The study was undertaken to determine the aetiology and prevalence of mastitis in hand-milked cows (n = 186) in two major Ethiopian dairies. The California Mastitis Test and culturing for bacteria revealed that 21.5% of the cows were clinically infected and 38.2% had subclinical mastitis. Most mastitis pathogens isolated from milk samples testing positive by the California Mastitis Test were Gram-positive cocci. Staphylococci constituted 57% of the isolates, of which the predominant cause of bovine mastitis was Staphylococcus aureus (40.5%). Other mastitis pathogens isolated include streptococci (16.5%), coliforms (9%) and corynebacteria (5%). Retrospective analysis of farm records indicated that mastitis was the second most important cause of culling and accounted for 27% of the cows removed from these two dairies.

We present an ultrahigh resolution spectral-domain optical coherence tomography imaging system using a broadband superluminescent diode light source emitting at a center wavelength of 1.3 µm. The light source consists of two spectrally shifted superluminescent diodes that are coupled together into a single mode fiber. The effective emission power spectrum has a full width at half maximum of 200 nm and the source output power is 10 mW. The imaging system has an axial resolution of 3.9 µm in air (< 3.0 µm in biological tissue), and a lateral resolution of 6.5 µm. The sensitivity and the maximum line rate are 95 dB and 46 kHz, respectively. Images of an infrared viewing card and a cornea from human eye suffering from glaucoma showing Schlemm's canal are presented to illustrate the performance of the system.

Du lait de quartier (n = 828) a été prélevé chez 207 femelles dromadaires (Camelus dromedarius) en lactation, provenant de troupeaux du Borana, au sud-ouest de l’Ethiopie, et élevées de manière traditionnelle. L’objectif de l’étude a été de décrire la prévalence des mammites et certaines étiologies bactériennes chez le dromadaire. Le California mastitis test (Cmt) a été utilisé comme test de dépistage et des examens bactériologiques ont été effectués pour identifier les agents pathogènes impliqués dans les mammites. La numération cellulaire somatique du lait de quartier des chamelles a également été déterminée. Vingt-cinq quartiers (12,1 p. 100) ont été trouvés non productifs parmi les 828 examinés. Un pourcentage de correspondance de 100 p. 100 a été trouvé pour les échantillons classés 3+ et 2+ avec Cmt, alors qu’un pourcentage de correspondance de 35, 71 et 85 p. 100 a été relevé pour ceux classés respectivement 0, traces et 1+ avec Cmt. Une association significative a été observée dans le lait de quartier des chamelles entre les classements positifs obtenus avec Cmt et la présence d’agents pathogènes principaux. La numération cellulaire somatique a été comprise entre 3 x 105 et 1,5 x 107 leucocytes par millilitre de lait. Les moyennes du comptage cellulaire ont montré une évolution numérique positive en fonction des classes croissantes du Cmt avec Anova. Parmi les femelles en lactation examinées, quatre (1,9 p. 100) cas cliniques de mammites ont été détectés. Des bactéries pathogènes ont été présentes dans 171 échantillons (74 p. 100) de lait de quartier examiné positif avec Cmt. Parmi les principaux agents pathogènes isolés ont été trouvées des espèces de Staphylococcus, Streptococcus, Micrococcus, Corynebacterium et Bacillus, ainsi que Actinomyces pyogenes, Escherichia coli et Pasteurella haemolytica.

Implementation of an artificial intelligencebased medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. Methods: Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The deep learning convolutional neural network (DLCNN) was utilized to fuse MRI and CT images with robust edge analysis (REA) properties, which are used to identify the slopes and edges of source images. Then, hybrid fuzzy c-means integrated k-means (HFCMIK) clustering is used to segment the disease affected region from the fused image. Further, hybrid features such as texture, colour, and low-level features are extracted from the fused image by using gray-level cooccurrence matrix (GLCM), redundant discrete wavelet transform (RDWT) descriptors. Finally, a deep learning based probabilistic neural network (DLPNN) is used to classify malignant and benign tumors. The BTFSC-Net attained 99.21% of segmentation accuracy and 99.46% of classification accuracy. Conclusions: The simulations showed that BTFSC-Net outperformed as compared to existing methods.

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BackgroundThe high anatomical complexity of maxillofacial defects caused by tumor can pose a formidable challenge for clinicians when designing an appropriate plan for surgical reconstruction. The intention of this work was to restore the complex anatomy with maximum possible facial functionality and aesthetics of the patient. Based on the medical images generated by computed tomography (CT) scan an optimal therapeutic planning for complex maxillofacial reconstruction was designed. MethodFirstly, the volumetric data sets were carefully evaluated and deeply inspected for accurate diagnosis. Regarding 3D visualization of the CT scan images 3D virtual models for regions of interest were created using a special software of 3D Slicer. Using the resulting 3D virtual models a well-defined virtual surgical planning was generated for multiple surgical procedures, including the osteotomies for bone defects, harvesting autogenous bone graft and creating a customized implant. ResultsThe relevant patient-specific anatomical models for real surgery were translated into the 3D printed physical models, with which the surgeons can rehearse the surgery before coming into the operating room. Precisely defined multiple surgeries for complex maxillofacial reconstruction were proposed in this research that could be transferred to the real-time surgery. ConclusionsThe proposed surgical approach will be beneficial both for the surgeons and patient, including improvement in surgical precision and outcomes, reduction in operating time, as well as understanding surgical procedures and decision making etc.

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