Myosin-X recruits lamellipodin to filopodia tips.

Myosin-X (MYO10), a molecular motor localizing to filopodia, is thought to transport various cargo to filopodia tips, modulating filopodia function. However, only a few MYO10 cargoes have been described. Here, using GFP-Trap and BioID approaches combined with mass spectrometry, we identified lamellipodin (RAPH1) as a novel MYO10 cargo. We report that the FERM domain of MYO10 is required for RAPH1 localization and accumulation at filopodia tips. Previous studies have mapped the RAPH1 interaction domain for adhesome components to its talin-binding and Ras-association domains. Surprisingly, we find that the RAPH1 MYO10-binding site is not within these domains. Instead, it comprises a conserved helix located just after the RAPH1 pleckstrin homology domain with previously unknown functions. Functionally, RAPH1 supports MYO10 filopodia formation and stability but is not required to activate integrins at filopodia tips. Taken together, our data indicate a feed-forward mechanism whereby MYO10 filopodia are positively regulated by MYO10-mediated transport of RAPH1 to the filopodium tip.

Fast4DReg - fast registration of 4D microscopy datasets.

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal visualization and quantification of biological processes. Although multiple methods and tools exist to correct images post acquisition, performing drift correction of three-dimensional (3D) videos using open-source solutions remains challenging and time consuming. Here, we present a new tool developed for ImageJ or Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video-microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using two-dimensional cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg can perform better than other state-of-the-art open-source drift-correction tools and significantly outperforms them in speed. We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether, Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis.

Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks.

Streamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s-1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.

Capture of Pb2+ and Cu2+ Metal Cations by Neisseria meningitidis-type Capsular Polysaccharides.

Heavy metal pollution of water is a significant environmental and public health concern. Current biological strategies for heavy metal removal from water are performed using microbial biopolymers, including polysaccharides, that are already fully formed. This creates limitations in adapting polysaccharides to increase binding affinity for specific metals. We propose that altering the specificity of polysaccharide-producing enzymes could be beneficial to improving metal capture by modified polysaccharides. We assess binding of Cu2+ and Pb2+ metal cations to Neisseria meningitidis-type polysaccharides. All concentrations of metal cations tested were able to completely bind to colominic acid. This polymer is equivalent to the capsular polysaccharide of N. meningitidis serogroup B comprised of a homopolymer of negatively charged sialic acid. There was slightly less binding observed with N. meningitidis serogroup W, which contains repeating units of the neutral sugar galactose and sialic acid. Our work represents the first assessment of the metal-binding properties of these capsular polysaccharides. Future work will seek to optimize metal-binding with Neisseria meningitidis serogroup W polysaccharide.

Unprecedented anticancer activities of organorhenium sulfonato and carboxylato complexes against hormone-dependent MCF-7 and hormone-independent triple-negative MDA-MB-231 breast cancer cells.

Cisplatin and other metal-based drugs often display side effects and tumor resistance after prolonged use. Because rhenium-based anticancer complexes are often less toxic, a novel series of organorhenium complexes were synthesized of the types: XRe(CO)3Z (X = α-diimines and Z = p-toluenesulfonate, 1-naphthalenesulfonate, 2-naphthalenesulfonate, picolinate, nicotinate, aspirinate, naproxenate, flufenamate, ibuprofenate, mefenamate, tolfenamate, N-acetyl-tryptophanate), and their biological properties were examined. Specifically, in hormone-dependent MCF-7 and hormone-independent triple-negative MDA-MB-231 breast cancer cells, the p-toluenesulfonato, 1-naphthalenesulfonato, 2-naphthalenesulfonato, picolinato, nicotinato, acetylsalicylato, flufenamato, ibuprofenato, mefenamato, and N-acetyl-tryptophanato complexes were found to be far more potent than conventional drug cisplatin. DNA-binding studies were performed in each case via UV-Vis titrations, cyclic voltammetry, gel electrophoresis, and viscosity, which suggest DNA partial intercalation interaction, and the structure-activity relationship studies suggest that the anticancer activities increase with the increasing lipophilicities of the compounds, roughly consistent with their DNA-binding activities.