Beta-turn formation in the processing region is important for efficient maturation of Escherichia coli maltose-binding protein by signal peptidase I in vivo.

Signal peptidase I (also called leader peptidase) is the endopeptidase that removes the signal peptides of most secreted proteins during or after translocation in Escherichia coli. Precursor recognition is contingent in part on the presence of small, uncharged residues in the -3 and -1 positions relative to the cleavage site, and may also depend on the structure of the processing region. Most precursor processing regions include residues likely to form a beta-turn. Mutations were introduced into the processing region of maltose-binding protein (MBP) that altered the prediction of beta-turn formation in this region. MBP species with a decreased probability of beta-turn formation were processed slowly or not at all, whereas MBP species with an increased probability of beta-turn formation were processed efficiently. Mutations altering the prediction of beta-turn formation in the MBP processing region were also made in cis to a proline in the +1 position. Cleavage at the normal processing site is blocked by proline in the +1 position; this MBP species, MBP27-P, inhibits processing of other proteins by signal peptidase I. Decreasing the probability of beta-turn formation in the processing region of MBP27-P eliminated the inhibition of signal peptidase I, and these MBP27-P derivatives remained unprocessed, suggesting that the formation of a beta-turn in the MBP processing region was necessary for recognition by signal peptidase I. Increasing the probability of beta-turn formation in cis to proline at +1 in MBP did not alter recognition of the protein by the processing enzyme. The results presented here are consistent with the hypothesis that the efficiency of recognition and processing by signal peptidase I is increased by the formation of a beta-turn in the processing region of the MBP signal peptide

Predictive feedback control and Fitts' law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”

What makes an operational Farm Soil Carbon Code? Insights from a global comparison of existing soil carbon codes using a structured analytical framework

Soils have the potential to sequester and store significant amounts of carbon, contributing towards climate change mitigation. Soil carbon markets are now emerging to pay farmers for changes in land use or management that absorb carbon from the atmosphere, governed by codes that ensure additionality, permanence and non-leakage whilst protecting against unintentional reversals. This paper represents the first global comparative analysis of agricultural soil carbon codes, providing new insights into the wide range of approaches currently used to govern these emerging markets internationally. To do this, the paper first develops an analytical framework for the systematic comparison of codes, which could be applied to the analysis of codes in other land uses and habitats. This framework was then used to identify commonalities and differences in methods, projects, administration and commercialisation and associated code documents for 12 publicly available codes from the UK, France, Australia, USA and international bodies. Codes used a range of mechanisms to manage: additionality (including legal, adoption, financial viability and investment tests); uncertainty and risks around soil carbon sequestration (including minimum permanence periods, carbon buffers, contractual arrangements and/or insurance policies); leakage (including restriction of eligible practices and monitoring to subtract leakage from verified sequestration); baselines (including multi-year and variable buffers based on empirical data or models); measurement, reporting and verification methods (stipulating time intervals, methods, data sources and assessments of uncertainty); auditing; resale of carbon units; stakeholder engagement; and approaches to ensure market integrity (such as buyer checks). The paper concludes by discussing existing MRV methods and codes that could be adapted for use in the UK and evaluates the need for an over-arching standard for soil carbon codes in the UK, to which existing codes and other schemes already generating soil carbon credits could be assessed and benchmarked

Effect of parasympathetic stimulation on brain activity during appraisal of fearful expressions

Autonomic nervous system activity is an important component of human emotion. Mental processes influence bodily physiology, which in turn feeds back to influence thoughts and feelings. Afferent cardiovascular signals from arterial baroreceptors in the carotid sinuses are processed within the brain and contribute to this two-way communication with the body. These carotid baroreceptors can be stimulated non-invasively by externally applying focal negative pressure bilaterally to the neck. In an experiment combining functional neuroimaging (fMRI) with carotid stimulation in healthy participants, we tested the hypothesis that manipulating afferent cardiovascular signals alters the central processing of emotional information (fearful and neutral facial expressions). Carotid stimulation, compared with sham stimulation, broadly attenuated activity across cortical and brainstem regions. Modulation of emotional processing was apparent as a significant expression-by-stimulation interaction within left amygdala, where responses during appraisal of fearful faces were selectively reduced by carotid stimulation. Moreover, activity reductions within insula, amygdala, and hippocampus correlated with the degree of stimulation-evoked change in the explicit emotional ratings of fearful faces. Across participants, individual differences in autonomic state (heart rate variability, a proxy measure of autonomic balance toward parasympathetic activity) predicted the extent to which carotid stimulation influenced neural (amygdala) responses during appraisal and subjective rating of fearful faces. Together our results provide mechanistic insight into the visceral component of emotion by identifying the neural substrates mediating cardiovascular influences on the processing of fear signals, potentially implicating central baroreflex mechanisms for anxiolytic treatment targets

Discovery of the peculiar supernova 1998bw in the error box of GRB980425

The discovery of X-ray, optical and radio afterglows of gamma-ray bursts (GRBs) and the measurements of the distances to some of them have established that these events come from Gpc distances and are the most powerful photon emitters known in the Universe, with peak luminosities up to 10^52 erg/s. We here report the discovery of an optical transient, in the BeppoSAX Wide Field Camera error box of GRB980425, which occurred within about a day of the gamma-ray burst. Its optical light curve, spectrum and location in a spiral arm of the galaxy ESO 184-G82, at a redshift z = 0.0085, show that the transient is a very luminous type Ic supernova, SN1998bw. The peculiar nature of SN1998bw is emphasized by its extraordinary radio properties which require that the radio emitter expand at relativistical speed. Since SN1998bw is very different from all previously observed afterglows of GRBs, our discovery raises the possibility that very different mechanisms may give rise to GRBs, which differ little in their gamma-ray properties.Comment: Under press embargo at Nature (submitted June 10, 1998

A gene signature for post-infectious chronic fatigue syndrome

Background: At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition. Methods: Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7). Results: Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significance Conclusion: Differential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment

Risk-Targeted Selection of Agricultural Holdings for Post-Epidemic Surveillance: Estimation of Efficiency Gains

Current post-epidemic sero-surveillance uses random selection of animal holdings. A better strategy may be to estimate the benefits gained by sampling each farm and use this to target selection. In this study we estimate the probability of undiscovered infection for sheep farms in Devon after the 2001 foot-and-mouth disease outbreak using the combination of a previously published model of daily infection risk and a simple model of probability of discovery of infection during the outbreak. This allows comparison of the system sensitivity (ability to detect infection in the area) of arbitrary, random sampling compared to risk-targeted selection across a full range of sampling budgets. We show that it is possible to achieve 95% system sensitivity by sampling, on average, 945 farms with random sampling and 184 farms with risk-targeted sampling. We also examine the effect of ordering samples by risk to expedite return to a disease-free status. Risk ordering the sampling process results in detection of positive farms, if present, 15.6 days sooner than with randomly ordered sampling, assuming 50 farms are tested per day

Emerging Infectious Disease leads to Rapid Population Decline of Common British Birds

Emerging infectious diseases are increasingly cited as threats to wildlife, livestock and humans alike. They can threaten geographically isolated or critically endangered wildlife populations; however, relatively few studies have clearly demonstrated the extent to which emerging diseases can impact populations of common wildlife species. Here, we report the impact of an emerging protozoal disease on British populations of greenfinch Carduelis chloris and chaffinch Fringilla coelebs, two of the most common birds in Britain. Morphological and molecular analyses showed this to be due to Trichomonas gallinae. Trichomonosis emerged as a novel fatal disease of finches in Britain in 2005 and rapidly became epidemic within greenfinch, and to a lesser extent chaffinch, populations in 2006. By 2007, breeding populations of greenfinches and chaffinches in the geographic region of highest disease incidence had decreased by 35% and 21% respectively, representing mortality in excess of half a million birds. In contrast, declines were less pronounced or absent in these species in regions where the disease was found in intermediate or low incidence. Also, populations of dunnock Prunella modularis, which similarly feeds in gardens, but in which T. gallinae was rarely recorded, did not decline. This is the first trichomonosis epidemic reported in the scientific literature to negatively impact populations of free-ranging non-columbiform species, and such levels of mortality and decline due to an emerging infectious disease are unprecedented in British wild bird populations. This disease emergence event demonstrates the potential for a protozoan parasite to jump avian host taxonomic groups with dramatic effect over a short time period

Blur-Robust Face Recognition via Transformation Learning

Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred im-ages to a same feature subspace by the method of multidimensional s-caling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the mani-fold structure of the original shape images and, at the same time, en-hances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of un-known blur degree, by applying the feature transformation correspond-ing to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance a-gainst the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.