Lay-up optimization for the hull of a racing sailing yacht

Deformability and buckling load of yacht hulls with fiber reinforced plastic sandwich structure depend on the stack sequence of the skins. In this work an optimization of fiber directions of the laminae for a racing yacht is proposed. This procedure has been divided into three parts (i.e. material characterization, surface model definition, lay-up optimization). First of all a set of unidirectional specimens has been realized, by using the same fibers and matrix (carbon/epoxy) used for the hull as well as the same procedure and workers, in order to characterize the material according to American Society for Testing and Materials (ASTM) Standard D3039, employing strain gage technique. In the second part, by means of an original software in Turbo-Pascal (which uses the half-width value matrix as an input) linked to Pro/ENGINEER, it has been possible to obtain the body plan and surface and finite element (FE) models of the sailing yacht for the subsequent analyses. In the third step, an optimization procedure that uses the results of FE structural analyses in three different sailing configurations is performed, with the aim of obtaining the fiber directions that are able to minimize the yacht deformability, also taking into account the buckling loads. An approximate analytical model has been used in conjunction with a sweep technique in order to evaluate the best of the solutions

Additional file 2: Table S2. of Population genetic structure and temporal stability among Trypanosoma brucei rhodesiense isolates in Uganda

Multi-locus genotypes summarized by temporal group (year of isolation). (DOCX 12 kb

Additional file 6: Figure S2. of Transcriptomes of newly-isolated Trypanosoma brucei rhodesiense reveal hundreds of mRNAs that are co-regulated with stumpy-form markers

Heat map of the regulation factors (log2) for mRNAs that are increased in the LW032 and LW042 transcriptomes. Red is least and white is most. (PDF 617 kb

MOESM4 of Relationship between Trypanosoma brucei rhodesiense genetic diversity and clinical spectrum among sleeping sickness patients in Uganda

Additional file 4. Allele frequencies across the different microsatellite loci

MOESM2 of Relationship between Trypanosoma brucei rhodesiense genetic diversity and clinical spectrum among sleeping sickness patients in Uganda

Additional file 2. Multi-locus genotypes (MLGs) and allele sizes for 7 microsatellite markers

Additional file 1: Figure S1. of Transcriptomes of newly-isolated Trypanosoma brucei rhodesiense reveal hundreds of mRNAs that are co-regulated with stumpy-form markers

Reads per Kilobase per million reads (RPKMs) for the set of unique open reading frames: examples from the genomic sequence data. Open reading frames were separated into “bins” according to the measured RPKM. The number of open reading frames in each bin was then calculated. The plots show the results for three of the genome datasets. The modal values were assumed to represent the average RPKM for a single-copy gene. (PDF 414 kb

Additional file 4: Table S3. of Transcriptomes of newly-isolated Trypanosoma brucei rhodesiense reveal hundreds of mRNAs that are co-regulated with stumpy-form markers

Raw poly(A)+ mRNA transcriptome data and calculations based on reads per million (RPM). Sheet 1: Alignment statistics and correlations between the different datasets, calculated from RPM using the unique gene set only. cBS: cultured Lister 427 bloodstream forms expressing the tet repressor from pHD1313. BT1-3: Tbr729; LW - new isolates; PC - cultured Lister 427 procyclic forms. Sheet 2: Read counts for all genes. Sheet 3: Data for unique gene list [39] with reads per million (RPM). Column R shows the regulation pattern according to a custom script. Samples giving significantly peak RPMs (relative to other samples) are indicated by “max” and troughs are indicated by “min”. The threshold was set at 2-fold. Sheet 4: Sheet 3 results, filtered to remove genes for which the maximum RPM value was less than 10. Column T shows correlation coefficients of the RPM results with PIP39 (Tb927.9.6090). Colour code for ratios: Blue is less than 0.25×, green is less than 0.5×, orange is greater than 2× and red is greater than 4×. Comparisons with other datasets are indicated as follows: ST/SL Kabani; stumpy/slender ratio from [6]; ST/SL BS Jensen - stumpy/slender ratio from [7]; SLvST mRNA Capewell and ST polysomes/mRNA Capewell - data from [13]; SG/cBS: salivary gland trypanosomes relative to cultured bloodstream forms from [30]; SG/PC: salivary gland trypanosomes [30] relative to our results for cultured Lister 427 procyclic forms. Sheet 5: calculated RPMs for selected recognised stumpy-form markers. Reads for the ESAG9s and the PAD genes are not unique and precise allocation to a particular variant was not attempted. Sheet 6: Genes showing increased RPM values in both LW032 and LW042 and metacyclic (salivary gland, SG) forms. Sheet 7: Genes showing reduced RPM values in both LW032 and LW042 and metacyclic (salivary gland, SG) forms. (XLSX 3928 kb

Patient’s baseline characteristics.

<p>*Significantly higher in late stage patients.</p><p>Patient’s baseline characteristics.</p

Interleukin (IL)-6 and IL-10 Are Up Regulated in Late Stage <i>Trypanosoma brucei rhodesiense</i> Sleeping Sickness

<div><p>Background</p><p>Sleeping sickness due to <i>Trypanosoma brucei rhodesiense</i> has a wide spectrum of clinical presentations coupled with differences in disease progression and severity across East and Southern Africa. The disease progresses from an early (hemo-lymphatic) stage to the late (meningoencephalitic) stage characterized by presence of parasites in the central nervous system. We hypothesized that disease progression and severity of the neurological response is modulated by cytokines.</p><p>Methods</p><p>A total of 55 sleeping sickness cases and 41 healthy controls were recruited passively at Lwala hospital, in Northern Uganda. A panel of six cytokines (IFN-γ, IL1-β, TNF-α, IL-6, TGF-β and IL-10) were assayed from paired plasma and cerebrospinal fluid (CSF) samples. Cytokine concentrations were analyzed in relation to disease progression, clinical presentation and severity of neurological responses.</p><p>Results</p><p>Median plasma levels (pg/ml) of IFN-γ (46.3), IL-6 (61.7), TGF-β (8755) and IL-10 (256.6) were significantly higher in cases compared to controls (p< 0.0001). When early stage and late stage CSF cytokines were compared, IL-10 and IL-6 were up regulated in late stage patients and were associated with a reduction in tremors and cranioneuropathy. IL-10 had a higher staging accuracy with a sensitivity of 85.7% (95% CI, 63.7%-97%) and a specificity of 100% (95% CI, 39.8%-100%) while for IL-6, a specificity of 100% (95% CI, 47.8%-100%) gave a sensitivity of 83.3% (95% CI, 62.2%-95.3%).</p><p>Conclusion</p><p>Our study demonstrates the role of host inflammatory cytokines in modulating the progression and severity of neurological responses in sleeping sickness. We demonstrate here an up-regulation of IL-6 and IL-10 during the late stage with a potential as adjunct stage biomarkers. Given that both cytokines could potentially be elevated by other CNS infections, our findings should be further validated in a large cohort of patients including those with other inflammatory diseases such as cerebral malaria.</p></div

Plasma cytokine profiles in HAT cases (N = 49) and controls (N = 41).

<p>Boxes indicate median and interquartile range, whiskers are defined as 10^th -90^th percentiles. Dots define outliers. Asterisk (*) indicate significant increase in cases over controls (Mann-Whitney U test, P< 0.05).</p