Phase transitions in the Shastry-Sutherland lattice

Two recently developed theoretical approaches are applied to the Shastry-Sutherland lattice, varying the ratio $J'/J$ between the couplings on the square lattice and on the oblique bonds. A self-consistent perturbation, starting from either Ising or plaquette bond singlets, supports the existence of an intermediate phase between the dimer phase and the Ising phase. This existence is confirmed by the results of a renormalized excitonic method. This method, which satisfactorily reproduces the singlet triplet gap in the dimer phase, confirms the existence of a gapped phase in the interval $0.66<J'/J<0.86$Comment: Submited for publication in Phys. Rev.

A self-consistent perturbative evaluation of ground state energies: application to cohesive energies of spin lattices

The work presents a simple formalism which proposes an estimate of the ground state energy from a single reference function. It is based on a perturbative expansion but leads to non linear coupled equations. It can be viewed as well as a modified coupled cluster formulation. Applied to a series of spin lattices governed by model Hamiltonians the method leads to simple analytic solutions. The so-calculated cohesive energies are surprisingly accurate. Two examples illustrate its applicability to locate phase transition.Comment: Accepted by Phys. Rev.

Breast cancer stem cell markers – the rocky road to clinical applications

Lately, understanding the role of cancer stem cells in tumor initiation and progression became a major focus in stem cell biology and in cancer research. Considerable efforts, such as the recent studies by Honeth and colleagues, published in the June issue of Breast Cancer Research, are directed towards developing clinical applications of the cancer stem cell concepts. This work shows that the previously described CD44+CD24- stem cell phenotype is associated with basal-type breast cancers in human patients, in particular BRCA1 inherited cancers, but does not correlate with clinical outcome. These very interesting findings caution that the success of our efforts in translating cancer stem cell research into clinical practice depends on how thorough and rigorous we are at characterizing these cells

Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics

Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and clinically applicable diagnostics. Here, we argue that a causal perspective improves the identification of these challenges, and formalizes their relation to the robustness and generalization of machine learning-based diagnostics. To make for a concrete discussion, we focus on a specific, recently established high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs). We discuss how the main biological and experimental factors of the AIRR domain may influence the learned biomarkers and provide easily adjustable simulations of such effects. In conclusion, we find that causal modeling improves machine learning-based biomarker robustness by identifying stable relations between variables and by guiding the adjustment of the relations and variables that vary between populations

An integrated life cycle costing database: a conceptual framework

Life cycle costing (LCC) is a management technique that has been available to the industry for some time, but despite this it continues to languish in obscurity. Some clients, most apparently from the public sector, are fostering the technique by commissioning studies based on the LCC appraisal techniques. However, the majority of building designs are still currently produced unsullied by thoughts of maintenance implications, life expectancy or energy consumption. Recent technological developments, particularly in Web, Virtual Reality (VR), and Object Oriented technologies and mathematical and computational modelling techniques will undoubtedly help in resolving some of the problems associated with life cycle costing techniques. This paper outlines a conceptual framework for an innovative system that facilitates the implementation of LCC in various design and occupancy stages. This system is being developed within an EPSRC-funded research project, undertaken through a joint collaboration between the Robert Gordon University and the University of Salford

Evaluation of medication adherence among Lebanese diabetic patients

Background: Diabetes type 2 is considered one of the main public health concerns. Lack of adherence to treatment leads to poor therapeutic outcome, poor glycemic control, and high risk for developing diabetes complications. Objectives: The aim of this study is to evaluate adherence to oral antidiabetic medication in Diabetes type 2 Lebanese patients, and to evaluate factors leading to low adherence. Methods: A cross-sectional study was conducted in outpatients endocrinology clinics of two hospitals and four private clinics located in Beirut-Lebanon. Data was collected using a well-structured questionnaire by trained pharmacists. Adherence level was measured by the Lebanese Medication Adherence Scale (LMAS-14). Bivariate and multivariate analyses were conducted using SPSS version 20. Results: Overall, 245 patients were included in the study with the majority being females (54.3%) and obese (47.8%). Only 29% of the participants had controlled glycemia (HbA1c <7%) with 31.8% of subjects had high adherence to their medication compared to 68.2% with low adherence. Increased working hours/day was associated with a decrease in adherence to oral antidiabetic medication (OR=0.31; 95% CI 0.11:0.88; p=0.029). Other factors significantly associated with decreased adherence to treatment were forgetfulness, high drug costs, complex treatment regimens, experiencing side effects, and perception of treatment inefficacy. Postponing physician office visits also decreased the probability of being adherent to oral antidiabetic medication (OR=0.36; 95% CI 0.15:0.86; p=0.022). Skipping or doubling the dose in case of hypo/hyperglycemia and the sensation of treatment burden also decreased medication adherence (OR=0.09; 95% CI 0.02:0.34; p=0.001, and OR=0.04; 95% CI 0.01:0.13; p<0.001 respectively). Conclusions: Adherence to oral antidiabetic medication is low for Lebanese patients, which leads to a poor glycemic control and increases the diabetes complications. Intervention programs including patient education strategies are essential to improve medication adherence

Stilbene derivatives promote Ago2-dependent tumour-suppressive microRNA activity

It is well known that natural products are a rich source of compounds for applications in medicine, pharmacy, and biology. However, the exact molecular mechanisms of natural agents in human health have not been clearly defined. Here, we demonstrate for the first time that the polyphenolic phytoalexin resveratrol promotes expression and activity of Argonaute2 (Ago2), a central RNA interference (RNAi) component, which thereby inhibits breast cancer stem-like cell characteristics by increasing the expression of a number of tumour-suppressive miRNAs, including miR-16, -141, -143, and -200c. Most importantly, resveratrol-induced Ago2 resulted in a long-term gene silencing response. We also found that pterostilbene, which is a natural dimethylated resveratrol analogue, is capable of mediating Ago2-dependent anti-cancer activity in a manner mechanistically similar to that of resveratrol. These findings suggest that the dietary intake of natural products contributes to the prevention and treatment of diseases by regulating the RNAi pathway

Cancer stem cell heterogeneity in hereditary breast cancer

The cancer stem cell hypothesis proposes that tumors arise in stem or progenitor cells generating in tumors driven by a subcomponent that retains cancer stem cell properties. Recent evidence supports the hypothesis that the BRCA1 gene involved in hereditary breast cancer plays a role in breast stem cell function. Furthermore, studies using mouse BRCA1 knockout models provide evidence for the existence of heterogeneous cancer stem cell populations in tumors generated in these mice. Although these populations may arise from different stem/progenitor cells, they share the expression of a common set of stem cell regulatory genes and show similar characteristics in in vitro mammosphere assays and xenograft models. Furthermore, these 'cancer stem cells' display resistance to chemotherapeutic agents. These studies suggest that breast tumors may display intertumor stem cell heterogeneity. Despite this heterogeneity, cancer stem cells may share common characteristics that can be used for their identification and for therapeutic targeting

Long-Term Sphere Culture Cannot Maintain a High Ratio of Cancer Stem Cells: A Mathematical Model and Experiment

Acquiring abundant and high-purity cancer stem cells (CSCs) is an important prerequisite for CSC research. At present, researchers usually gain high-purity CSCs through flow cytometry sorting and expand them by short-term sphere culture. However, it is still uncertain whether we can amplify high-purity CSCs through long-term sphere culture. We have proposed a mathematical model using ordinary differential equations to derive the continuous variation of the CSC ratio in long-term sphere culture and estimated the model parameters based on a long-term sphere culture of MCF-7 stem cells. We found that the CSC ratio in long-term sphere culture presented as gradually decreased drift and might be stable at a lower level. Furthermore, we found that fitted model parameters could explain the main growth pattern of CSCs and differentiated cancer cells in long-term sphere culture

TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions even during ambiguous transitions. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information. Outperforming various state-of-the-art LSTM approaches, we verify the suitability of the proposed causal MS-TCN for surgical phase recognition.Comment: 10 pages, 2 figure