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Abstract The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper NADW (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014–20 observations from the transatlantic Overturning in the Subpolar North Atlantic Program (OSNAP) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre [−13.0 ± 0.5 Sv (1 Sv ≡ 10⁶m³s⁻¹)] mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intraseasonal changes across the region that result from a basinwide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation. 

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms. 

Abstract Mitochondrial features and activities vary in a cell type- and developmental stage-dependent manner to critically impact cell function and lineage development. Particularly in male germ cells, mitochondria are uniquely clustered into intermitochondrial cement (IMC), an electron-dense granule in the cytoplasm to support proper spermatogenesis. But it remains puzzling how mitochondria assemble into such a stable structure as IMC without limiting membrane during development. Here, we showed that GASZ (germ cell-specific, ankyrin repeat, SAM and basic leucine zipper domain containing protein), a mitochondrion-localized germ cell-specific protein, self-interacted with each other to cluster mitochondria and maintain protein stability for IMC assembling. When the self-interaction of GASZ was disrupted by either deleting its critical interaction motif or using a blocking peptide, the IMC structure was destabilized, which in turn led to impaired spermatogenesis. Notably, the blocked spermatogenesis was reversible once GASZ self-interaction was recovered. Our findings thus reveal a critical mechanism by which mitochondrion-based granules are properly assembled to support germ cell development while providing an alternative strategy for developing nonhormonal male contraceptives by targeting IMC protein interactions. 

Abstract Saharan dust exerts profound impacts on the genesis and intensification of tropical cyclones (TCs). Such impacts on various stages of the TCs have yet to be explored. In this study, we utilize the Cloud‐Resolving weather research and forecasting model (WRF) to investigate the relative importance of the microphysical and radiative effects of dust on two hurricanes (Earl and Danielle) at different life stages under similar dynamical conditions in 2010. Both TCs were embedded in a dusty environment throughout their lifetime. A new dust ice nucleation scheme was implemented into the aerosol‐aware Texas A&M University two‐moment microphysical scheme in WRF. Moreover, the dust radiative effect was included in the Goddard Shortwave Scheme of WRF. Our sensitivity experiments show that the radiative effect of dust (DRAD) amplified the mid‐level ridge in the Central Atlantic Ocean through temperature perturbation, changing the tracks of Danielle and Earl. Further analyses reveal an early shift of Danielle's maximum intensity for 12 hours but a significantly suppressed Earl in DRAD. In addition, the microphysical effect of dust had little impact on the large‐scale dynamical fields and storm tracks. The inclusion of dust as ice nucleation particles results in more variations in the intensity of Danielle and Earl than in other scenarios. This is owing to the higher maximum diabatic heating rate in the rainband region that perturbs the size of the TC. This study shows the dominant dust radiative effects on both intensity and track of the storm. In addition, there is evidence that dust suppresses the early stage TC but provides favorable conditions for matured TC. Both findings have profound implications for hurricane forecast and address the importance of accounting for detailed cloud microphysics and aerosol‐TC interactions in the operational forecasting models. 

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality—reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period—we estimate a potential of 41 to 50 Mt net additional annual CO₂uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity. 

Abstract Primordial germ cells (PGCs) are the germline precursors that give rise to oocytes and sperm, ensuring the continuation of life. While the PGC specification is extensively studied, it remains elusive how the PGC population is sustained and expanded after they migrate to embryonic gonads before birth. This study demonstrates that NRF1, a known regulator for mitochondrial metabolism, plays critical roles in post‐migrating PGC development. We show that NRF1 protein level gradually increases in post‐migrating PGCs during embryonic development. ConditionalNrf1knockout from embryonic germ cells leads to impaired PGC proliferation and survival. In addition, NRF1 may also actively drive PGC derivation from pluripotent stem cells. Using whole genome transcriptome profiling and ChIP‐seq analyses, we further reveal that NRF1 directly regulates key signalling molecules in PGC formation, transcription factors in proliferation and cell cycle and enzymes in mitochondrial metabolism. Overall, our findings highlight an essential requirement of NRF1 in regulating a broad transcriptional network to support post‐migrating PGC development both in vitro and in vivo. 

Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined conditions, i.e., triggers, but behave normally otherwise. The triggers of existing attacks are mainly injected in the pixel space, which tend to be visually identifiable at both training and inference stages and detectable by existing defenses. In this paper, we propose a simple but effective and invisible black-box backdoor attack FTROJAN through trojaning the frequency domain. The key intuition is that triggering perturbations in the frequency domain correspond to small pixel-wise perturbations dispersed across the entire image, breaking the underlying assumptions of existing defenses and making the poisoning images visually indistinguishable from clean ones. Extensive experimental evaluations show that FTROJAN is highly effective and the poisoning images retain high perceptual quality. Moreover, we show that FTROJAN can robustly elude or significantly degenerate the performance of existing defenses. 

ABSTRACT IntroductionCurrent wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3–8 yr. MethodsChildren (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin’s concordance correlation coefficient (CCC). ResultsMean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. ConclusionsThe PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.