Parametrization of Scale-Invariant Self-Adjoint Extensions of Scale-Invariant Symmetric Operators

On a Hilbert space H, we consider a symmetric scale-invariant operator with equal defect numbers. It is assumed that the operator has at least one scale invariant self-adjoint extension in H. We prove that there is a one-to-one correspondence between (generalized) resolvents of scale-invariant extensions and solutions of some functional equation. Two examples of Dirac-type operators are considered

Chromatin signature of widespread monoallelic expression

In mammals, numerous autosomal genes are subject to mitotically stable monoallelic expression (MAE), including genes that play critical roles in a variety of human diseases. Due to challenges posed by the clonal nature of MAE, very little is known about its regulation; in particular, no molecular features have been specifically linked to MAE. In this study, we report an approach that distinguishes MAE genes in human cells with great accuracy: a chromatin signature consisting of chromatin marks associated with active transcription (H3K36me3) and silencing (H3K27me3) simultaneously occurring in the gene body. The MAE signature is present in ∼20% of ubiquitously expressed genes and over 30% of tissue-specific genes across cell types. Notably, it is enriched among key developmental genes that have bivalent chromatin structure in pluripotent cells. Our results open a new approach to the study of MAE that is independent of polymorphisms, and suggest that MAE is linked to cell differentiation. DOI: http://dx.doi.org/10.7554/eLife.01256.00

An observation of Lagenorhynchus albirostris (Delphinidae, Odontoceti) in Kola Peninsula, Barents Sea in 2011

Lagenorhynchus albirostris is one of the most common Cetacean species in the Barents Sea. However, there is not a mention of its appearance in the Kola Bay. The present report confirms the appearance of a group of Lagenorhynchus albirostris in the Kola Bay near the aquacomplex of the Murmansk Marine Biological Institute of the Kola Research Centre of RAS, Polyarny town, in autumn 2011

Digital competence in laboratory medicine

Objectives: Even though most physicians and professionals in laboratory medicine have received basic training in statistics, experience shows that a general understanding of data analysis is not yet available on a broad scale. Therefore, data literacy, data-driven decision making, and computational thinking should be implemented in future educational training. To evaluate the state of digital competence among young scientists (YS) in laboratory medicine, we launched a worldwide online survey. Methods: A global online survey was conducted from 25/05/2022 to 26/06/2022 and was disseminated to YS who are listed in three large networks: YS of the DGKL, the EFLM Task Group-YS, and IFCC Task Force-YS and its corresponding members, covering a base of 53 countries. Results: A total of 119 young scientists from 40 countries participated in this survey. 80% did not learn digital skills in their academic education but 96% felt they needed to. Digital literacy was associated with terms such as programming, artificial intelligence and machine learning, statistics, communication, Big Data and data analytics. Conclusions: The results of our survey show that more knowledge and training in the area of digital skills is not just necessary, but also wanted by young scientists. A varied learning environment consisting of tutorial articles, videos, exercises, technical articles, collection of helpful links, online meetings and in person bootcamps is crucial to meet the challenges of an international project with different languages, health systems and time zones

Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data

Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.Statistic

X chromosomal abnormalities in basal-like human breast cancer

SummarySporadic basal-like cancers (BLC) are a distinct class of human breast cancers that are phenotypically similar to BRCA1-associated cancers. Like BRCA1-deficient tumors, most BLC lack markers of a normal inactive X chromosome (Xi). Duplication of the active X chromosome and loss of Xi characterized almost half of BLC cases tested. Others contained biparental but nonheterochromatinized X chromosomes or gains of X chromosomal DNA. These abnormalities did not lead to a global increase in X chromosome transcription but were associated with overexpression of a small subset of X chromosomal genes. Other, equally aneuploid, but non-BLC rarely displayed these X chromosome abnormalities. These results suggest that X chromosome abnormalities contribute to the pathogenesis of BLC, both inherited and sporadic

Prevalence and Predictors of Loss of Wild Type BRCA1 in Estrogen Receptor Positive and Negative BRCA1-Associated Breast Cancers

Introduction: The majority of breast cancers that occur in BRCA1 mutation carriers (BRCA1 carriers) are estrogen receptor-negative (ER-). Therefore, it has been suggested that ER negativity is intrinsic to BRCA1 cancers and reflects the cell of origin of these tumors. However, approximately 20% of breast cancers that develop in BRCA1 carriers are ER-positive (ER+); these cancers are more likely to develop as BRCA1 carriers age, suggesting that they may be incidental and unrelated to BRCA1 deficiency. The purpose of this study was to compare the prevalence of loss of heterozygosity due to loss of wild type (wt) BRCA1 in ER+ and ER- breast cancers that have occurred in BRCA1 carriers and to determine whether age at diagnosis or any pathologic features or biomarkers predict for loss of wt BRCA1 in these breast cancers. Methods: Relative amounts of mutated and wt BRCA1 DNA were measured by quantitative polymerase chain reaction performed on laser capture microdissected cancer cells from 42 ER+ and 35 ER- invasive breast cancers that developed in BRCA1 carriers. BRCA1 gene methylation was determined on all cancers in which sufficient DNA was available. Immunostains for cytokeratins (CK) 5/6, 14, 8 and 18, epidermal growth factor receptor and p53 were performed on paraffin sections from tissue microarrays containing these cancers. Results: Loss of wt BRCA1 was equally frequent in ER+ and ER- BRCA1-associated cancers (81.0% vs 88.6%, respectively; P = 0.53). One of nine cancers tested that retained wt BRCA1 demonstrated BRCA1 gene methylation. Age at diagnosis was not significantly different between first invasive ER+ BRCA1 breast cancers with and without loss of wt BRCA1 (mean age 45.2 years vs 50.1 years, respectively; P = 0.51). ER+ BRCA1 cancers that retained wt BRCA1 were significantly more likely than those that lost wt BRCA1 to have a low mitotic rate (odds ratio (OR), 5.16; 95% CI, 1.91 to ∞). BRCA1 cancers with loss of wt BRCA1 were more likely to express basal cytokeratins CK 5/6 or 14 (OR 4.7; 95% CI, 1.85 to ∞). Conclusions: We found no difference in the prevalence of loss of wt BRCA1 between ER+ and ER- invasive BRCA1-associated breast cancers. Our findings suggest that many of the newer therapies for BRCA1 breast cancers designed to exploit the BRCA1 deficiency in these cancers may also be effective in ER+ cancers that develop in this population

Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab.

PURPOSE: This study examined the utility of sets of single-nucleotide polymorphisms (SNPs) in familial but non-BRCA-associated breast cancer (BC). METHODS: We derived a polygenic risk score (PRS) based on 24 known BC risk SNPs for 4,365 women from the Breast Cancer Family Registry and Kathleen Cuningham Consortium Foundation for Research into Familial Breast Cancer familial BC cohorts. We compared scores for women based on cancer status at baseline; 2,599 women unaffected at enrollment were followed-up for an average of 7.4 years. Cox proportional hazards regression was used to analyze the association of PRS with BC risk. The BOADICEA risk prediction algorithm was used to measure risk based on family history alone. RESULTS: The mean PRS at baseline was 2.25 (SD, 0.35) for affected women and was 2.17 (SD, 0.35) for unaffected women from combined cohorts (P < 10-6). During follow-up, 205 BC cases occurred. The hazard ratios for continuous PRS (per SD) and upper versus lower quintiles were 1.38 (95% confidence interval: 1.22-1.56) and 3.18 (95% confidence interval: 1.84-5.23) respectively. Based on their PRS-based predicted risk, management for up to 23% of women could be altered. CONCLUSION: Including BC-associated SNPs in risk assessment can provide more accurate risk prediction than family history alone and can influence recommendations for cancer screening and prevention modalities for high-risk women.Genet Med 19 1, 30-35.National Institutes of HealthThis is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/gim.2016.4

The Crucial p53-Dependent Oncogenic Role of JAB1 in Osteosarcoma in vivo

Osteosarcoma (OS) is the most common primary bone cancer and ranks amongst the leading causes of cancer mortality in young adults. Jun activation domain binding protein 1 (JAB1) is overexpressed in many cancers and has recently emerged as a novel target for cancer treatment. However, the role of JAB1 in osteosarcoma was virtually unknown. In this study, we demonstrate that JAB1-knockdown in malignant osteosarcoma cell lines significantly reduced their oncogenic properties, including proliferation, colony formation, and motility. We also performed RNA-sequencing analysis in JAB1-knockdown OS cells and identified 4110 genes that are significantly differentially expressed. This demonstrated for the first time that JAB1 regulates a large and specific transcriptome in cancer. We also found that JAB1 is overexpressed in human OS and correlates with a poor prognosis. Moreover, we generated a novel mouse model that overexpresses Jab1 specifically in osteoblasts upon a TP53 heterozygous sensitizing background. Interestingly, by 13 months of age, a significant proportion of these mice spontaneously developed conventional OS. Finally, we demonstrate that a novel, highly specific small molecule inhibitor of JAB1, CSN5i-3, reduces osteosarcoma cell viability and has specific effects on the ubiquitin-proteasome system in OS. Thus, we show for the first time that the overexpression of JAB1 in vivo can result in accelerated spontaneous tumor formation in a p53-dependent manner. In summary, JAB1 might be a unique target for the treatment of osteosarcoma and other cancers