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The Cryogenian Sturtian and Marinoan Snowball Earth glaciations bracket a nonglacial interval during which Demosponge and green-algal biomarkers first appear. To understand the relationships between environmental perturbations and early animal evolution, we measured sulfur and mercury isotopes from the Datangpo Formation from South China. Hg enrichment with positive Δ199Hg excursion suggests enhanced volcanism, potentially due to depressurization of terrestrial magma chambers during deglaciation. A thick stratigraphic interval of negative Δ33Spy indicates that the nonglacial interlude was characterized by low but rising sulfate levels. Model results reveal a mechanism to produce the Δ33S anomalies down to -0.284‰ through Rayleigh distillation. We propose that extreme temperatures and anoxia contributed to the apparent delay in green algal production in the aftermath of the Sturtian glaciation and the subsequent reoxygenation of the iron-rich and sulfate-depleted ocean paved the way for evolution of animals.

There has been a longstanding debate on whether plutonic and subvolcanic rocks have a petrogenetic connection, and the Jurassic Shuikoushan caldera complex in South China serves as an ideal case study for unraveling this geological puzzle. SIMS U–Pb dating of zircon indicates the formation age of plutonic (granodiorite) and subvolcanic rocks (dacite porphyry and rhyolite porphyry) from the Shuikoushan caldera complex at ca.159 Ma. In terms of geochemical composition, granodiorite and dacite porphyry exhibit higher levels of MgO (1.98–3.63%), MnO (0.07–0.11%), FeOt (5.12–6.15%), Sr (342–547 ppm), and Ba (754–1200 ppm) compared to the rhyolite porphyry. Conversely, they show lower concentrations of Rb (2.04–27.1 ppm), as well as lower ratios of Rb/Sr (0.004–0.055) and Rb/Ba (0.004–0.023). The distinct but complementary geochemical characteristics between these rock types are evident in the Shuikoushan caldera complex. Overall, zircon grains from all three rock types exhibit similar negative ε Hf(t) values (–8.1 to –12.4) with high δ 18O values (8.3–9.7‰), suggesting a common source region. Based on lithological and geochemical evidence, it is proposed that the rhyolite porphyry represents extracted melt from the mush reservoir. In contrast, the granodiorite and dacite porphyry are residual melts enriched in cumulates. This interpretation supports the idea that crystal–melt segregation processes were crucial in connecting the late Jurassic subvolcanic and plutonic rocks within the Shuikoushan complex.

The study analyzed the electrical properties, mechanical performance, and rheological behavior of three kinds of high plasticizer-content polyvinyl chloride (PVC) composites, herein referred as SCBC, EGC and MCNTC, respectively filled with porous superconductive carbon black (SCB), flake-like expanded graphite (EG), and tubular multi-walled carbon nanotubes (MCNT). The impact of the conductive fillers' morphology on the electrical percolation and viscoelastic percolation performance of the composites were investigated to understand the conductive mechanisms. It revealed that the incorporation of SCB and MCNT can induce both viscoelastic and electrical percolation in the composites. The thresholds for viscoelastic percolation for SCBC and MCNTC are between 2.35-3.11 vol% and 2.72–3.59 vol%, respectively, which are lower than their electrical percolation thresholds of 3.85 vol% for SCBC and 3.65 vol% for MCNTC. The nature of PVC chains, which can transmit force but not electrons, results in a higher electrical percolation threshold than the rheological percolation threshold. Despite MCNT's poorer dispersion, its high aspect ratio and one-dimensional tubular shape enabled it to achieve electrical percolation at a lower loading level. Due to the poor dispersion, EG did not cause either viscoelastic or electrical percolation in the composites within the 0–9 vol% range.

Background and objectivesUpwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.MethodsA total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient’ demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants’ prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.ResultsFeature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.ConclusionThese findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

Abstract Point cloud completion is a challenging task that involves predicting missing parts in incomplete 3D shapes. While existing strategies have shown effectiveness on point cloud datasets with regular shapes and continuous surfaces, they struggled to manage the morphologically diverse structures commonly encountered in real-world scenarios. This research proposed a new point cloud completion method, called SegCompletion, to derive complete 3D geometries from a partial shape with different structures and discontinuous surfaces. To achieve this, morphological segmentation was introduced before point cloud completion by deep hierarchical feature learning on point sets, and thus, the complex morphological structure was segmented into regular shapes and continuous surfaces. Additionally, each instance of a point cloud that belonged to the same type of feature could also be effectively identified using HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). Furthermore, the multiscale generative network achieved sophisticated patching of missing point clouds under the same geometric feature based on feature points. To compensate for the variance in the mean distances between the centers of the patches and their closest neighbors, a simple yet effective uniform loss was utilized. A number of experiments on ShapeNet and Pheno4D datasets have shown the performance of SegCompletion on public datasets. Moreover, the contribution of SegCompletion to our dataset (Cotton3D) was discussed. The experimental results demonstrated that SegCompletion performed better than existing methods reported in the literature.