Requirements for building information modeling based lean production management systems for construction

Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face

Quantum fast Fourier transform and quantum computation by linear optics

Using the quantum fast Fourier transform in linear optics the input mode annihilation operators ͕â 0 , â 1 , . . . ,â s−1 ͖ are transformed into output mode annihilation operators ͕b 0 , b 1 , . . . ,b s−1 ͖. We show how to implement experimentally such transformations based on the Cooley-Tukey algorithm, by the use of beam splitters and phase shifters in a linear optical system. Optical systems implementing 1,2, and 3 qubits discrete Fourier transform (DFT) are described, and a general method for implementing the n-qubit DFT is analyzed. These transformations are used on various input radiation states by which phase estimation and order finding can be computed

Lymphopenia and mortality among patients undergoing coronary angiography: Long-term follow-up study

Background: Lymphopenia is associated with adverse prognosis in chronic disease states that are related to immune dysregulation. We aimed to determine the association between lymphopenia and mortality in patients presenting to coronary angiography and investigate whether elevated red blood cell distribution width (RDW), an established cardiovascular prognostic marker, further refines risk stratification. Methods: Retrospective analysis of patients undergoing coronary angiography for evaluation or treatment of coronary artery disease between 2003 and 2018. Mortality risk associated with relative (1000–1500/μL) or severe (< 1000/μL) lymphopenia was analyzed using adjusted Cox proportional hazards regression models. Results: Overall, 15,179 patients aged 65 ± 12 years underwent coronary angiography. During a median follow-up of 8 years, 4253 patients died. Compared to normal lymphocyte count, the adjusted hazard ratio (HR) for mortality was 1.31 (95% confidence interval [CI] 1.21–1.41) and 1.97 (95% CI 1.75–2.22) for relative and severe lymphopenia, respectively. The increase in mortality associated with severe lymphopenia was significant in patients presenting in the non-acute setting (HR 2.18, 95% CI 1.74–2.73), ST-segment elevation myocardial infarction (STEMI) (HR 1.59, 95% CI 1.15–2.21), or unstable angina/non-STEMI (HR 2.00, 95% CI 1.70–2.34); p-value for interaction 0.626. The association of lymphopenia with mortality remained significant after additional adjustment to RDW. High RDW (> 14.5%) was associated with reduced survival, and it improved the predictive accuracy of lymphocytes count with an increase in Harrell’s Concordance statistic from 0.634 (SE = 0.005) to 0.672 (SE = 0.005), p < 0.001. Conclusions: lymphopenia is associated with increased risk of mortality during long-term follow-up in patients undergoing coronary angiography, regardless of the coronary presentation. High RDW may enhance the predictive ability of lymphopenia

DNA Repair Biomarker for Lung Cancer Risk and its Correlation With Airway Cells Gene Expression.

Background: Improving lung cancer risk assessment is required because current early-detection screening criteria miss most cases. We therefore examined the utility for lung cancer risk assessment of a DNA Repair score obtained from OGG1, MPG, and APE1 blood tests. In addition, we examined the relationship between the level of DNA repair and global gene expression. Methods: We conducted a blinded case-control study with 150 non-small cell lung cancer case patients and 143 control individuals. DNA Repair activity was measured in peripheral blood mononuclear cells, and the transcriptome of nasal and bronchial cells was determined by RNA sequencing. A combined DNA Repair score was formed using logistic regression, and its correlation with disease was assessed using cross-validation; correlation of expression to DNA Repair was analyzed using Gene Ontology enrichment. Results: DNA Repair score was lower in case patients than in control individuals, regardless of the case's disease stage. Individuals at the lowest tertile of DNA Repair score had an increased risk of lung cancer compared to individuals at the highest tertile, with an odds ratio (OR) of 7.2 (95% confidence interval [CI] = 3.0 to 17.5; P < .001), and independent of smoking. Receiver operating characteristic analysis yielded an area under the curve  of 0.89 (95% CI = 0.82 to 0.93). Remarkably, low DNA Repair score correlated with a broad upregulation of gene expression of immune pathways in patients but not in control individuals. Conclusions: The DNA Repair score, previously shown to be a lung cancer risk factor in the Israeli population, was validated in this independent study as a mechanism-based cancer risk biomarker and can substantially improve current lung cancer risk prediction, assisting prevention and early detection by computed tomography scanning.This work was funded by grants from NIH/NCI/EDRN (#1 U01 CA111219), the Flight Attendant Medical Research Institute, Florida, the Mike Rosenbloom Foundation and Weizmann Institute of Science to ZL and TPE; and by grants from Cancer Research UK to BP and to the Cancer Research UK Cambridge Centre; and by a UK National Institute for Health Research Senior Fellowship to BP; and by the Cambridge Biomedical Research Centre and the Cancer Research UK Cambridge Centre to RCR. Volunteer participant recruitment through the Cambridge Bioresource was funded by the Cambridge Biomedical Research Centre

Thermodynamic State Ensemble Models of cis-Regulation

A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2) the binding constants that describe the affinity of the protein–protein and protein–DNA interactions that occur in each state, and (3) whether each state is capable of transcribing. Using these components, we demonstrate how to compute a cis-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own cis-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of cis-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial cis-regulation and in generating formalisms that accurately predict gene expression from DNA sequence

Age and work-related motives: Results of a meta-analysis

Item does not contain fulltextAn updated literature review was conducted and a meta-analysis was performed to investigate the relationship between age and work-related motives. Building on theorizing in life span psychology, we hypothesized the existence of age-related differences in work-related motives. Specifically, we proposed an age-related increase in the strength of security and social motives, and an age-related decrease in the strength of growth motives. To investigate life span developmental theory predictions about age-related differences in control strategies, we also examined the relationship between age and intrinsic and extrinsic motives. Consistent with our predictions, meta-analytic results showed a significant positive relationship between age and intrinsic motives, and a significant negative relationship between age and strength of growth and extrinsic motives. The predicted positive relation between age and strength of social and security motives was only found among certain subgroups. Implications of these findings for work motivation and life span theories and future research are discussed