In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: The sudden death of three calves, one diarrheic calf, and one aborted fetus from four farms in southern Brazil was investigated. Two Histophilus somni-associated syndromes were identified: systemic histophilosis (n=4) and abortion (n=1). The principal pathological findings included vasculitis, meningoencephalitis with thrombosis, necrotizing myocarditis, renal infarctions, hepatic abscesses, and bronchopneumonia. PCR assays were used to amplify specific amplicons of the ovine herpesvirus 2, bovine herpesvirus 1 and −5, Listeria monocytogenes, H. somni, and pestivirus; bovine group A rotavirus (BoRV-A) and bovine coronavirus (BCoV) were investigated in calves with diarrhea. H. somni DNA was amplified in tissues from all calves and the brain of the aborted fetus with pathological alterations consistent with histophilosis. All other PCR assays were negative; BoRV-A and BCoV were not identified. These findings confirm the participation of H. somni in the pathological alterations observed in this study and represent the first description of histophilosis in cattle from Brazil.

A: Histophilus somni-induced infections in cattle from southern Brazil
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Q: Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

A: Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images
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Q: Serum and CSF specimens from clinically healthy Brown Norway (BN) rats inoculated intracerebrally with corona virus JHM were analysed with respect to the state of the blood-brain barrier (BBB) and the intrathecal synthesis and isoelectric distribution of immunoglobulins (Ig). Increased CSF/serum ratios for Ig in the context of an intact BBB were never seen in the absence of intrathecal synthesis of virus-specific antibodies. Affinity-mediated immunoblot analysis revealed a broad pattern of virus-specific antibodies with embedded clusters of restricted heterogeneity, but no signs of oligoclonal Ig production carrying non-viral specificity. From these data it was concluded that BN rats do control the intracerebral spread of JHM virus effectively by a strong local virus-specific antibody response, thereby preventing a clinically apparent disease.

A:
Analysis of the intrathecal humoral immune response in Brown Norway (BN) rats, infected with the murine coronavirus JHM
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