Soil legacy phosphorus and loss risk in subtropical grasslands.

The accumulation of soil legacy phosphorus (P) due to past fertilization practices poses a persistent challenge for agroecosystem management and water quality conservation. This study investigates the spatial distribution and risk assessment of soil legacy P in subtropical grasslands managed for cow-calf operations in Florida, with two pasture types along the intensity gradient: improved vs semi-native pastures. Soil samples from 1438 locations revealed substantial spatial variation in soil legacy P, with total P concentrations ranging from 11.46 to 619.54 mg/kg and Mehlich-1 P concentrations spanning 0.2-187.27 mg/kg. Our analyses revealed that most of the sites in semi-native pastures may function as P sinks by exhibiting positive Soil P Storage Capacity (SPSC) values, despite having high levels of soil total P. These locales of higher SPSC values were associated with high levels of aluminum, iron, and organic matter that can adsorb P. In addition, our results from spatial random forest modelling demonstrated that factors including elevation, soil organic matter, available water storage, pasture type, soil pH, and soil order are important to explain and predict spatial variations in SPSC. Incorporating SPSC into the Phosphorus Index (PI) spatial assessment, we further determined that only 3% of the study area was considered as high or very high PI categories indicative of a significant risk for P loss. Our evaluation of SPSC and PI underscores the complexity inherent in P dynamics, emphasizing the need for a holistic approach to assessing P loss risk. Insights from this work not only help optimize agronomic practices but also promote sustainable land management, thus ensuring the long-term health and sustainability of grass-dominated agroecosystems.

Unraveling spatial heterogeneity of soil legacy phosphorus in subtropical grasslands.

Humans have profoundly altered phosphorus (P) cycling across scales. Agriculturally driven changes (e.g., excessive P-fertilization and manure addition), in particular, have resulted in pronounced P accumulations in soils, often known as "soil legacy P." These legacy P reserves serve as persistent and long-term nonpoint sources, inducing downstream eutrophication and ecosystem services degradation. While there is considerable scientific and policy interest in legacy P, its fine-scale spatial heterogeneity, underlying drivers, and scales of variance remain unclear. Here we present an extensive field sampling (150-m interval grid) and analysis of 1438 surface soils (0-15 cm) in 2020 for two typical subtropical grassland types managed for livestock production: Intensively managed (IM) and Semi-natural (SN) pastures. We ask the following questions: (1) What is the spatial variability, and are there hotspots of soil legacy P? (2) Does soil legacy P vary primarily within pastures, among pastures, or between pasture types? (3) How does soil legacy P relate to pasture management intensity, soil and geographic characteristics? and (4) What is the relationship between soil legacy P and aboveground plant tissue P concentration? Our results showed that three measurements of soil legacy P (total P, Mehlich-1, and Mehlich-3 extractable P representing labile P pools) varied substantially across the landscape. Spatial autoregressive models revealed that soil organic matter, pH, available Fe and Al, elevation, and pasture management intensity were crucial predictors for spatial patterns of soil P, although models were more reliable for predicting total P (68.9%) than labile P. Our analysis further demonstrated that total variance in soil legacy P was greater in IM than SN pastures, and intensified pasture management rescaled spatial patterns of soil legacy P. In particular, after controlling for sample size, soil P was extremely variable at small scales, with variance diminished as spatial scale increased. Our results suggest that broad pasture- or farm-level best management practices may be limited and less efficient, especially for more IM pastures. Rather, management to curtail soil legacy P and mitigate P loading and losses should be implemented at fine scales designed to target spatially distinct P hotspots across the landscape.

Interpretable Machine Learning Models Based on SHapley Additive exPlanations for Predicting the Risk of Cerebrospinal Fluid Leakage in Lumbar Fusion Surgery.

STUDY DESIGN: Retrospective study. OBJECTIVES: The objective of this investigation was to formulate and internally verify a customized machine learning (ML) framework for forecasting cerebrospinal fluid leakage (CSFL) in lumbar fusion surgery. This was accomplished by integrating imaging parameters and employing the SHapley Additive exPlanation (SHAP) technique to elucidate the interpretability of the model. SUMMARY OF BACKGROUND DATA: Given the increasing incidence and surgical volume of spinal degeneration worldwide, accurate predictions of postoperative complications are urgently needed. SHAP-based interpretable ML models have not been used for CSFL risk factor analysis in lumbar fusion surgery. METHODS: Clinical and imaging data were retrospectively collected from 3505 patients who underwent lumbar fusion surgery. Six distinct machine learning models were formulated: extreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GaussianNB), and K-nearest neighbors (KNN) models. Evaluation of model performance on the test dataset was performed using performance metrics, and the analysis was executed through the SHAP framework. RESULTS: CSFL was detected in 95 out of 3505 patients (2.71%). Notably, the XGBoost model exhibited outstanding accuracy in forecasting CSFLs, with high precision (0.9815), recall (0.6667), accuracy (0.8182), F1 score (0.7347), and AUC (0.7343). Additionally, through SHAP analysis, significant predictors of CSFL were identified, including ligamentum flavum thickness, zygapophysial joint degeneration grade, central spinal stenosis grade, decompression segment count, decompression mode, intervertebral height difference, Cobb angle, intervertebral height index difference, operation mode, lumbar segment lordosis angle difference, Meyerding grade of lumbar spondylolisthesis, and revision surgery. CONCLUSION: The combination of the XGBoost model with the SHAP is an effective tool for predicting the risk of CSFL during lumbar fusion surgery. Its implementation could aid clinicians in making informed decisions, potentially enhancing patient outcomes and lowering healthcare expenses. This study advocates for the adoption of this approach in clinical settings to enhance the evaluation of CSFL risk among patients undergoing lumbar fusion.

UBE2D1 promotes glioblastoma proliferation by modulating p21 ubiquitination.

Glioblastoma (GBM) cells exhibit aberrant proliferative abilities and resistance to conventional therapies. However, the mechanisms underlying these malignant phenotypes are poorly understood. In this study, we identified ubiquitin-conjugating enzyme E2D1 (UBE2D1) as a crucial stimulator of GBM development. It is highly expressed in GBM and closely associated with poor prognosis in patients with GBM. UBE2D1 knockdown inhibits GBM cell growth and leads to G1 cell cycle arrest. Mechanistically, UBCH5A binds to p21 at the protein level and induces the ubiquitination and degradation of p21. This negative regulation is mediated by STUB1. Our findings are the first to identify UBE2D1 as a key driver of GBM growth and provide a potential target for improving prognosis and therapy.

Habitual Daily Intake of Fried Foods Raises Transgenerational Inheritance Risk of Heart Failure Through NOTCH1-Triggered Apoptosis.

Consumption of fried foods is highly prevalent in the Western dietary pattern. Western diet has been unfavorably linked with high risk of developing cardiovascular diseases. Heart failure (HF) as a cardiovascular disease subtype is a growing global pandemic with high morbidity and mortality. However, the causal relationship between long-term fried food consumption and incident HF remains unclear. Our population-based study revealed that frequent fried food consumption is strongly associated with 15% higher risk of HF. The causal relationship may be ascribed to the dietary acrylamide exposure in fried foods. Further cross-sectional study evidenced that acrylamide exposure is associated with an increased risk of HF. Furthermore, we discover and demonstrate that chronic acrylamide exposure may induce HF in zebrafish and mice. Mechanistically, we reveal that acrylamide induces energy metabolism disturbance in heart due to the mitochondria dysfunction and metabolic remodeling. Moreover, acrylamide exposure induces myocardial apoptosis via inhibiting NOTCH1-phosphatidylinositol 3-kinase/AKT signaling. In addition, acrylamide exposure could affect heart development during early life stage, and the adverse effect of acrylamide exposure is a threat for next generation via epigenetic change evoked by DNA methyltransferase 1 (DNMT1). In this study, we reveal the adverse effects and underlying mechanism of fried foods and acrylamide as a typical food processing contaminant on HF from population-based observations to experimental validation. Collectively, these results both epidemiologically and mechanistically provide strong evidence to unravel the mechanism of acrylamide-triggered HF and highlight the significance of reducing fried food consumption for lower the risk of HF.

E-Nose: Time-Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction.

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model's robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.

Favored Amorphous LixSi Process with Restrained Volume Change Enabling Long Cycling Quasi-solid-state SiOx anode.

Silicon-based anodes are becoming promising materials due to their high specific capacity. However, the intrinsically large volume change brought about by the alloying reaction results in the crushing of the active particles and destruction of the electrode structure, which severely limits its practical application. Various structured and modified silica-based anodes exhibit improved cycling stability and the demonstrated ability to mitigate their volume changes through interfacial and binder strategies. However, the issue of large volume changes in silicon-based anodes remains. Herein, we report a gel polymer electrolyte (GPE) prepared through an in situ thermal polymerization process that is suitable for SiOx anode materials and achieving long-term cycling stability. GPE-based cells essentially mitigate the volume change of SiOx anodes by guiding the unique lithiation/delithiation mechanism that tends to favor the formation and delithiation of amorphous-LixSi (a-LixSi) with smaller volume change, thereby mitigating electrode damage and cracking, and achieving the significant improvement in cycling performance. The prepared GPE-SiOx cells retained 693.80 mAh g-1 reversible capacity after 450 cycles at 500 mA g-1. In addition, the prelithiation process was incorporated to mitigate capacity fluctuations and improve the Initial Coulombic Efficiency (ICE), and a reversible capacity of 641.90 mAh g-1 was retained after 480 cycles.

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.

OBJECTIVE: Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not included in the training datasets, owing to bio-individual differences and noisy labels. This study aims to develop an effective framework for cross-subject fatigue detection by addressing these challenges. APPROACH: In this study, we propose a novel framework, termed DP-MP, for cross-subject fatigue detection, which utilizes a Domain-Adversarial Neural Network (DANN)-based prototypical representation in conjunction with Mix-up pairwise learning. Our proposed DP-MP framework aims to mitigate the impact of bio-individual differences by encoding fatigue-related semantic structures within EEG signals and exploring shared fatigue prototype features across individuals. Notably, to the best of our knowledge, this work is the first to conceptualize fatigue detection as a pairwise learning task, thereby effectively reducing the interference from noisy labels. Furthermore, we propose the Mix-up pairwise learning (MixPa) approach in the field of fatigue detection, which broadens the advantages of pairwise learning by introducing more diverse and informative relationships among samples. RESULTS: Cross-subject experiments were conducted on two benchmark databases, SEED-VIG and FTEF, achieving state-of-the-art performance with average accuracies of 88.14% and 97.41%, respectively. These promising results demonstrate our model's effectiveness and excellent generalization capability. SIGNIFICANCE: This is the first time EEG-based fatigue detection has been conceptualized as a pairwise learning task, offering a novel perspective to this field. Moreover, our proposed DP-MP framework effectively tackles the challenges of bio-individual differences and noisy labels in the fatigue detection field and demonstrates superior performance. Our work provides valuable insights for future research, promoting the application of brain-computer interfaces for fatigue detection in real-world scenarios. .

Disentangling the "tip-effects" enhanced antibacterial mechanism of Ag nanoparticles.

Silver nanoparticles (Ag NPs) exhibit strong antibacterial activity and are widely used in industries such as medical, food and cosmetics. In this study, Ag nanospheres and Ag nanotriangles are selected as antibacterial agents to reveal the distinct mechanism of tip effects towards their antibacterial performance. A series of antibacterial experiments were implemented, including in situ monitoring as well as studying and determining the evolution of the inhibition zone, minimum inhibitory concentration (MIC)/minimum bactericidal concentration (MBC) values, growth kinetics, bactericidal curve, bacterial morphologies and intracellular reactive oxygen species (ROS). Ag nanotriangles can eradicate E. coli and S. aureus at extremely low concentrations in comparison to Ag nanospheres, in particular under sunlight irradiation. The destroyed bacterial cell walls were examined by scanning electron microscopy. Through the investigation of ROS production, the generation efficiency of ROS is improved by the merit of sunlight irradiation thanks to the localized surface plasmon resonance (LSPR) properties of Ag NPs. However, a more significant improvement in ROS generation efficiency occurred in the presence of Ag nanotriangles contributed by the pronounced "tip effects". This study sheds light on the structure-performance relationship for the rational design of antibacterial agents.

Mechanism and kinetics study of vanadium leaching from landfilled metallurgical residues by ultrasonic with ozonation enhancement in a low-acid medium.

Landfilled metallurgical residues are valuable raw materials for the recovery of strategic vanadium resources. However, efficient separation of vanadium from these residues is challenging due to its strong oxidation resistance and coating within silicate inclusions. To address this issue, this study proposes an enhanced leaching process utilizing the synergistic effect of O3-catalyzed ultrasonic field in a low concentration sulfuric acid system. Results show that following a 10-minute O3 and ultrasonic treatment, the direct leaching rate of vanadium experienced a remarkable 46.7 % increase. Quenching experiments revealed a hierarchical order of active species within the reaction process:⋅OH >⋅O2-> H+, with⋅OH oxidation exhibiting the most pronounced capacity for disrupting the inclusion structure. Electron Paramagnetic Resonance analysis indicated that the highest⋅OH yield arose from the combined application of ultrasound and ozone. Kinetic investigations demonstrated that the vanadium leaching process is governed by interfacial chemical reactions. The activation energy of vanadium oxidation leaching under ultrasonic-O3 conditions was determined to be 40.41 kJ/mol, representing a 20.19 % reduction compared to ultrasonic conditions alone. Through the integration of analysis, characterization, and comparative evaluations, it was discerned that the synergistic impact of ultrasonic and ozone treatments significantly enhances the breakdown of silicate inclusions by low-concentration HF, particularly in the conversion of SiOSi bonds into SiOH bonds and SiF bonds. In summary, the refined leaching methodology incorporating ozone catalysis in conjunction with ultrasonic treatment provides a new idea for the separation and extraction of refractory residual vanadium.

Effect of alveolar ridge preservation at periodontally compromised molar extraction sockets: A retrospective cohort study.

BACKGROUND: To date, the clinical evidence regarding the effectiveness of alveolar ridge preservation (ARP) in restricting alveolar bone height and width change after extraction at periodontally compromised molar extraction sockets still remains controversial. This retrospective cohort study aims to evaluate the effect of ARP in molars extracted for periodontal reasons. METHODS: Retrospective data were collected from patient electronic records from January 2019 to December 2023. Patients with Stage III/IV periodontitis who underwent extraction of molars for periodontal reasons were screened for eligibility. The outcomes included the horizontal and vertical dimensions of alveolar bone. The need for additional augmentation procedure during implantation was also evaluated. A linear regression model was used to adjust for known confounders. RESULTS: A total of 80 sockets were included in this study, of which 27 sockets received ARP therapy after extraction while 53 sockets experienced natural healing (NH). ARP resulted in significantly less bone height change in the periodontally compromised molar sites compared to the NH group (p < 0.001). In sockets displaying a height disparity of >2 mm between the buccal and palatal/lingual walls, the ARP group exhibited advantageous outcomes in terms of ridge width change, surpassing the NH group (p = 0.004). Moreover, the percentage for additional augmentation was significantly reduced in the ARP compared to the NH group (p = 0.006). Age, sex, smoking, jaw, location, and buccal wall thickness did not show any significant effect on bone height change. CONCLUSION: ARP had benefits on limiting ridge resorption subsequent to molar extraction for periodontal reasons.

Diagnostic yield and complication rates of percutaneous transthoracic CT-guided coaxial needle biopsy in persistent pulmonary consolidation.

OBJECTIVES: To investigate the diagnostic performance and complication rates of percutaneous transthoracic CT-guided coaxial core needle biopsy (PTCNB) in persistent consolidations and evaluate its safety in routine clinical practice. METHODS: A total of 685 patients (404 males, 281 females) underwent PTCNB with coaxial core technique for persisted consolidation were reviewed in this study. According to histopathological and microbiological analysis, the results of biopsy specimens were categorized as follows: malignant, specific benign, non-specific benign and non-diagnostic. The final diagnosis was established through surgical resection or clinicoradiological follow-up for at least 12 months following biopsy. Diagnostic yield of PTCNB was defined as the percentage of the true diagnosis from biopsy as malignant and specific benign lesions. RESULTS: With respect to the final diagnosis, 54 (54/685; 7.88%) cases were obtained by surgery and the remaining were by follow-up. The total accuracy, sensitivity, specificity of PTCNB for malignancy diagnosis was 94.45%, 84.87%, 100%, respectively. Diagnostic yield of PTCNB was 66.28%. Compared to lesions smaller than 3 cm, higher diagnostic yield (70.89%), lower complication incidence (38.22%) and shorter procedure time (8.78 min) were observed in lesions ≥ 3 cm group. CONCLUSION: PTCNB in persistent consolidation is a safe and effective procedure, which provide relatively high diagnostic yield and acceptable complication, especially in size over 3 cm lesions. CRITICAL RELEVANCE STATEMENT: CT-guided coaxial needle biopsy for pulmonary consolidation is a safe and effective procedure. The coaxial needle biopsy yielded high diagnostic rates and low complication rates (including pneumothorax and intrapulmonary hemorrhage), especially in larger lesions.

High Sensitive and Stable UV-Vis Photodetector Based on MoS2/MoO3 vdW Heterojunction.

The development of new high-performance photodetectors (PDs) is currently focused on achieving small size, low power consumption, low cost, and large bandwidth. Two-dimensional (2D) materials and heterostructures offer promising approaches for the future development of optoelectronic devices. However, there has been limited research on 2D wide-bandgap semiconductor heterostructures. In this study, we successfully constructed a MoS2/MoO3 vdW heterojunction PD. This PD exhibited excellent response and significant photovoltaic behavior in the ultraviolet (UV) to visible (Vis) range. Under 365 nm UV light and 1 V bias voltage, the PD demonstrated a high responsivity of 645 mA/W, a high specific detectivity of 8.98 × 1010 Jones, and fast response speeds of 55.9/59.6 ms. At 0 V bias voltage, the responsivity reached as high as 157 mA/W. Furthermore, the PD exhibited remarkable stability in its performance. These outstanding characteristics can be attributed to the strong internal electric field created by the type II heterojunction structure and the chemical stability of the materials. This work opens a route for the application of 2D wide-bandgap semiconductor materials in optoelectronic devices.

Solvation Effect: The Cornerstone of High-Performance Battery Design for Commercialization-Driven Sodium Batteries.

Sodium batteries (SBs) emerge as a potential candidate for large-scale energy storage and have become a hot topic in the past few decades. In the previous researches on electrolyte, designing electrolytes with the solvation theory has been the most promising direction is to improve the electrochemical performance of batteries through solvation theory. In general, the four essential factors for the commercial application of SBs, which are cost, low temperature performance, fast charge performance and safety. The solvent structure has significant impact on commercial applications. But so far, the solvation design of electrolyte and the practical application of sodium batteries have not been comprehensively summarized. This review first clarifies the process of Na+ solvation and the strategies for adjusting Na+ solvation. It is worth noting that the relationship between solvation theory and interface theory is pointed out. The cost, low temperature, fast charging, and safety issues of solvation are systematically summarized. The importance of the de-solvation step in low temperature and fast charging application is emphasized to help select better electrolytes for specific applications. Finally, new insights and potential solutions for electrolytes solvation related to SBs are proposed to stimulate revolutionary electrolyte chemistry for next generation SBs.

The Role and Therapeutic Potential of Melatonin in Degenerative Fundus Diseases: Diabetes Retinopathy and Age-Related Macular Degeneration.

Degenerative fundus disease encompasses a spectrum of ocular diseases, including diabetic retinopathy (DR) and age-related macular degeneration (AMD), which are major contributors to visual impairment and blindness worldwide. The development and implementation of effective strategies for managing and preventing the onset and progression of these diseases are crucial for preserving patients' visual acuity. Melatonin, a neurohormone primarily produced by the pineal gland, exhibits properties such as circadian rhythm modulation, antioxidant activity, anti-inflammatory effects, and neuroprotection within the ocular environment. Furthermore, melatonin has been shown to suppress neovascularization and reduce vascular leakage, both of which are critical in the pathogenesis of degenerative fundus lesions. Consequently, melatonin emerges as a promising therapeutic candidate for degenerative ocular diseases. This review provides a comprehensive overview of melatonin synthesis, its localization within ocular tissues, and its mechanisms of action, particularly in regulating melatonin production, thereby underscoring its potential as a therapeutic agent for degenerative fundus diseases.

Recent advances in zinc-ion dehydration strategies for optimized Zn-metal batteries.

Aqueous Zn-metal batteries have attracted increasing interest for large-scale energy storage owing to their outstanding merits in terms of safety, cost and production. However, they constantly suffer from inadequate energy density and poor cycling stability due to the presence of zinc ions in the fully hydrated solvation state. Thus, designing the dehydrated solvation structure of zinc ions can effectively address the current drawbacks of aqueous Zn-metal batteries. In this case, considering the lack of studies focused on strategies for the dehydration of zinc ions, herein, we present a systematic and comprehensive review to deepen the understanding of zinc-ion solvation regulation. Two fundamental design principles of component regulation and pre-desolvation are summarized in terms of solvation environment formation and interfacial desolvation behavior. Subsequently, specific strategy based distinct principles are carefully discussed, including preparation methods, working mechanisms, analysis approaches and performance improvements. Finally, we present a general summary of the issues addressed using zinc-ion dehydration strategies, and four critical aspects to promote zinc-ion solvation regulation are presented as an outlook, involving updating (de)solvation theories, revealing interfacial evolution, enhancing analysis techniques and developing functional materials. We believe that this review will not only stimulate more creativity in optimizing aqueous electrolytes but also provide valuable insights into designing other battery systems.

Dietary Strategies to Improve Exercise Performance by Modulating the Gut Microbiota.

Numerous research studies have shown that moderate physical exercise exerts positive effects on gastrointestinal tract health and increases the variety and relative number of beneficial microorganisms in the intestinal microbiota. Increasingly, studies have shown that the gut microbiota is critical for energy metabolism, immunological response, oxidative stress, skeletal muscle metabolism, and the regulation of the neuroendocrine system, which are significant for the physiological function of exercise. Dietary modulation targeting the gut microbiota is an effective prescription for improving exercise performance and alleviating exercise fatigue. This article discusses the connection between exercise and the makeup of the gut microbiota, as well as the detrimental effects of excessive exercise on gut health. Herein, we elaborate on the possible mechanism of the gut microbiota in improving exercise performance, which involves enhancing skeletal muscle function, reducing oxidative stress, and regulating the neuroendocrine system. The effects of dietary nutrition strategies and probiotic supplementation on exercise from the perspective of the gut microbiota are also discussed in this paper. A deeper understanding of the potential mechanism by which the gut microbiota exerts positive effects on exercise and dietary nutrition recommendations targeting the gut microbiota is significant for improving exercise performance. However, further investigation is required to fully comprehend the intricate mechanisms at work.

Iterative Design of Near-Infrared Fluorescent Probes for Early Diagnosis of Alzheimer's Disease by Targeting Aß Oligomers.

Amyloid-ß oligomers (AßOs), crucial toxic proteins in early Alzheimer's disease (AD), precede the formation of Aß plaques and cognitive impairment. In this context, we present our iterative process for developing novel near-infrared fluorescent (NIRF) probes specifically targeting AßOs, aimed at early AD diagnosis. An initial screening identified compound 18 as being highly selective for AßOs. Subsequent analysis revealed that compound 20 improved serum stability while retaining affinity for AßOs. The most promising iteration, compound 37, demonstrated exceptional qualities: a high affinity for AßOs, emission in the near-infrared region, and good biocompatibility. Significantly, ex vivo double staining indicated that compound 37 detected AßOs in AD mouse brain and in vivo imaging experiments showed that compound 37 could differentiate between 4-month-old AD mice and age-matched wild-type mice. Therefore, compound 37 has emerged as a valuable NIRF probe for early detection of AD and a useful tool in exploring AD's pathological mechanisms.

Optimal dose and type of exercise to improve depressive symptoms in older adults: a systematic review and network meta-analysis.

BACKGROUND: Depression is a prevalent issue among older adults, affecting their quality of life and overall well-being. Exercise is an effective means of relieving depressive symptoms in older adults, but the optimal dose for different exercise types remains unclear. As such, the aim of this meta-analysis was to examine the dose-response relationship between overall and specific types of exercise with depression symptoms in older adults. METHODS: This systematic review and network meta-analysis included a search of PubMed, Medline, Embase, PsycINFO, Cochrane library, and Web of Science for randomized controlled trials of exercise in older adults with depression symptoms from inception to 15 July 2023. Comprehensive data extraction covered dose, treatment regimen, demographics and study duration. Dosage metrics, encompassing METs-min/week, were scrutinized in correlation with the Minimal Clinically Importance Difference (MCID). RESULTS: A total of 47 studies involving 2895 participants and 7 kinds of exercise were included in the review. Without considering the dose, the results of our network meta-analysis indicated that Walking was the most effective in alleviating depression in older adults, in addition to Aerobic exercise (AE), Yoga, Qigong, Resistance training (RT), and Tai Chi (TC), which were equally effective. However, the results of the dose-response analysis found that Aerobic exercise was most effective at a dose of 1000 METs-min/week. It is noteworthy that Walking is significantly effective in alleviating depressive symptoms in older adults at very low doses. In terms of clinical benefits, we found that overall exercise doses in the range of 600 ~ 970 METs-min/week were clinically effective. Considering the specific types of exercise, Aerobic exercise, Resistance training, Walking, and Yoga were found to be effective at doses ranging from 820 ~ 1000 METs-min/week, 520 ~ 1000 METs-min/week, 650 ~ 1000 METs-min/week, 680 ~ 1000 METs-min/week, respectively. At the same time, we found that when the age exceeded 81 years, even when participating in exercise, it did not achieve the effect of alleviating depressive symptoms in older adults. CONCLUSIONS: In conclusion, including Walking, AE, Yoga, Qigong, RT, and TC, effectively alleviate depressive symptoms in older adults. Furthermore, we established statistically and clinically significant threshold doses for various exercise types. Early initiation of exercise is beneficial, but its efficacy diminishes from the age of 80, and beyond 81, exercise no longer significantly alleviates depressive symptoms.

Electrically tunable on-chip quantum Deutsch-Jozsa algorithm with lithium niobate metasurfaces.

Owing to the inherent advantages of parallelism, rapid processing speed, and minimal energy consumption, optical analog computing has witnessed a progressive development. Quantum optical computing exceeds the capabilities of classical computing in terms of computational speed in numerous tasks. However, existing metamaterial-based quantum Deutsch-Jozsa (DJ) algorithm devices have large structural dimensions and are not suitable for miniaturized optical computing systems. Furthermore, most reported on-chip metasurface devices, rendered monofunctional after fabrication, do not possess sophisticated optical systems. In this work, we develop an electrically tunable on-chip DJ algorithm device on a lithium-niobate-on-insulator (LNOI) platform. The on-chip device consists of various etched slots, each with carefully designed size. By applying different external voltages to each individual unit, precise phase redistribution across the device is attainable, enabling the realization of tunable DJ algorithm. Notably, we can determine whether the oracle metasurface yields a constant or balance function by measuring the output electric field. The on-chip device is miniaturized and easy to integrate while enabling functional reconfiguration, which paves the way for numerous applications in optical computing.