Isolation of phage-display library-derived scFv antibody specific to Listeria monocytogenes by a novel immobilized method.

AIMS: To select Listeria monocytogenes-specific single-chain fragment variable (scFv) antibodies from a phage-display library by a novel simple and cost-effective immobilization method. METHODS AND RESULTS: Light expanded clay aggregate (LECA) was used as biomass support matrix for biopanning of a phage-display library to select L. monocytogenes-specific scFv antibody. Four rounds of positive selection against LECA-immobilized L. monocytogenes and an additional subtractive panning against Listeria innocua were performed. The phage clones selected using this panning scheme and LECA-based immobilization method exhibited the ability to bind L. monocytogenes without cross-reactivity toward 10 other non-L. monocytogenes bacteria. One of the selected phage clones was able to specifically recognize three major pathogenic serotypes (1/2a, 1/2b and 4b) of L. monocytogenes and 11 tested L. monocytogenes strains isolated from foods. CONCLUSIONS: The LECA-based immobilization method is applicable for isolating species-specific anti-L. monocytogenes scFv antibodies by phage display. SIGNIFICANCE AND IMPACT OF THE STUDY: The isolated scFv antibody has potential use in development of immunoassay-based methods for rapid detection of L. monocytogenes in food and environmental samples. In addition, the LECA immobilization method described here could feasibly be employed to isolate specific monoclonal antibodies against any given species of pathogenic bacteria from phage-display libraries.

An allometric model-based approach for estimating biomass in seven Indian bamboo species in western Himalayan foothills, India.

The rapid growth rate, high biomass production, and annual harvesting make bamboo a suitable species for commercial production. Allometric equations for many broadleaf and conifer tree species are available. However, knowledge of biomass production and allometric equations of bamboos is limited. This study aims to develop species- specific allometric models for predicting biomass and synthetic height values as a proxy variable for seven bamboo species in Himalayan foothills. Two power form-based allometric models were used to predict aboveground and culm biomass using diameter at breast height (D) alone and D combined with culm height (H) as an independent variable. This study also extended to establishing an H-D allometric model that can be used to generate synthetic H values as a proxy to missing H. In the seven bamboo species studied, among three major biomass components (culm, branch and foliage), culm is the most important component with the highest share (69.56-78.71%). The distribution of percentage (%) share of culm, branch and foliage to above-ground fresh weight varies significantly between different bamboo species. D. hamiltonii has the highest productivity for above-ground biomass components. Ratio of dry to fresh weight of seven bamboo species was estimated for culm, branch, foliage and above-ground biomass to convert fresh weight to dry weight.

SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images.

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.

Multi-Scale Binary Pattern Encoding Network for Cancer Classification in Pathology Images.

Multi-scale approaches have been widely studied in pathology image analysis. These offer an ability to characterize tissues in an image at various scales, in which the tissues may appear differently. Many of such methods have focused on extracting multi-scale hand-crafted features and applied them to various tasks in pathology image analysis. Even, several deep learning methods explicitly adopt the multi-scale approaches. However, most of these methods simply merge the multi-scale features together or adopt the coarse-to-fine/fine-to-coarse strategy, which uses the features one at a time in a sequential manner. Utilizing the multi-scale features in a cooperative and discriminative fashion, the learning capabilities could be further improved. Herein, we propose a multi-scale approach that can identify and leverage the patterns of the multiple scales within a deep neural network and provide the superior capability of cancer classification. The patterns of the features across multiple scales are encoded as a binary pattern code and further converted to a decimal number, which can be easily embedded in the current framework of the deep neural networks. To evaluate the proposed method, multiple sets of pathology images are employed. Under the various experimental settings, the proposed method is systematically assessed and shows an improved classification performance in comparison to other competing methods.

Diagnostic value of cerebral vasospasm by transcranial doppler ultrasound in Vietnamese patients with subarachnoid hemorrhage.

OBJECTIVE: Although the application of transcranial Doppler (TCD) ultrasonography in clinical diagnosis of cerebral vasospasm is popular in clinical practice in Vietnam, available evidence of the predictive value of vasospasm on TCD in the literature was mostly reported from large institutions in developed countries. Hence, this study was conducted to evaluate the value of TCD ultrasonography in the diagnosis of vasospasm in patients with subarachnoid hemorrhage (SAH) in Vietnam. PATIENTS AND METHODS: This is a prospective observational study of all aneurysmal SAH patients consecutively admitted to a single center between 2008 and December 2011. TCD and 64-slice computed tomographic angiography (CTA) were used to cerebral vasospasm in SAH patients. RESULTS: 316 patients were analyzed (mean age = 52.97±12.27 years, 52.2% males). There were statistically significant difference rates of the cerebral vasospasm by Hunt and Hess Classification and Fisher classification (p <0.01). The proportion of the patients with cerebral vasospasm who were diagnosed exactly by TCD was 95.2%, while the proportion of the patients without cerebral vasospasm diagnosed exactly was 91.5%. TCD predictive diagnostic value was the highest, with the sensitivity of 0.95 (95% CI: 0.91-0.98), specificity of 0.91 (95% CI: 0.85-0.96), positive predictive value of 0.94 (5% CI: 0.90-0.97) and negative predictive value of 0.93 (95 CI: 0.87-0.97). Hemiplegia was the clinical symptom with the highest diagnostic value with the sensitivity of 0.34 (95% CI: 0.27-0.41), specificity of 0.92 (95% CI: 0.86-0.96), positive predictive value of 0.86 (95% CI: 0.76-0.93) and negative predictive value of 0.49 (95% CI: 0.41-0.54). CONCLUSIONS: Evidence of vasospasm diagnosis on TCD ultrasonography was found with high accuracy. Current study enables to suggest the wide application of TCD in Vietnam health facilities from central to grassroots levels instead of the CTA use.