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Sulforaphane (SFN) is an organosulfur compound categorized as an isothiocyanate (ITC), primarily extracted from cruciferous vegetables like broccoli and cabbage. The molecular formula of sulforaphane (SFN) is C6H11NOS2. SFN is generated by the hydrolysis of glucoraphanin (GRP) through the enzyme myrosinase, showing notable properties including anti‐diabetic, anti‐inflammatory, antimicrobial, anti‐angiogenic, and anticancer attributes. Ongoing clinical trials are investigating its potential in diseases such as cancer, neurodegenerative diseases, diabetes‐related complications, chronic kidney disease, cardiovascular disease, and liver diseases. Several animal carcinogenesis models and cell culture models have shown it to be a very effective chemopreventive agent, and the protective effects of SFN in ophthalmic diseases have been linked to multiple mechanisms. In murine models of diabetic retinopathy and age‐related macular degeneration, SFN delays retinal photoreceptor cell degeneration through the Nrf2 antioxidative pathway, NF‐κB pathway, AMPK pathway, and Txnip/mTOR pathway. In rabbit models of keratoconus and cataract, SFN has been shown to protect corneal and lens epithelial cells from oxidative stress injury by activating the Keap1‐Nrf2‐ARE pathway and the Nrf‐2/HO‐1 antioxidant pathway. Oral delivery or intraperitoneal injection at varying concentrations are the primary strategies for SFN intake in current preclinical studies. Challenges remain in the application of SFN in eye disorders due to its weak solubility in water and limited bioavailability because of the presence of blood–ocular barrier systems. This review comprehensively outlines recent research on SFN, elucidates its mechanisms of action, and discusses potential therapeutic benefits for eye disorders such as age‐related macular degeneration (AMD), diabetic retinopathy (DR), cataracts, and other ophthalmic diseases, while also indicating directions for future clinical research to achieve efficient SFN treatment for ophthalmic diseases. [ABSTRACT FROM AUTHOR]

Zinc (Zn)–dysprosium (Dy) binary alloys are promising biodegradable bone fracture fixation implants owing to their attractive biodegradability and mechanical properties. However, their clinical application is a challenge for bone fracture healing, due to the lack of Zn–Dy alloys with tailored proper bio‐mechanical and osteointegration properties for bone regeneration. A Zn‐5Dy alloy with high strength and ductility and a degradation rate aligned with the bone remodeling cycle is developed. Here, mechanical stability is further confirmed, proving that Zn‐5Dy alloy can resist aging in the degradation process, thus meeting the mechanical requirements of fracture fixation. In vitro cellular experiments reveal that the Zn‐5Dy alloy enhances osteogenesis and angiogenesis by elevating SIRT4‐mediated mitochondrial function. In vivo Micro‐CT, SEM‐EDS, and immunohistochemistry analyses further indicate good biosafety, suitable biodegradation rate, and great osteointegration of Zn‐5Dy alloy during bone healing, which also depends on the upregulation of SIRT4‐mediated mitochondrial events. Overall, the study is the first to report a Zn‐5Dy alloy that exerts remarkable osteointegration properties and has a strong potential to promote bone healing. Furthermore, the results highlight the importance of mitochondrial modulation and shall guide the future development of mitochondria‐targeting materials in enhancing bone fracture healing. [ABSTRACT FROM AUTHOR]

The Qinghai‐Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub‐daily precipitation observations in this region has hindered our understanding of rainstorm‐related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub‐daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub‐daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub‐daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1‐km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm‐related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub‐daily precipitation and assessing its uncertainty in ungauged regions. Plain Language Summary: As one of famous hotspots for natural disaster studies on Earth, the Qinghai‐Tibet Plateau (QTP) is highly vulnerable to destructive rainstorm hazard and related natural disasters, causing significant damage to property, infrastructure, agriculture, and resulting in extensive loss of life. Short‐duration heavy precipitation at sub‐daily scales is an important trigger for flash flood, debris flows and other disasters in QTP. However, it is a poorly gauged high mountain region, observed data for sub‐daily precipitation is extremely limited. Although there have been several satellite products and reanalysis data for sub‐daily precipitation in QTP, their quality has large bias and uncertainty compared to observations. It leaves a large data gap of sub‐daily precipitation, hindering the studies of rainstorm‐related natural disasters in the region. In this work, we develop a new strategy to quantify the temporal scaling characteristics of sub‐daily precipitation, as a basis of temporal downscaling. Then we use the new strategy to generate a parameters data set, to fill the data gap of sub‐daily precipitation in QTP. The parameters data set generated provides an effective way to estimate sub‐daily precipitation and its uncertainty, which can effectively serve for the rainstorm‐related natural disasters study in QTP. Key Points: A high‐resolution gridded parameters data set is generated to estimate sub‐daily precipitation and its uncertainty in QTPThe temporal scaling characteristics of sub‐daily precipitation in QTP is well described by a logarithmic functionSpatial heterogeneity in the temporal scaling characteristics of sub‐daily precipitation in QTP is closely related to geographical conditions [ABSTRACT FROM AUTHOR]

The aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs' behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs' V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA. [ABSTRACT FROM AUTHOR]

Cortical gray to white matter signal intensity ratio (GWR) measured from T1‐weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross‐sectional dataset and a longitudinal dataset. The cross‐sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross‐sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with β‐amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with β‐amyloid deposition, MCI without β‐amyloid deposition, and controls. The spatial pattern of GWR‐increased regions was not influenced by β‐amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with β‐amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR‐increased regions was independent of β‐amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD. [ABSTRACT FROM AUTHOR]

Optoelectronic synapses integrating sensing–memory–processing functions have great advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy‐efficient manner. Herein, a light‐induced bidirectional response is demonstrated in the proposed WSe2/MoS2 junction field‐effect transistor (JFET) with an extra Ge back gate. The WSe2/MoS2 JFET exhibits high responsivity and detectivity owing to effective modulation by the top junction and back dielectric gates. The unique bidirectional photoresponse and interfacial state storage properties of the device result in significant synaptic excitatory effects under visible light stimulation and remarkable synaptic inhibitory effects under infrared illumination. Optical storage with visible light and optical erasure with infrared light can be achieved in the device based on its synaptic behavior. The conductance changes under different types of illumination can be used to mimic the weight update in a neural network for image recognition. A high accuracy exceeding 98% was achieved in handwritten digit recognition. This visible and infrared dual‐band phototransistor, which integrates image perception, memory, and recognition functionalities, provides a promising solution for self‐driving, surveillance, computer vision, and biomedical imaging applications. [ABSTRACT FROM AUTHOR]

Gaussianity, non‐Gaussianity, linearity, and nonlinearity generally coexist within industrial process variables, and should be taken into account simultaneously for process modelling with monitoring. This paper presents a comprehensive monitoring method of industrial processes using multivariable characteristics evaluation and subspace decomposition. First, a multivariable characteristics evaluation method is presented to divide the process variables into the Gaussian linear, Gaussian nonlinear, non‐Gaussian linear, and non‐Gaussian nonlinear subspaces. Second, the PCA‐ICA‐KPCA‐KICA‐based multivariable subspace decomposition is proposed for process modelling. Furthermore, comprehensive monitoring is developed and final results are combined using comprehensive statistics. By multivariable characteristics evaluation and subspace decomposition, the proposed method could evaluate and seek the multivariable characteristics and enhance the performance of process monitoring. The effectiveness and feasibility of the proposed comprehensive monitoring method are demonstrated by a numerical system and the benchmark Tennessee Eastman (TE) process. [ABSTRACT FROM AUTHOR]