Value at Risk models with long memory features and their economic performance

We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms

I Know I Can: Feeling Confident About Discussing Cancer May Help Couples\u27 Cancer Management

Managing chronic illness, especially something like cancer, affects more than the diagnosed person. Cancer also affects partners, families, and loved ones. In our study, we were interested in how cancer affects communication patterns between survivors and their partners. We wanted to apply a model that explains chronic illness management in couples to see if we could determine how parts of a relationship, uncertainty about cancer prognosis, and the confidence people have in talking with partners about the cancer affect couples’ ability to manage the disease

Fixed-bed gasification and pyrolysis of organic fraction of MSW blended with coal samples

Buildup of vast quantities of municipal solid waste (MSW) including refuse derived fuel, organic fraction around the urban areas has negative environmental consequences. Gasification and pyrolysis of municipal solid waste could be an attractive option to utilize or convert to a valuable product. This study investigates the thermochemical properties of refuse derived fuel (RDF), organic fraction of MSW (Org MSW) and coal samples. Along with proximate and elemental analysis, calorific values were provided for RDF, MSW organic fraction, and coal samples. This followed by the thermogravimetric analysis of the same samples. In addition, Org MSW MSW and coal samples were blended in a proportion of 0.5/0.5 and 0.25/0.75 and then thermally treated in horizontal tube furnace both under air and inert gases to investigate the pyrolysis and gasification processes. TGA tests revealed that volatile content from Org MSW and RDF begin to be emitted at temperatures above 180-200 °C. Org MSW and RDF lose all their volatile contents at 500 °C and 700 °C. Pyrolysis experiments revealed that below 500 °C mostly tars are formed from Org MSW. Organic MSW and coal 0.5/0.5 blends yielded higher methane concentrations than coal or MSW alone, reaching 35-37 % at 800 °C. It could be concluded that both fixed bed and thermogravimetric method analysis have provided a good result to investigate the gasification and pyrolysis processes

Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help

Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods

Triple-negative breast cancer (TNBC) poses a significant challenge in terms of prognosis and disease recurrence. The limited treatment options and the development of resistance to chemotherapy make it particularly difficult to manage these patients. However, recent research has been shifting its focus towards biomarker-based approaches for TNBC, with a particular emphasis on the tumor immune landscape. Immune biomarkers in TNBC are now a subject of great interest due to the presence of tumor-infiltrating lymphocytes (TILs) in these tumors. This characteristic often coincides with the presence of PD-L1 expression on both neoplastic cells and immune cells within the tumor microenvironment. Furthermore, a subset of TNBC harbor mismatch repair deficient (dMMR) TNBC, which is frequently accompanied by microsatellite instability (MSI). All of these immune biomarkers hold actionable potential for guiding patient selection in immunotherapy. To fully capitalize on these opportunities, the identification of additional or complementary biomarkers and the implementation of highly customized testing strategies are of paramount importance in TNBC. In this regard, this article aims to provide an overview of the current state of the art in immune-related biomarkers for TNBC. Specifically, it focuses on the various testing methodologies available and sheds light on the immediate future perspectives for patient selection. By delving into the advancements made in understanding the immune landscape of TNBC, this study aims to contribute to the growing body of knowledge in the field. The ultimate goal is to pave the way for the development of more personalized testing strategies, ultimately improving outcomes for TNBC patients

Pten alterations and their role in cancer management: Are we making headway on precision medicine?

Alterations in the tumor suppressor phosphatase and tensin homolog (PTEN) occur in a substantial proportion of solid tumors. These events drive tumorigenesis and tumor progression. Given its central role as a downregulator of the phosphoinositide 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) pathway, PTEN is deeply involved in cell growth, proliferation, and survival. This gene is also implicated in the modulation of the DNA damage response and in tumor immune microenvironment modeling. Despite the actionability of PTEN alterations, their role as biomarkers remains controversial in clinical practice. To date, there is still a substantial lack of validated guidelines and/or recommendations for PTEN testing. Here, we provide an update on the current state of knowledge on biologic and genetic alterations of PTEN across the most frequent solid tumors, as well as on their actual and/or possible clinical applications. We focus on possible tailored schemes for cancer patients\u2019 clinical management, including risk assessment, diagnosis, prognostication, and treatment

Targeting immune-related biological processes in solid tumors : we do need biomarkers

Immunotherapy has become the standard-of-care in many solid tumors. Despite the significant recent achievements in the diagnosis and treatment of cancer, several issues related to patients' selection for immunotherapy remain unsolved. Multiple lines of evidence suggest that, in this setting, the vision of a single biomarker is somewhat na\uefve and imprecise, given that immunotherapy does not follow the rules that we have experienced in the past for targeted therapies. On the other hand, additional immune-related biomarkers that are reliable in real-life clinical practice remain to be identified. Recently, the immune-checkpoint blockade has been approved in the US irrespective of the tumor site of origin. Further histology-agnostic approvals, coupled with with tumor-specific companion diagnostics and guidelines, are expected in this field. In addition, immune-related biomarkers can also have a significant prognostic value. In this review, we provide an overview of the role of these biomarkers and their characterization in the management of lung cancer, melanoma, colorectal cancer, gastric cancer, head and neck cancer, renal cell carcinoma, urothelial cancers, and breast cancer

Immunotherapy in breast cancer patients: A focus on the use of the currently available biomarkers in oncology

Immune checkpoint inhibitors (ICIs) have remarkably modified the way solid tumors are managed, including breast cancer. Unfortunately, only a relatively small number of breast cancer patients significantly respond to these treatments. To maximize the immunotherapy benefit in breast cancer, several efforts are currently being put forward for the identification of i) the best therapeutic strategy (i.e. ICI monotherapy or in association with chemotherapy, radiotherapy, or other drugs); ii) the optimal timing for administration (e.g. early/advanced stage of disease; adjuvant/neoadjuvant setting); iii) the most effective and reliable predictive biomarkers of response (e.g. tumor-infiltrating lymphocytes, programmed death-ligand 1, microsatellite instability associated with mismatch repair deficiency, and tumor mutational burden). In this article, we review the impacts and gaps in the characterization of immune-related biomarkers raised by clinical and translational research studies with immunotherapy treatments. Particular emphasis has been put on the documented evidence of significant clinical benefits of ICI in different randomized clinical trials, along with preanalytical and analytical issues in predictive biomarkers pathological assessment

Standardized pathology report for HER2 testing in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer

Since the release of the DESTINY-Breast04 (DB-04) trial findings in June 2022, the field of pathology has seen a renaissance of HER2 as a predictive biomarker in breast cancer. The trial focused on patients with metastatic breast cancer who were classified as “HER2-low,” i.e., those with immunohistochemistry (IHC) HER2 1 + or 2 + and negative in situ hybridization (ISH) results. The study revealed that treating these patients with trastuzumab deruxtecan (T-DXd) instead of the oncologist’s chosen chemotherapy led to outstanding improvements in survival. This has challenged the existing binary HER2 pathological classification system, which categorized tumors as either positive (overexpression/amplification) or negative, as per the ASCO/CAP 2018 guideline reaffirmed by ASCO/CAP 2023 guideline update. Given that DB-04 excluded patients with HER2 IHC score 0 status, the results of the ongoing DB-06 trial may shed further light on the potential benefits of T-DXd therapy for these patients. Roughly half of all breast cancers are estimated to belong to the HER2-low category, which does not represent a distinct or specific subtype of cancer. Instead, it encompasses a diverse group of tumors that exhibit clinical, morphological, immunohistochemical, and molecular variations. However, HER2-low offers a distinctive biomarker status that identifies a specific therapeutic regimen (i.e., T-DXd) linked to a favorable prognosis in breast cancer. This unique association emphasizes the importance of accurately identifying these tumors. Differentiating between a HER2 IHC score 0 and score 1 + has not been clinically significant until now. To ensure accurate classification and avoid misdiagnosis, it is necessary to adopt standardized procedures, guidelines, and specialized training for pathologists in interpreting HER2 expression in the lower spectrum. Additionally, the utilization of artificial intelligence holds promise in supporting this endeavor. Here, we address the current state of the art and unresolved issues in assessing HER2-low status, with a particular emphasis on the score 0. We explore the dilemma surrounding the exclusion of HER2-zero patients from potentially beneficial therapy based on traditional HER2 testing. Additionally, we examine the clinical context, considering that DB-04 primarily involved heavily pretreated late-stage metastatic breast cancers. We also delve into emerging evidence suggesting that extrapolating HER2-low status from the original diagnosis may lead to misleading results. Finally, we provide recommendations for conducting high-quality testing and propose a standardized pathology report in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer

Assessment of estrogen receptor low positive status in breast cancer: Implications for pathologists and oncologists

Estrogen receptor (ER) status assessment by immunohistochemistry (IHC) is the gold standard test for the identification of patients with breast cancer who may benefit from endocrine therapy (ET). Whilst most ER+ breast cancers have a high IHC score, about 3% of cases display a low positivity, with 1% to 10% of cells being weakly stained. These tumors are generally classified within the luminal-like category; however, their risk profile seems to be more similar to that of ER-negative breast cancers. The decision on ET for patients with a diagnosis of ER-low breast cancer should be carefully considered in light of the risks and possible benefits of the treatment. Potential pitfalls hinder pathologists and oncologists from establishing an appropriate threshold for "low positivity". Furthermore, several pre-analytical and analytical variables might trouble the pathological identification of these clinically challenging cases. In this review, we sought to discuss the adversities that can be accounted for the pathological identification of ER-low breast cancers in real-world clinical practice, and to provide practical suggestions for the perfect ER testing in light of the most updated recommendations and guidelines