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Decarbonization plans depend on the rapid, large-scale deployment of batteries to sufficiently decarbonize the electricity system and on-road transport. This can take many forms, shaped by technology, materials, and supply chain selection, which will have local and global environmental and social impacts. Current knowledge gaps limit the ability of decision-makers to make choices in facilitating battery deployment that minimizes or avoids unintended environmental and social consequences. These gaps include a lack of harmonized, accessible, and up-to-date data on manufacturing and supply chains and shortcomings within sustainability and social impact assessment methods, resulting in uncertainty that limits incorporation of research into policy making. These gaps can lead to unintended detrimental effects of large-scale battery deployment. To support decarbonization goals while minimizing negative environmental and social impacts, we elucidate current barriers to tracking how decision-making for large-scale battery deployment translates to environmental and social impacts and recommend steps to overcome them.

Abstract Background Fall prevention and balance control constitute critical components of rehabilitation for stroke survivors. Kneeling training, characterized by its low center of gravity focus, has been incorporated into rehabilitation regimens to enhance postural control across various pathological conditions. Despite its widespread use, empirical evidence substantiating the efficacy of kneeling training is limited, particularly in the context of mobility and balance improvement for patients who have had a stroke. This study aims to substantiate the safety and effectiveness of kneeling training in individuals recovering from stroke. Methods A randomized controlled trial comparing kneeling training and conventional rehabilitation training was conducted, involving sixty-seven participants allocated to the Kneeling Training Group (KNT) and the Conventional Rehabilitation Group (CVR). The KNT group underwent 30-minute sessions of kneeling training, while the CVR group received conventional treadmill walking training, both administered six times per week over four weeks. Evaluation encompassed the Fugl-Meyer Assessment for Lower Extremity (FMA-LE), the Berg Balance Scale (BBS), and gait analysis was conducted at baseline, as well as at the 2 and 4-week intervals. Results Our study established the safety of a 4-week kneeling training program. Notably, the KNT group exhibited more pronounced improvements in BBS scores at weeks 2 and 4 compared to the CVR group. However, no significant disparities emerged in FMA-LE and gait analysis between the two groups. Our findings suggest that kneeling training may serve as a viable option for enhancing lower limb balance in survivors who have had a stroke. Conclusions We conclude that kneeling training, characterized by its safety, simplicity, and no restrictions on location or equipment, represents a valuable therapeutic approach for enhancing walking balance in individuals recovering from stroke. Trial registration Clinical trials ChiCTR1900028385, December 20, 2019.

Abstract As spaceflight becomes more common with commercial crews, blood-based measures of crew health can guide both astronaut biomedicine and countermeasures. By profiling plasma proteins, metabolites, and extracellular vesicles/particles (EVPs) from the SpaceX Inspiration4 crew, we generated “spaceflight secretome profiles,” which showed significant differences in coagulation, oxidative stress, and brain-enriched proteins. While >93% of differentially abundant proteins (DAPs) in vesicles and metabolites recovered within six months, the majority (73%) of plasma DAPs were still perturbed post-flight. Moreover, these proteomic alterations correlated better with peripheral blood mononuclear cells than whole blood, suggesting that immune cells contribute more DAPs than erythrocytes. Finally, to discern possible mechanisms leading to brain-enriched protein detection and blood-brain barrier (BBB) disruption, we examined protein changes in dissected brains of spaceflight mice, which showed increases in PECAM-1, a marker of BBB integrity. These data highlight how even short-duration spaceflight can disrupt human and murine physiology and identify spaceflight biomarkers that can guide countermeasure development.

Abstract Background Hepatic encephalopathy (HE) is closely associated with inflammatory responses. However, as a crucial regulator of the immune and inflammatory responses, the role of leucine-rich repeat kinase 2 (LRRK2) in the pathogenesis of HE remains unraveled. Herein, we investigated this issue in thioacetamide (TAA)-induced HE following acute liver failure (ALF). Methods TAA-induced HE mouse models of LRRK2 wild type (WT), LRRK2 G2019S mutation (Lrrk2 G2019S) and LRRK2 knockout (Lrrk2 −/−) were established. A battery of neurobehavioral experiments was conducted. The biochemical indexes and pro-inflammatory cytokines were detected. The prefrontal cortex (PFC), striatum (STR), hippocampus (HIP), and liver were examined by pathology and electron microscopy. The changes of autophagy-lysosomal pathway and activity of critical Rab GTPases were analyzed. Results The Lrrk2 −/−-HE model reported a significantly lower survival rate than the other two models (24% vs. 48%, respectively, p

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a five-year survival rate of approximately 10%. Genetic mutations are pivotal drivers in PDAC pathogenesis, but recent investigations also revealed the involvement of non-genetic alterations in the disease development. In this study, we undertook a multi-omics approach, encompassing ATAC-seq, RNA-seq, ChIP-seq, and Hi-C methodologies, to dissect gene expression alterations arising from changes in chromosome accessibility and chromatin three-dimensional interactions in PDAC. Results Our findings indicate that chromosomal structural alterations can lead to abnormal expressions on key genes during PDAC development. Notably, overexpression of oncogenes FGFR2, FOXA2, CYP2R1, and CPOX can be attributed to the augmentation of promoter accessibility, coupled with long-range interactions with distal elements. Additionally, our findings indicate that chromosomal structural alterations caused by genomic instability can lead to abnormal expressions in PDACs. As an example, by analyzing chromosomal changes, we identified a putative oncogenic gene, LPAR1, which shows upregulated expression in both PDAC cell lines and clinical samples. The overexpression is correlated with alterations in LPAR1-associated 3D genome structure and chromatin state. We further demonstrated that high LPAR1 activity is required for enhanced PDAC cell migration in vitro. Conclusions Collectively, our findings reveal that the chromosomal conformational alterations, in addition to the well-known genetic mutations, are critical for PDAC tumorigenesis.

In recent years, with the continuous promotion of the " Ocean Power" strategy, Underwater Wireless Optical Communication (UWOC) has played an important role in marine military, underwater environment monitoring, submarine oil exploration and marine science research due to its advantages in large bandwidth, fast rate, low power consumption and high security. UWOC has become a new feasible underwater communication technology. The positioning technology is now regarded as the basis of UWOC applications and one of the key techniques in underwater target detection & moving object tracking, which has been favored by researchers. In this paper, the development of UWOC and related research results are introduced in detail, with diverse UWOC localization methods being analyzed. Besides, the research status, the challenges and the development prospects for UWOC localization technology are also discussed.

The risk factor of the receiving water body is one of the important factors that affect the self-purification ability of the water body. Analyzing the concentration and removal rate of the target substances such as suspended solid (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in the receiving water body during the life cycle can trace the risk factor. Based on this, this article takes a sewage treatment plant and its receiving water body in Nanjing as the research object, sets up five sampling points, and calculates the target concentration and removal rate using the cellular automata-Markov chain Monte Carlo method. Thus, risk assessment of water bodies can be achieved. The results show that: (1) the concentration of the target substance in each process stage has obviously decreased during the life cycle, with an average concentration of 3–18 mg/L; (2) in the life cycle, the highest removal rate of target concentration in each process section can reach 85%; (3) the concentration of target substances SS, COD, TN, TN, TP, and TP at the outlet of the sewage treatment plant is relatively high. Practice has proven that this research method can assess the risk factors of receiving water bodies, so as to improve the scientific, refined and standardized environmental management.

Abstract This paper gives concrete examples to exhibit how to use the replica trick to calculate the quantum (quasi-)distances holographically. First, we consider the fidelity and relative entropy between thermal states that are dual to the Schwarzschild-AdS black holes. Then we generalize our method into the RN-AdS black holes by adding a U(1) gauge field. We also investigate the fidelity between states excited by scalar operator in probe limit. In this case, it is surprising that the fidelity in standard quantization will suffer from new UV divergence though the usual holographic renormalization has been applied. We call for deep understanding for such divergence in the future. We also discover a holographic method to check whether the density matrices of two holographic states are commutative.

Abstract The gradient nanostructure is machined on the aluminum (Al) alloy by the two-dimensional ultrasonic surface burnishing process (2D-USBP). The mechanism of why the gradient nanostructure enhances wear resistance is investigated. The mechanical properties and microstructure characterization for the gradient nanostructure are performed by operating a nanoindenter, transmission electron microscopy (TEM), and electron backscattered diffraction (EBSD). Dry wear tests are performed on the samples before and after machining to evaluate the wear resistance and mechanisms. The effect of the gradient nanostructure on the wear resistance is explored by developing the crystal plasticity (CP) finite element and molecular dynamics (MD) models. The characterization results show that the 2D-USBP sample prepared a gradient structure of ∼600 µm thick on the aluminum surface, increasing the surface hardness from 1.13 to 1.71 GPa and reducing the elastic modulus from 78.84 to 70.14 GPa. The optimization of the surface microstructure and the increase of the mechanical properties effectively enhance the wear resistance of the sample, with 41.20%, 39.07%, and 54.58% of the wear scar areas for the 2D-USBP treated samples to the original samples under 5, 10, and 15 N loads, respectively. The gradient nanostructure hinders the slip of dislocations inside the sample during the wear process and reduces the size and scope of plastic deformation; meanwhile, the resistance to deformation, adhesion, and crack initiation and propagation of the sample surface is improved, resulting in enhanced wear resistance.

Obtaining discriminative joint features is crucial for skeleton-based human action recognition. Current models mainly focus on the research of skeleton topology encoding. However, their predefined topology is the same and fixed for all action samples, making it challenging to obtain discriminative joint features. Although some studies have considered the complex non-natural connection relationships between joints, the existing methods cannot fully capture this complexity by using high-order adjacency matrices or adding trainable parameters and instead increase the computation parameters. Therefore, this study constructs a novel adaptive channel-enhanced graph convolution (ACE-GCN) model for human action recognition. The model generates similar and affinity attention maps by encoding channel attention in the input features. These maps are complementarily applied to the input feature map and graph topology, which can realize the refinement of joint features and construct an adaptive and non-shared channel-based adjacency matrix. This method of constructing the adjacency matrix improves the model’s capacity to capture intricate non-natural connections between joints, prevents the accumulation of unnecessary information, and minimizes the number of computational parameters. In addition, integrating the Edgeconv module into a multi-branch aggregation improves the model’s ability to aggregate different scale and temporal features. Ultimately, comprehensive experiments were carried out on NTU-RGB+D 60 and NTU-RGB+D 120, which are two substantial datasets. On the NTU RGB+D 60 dataset, the accuracy of human action recognition was 92% (X-Sub) and 96.3% (X-View). The model achieved an accuracy of 96.6% on the NW-UCLA dataset. The experimental results confirm that the ACE-GCN exhibits superior recognition accuracy and lower computing complexity compared to current methodologies.

The spatial characteristics, pollution sources, and risks of soil heavy metals were analyzed on Hainan Island. The results showed that the heavily polluted points accounted for 0.56%, and the number of mildly and above polluted points accounted for 15.27%, respectively, which were mainly distributed in the northern part of the study area. The principal component analysis–absolute principal component score–multiple linear regression (APCS-MLR) and the positive matrix factorization (PMF) revealed four sources of heavy metals: agricultural pollution sources for cadmium, (Cd), industrial and mining pollution sources for arsenic, (As), transportation pollution sources for zinc and lead (Zn and Pb), and natural pollution sources for chromium, nickel, and copper (Cr, Ni, and Cu). The human health risk assessment indicated that the average non-carcinogenic risk (HI) for both adults and children was within the safe threshold (1, indicating a potential health risk above the safe threshold. Meanwhile, nearly 100% and 99.94% of the TCR values exceeded 1 × 10−4 for children and adults, indicating that Cr and Ni are priority heavy metals for control. The research results provide the necessary scientific basis for the prevention and control of heavy metals in agricultural soils.

Overuse of chemical nitrogen (N) fertilizers leads to N leaching and soil degradation. Replacing chemical N fertilizers with organic fertilizers can enhance soil nutrition, reduce N loss, and improve soil productivity. However, the effects of combining organic and chemical fertilizers on soil N components and N transformation remain unclear. A 12-year field study included four treatments: no fertilizer (CK), chemical fertilizer alone (CF), 50% chemical N fertilizer combined with co-composted organic fertilizer (CFM), and composted (CFMC) organic fertilizer. The results showed that CFM and CFMC significantly enhanced SOC, TN, LFON, DON, NH4+-N, and MIN levels compared to CF. The CFM and CFMC treatments enhanced the soil N supply capacity and N pool stability by increasing the N mineralization potential (N0) and decreasing the N0/TN ratio. The CFM and CFMC treatments decreased net N ammonification rates by 108.03%–139.83% and 0.44%–64.91% and net mineralization rates by 60.60%–66.30% and 1.74%–30.38%, respectively. Changes in N transformation have been attributed to increased soil pH, enzyme activity, and substrate availability. These findings suggest that partial organic fertilizer substitution, particularly with co-composted organic fertilizers, is a viable strategy for enhancing soil fertility, improving soil N supply and stability, and reducing N loss in rubber plantations.