The human GCOM1 complex gene interacts with the NMDA receptor and internexin-alpha

The known functions of the human GCOM1 complex hub gene include transcription elongation and the intercalated disk of cardiac myocytes. However, in all likelihood, the gene's most interesting, and thus far least understood, roles will be found in the central nervous system. To investigate the functions of the GCOM1 gene in the CNS, we have cloned human and rat brain cDNAs encoding novel, 105 kDa GCOM1 combined (Gcom) proteins, designated Gcom15, and identified a new group of GCOM1 interacting genes, termed Gints, from yeast two-hybrid (Y2H) screens. We showed that Gcom15 interacts with the NR1 subunit of the NMDA receptor by co-expression in heterologous cells, in which we observed bi-directional co-immunoprecipitation of human Gcom15 and murine NR1. Our Y2H screens revealed 27 novel GCOM1 interacting genes, many of which are synaptic proteins and/or play roles in neurologic diseases. Finally, we showed, using rat brain protein preparations, that the Gint internexin-alpha (INA), a known interactor of the NMDAR, co-IPs with GCOM1 proteins, suggesting a GCOM1-GRIN1-INA interaction and a novel pathway that may be relevant to neuroprotection

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

Two-dimensional method for time aligning liquid chromatography-mass spectrometry data

We describe a new time alignment method that takes advantage of both dimensions of LC-MS data to resolve ambiguities in peak matching while remaining computationally efficient. This approach, Warp2D, combines peak extraction with a two-dimensional correlation function to provide a reliable alignment scoring function that is insensitive to spurious peaks and background noise. One-dimensional alignment methods are often based on the total-ion-current elution profile of the spectrum and are unable to distinguish peaks of different masses. Our approach uses one-dimensional alignment in time, but with a scoring function derived from the overlap of peaks in two dimensions, thereby combining the specificity of two-dimensional methods with the computational performance of one-dimensional methods. The peaks are approximated as two-dimensional Gaussians of varying width. This approximation allows peak overlap (the measure of alignment quality) to be calculated analytically, without computationally intensive numerical integration in two dimensions. To demonstrate the general applicability of Warp2D, we chose a variety of complex samples that have substantial biological and analytical variability, including human serum and urine. We show that Warp2D works well with these diverse sample sets and with minimal tuning of parameters, based on the reduced standard deviation of peak elution times after warping. The combination of high computational speed, robustness with complex samples, and lack of need for detailed tuning makes this alignment method well suited to high-throughput LC-MS studies

The ecological system and the regionalization of landscape reconstruction in northwest of China

The northwest of China is a vast area with abundant resources and significant potential for development. However, the ecological system is extremely vulnerable to damage and must be managed carefully. Thus, the Chinese government is strengthening research on improvement and reconstruction of the ecological system and landscape in northwest of China while moving forward with large-scale development in west China. The disadvantages and vulnerabilities in the northwest area in China are presented. It is suggested that the reconstruction of landscape should be conducted by step by step regionalization across the various ecological systems in the 3.04 million km2 northwest area of China. The first level regionalization results of reconstruction of landscape are discussed

ST-MAE: robust lane detection in continuous multi-frame driving scenes based on a deep hybrid network

Abstract Lane detection is one of the key techniques to realize advanced driving assistance and automatic driving. However, lane detection networks based on deep learning have significant shortcomings. The detection results are often unsatisfactory when there are shadows, degraded lane markings, and vehicle occlusion lanes. Therefore, a continuous multi-frame image sequence lane detection network is proposed. Specifically, the continuous six-frame image sequence is input into the network, in which the scene information of each frame image is extracted by an encoder composed of Swin Transformer blocks and input into the PredRNN. Continuous multi-frame of the driving scene is modeled as time-series by ST-LSTM blocks, and then, the shape changes and motion trajectory in the spatiotemporal sequence are effectively modeled. Finally, through the decoder composed of Swin Transformer blocks, the features are obtained and reconstructed to complete the detection task. Extensive experiments on two large-scale datasets demonstrate that the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations. Experiments are carried out based on the TuSimple dataset. The results show: for easy scenes, the validation accuracy is 97.46%, the test accuracy is 97.37%, and the precision is 0.865. For complex scenes, the validation accuracy is 97.38%, the test accuracy is 97.29%, and the precision is 0.859. The running time is 4.4 ms. Experiments are carried out based on the CULane dataset. The results show that, for easy scenes, the validation accuracy is 97.03%, the test accuracy is 96.84%, and the precision is 0.837. For complex scenes, the validation accuracy is 96.18%, the test accuracy is 95.92%, and the precision is 0.829. The running time is 6.5 ms