Dynamical tunneling in molecules: Quantum routes to energy flow

Dynamical tunneling, introduced in the molecular context, is more than two decades old and refers to phenomena that are classically forbidden but allowed by quantum mechanics. On the other hand the phenomenon of intramolecular vibrational energy redistribution (IVR) has occupied a central place in the field of chemical physics for a much longer period of time. Although the two phenomena seem to be unrelated several studies indicate that dynamical tunneling, in terms of its mechanism and timescales, can have important implications for IVR. Examples include the observation of local mode doublets, clustering of rotational energy levels, and extremely narrow vibrational features in high resolution molecular spectra. Both the phenomena are strongly influenced by the nature of the underlying classical phase space. This work reviews the current state of understanding of dynamical tunneling from the phase space perspective and the consequences for intramolecular vibrational energy flow in polyatomic molecules.Comment: 37 pages and 23 figures (low resolution); Int. Rev. Phys. Chem. (Review to appear in Oct. 2007

Changing Pattern of Esophageal Cancer Incidence in New Mexico: A 30-Year Evaluation

The incidence of esophageal adenocarcinoma has increased over the last 30 years, especially in non-Hispanic whites (nHw). Recent work indicates an increase in Hispanic Americans (HA). It is important to understand the effect of ethnicity on cancer occurrence over a prolonged interval. We searched the New Mexico Tumor Registry for all cases of esophageal cancer from 1 January 1973 to 31 December 2002. Inclusion criteria were histologic diagnosis of adenocarcinoma or squamous cell carcinoma, ethnicity and gender. Incidence rates for both were compared among ethnic groups in 5-year intervals. Nine hundred eighty-eight patients met the criteria. Esophageal adenocarcinoma incidence rates/100,000 population increased significantly over 30 years; 1973–1977, 0.4 cases; 1978–1982, 0.4 cases; 1983–1987, 0.6 cases; 1988–1992, 1.2 cases, 1993–1997, 1.6 cases and 1998–2002, 2.2 cases; P < 0.001. Squamous cell carcinoma incidence rates remained unchanged during the interval. In nHw and HA, adenocarcinoma incidence rates increased significantly during the study period. In all minority groups, squamous cell carcinoma remained the major type. Esophageal adenocarcinoma incidence among nHw and HA increased from 1973 to 2002 in New Mexico. Squamous cell carcinoma remains predominant in minorities. Ethnicity may influence the histology or indicate an increased risk for certain types of esophageal cancer

Sequential activation of different pathway networks in ischemia-affected and non-affected myocardium, inducing intrinsic remote conditioning to prevent left ventricular remodeling

We have analyzed the pathway networks of ischemia-affected and remote myocardial areas after repetitive ischemia/reperfusion (r-I/R) injury without ensuing myocardial infarction (MI) to elaborate a spatial- and chronologic model of cardioprotective gene networks to prevent left ventricular (LV) adverse remodeling. Domestic pigs underwent three cycles of 10/10 min r-I/R by percutaneous intracoronary balloon inflation/deflation in the mid left anterior descending artery, without consecutive MI. Sham interventions (n = 8) served as controls. Hearts were explanted at 5 h (n = 6) and 24 h (n = 6), and transcriptomic profiling of the distal (ischemia-affected) and proximal (non-affected) anterior myocardial regions were analyzed by next generation sequencing (NGS) and post-processing with signaling pathway impact and pathway network analyses. In ischemic region, r-I/R induced early activation of Ca-, adipocytokine and insulin signaling pathways with key regulator STAT3, which was also upregulated in the remote areas together with clusterin (CLU) and TNF-alpha. During the late phase of cardioprotection, antigen immunomodulatory pathways were activated with upregulation of STAT1 and CASP3 and downregulation of neprilysin in both zones, suggesting r-I/R induced intrinsic remote conditioning. The temporo-spatially differently activated pathways revealed a global myocardial response, and neprilysin and the STAT family as key regulators of intrinsic remote conditioning for prevention of adverse remodeling

The Problem of Ethical Vagueness for Expressivism

Ethical vagueness has garnered little attention. This is rather surprising since many philosophers have remarked that the science of ethics lacks the precision that other fields of inquiry have. Of the few philosophers who have discussed ethical vagueness the majority have focused on the implications of vagueness for moral realism. Because the relevance of ethical vagueness for other metaethical positions has been underexplored, my aim in this paper is to investigate the ramifications of ethical vagueness for expressivism. Ultimately, I shall argue that expressivism does not have the resources to adequately account for ethical vagueness, while cognitivism does. This demonstrates an advantage that cognitivism holds over expressivis

A classification-based framework for predicting and analyzing gene regulatory response

BACKGROUND: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem — predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree. METHODS: In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more shallow and interpretable tree. We also show how to incorporate genome-wide protein-DNA binding data from ChIP chip experiments into the GeneClass algorithm, and we use an improved noise model for gene expression data. RESULTS: Using the improved scalability of Robust GeneClass, we present larger scale experiments on a yeast environmental stress dataset, training and testing on all genes and using a comprehensive set of potential regulators. We demonstrate the improved stability of the features in the learned prediction tree, and we show the utility of the post-processing framework by analyzing two groups of genes in yeast — the protein chaperones and a set of putative targets of the Nrg1 and Nrg2 transcription factors — and suggesting novel hypotheses about their transcriptional and post-transcriptional regulation. Detailed results and Robust GeneClass source code is available for download from

Preservation and stability of cell therapy products: recommendations from an expert workshop

If the field of regenerative medicine is to deliver therapies, rapid expansion and delivery over considerable distances to large numbers of patients is needed. This will demand efficient stabilization and shipment of cell products. However, cryopreservation science is poorly understood by life-scientists in general and in recent decades only limited progress has been made in the technology of preservation and storage of cells. Rapid translation of new developments to a broader range of cell types will be vital, as will assuring a deeper knowledge of the fundamental cell biology relating to successful preservation and recovery of cell cultures. This report presents expert consensus on these and other issues which need to be addressed for more efficient delivery of cell therapies

Stereotyping of medical disability claimants' communication behaviour by physicians: towards more focused education for social insurance physicians

Background: Physicians who hold medical disability assessment interviews (social insurance physicians) are probably influenced by stereotypes of claimants, especially because they have limited time available and they have to make complicated decisions. Because little is known about the influences of stereotyping on assessment interviews, the objectives of this paper were to qualitatively investigate: (1) the content of stereotypes used to classify claimants with regard to the way in which they communicate; (2) the origins of such stereotypes; (3) the advantages and disadvantages of stereotyping in assessment interviews; and (4) how social insurance physicians minimise the undesirable influences of negative stereotyping. Methods: Data were collected during three focus group meetings with social insurance physicians who hold medical disability assessment interviews with sick-listed employees (i.e. claimants). The participants also completed a questionnaire about demographic characteristics. The data were qualitatively analysed in Atlas.ti in four steps, according to the grounded theory and the principle of constant comparison. Results: A total of 22 social insurance physicians participated. Based on their responses, a claimant's communication was classified with regard to the degree of respect and acceptance in the physician-claimant relationship, and the degree of dominance. Most of the social insurance physicians reported that they classify claimants in general groups, and use these classifications to adapt their own communication behaviour. Moreover, the social insurance physicians revealed that their stereotypes originate from information in the claimants' files and first impressions. The main advantages of stereotyping were that this provides a framework for the assessment interview, it can save time, and it is interesting to check whether the stereotype is correct. Disadvantages of stereotyping were that the stereotypes often prove incorrect, they do not give the complete picture, and the claimant's behaviour changes constantly. Social insurance physicians try to minimise the undesirable influences of stereotypes by being aware of counter transference, making formal assessments, staying neutral to the best of their ability, and being compassionate. Conclusions: We concluded that social insurance physicians adapt their communication style to the degree of respect and dominance of claimants in the physician-claimant relationship, but they try to minimise the undesirable influences of stereotypes in assessment interviews. It is recommended that this issue should be addressed in communication skills trainin

Graphical models for inferring single molecule dynamics

<p>Abstract</p> <p>Background</p> <p>The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET)<it> versus</it> time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.</p> <p>Results</p> <p>The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.</p> <p>Conclusions</p> <p>The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.</p

What evidence exists on the links between natural climate solutions and climate change mitigation outcomes in subtropical and tropical terrestrial regions? A systematic map protocol

Background Natural climate solutions (NCS)—actions to conserve, restore, and modify natural and modified ecosystems to increase carbon storage or avoid greenhouse gas (GHG) emissions—are increasingly regarded as important pathways for climate change mitigation, while contributing to our global conservation efforts, overall planetary resilience, and sustainable development goals. Recently, projections posit that terrestrial-based NCS can potentially capture or avoid the emission of at least 11 Gt (gigatons) of carbon dioxide equivalent a year, or roughly encompassing one third of the emissions reductions needed to meet the Paris Climate Agreement goals by 2030. NCS interventions also purport to provide co-benefits such as improved productivity and livelihoods from sustainable natural resource management, protection of locally and culturally important natural areas, and downstream climate adaptation benefits. Attention on implementing NCS to address climate change across global and national agendas has grown—however, clear understanding of which types of NCS interventions have undergone substantial study versus those that require additional evidence is still lacking. This study aims to conduct a systematic map to collate and describe the current state, distribution, and methods used for evidence on the links between NCS interventions and climate change mitigation outcomes within tropical and sub-tropical terrestrial ecosystems. Results of this study can be used to inform program and policy design and highlight critical knowledge gaps where future evaluation, research, and syntheses are needed. Methods To develop this systematic map, we will search two bibliographic databases (including 11 indices) and 67 organization websites, backward citation chase from 39 existing evidence syntheses, and solicit information from key informants. All searches will be conducted in English and encompass subtropical and tropical terrestrial ecosystems (forests, grasslands, mangroves, agricultural areas). Search results will be screened at title and abstract, and full text levels, recording both the number of excluded articles and reasons for exclusion. Key meta-data from included articles will be coded and reported in a narrative review that will summarize trends in the evidence base, assess gaps in knowledge, and provide insights for policy, practice, and research. The data from this systematic map will be made open access