We consider the task of learning Ising models when the signs of different random variables are flipped independently with possibly unequal, unknown probabilities. In this paper, we focus on the problem of robust estimation of tree-structured Ising models. Without any additional assumption of side information, this is an open problem. We first prove that this problem is unidentifiable, however, this unidentifiability is limited to a small equivalence class of trees formed by leaf nodes exchanging positions with their neighbors. Next, we propose an algorithm to solve the above problem with logarithmic sample complexity in the number of nodes and polynomial run-time complexity. Lastly, we empirically demonstrate that, as expected, existing algorithms are not inherently robust in the proposed setting whereas our algorithm correctly recovers the underlying equivalence class.

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BackgroundCancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue.MethodsIn this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework.ResultsThe algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma).ConclusionThe algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research.

Background:Formalin is widely used to fix histological preparations and as preservatives in embalming solutions and is an age-long practice in medical laboratories. It is generally accepted that the risk of contracting infections is relatively high among medical laboratory workers and pathologists. Recent studies have, however, suggested that formalin does not effectively inactivate all kinds of microbes in formalin-fixed tissue (FFT). Long time preserved tissues in formalin may develop growth of microbes on the surface of the formalin.Aims and Objectives:The purpose of the study is to determine the growth of microorganisms on the surface of FFTs.Materials and Methods:Fifty-one containers of 10% formalin with fixed tissues and undiscarded formalin solution not containing tissues of years 2013–2015 (17 in each year) were selected, and samples for inoculation onto the cysteine lactose electrolyte deficient agar plates were taken from the surface of the FFT using sterile cotton tips. The growth of the colonies was checked for after 48 h.Results:Out of 51 samples from 2013 to 2015, 17 had shown growth of microbial colonies. Six out of 17 samples of 2013, 7 out of 17 of 2014 and 4 out of 17 samples of 2015 had colonies of microbes on agar plates. Gram-negative bacilli, Bacillus subtilis and micrococci were mostly found.Conclusion:There were viable microbes on the surfaces of formalin solution containing pathology tissue. Since cross-contamination by microbes may occur during regrossing or processing, protocols to decrease cross-contamination should be instituted.

Odontomas are usually one of the most common odontogenic tumors of the jaw and are perhaps more accurately defined as a hamartoma than a true neoplasm. It is asymptomatic, nonaggressive, slow growing, and benign nature. They are considered to be the malformations of the dental tissue and can sometimes interfere with the eruption of the associated tooth leading to its impaction or delayed eruption. Complex odontomas in primary teeth are rare and unusually diagnosed before the second decade of life. This article aims to present the case of a child with complex odontoma and its effective treatment planning. The results indicate that early diagnosis and proper treatment planning can ensure a better prognosis and can prevent later craniofacial complications and other developmental problems.