Long wavelength single photon like driven photolysis via triplet triplet annihilation

Photolysis has enabled the occurrence of numerous discoveries in chemistry, drug discovery and biology. However, there is a dearth of efficient long wavelength light mediated photolysis. Here, we report general and efficient long wavelength single photon method for a wide array of photolytic molecules via triplet-triplet annihilation photolysis. This method is versatile and LEGO -like. The light partners (the photosensitizers and the photolytic molecules) can be energetically matched to adapt to an extensive range of electromagnetic spectrum wavelengths and the diversified chemical structures of photoremovable protecting groups, photolabile linkages, as well as a broad array of targeted molecules. Compared to the existing photolysis methods, our strategy of triplet-triplet annihilation photolysis not only exhibits superior reaction yields, but also resolves the photodamage problem, regardless of whether they are single photon or multiple photon associated. Furthermore, the biological promise of this LEGO system was illustrated via developing ambient air-stable nanoparticles capable of triplet-triplet annihilation photolysis

Metal source and ore precipitation mechanism of the Ashawayi orogenic gold deposit, southwestern Tianshan Orogen, western China: Constraints from textures and trace elements in pyrite

The metal source and ore precipitation mechanism of orogenic gold mineralization are not yet well understood, partly because ore metals may be derived from different sources. Pyrite is a dominant Au-hosting mineral in the Ashawayi orogenic gold deposit in the southwestern Tianshan orogen, western China. Petrographic features of pyrite in host rocks and orebodies define four generations: diagenetic preore (Py1), hydrothermal early-ore (Py2), main-ore (Py3), and late-ore (Py4) pyrites. Trace element abundances were analyzed in situ by femtosecond laser ablation inductively coupled plasma mass spectrometry (fs-LA-ICP-MS) to unravel the pyrite formation history. Preore Py1 contains the lowest Cu, Mo, Se, Au and As contents, consistent with a diagenetic origin. Py2 has higher Au and As contents than Py1 and may have formed by the reaction between hydrothermal fluid and preexisting Py1, as indicated by diagenetic pyrite-like As/Ni and Bi/Au ratios but lower hydrothermal pyrite-like Sb/Au ratios in Py2. Hydrothermal pyrite (Py3) contains more abundant As (1723–65182 ppm) and Au (0.32–107 ppm) but lower Co and Ni contents than Py2, suggesting a greater hydrothermal fluid contribution. Oscillatory zoning and abundant mineral inclusions (e.g., arsenopyrite and chalcopyrite) in porous Py3 indicate that fluid boiling was responsible for gold deposition during the main-ore stage. Py4 is a relict of hydrothermal pyrite (Py3) but not diagenetic pyrite, as supported by Py4 and Py3 clustering into a class based on hierarchical cluster analysis. The application of a machine learning method (i.e., artificial neural network) to the syn-ore pyrite indicates that the Ashawayi gold deposit has affinity to those from orogenic-type gold deposits worldwide. Our study, therefore, highlights that ore metals in orogenic gold deposits may originate from different sources, such as Au and As, which are largely sourced from metamorphic fluids, while Co and Ni are mainly released from preore sedimentary pyrite, fluid boiling and fluid-rock interaction triggered precipitation of Au and other metals

Broad activation of the Parkin pathway induces synaptic mitochondrial deficits in early tauopathy

Mitochondrial defects are a hallmark of early pathophysiology in Alzheimer’s disease, with pathologically phosphorylated tau reported to induce mitochondrial toxicity. Mitophagy constitutes a key pathway in mitochondrial quality control by which damaged mitochondria are targeted for autophagy. However, few details are known regarding the intersection of mitophagy and pathologies in tauopathy. Here, by applying biochemical and cell biological approaches including time-lapse confocal imaging in live tauopathy neurons, combined with gene rescue experiments via stereotactic injections of adeno-associated virus particles into tauopathy mouse brains, electrophysiological recordings and behavioural tests, we demonstrate for the first time that mitochondrial distribution deficits at presynaptic terminals are an early pathological feature in tauopathy brains. Furthermore, Parkin-mediated mitophagy is extensively activated in tauopathy neurons, which accelerates mitochondrial Rho GTPase 1 (Miro1) turnover and consequently halts Miro1-mediated mitochondrial anterograde movement towards synaptic terminals. As a result, mitochondrial supply at tauopathy synapses is disrupted, impairing synaptic function. Strikingly, increasing Miro1 levels restores the synaptic mitochondrial population by enhancing mitochondrial anterograde movement and thus reverses tauopathy-associated synaptic failure. In tauopathy mouse brains, overexpression of Miro1 markedly elevates synaptic distribution of mitochondria and protects against synaptic damage and neurodegeneration, thereby counteracting impairments in learning and memory as well as synaptic plasticity. Taken together, our study reveals that activation of the Parkin pathway triggers an unexpected effect—depletion of mitochondria from synaptic terminals, a characteristic feature of early tauopathy. We further provide new mechanistic insights into how parkin activation-enhanced Miro1 degradation and impaired mitochondrial anterograde transport drive tauopathy-linked synaptic pathogenesis and establish a foundation for future investigations into new therapeutic strategies to prevent synaptic deterioration in Alzheimer’s disease and other tauopathies

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

ObjectiveTo establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.MethodsThe cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.ResultsThe area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).ConclusionMachine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application

Building Circularity Assessment in the Architecture, Engineering, and Construction Industry: A New Framework

Circular Economy (CE) has proved its contribution to addressing environmental impacts in the Architecture, Engineering, and Construction (AEC) industries. Building Circularity (BC) assessment methods have been developed to measure the circularity of building projects. However, there still exists ambiguity and inconsistency in these methods. Based on the reviewed literature, this study proposes a new framework for BC assessment, including a material flow model, a Material Passport (MP), and a BC calculation method. The material flow model redefines the concept of BC assessment, containing three circularity cycles and five indicators. The BC MP defines the data needed for the assessment, and the BC calculation method provides the equations for building circularity scoring. The proposed framework offers a comprehensive basis to support a coherent and consistent implementation of CE in the AEC industry