Rate-dependent Mechanical Properties of the Interfaces in Biological Composites

Biology produces a range of composite structures that evolve to resist a wide range of loading conditions from their environments. The mechanical function of these biological composites is expected to be governed by the properties of the interfaces between distinct hard and soft constituents at different length scales. However, difficulties exist in applying composite theories to biological structures since the interfaces present between the nanoscale biological constituents are typically below standard measurement length scales. Hierarchical biological composites found in nacre and arthropod exoskeleton are distinct examples of structures potentially optimized to resist dynamic loading conditions. Understanding the deformation and failure of such biological composites thus require evaluations beyond quasi-static conditions. Knowing the dynamic mechanical properties of the biological interfaces at small scales would make a better understanding how nature designs different biological structures to serve their specific mechanical functions and potentially provide better guidance for synthesizing bio-inspired composites. Therefore, the aim of this PhD project is to examine the rate-dependent mechanical properties of the interfaces in different biological composites using a novel mechanical testing technique incorporating scanning electron microscopy (SEM), focused ion beam (FIB) microscopy and atomic force microscopy (AFM) to understand their interfacial mechanical behaviour under different loading conditions and establish a relationship between their interfacial mechanics and their physiological loading conditions. As most biological composites are physiologically in hydrated condition, it is therefore critical to justify the applied experimental methodology capable of mechanically testing biological samples in hydrated condition effectively. Elastic modulus of nacre fabricated using FIB at the microscale were shown to be similar for both dry and hydrated samples under SEM vacuum and ambient air conditions, validating our methodology of mechanically testing hydrated biological samples under SEM vacuum condition at the sub-microscale. Nacre was then studied by performing the AFM nanoscale interfacial shear test under loading rates with the range of two orders of magnitude and a shear strength decrease of around 10% was found. General interfacial mechanical behaviour within biological composites was further explored by comparing interfacial mechanical behaviour from nacre and arthropod exoskeleton to the interfacial shear behaviour of the NCP-MCF interface in antler bone. All the three biological composites exhibited a weakened interface with increasing loading rates, but the biological interface with less confinement showed a shear strength more sensitive to varying loading rates and appeared to adapt to less dynamic physiological loading conditions. Finally, this work evaluated mechanically graded tendon-to-bone interfaces, highlighting the flexibility of the experimental approach used. Microscale beams of tendon-to-bone attachment fabricated using FIB were successfully tensile tested using in situ AFM. An analytical model based on a simple rule of mixtures was used to predict the elastic moduli of the tendon-to-bone beams by consideration the spatial compositional variations within the larger interfacial regions, again providing a more complex-structure function relationship in a biological composite. Therefore, this PhD work highlights the use of mechanical testing using AFM and SEM to investigate the rate-dependent mechanical behaviour of small scale interfaces in a variety of structural biological composites

NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference

The inherent diversity of computation types within individual deep neural network (DNN) models necessitates a corresponding variety of computation units within hardware processors, leading to a significant constraint on computation efficiency during neural network execution. In this study, we introduce NeuralMatrix, a framework that transforms the computation of entire DNNs into linear matrix operations, effectively enabling their execution with one general-purpose matrix multiplication (GEMM) accelerator. By surmounting the constraints posed by the diverse computation types required by individual network models, this approach provides both generality, allowing a wide range of DNN models to be executed using a single GEMM accelerator and application-specific acceleration levels without extra special function units, which are validated through main stream DNNs and their variant models.Comment: 12 pages, 4figures, Submitted to 11th International Conference on Learning Representation

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Lipids, obesity and gallbladder disease in women: insights from genetic studies using the cardiovascular gene-centric 50K SNP array

Gallbladder disease (GBD) has an overall prevalence of 10-40% depending on factors such as age, gender, population, obesity and diabetes, and represents a major economic burden. Although gallstones are composed of cholesterol by-products and are associated with obesity, presumed causal pathways remain unproven, although BMI reduction is typically recommended. We performed genetic studies to discover candidate genes and define pathways involved in GBD. We genotyped 15,241 women of European ancestry from three cohorts, including 3216 with GBD, using the Human cardiovascular disease (HumanCVD) BeadChip containing up to ~ 53,000 single-nucleotide polymorphisms (SNPs). Effect sizes with P-values for development of GBD were generated. We identify two new loci associated with GBD, GCKR rs1260326:T>C (P = 5.88 × 10(-7), ß = -0.146) and TTC39B rs686030:C>A (P = 6.95 x 10(-7), ß = 0.271) and detect four independent SNP effects in ABCG8 rs4953023:G>A (P=7.41 × 10(-47), ß = 0.734), ABCG8 rs4299376:G(>)T (P = 2.40 × 10(-18), ß = 0.278), ABCG5 rs6544718:T>C (P = 2.08 × 10(-14), ß = 0.044) and ABCG5 rs6720173:G>C (P = 3.81 × 10(-12), ß(=)0.262) in conditional analyses taking genotypes of rs4953023:G>A as a covariate. We also delineate the risk effects among many genotypes known to influence lipids. These data, from the largest GBD genetic study to date, show that specific, mainly hepatocyte-centred, components of lipid metabolism are important to GBD risk in women. We discuss the potential pharmaceutical implications of our findings

Enhancing Scientific Papers Summarization with Citation Graph

Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries

Ratio of Immune Response to Tumor Burden Predicts Survival Via Regulating Functions of Lymphocytes and Monocytes in Diffuse Large B-Cell Lymphoma

Background/Aims: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, and is the most common type of lymphoma in adults. Although significant progress in treatment has been made using chemotherapy combinations, there exist a large amount of relapse or refractory cases. Thus, effective clinical biomarkers for DLBCL are urgently needed. Our study aims to explore the predictive significance of using the immune response to tumor burden ratio [defined as the lymphocyte to monocyte ratio (LMR)/lactate dehydrogenase (LDH) levels] in 184 DLBCL patients and the potential mechanism underlying the use of the LMR to tumor burden ratio in predicting patient survival. Methods: The correlation between serum LDH levels and tumor levels assessed by PET-CT was determined using Spearman’s correlation analysis. Clinical data from 184 DLBCL patients was assessed using receiver operating characteristic curve analysis and survival analysis. The potential correlation between tumor burden and lymphocytes or monocytes was analyzed by immunohistochemical staining, flow cytometry, and ELISA analysis of patient samples. In addition, we performed in vitro studies to further determine the effects of tumor burden on the anti-tumor activity of T lymphocytes. Results: We observed that serum LDH was an excellent surrogate marker of tumor burden in DLBCL patients, and that the ratio of LMR to LDH was an independent prognostic biomarker capable of predicting survival in DLBCL patients. Further analysis showed that a high tumor burden was correlated with decreased Ki67 expression in T cells, either in the solid tumor tissue or in the circulating blood. In addition, based on an in vitro co-culture study, a higher tumor burden led to the suppression of the anti-tumor response of T cells. Furthermore, we found that a higher tumor burden was correlated with the differentiation of monocytes to tumor associated macrophages in the tumor micro-environment. Both results demonstrate the importance of considering both the immune system and tumor burden for prognostic analysis. Conclusion: Our study has identified a novel clinical biomarker, namely, the immune response to tumor burden ratio, that can be used to distinguish survival outcomes in DLBCL patients, and demonstrated the potential mechanism underlying the use of this biomarker, that incorporates both the immune system and tumor burden, for use in future clinical applications