatment, the oil content of the OBDCs decreased from 5.00 wt% to 2.01 wt%. Advanced oxidation was performed at room temperature with a mass ratio of OBDCs to water of 1:10, 3.57 wt% sodium persulphate in water, 4.17 wt% ferrous sulphate heptahydrate in water and ultrasonication power of 1000 W for 100 min. Following the advanced oxidation treatment, the oil content of the OBDCs decreased from 2.01 wt% to 0.58 wt%. The results of this study provide a new method and idea for treating OBDCs with high oil content.

BackgroundPrevious studies have suggested a potential association between nutrients and cerebral small vessel disease (CSVD), but this association has not been fully addressed.ObjectWe intended to clarify the causal associations between four categories of essential nutrients (amino acids, polyunsaturated fatty acids, minerals and vitamins) and two acute manifestations of CSVD (intracerebral hemorrhage and small vessel stroke) using two-sample Mendelian randomization (MR) analysis.MethodWe obtained European-based large-scale genome-wide association studies (GWASs) related to CSVD (6,255 cases and 233,058 controls) and nutrient concentrations. Causality evaluation mainly included the results of the inverse variance-weighted (IVW) method. The simple median method, the weighted median method and the MR–Egger method were adopted for sensitivity analyses.ResultsFor ICH or SVS, increased levels of phenylalanine (OR = 1.188, p

Abstract We aimed to investigate the causality between potentially modifiable risk factors and the risk of intracranial aneurysm. Genetic instruments for 51 modifiable factors and intracranial aneurysm data were obtained from recently published genome-wide association studies. We applied two-sample Mendelian randomization methods to investigate their causal relationships. Genetically predicted cigarettes per day, smoking initiation, systolic blood pressure, hypertension and body fat percentage were significantly associated with an increased risk of intracranial aneurysm [odds ratios (OR) 2.67, 95% confidence interval (CI) 1.75–4.07, p = 5.36 × 10–6, OR 1.53, 95% CI 1.32–1.77, p = 9.58 × 10–9, OR 1.05, 95% CI 1.02–1.08, p = 1.18 × 10–3, OR 1.65, 95% CI 1.19–2.28, p = 2.56 × 10–3 and OR 1.29, 95% CI 1.11–1.52, p = 1.33 × 10–3, respectively]. Type 2 diabetes mellitus was significantly associated with a decreased risk of intracranial aneurysm (OR 0.89, 95% CI 0.83–0.95, p = 8.54 × 10–4). Body fat percentage was significantly associated with subarachnoid haemorrhage (p = 5.70 × 10–5). This study provided genetic evidence of causal effects of smoking, blood pressure, type 2 diabetes mellitus and obesity on the risk of intracranial aneurysm.

The recently discovered Mida nephrite deposit, located in the East Kunlun Mountains, Qiemo County, Xinjiang, Northwest China, contains new types of white and greenish white nephrite formed by limestone replacement, which shows microstructures, macroscopic features and country rocks typologies that are quite different from those of the other deposits along the Kunlun–Altyn Tagh Mountains. The gemological and mineralogical characteristics of Mida nephrite are presented here. These nephrites show an ivory white color and a porcelain-like appearance, with semitranslucent-to-opaque transparency and a porcelain-to-greasy luster. Petrographic study, electron probe microanalysis (EPMA) data and scanning electron microscopy (SEM) images have indicated that the nephrite is composed of tremolite, accompanied by minor quartz, calcite and diopside. Tremolite aggregates have shown different textures, like flaky, granular, fibrous–felted, bundle, radial and metasomatic relict textures. Quartz has appeared in granular or disseminated form, dispersed in the tremolite matrix. Calcite has shown a metasomatic relict texture in the white nephrite samples. Diopside has shown euhedral grains, with some distributed with a certain geometric appearance. Based on our observations, it is suggested that the quartz in the nephrite originated from Si-rich hydrothermal fluids. We propose that the substantial size difference of mineral grains, together with uncompacted grains with inter-particle pores, are the main reasons for the internal reflection and refraction under transmitted light, which allow less transmitted light to pass through the nephrite body and generate the appearance of a semitranslucent-to-opaque transparency, ivory white color and porcelain luster. Our study has unveiled that the Mida nephrite is not typical of the two known types (D-type: dolomite-related; S-type: serpentinite-related) and is overlapped by quartz grains dispersed throughout the less compact tremolite matrix. These observations would help set it apart from the majority of nephrite jades found in the Kunlun Mountains region and provide valuable insights for enhancing comprehension of the diversity of the nephrite deposits.

The identification of knee points in lithium-ion (Li-ion) batteries is crucial for predicting the battery life, designing battery products, and managing battery health. Knee points (KPs) refer to the transition points in the aging speed and aging trajectory of Li-ion batteries. KPs can be identified using a wealth of aging data and various regression-based methods. However, KP identification relies on a large amount of aging data, which is exceedingly time-consuming and resource-intensive. To overcome this issue, we propose a novel method based on KP characteristics to identify the KPs and critical aging speed. Firstly, we extract the main aging trajectory using curve-fitting techniques. Secondly, we calculate the aging speed at each cycle to identify the KPs. We then explore the relationship between the KPs and cycle life and develop a knee point identification algorithm. The correlation coefficient between the KPs and cycle life provides a valuable indicator of the critical aging speed, enabling accurate identification of KPs. To validate our approach, we apply it to the Li(NiCoMn)O2, LiFePO4, and LiCoO2 cell datasets. Our results demonstrate a strong correlation between the KPs and cycle life for these battery types. By employing our proposed method, KPs can be identified for battery life prediction, product design, and health management. Moreover, we summarize a critical degradation speed of −0.03%/cycle can serve as an empirical threshold for warning against capacity diving and KPs. The statistical transition speed threshold can eliminate the dependence on extensive aging data throughout the entire battery’s lifecycle for identifying capacity knee points.

Currently, only the general control of the risk factors is known to prevent lacunar cerebral infarction, but it is unknown which type of medication for controlling the risk factors has a causal relationship with reducing the risk of lacunar infarction. To unlock this medical mystery, drug-target Mendelian randomization analysis was applied to estimate the effect of common antihypertensive agents, hypolipidemic agents, and hypoglycemic agents on lacunar stroke. Lacunar stroke data for the transethnic analysis were derived from meta-analyses comprising 7338 cases and 254,798 controls. We have confirmed that genetic variants mimicking calcium channel blockers were found to most stably prevent lacunar stroke. The genetic variants at or near HMGCR, NPC1L1, and APOC3 were predicted to decrease lacunar stroke incidence in drug-target MR analysis. These variants mimic the effects of statins, ezetimibe, and antisense anti-apoC3 agents, respectively. Genetically proxied GLP1R agonism had a marginal effect on lacunar stroke, while a genetically proxied improvement in overall glycemic control was associated with reduced lacunar stroke risk. Here, we show that certain categories of drugs currently used in clinical practice can more effectively reduce the risk of stroke. Repurposing several drugs with well-established safety and low costs for lacunar stroke prevention should be given high priority when doctors are making decisions in clinical practice. This may contribute to healthier brain aging.

Abstract Type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) are associated consistently. However, it is currently unknown whether this association is causal. We aimed to estimate the unconfounded, causal association between T2D on ALS using a two-sample Mendelian randomization approach both in European and East Asian ancestry. Genetic variants strongly associated with T2D and each T2D markers were used to investigate the effect of T2D on ALS risk in European (involving 20,806 ALS cases and 59,804 controls) and East Asian (involving 1234 ALS cases and 2850 controls) ancestry. We found that the OR of ALS per 1 SD increase in T2D was estimated to be 0.96 [95% confidence interval (CI) 0.92–0.996; p = 0.03] in European populations. Similarly, all 8 SNPs were associated with T2D in East Asian ancestry, the OR of ALS per 1 SD increase in T2D was estimated to be 0.83 [95% CI 0.70–0.992; p = 0.04] in East Asian populations. Examining the intercept estimates from MR-Egger regression also leads to the same conclusion, in that horizontal pleiotropy unlikely influences the results in either population. We found that genetically predicted T2D was associated with significantly lower odds of amyotrophic lateral sclerosis both in European and East Asian populations. It is now critical to identify a clear molecular explanation for this association between T2D and ALS and to focus on its potential therapeutic implications.

Abstract Background Observational studies have suggested a close but controversial relationship between blood pressure (BP) and amyotrophic lateral sclerosis (ALS). It remains unclear whether this association is causal. The authors employed a bidirectional two-sample Mendelian randomization (MR) approach to evaluate the causal relationship between BP and ALS. Genetic proxies for systolic blood pressure (SBP), diastolic blood pressure (DBP), antihypertensive drugs (AHDs), ALS, and their corresponding genome-wide association study (GWAS) summary datasets were obtained from the most recent studies with the largest sample sizes. The inverse variance weighted (IVW) method was adopted as the main approach to examine the effect of BP on ALS and four other MR methods were used for sensitivity analyses. To exclude the interference between SBP and DBP, a multivariable MR approach was used. Results We found that genetically determined increased DBP was a protective factor for ALS (OR = 0.978, 95% CI 0.960–0.996, P = 0.017) and that increased SBP was an independent risk factor for ALS (OR = 1.014, 95% CI 1.003–1.025, P = 0.015), which is supported by sensitivity analyses. The use of calcium channel blocker (CCB) showed a causal relationship with ALS (OR = 0.985, 95% CI 0.971–1.000, P = 0.049). No evidence was revealed that ALS caused changes in BP. Conclusions This study provides genetic support for a causal effect of BP and ALS that increased DBP has a protective effect on ALS, and increased SBP is a risk factor for ALS, which may be related to sympathetic excitability. Blood pressure management is essential in ALS, and CCB may be a promising candidate.

Abstract Background Growing evidence suggests a mutual interaction between gut microbiome alterations and ALS pathogenesis. However, previous studies were susceptible to potential confounding factors and reverse causation bias, likely leading to inconsistent and biased results. Objectives To decipher the potentially mutual relationship between gut microbiota and ALS, we used a bidirectional two-sample MR approach to examine the associations between the gut microbiome and ALS. Results Using the inverse variance-weighted method, OTU10032 unclassified Enterobacteriaceae species-level OTU and unclassified Acidaminococcaceae were associated with a higher risk of ALS (per relative abundance: OR, 1.04; 95% CI, 1.01–1.07; P = 0.011 and OR, 1.02; 95% CI, 1.01–1.04; P = 0.009, respectively). Importantly, Gamma-Glu-Phe was showed potential deleterious effects on the risk of ALS (genetically predicted per a 1-standard deviation increase in the level of Gamma-Glu-Phe: OR, 1.96; 95% CI, 1.50–2.55; P = 0.012). Sensitivity analysis of the two candidate genera and metabolites using the MR-Egger and weighted-median methods produced similar estimates, and no horizontal pleiotropy or outliers were observed. Intriguingly, genetically predicted ALS was associated with an increase in the relative abundance of OTU4607_Sutterella (per 1-unit higher log odds: β, 2.23; 95% CI, 1.27–3.18; P = 0.020) and Lactobacillales_ORDER (per 1-unit higher log odds: β, 0.51; 95% CI, 0.09–0.94; P = 0.019). Conclusions Our findings provide novel evidence supporting the bidirectional relationship between the gut microbiota and ALS. These results may contribute to designing microbiome- and microbiome-dependent metabolite interventions in future ALS clinical trials.

Abstract Background Observational studies have suggested that telomere length is associated with amyotrophic lateral sclerosis (ALS). However, whether this association is causal remains unclear. In this study, we aimed to explore the causal relationship between leukocyte telomere length (LTL) and ALS by a two-sample Mendelian randomization (MR) approach. Single-nucleotide polymorphisms (SNPs) for LTL were identified through high-quality genome-wide association studies (GWASs). The ALS GWAS summary data (20,806 cases; 59,804 controls) with largest sample size to date was obtained. We adopted the inverse variance weighted (IVW) method to examine the effect of LTL on ALS and used the weighted median method, simple median method, MR Egger method and MR-PRESSO method to perform sensitivity analyses. Results We found that genetically determined increased LTL was inversely associated with the risk of ALS (odds ratio (OR) = 0.846, 95% confidence interval (CI): 0.744–0.962, P = 0.011), which was mainly driven by rs940209 in the OBFC1 gene, suggesting a potential effect of OBFC1 on ALS. The results were further confirmed by sensitivity analysis with the MR Egger method (OR = 0.647, 95% CI = 0.447–0.936, P = 0.050). Analyses by the weighted median method (OR = 0.893, P = 0.201) and simple median method (OR = 0.935, P = 0.535) also showed a similar trend. The MR Egger analysis did not suggest directional pleiotropy, with an intercept of 0.025 (P = 0.168). Neither the influence of instrumental outliers nor heterogeneity was found. Conclusions Our results suggest that genetically predicted increased LTL has a causal relationship with a lower risk of ALS. Protecting against telomere loss may be of great importance in the prevention and treatment of ALS.

Background: Systematically assessing the causal associations between medications and neurodegenerative diseases is significant in identifying disease etiology and novel therapies. Here, we investigated the putative causal associations between 23 existing medication categories and major neurodegenerative diseases (NDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Methods: A two-sample mendelian randomization (MR) approach was conducted. Estimates were calculated using the inverse-variance weighted (IVW) method as the main model. A sensitivity analysis and a pleiotropy analysis were performed to identify potential violations. Results: Genetically predisposition to antihypertensives (OR = 0.809, 95% CI = 0.668–0.981, p = 0.031), thyroid preparations (OR = 0.948, 95% CI = 0.909–0.988, p = 0.011), and immunosuppressants (OR = 0.879, 95% CI = 0.789–0.979, p = 0.018) was associated with a decreased risk of AD. Genetic proxies for thyroid preparations (OR = 0.934, 95% CI = 0.884–0.988, p = 0.017), immunosuppressants (OR = 0.825, 95% CI = 0.699–0.973, p = 0.022), and glucocorticoids (OR = 0.862, 95% CI = 0.756–0.983, p = 0.027) were causally associated with a decreased risk of PD. Genetically determined antithrombotic agents (OR = 1.234, 95% CI = 1.042–1.461, p = 0.015), HMG CoA reductase inhibitors (OR = 1.085, 95% CI = 1.025–1.148, p = 0.005), and salicylic acid and derivatives (OR = 1.294, 95% CI = 1.078–1.553, p = 0.006) were associated with an increased risk of ALS. Conclusions: We presented a systematic view concerning the causal associations between medications and NDs, which will promote the etiology discovery, drug repositioning and patient management for NDs.

Abstract Cerebral small vessel disease (cSVD) is correlated with a high risk of stroke and cognitive impairment. Previous studies between heart rate variability (HRV) and cSVD revealed paradoxical results. The authors aimed to investigate the relationship between HRV and cSVD using Mendelian randomization analysis. Genetic instruments for HRV were obtained from previous genome‐wide association studies. They applied inverse variance‐weighted analysis, weighted median analysis, simple median analysis, and Mendelian randomization–Egger regression to evaluate the associations of HRV with white matter hyperintensity (WMH) and small vessel stroke (SVS) in the UK Biobank neuroimaging dataset and the MEGASTROKE genome‐wide association study dataset. Two genetically predicted traits of HRV (the root mean square of the successive differences of inter beat intervals [RMSSD] and the peak‐valley respiratory sinus arrhythmia or high frequency power [pvRSA/HF]) were suggestively associated with WMH (β 0.26, 95% confidence interval [CI] 0.04–0.49, p = .02; β 0.14, 95% CI 0.02–0.27, p = .03, respectively). Genetically predicted traits of HRV were not significantly associated with SVS. This study provides genetic support for a suggestive causal effect of HRV (RMSSD, pvRSA/HF) on WMH but not SVS.

Abstract Observational studies have shown that several risk factors are associated with cardioembolic stroke. However, whether such associations reflect causality remains unknown. We aimed to determine whether established and provisional cardioembolic risk factors are causally associated with cardioembolic stroke. Genetic instruments for atrial fibrillation (AF), myocardial infarction (MI), electrocardiogram (ECG) indices and N-terminal pro-brain natriuretic peptide (NT-pro BNP) were obtained from large genetic consortiums. Summarized data of ischemic stroke and its subtypes were extracted from the MEGASTROKE consortium. Causal estimates were calculated by applying inverse-variance weighted analysis, weighted median analysis, simple median analysis and Mendelian randomization (MR)-Egger regression. Genetically predicted AF was significantly associated with higher odds of ischemic stroke (odds ratio (OR): 1.20, 95% confidence intervals (CI): 1.16–1.24, P = 6.53 × 10–30) and cardioembolic stroke (OR: 1.95, 95% CI: 1.85–2.06, P = 8.81 × 10–125). Suggestive associations were found between genetically determined resting heart rate and higher odds of ischemic stroke (OR: 1.01, 95% CI: 1.00–1.02, P = 0.005), large-artery atherosclerotic stroke (OR: 1.02, 95% CI: 1.00–1.04, P = 0.026) and cardioembolic stroke (OR: 1.02, 95% CI: 1.00–1.04, P = 0.028). There was no causal association of P‐wave terminal force in the precordial lead V1 (PTFVI), P-wave duration (PWD), NT-pro BNP or PR interval with ischemic stroke or any subtype.

The rapid urban expansion of China has led to a large amount of water and energy consumption, and caused drastic growth of carbon emissions. Discovering the water–energy–carbon nexus of different land use types helps explain the interactions between resources capacity and environmental effects of land use activities, as well as provides reasonable options for land use management based on water–energy conservation and carbon emission reduction. In this paper, carbon emission/absorption of different land use types was estimated and the water–energy–carbon nexus of different land use types in Zhengzhou was analyzed. Through the SD model, the future carbon emissions of different scenarios were predicted under water, energy, and land constraints. It showed that total carbon emission increased and carbon absorption reduced along with the land use change during the rapid urbanization process of Zhengzhou city. The water and energy consumption and carbon emissions of different land use types were determined by the different intensities and their spatial patterns of human activities. Therefore, carbon emission and its intensity varied with land use types and water–energy–carbon nexus was closely related to land use types. The carbon emission prediction showed that the carbon emission reduction potential was the highest and the peak carbon emission (40.18 × 106 t) occurred in 2025 under a comprehensive scenario, which was designed with water–energy saving and optimization of land use structure. This study could provide references for policy–makers to formulate differentiated strategies for different land use activities, and contribute to realize water–energy saving and efficient utilization of land and the emission reduction of Zhengzhou city.

Grassland aboveground biomass (AGB) is a crucial indicator when studying the carbon sink of grassland ecosystems. The exploration of the grassland AGB inversion method with viable reproducibility is significant for promoting the practicability and efficiency of grassland quantitative monitoring. Therefore, this study provides a novel retrieval method for grassland AGB by coupling the PROSAIL (PROSPECT + SAIL) model and the random forest (RF) model on the basis of the lookup-table (LUT) method. These sensitive spectral characteristics were optimized to significantly correlate with AGB (ranging from 0.41 to 0.68, p < 0.001). Four methods were coupled with the PROSAIL model to estimate grassland AGB in the West Ujimqin grassland, including the LUT method, partial least square (PLSR), RF and support vector machine (SVM) models. The ill-posed inverse problem of the PROSAIL model was alleviated using the MODIS products-based algorithm. Inversion results using sensitive spectral characteristics showed that the PROSAIL + RF model offered the best performance (R2 = 0.70, RMSE = 21.65 g/m2 and RMESr = 27.62%), followed by the LUT-based method, which was higher than the PROSAIL + PLSR model. Relatively speaking, the PROSAIL + SVM model was more challenging in this study. The proposed method exhibited strong robustness and universality for AGB estimation in large-scale grassland without field measurements.

Background: Observational studies suggest that statins may affect amyotrophic lateral sclerosis (ALS). However, they are limited by confounding and reverse causality biases. Therefore, we aimed to investigate the potential causal associations between statins and ALS using a mendelian randomization (MR) approach. Methods: Two-sample MR and drug-target MR were performed. Exposure sources included GWAS summary statistics of statin use, low-density-lipoprotein cholesterol (LDL-C), HMGCR-mediated LDL-C and LDL-C response to statins. Results: Genetic predisposition to statin medication was associated with increased ALS risk (OR = 1.085, 95% CI = 1.025–1.148, p = 0.005). After removing SNPs significantly associated with statin use from the instrumental variables (IVs), LDL-C-related higher ALS risk was absent (before removing: OR = 1.075, 95% CI = 1.013–1.141, p = 0.017; after removing: OR = 1.036, 95% CI = 0.949–1.131, p = 0.432). HMGCR-mediated LDL-C (OR = 1.033, 95% CI = 0.823–1.296, p = 0.779) and blood LDL-C response to statins (OR = 0.998, 95% CI = 0.991–1.005, p = 0.538) had no association with ALS. Conclusions: Here, we show that statins may be a risky exposure that increases ALS risk independent of the lowering effect of LDL-C in peripheral circulation. This provides insights into ALS development and prevention.

Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. However, low interclass spectral variability and high intraclass spectral variability in VHR-HSI, shadows, pedestrians, and low signal-to-noise ratio increase the fuzziness of different categories. To address the known challenges of VHR-HSI classification, an effective classification method based on encoder-decoder architecture is proposed. The proposed algorithm is an object-level contextual convolution neural network based on an improved residual network backbone with 3-D convolution, which fully considers the spatial-spectral and contextual features of HSIs. Two different spatial resolution aerial HSIs are used as experimental data. The results show that the overall accuracy of the proposed method is improved by 7.42% and 18.82%, respectively, compared to the pixelwise convolution neural network and DeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.

Abstract Objective To systematically investigate causal relationships between educational attainment, cognitive‐related phenotypes, and amyotrophic lateral sclerosis (ALS). Methods Summary statistics from genome‐wide association studies for educational attainment, math ability, highest math class taken, cognitive performance, intelligence, and ALS were used. A two‐sample Mendelian randomization (MR) design was applied to explore the potential causal associations between them. Results Genetically predisposition to longer educational attainment and harder math class taken were associated with significantly lower statistical odds ratio of ALS. For per year increase in education completed there was a 21% lower (95% confidence interval [CI] = 27–14%) in risk for ALS. For per 1‐SD increase in the highest math class taken obtained there was a 19% lower (95% CI = 9–28%) in risk for ALS. Genetically predisposition to math ability, cognitive performance, and intelligence did not decrease the risk of ALS. Interpretation This study provides genetic support for a causal association between higher educational attainment and a lower risk of ALS. The genes related to these phenotypes are involved in almost all aspects of neuron‐to‐neuron communication. ALS patients are occasionally accompanied by varying degrees of cognitive impairment. People with greater cognitive reserve may be better able to offset damages of degenerative brain changes associated with dementia or other brain diseases, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, or ALS.

Background and ObjectiveIn amyotrophic lateral sclerosis (ALS), progressive weakness significantly limits the ability to exercise. However, measurements of the impaired exercise function and their practical value to assess disease progression in ALS are scarce. Cardiopulmonary exercise testing (CPET) is a non-invasive accurate method used to comprehensively quantify exercise physiology in a variety of diseases. This study aimed to evaluate the clinical value of CPET and to explore its association with disease severity and prognosis prediction in ALS.MethodsA total of 319 participants were enrolled in this 3-year prospective study. After strict quality control, 109 patients with ALS and 150 age- and sex-matched healthy controls were included with comprehensive clinical assessment and follow-ups. The incremental ramp protocol for symptom-limited CPET was applied in both groups. The exercise physiology during peak effort exercise was systematically measured, including the overall aerobic capacity of exercise (VO2 peak) and the respective capacity of the exercise-involved organs [cardiac response (heart rate peak—HR peak), ventilatory efficiency (VE/VCO2 slope), breathing economy (VE/VO2 peak), and other relevant parameters]. Disease severity and progression were evaluated using recognized scales. Survival was monitored with regular follow-ups every 6 months.ResultsDecreased exercise capacity (VO2 peak < 16 ml/kg/min) occurred more frequently in patients with ALS than in controls (44.95% vs. 9.33%, p < 0.01). In patients with ALS, the average VO2 peak (16.16 ± 5.43 ml/kg/min) and HR peak [135 (112–153) bpm] were significantly lower (p < 0.01) than in controls [22.26 ± 7.09 ml/kg/min; 148 (135–164) bpm], but the VE/VCO2 slope was significantly higher [28.05 (25.03–32.16) vs. 26.72 (24.37–29.58); p = 0.03]. In patients with ALS, the VO2 peak and HR peak were significantly correlated with disease severity and progression scores (p < 0.05). Survival analyses revealed the VO2 peak and HR peak as protective indicators while the VE/VO2 peak as a detrimental indicator for the prognostic prediction in ALS (HR = 0.839, p = 0.001; HR = 0.967, p < 0.001; HR = 1.137, p = 0.028, respectively).ConclusionOur prospective study quantified the significantly decreased exercise capacity in ALS through non-invasive CPET. The impaired VO2 peak and HR peak closely correlated with disease severity and independently predicted a worse prognosis. Our findings identified the clinical value of CPET as an objective indicator of disease progression in ALS.

Objectives The aims of this study were to investigate whether serum neurofilament light chain (NfL) levels were correlated with the severity of the axonal degeneration of lower motor neurons (LMNs) in the early symptomatic phase of amyotrophic lateral sclerosis (ALS). Methods In this prospective study, the serum samples used for NfL measurement were obtained from 103 sporadic ALS outpatients within 2 years of disease duration. The severity of axonal degeneration was assessed by assessing the decrease in the compound muscle action potentials (CMAPs) within a 1-month interval from serum sampling. Results The NfL levels showed a significant positive correlation with the relative score as a proxy for the axonal damage of LMNs in patients with ALS (coefficient: 0.264, p = 0.009). Furthermore, this correlation became stronger (coefficient: 0.582, p = 0.037) when estimated only among patients with disease subtypes that involve only LMNs, that is, patients with flail arm or leg syndrome (FAS or FLS). The levels of NfL increased with the severity of axonal damage of LMNs (F = 6.694, P = 0.0001). Conclusions Serum NfL levels mirrored the severity of the axonal degeneration of LMNs, particularly in patients with signs of predominant LMN involvement. These results may have a profound effect on the selection of patients and the monitoring of treatment efficacy in future disease-modifying clinical trials.

Accurate high-resolution soil moisture mapping is critical for surface studies as well as climate change research. Currently, regional soil moisture retrieval primarily focuses on a spatial resolution of 1 km, which is not able to provide effective information for environmental science research and agricultural water resource management. In this study, we developed a quantitative retrieval framework for high-resolution (250 m) regional soil moisture inversion based on machine learning, multisource data fusion, and in situ measurement data. Specifically, we used various data sources, including the normalized vegetation index, surface temperature, surface albedo, soil properties data, precipitation data, topographic data, and soil moisture products from passive microwave data assimilation as input parameters. The soil moisture products simulated based on ground model simulation were used as supplementary data of the in situ measurements, together with the measured data from the Maqu Observation Network as the training target value. The study was conducted in the Zoige region of the Tibetan Plateau during the nonfreezing period (May–October) from 2009 to 2018, using random forests for training. The random forest model had good accuracy, with a correlation coefficient of 0.885, a root mean square error of 0.024 m³/m³, and a bias of −0.004. The ground-measured soil moisture exhibited significant fluctuations, while the random forest prediction was more accurate and closely aligned with the field soil moisture compared to the soil moisture products based on ground model simulation. Our method generated results that were smoother, more stable, and with less noise, providing a more detailed spatial pattern of soil moisture. Based on the permutation importance method, we found that topographic factors such as slope and aspect, and soil properties such as silt and sand have significant impacts on soil moisture in the southeastern Tibetan Plateau. This highlights the importance of fine-scale topographic and soil property information for generating high-precision soil moisture data. From the perspective of inter-annual variation, the soil moisture in this area is generally high, showing a slow upward trend, with small spatial differences, and the annual average value fluctuates between 0.3741 m3/m3 and 0.3943 m3/m3. The intra-annual evolution indicates that the monthly mean average soil moisture has a large geographical variation and a small multi-year linear change rate. These findings can provide valuable insights and references for regional soil moisture research.

The existence of the consistency degradation of the battery pack hinders the accurate estimation of pack capacity and cell capacity in the battery pack. The paper focuses on the capacity estimation of cells in the serial battery pack. The shape invariance of the charging voltage curve is discussed and used as the theoretical foundation of cell capacity difference identification. The matching relationship between two voltage curves is obtained based on the dynamic time warping algorithm. Then the capacity difference identification algorithm to calculate the capacity difference between the two cells is proposed. Based on the algorithm, a three-step capacity estimation method is established. The proposed method can only use the previous charging curve of one cell in the pack and the current charging data of the battery pack to rapidly estimate the capacity of each cell in the battery pack. A 16 serial LiFePO4 battery pack is employed to verify the method. The result shows the estimation error of cell capacities is less than 3% rated capacity. With this method, the cell capacities in the pack can be rapidly and accurately estimated, providing a foundation for the consistency analysis and equalization of the battery pack.

Atmospheric methane (CH4) is an important greenhouse gas that can reflect variations of CH4 emissions and sinks. This study aimed to detect spatial and temporal variations of atmospheric CH4 concentrations in China during 2003–2021 based on CH4 column-averaged dry-air mole fraction (XCH4) products from three satellites, namely, Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT), and Copernicus Sentinel-5 Precursor (S5P). The results revealed that XCH4 observed from three satellites showed high agreement in spatiotemporal variations and demonstrated good consistency with ground station measurements. The correlation coefficients (r) between the three satellites were 0.72 and 0.73, and the correlation coefficients for the ground stations were 0.79, 0.66, 0.03, 0.21, 0.70, and 0.80. The spatial distribution of XCH4 in China was generally high in the east and low in the west and close to that of CH4 emissions, indicating that CH4 emission sources dominated the spatial variations of atmospheric XCH4. From 2003 to 2006, XCH4 remained stable with an annual growth rate of 0.51 ppb·yr−1 and then abruptly increased with an overall growth rate of 6.96 ppb·yr−1. There were obvious seasonal changes in XCH4, with peaks in autumn and summer and nadir in winter and spring. These seasonal variations of XCH4 were related to CH4 emissions from rice planting. Rice cultivation areas generally had high XCH4 concentrations, and the growth cycle of rice plants significantly contributed to seasonal variations of XCH4 in the main rice planting areas. These results provide scientific data that could encourage decision-makers to enact policies and processes to reduce methane emissions.

Objective: Attachment and rumination were examined as the intermediary variables on post-traumatic stress disorder and medication compliance in stroke or TIA patients. Methods: A total of 300 participants with stroke or TIA from the Second Hospital of Hebei Province were selected. Patients accomplished NIHSS, ABCD2, ECR, RSQ, and RRS on admission. After 3 months, the PCL-C and MMAS were collected. Results: In the stroke or TIA patients, the incident of PTSD was 7.7%; PTSD scores were significantly associated with attachment anxiety (r = 0.225, p < 0.01), symptom rumination (r = 0.197, p < 0.01), and obsessive thinking (r = 0.187, p < 0.01). After the Sobel test analysis and verification by the Baron and Kenny’s stepwise approach we found that ruminant mediated the relationship between attachment anxiety and PTSD; obsessive thinking mediated the relationship between attachment anxiety and PTSD. Conclusions: The relationship between attachment anxiety and PTSD was positively predicted by rumination and obsessive thinking. Adult attachment style, rumination, and PTSD scores may not predict medication compliance.

Type 2 diabetes mellitus (T2DM), characterized by insulin resistance and unclear pathogenesis, is a serious menace to human health. Bile acids are the end products of cholesterol catabolism and play an important role in maintaining cholesterol homeostasis. Furthermore, increasing studies suggest that bile acids may regulate glucose tolerance, insulin sensitivity, and energy metabolism, suggesting that bile acids may represent a potential therapeutic target for T2DM. This study summarizes the metabolism of bile acids and, more importantly, changes in their concentrations, constitution, and receptors in diabetes. Furthermore, we provide an overview of the mechanisms underlying the role of bile acids in glucose and lipid metabolism, as well as the occurrence and development of T2DM. Bile acid-targeted therapy may represent a valid approach for T2DM treatment.

Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health management systems and life evaluation before accelerated aging. Capacity recovery, test errors and accelerated aging all affect life model building during normal aging. Therefore, this paper proposes an improved life model based on wavelet transform (WT) signal processing to accurately predict the decline trend of the battery in the normal aging stage. In this paper, the capacity recovery, test noise and capacity diving in the aging trend are effectively removed by wavelet transform. We obtained an optimized life model through the analysis of the model structure and the analysis of the parameter sensitivity of the life model. The particle swarm algorithm (PSO) is employed to identify the parameters of the empirical models with the normal aging data extracted by the WT. Through verification, it is found that the modified cycle life model proposed in this paper can accurately predict the normal aging trajectory of batteries under different discharge rates and temperatures. The prediction error of the improved life model for normal aging is 1.09%.

Aiming at the accelerated aging problem that may occur during the use of high specific energy lithium-ion batteries, this article proposes a method to judge the accelerated aging of lithium-ion batteries. Taking the IC curve and DV curve as the starting point, the complete characteristic curve of the new battery is used as a comparison benchmark, and the battery characteristic curves of different aging stages are compared and analyzed, and the parameters that have obvious changes before and after the accelerated aging of the battery are extracted as the characteristics that the battery has accelerated aging. It is important to establish the relevant characteristic parameter matrix, and use the logistic regression method to train the accelerating aging model of the battery to realize the diagnosis of accelerating aging fault. In view of the fact that lithium batteries rarely undergo a complete charging process, the characteristic parameter sets established in this paper are based on the IC curve and the DV curve in the 15–75% SOC range, which have certain practicability.

Understanding the characteristics of water use efficiency (WUE) could enhance our understanding of the integrated response of terrestrial ecosystems to climate change. In this study, the vegetation photosynthesis model (VPM) and the revised remote sensing-Penman Monteith (RRS-PM) model were used to characterize the spatial-temporal dynamics of gross primary productivity (GPP) and evapotranspiration (ET) in the Yangtze River Delta (YRD) region. Our predicted GPP and ET results were in the range of those reported by other models. To be specific, the RRS-PM model could explain 85% of the variability in ET at the Anji site, China. The root mean square error between the simulation value and the measured ET was 8.543 mm month-1. For the evergreen broadleaf forest, deciduous needle leaf forest and deciduous broadleaf forest, GPP decreased and ET increased, thus the WUE = (GPP/ET) inevitably decreased. The increase in WUE in grassland was due to the decreased ET, for which the rate of decrease was greater than for GPP. For the spatial distribution of WUE, the maximum value occurred in the southern and northeastern parts of the YRD region, while the minimum value was distributed in the middle and northwest. This is likely due to the different land-cover types. In the south, there is more forest, while in the north the land-cover type is almost entirely cropland. In our study, GPP increased with increasing LAI across all of the plant function types. However, ET only increased with LAI when LAI was less than 2 m2 m-2 across all of the plant function types, once LAI was larger than 2 m2 m-2, ET did not continue to increase with LAI.

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

Timely monitoring of marine aquaculture has considerable significance for marine ecological protection and maritime safety and security. Considering that supervised learning needs to rely on a large number of training samples and the characteristics of intensive and regular distribution of the laver aquaculture zone, in this paper, an inaccurate supervised classification model based on fully convolutional neural network and conditional random filed (FCN-CRF) is designed for the study of a laver aquaculture zone in Lianyungang, Jiangsu Province. The proposed model can extract the aquaculture zone and calculate the area and quantity of laver aquaculture net simultaneously. The FCN is used to extract the laver aquaculture zone by roughly making the training label. Then, the CRF is used to extract the isolated laver aquaculture net with high precision. The results show that the k a p p a coefficient of the proposed model is 0.984, the F 1 is 0.99, and the recognition effect is outstanding. For label production, the fault tolerance rate is high and does not affect the final classification accuracy, thereby saving more label production time. The findings provide a data basis for future aquaculture yield estimation and offshore resource planning as well as technical support for marine ecological supervision and marine traffic management.

Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance.

Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a novel synergistic approach to estimating biomass by integrating LiDAR data with Landsat 8 imagery through a deep learning-based workflow. First the relationships between biomass and spectral vegetation indices (SVIs) and LiDAR metrics were separately investigated. Next, two groups of combined optical and LiDAR indices (i.e., COLI1 and COLI2) were designed and explored to identify their performances in biomass estimation. Finally, five prediction models, including K-nearest Neighbor, Random Forest, Support Vector Regression, the deep learning model, i.e., Stacked Sparse Autoencoder network (SSAE), and multiple stepwise linear regressions, were individually used to estimate biomass with input variables of different scenarios, i.e., (i) all the COLI1 (ACOLI1), (ii) all the COLI2 (ACOLI2), (iii) ACOLI1 and all the optical (AO) and LiDAR variables (AL), and (iv) ACOLI2, AO and AL. Results showed that univariate models with the combined optical and LiDAR indices as explanatory variables presented better modeling performance than those with either optical or LiDAR data alone, regardless of the combination mode. The SSAE model obtained the best performance compared to the other tested prediction algorithms for the forest biomass estimation. The best predictive accuracy was achieved by the SSAE model with inputs of combined optical and LiDAR variables (i.e., ACOLI1, AO and AL) that yielded an R2 of 0.935, root mean squared error (RMSE) of 15.67 Mg/ha, and relative root mean squared error (RMSEr) of 11.407%. It was concluded that the presented combined indices were simple and effective by integrating LiDAR-derived structure information with Landsat 8 spectral data for estimating forest biomass. Overall, the SSAE model with inputs of Landsat 8 and LiDAR integrated information resulted in accurate estimation of forest biomass. The presented modeling workflow will greatly facilitate future forest biomass estimation and carbon stock assessments.

Lithium ion batteries are widely used in portable electronics and transportations due to their high energy and high power with low cost. However, they suffer from capacity degradation during long cycling, thus making it urgent to study their decay mechanisms. Commercial 18650-type LiCoO2 + LiNi0.5Mn0.3Co0.2O2/graphite cells are cycled at 1 C rate for 700 cycles, and a continuous post-mortem analysis is performed. Based on these tests, the decay mechanism of the cells is finally proposed. The changes of differential capacity curves of the full cells show that the loss of active materials, loss of lithium ions and cell polarization are the main factors contributing to capacity loss. Non-fully charging of the electrodes is also one of the reasons, but only takes up a minor portion. Impedance results indicate that the charge transfer resistance becomes larger during cycling, especially at low state of charge. Morphology and surface chemistry analysis demonstrates that the electrode particles after cycling exhibit some minor cracks and some additional layers are formed on surfaces of both the cathode and anode electrodes. All of these effects may contribute to the impedance increase, and consequently lead to degradation of the full cells. Thus, a good protection of the surface of the cathode and anode shows great potential to improve the capacity maintenance and prolong the cycle life of the cells.

A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass.