Biofilms and core pathogens shape the tumor microenvironment and immune phenotype in colorectal cancer.

Extensive research has explored the role of gut microbiota in colorectal cancer (CRC). Nonetheless, metatranscriptomic studies investigating the in situ functional implications of host-microbe interactions in CRC are scarce. Therefore, we characterized the influence of CRC core pathogens and biofilms on the tumor microenvironment (TME) in 40 CRC, paired normal, and healthy tissue biopsies using fluorescence in situ hybridization (FISH) and dual-RNA sequencing. FISH revealed that Fusobacterium spp. was associated with increased bacterial biomass and inflammatory response in CRC samples. Dual-RNA sequencing demonstrated increased expression of pro-inflammatory cytokines, defensins, matrix-metalloproteases, and immunomodulatory factors in CRC samples with high bacterial activity. In addition, bacterial activity correlated with the infiltration of several immune cell subtypes, including M2 macrophages and regulatory T-cells in CRC samples. Specifically, Bacteroides fragilis and Fusobacterium nucleatum correlated with the infiltration of neutrophils and CD4+ T-cells, respectively. The collective bacterial activity/biomass appeared to exert a more significant influence on the TME than core pathogens, underscoring the intricate interplay between gut microbiota and CRC. These results emphasize how biofilms and core pathogens shape the immune phenotype and TME in CRC while highlighting the need to extend the bacterial scope beyond CRC pathogens to advance our understanding and identify treatment targets.

Low cost MEMS accelerometer and microphone based condition monitoring sensor, with LoRa and Bluetooth Low Energy radio.

Vibration-based Condition Monitoring (CM) is an essential tool for identifying potential defects in industrial machinery. However, the implementation of an efficient CM system often necessitates the use of high-cost accelerometers with a large bandwidth. To address this challenge, this study introduces a low-cost CM sensor composed of an ultrasonic MEMS microphone - SPH0641LU and an ADXL1002 MEMS accelerometer. The combination of these two sensor types allows for comparative analysis of the captured data. The SPH641LU microphone is capable of detecting audible vibration signals with a frequency range up to 80 kHz, while the ADXL1002 accelerometer can measure vibrations up to 21 kHz. In addition a three axis ultra low power accelerometer is included, allowing measurement of unbalance or rotating speed, below 200Hz. Moreover, the sensor is designed to operate on battery power and provides the capability for raw data transmission via Bluetooth Low Energy (BLE) or the transmission of pre-processed features from the raw data using LoRaWAN.

Seismic and medical ultrasound imaging of velocity and density variations by nonlinear vectorial inverse scattering.

We present an iterative nonlinear inverse scattering algorithm for high-resolution acoustic imaging of density and velocity variations. To solve the multi-parameter nonlinear direct scattering problem, the acoustic wave equation for inhomogeneous media in the frequency domain is transformed into a vectorial integral equation of the Lippmann-Schwinger type for the combined pressure and pressure-gradient field. To solve the multi-parameter nonlinear inverse scattering problem, we use the Newton-Kantorovich method in conjunction with matrix-free representations of the Fréchet derivative operators and their adjoints. The approximate Hessian information that is accounted for in our iterative solution of the (nonlinear) multi-parameter inverse scattering problem is essential for the mitigation of multi-parameter cross talk effects. Numerical examples related to seismic and medical ultrasound breast imaging illustrate the performance of the new algorithm for multi-parameter acoustic imaging.

Comparison between digital image analysis and visual assessment of immunohistochemical HER2 expression in breast cancer.

BACKGROUND: Assessment of HER2 status is considered standard of care in the histopathologic workup of breast cancer and conveys prognostic and predictive information used to guide treatment decisions. The assessment is often carried out in a two-step approach where immunohistochemical expression of HER2 protein is first evaluated by conventional microscopy and equivocal cases are further analyzed by in-situ hybridization techniques to assess gene amplification status. In this study we compared conventional manual assessment of immunohistochemical HER2 expression with digital image analysis (DIA) and consensus manual assessment by a panel of three pathologists. METHODS: From our archive we retrieved sections of 109 invasive breast carcinomas stained for HER2 with corresponding HER2 score from the original pathology report. The glass slides were assessed by three pathologists to reach a consensus score. Next, the slides were scanned into whole slide images and DIA was performed using Aperio Imagescope. The scoring results were then compared with gene amplification status evaluated by dual in-situ hybridization (DISH). RESULTS: Comparing manual assessment with consensus assessment and DIA, good agreement was obtained with weighted kappa coefficients of 0.79 (manual vs. consensus) and 0.67 (manual vs. DIA). When compared with gene status assessment by DISH, agreement analysis yielded weighted kappa coefficients of 0.52 (manual vs. DISH), 0.58 (consensus vs. DISH) and 0.78 (DIA vs. DISH). There were no false negatives by any of the three methods and false positives ranging from 0.9 to 2.8%. The proportion of equivocal cases by each method was 44% (manual), 33.3% (consensus) and 14.7% (DIA). Application of DIA reduced the number of equivocal cases by 67% without increasing the proportion of false negatives. CONCLUSION: We conclude that DIA is an accurate method to reduce the number of HER2 equivocal cases without affecting the sensitivity of the HER2 assessment.

Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering.

Data assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. When applying ensemble-based methods to assimilate big geophysical data, substantial computational resources are needed in order to compute and/or store certain quantities (e.g., the Kalman-gain-type matrix), given both big model and data sizes. In addition, uncertainty quantification of observational data, e.g., in terms of estimating the observation error covariance matrix, also becomes computationally challenging, if not infeasible. To tackle the aforementioned challenges in the presence of big data, in a previous study, the authors proposed a wavelet-based sparse representation procedure for 2D seismic data assimilation problems (also known as history matching problems in petroleum engineering). In the current study, we extend the sparse representation procedure to 3D problems, as this is an important step towards real field case studies. To demonstrate the efficiency of the extended sparse representation procedure, we apply an ensemble-based seismic history matching framework with the extended sparse representation procedure to a 3D benchmark case, the Brugge field. In this benchmark case study, the total number of seismic data is in the order of [Formula: see text]. We show that the wavelet-based sparse representation procedure is extremely efficient in reducing the size of seismic data, while preserving the salient features of seismic data. Moreover, even with a substantial data-size reduction through sparse representation, the ensemble-based seismic history matching framework can still achieve good estimation accuracy.