Diagnosis of bacterial infection

Accurate diagnosis of bacterial infection is crucial to avoid unnecessary antibiotic use and to focus  appropriate therapy. Bacterial infection is the combination of the presence of bacteria and inflammation or  systemic dysfunction; therefore, more than one diagnostic modality is usually required for  confirmation. History and examination to determine if a patient fits a clinical case definition is sometimes adequate to confirm or exclude a diagnosis. The second stage is bedside tests – some are used widely, such as urine dipstick tests, but  others, such as skin scrapings of petechial rashes, are underutilised. The third stage is laboratory tests – indirect non-culture-based tests, including C-reactive protein and procalcitonin tests, when negative, can be used to prevent the unnecessary use of antibiotics. Direct non-culture-based tests detect antigens or specific antibodies, e.g. group A streptococcal antigen testing can be employed to reduce antibiotic use. Culture-based tests are often considered the reference standard in modern microbiology. Because of slow turnaround times, these tests are frequently used to focus or stop antibiotic therapy after empiric initiation. Nucleic acid  amplification tests raise the possibility of detecting organisms with high sensitivity, specificity and reduced turnaround time, and novel diagnostic modalities relying on nanotechnology and mass spectrometry may dramatically alter the practice of microbiology in future

Astrometry with Hubble Space Telescope: A Parallax of the Fundamental Distance Calibrator RR Lyrae

We present an absolute parallax and relative proper motion for the fundamental distance scale calibrator, RR Lyr. We obtain these with astrometric data from FGS 3, a white-light interferometer on HST. We find $\pi_{abs} = 3.82 \pm 0.2$ mas. Spectral classifications and VRIJHKT$_2$M and DDO51 photometry of the astrometric reference frame surrounding RR Lyr indicate that field extinction is low along this line of sight. We estimate =0.07\pm0.03 for these reference stars. The extinction suffered by RR Lyr becomes one of the dominant contributors to the uncertainty in its absolute magnitude. Adopting the average field absorption, =0.07 \pm 0.03, we obtain M_V^{RR} = 0.61 ^{-0.11}_{+0.10}. This provides a distance modulus for the LMC, m-M = 18.38 - 18.53^{-0.11}_{+0.10} with the average extinction-corrected magnitude of RR Lyr variables in the LMC, , remaining a significant uncertainty. We compare this result to more than 80 other determinations of the distance modulus of the LMC.Comment: Several typos corrected. To appear in The Astronomical Journal, January 200

A transgenic mouse model for monitoring oxidative stress

Oxidative stress conditions enhance the production of reactive oxygen species resulting from a variety of stimuli, and are associated with various human diseases, including neurodegenerative disorders, inflammation, and various cancers. Though such associations have been closely studied using animal models, there has been no in vivo system for monitoring oxidative stress. We have developed an oxidative stress indicator that is dually regulated by induction at the transcriptional level, and by protein stabilisation at the post-translational level in Keap1-Nrf2 pathway. In vitro, our indicator elicited an intense and specific signal to oxidative stress among various agents, in a Keap1-Nrf2-dependent manner. Moreover, the transgenic animal expressing the indicator exhibited significant signals upon oxidative stress. These results indicate the usefulness of our system as an indicator of oxidative stress both in vitro and in vivo

Testing density-functional approximations on a lattice and the applicability of the related Hohenberg-Kohn-like theorem

We present a metric-space approach to quantify the performance of approximations in lattice density-functional theory for interacting many-body systems and to explore the regimes where the Hohenberg-Kohn-type theorem on fermionic lattices is applicable. This theorem demonstrates the existence of one-to-one mappings between particle densities, wave functions and external potentials. We then focus on these quantities, and quantify how far apart in metric space the approximated and exact ones are. We apply our method to the one-dimensional Hubbard model for different types of external potentials, and assess the regimes where it is applicable to one of the most used approximations in density-functional theory, the local density approximation (LDA). We find that the potential distance may have a very different behaviour from the density and wave function distances, in some cases even providing the wrong assessments of the LDA performance trends. We attribute this to the systems reaching behaviours which are borderline for the applicability of the one-to-one correspondence between density and external potential. On the contrary the wave function and density distances behave similarly and are always sensitive to system variations. Our metric-based method correctly predicts the regimes where the LDA performs fairly well and the regimes where it fails. This suggests that our method could be a practical tool for testing the efficiency of density-functional approximations

Computational analyses of eukaryotic promoters

Computational analysis of eukaryotic promoters is one of the most difficult problems in computational genomics and is essential for understanding gene expression profiles and reverse-engineering gene regulation network circuits. Here I give a basic introduction of the problem and recent update on both experimental and computational approaches. More details may be found in the extended references. This review is based on a summer lecture given at Max Planck Institute at Berlin in 2005

Task-related oxygen uptake and symptoms during activities of daily life in CHF patients and healthy subjects

Patients with chronic heart failure (CHF) have a significantly lower peak aerobic capacity compared to healthy subjects, and, may therefore experience more inconvenience during the performance of domestic activities of daily life (ADLs). To date, the extent to which task-related oxygen uptake, heart rate, ventilation and symptoms during the performance of ADLs in CHF patients is different than in healthy subjects remains uncertain. General demographics, pulmonary function, body composition and peak aerobic capacity were assessed in 23 CHF outpatients and 20 healthy peers. In addition, the metabolic requirement of five simple self-paced domestic ADLs was assessed using a mobile oxycon. Task-related oxygen uptake (ml/min) was similar or lower in CHF patients compared to healthy subjects. In contrast, patients with CHF performing ADLs consumed oxygen at a higher proportion of their peak aerobic capacity than healthy subjects (p < 0.05). For example, getting dressed resulted in a mean task-related oxygen uptake of 49% of peak aerobic capacity, while sweeping the floor resulted in a mean task-related oxygen uptake of 52% of peak aerobic capacity, accompanied by significantly higher Borg symptom scores for dyspnea and fatigue (p < 0.05). Patients with CHF experience use a higher proportion of their peak aerobic capacity, peak ventilation and peak heart rate during the performance of simple self-paced domestic ADL than their healthy peers. These findings represent a necessary step in improving our understanding of improving what troubles patients the most—not being able to do the things that they could when they were healthy

Reduced Exercise Tolerance and Pulmonary Capillary Recruitment with Remote Secondhand Smoke Exposure

RATIONALE: Flight attendants who worked on commercial aircraft before the smoking ban in flights (pre-ban FAs) were exposed to high levels of secondhand smoke (SHS). We previously showed never-smoking pre-ban FAs to have reduced diffusing capacity (Dco) at rest. METHODS: To determine whether pre-ban FAs increase their Dco and pulmonary blood flow (Qc) during exercise, we administered a symptom-limited supine-posture progressively increasing cycle exercise test to determine the maximum work (watts) and oxygen uptake (VO2) achieved by FAs. After 30 min rest, we then measured Dco and Qc at 20, 40, 60, and 80 percent of maximum observed work. RESULTS: The FAs with abnormal resting Dco achieved a lower level of maximum predicted work and VO2 compared to those with normal resting Dco (mean±SEM; 88.7±2.9 vs. 102.5±3.1%predicted VO2; p = 0.001). Exercise limitation was associated with the FAs' FEV(1) (r = 0.33; p = 0.003). The Dco increased less with exercise in those with abnormal resting Dco (mean±SEM: 1.36±0.16 vs. 1.90±0.16 ml/min/mmHg per 20% increase in predicted watts; p = 0.020), and amongst all FAs, the increase with exercise seemed to be incrementally lower in those with lower resting Dco. Exercise-induced increase in Qc was not different in the two groups. However, the FAs with abnormal resting Dco had less augmentation of their Dco with increase in Qc during exercise (mean±SEM: 0.93±0.06 vs. 1.47±0.09 ml/min/mmHg per L/min; p<0.0001). The Dco during exercise was inversely associated with years of exposure to SHS in those FAs with ≥10 years of pre-ban experience (r = -0.32; p = 0.032). CONCLUSIONS: This cohort of never-smoking FAs with SHS exposure showed exercise limitation based on their resting Dco. Those with lower resting Dco had reduced pulmonary capillary recruitment. Exposure to SHS in the aircraft cabin seemed to be a predictor for lower Dco during exercise

Widespread Site-Dependent Buffering of Human Regulatory Polymorphism

The average individual is expected to harbor thousands of variants within non-coding genomic regions involved in gene regulation. However, it is currently not possible to interpret reliably the functional consequences of genetic variation within any given transcription factor recognition sequence. To address this, we comprehensively analyzed heritable genome-wide binding patterns of a major sequence-specific regulator (CTCF) in relation to genetic variability in binding site sequences across a multi-generational pedigree. We localized and quantified CTCF occupancy by ChIP-seq in 12 related and unrelated individuals spanning three generations, followed by comprehensive targeted resequencing of the entire CTCF–binding landscape across all individuals. We identified hundreds of variants with reproducible quantitative effects on CTCF occupancy (both positive and negative). While these effects paralleled protein–DNA recognition energetics when averaged, they were extensively buffered by striking local context dependencies. In the significant majority of cases buffering was complete, resulting in silent variants spanning every position within the DNA recognition interface irrespective of level of binding energy or evolutionary constraint. The prevalence of complex partial or complete buffering effects severely constrained the ability to predict reliably the impact of variation within any given binding site instance. Surprisingly, 40% of variants that increased CTCF occupancy occurred at positions of human–chimp divergence, challenging the expectation that the vast majority of functional regulatory variants should be deleterious. Our results suggest that, even in the presence of “perfect” genetic information afforded by resequencing and parallel studies in multiple related individuals, genomic site-specific prediction of the consequences of individual variation in regulatory DNA will require systematic coupling with empirical functional genomic measurements

A Primer on Regression Methods for Decoding cis-Regulatory Logic

The rapidly emerging field of systems biology is helping us to understand the molecular determinants of phenotype on a genomic scale [1]. Cis-regulatory elements are major sequence-based determinants of biological processes in cells and tissues [2]. For instance, during transcriptional regulation, transcription factors (TFs) bind to very specific regions on the promoter DNA [2,3] and recruit the basal transcriptional machinery, which ultimately initiates mRNA transcription (Figure 1A). Learning cis-Regulatory Elements from Omics Data A vast amount of work over the past decade has shown that omics data can be used to learn cis-regulatory logic on a genome-wide scale [4-6]--in particular, by integrating sequence data with mRNA expression profiles. The most popular approach has been to identify over-represented motifs in promoters of genes that are coexpressed [4,7,8]. Though widely used, such an approach can be limiting for a variety of reasons. First, the combinatorial nature of gene regulation is difficult to explicitly model in this framework. Moreover, in many applications of this approach, expression data from multiple conditions are necessary to obtain reliable predictions. This can potentially limit the use of this method to only large data sets [9]. Although these methods can be adapted to analyze mRNA expression data from a pair of biological conditions, such comparisons are often confounded by the fact that primary and secondary response genes are clustered together--whereas only the primary response genes are expected to contain the functional motifs [10]. A set of approaches based on regression has been developed to overcome the above limitations [11-32]. These approaches have their foundations in certain biophysical aspects of gene regulation [26,33-35]. That is, the models are motivated by the expected transcriptional response of genes due to the binding of TFs to their promoters. While such methods have gathered popularity in the computational domain, they remain largely obscure to the broader biology community. The purpose of this tutorial is to bridge this gap. We will focus on transcriptional regulation to introduce the concepts. However, these techniques may be applied to other regulatory processes. We will consider only eukaryotes in this tutorial