Surface Free Energies, Interfacial Tensions and Correlation Lengths of the ABF Models

The surface free energies, interfacial tensions and correlation lengths of the Andrews-Baxter-Forrester models in regimes III and IV are calculated with fixed boundary conditions. The interfacial tensions are calculated between arbitrary phases and are shown to be additive. The associated critical exponents are given by $2-\alpha_s=\mu=\nu$ with $\nu=(L+1)/4$ in regime III and $4-2\alpha_s=\mu=\nu$ with $\nu=(L+1)/2$ in regime IV. Our results are obtained using general commuting transfer matrix and inversion relation methods that may be applied to other solvable lattice models.Comment: 21 pages, LaTeX 2e, requires the amsmath packag

Interaction-Round-a-Face Models with Fixed Boundary Conditions: The ABF Fusion Hierarchy

We use boundary weights and reflection equations to obtain families of commuting double-row transfer matrices for interaction-round-a-face models with fixed boundary conditions. In particular, we consider the fusion hierarchy of the Andrews-Baxter-Forrester models, for which we find that the double-row transfer matrices satisfy functional equations with an su(2) structure.Comment: 48 pages, LaTeX, requires about 79000 words of TeX memory. Submitted to J. Stat. Phy

Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip

Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, non-fault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a Silicon quantum photonic device. The approach is verified to be well suited for pre-threshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed

Implications of ocean acidification for marine microorganisms from the free-living to the host-associated

Anthropogenic CO2 emissions are causing oceans to become more acidic, with consequences for all marine life including microorganisms. Studies reveal that from the microbes that occupy the open ocean to those intimately associated with their invertebrate hosts changing ocean chemistry will alter the critical functions of these important organisms. Our current understanding indicates that bacterial communities associated with their host will shift as pH drops by another 0.2–0.4 units over the next 100 years. It is unclear what impacts this will have for host health, however, increased vulnerability to disease seems likely for those associated with reef corals. Natural CO2 seeps have provided a unique setting for the study of microbial communities under OA in situ, where shifts in the bacterial communities associated with corals at the seep are correlated with a decline in abundance of the associated coral species. Changes to global biogeochemical cycles also appear likely as photosynthesis and nitrogen fixation by pelagic microbes becomes enhanced under low pH conditions. However, recent long-term studies have shown that pelagic microbes are also capable of evolutionary adaptation, with some physiological responses to a decline in pH restored after hundreds of generations at high pCO2 levels. The impacts of ocean acidification (OA) also will not work in isolation, thus synergistic interactions with other potential stressors, such as rising seawater temperatures, will likely exacerbate the microbial response to OA. This review discusses our existing understanding of the impacts of OA on both pelagic and host-associated marine microbial communities, whilst highlighting the importance of controlled laboratory studies and in situ experiments, to fill the current gaps in our knowledge

The effect of different precooling rates and cold storage on milk microbiological quality and composition

peer-reviewedThe objective of this study was to measure the effect of different milk cooling rates, before entering the bulk tank, on the microbiological load and composition of the milk, as well as on energy usage. Three milk precooling treatments were applied before milk entered 3 identical bulk milk tanks: no plate cooler (NP), single-stage plate cooler (SP), and double-stage plate cooler (DP). These precooling treatments cooled the milk to 32.0 ± 1.4°C, 17.0 ± 2.8°C, and 6.0 ± 1.1°C, respectively. Milk was added to the bulk tank twice daily for 72 h, and the tank refrigeration temperature was set at 3°C. The blend temperature within each bulk tank was reduced after each milking event as the volume of milk at 3°C increased simultaneously. The bacterial counts of the milk volumes precooled at different rates did not differ significantly at 0 h of storage or at 24-h intervals thereafter. After 72 h of storage, the total bacterial count of the NP milk was 3.90 ± 0.09 log10 cfu/mL, whereas that of the precooled milk volumes were 3.77 ± 0.09 (SP) and 3.71 ± 0.09 (DP) log10 cfu/mL. The constant storage temperature (3°C) over 72 h helped to reduce bacterial growth rates in milk; consequently, milk composition was not affected and minimal, if any, proteolysis occurred. The DP treatment had the highest energy consumption (17.6 ± 0.5 Wh/L), followed by the NP (16.8 ± 2.7 Wh/L) and SP (10.6 ± 1.3 Wh/L) treatments. This study suggests that bacterial count and composition of milk are minimally affected when milk is stored at 3°C for 72 h, regardless of whether the milk is precooled; however, milk entering the tank should have good initial microbiological quality. Considering the numerical differences between bacterial counts, however, the use of the SP or DP precooling systems is recommended to maintain low levels of bacterial counts and reduce energy consumption

The Enduring Challenge of Determining Pneumonia Etiology in Children: Considerations for Future Research Priorities.

Pneumonia kills more children each year worldwide than any other disease. Nonetheless, accurately determining the causes of childhood pneumonia has remained elusive. Over the past century, the focus of pneumonia etiology research has shifted from studies of lung aspirates and postmortem specimens intent on identifying pneumococcal disease to studies of multiple specimen types distant from the lung that are tested for multiple pathogens. Some major challenges facing modern pneumonia etiology studies include the use of nonspecific and variable case definitions, poor access to pathologic lung tissue and to specimens from fatal cases, poor diagnostic accuracy of assays (especially when testing nonpulmonary specimens), and the interpretation of results when multiple pathogens are detected in a given individual. The future of childhood pneumonia etiology research will likely require integrating data from complementary approaches, including applications of advanced molecular diagnostics and vaccine probe studies, as well as a renewed emphasis on lung aspirates from radiologically confirmed pneumonia and postmortem examinations

MicroRNA profiling reveals marker of motor neuron disease in ALS models

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder marked by the loss of motor neurons (MNs) in the brain and spinal cord, leading to fatally debilitating weakness. Because this disease predominantly affects MNs, we aimed to characterize the distinct expression profile of that cell type to elucidate underlying disease mechanisms and to identify novel targets that inform on MN health during ALS disease time course. microRNAs (miRNAs) are short, noncoding RNAs that can shape the expression profile of a cell and thus often exhibit cell-type-enriched expression. To determine MN-enriched miRNA expression, we used Cre recombinase-dependent miRNA tagging and affinity purification in mice. By defining thein vivomiRNA expression of MNs, all neurons, astrocytes, and microglia, we then focused on MN-enriched miRNAs via a comparative analysis and found that they may functionally distinguish MNs postnatally from other spinal neurons. Characterizing the levels of the MN-enriched miRNAs in CSF harvested from ALS models of MN disease demonstrated that one miRNA (miR-218) tracked with MN loss and was responsive to an ALS therapy in rodent models. Therefore, we have used cellular expression profiling tools to define the distinct miRNA expression of MNs, which is likely to enrich future studies of MN disease. This approach enabled the development of a novel, drug-responsive marker of MN disease in ALS rodents.SIGNIFICANCE STATEMENTAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease in which motor neurons (MNs) in the brain and spinal cord are selectively lost. To develop tools to aid in our understanding of the distinct expression profiles of MNs and, ultimately, to monitor MN disease progression, we identified small regulatory microRNAs (miRNAs) that were highly enriched or exclusive in MNs. The signal for one of these MN-enriched miRNAs is detectable in spinal tap biofluid from an ALS rat model, where its levels change as disease progresses, suggesting that it may be a clinically useful marker of disease status. Furthermore, rats treated with ALS therapy have restored expression of this MN RNA marker, making it an MN-specific and drug-responsive marker for ALS rodents.</jats:p

Addressing the Analytic Challenges of Cross-Sectional Pediatric Pneumonia Etiology Data.

Despite tremendous advances in diagnostic laboratory technology, identifying the pathogen(s) causing pneumonia remains challenging because the infected lung tissue cannot usually be sampled for testing. Consequently, to obtain information about pneumonia etiology, clinicians and researchers test specimens distant to the site of infection. These tests may lack sensitivity (eg, blood culture, which is only positive in a small proportion of children with pneumonia) and/or specificity (eg, detection of pathogens in upper respiratory tract specimens, which may indicate asymptomatic carriage or a less severe syndrome, such as upper respiratory infection). While highly sensitive nucleic acid detection methods and testing of multiple specimens improve sensitivity, multiple pathogens are often detected and this adds complexity to the interpretation as the etiologic significance of results may be unclear (ie, the pneumonia may be caused by none, one, some, or all of the pathogens detected). Some of these challenges can be addressed by adjusting positivity rates to account for poor sensitivity or incorporating test results from controls without pneumonia to account for poor specificity. However, no classical analytic methods can account for measurement error (ie, sensitivity and specificity) for multiple specimen types and integrate the results of measurements for multiple pathogens to produce an accurate understanding of etiology. We describe the major analytic challenges in determining pneumonia etiology and review how the common analytical approaches (eg, descriptive, case-control, attributable fraction, latent class analysis) address some but not all challenges. We demonstrate how these limitations necessitate a new, integrated analytical approach to pneumonia etiology data