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Increased use and improved methodology of carbonate clumped isotope thermometry has greatly enhanced our ability to interrogate a suite of Earth-system processes. However, interlaboratory discrepancies in quantifying carbonate clumped isotope (Δ47) measurements persist, and their specific sources remain unclear. To address interlaboratory differences, we first provide consensus values from the clumped isotope community for four carbonate standards relative to heated and equilibrated gases with 1,819 individual analyses from 10 laboratories. Then we analyzed the four carbonate standards along with three additional standards, spanning a broad range of δ47 and Δ47 values, for a total of 5,329 analyses on 25 individual mass spectrometers from 22 different laboratories. Treating three of the materials as known standards and the other four as unknowns, we find that the use of carbonate reference materials is a robust method for standardization that yields interlaboratory discrepancies entirely consistent with intralaboratory analytical uncertainties. Carbonate reference materials, along with measurement and data processing practices described herein, provide the carbonate clumped isotope community with a robust approach to achieve interlaboratory agreement as we continue to use and improve this powerful geochemical tool. We propose that carbonate clumped isotope data normalized to the carbonate reference materials described in this publication should be reported as Δ47 (I-CDES) values for Intercarb-Carbon Dioxide Equilibrium Scale.

Abstract Background Establishing the relationship between an organism's genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel high-throughput and single cell sequencing techniques. Results We present a novel approach to predict the phenotype of prokaryotes directly from their protein domain frequencies. Our discriminative machine learning approach provides high prediction accuracy of relevant phenotypes such as motility, oxygen requirement or spore formation. Moreover, the set of discriminative domains provides biological insight into the underlying phenotype-genotype relationship and enables deriving hypotheses on the possible functions of uncharacterized domains. Conclusions Fast and accurate prediction of microbial phenotypes based on genomic protein domain content is feasible and has the potential to provide novel biological insights. First results of a systematic check for annotation errors indicate that our approach may also be applied to semi-automatic correction and completion of the existing phenotype annotation.

Abstract Background Functional profiling is a key technique to characterize and compare the functional potential of entire genomes. The estimation of profiles according to an assignment of sequences to functional categories is a computationally expensive task because it requires the comparison of all protein sequences from a genome with a usually large database of annotated sequences or sequence families. Description Based on machine learning techniques for Pfam domain detection, the UFO web server for ultra-fast functional profiling allows researchers to process large protein sequence collections instantaneously. Besides the frequencies of Pfam and GO categories, the user also obtains the sequence specific assignments to Pfam domain families. In addition, a comparison with existing genomes provides dissimilarity scores with respect to 821 reference proteomes. Considering the underlying UFO domain detection, the results on 206 test genomes indicate a high sensitivity of the approach. In comparison with current state-of-the-art HMMs, the runtime measurements show a considerable speed up in the range of four orders of magnitude. For an average size prokaryotic genome, the computation of a functional profile together with its comparison typically requires about 10 seconds of processing time. Conclusion For the first time the UFO web server makes it possible to get a quick overview on the functional inventory of newly sequenced organisms. The genome scale comparison with a large number of precomputed profiles allows a first guess about functionally related organisms. The service is freely available and does not require user registration or specification of a valid email address.

Abstract Background A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters. Results We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data. Conclusion MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

Abstract Background One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters. Results We present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers. Conclusion Our specialized realization of self-organizing maps is well-suitable to gain insight into complex pattern variation in a large set of metabolite profiles. In comparison to other methods our visualization approach facilitates the identification of interesting groups of metabolites by means of a convenient overview on relevant intensity patterns. In particular, the visualization effectively supports researchers in analyzing many putative clusters when the true number of biologically meaningful groups is unknown.

Abstract Background Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive. Results In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection. Conclusion Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biologically meaningful features. In particular, analysis of discriminative words allows the identification of characteristic regions in biological sequences. Because of its high computational efficiency, our method can be applied to ranking of potential homologs in large databases.

Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).

Abstract Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases.

Abstract Background Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes.

Abstract Background Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes. Results We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum. Conclusion On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool »TICO«(TIs COrrector) which is publicly available from our web site.

Abstract Background Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon. Conclusions We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems.

The Pliocene sedimentary record provides a window into Earth's climate dynamics under warmer-than-present boundary conditions. However, the Pliocene cannot be considered a stable warm climate that constitutes a solid baseline for middle-of-the-road future climate projections. The increasing availability of time-continuous sedimentary archives (e.g., marine sediment cores) reveals complex temporal and spatial patterns of Pliocene ocean and climate variability on astronomical timescales. The Perth Basin is particularly interesting in that respect because it remains unclear if and how the Leeuwin Current sustained the comparably wet Pliocene climate in Western Australia, as well as how it influenced Southern Hemisphere paleoclimate variability. To constrain Leeuwin Current dynamics in time and space, this project obtained eight clumped-isotope Δ47 paleotemperatures and constructed a new orbitally resolved planktonic foraminifera (Trilobatus sacculifer) stable isotope record (δ18O) for the Plio-Pleistocene (4–2 Ma) interval of International Ocean Discovery Program (IODP) Site U1459. These new data complement an existing TEX86 record from the same site and similar planktonic isotope records from the Northern Carnarvon Basin (Ocean Drilling Program (ODP) Site 763 and IODP Site U1463). The comparison of TEX86 and Δ47 paleothermometers reveals that TEX86 likely reflects sea surface temperatures (SSTs) with a seasonal warm bias (23.8–28.9 ∘C), whereas T. sacculifer Δ47 calcification temperatures probably echo mixed-layer temperatures at the studied Site U1459 (18.9–23.2 ∘C). The isotopic δ18O gradient along a 19–29∘ S latitudinal transect, between 3.9 and 2.2 Ma, displays large variability, ranging between 0.5 ‰ and 2.0 ‰. We use the latitudinal δ18O gradient as a proxy for Leeuwin Current strength, with an inverse relationship between both. The new results challenge the interpretation that suggested a tectonic event in the Indonesian Throughflow as the cause for the rapid steepening of the isotopic gradient (0.9 ‰ to 1.5 ‰) around 3.7 Ma. The tectonic interpretation appears obsolete as it is now clear that the 3.7 Ma steepening of the isotopic gradient is intermittent, with flat latitudinal gradients (∼0.5 ‰) restored in the latest Pliocene (2.9–2.6 Ma). Still, the new analysis affirms that a combination of astronomical forcing of wind patterns and eustatic sea level controlled Leeuwin Current intensity. On secular timescales, a period of relatively weak Leeuwin Current is observed between 3.7 and 3.1 Ma. Notably, this interval is marked by cooler conditions throughout the Southern Hemisphere. In conclusion, the intensity of the Leeuwin Current and the latitudinal position of the subtropical front are both long-range effects of the same forcing: heat transport through the Indonesian Throughflow (ITF) valve and its propagation through Indian Ocean poleward heat transport. The common ITF forcing explains the observed coherence of Southern Hemisphere ocean and climate records.

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

The regulation of surface tension (ST) by surfactants plays an important role in the human respiratory system but is largely unexplored in brain homeostasis. The aim of this study was to evaluate changes in ST in relation to surfactant proteins (SPs) in children with intraventricular hemorrhage (IVH). CSF samples from 93 patients were analyzed for ST with a force tensiometer and SP-A-D and -G with ELISA assays. Patients belonged to six groups: (i) IVH before primary intervention (PI), (ii) IVH 4–28 days after PI, (iii) IVH 44–357 days after PI, (iv) hydrocephalus, (v) sepsis and (vi) controls. We found indirect correlations and significant differences in ST and SPs (all p < 0.001; except for SP-C, p = 0.007). Post hoc analyses showed significantly decreased ST in IVH patients before PI compared with patients with hydrocephalus, sepsis or controls (p < 0.001), but it increased in IVH patients over time. All SPs were significantly elevated when comparing IVH patients before PI with controls (all p < 0.001; except for SP-C, p = 0.003). Children suffering from IVH displayed an increase in SPs and a decrease in ST as coping mechanisms to preserve CSF flow. The increase in ST over time could serve as prognostic marker for the healing process.

Abstract Increased use and improved methodology of carbonate clumped isotope thermometry has greatly enhanced our ability to interrogate a suite of Earth‐system processes. However, interlaboratory discrepancies in quantifying carbonate clumped isotope (Δ47) measurements persist, and their specific sources remain unclear. To address interlaboratory differences, we first provide consensus values from the clumped isotope community for four carbonate standards relative to heated and equilibrated gases with 1,819 individual analyses from 10 laboratories. Then we analyzed the four carbonate standards along with three additional standards, spanning a broad range of δ47 and Δ47 values, for a total of 5,329 analyses on 25 individual mass spectrometers from 22 different laboratories. Treating three of the materials as known standards and the other four as unknowns, we find that the use of carbonate reference materials is a robust method for standardization that yields interlaboratory discrepancies entirely consistent with intralaboratory analytical uncertainties. Carbonate reference materials, along with measurement and data processing practices described herein, provide the carbonate clumped isotope community with a robust approach to achieve interlaboratory agreement as we continue to use and improve this powerful geochemical tool. We propose that carbonate clumped isotope data normalized to the carbonate reference materials described in this publication should be reported as Δ47 (I‐CDES) values for Intercarb‐Carbon Dioxide Equilibrium Scale.

Introduction: Antibiotic resistance is a serious threat to global public health. It reduces the effectiveness of treatments for serious bacterial infections and thus increases the risk of fatal outcomes. Antibiotic prescriptions are often not in line with clinical evidence-based guidelines. The process of emergence of resistant bacteria can be slowed down by adherence to guidelines. Yet this adherence seems to be lacking in primary health care.Methods and Analysis: This pragmatic quasi-experimental study using a controlled before-after design was carried out in South-East-Lower Saxony in 2018–2020. The voluntary attendance of interactive trainings with condensed presentation of current guidelines for general practitioners (GP) on antibiotic management for urinary and respiratory tract infections is regarded as intervention. Those GP not attending the trainings constitute the control group. Data were collected via questionnaires; routine health records are provided by a statutory health insurance. The primary outcome is the proportion of (guideline-based) prescriptions in relation to the relevant ICD-10 codes as well as daily defined doses and the difference in proportion of certain prescriptions according to guidelines before and after the intervention as compared to the control group. Further outcomes are among others the subjectively perceived risk of antibiotic resistance and the attitude toward the guidelines. The questionnaires to assess this are based on theory of planned behavior (TPB) and health action process approach (HAPA). Variations over time and effects caused by measures other than WASA (Wirksamkeit von Antibiotika-Schulungen in der niedergelassenen Aerzteschaft-Effectiveness of antibiotic management training in the primary health care sector) training are taken into account by including the control group and applying interrupted time series analysis.Ethics and Dissemination: The study protocol and the data protection concept respectively were reviewed and approved by the Ethics Committee of the Hannover Medical School and the Federal Commissioner for Data Protection and Freedom of Information.Trial Registration:https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013951, identifier DRKS00013951.

Introduction Elderly people (≥65 years) with type 2 diabetes mellitus (T2DM) are becoming increasingly prevalent, notably in Japan. As cardiovascular (CV) risk increases with age and sodium–glucose cotransporter-2 (SGLT2) inhibitors reduce CV risk, elderly patients with T2DM are increasingly likely to be prescribed these glucose-lowering drugs. There is controversy surrounding the effects of SGLT2 inhibitors on muscle mass, particularly in elderly patients for whom loss of muscle is especially undesirable; however, robust evidence on this important issue is lacking. Consequently, we have designed a clinical trial of the SGLT2 inhibitor empagliflozin in elderly Japanese patients with T2DM (Empagliflozin in Elderly T2DM Patients (EMPA-ELDERLY)) to assess its effects on body composition as well as glycaemic control. EMPA-ELDERLY will be the first randomised clinical trial of an SGLT2 inhibitor in elderly patients with T2DM to evaluate effects on skeletal muscle mass, muscle strength and physical performance concurrently.Methods and analysis EMPA-ELDERLY is a randomised, double-blind, placebo-controlled, parallel-group clinical trial to be conducted in Japan. Patients with T2DM aged ≥65 years are eligible if they are Japanese with a body mass index of ≥22 kg/m2 and glycated haemoglobin (HbA1c) levels from ≥7.0% to ≤10.0% from either diet and exercise alone or treatment with oral glucose-lowering drugs. Approximately 128 participants will be randomised 1:1 to once per day, oral, double-blind treatment with empagliflozin 10 mg or matching placebo for 52 weeks. The primary endpoint is the change in HbA1c level from baseline at week 52. Secondary endpoints include changes from baseline to 52 weeks in body composition, including muscle mass and body fat, measured by bioelectrical impedance analysis, as well as skeletal muscle index, grip strength and time in the five-time chair stand test. Other endpoints include changes in patient-reported outcomes (including quality of life), cognitive function and safety.Ethics and dissemination We will submit the trial results to conferences and peer-reviewed journals.Trial registration number NCT04531462.

Bacteria with lipolytic activity are widespread in the marine environment. These organisms can be used as a source of lipases with activity in unusual conditions such as low temperatures and high salinities. These characteristics allow them to be applied to several industrial processes and products including wastewater treatment and detergent production. In this context the lipolytic activity of bacteria isolated from deep-sea sediments of the South Atlantic was evaluated. Among the collection studied, two strains, Halomonas sulfidaeris LAMA 838 and Marinobacter excellens LAMA 842, were selected for further analysis concerning their growth and lipolytic activity in different temperatures and culture media. Both strains were characterized as mesophiles: H. sulfidaeris LAMA 838 grew best at 30ºC, while M. excellens LAMA 842 showed a maximum growth rate between 20 and 25ºC. Maximum lipolytic activity for both microorganisms was observed above 45ºC, but only M. excellens LAMA 842 had lipolytic activity at low temperatures (5ºC) as well. Considering the culture media employed, H. sulfidaeris LAMA 838 grew better in marine broth without Tween 40, while M. excellens LAMA 842 grew best in marine broth supplemented with Tween 40. Both organisms had maximum lipolytic activity in rich or minimal media supplemented with Tween 40. These results demonstrate the potential of deep-sea bacteria as sources of lipases and indicate conditions that may be used in their cultivation and for the production of industrially relevant enzymes.

The classic surfactant proteins (SPs) A, B, C, and D were discovered in the lungs, where they contribute to host defense and regulate the alveolar surface tension during breathing. Their additional importance for brain physiology was discovered decades later. SP-G, a novel amphiphilic SP, was then identified in the lungs and is mostly linked to inflammation. In the brain, it is also present and significantly elevated after hemorrhage in premature infants and in distinct conditions affecting the cerebrospinal fluid circulation of adults. However, current knowledge on SP-G-expression is limited to ependymal cells and some neurons in the subventricular and superficial cortex. Therefore, we primarily focused on the distribution of SP-G-immunoreactivity (ir) and its spatial relationships with components of the neurovascular unit in murine forebrains. Triple fluorescence labeling elucidated SP-G-co-expressing neurons in the habenula, infundibulum, and hypothalamus. Exploring whether SP-G might play a role in Alzheimer’s disease (AD), 3xTg-AD mice were investigated and displayed age-dependent hippocampal deposits of β-amyloid and hyperphosphorylated tau separately from clustered, SP-G-containing dots with additional Reelin-ir—which was used as established marker for disease progression in this specific context. Semi-quantification of those dots, together with immunoassay-based quantification of intra- and extracellular SP-G, revealed a significant elevation in old 3xTg mice when compared to age-matched wildtype animals. This suggests a role of SP-G for the pathophysiology of AD, but a confirmation with human samples is required.

This paper presents results of an experimental investigation on pressure drop and heat transfer for a wide range of Reynolds and Prandtl numbers ranging from 8 < Pr < 60 and 40 < Re < 3500, for flat tubes without and with passive inserts. For three different kinds of passive insert designs, the impact on heat and momentum transfer due to coaction of the total set of passive inserts with different shape and amount was investigated. Experimental results were analyzed regarding two main aspects: Heat transfer mechanisms and pressure drop induced by friction and form drag forces due to the presence of different shapes. After heat and momentum transfer mechanisms for each passive insert design were analyzed, heat transfer and pressure drop enhancement were compared to each other, leading to an efficiency discussion. Different concepts for efficiency evaluation, which are cited in literature, were applied to the presented experimental data. Pros and cons of the different concepts are discussed. Finally, we propose an equation for evaluation of total performance, which fully respects the energetic and exergetic aspects of heat transfer and pressure drop enhancement.

O sorgo forrageiro é uma gramínea indicada para cultivo em ambientes secos e quentes, nos quais a produtividade de outras forrageiras pode ser antieconômica. Objetivou-se, com este trabalho, estudar diferentes sistemas de manejo e diferentes velocidades de semeadura, no desenvolvimento do sorgo forrageiro cv. BRS 610. O solo da área experimental foi um Nitossolo Vermelho distroférrico. O experimento foi instalado em condições de campo, na FCA/UNESP, Campus de Botucatu-SP, utilizando-se o delineamento em blocos ao acaso, em esquema de parcela subdividida, com dezesseis tratamentos (quatro sistemas de manejo e quatro velocidades de semeadura). Os sistemas de manejo utilizados foram: semeadura direta; gradagem pesada; gradagem pesada + duas gradagens leves e escarificação. As velocidades na operação de semeadura utilizadas foram: 3, 5, 6 e 9 km h-1. Foram analisadas as seguintes variáveis: população de plantas, altura de plantas, diâmetro do colmo, massa de mil grãos e produtividades de massa verde e de massa seca. O sistema de semeadura direta foi o que proporcionou maior população de plantas, maior diâmetro do colmo e maior produção de massas verde e seca. Com o aumento da velocidade na operação de semeadura, houve redução da população de plantas e aumento do diâmetro do colmo. A velocidade de 5 km h-1 foi a que proporcionou maior produção de massas verde e seca (kg ha-1), seguida da velocidade de 3 km h-1.