All-optical spatio-temporal metrology for isolated attosecond pulses

Characterizing an isolated attosecond pulse (IAP) is essential for its potential applications. A complete characterization of an IAP ultimately requires the determination of its electric field in both time and space domains. However, previous methods, like the widely-used RABBITT and attosecond streaking, only measure the temporal profile of the attosecond pulse. Here we demonstrate an all-optical method for the measurement of the space-time properties of an IAP. By introducing a non-collinear perturbing pulse to the driving field, the process of IAP generation is modified both spatially and temporally, manifesting as a spatial and a frequency modulation in the harmonic spectrum. By using a FROG-like retrieval method, the spatio-spectral phases of the harmonic spectrum are faithfully extracted from the induced spatio-spectral modulations, which allows a thoroughgoing characterization of the IAP in both time and space. With this method, the spatio-temporal structures of the IAP generated in a two-color driving field in both the near- and far-field are fully reconstructed, from which a weak spatio-temporal coupling in the IAP generation is revealed. Our approach overcomes the limitation in the temporal measurement in conventional in situ scheme, providing a reliable and holistic metrology for IAP characterization.Comment: 18 pages, 5 figure

Unraveling the Effects of Mobile Application Usage on Users’ Health Status: Insights from Conservation of Resources Theory

Numerous studies have documented adverse consequences arising from increased technology usage and advocated for a reduction in such usage as a plausible remedy. However, such recommendations are often infeasible and oversimplistic given mounting evidence attesting to users’ growing reliance on technology in both their personal and professional lives. Building on conservation of resources (COR) theory, we construct a research model to explain how mobile application usage, as delineated by its breadth and depth, affects users’ nomophobia and sleep deprivation, which can have negative impacts on users’ health status. We also consider the moderating influence of physical activity in mitigating the effects of mobile application usage on users’ health. We validated our hypotheses via data collected by surveying 5,842 respondents. Empirical findings reveal that (1) nomophobia is positively influenced by mobile application usage breadth but negatively influenced by mobile application usage depth, (2) sleep deprivation is negatively influenced by mobile application usage breadth but positively influenced by mobile application usage depth, and (3) sleep deprivation and nomophobia negatively impact users’ health status, whereas (4) physical activity attenuates the impact of mobile application usage on sleep deprivation but not nomophobia. The findings from this study not only enrich the extant literature on the health outcomes of mobile application usage by unveiling the impact of mobile application usage patterns and physical activity on users’ health but they also inform practitioners on how calibrating usage breadth and depth, along with encouraging physical activity, can promote healthy habits among users

30+ year evolution of Cu in the surface sediment of Lake Poyang, China.

Poyang Lake, the largest freshwater lake in China, is increasingly experiencing Cu crises. Combining field data, laboratory experiments, and long-term simulations, we retrieved 30 + year evolution of Cu in surface sediments (SCu). SCu evolution between 1983 and 2015 may be separated into two distinguishable periods. During the first period (1983-2003), SCu underwent a continuous increase at a mean rate of 1.80 mg/kg/yr; however, since 2003 it displayed a stepwise reduction tendency. The average SCu concentration of the entire lake in 2015 fell to 54.26 mg/kg, which is approximately 30.01% lower than that in 2003. The operation of Three Gorges Dam (TGD) markedly altered the river-lake relationship, pulled more deposited Cu along with sediment out toward the Yangtze River, and made the regions of high SCu emanate from the southeastern lake extend northwestward between 2003 and 2015. SCu in the reserves showed significant inter-annual variations, with the exception of the Jiangxi Whitebait Spawning Reserve (JWSR), where SCu generally has not been significantly impacted and has displayed no departure from the 30 + year mean of 30.57 mg/kg. The National Germplasm Reserve (NGR) and Nanjishan National Nature Reserve (NNNR) were detected with the highest SCu, with the peak concentrations, respectively, of 123.15 mg/kg and 103.1 mg/kg

High-resolution face swapping via latent semantics disentanglement

We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure attributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending to produce a plausible swapping result. We further extend our method to video face swapping by enforcing two spatio-temporal constraints on the latent space and the image space. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art image/video face swapping methods in terms of hallucination quality and consistency. Code can be found at: https://github.com/cnnlstm/FSLSD_HiRes

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

BackgroundMethamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.MethodFecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.ResultsMUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.ConclusionIn conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings

A two-dimensional angular-resolved proton spectrometer

We present a novel design of two-dimensional (2D) angular-resolved spectrometer for full beam characterization of ultrashort intense laser driven proton sources. A rotated 2D pinhole array was employed, as selective entrance before a pair of parallel permanent magnets, to sample the full proton beam into discrete beamlets. The proton beamlets are subsequently dispersed without overlapping onto a planar detector. Representative experimental result of protons generated from femtosecond intense laser interaction with thin foil target is presented

Lycium barbarum Polysaccharides Attenuate Cisplatin-Induced Hair Cell Loss in Rat Cochlear Organotypic Cultures

The aim of the present study was to investigate the effects of Lycium barbarum polysaccharides (LBP) on cisplatin-induced hair cell damage in the organ of Corti explant. The neonatal (P2–3) rat organ of Corti explant was exposed to cisplatin (20 μM; 48 h) with or without LBP pretreatment (150 and 600 μg/mL; 24 h). Hair cell loss was indicated by FITC-labeled phalloidin staining. The level of reactive oxygen species (ROS) and alteration of mitochondrial membrane potential (ΔΨm) in hair cells were analyzed using fluorescent probes 2′,7′-dichlorofluorescein diacetate and JC-1, respectively. The results showed that LBP significantly attenuated hair cell loss (p < 0.01). Hair cells pretreated with LBP showed significant reduction in ROS production and the decline of ΔΨm compared with cisplatin alone group (p < 0.01), indicating the protective effect of LBP on cisplatin-induced hair cell loss. Taken together, these results indicate that LBP was effective in attenuating cisplatin-induced hair cell loss by reducing the production of ROS and maintaining mitochondrial ΔΨm

A flexible, on-line magnetic spectrometer for ultra-intense laser produced fast electron measurement

We have developed an on-line magnetic spectrometer to measure energy distributions of fast electrons generated from ultra-intense laser-solid interactions. The spectrometer consists of a sheet of plastic scintillator, a bundle of non-scintillating plastic fibers, and an sCMOS camera recording system. The design advantages include on-line capturing ability, versatility of detection arrangement, and resistance to harsh in-chamber environment. The validity of the instrument was tested experimentally. This spectrometer can be applied to the characterization of fast electron source for understanding fundamental laser-plasma interaction physics and to the optimization of high-repetition-rate laser-driven applications