A Single Human VH-gene Allows for a Broad-Spectrum Antibody Response Targeting Bacterial Lipopolysaccharides in the Blood

B cell receptors (BCRs) display a combination of variable (V)-gene-encoded complementarity determining regions (CDRs) and adaptive/hypervariable CDR3 loops to engage antigens. It has long been proposed that the former tune for recognition of pathogens or groups of pathogens. To experimentally evaluate this within the human antibody repertoire, we perform immune challenges in transgenic mice that bear diverse human CDR3 and light chains but are constrained to different human V_H-genes. We find that, of six commonly deployed V_H sequences, only those CDRs encoded by IGHV1-2^∗02 enable polyclonal antibody responses against bacterial lipopolysaccharide (LPS) when introduced to the bloodstream. The LPS is from diverse strains of gram-negative bacteria, and the V_H-gene-dependent responses are directed against the non-variable and universal saccrolipid substructure of this antigen. This reveals a broad-spectrum anti-LPS response in which germline-encoded CDRs naturally hardwire the human antibody repertoire for recognition of a conserved microbial target.

Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow

Artificial intelligence (AI) is widely used in pattern recognition and positioning. In most of the geological exploration applications, it needs to locate and identify underground objects according to electromagnetic wave characteristics from the ground-penetrating radar (GPR) images. Currently, a few robust AI approach can detect targets by real-time with high precision or automation for GPR images recognition. This paper proposes an approach that can be used to identify parabolic targets with different sizes and underground soil or concrete structure voids based on you only look once (YOLO) v3. With the TensorFlow 1.13.0 developed by Google, we construct YOLO v3 neural network to realize real-time pattern recognition of GPR images. We propose the specific coding method for the GPR image samples in Yolo V3 to improve the prediction accuracy of bounding boxes. At the same time, K-means algorithm is also applied to select anchor boxes to improve the accuracy of positioning hyperbolic vertex. For some instances electromagnetic-vacillated signals may occur, which refers to multiple parabolic electromagnetic waves formed by strong conductive objects among soils or overlapping waveforms. This paper deals with the vacillating signal similarity intersection over union (IoU) (V-IoU) methods. Experimental result shows that the V-IoU combined with non-maximum suppression (NMS) can accurately frame targets in GPR image and reduce the misidentified boxes as well. Compared with the single shot multi-box detector (SSD), YOLO v2, and Faster-RCNN, the V-IoU YOLO v3 shows its superior performance even when implemented by CPU. It can meet the real-time output requirements by an average 12 fps detected speed. In summary, this paper proposes a simple and high-precision real-time pattern recognition method for GPR imagery, and promoted the application of artificial intelligence or deep learning in the field of the geophysical science.

Use of Inductive, Problem-Based Clinical Reasoning Enhances Diagnostic Accuracy in Final-Year Veterinary Students

Despite tremendous progression in the medical field, levels of diagnostic error remain unacceptably high. Cognitive failures in clinical reasoning are believed to be the major contributor to diagnostic error. There is evidence in the literature that teaching problem-based, inductive reasoning has the potential to improve clinical reasoning skills. In this study, 47 final-year veterinary medicine students at the Royal Veterinary College (RVC) were presented with a complex small animal medicine case. The participants were divided into two groups, one of which received a prioritized problem list in addition to the history, physical exam, and diagnostic test results provided to both groups. The students' written approaches to the case were then analyzed and assigned a diagnostic accuracy score (DAS) and an inductive reasoning score (IRS). The IRS was based on a series of predetermined characteristics consistent with the inductive reasoning framework taught at the RVC. No significant difference was found between the DAS scores of each group, indicating that the provision of a prioritized problem list did not impact diagnostic accuracy. However, a significant positive correlation between the IRS and DAS was illustrated for both groups of students, suggesting increased use of inductive reasoning is associated with increased diagnostic accuracy. These results contribute to a body of research proposing that inductive, problem-based reasoning teaching delivered in an additive model, can enhance the clinical reasoning skills of students and reduce diagnostic error.