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As an important part of electric vehicles, lithium-ion battery packs will have a certain environmental impact in the use stage. To analyze the comprehensive environmental impact, 11 lithium-ion battery packs composed of different materials were selected as the research object. By introducing the life cycle assessment method and entropy weight method to quantify environmental load, a multilevel index evaluation system was established based on environmental battery characteristics. The results show that the Li–S battery is the cleanest battery in the use stage. In addition, in terms of power structure, when battery packs are used in China, the carbon footprint, ecological footprint, acidification potential, eutrophication potential, human toxicity cancer and human toxicity noncancer are much higher than those in the other four regions. Although the current power structure in China is not conducive to the sustainable development of electric vehicles, the optimization of the power structure is expected to make electric vehicles achieve clean driving in China.

The objective of this study was to evaluate clinical outcomes for patients undergoing IVF treatment where an artificial intelligence (AI) platform was utilized by clinicians to help determine the optimal starting dose of FSH and timing of trigger injection. This was a prospective clinical trial with historical control arm. Four physicians from two assisted reproductive technology treatment centers in the United States participated in the study. The treatment arm included patients undergoing autologous IVF cycles between December 2022–April 2023 where the physician use AI to help select starting dose of follicle stimulating hormone (FSH) and trigger injection timing (N = 291). The control arm included historical patients treated where the same doctor did not use AI between September 2021 and September 2022. The main outcome measures were total FSH used and average number of mature metaphase II (MII) oocytes. There was a non-significant trend towards improved patient outcomes and a reduction in FSH with physician use of AI. Overall, the average number of MIIs in the treatment vs. control arm was 12.20 vs 11.24 (improvement = 0.96, p = 0.16). The average number of oocytes retrieved in the treatment vs. control arm was 16.01 vs 14.54 (improvement = 1.47, p = 0.08). The average total FSH in the treatment arm was 3671.95 IUs and the average in the control arm was 3846.29 IUs (difference = −174.35 IUs, p = 0.13). These results suggests that AI can safely assist in refining the starting dose of FSH while narrowing down the timing of the trigger injection during ovarian stimulation, benefiting the patient in optimizing the count of MII oocytes retrieved.

Internal solitary waves (ISWs) are extreme oceanic dynamic processes that significantly modulate sea surface currents and roughness, but their roles in air-sea interactions are still poorly understood. Here, synchronous ship-board observations of the ocean interior, sea surface and near-surface air conditions in the northern South China Sea showed that, influenced by one ISW front, skin temperature, air temperature, and wind speed decreased by up to 0.9℃, 0.72℃, and 3.1 m·s −1 , respectively. Surface waves enhanced significantly over the strong convergence zone of the ISW front, which perturbed the sea surface and likely resulted in the decrease of skin temperature to bulk water temperature. Air-sea heat flux was estimated to decrease by up to ~ 50% therein. A satellite image acquired during the experiment period showed that ISW fronts occupied 13.4% of the area around the Dongsha Island. Those results suggest that ISWs could be a potentially relevant modulator of regional air-sea interactions.

Two-dimensional layered materials are considered ideal platforms to study novel small-scale optoelectronic devices due to their unique electronic structures and fantastic physical properties. However, it is urgent to further improve the light–matter interaction in these materials because their light absorption efficiency is limited by the atomically thin thickness. One of the promising approaches is to engineer the plasmonic environment around 2D materials for modulating light–matter interaction in 2D materials. This method greatly benefits from the advances in the development of nanofabrication and out-plane van der Waals interaction of 2D materials. In this paper, we review a series of recent works on 2D materials integrated with plasmonic environments, including the plasmonic-enhanced photoluminescence quantum yield, strong coupling between plasmons and excitons, nonlinear optics in plasmonic nanocavities, manipulation of chiral optical signals in hybrid nanostructures, and the improvement of the performance of optoelectronic devices based on composite systems.

Transient thermal strain (TS) is a unique compressive strain that reactive powder concrete (RPC) experiences during temperature rise. RPC has a more rapid TS development than normal concrete (NC) during temperatures of 300 °C~800 °C, and under the same load level, the TS of RPC is 40% to 60% higher than that of NC. However, while TS is known to be significant in RPC, its quantitative influence on the structural fire response and ultimate fire resistance of RPC columns remains insufficiently understood and inadequately modeled, posing a potential risk to fire safety design. In this study, a method for modelling the fire response of RPC columns with due consideration to TS was developed using ABAQUS. The Drucker–Prager model was applied to assess the impact of TS on the fire resistance of RPC columns. The results indicate that ignoring the effect of TS could lead to unsafe fire resistance predictions for RPC columns. The influence of TS on the fire resistance performance of RPC columns increases with the increase in cross-sectional dimensions. When the cross-sectional dimension of RPC columns increases from 305 mm to 500 mm, the influence of TS on the fire resistance of RPC columns increases from 22% to 43%. Under the same load, the influence of TS on the fire resistance of RPC columns is 31.3%, which is greater than that on NC columns. When the hydrocarbon heating curve is used, if the influence of TS is not considered, the fire resistance will be overestimated by 18.2% and 37.7%. Under fire, the existence of TS will lead to a further increase in the compressive stress of the RPC element in the relatively low temperature region, resulting in a greater stress redistribution, and accelerating the RPC column to reach the fire resistance. Therefore, it is crucial to clearly consider TS for the accurate fire resistance prediction and safe fire protection design of RPC columns. Crucially, these findings have direct significance for the fire protection design of actual projects, such as liquefied petroleum stations.

Background Spectral analysis is a model-free PET quantification technique that treats the time-space signal as an impulse response to a bolus injection. Band-pass spectral analysis, considering specific frequency ranges, enables calculation of separate parametric maps of receptor subtype tracer binding for suitable radiopharmaceuticals such as [ 11 C]Ro15-4513 binding to GABA A α 1/5 subunits. Frequency ranges are based on inspection of spectra, prior knowledge of receptor distribution, and blocking studies. The process currently requires the manual selection of frequency ranges based on the data. To enhance the efficiency of band-pass spectral analysis and extend its application to a broader range of tracers, we propose employing machine learning to automate the selection of spectral boundaries. Based on these boundaries, voxel-wise parametric maps can be generated. The machine learning models utilized in this study include 1D Convolutional Neural Network, Neural Network, Support Vector Machine, Logistic Regression, K-nearest neighbors, and Fine Tree. Results The best machine learning model, Fine Tree, agreed with the manual frequency boundary in 96.92% of 3185 ROIs. The absolute mean error was 3.80% for slow component volume-of-distribution ( V slow , largely representing α 5) and 4.74% for fast component volume-of-distribution( V fast , largely representing α 5), while the relative error was 2.83% ± 43.47% for V slow and - 2.01% ± 78.04% for V fast . The median test-retest intraclass correlation coefficient across six representative regions was 0.770 for V slow , 0.670 for V fast , and 0.502 for total component volume-of-distribution( V d ). Parametric maps applying different boundaries for different ROIs were generated. Conclusion The machine learning model developed provided accurate boundary predictions in 96.92% of regions, with minimal average bias. However, when errors occur, they can be large, owing to the sparsity of peaks. The model enables setting boundaries automatically for the vast majority of regions, followed by manual checking of the outliers. It opens the possibility of accelerating analyses e.g. of GABA A α 1/2/3/5 subunit binding using [ 11 C]flumazenil and of extending band-pass spectral analysis to other receptor systems.

Instability within internal solitary waves (ISWs), featured by temperature inversions with vertical lengths of dozens of meters and current reversals in the upper shoreward velocity layer, was observed in the northern South China Sea at a water depth of 982 m by using mooring measurements between June 2017 and May 2018. Regions of shear instability satisfying Ri < 1/4 were found within those unstable ISWs, and some large ISWs were even possibly in the breaking state, indicated by the ratio of L x (wave width satisfying Ri < 1/4) to λ η /2 (wavelength at half amplitude) larger than 0.86. Wave stability analyses revealed that the observed wave shear instability was induced by strong background current shear associated with multiscale dynamic processes, which greatly strengthened wave shear by introducing sharp perturbations to the fine-scale vertical structures of ISWs. During the observational period, wave shear instability was strong in summer (July–September) while weak in winter (January–March). Sensitivity experiments revealed that the observed shear instability was prone to be triggered within large ISWs by the background current shear and sensitive to the pycnocline depth in the background stratification. However, shear instability within ISWs was observed to be promoted during mid-January, as the near-inertial waves trapped inside an anticyclonic eddy resulted in enhanced background current shear between 150 and 300 m. This work emphasizes the notable impacts of multiscale background processes on ISWs in the oceans.

Spectral analysis is a model-free PET quantification technique that treats the time-space signal as an impulse response to a bolus injection. Band-pass spectral analysis, considering specific frequency ranges, enables calculation of separate parametric maps of receptor subtype tracer binding for suitable radiopharmaceuticals such as [ 11 C]Ro15-4513 binding to GABAA α 1/5 subunits. Frequency ranges are based on inspection of spectra, prior knowledge of receptor distribution, and blocking studies. The process currently requires the manual selection of frequency ranges based on the data. To enhance the efficiency of band-pass spectral analysis and extend its application to a broader range of tracers, we propose employing machine learning to automate the selection of spectral boundaries. Based on these boundaries, voxel-wise parametric maps can be generated. The machine learning models utilized in this study include 1D Convolutional Neural Network, Neural Network, Support Vector Machine, Logistic Regression, K-nearest neighbors, and Fine Tree.BACKGROUNDSpectral analysis is a model-free PET quantification technique that treats the time-space signal as an impulse response to a bolus injection. Band-pass spectral analysis, considering specific frequency ranges, enables calculation of separate parametric maps of receptor subtype tracer binding for suitable radiopharmaceuticals such as [ 11 C]Ro15-4513 binding to GABAA α 1/5 subunits. Frequency ranges are based on inspection of spectra, prior knowledge of receptor distribution, and blocking studies. The process currently requires the manual selection of frequency ranges based on the data. To enhance the efficiency of band-pass spectral analysis and extend its application to a broader range of tracers, we propose employing machine learning to automate the selection of spectral boundaries. Based on these boundaries, voxel-wise parametric maps can be generated. The machine learning models utilized in this study include 1D Convolutional Neural Network, Neural Network, Support Vector Machine, Logistic Regression, K-nearest neighbors, and Fine Tree.The best machine learning model, Fine Tree, agreed with the manual frequency boundary in 96.92% of 3185 ROIs. The absolute mean error was 3.80% for slow component volume-of-distribution ( V slow , largely representing α 5) and 4.74% for fast component volume-of-distribution( V fast , largely representing α 5), while the relative error was 2.83% ± 43.47% for V slow and - 2.01% ± 78.04% for V fast . The median test-retest intraclass correlation coefficient across six representative regions was 0.770 for V slow , 0.670 for V fast , and 0.502 for total component volume-of-distribution( V d ). Parametric maps applying different boundaries for different ROIs were generated.RESULTSThe best machine learning model, Fine Tree, agreed with the manual frequency boundary in 96.92% of 3185 ROIs. The absolute mean error was 3.80% for slow component volume-of-distribution ( V slow , largely representing α 5) and 4.74% for fast component volume-of-distribution( V fast , largely representing α 5), while the relative error was 2.83% ± 43.47% for V slow and - 2.01% ± 78.04% for V fast . The median test-retest intraclass correlation coefficient across six representative regions was 0.770 for V slow , 0.670 for V fast , and 0.502 for total component volume-of-distribution( V d ). Parametric maps applying different boundaries for different ROIs were generated.The machine learning model developed provided accurate boundary predictions in 96.92% of regions, with minimal average bias. However, when errors occur, they can be large, owing to the sparsity of peaks. The model enables setting boundaries automatically for the vast majority of regions, followed by manual checking of the outliers. It opens the possibility of accelerating analyses e.g. of GABAA α 1/2/3/5 subunit binding using [11C]flumazenil and of extending band-pass spectral analysis to other receptor systems.CONCLUSIONThe machine learning model developed provided accurate boundary predictions in 96.92% of regions, with minimal average bias. However, when errors occur, they can be large, owing to the sparsity of peaks. The model enables setting boundaries automatically for the vast majority of regions, followed by manual checking of the outliers. It opens the possibility of accelerating analyses e.g. of GABAA α 1/2/3/5 subunit binding using [11C]flumazenil and of extending band-pass spectral analysis to other receptor systems.

Signals from the gut are increasingly recognized as modulators of brain function and behavior. However, the pathways through which the gut conveys adverse or unpleasant information to the brain are still not well understood. In this study, we identify an aversive gut-brain axis in Drosophila melanogaster that detects toxin-induced gut damage and triggers both innate and learned anticipatory emesis (vomiting). After toxin ingestion, reactive oxygen species are produced by midgut enterocytes and detected by the transient receptor potential channel TrpA1 on nearby enteroendocrine cells. This sensing stimulates the release of neuropeptides from enteroendocrine cells, likely representing the gastric malaise flies experience after eating. We show that these neuropeptides act on specific serotonergic and dopaminergic neurons in the brain. These neurons interact with each other and signal to the downstream memory-related mushroom bodies to promote emesis. This circuit not only drives an immediate emetic response but also represents a malaise-driven aversive signal. The signal manifests as the persistent activity of dopaminergic neurons, which reinforces aversive valence to odor cues in the mushroom bodies. Thus, the flies learn that a specific odor predicts the presence of a toxin in food and exhibit anticipatory emesis upon re-exposure to the same odor. Taken together, we have identified an interoceptive signaling pathway that may be conserved for detecting harmful gut conditions and for remembering how to avoid them. Our work offers a mechanistic framework for studying aversive gut-brain communication involved in feeding, metabolism, depression, brain injury, and neurodegenerative diseases.

Neurotransmitter receptors guide the propagation of signals between brain regions. Mapping receptor distributions in the brain is therefore necessary for understanding how neurotransmitter systems mediate the link between brain structure and function. Normative receptor density can be estimated using group averages from Positron Emission Tomography (PET) imaging. However, the generalizability and reliability of group-average receptor maps depends on the inter-individual variability of receptor density, which is currently unknown. Here we collect group standard deviation brain maps of PET-estimated protein abundance for 12 different neurotransmitter receptors and transporters across 7 neurotransmitter systems, including dopamine, serotonin, acetylcholine, glutamate, GABA, cannabinoid, and opioid. We illustrate how cortical and subcortical inter-individual variability of receptor and transporter density varies across brain regions and across neurotransmitter systems. We complement inter-individual variability with inter-regional variability, and show that receptors that vary more across brain regions than across individuals also demonstrate greater out-of-sample spatial consistency. Altogether, this work quantifies how receptor systems vary in healthy individuals, and provides a means of assessing the generalizability of PET-derived receptor density quantification.