Maxillofacial Prosthetic Materials: A Literature Review

Rehabilitation of patients with disabilities of head and neck region, due to either congenital or acquired defects is a challenging task. These defects range from minor cosmeticdiscrepancies to major functional limitation. The prosthodontics management of these patients should aim at not only restoring the functional and esthetic handicap, but also ensurepsychological well being. For facial rehabilitation assessment of materials used in maxillofacial prosthesis is necessary. Till date we have come a cross various materials which exhibitsome excellent properties but also have many deficiencies. This article will review various materials used in maxillofacial prosthesis

Synthpop++: A Hybrid Framework for Generating A Country-scale Synthetic Population

Population censuses are vital to public policy decision-making. They provide insight into human resources, demography, culture, and economic structure at local, regional, and national levels. However, such surveys are very expensive (especially for low and middle-income countries with high populations, such as India), time-consuming, and may also raise privacy concerns, depending upon the kinds of data collected. In light of these issues, we introduce SynthPop++, a novel hybrid framework, which can combine data from multiple real-world surveys (with different, partially overlapping sets of attributes) to produce a real-scale synthetic population of humans. Critically, our population maintains family structures comprising individuals with demographic, socioeconomic, health, and geolocation attributes: this means that our ``fake'' people live in realistic locations, have realistic families, etc. Such data can be used for a variety of purposes: we explore one such use case, Agent-based modelling of infectious disease in India. To gauge the quality of our synthetic population, we use both machine learning and statistical metrics. Our experimental results show that synthetic population can realistically simulate the population for various administrative units of India, producing real-scale, detailed data at the desired level of zoom -- from cities, to districts, to states, eventually combining to form a country-scale synthetic population.Comment: 9 pages, 6 figures, Accepted for oral presentation at AI4ABM workshop at ICLR 202

Broken Neural Scaling Laws

We present a smoothly broken power law functional form that accurately models and extrapolates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as the amount of compute used for training, number of model parameters, training dataset size, or upstream performance varies) for various architectures and for each of various tasks within a large and diverse set of upstream and downstream tasks, in zero-shot, prompted, and fine-tuned settings. This set includes large-scale vision, language, audio, video, diffusion generative modeling, multimodal learning, contrastive learning, AI alignment, robotics, out-of-distribution generalization, continual learning, arithmetic, unsupervised/self-supervised learning, and reinforcement learning (single agent and multi-agent). When compared to other functional forms for neural scaling behavior, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic. Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior. Code is available at https://github.com/ethancaballero/broken_neural_scaling_law

Orthodontic Treatment Considerations in Pregnancy: An Insight

Introduction: This article presents an insight on little known fact regarding orthodontic treatment in pregnancy and to find literature support in favor regarding orthodontic treatment during pregnancy. Literature was reviewed extensively to get results dental and orthodontic treatment during pregnancy. Discussion: Nowadays there are many adult patients seekingorthodontic treatment because of increase in awareness. In these adult patients, there are many pregnant females coming to orthodontist for treatment or a lady getting pregnant during the treatment. ‘Can a pregnant woman continue with orthodontic treatment or can she start with orthodontic treatment during pregnancy?’ This is a difficult question to answer but ‘Yes’, pregnant women can go for orthodontic treatment but with precautions. Present article gives us the information how to go about the treatment in pregnant women, the precautions to be taken, effect of drugs and hormonal changes on orthodontic treatment. Conclusion: Pregnant women can go for orthodontic treatment but with some precaution and some systemic and local condition limit the treatment modalities

Pectoralis major myocutaneous flap in head and neck reconstruction: an interesting experience from central India regional cancer center

Background: Head and neck cancer are sixth most common cancers worldwide with cancer of oral cavity most common. The primary treatment modality for oral cavity cancer has been surgery and defects resulting from the ablation of the tumors require reconstruction. the PMMC flap offer an easy, less time consuming with minimal postoperative complication as a reconstructive option in the hands of reconstructive surgeon. The objective of our study was to give a precise description of our experience with the PMMC flap as a reconstructive option in post-ablative head and cancer surgery.Methods: The current prospective study was conducted in the Department of Surgical Oncology, Regional cancer center, Pt. JNMC, Raipur (C.G.), India from the January 2014 to June 2015. Detailed clinical history and examination of the patients were recorded. All Investigations relevant to the study were done before the surgical procedure. Procedure was performed as per standard protocol and reconstruction was made with PMMC flap. Data was compiled in MS Excel and checked for its completeness and correctness. Then it was analyzed.Results: In the present study male to female ratio was 2:1. Most of the patients belongs to the age group of 41-60 (55.55%) followed by 21-40 (30.15%). In the present study majority of patient of oral malignancy presented with lower alveolus malignancy (36.5%) followed by buccal mucosa malignancy (19.06%).Conclusions: Pectoralis major myocutaneous flap was found to be a versatile flap for reconstruction of large defects in Head and Neck region with minimal complication rate.

Continual Pre-Training of Large Language Models: How to (re)warm your model?

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes available. A much cheaper and more efficient solution would be to enable the continual pre-training of these models, i.e. updating pre-trained models with new data instead of re-training them from scratch. However, the distribution shift induced by novel data typically results in degraded performance on past data. Taking a step towards efficient continual pre-training, in this work, we examine the effect of different warm-up strategies. Our hypothesis is that the learning rate must be re-increased to improve compute efficiency when training on a new dataset. We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule. We conduct all experiments on the Pythia 410M language model architecture and evaluate performance through validation perplexity. We experiment with different pre-training checkpoints, various maximum learning rates, and various warmup lengths. Our results show that while rewarming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch\unicode{x2013}even for a large downstream dataset

Differential Regulation of Mas-Related G Protein-Coupled Receptor X2- Mediated Mast Cell Degranulation by Antimicrobial Host Defense Peptides and Porphyromonas Gingivalis Lipopolysaccharide

Porphyromonas gingivalis is a keystone pathogen that contributes to periodontal pathogenesis by disrupting host-microbe homeostasis and promoting dysbiosis. The virulence of P. gingivalis likely reflects an alteration in the lipid A composition of its lipopolysaccharide (LPS) from the penta-acylated (PgLPS1690) to the tetra-acylated (PgLPS1435/1449) form. Mast cells play an important role in periodontitis, but the mechanisms of their activation and regulation remain unknown. The expression of epithelium- and neutrophil-derived host defense peptides (HDPs) (LL-37 and human β-defensin-3), which activate mast cells via Mas-related G protein-coupled receptor X2 (MRGPRX2), is increased in periodontitis. We found that MRGPRX2-expressing mast cells are present in normal gingiva and that their numbers are elevated in patients with chronic periodontitis. Furthermore, HDPs stimulated degranulation in a human mast cell line (LAD2) and in RBL-2H3 cells stably expressing MRGPRX2 (RBL-MRGPRX2). PgLPS1690 caused substantial inhibition of HDP-induced mast cell degranulation, but PgLPS1435/1449 had no effect. A fluorescently labeled HDP (FAM-LL-37) bound to RBLMRGPRX2 cells, and PgLPS1690 inhibited this binding, but PgLPS1435/1449 had no effect. These findings suggest that low-level inflammation induced by HDP/MRGPRX2- mediated mast cell degranulation contributes to gingival homeostasis but that sustained inflammation due to elevated levels of both HDPs and MRGPRX2-expressing mast cells promotes periodontal disease. Furthermore, differential regulation of HDP-induced mast cell degranulation by PgLPS1690 and PgLPS1435/1449 may contribute to the modulation of disease progression. © 2017 American Society for Microbiology

ARB: Advanced Reasoning Benchmark for Large Language Models

Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. ARB presents a more challenging test than prior benchmarks, featuring problems in mathematics, physics, biology, chemistry, and law. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks. In order to improve both automatic and assisted evaluation capabilities, we introduce a rubric-based evaluation approach, allowing GPT-4 to score its own intermediate reasoning steps. Further, we conduct a human evaluation of the symbolic subset of ARB, finding promising agreement between annotators and GPT-4 rubric evaluation scores.Comment: Submitted to NeurIPS Datasets and Benchmarks Trac