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Reading Disability (RD) is often characterized by difficulties in the phonology of the language. While the molecular mechanisms underlying it are largely undetermined, loci are being revealed by genome-wide association studies (GWAS). In a previous GWAS for word reading (Price, 2020), we observed that top single-nucleotide polymorphisms (SNPs) were located near to or in genes involved in neuronal migration/axon guidance (NM/AG) or loci implicated in autism spectrum disorder (ASD). A prominent theory of RD etiology posits that it involves disturbed neuronal migration, while potential links between RD-ASD have not been extensively investigated. To improve power to identify associated loci, we up-weighted variants involved in NM/AG or ASD, separately, and performed a new Hypothesis-Driven (HD)–GWAS. The approach was applied to a Toronto RD sample and a meta-analysis of the GenLang Consortium. For the Toronto sample (n = 624), no SNPs reached significance; however, by gene-set analysis, the joint contribution of ASD-related genes passed the threshold (p~1.45 × 10–2, threshold = 2.5 × 10–2). For the GenLang Cohort (n = 26,558), SNPs in DOCK7 and CDH4 showed significant association for the NM/AG hypothesis (sFDR q = 1.02 × 10–2). To make the GenLang dataset more similar to Toronto, we repeated the analysis restricting to samples selected for reading/language deficits (n = 4152). In this GenLang selected subset, we found significant association for a locus intergenic between BTG3-C21orf91 for both hypotheses (sFDR q < 9.00 × 10–4). This study contributes candidate loci to the genetics of word reading. Data also suggest that, although different variants may be involved, alleles implicated in ASD risk may be found in the same genes as those implicated in word reading. This finding is limited to the Toronto sample suggesting that ascertainment influences genetic associations., Translational Psychiatry, 12 (1), ISSN:2158-3188

Published August 23, 2022 The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 1028) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits. We thank all the children, twins, families, and participants who took part and are taking part in the 22 cohorts whose data contributed to these GWAS meta-analyses; the staff working on the different cohorts, including volunteers, study coordinators, interviewers, teachers, nurses, research scientists, general practitioners, midwives, psychologists, psychometrists, computer and laboratory technicians, and colleagues who assisted in the quality control and preparation of the imputed GWAS data; and the pharmacies and hospitals that were involved. B.M., B.M.-M., B.S.P., C.F., E.E., E.V., G.A., M.v.D., and S.E.F. are supported by the Max Planck Society. A.G. and T.F.M.A. were supported by the Munich Cluster for Systems Neurology (SyNergy), and A.G. was supported by Fondazione Umberto Veronesi. A.T.M. is supported by National Health and Medical Research Council of Australia (NHMRC) Grants 1105008 and 1195955 and Centre of Research Excellence Grant 1116976. A.J.O.W. is supported by NHMRC Grant 1173896. B.S.P. is supported by Simons Foundation Autism Research Initiative Grant 514787. C.Y.S. works in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/3). D.I.B. acknowledges Royal Netherlands Academy of Science Professor Award PAH/6635. E.E. is supported by NIH Grant R01DC016977. E.G.W. and J.R.G. are supported by National Institute of Child Health and Human Development (NICHD) Grant P50 HD 27802. F.R. is supported by Agence Nationale de la Recherche Grants ANR-06-NEURO-019-01, ANR-17-EURE-0017 IEC, ANR-10-IDEX-0001-02 PSL, and ANR-11-BSV4-014-01 and European Commission Grant LSHM-CT-2005-018696. H.T. is supported by the Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) Grant VICI 016.VICI.170.200. J.C.D. was supported by NICHD Grant P50 HD 27802. J.J.M., J.B.To., and T.K. were supported by NIH Grant R01 DC014489. K.M.P. was supported by the Hospital for Sick Children Research Training Program (Restracomp). K.R. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (213514/Z/18/Z). M.J.S. is supported by Wellcome Trust Grant WT082032MA. S.P. and F.A. are supported by Royal Society Grants UF150663 and RGF\EA\180141. T.B. is supported by Institut Pasteur, the Bettencourt-Schueller Foundation, and Université de Paris. The Adolescent Brain Cognitive Development Study is supported by the NIH and additional federal partners (NIH Grants U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147). The Aston Cohort was supported by funding from European Union (EU) Horizon 2020 Programme 641652 and Waterloo Foundation Grant 797/17290. The St. Andrews Bioinformatics Unit is funded by Wellcome Trust Grants 105621/Z/14/Z and 204821/Z/16/Z. ALSPAC is supported by UK Medical Research Council and Wellcome Grant 217065/Z/19/Z and the University of Bristol. A comprehensive list of grant funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). The Basque Center on Cognition, Brain and Language (BCBL) cohort was supported by the Basque Government through the Basic Excellence Research Centre program and the Agencia Estatal de Investigación through BCBL Severo Ochoa excellence accreditation. The Brisbane Adolescent Twin Sample was supported by Australian Research Council Grants A7960034, A79906588, A79801419, DP0212016, and DP0343921, with genotyping funded by the NHMRC Grant 389891. The Colorado Learning Disabilities Research Center cohort was supported by NICHD Grant P50 HD 27802. The Early Language in Victoria Study was supported by NHMRC Grant 436958. The Familial Influences on Literacy Abilities cohort is supported by the University of Amsterdam, the Max Planck Institue Nijmegen, and NWO Grants Rubicon 446-12-005 and VENI 451-15-017. The GRaD study was funded by the Manton Foundation, NIH Grants P50-HD027802 and K99-HD094902, and the Lambert Family. NeuroDys was funded by an EU Sixth Framework Program grant to the NeuroDys Consortium, Swiss National Science Foundation Grant 32-108130, and Austrian Science Fund Grant 18351-B02. The Netherlands Twin Register is funded by NWO Grants 480-04-004, 481-08-011, 056-32-010, 024.001.003, 480-15-001/674, 184.021.007, 184.033.111, and 56-464-14192; ZonMW Grants 911-09-032 and 912-10-020; the Amsterdam Public Health and Amsterdam Reproduction and Development Research Institutes; European Science Council Grant ERC Advanced 230374; EU Seventh Framework Program (FP7) Grant FP7/2007-2013: 602768; National Institute of Mental Health (NIMH) Grants U24 MH068457-06, R01 MH58799-03, and 1RC2 MH089995; and the Avera Institute for Human Genetics. The Pediatric Imaging, Neurocognition, and Genetics cohort is funded by NIH Grant RC2DA029475, the National Institute on Drug Abuse, and the Eunice Kennedy Shriver NICHD. The Philadelphia Neurodevelopmental Cohort is funded by NIH Grants RC2MH089983 and RC2MH089924, an institutional development award to the Center for Applied Genomics from The Children’s Hospital of Philadelphia, and a donation from Adele and Daniel Kubert and thanks the NIH data repository. The Raine study was supported by long-term funding from NHMRC Grants 572613, 403981, and 1059711 and Canadian Institutes of Health Research (CIHR) Grant MOP-82893. Funding was also provided by the University of Western Australia, Curtin University, the Women and Infants Research Foundation, the Telethon Kids Institute, Edith Cowan University, Murdoch University, the University of Notre Dame Australia, and the Raine Medical Research Foundation. The Raine study analyses were supported by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. The Saguenay Youth Study is supported by the CIHR, the Heart and Stroke Foundation of Quebec, and the Canadian Foundation for Innovation. The SLI Consortium was funded by Wellcome Trust Grant 076566 and UK Medical Research Council Grant G1000569. The Twins Early Development Study is supported by UK Medical Research Council Grants MR/V012878/1 and MR/M021475/1, NIH Grant AG046938, and the EU FP7 grant FP7/2007-2013/: 602768. Toronto was supported by CIHR Grant MOP-133440. UK Dyslexia was supported by Wellcome Trust Grants 076566/Z/05/Z and 075491/Z/04, Waterloo Foundation Grant 797–1720, EU Grant 018696, and Royal Society Grant UF100463. The York cohort was funded by Wellcome Trust Grant 082036/B/07/Z. We acknowledge iPSYCH for sharing their summary statistics. The iPSYCH team was supported by Lundbeck Foundation Grants R102-A9118, R155-2014-1724, and R248-2017-2003; NIMH Grant 1U01MH109514-01; and the Universities and University Hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, Aarhus University, Denmark.

Microbes in aquifers are present suspended in groundwater or attached to the aquifer sediment. Groundwater is often sampled at gasoline ether oxygenate (GEO)-impacted sites to assess the potential biodegradation of organic constituents. However, the distribution of GEO-degrading microorganisms between the groundwater and aquifer sediment must be understood to interpret this potential. In this study, the distribution of ethyl tert-butyl ether (ETBE)-degrading organisms and ETBE biodegradation potential was investigated in laboratory microcosm studies and mixed groundwater-aquifer sediment samples obtained from pumped monitoring wells at ETBE-impacted sites. ETBE biodegradation potential (as determined by quantification of the ethB gene) was detected predominantly in the attached microbial communities and was below detection limit in the groundwater communities. The copy number of ethB genes varied with borehole purge volume at the field sites. Members of the Comamonadaceae and Gammaproteobacteria families were identified as responders for ETBE biodegradation. However, the detection of the ethB gene is a more appropriate function-based indicator of ETBE biodegradation potential than taxonomic analysis of the microbial community. The study shows that a mixed groundwater-aquifer sediment (slurry) sample collected from monitoring wells after minimal purging can be used to assess the aquifer ETBE biodegradation potential at ETBE-release sites using this function-based concept.

We regret to inform you about a calculation error we recently detected in the above published article. We re-analyzed the data after discovering this error to determine the impact it had on the results. This error did not affect the main interpretation of the study but it had an impact in the specific network metrics that showed sensitivity to group differences. With the present corrigendum we propose corrections for the result sections 3.2 and 3.3 and removal of supplementary material. The specific error is related to one of the latest steps of the data analysis, in which each EEG segment per participant is exported as an ASCII file to be further analyzed in Brainwave software (http://home.kpn.nl/stam7883/brainwave.html; by C.J. Stam). In Brainwave, functional connectivity and graph network metrics are computed for each of those segments and then averaged across segments for each participant. The ASCII files were exported including a header with the electrode labels and this was not taken into account when selecting the analysis options in Brainwave (the option asks to specify the number of header lines to be ignored and there is a specific warning about this in the software's manual). This resulted in erroneous reading of the initial lines of each data segment. The following report describes the updated results after re-analysis. Comparably to the published article, the main effects are localized in the Theta band and indicate lower network integration in dyslexics compared to controls. Similar to the published report, the group effect in Leaf is significant (at p < 0.05). However, after re-analyzing the data two additional metrics showed significant differences between the groups at p < 0.05, namely Degree and Kappa. The effect in Diameter from our previous report is not significant in the current re-analysis. The interpretation of these metrics is described in the following report. The group effects suggest higher values for those metrics, associated with network integration, in controls vs dyslexic readers. These effects did not reach significance levels in the control analyses presented as supplementary material. The following report concludes with a paragraph in which the significance of these new findings is highlighted. In view of the current report, we consider that the main interpretation of this study remains valid and interesting for the journal's readers. Thus we would be inclined to present a corrigendum note with the corrected report and table, but we entrust the ultimate decision to the editor. On behalf of all coauthors, Dr. Gorka Fraga González The following report contains the corrected text and results. Results: The inspection of individual peak frequencies in the spectra averaged across parietal-occipital sites indicated that for the majority of participants the peak frequency fell within the low alpha (8–10 Hz) and high alpha (10–13 Hz) range (see Section 2.5). We discarded data from children with a peak frequency equal or lower than 8 Hz as this might bias subsequent analysis in the lower frequency bands. A total of 12 subjects were excluded; 8 dyslexics (N = 26) and 4 controls (N = 15). Spectral power and functional connectivity: The power spectra averaged across all electrodes for each group are shown in Fig. 3. Controls and dyslexics both showed prominent peak frequencies in the alpha band, which did not differ between groups. The mixed model ANOVAs performed on the power values in each frequency band revealed no significant differences (in the total average or regional sub-averages) between groups. The analysis of connectivity strength, i.e., PLI, revealed a trend in the Delta band for higher PLI values in dyslexics compared to typical readers, F (1, 59) = 3.67, p =.063, η2 = 0.09. There were no other significant group differences in PLI for any of the other frequency bands, ps >.144. MST analysis: MST analysis yielded significant between group effects in the theta band (see Table 1). Leaf fraction, reflecting the integration of information within the network, was significantly lower in dyslexics relative to typical readers, F (1, 39) = 5.32, p =.027, η2 = 0.12. The group effect on degree, representing the connections that each node has on average with the rest of the network, was significant also, F (1, 39) = 5.62, p =.023, η2 = 0.13 indicating lower degree in dyslexics relative to controls. In addition, there was a significant group effect on kappa, relating to degree diversity within the network, F (1, 39) = 5.91, p =.020, η2 = 0.13, suggesting lower kappa in dyslexics compared to controls. Kappa is specially high in scale-free network models which are characterized by a few nodes (hubs) with an exceptionally high degree compared to the majority of all other nodes in the network (Stam and van Straaten, 2012). Collectively these results indicate a less integrated network organization in dyslexic children compared to controls. Group effects in all other graph measures and frequency bands were not significant, ps >.093. Significance: Graph metrics relate to the intrinsic organization of functional brain networks. The current findings point to a less efficient network configuration in dyslexics relative to a more proficient configuration in the control group. MST metrics could help characterizing the heterogeneity within dyslexia, as different underlying deficits may result in similar reading impairments. This study provides additional insights on the cognitive deficits underlying dyslexia and, thus, may advance our knowledge on reading development. Our findings add to the growing body literature suggesting compromised networks rather than specific dysfunctional brain regions in dyslexia.

While MEPDG is an effective pavement design tool for INDOT, low volume LPA projects may not require that level of complexity. Many agencies have long been comfortable with the 1993 AASHTO Guide for the Design of Pavement Structures and the associated software known as DARWinTM. However, DARWinTM is no longer available, and local agencies and consultants are faced with a lack of well-supported pavement design tools in the marketplace. PaveXpress is a free online tool to help you create simplified pavement designs using key engineering inputs, based on the AASHTO 1993 and 1998 supplement pavement design process. It is user friendly and works on your phone or tablet. Come learn how simple it is to use this tool.

The session will present a “user’s guide” for public employees to insure cost-effective decision making when it comes to specifying asphalt pavements for affordable initial and life cycle cost. There will be time for questions and answers on the recommended specs.

This report provides an assessment of the consequences of changing water availability for production of drinking water, the manufacturing industry and power production in Europe, due to climate change and socio-economic developments. The report is based up on projections of demographic and socio-economic trends and climate change impacts, according to the SRES A2 and B1 scenario’s also used by IPCC

Het Markermeer is meer ‘climate proof’ dan het IJsselmeer en heeft onder het huidige waterbeheer minder last van verzilting als gevolg van klimaatverandering, zeespiegelstijging en veranderende hydrologie van de Rijn dan het IJsselmeer. Dit blijkt uit een modelstudie waarbij water en chloride dynamisch in de tijd zijn gesimuleerd. Herinrichtingsmaatregelen die gepland staan voor het Markermeer (zoals het aanleggen van een oermoeras of een slibput) hebben vrijwel geen effect op de chlorideconcentratie. Opvallend resultaat is dat het mee laten stijgen van het IJsselmeerpeil met de Waddenzee een positief effect heeft op de chlorideconcentratie in het IJsselmeer. De op korte termijn geplande omzetting van het bemalingsregime in zuidelijk Flevoland veroorzaakt een stijging van het chloridegehalte van maximaal 80 mg/l (tot boven 200 mg/l) in het Markermeer en maakt het water uit het Markermeer ongeschikt als grondstof voor drinkwater. Dit is gezien de kwetsbaarheid van het IJsselmeer voor toekomstige verzilting een ongewenste ontwikkeling. Uit een analyse van de monitoringsdata uit de DONAR database van Rijkswaterstaat en monitoringsdata van RIWA Rijn volgt dat macroionen als chloride, natrium en sulfaat in de huidige situatie geen bezwaar vormen om water uit het Markermeer te gebruiken als grondstof voor drinkwater. Problematisch voor het Markermeer is de concentratie zwevend stof die vrijwel ieder jaar boven de BKMO norm ligt. Mogelijk dat de helderheid in de Gouwzee hoger is door de aanwezigheid van kranswieren.

Shallow geothermal systems are increasingly being used to store or harvest thermal energy for heating or cooling purposes. This technology causes temperature perturbations exceeding the natural variations in aquifers, which may impact groundwater quality. Here, we report the results of laboratory experiments on the effect of temperature variations (5-80 °C) on redox processes and associated microbial communities in anoxic unconsolidated subsurface sediments. Both hydrochemical and microbiological data showed that a temperature increase from 11 °C (in situ) to 25 °C caused a shift from iron-reducing to sulfate-reducing and methanogenic conditions. Bioenergetic calculations could explain this shift. A further temperature increase (45 °C) resulted in the emergence of a thermophilic microbial community specialized in fermentation and sulfate reduction. Two distinct maxima in sulfate reduction rates, of similar orders of magnitude (5 × 10(-10) M s(-1)), were observed at 40 and 70 °C. Thermophilic sulfate reduction, however, had a higher activation energy (100-160 kJ mol(-1)) than mesophilic sulfate reduction (30-60 kJ mol(-1)), which might be due to a trade-off between enzyme stability and activity with thermostable enzymes being less efficient catalysts that require higher activation energies. These results reveal that while sulfate-reducing functionality can withstand a substantial temperature rise, other key biochemical processes appear more temperature sensitive.