AbsorbanceQ: An App for Generating Absorbance Images from Brightfield Images

The AbsorbanceQ app converts brightfield microscope images into absorbance images that can be analyzed and compared across different operators, microscopes, and time. Because absorbance-based measurements are comparable across these parameters, they are useful when the aim is to manufacture biotherapeutics with consistent quality. AbsorbanceQ will be of value to those who want to capture quantitative absorbance images of cells. The AbsorbanceQ app has two modes - a single image processing mode and a batch processing mode for multiple images. Instructions for using the app are given on the ‘App Information’ tab when the app is opened. The input and output images for the app have been defined, and synthetic images were used to validate that the output images are correct. This article provides a description of how to use the app, software specifications, a description of how the app works, instructive advice on how to use the tools and a description of the methods used to generate the software. In addition, links are provided to a website where the app and test images are deployed.

The Ryazan Pendant Seals from I. N. Parusimov’s Collection

The article is the last one in a series of publications on Old Rus’ pendant seals from the collection of the Rostov archaeologist I. N. Parusimov. It reproduces and comments on 75 pendant seals originating from the territory of Ryazan Oblast. These objects were partly published by the owner of the collection. This article offers clarifications to previous suggestions. In I. N. Parusimov’s collection, pendant seals originating from Ryazan Oblast are the most representative series. It seems that, by the present time, no pendant seals impressed by similar matrices have been found in other regions of Old Rus. In this collection, there are 13 seal-types among Ryazan pendant seals. All these belong to common sphragistic groups. The majority of signs are attributed to already known Old Rus’ princely signs. However, signs of four types are encountered for the first time. Among the signs from the Ryazan seals, there are multiple images of bidents with one prong pointing outwards. No narrow dating is available for the signs on the Ryazan seals from I. N. Parusimov’s collection. It can be confidently maintained that the objects carrying the depictions of similar signs date to pre-Mongolian time. Definitely certain are only the dates of the bidents attributed to Vladimir Monomakh (†1125) and his eldest son Mstislav the Great (†1132).

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

Temporal SNR optimization through RF coil combination in fMRI: The more, the better?

For functional MRI with a multi-channel receiver RF coil, images are often reconstructed channel by channel, resulting into multiple images per time frame. The final image to analyze usually is the result of the covariance Sum-of-Squares (covSoS) combination across these channels. Although this reconstruction is quasi-optimal in SNR, it is not necessarily the case in terms of temporal SNR (tSNR) of the time series, which is yet a more relevant metric for fMRI data quality. In this work, we investigated tSNR optimality through voxel-wise RF coil combination and its effects on BOLD sensitivity. An analytical solution for an optimal RF coil combination is described, which is somewhat tied to the extended Krueger-Glover model involving both thermal and physiological noise covariance matrices. Compared experimentally to covSOS on four volunteers at 7T, the method yielded great improvement of tSNR but, surprisingly, did not result into higher BOLD sensitivity. Solutions to improve the method such as for example the t-score for the mean recently proposed are also explored, but result into similar observations once the statistics are corrected properly. Overall, the work shows that data-driven RF coil combinations based on tSNR considerations alone should be avoided unless additional and unbiased assumptions can be made.

A 3D Reconstruction Framework of Buildings Using Single Off-Nadir Satellite Image

A novel framework for 3D reconstruction of buildings based on a single off-nadir satellite image is proposed in this paper. Compared with the traditional methods of reconstruction using multiple images in remote sensing, recovering 3D information that utilizes the single image can reduce the demands of reconstruction tasks from the perspective of input data. It solves the problem that multiple images suitable for traditional reconstruction methods cannot be acquired in some regions, where remote sensing resources are scarce. However, it is difficult to reconstruct a 3D model containing a complete shape and accurate scale from a single image. The geometric constraints are not sufficient as the view-angle, size of buildings, and spatial resolution of images are different among remote sensing images. To solve this problem, the reconstruction framework proposed consists of two convolutional neural networks: Scale-Occupancy-Network (Scale-ONet) and model scale optimization network (Optim-Net). Through reconstruction using the single off-nadir satellite image, Scale-Onet can generate water-tight mesh models with the exact shape and rough scale of buildings. Meanwhile, the Optim-Net can reduce the error of scale for these mesh models. Finally, the complete reconstructed scene is recovered by Model-Image matching. Profiting from well-designed networks, our framework has good robustness for different input images, with different view-angle, size of buildings, and spatial resolution. Experimental results show that an ideal reconstruction accuracy can be obtained both on the model shape and scale of buildings.

Asymmetric optical cryptosystem for multiple images based on devil’s spiral Fresnel lens phase and random spiral transform in gyrator domain

An asymmetric cryptosystem is presented for encrypting multiple images in gyrator transform domains. In the encryption approach, the devil’s spiral Fresnel lens variable pure phase mask is first designed for each image band to be encrypted by using devil’ mask, random spiral phase and Fresnel mask, respectively. Subsequently, a novel random devil’ spiral Fresnel transform in optical gyrator transform is implemented to achieved the intermediate output. Then, the intermediate data is divided into two masks by employing random modulus decomposition in the asymmetric process. Finally, a random permutation matrix is utilized to obtain the ciphertext of the intact algorithm. For the decryption approach, two divided masks (private key and ciphertext) need to be imported into the optical gyrator input plane simultaneously. Some numerical experiments are given to verify the effectiveness and capability of this asymmetric cryptosystem.