Testing Evolutionary Models with Red Supergiant and Wolf–Rayet Populations

Despite the many successes that modern massive star evolutionary theory has enjoyed, reproducing the apparent trend in the relative number of red supergiants (RSGs) and Wolf–Rayet (WR) stars has remained elusive. Previous estimates show the RSG/WR ratio decreasing strongly with increasing metallicity. However, the evolutionary models have always predicted a relatively flat distribution for the RSG/WR ratio. In this paper we reexamine this issue, drawing on recent surveys for RSGs and WRs in the Magellanic Clouds, M31, and M33. The RSG surveys have used Gaia astrometry to eliminate foreground contamination and have separated RSGs from asymptotic giant branch stars using near-infrared colors. The surveys for WRs have utilized interference-filter imaging, photometry, and image subtraction techniques to identify candidates, which have then been confirmed spectroscopically. After carefully matching the observational criteria to the models, we now find good agreement in both the single-star Geneva and binary BPASS models with the new observations. The agreement is better when we shift the RSG effective temperatures derived from J − Ks photometry downwards by 200 K in order to agree with the Levesque TiO effective temperature scale. In an appendix we also present a source list of RSGs for the SMC which includes effective temperatures and luminosities derived from near-infrared 2MASS photometry, in the same manner as used for the other galaxies.

Static permeability assessment method to distinguish brain tumour recurrence from pseudoprogression

Aims It is common to have adjuvant chemo-radiotherapy after primary brain tumour resection. It is a known side effect that enhancing lesion could be seen in radiation territory after treatment, termed as pseudoprogression. It has been a difficult task to distinguish brain between tumour recurrence from pseudoprogression after radiotherapy. Timing of occurrence of these can overlap. It is important to distinguish the two as management is completely different. Early intervention in recurrence could improve survival time while pseudoprogression could be self-limiting. Surgical resection of pseudoprogression could be counter-productive. The radiological approach has been relying on multimodality investigation and close follow up. It has come to our institution notice that there is a new technique which could distinguish the two conditions efficiently. That's static permeability assessment method, also known as treatment response assessment maps (TRAMs). Our experience with it so far has been beneficial. Method This is a retrospective case series review of primary brain tumour treatment in our neurosurgical institution in 2020. Two high resolution 3D T1-weighted brain MRI images were acquired after a standard dose of gadolinium based contrast agent was injected. The first acquisition began five minutes after injection, and the second began 60 – 105 minutes post contrast injection. The TRAMs technique is based on image subtraction that is post processed after acquisition. The resultant subtracted image set was mapped to grey scale values, where voxels showing contrast clearance were light grey/white, and those showing contrast accumulation were dark grey/black. The zero value (i.e. no clearance or accumulation) was therefore mid-grey. Those with contrast clearance is associated with tumour recurrence. TRAMs images were compared to serial follow up imaging and histopathology results to determine the diagnostic accuracy of the technique. Results We have identified 21 patients in this period who had concern of either of pseudoprogression or tumour recurrence/progression. There were 6 females and 15 males, mean age 51. There were 14 glioblastoma multiforme (GBM), 5 astrocytoma, 1 oligodendroglioma and 1 post radiotherapy arteriovenous malformation. 17 cases were found to have clear cut recurrence, pseudoprogression or mixture of both in TRAMS. These findings are backed up by histology or repeated follow up scan. 4 cases were considered as equivocal. In retrospect, these cases have challenging interpretation due to poor case selection. TRAMs could distinguish high grade transformation as well as detecting recurrence. In some difficult cases, it is found that both pseudoprogression and recurrence could happen together. Conclusion TRAMs is a useful adjunct to the multimodalities of diagnostic techniques in tricky situation. This has provided an efficient and easy to use tool for radiologists to come up with the answer. We are the first independent centre to report on this technique. This is still early days and fine-tuning of its use is still undergoing. It is clear this has saved precious resources and has given patients more suitable care. We think it would be beneficial for us to share our experience with others and hope to get future collaboration with other centres.

Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique

A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.

Non-Diffracting Light Wave: Fundamentals and Biomedical Applications

The light propagation in the medium normally experiences diffraction, dispersion, and scattering. Studying the light propagation is a century-old problem as the photons may attenuate and wander. We start from the fundamental concepts of the non-diffracting beams, and examples of the non-diffracting beams include but are not limited to the Bessel beam, Airy beam, and Mathieu beam. Then, we discuss the biomedical applications of the non-diffracting beams, focusing on linear and nonlinear imaging, e.g., light-sheet fluorescence microscopy and two-photon fluorescence microscopy. The non-diffracting photons may provide scattering resilient imaging and fast speed in the volumetric two-photon fluorescence microscopy. The non-diffracting Bessel beam and the Airy beam have been successfully used in volumetric imaging applications with faster speed since a single 2D scan provides information in the whole volume that adopted 3D scan in traditional scanning microscopy. This is a significant advancement in imaging applications with sparse sample structures, especially in neuron imaging. Moreover, the fine axial resolution is enabled by the self-accelerating Airy beams combined with deep learning algorithms. These additional features to the existing microscopy directly realize a great advantage over the field, especially for recording the ultrafast neuronal activities, including the calcium voltage signal recording. Nonetheless, with the illumination of dual Bessel beams at non-identical orders, the transverse resolution can also be improved by the concept of image subtraction, which would provide clearer images in neuronal imaging.

Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method

In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM-convolutional neural network (CNN) is proposed to address the non-stationary nature, the temporal localization of excitation occurrence, and the frequency band distribution characteristics of the MI-EEG signal in this paper. First, according to the logically symmetrical relationship between the C3 and C4 channels, the result of the time-frequency image subtraction (IS) for the MI-EEG signal is used as the input of the classifier. It both reduces the redundancy and increases the feature differences of the input data. Second, the attention module is added to the classifier. A convolutional neural network is built as the base classifier, and information on the temporal location and frequency distribution of MI-EEG signal occurrences are adaptively extracted by introducing the Convolutional Block Attention Module (CBAM). This approach reduces irrelevant noise interference while increasing the robustness of the pattern. The performance of the framework was evaluated on BCI competition IV dataset 2b, where the mean accuracy reached 79.6%, and the average kappa value reached 0.592. The experimental results validate the feasibility of the framework and show the performance improvement of MI-EEG signal classification.

Comparative Study of Image Processing and Transfer Learning Techniques for an Automated PCB Fault Detection System

Printed circuit board (PCB) is one of the most crucial components in most electronic devices. PCBs are manufactured in large quantities, and therefore, maintaining the quality of such large numbers of PCBs is important. An automated inspection system can help in the aspect of quality maintenance. Such a system is able to overcome the limitations of manual inspection for a large number of PCBs. It provides fast detection of defects and hence can prove to be an asset in the manufacturing process. This project aims to achieve fault detection of bare PCBs through two different methods; the traditional algorithmic approach using image processing, which makes use of image subtraction method, and a transfer learning approach, which involves the pre-trained CNN VGG16 model. A comparative study of both the methods is done.

Estrus Detection Using Background Image Subtraction Technique in Tie-Stalled Cows

In this study, we determined the applicability of the background image subtraction technique to detect estrus in tie-stalled cows. To investigate the impact of the camera shooting direction, webcams were set up to capture the front, top, and rear views of a cow simultaneously. Video recording was performed for a total of ten estrous cycles in six cows. Standing estrus was confirmed by testing at 6 h intervals. From the end of estrus, transrectal ultrasonography was performed every 2 h to confirm ovulation time. Foreground objects (moving objects) were extracted in the videos using the background subtraction technique, and the pixels were counted at each frame of five frames-per-second sequences. After calculating the hourly averaged pixel counts, the change in values was expressed as the pixel ratio (total value during the last 24 h/total value during the last 24 to 48 h). The mean pixel ratio gradually increased at approximately 48 h before ovulation, and the highest value was observed at estrus, regardless of the camera shooting direction. When using front-view videos with an appropriate threshold, estrus was detected with 90% sensitivity and 50% precision. The present method in particular has the potential to be a non-contact estrus detection method for tie-stalled cows.

Computer-automated shadow moiré method and applications for 3D surface profiling

Among numerous methods for 3D surface profiling, classic shadow moiré method has been kept as the most popular one due to its full-field feature and low cost. This thesis focuses on a computer-vision shadow moiré method with a scope to improve the measurement resolution, accuracy and efficiency. The computer automation is basically realized through the introduction of a phase-shifting technique that is incorporated with a new multi-grid least-square unwrapping algorithm. The method is enhanced by implementing a few additional image processing techniques. These techniques, when implemented, result in improved measurement accuracy and enable easy applications to irregularly shaped surfaces. The study also proposes a new, automated system calibration approach that is based on a real-time image subtraction. A data normalization process is studied to resolve possible confusions in the presentation of the original data. The verification test results show that the modified shadow moiré technique has achieved the initial goal, in that the measurement resolution now reaches a few percentage of the fringe sensititivity.

Computer-automated shadow moiré method and applications for 3D surface profiling

Among numerous methods for 3D surface profiling, classic shadow moiré method has been kept as the most popular one due to its full-field feature and low cost. This thesis focuses on a computer-vision shadow moiré method with a scope to improve the measurement resolution, accuracy and efficiency. The computer automation is basically realized through the introduction of a phase-shifting technique that is incorporated with a new multi-grid least-square unwrapping algorithm. The method is enhanced by implementing a few additional image processing techniques. These techniques, when implemented, result in improved measurement accuracy and enable easy applications to irregularly shaped surfaces. The study also proposes a new, automated system calibration approach that is based on a real-time image subtraction. A data normalization process is studied to resolve possible confusions in the presentation of the original data. The verification test results show that the modified shadow moiré technique has achieved the initial goal, in that the measurement resolution now reaches a few percentage of the fringe sensititivity.