Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The characteristics analysis of strain variation associated with Wenchuan earthquake using principal component analysis

Borehole strainmeters that are installed deeply into bedrock are capable of recording both continuous stress and strain measurements, and have consequently become an important tool for monitoring crustal deformation. A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Wenchuan earthquake on May 12, 2008 (UTC) (=8.0). We apply principal component analysis (PCA) to analyze borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors are calculated. The fitted results of the cumulative number of anomalous eigenvalues demonstrate that an acceleration occurred approximately 4 months before the earthquake (from January 2008). The results of the combined eigenvalue and eigenvector analyses show that the spatial distribution of eigenvectors and accelerated occurrence of eigenvalue anomalies represents the stress evolution characteristics of the fault from a steady state to a sub-instability state in rock experiments. We tentatively infer that this process may also be linked to the preparation phase of a large earthquake

DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning

Abstract To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their technical limitations. To overcome the limitations, we develop DeepETPicker, a deep learning model for fast and accurate picking of particles from cryo-electron tomograms. Training of DeepETPicker requires only weak supervision with low numbers of simplified labels, reducing the burden of manual annotation. The simplified labels combined with the customized and lightweight model architecture of DeepETPicker and accelerated pooling enable substantial performance improvement. When tested on simulated and real tomograms, DeepETPicker outperforms the competing state-of-the-art methods by achieving the highest overall accuracy and speed, which translate into higher authenticity and coordinates accuracy of picked particles and higher resolutions of final reconstruction maps. DeepETPicker is provided in open source with a user-friendly interface to support cryo-electron tomography in situ

X-ray radiation excited ultralong (>20,000 seconds) intrinsic phosphorescence in aluminum nitride single-crystal scintillators

Phosphorescence emission from extrinsic rare-earth element and organic component remains a challenge. Here, the authors demonstrate an ultraviolet ultralong intrinsic phosphorescence (UIP, >20,000 seconds) in aluminum nitride (AlN) single-crystal scintillator through X-ray excitation

Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve

In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, Nano, and the length of alarm window, Talm. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, tpr, and false alarm rate, fpr, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting

Isolation, Characterization and Antibacterial Activity of 4-Allylbenzene-1,2-diol from <i>Piper austrosinense</i>

Isolation for antibacterial compounds from natural plants is a promising approach to develop new pesticides. In this study, two compounds were obtained from the Chinese endemic plant Piper austrosinense using bioassay-guided fractionation. Based on analyses of 1H-NMR, 13C-NMR, and mass spectral data, the isolated compounds were identified as 4-allylbenzene-1,2-diol and (S)-4-allyl-5-(1-(3,4-dihydroxyphenyl)allyl)benzene-1,2-diol. 4-Allylbenzene-1,2-diol was shown to have strong antibacterial activity against four plant pathogens, including Xanthomonas oryzae pathovar oryzae (Xoo), X. axonopodis pv. citri (Xac), X. oryzae pv. oryzicola (Xoc) and X. campestris pv. mangiferaeindicae (Xcm). Further bioassay results exhibited that 4-allylbenzene-1,2-diol had a broad antibacterial spectrum, including Xoo, Xac, Xoc, Xcm, X. fragariae (Xf), X. campestris pv. campestris (Xcc), Pectobacterium carotovorum subspecies brasiliense (Pcb) and P. carotovorum subsp. carotovorum (Pcc), with minimum inhibitory concentration (MIC) values ranging from 333.75 to 1335 μmol/L. The pot experiment showed that 4-allylbenzene-1,2-diol exerted an excellent protective effect against Xoo, with a controlled efficacy reaching 72.73% at 4 MIC, which was superior to the positive control kasugamycin (53.03%) at 4 MIC. Further results demonstrated that the 4-allylbenzene-1,2-diol damaged the integrity of the cell membrane and increased cell membrane permeability. In addition, 4-allylbenzene-1,2-diol also prevented the pathogenicity-related biofilm formation in Xoo, thus limiting the movement of Xoo and reducing the production of extracellular polysaccharides (EPS) in Xoo. These findings suggest the value of 4-allylbenzene-1,2-diol and P. austrosinense could be as promising resources for developing novel antibacterial agents

Extracting borehole strain precursors associated with the Lushan earthquake through principal component analysis

A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Lushan earthquake on April 20, 2013 (UTC) ( =7.0). To identify earthquake-induced abnormal strain changes, we apply principal component analysis (PCA) for the first time to analyse the borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors demonstrate that the anomalous days are mainly concentrated within two time periods:1) October 25-December 30, 2012, and 2) April 15-19,2013. A combined eigenvalue and eigenvector analysis reveals that the abnormal days exhibit a clustered distribution that is aggregated in the same location for both periods, intuitively indicating that there is a forceful correlation between the two anomalies. We infer that a similar process contributed to the formation of both anomalies and that these two anomalies are both earthquake precursors associated with the Lushan earthquake. These findings also indicate that the PCA approach exhibits potential for the extraction of earthquake precursor anomalies

Proteomic Studies of the Mechanism of Cytotoxicity, Induced by Palytoxin on HaCaT Cells

Palytoxin (PLTX) is a polyether marine toxin isolated from sea anemones. It is one of the most toxic nonprotein substances, causing many people to be poisoned every year and to die in severe cases. Despite its known impact on Na+,K+-ATPase, much still remains unclear about PLTX&rsquo;s mechanism of action. Here, we tested different concentrations of PLTX on HaCaT cells and studied its distributions in cells, its impact on gene expression, and the associated pathways via proteomics combined with bioinformatics tools. We found that PLTX could cause ferroptosis in HaCaT cells, a new type of programmed cell death, by up-regulating the expression of VDAC3, ACSL4 and NCOA4, which lead to the occurrence of ferroptosis. PLTX also acts on the MAPK pathway, which is related to cell apoptosis, proliferation, division and differentiation. Different from its effect on ferroptosis, PLTX down-regulates the expression of ERK, and, as a result, the expressions of MAPK1, MAP2K1 and MAP2K2 are also lower, affecting cell proliferation. The genes from these two mechanisms showed interactions, but we did not find overlap genes between the two. Both ferroptosis and MAPK pathways can be used as anticancer targets, so PLTX may become an anticancer drug with appropriate modification