7 research outputs found
trans-(į2-Alkene)(4'-alkyloxy-4-stilbazole)dichloro-platinum; Low Melting Organometallic Mesogens
No full text-[PtCl(CHOCHCH[double bond{,} length half m-dash]CHCHN)([small eta]-CH[double bond{,} length half m-dash]CHCH)]() form stable smectic A mesophases on heating (for [gt-or-equal]7{,} [gt-or-equal]7; [gt-or-equal]8{,} [gt-or-equal]5; [gt-or-equal]9{,} [gt-or-equal]2; and [gt-or-equal]11{,} [gt-or-equal]0); melting temperatures below 50 [degree]C can easily be achieved
Clearing transcription barriers to replication
No full textSupplementary Data are available at NAR Online at https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkz170#supplementary-data .Copyright © The Author(s) 2019. Bacterial genome duplication and transcription require simultaneous access to the same DNA template. Conflicts between the replisome and transcription machinery can lead to interruption of DNA replication and loss of genome stability. Pausing, stalling and backtracking of transcribing RNA polymerases add to this problem and present barriers to replisomes. Accessory helicases promote fork movement through nucleoprotein barriers and exist in viruses, bacteria and eukaryotes. Here, we show that stalled Escherichia coli transcription elongation complexes block reconstituted replisomes. This physiologically relevant block can be alleviated by the accessory helicase Rep or UvrD, resulting in the formation of full-length replication products. Accessory helicase action during replication-transcription collisions therefore promotes continued replication without leaving gaps in the DNA. In contrast, DinG does not promote replisome movement through stalled transcription complexes in vitro. However, our data demonstrate that DinG operates indirectly in vivo to reduce conflicts between replication and transcription. These results suggest that Rep and UvrD helicases operate on DNA at the replication fork whereas DinG helicase acts via a different mechanism.UK Biotechnology and Biological Sciences Research Council (BBSRC) [BB/I001859/2, BB/N014863/1 to P.M., BB/K015729/1, BB/N014995/1 to C.J.R. and BB/I003142/1 to N.J.S. and M.S.D.]. Funding for open access charge: York Open Access Fund
Metallomesogens: Organometallic and Coordination Complexes with Liquid Crystalline Properties
No full textCoral reef biofilm bacterial diversity and successional trajectories are structured by reef benthic organisms and shift under chronic nutrient enrichment
No full text© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remple, K. L., Silbiger, N. J., Quinlan, Z. A., Fox, M. D., Kelly, L. W., Donahue, M. J., & Nelson, C. E. Coral reef biofilm bacterial diversity and successional trajectories are structured by reef benthic organisms and shift under chronic nutrient enrichment. Npj Biofilms and Microbiomes, 7(1), (2021): 84, https://doi.org/10.1038/s41522-021-00252-1.Work on marine biofilms has primarily focused on host-associated habitats for their roles in larval recruitment and disease dynamics; little is known about the factors regulating the composition of reef environmental biofilms. To contrast the roles of succession, benthic communities and nutrients in structuring marine biofilms, we surveyed bacteria communities in biofilms through a six-week succession in aquaria containing macroalgae, coral, or reef sand factorially crossed with three levels of continuous nutrient enrichment. Our findings demonstrate how biofilm successional trajectories diverge from temporal dynamics of the bacterioplankton and how biofilms are structured by the surrounding benthic organisms and nutrient enrichment. We identify a suite of biofilm-associated bacteria linked with the orthogonal influences of corals, algae and nutrients and distinct from the overlying water. Our results provide a comprehensive characterization of marine biofilm successional dynamics and contextualize the impact of widespread changes in reef community composition and nutrient pollution on biofilm community structure.This work was supported through grants from the National Science Foundation for Biological Oceanography (1923877 to C.E.N. and M.J.D., 1949033 to C.E.N. and 2118687 to L.W.K., and 1924281 to N.J.S.) and the National Fish and Wildlife Foundation (grant no. 44447 to C.E.N.). This paper is funded in part by the National Oceanic and Atmospheric Administration, Project A/AS-1, which is sponsored by the University of Hawaii Sea Grant College Program, SOEST, under Institutional Grant No. NA18OAR4170076 from NOAA Office of Sea Grant, Department of Commerce. This is CSUN marine biology contribution #365, UH Sea Grant contribution UNIHI-SEAGRANT-JC-21-06, and UH SOEST contribution 11435
Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide
No full textPublisher Copyright: © The Author(s) (2021).Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P<0.004). We tested subacute (≤90 days) and chronic (≥180 days) stroke subgroups separately, with exploratory analyses in early stroke (≤21 days) and across all time. Sub-analyses in chronic stroke were also performed based on class of sensorimotor deficits (impairment, activity limitations) and side of lesioned hemisphere. Worse sensorimotor behaviour was associated with a smaller ipsilesional thalamic volume in both early (n=179; d=0.68) and subacute (n=274, d=0.46) stroke. In chronic stroke (n=404), worse sensorimotor behaviour was associated with smaller ipsilesional putamen (d=0.52) and nucleus accumbens (d=0.39) volumes, and a larger ipsilesional lateral ventricle (d=-0.42). Worse chronic sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n=256) was associated with smaller ipsilesional putamen (d=0.72) and larger lateral ventricle (d=-0.41) volumes, while several measures of activity limitations (n=116) showed no significant relationships. In the full cohort across all time (n=828), sensorimotor behaviour was associated with the volumes of the ipsilesional nucleus accumbens (d=0.23), putamen (d=0.33), thalamus (d=0.33) and lateral ventricle (d=0.23). We demonstrate significant relationships between post-stroke sensorimotor behaviour and reduced volumes of deep grey matter structures that were spared by stroke, which differ by time and class of sensorimotor measure. These findings provide additional insight into how different cortico-thalamo-striatal circuits support post-stroke sensorimotor outcomes.S.-L.L. was supported by National Institutes of Health (NIH) K01 HD091283; NIH R01 NS115845. N.S. was supported by NIH R56 NS100528. N.J. was supported by NIH R01 AG059874; NIH R01 MH117601. A.B. was supported by National Health and Medical Research Council of Australia GNT1020526; GNT1094974; Heart Foundation Future Leader Fellowship 100784. C.M.B was supported by NIH R21 HD067906; NIH R01 NS090677. W.D.B. was supported by the Health Research Council of New Zealand (09/ 164R; 14/136). J.M.C was supported by NIH R00 HD091375. A.B.C. was supported by NIH R01 NS076348; IIEP-2250-14. A.N.D. was supported by the Lone Star Stroke Research Consortium. N.E. was supported by Australian Research Council DE180100893. W.F. was supported by NIH P20 GM109040. C.A.H. was supported by NIH P20 GM109040. K.S.H. was supported by National Health and Medical Research Council of Australia #1088449; NIH R01 NS115845. B.H. was supported by National Health and Medical Research Council fellowship (1125054). S.A.K. was supported by VA1IK6RX003075; NIH P20 GM109040. B.K was supported by NIH R01 HD065438; NIH R56 NS100528. H.K. was supported by a BrightFocus Faculty Award. B.J.M. was supported by Canadian Partnership for Stroke Recovery; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council; Brain & Behavior Research Foundation. A.R.-M. was supported by Basque Government Elkartek MODULA; H2020-EIC-FETPROACT-2019 MAIA 951910, Bundesministerium für Bildung und Forschung BMBF AMORSA (FKZ 16SV7754); and the Fortüne- Program of the University of Tübingen (2452-0-0/1). F.P. was supported by the Italian Ministry of Health, Grants RC 2016, 2017, 2018, 2019. K.P.R. was supported by NIH R21 HD067906; NIH R01 NS090677. H.M.S. was supported by NIH R01 NS110696; NIH R01 LM013316; NIH K02 NS104207. N.J.S. was supported by NIH U54 GM104941; NIH P20 GM109040. S.R.S. was supported by the European Research Council (ERC, Grant number 759370). G.S. was supported by Italian Ministry of Health grant RC 15-16-17-18-19-20/A. C.M.S was supported by the Health Research Council of New Zealand. L.T.W. was supported by the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073); the Norwegian ExtraFoundation for Health and Rehabilitation (2015/ FO5146); the Research Council of Norway (249795, 262372); and the European Research Council under the European Union's Horizon 2020 Research and Innovation program (ERC StG, Grant 802998). G.F.W. was supported by the Department of Veterans Affairs RR&D Program. S.L.W. was supported by NIH R01 NS115845, R01HD095975. S.C.C. was supported by NIH U01 NS086872, R01 NR015591, and R01 HD095457. P.M.T. was supported by NIH U54 EB020403.Peer reviewe
Dorsomitus tjederi Michel & Mansell 2018, gen. et sp. nov.
No full text<i>Dorsomitus tjederi</i> Michel gen. et sp. nov. <p>urn:lsid:zoobank.org:act:666BFC9D-4117-426E-880C-B0 CAD 5E9E600</p> <p>Fig. 2</p> Diagnosis <p> <b>Male</b></p> <p>Prongs of the forked projection of first abdominal segment twice as long as wide (Fig. 2D–E). Lateral surface of abdominal tergites 2 and 3 covered with short black spines (Fig. 2C–D).</p> Etymology <p>The first author dedicates this species to the late Dr. Bo Tjeder in gratitude for his huge contribution to the knowledge of Neuroptera.</p> Material examined <p> <b>Holotype</b></p> <p> ZIMBABWE: ♂, Marondera (“Rhodesia, Marandellas ”), -18.19, 31.55, alt. 1640 m, Nov. 1961, “ <i>Disparomitus neavei</i> det. D.E Kimmins 1963” (NHMUK 1963-439) (Fig. 2A).</p> <p> <b>Other specimens</b></p> <p>MALAWI: 1 ♂, Chisasira Forest Reserve, -11.85, 34.08, 25 Dec. 2001, R.J. Murphy leg. (SANC NEUR05937).</p> <p>SOUTH AFRICA: 1 ♂, Ben Lavin Nature Reserve, 9 km South of Louis Trichardt, -23.04, 29.91, alt. 950 m, 8–14 Dec. 2000, M. Krüger leg. (SANC NEUR09310); 1 ♂, same but 8 Dec. 2000 (SANC NEUR09310); 1 ♂, Bluegums Poort Farm, -22.97, 29.88, alt. 1445 m, 15 Oct. 1999, J. Joannou leg. (SANC NEUR05778); 1 ♂, D’Nyala Nature Reserve, -23.75, 27.83, alt. 893 m, 17 Dec. 1987, E. Van der Linde leg. (SANC NEUR00775); 1 ♂, Manoutsa Resort, -24.4, 30.62, alt. 504 m, 24 Nov. 1981, R.G. Oberprieler leg. (SANC NEUR00080); 1 ♂, same but 24 Nov. 1981, M.W. Mansell leg. (SANC NEUR00083); 1 ♂, Pafuri, -22.43, 31.21, alt. 216 m, 24 Dec. 1973, H. & A. Braack leg. (SANC NEUR09295); 1 ♂, same but 6 Dec. 1986, L.E.O. Braack leg. (SANC NEUR08750).</p> <p>ZIMBABWE: 1 ♂, Arcturus, -17.78, 31.32, 7 Nov. 1999, A.J. Gardiner leg. (SANC NEUR09311); 1 ♂, Harare, Glenborne, -17.25, 31.03, 3 Dec. 1992, A.J. Gardiner leg. (SANC NEUR09312); 1 ♂, Lawrenceville, -19.03, 32.73, alt. 1500 m, 6 Dec. 1994, N.J.S. Duke leg. (SANC NEUR01985).</p> Description <p> <b>Male</b></p> <p>HEAD. Frons black with luteous margins to eyes. Labrum and clypeus yellowish. Tufts of hairs between bases of antennae brownish. Antennae brown with yellowish mark on external apical part of each antennomere. This mark (not clearly visible on all the specimens) is more visible on the last segments. Segments of the club yellowish, bordered with brown apically.</p> <p>THORAX. Grey to yellowish-brown. Prescutum of mesothorax with dark oval marking divided medially by a narrow longitudinal clear line. Dorsal part of mesoscutum with an elongate dark marking (Fig. 2A). Lateral and ventral part of thorax testaceous, lighter than the dorsal part, covered with silky white hairs (Fig. 2B). Legs dark brown with apex of femora yellow and two yellow markings on the dorsal surface of tibiae (Fig. 2B). Tarsi dark brown.</p> <p>ABDOMEN. Prongs of the forked projection of abdominal segment 1 of male twice as long as wide (Fig. 2D–E). Lateral surfaces of abdominal tergites 2 and 3 covered with short black erect spines (Fig. 2C–D). Dorsal area of same tergites glabrous (Fig. 2C). Second abdominal tergite with two basal black shiny areas and two longitudinal sinuate dark lines (Fig. 2F). Ectoprocts extended laterally with one apical stout black seta (Fig. 2G). Subgenital plate convex, yellowish laterally, bordered with brown, tapering to a sub-rounded apex. Gonarcus-parameres complex as in Fig. 2H–J. Parameres not extended downwards by a carina (Fig. 2H, J). Pulvini well developed, lacking setimere (Fig. 2G).</p> Distribution <p>Known from South Africa, Zimbabwe and Malawi (Fig. 4).</p> Remarks <p> Considering the shape of the prongs of the forked projection of segment 1 of male, and the spine-like setae on the lateral surface of abdominal tergites 2 and 3, and pending the observation of additional specimens, we consider that the species briefly described and illustrated by Tjeder (1992) is <i>D. tjederi</i> gen. et sp. nov.</p>Published as part of <i>Michel, Bruno & Mansell, Mervyn W., 2018, A new genus and species of owlfly from eastern and southern Africa (Neuroptera: Ascalaphidae), pp. 1-12 in European Journal of Taxonomy 413</i> on pages 4-8, DOI: 10.5852/ejt.2018.413, <a href="http://zenodo.org/record/1200280">http://zenodo.org/record/1200280</a>
Modelling the seasonal and spatial variation of malaria transmission in relation to mortality in Africa
No full textAbout three billion people worldwide are estimated to be at risk of malaria transmission. In developing countries, malaria is believed to be a major cause of morbidity and mortality, mostly in children under five years. It is among the indirect causes of maternal mortality and infants’ deaths due to low-birth-weights. Malaria brings huge economic burden due to number of days lost during sickness and deaths, sustaining a vicious cycle of disease and poverty in sub Saharan Africa (SSA) and high attribute of disability-adjusted life years.
A number of malaria control interventions to reduce intensity of transmission have been successfully implemented in the regions of SSA, however, elimination of malaria is still a dream in many developing countries today. Failures in global eradication are related to resistance in insecticides and anti-malarial drugs, and health systems related factors. The Roll Back Malaria (RBM) partnership reinforced new strategies to combat malaria with long-term goal of eradicating the disease globally. This was facilitated by increasing funding for malaria research, improve multi disciplinary initiatives and make malaria among the main agenda of all international health and development forums. The reduction in mortality, especially in children has been reported recently and is associated with achievements in intervention strategies, improvements in malaria diagnosis and treatment. However, poor natural acquisition of malaria immunity in children as a consequence of weak or no exposure is a major epidemiological concern and brings a fear of higher mortality rates or shifting of age of death to older children. Understanding and quantify links between transmission, intervention, immunity and mortality is key for sustainable progress towards malaria control targets.
A comprehensive analysis of information on malaria transmission, vital events, drivers of transmission and mortality-related risk factors is required to achieve that. Lack of vital registration systems in developing countries hinders availability of appropriate data to conduct such analysis. Establishment of Demographic Surveillance Systems (DSS) in many developing countries aims to fill these information gaps. One of the initiatives integrated within DSSs is the Malaria Transmission Intensity and Mortality Burden across Africa (MTIMBA) project. The project compiled a database of mosquito collections at selected sites in Africa over a large number of locations, using standardized methodologies for a period of three years. The entomological parameters were linked with routinely monitored vital events within the DSS. The MTIMBA database is the most comprehensive entomological database ever collected in Africa which allows studying spatial-temporal variation in malaria transmission in relation to mortality.
Malaria is an environmental disease hence transmission varies with climate as it modifies population, survival, distribution and infectivity of malaria vectors. Quantification of association between climate and transmission is important to allow prediction of risk even in areas that field data cannot be easily obtained. Development in geographical information systems (GIS) and availability of remote sensing (RS) data facilitates availability of environment and climate data at high space and time resolutions allowing accurate estimation of outcome-factor relationship.
However, DSS data are large, sparse, zero-inflated and are characterized by seasonal patterns, spatial and temporal correlations. Standard models assume independence between observations, an assumption which do not hold for correlated data, hence utilizing these models might result into biased estimates. Geostatistical modeling of large, sparse and zero inflated space-time data is computational challenging specifically in the estimation of the spatial processes. The spatial correlation is accounted by introducing location-specific random effect parameters which are assumed to arise from a spatial process quantified by a multivariate normal distribution. The models are highly parameterized and their fit is computationally intensive. Bayesian computational algorithms such as Markov Chain Monte Carlo (MCMC) can be used to fit these models. Estimation of the spatial process requires inversion of the covariance matrix at each simulation point. The dimension of the matrix increases exponentially with number of locations and the inversion becomes infeasible when the size is too large. Recent techniques overcome this problem by approximating the spatial process from a subset of locations. These methods have been applied on Gaussian outcomes observed over a grid. Extension and formulation of rigorous methods to efficient model MTIMBA data are needed to allow precise prediction of malaria transmission at locations with mortality data to enhance studying the association. Lastly, seasonality in climatic conditions which introduces seasonal patterns in transmission and mortality data, should be accounted for when modelling such data.
The objectives of this thesis were to i) develop Bayesian geostatistical models to analyze very large and sparse geostatistical and temporal non-Gaussian data with seasonal patterns and ii) apply these models to (a) estimate space-time heterogeneity in malaria transmission (b) assess mortality variations between different ages during the first year of life while adjusting for seasonality and (c) determine the relation between transmission intensity and risk of mortality in children and adult population after taking into account control interventions. This work used an extract of MTIMBA data from the Rufiji DSS (RDSS) collected between October 2001 and September 2004.
Evaluation of approaches to capture seasonal pattern is discussed in Chapter 2 and applied to estimate mortality peaks at different stages of infant life. In Chapter 3, models approximating the spatial process from a subset of locations were developed to assess effect of climate, seasonal and spatial pattern of sporozoite rate (SR) of An. funestus and An. gambiae in RDSS. A rigorous approach to analyze malaria transmission data using Entomology Inoculation Rate (EIR) data, which is the product of mosquito density and SR, is discussed in Chapter 4. Zero-inflated models were used to account for over-dispersion and zero-inflation in the data. High resolution EIR estimates were produced for the RDSS. Exposure surfaces obtained in Chapter 4, were aligned with mortality events to assess the relationship between all-cause mortality and malaria transmission. Geostatistical Bernoulli discrete-time regression models adjusted for age and ITN possession were used for that analysis. The results of these analyses are presented in Chapters 5 and 6. The EIR was incorporated in the model as a covariate with measure of uncertainty.
This work is a building block on the insight and understanding of association between malaria transmission and all-cause mortality. The strength of results of this work relies on EIR estimates predicted at high spatial (household level) and temporal resolution by employing rigorous geostatistical models fitted on large entomological data. The better exposure estimates obtained are able to more accurately estimate the mortality-transmission relation
