Possible impact of neutrophils on immune responses during early pregnancy in ruminants.

The interaction between early embryo and maternal immune system for the establishment of pregnancy is the focus of several studies; however, it remains unclear. The maternal immune response needs to keep a balance between avoiding any damage to the conceptus and maintaining its function in combating microbes as well. When conceptus-maternal crosstalk cannot achieve this balance, pregnancy losses might occur. Intercommunication between mother and conceptus is fundamental during early pregnancy to dictate the outcome of pregnancy. In ruminants, the embryo reacts with the maternal system mainly via interferon tau (IFNT) release. IFNT can act locally on the embryo and endometrial cells and systemically in several tissues and cells to regulate their response via the expression of interferon-stimulated genes (ISGs). Also, IFNT can induce the expression of inflammatory-related genes in immune cells. Day 7 embryo induces a shift in the maternal immune response towards anti-inflammatory (Th2) immune responses. During maternal recognition of pregnancy, peripheral mononuclear cells (PBMCs) and polymorphonuclear cells (PMNs) express markers that configure an anti-inflammatory response. However, PMNs response is more sensitive to the effects of IFNT. PMNs are more likely to express interferon-stimulated genes (ISGs), transforming growth factor-beta (TGFB), interleukin 10 (IL10), and arginase-1 (ARG1), configuring one of the most rapid immune responses to early pregnancy. This review focus on the local and peripheral immune responses during early pregnancy in ruminants, mainly the PMNs function in the immune system.

YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms.

BACKGROUND AND OBJECTIVE: With the recent development in deep learning since 2012, the use of Convolutional Neural Networks (CNNs) in bioinformatics, especially medical imaging, achieved tremendous success. Besides that, breast masses detection and classifications in mammograms and their pathology classification are considered a critical challenge. Till now, the evaluation process of the screening mammograms is held by human readers which is considered very monotonous, tiring, lengthy, costly, and significantly prone to errors. METHODS: We propose an end to end computer-aided diagnosis system based on You Only Look Once (YOLO). The proposed system first preprocesses the mammograms from their DICOM format to images without losing data. Then, it detects masses in full-field digital mammograms and distinguishes between the malignant and benign lesions without any human intervention. YOLO has three different architectures, and, in this paper, the three versions are used for mass detection and classification in the mammograms to compare their performance. The use of anchors in YOLO-V3 on the original form of data and its augmented version is proved to improve the detection accuracy especially when the k-means clustering is applied to generate anchors corresponding to the used dataset. Finally, ResNet and Inception are used as feature extractors to compare their classification performance against YOLO. RESULTS: Mammograms with different resolutions are used and based on YOLO-V3, the best results are obtained through detecting 89.4% of the masses in the INbreast mammograms with an average precision of 94.2% and 84.6% for classifying the masses as benign and malignant respectively. YOLO's classification network is replaced with ResNet and InceptionV3 to get overall accuracy of 91.0% and 95.5%, respectively. CONCLUSION: The proposed system showed using the experimental results the YOLO impact on the breast masses detection and classification. Especially using the anchor boxes concept in YOLO-V3 that are generated by applying k-means clustering on the dataset, we can detect most of the challenging cases of masses and classify them correctly. Also, by augmenting the dataset using different approaches and comparing with other recent YOLO based studies, it is found that augmenting the training set only is the fairest and accurate to be applied in the realistic scenarios.

Hybrid, randomized and high capacity conservative mutations DNA-based steganography for large sized data.

In this paper, a well secured, high capacity, preserved algorithm is proposed through integrating the cryptography and steganography concepts with the molecular biology concepts. We achieved this by first encrypting the confidential data using the DNA Playfair cipher to avoid extra information sent to the receiver and it consequently acts as a trap for an attacker. Second, it achieves a randomized steganography process by exploiting the DNA conservative mutations. The DNA conservative mutations are utilized in a way that allows a DNA base to be substituted by another base to allow carrying two bits. Consequently, a high capacity feature is obtained with no payload for the used sequence. There are three main achieved contributions in this work. First, is hiding high capacity of data within DNA by exploiting each codon to hide two bits whilst preserving the sequence properties of protein after the steganography process, which is a trade off in the field. Secondly, using the conservative mutation with all its valid biological permutations, leads to the lowest cracking probability achieved and published till now, as proven in the security analysis section. Finally, a comparison is conducted between the proposed algorithm and five recent substitution based algorithms using large sized data up to three megabytes, to prove the algorithm's scalability.