Evaluation of Non-functionalized Single Walled Carbon Nanotubes Composites for Bone Tissue Engineering

Introduction: Bone defects and non-unions caused by trauma, tumor resection, pathological degeneration, or congenital deformity pose a great challenge in the field of orthopedics. Traditionally, these defects have been repaired by using autografts and allografts. Autografts have set the gold standard for clinical bone repair because of their osteoconductivity, osteoinductivity and osteogenicity. Nevertheless, the application of autografts is limited because of donor availability and donor site morbidity. Allografts have the advantage that the tissues are readily available and can be easily applied, especially when large segments of bone are to be reconstructed. However, their use is also limited by the risk of disease transfer and immune rejection. To circumvent these limitations tissue engineering has evolved as a means to develop viable bone grafts. An ideal bone graft should be both osteoconductive and osteoinductive, biomechanically strong, minimally antigenic, and eliminates donor site morbidity and quantity issues. The biodegradable polymer, Poly lactic-co-glycolic acid (PLAGA) was chosen because of its commercial availability, biocompatibility, non-immunogenicity, controlled degradation rate, and its ability to promote optimal cell growth. To improve the mechanical properties of PLAGA, Single Walled Carbon Nanotubes (SWCNT) were used as a reinforcing material to fabricate composite scaffolds. The overall goal of this project is to develop a Single Walled Carbon Nanotube composite (SWCNT/PLAGA) for bone regeneration and to examine the interaction of MC3T3-E1 cells (mouse fibroblasts) and hBMSCs (human bone marrow derived stem cells) with the SWCNT/PLAGA composite via focusing on extracellular matrix production and mineralization; and to evaluate its toxicity and bio-compatibility in-vivo in a rat subcutaneous implant model. We hypothesize that reinforcement of PLAGA with SWCNT to fabricate SWCNT/PLAGA composites increases both the mechanical strength of the composites as well as the cell proliferation rate on the surface of the composites while expressing osteoblasts phenotypic, differentiation and mineralization markers; and SWCNT/PLAGA composites are biocompatible and non-toxic, and are ideal candidates for bone tissue engineering. Methods: PLAGA and SWCNT/PLAGA composites were fabricated with various amounts of SWCNT (5, 10, 20, 40 and 100mg), characterized and degradation studies were performed. PLAGA (poly lactic-co-glycolic acid) and SWCNT/PLAGA microspheres and composites were fabricated; characterized and mechanical testing was performed. Cells were seeded and cell adhesion/morphology, growth/survival, proliferation and gene expression analysis were performed to evaluate biocompatibility. Sprague-Dawley rats were implanted subcutaneously with Sham, poly lactic-co-glycolic acid (PLAGA) and SWCNT/PLAGA composites, and sacrificed at 2, 4, 8 and 12 week post-implantation. The animals were observed for signs of morbidity, overt toxicity, weight gain, food consumption, hematological and urinalysis parameters, and histopathology. Results: Imaging studies demonstrated uniform incorporation of SWCNT into the PLAGA matrix and addition of SWCNT did not affect the degradation rate. Composites with 10mg SWCNT resulted in highest rate of cell proliferation (p\u3c0.05) among all composites. Imaging studies demonstrated microspheres with uniform shape and smooth surfaces, and uniform incorporation of SWCNT into PLAGA matrix. The microspheres bonded in a random packing manner while maintaining spacing, thus resembling trabeculae of cancellous bone. Addition of 10mg SWCNT led to greater compressive modulus and ultimate compressive strength. Imaging studies revealed that MC3T3-E1 cells adhered, grew/survived, and exhibited normal, non-stressed morphology on the composites. SWCNT/PLAGA composites exhibited higher cell proliferation rate and gene expression compared to PLAGA. No mortality and clinical signs were observed. All the groups showed consistent weight gain and rate-of-gain for each group was similar. All the groups exhibited similar pattern for food consumption. No difference in urinalysis parameters, hematological parameters; and absolute and relative organ weight was observed. A mild to moderate summary toxicity (sumtox) score was observed for animals treated with the PLAGA and SWCNT/PLAGA whereas the sham animals did not show any response. At all the time intervals both PLAGA and SWCNT/PLAGA showed a significantly higher sumtox score compared to the Sham group. However, there was no significant difference between PLAGA and SWCNT/PLAGA groups. Conclusion: Our SWCNT/PLAGA composites, which possess high mechanical strength and mimic the microstructure of human trabecular bone, displayed tissue compatibility similar to PLAGA, a well known biocompatible polymer over the 12 week study. Thus, the results obtained demonstrate the potential of SWCNT/PLAGA composites for application in BTE and musculoskeletal regeneration. Future studies will be designed to evaluate the efficacy of SWCNT/PLAGA composites in bone regeneration in a non-union ulnar bone defect rabbit model. As interest in carbon nanotube technology increases, studies must be performed to fully evaluate these novel materials at a nonclinical level to assess their safety. The ability to produce composites capable of promoting bone growth will have a significant impact on tissue regeneration and will allow greater functional recovery in injured patients

BERT & Family Eat Word Salad: Experiments with Text Understanding

In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. We define simple heuristics to construct such examples. Our experiments show that state-of-the-art models consistently fail to recognize them as ill-formed, and instead produce high confidence predictions on them. As a consequence of this phenomenon, models trained on sentences with randomly permuted word order perform close to state-of-the-art models. To alleviate these issues, we show that if models are explicitly trained to recognize invalid inputs, they can be robust to such attacks without a drop in performance.Comment: Accepted at AAAI 2021, Camera Ready Versio

IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery

Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classified into predefined categories or as a clustering task when new and previously unknown intent categories need to be discovered from these utterances. Further, the intent classification may be modeled in a multiclass (MC) or multilabel (ML) setup. While typically these tasks are modeled as separate tasks, we propose IntenDD, a unified approach leveraging a shared utterance encoding backbone. IntenDD uses an entirely unsupervised contrastive learning strategy for representation learning, where pseudo-labels for the unlabeled utterances are generated based on their lexical features. Additionally, we introduce a two-step post-processing setup for the classification tasks using modified adsorption. Here, first, the residuals in the training data are propagated followed by smoothing the labels both modeled in a transductive setting. Through extensive evaluations on various benchmark datasets, we find that our approach consistently outperforms competitive baselines across all three tasks. On average, IntenDD reports percentage improvements of 2.32%, 1.26%, and 1.52% in their respective metrics for few-shot MC, few-shot ML, and the intent discovery tasks respectively.Comment: EMNLP 2023 Finding

Crop growth and soil water balance modeling to explore water management options

The study was on the performance of the decision support system for agrotechnology transfer (DSSAT) and the soil water atmosphere plant (SWAP) under an acid sulphate soil. The comparison of these models was done as a prerequisite to the selection of an appropriate model, which is capable of simulating water management scenarios, water balance and crop growth, to be coupled with an adaptive optimization algorithm that can be used to explore water management options

Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture

We present an innovative approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties. The method is two-folds: (i) system characterization using a stochastic data assimilation procedure where the irrigation system properties and operational management practices are estimated using remote sensing (RS) data; and (ii) water management optimization where we explored water management options under various levels of water availability. We set up a soil–water–atmosphere–plant model (SWAP) in a deterministic–stochastic mode for regional modeling. The distributed data, e.g. sowing dates, irrigation practices, soil properties, depth to groundwater and water quality, required as inputs for the regional modeling were estimated by minimizing the residuals between the distributions of field-scale evapotranspiration (ET) simulated by the regional application of SWAP, and by surface energy balance algorithm for land (SEBAL) using two Landsat7 ETM+ images. The derived distributed data were used as inputs in exploring water management options. Genetic algorithm was used in data assimilation and water management optimizations. The case study was conducted in Bata minor (lateral canal), Kaithal, Haryana, India during 2000–2001 rabi (dry) season. Our results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently. Adjusting sowing dates and their distribution in the irrigated area could improve the regional yield, which also complements the practice of deficit irrigation when water availability is largely a constraint. This result was also found in agreement with the scenario that water is non-limited with the exception that the farmers have more degrees of freedom in their agricultural activities. An improvement of the regional yield to 8.5% is expected under the current scenario

Analytic formulas for frequency and size dependence of absorption and scattering efficiencies of astronomical polycyclic aromatic hydrocarbons

In a series of two recent papers, the frequency and size distribution dependence of extinction spectra for astronomical silicate and graphite grains was analyzed by us in the context of MRN type interstellar dust models. These grains were taken to be homogeneous spheres following the power law $(a^{-3.5})$ size distribution which is very much in use. The analytic formulas were obtained for the graphite and silicate grains in wavelength range 1000\AA - 22,500\AA and their utility was demonstrated. In this paper of the series, we present analytic formulas for the scattering and absorption spectrum of another important constituent of interstellar dust models, namely, the polycyclic aromatic hydrocarbons (PAHs). Relative contribution of the PAHs to extinction {\it vis a vis} carbonaceous classical grains has been examined.Comment: 19 pages, 4 figures, to appear in JQSRT 201