ALLOPOLYPLOIDY AND ORIGIN OF GENOMES IN THE ELYMUS L. SPECIES (A REVIEW)

All Elymus L. species are allopolyploids, whereas primary diploids are not part of this genus (with 2n = 14, x = 7). Widespread in Russia are mainly the species with genomic formulae StH, StY and StHY. According to the nomenclature system of Triticeae genomes, in the Russian Federation there are 53 species of Elymus with genomic constitutions StH, StY (2n = 4x = 28) and StHY (2n = 6x = 42). Key words: Elymus, molecular phylogeny, hybridization

Polymorphism of ITS sequences in 35S rRNA genes in Elymus dahuricus aggregate species: two cryptic species?

Nuclear ribosomal internal transcribed spacer (ITS) sequences were sequenced for 23 species and subspecies of Elymus sensu lato collected in Russia. The Neighbor-Net analysis of ITS sequences suggested that there are four ribotypes called Core Northern St-rDNA, Core Southern St-rDNA, Northern dahuricus St-rDNA and Southern dahuricus St-rDNA. The Core Southern variant of St-rDNA is closely related to rDNA of diploid Pseudoroegneria stipifolia (PI 313960) and P. spicata (PI 547161). The Core Northern St-rDNA is closely related to rDNA of P. cognata (PI 531720), a diploid species of Kyrgyzstan carrying StY variant of the St genome. The Core Northern St-rDNA is widespread among the Elymus species of Siberia and the Far East, including Yakutia and Chukotka. The Core Southern St-ribotype is typical of southern Elymus and Pseudoroegneria of the South Caucasus, Primorye, Pakistan, and South Korea. The Northern dahuricus St-ribotype and Southern dahuricus St-ribotype are derivatives of the Core Northern and Core Southern St-ribotypes, correspondingly. Both of them were found in all four studied species of the E. dahuricus aggregate: E. dahuricus Turcz. ex Griseb., E. franchetii Kitag., E. excelsus Turcz. ex Griseb. and Himalayan E. tangutorum (Nevski) Hand.-Mazz. In other words, there are at least two population groups (two races) of the Elymus dahuricus aggregate species that consistently differ in their ITS-sequences in Siberia, the Far East and Northern China. Each contains all morphological forms, which taxonomists now attribute either to different species of E. dahuricus aggr. (E. dahuricus sensu stricto, E. franchetii, E. tangutorum, E. excelsus) or subspecies of Campeiostachys dahurica (Turcz. ex Griseb.) B.R. Baum, J.L. Yang et C.C. Yen. At the moment it is unknown if there are any morphological differences between plants carrying either Northern or Southern dahuricus rDNA. Probably, they are cryptic species, but it is certain that if differences in morphology between the two races exist, they are not associated with signs that are now considered taxonomically significant and are used to separate E. dahuricus s. s., E. franchetii, E. tangutorum, and E. excelsus

Basal ganglia correlates of fatigue in young adults

Although the prevalence of chronic fatigue is approximately 20% in healthy individuals, there are no studies of brain structure that elucidate the neural correlates of fatigue outside of clinical subjects. We hypothesized that fatigue without evidence of disease might be related to changes in the basal ganglia and prefrontal cortex and be implicated in fatigue with disease. We aimed to identify the white matter structures of fatigue in young subjects without disease using magnetic resonance imaging (MRI). Healthy young adults (n = 883; 489 males and 394 females) were recruited. As expected, the degrees of fatigue and motivation were associated with larger mean diffusivity (MD) in the right putamen, pallidus and caudate. Furthermore, the degree of physical activity was associated with a larger MD only in the right putamen. Accordingly, motivation was the best candidate for widespread basal ganglia, whereas physical activity might be the best candidate for the putamen. A plausible mechanism of fatigue may involve abnormal function of the motor system, as well as areas of the dopaminergic system in the basal ganglia that are associated with motivation and reward

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p \u3c 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p \u3e 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis

Data Availability Statement: The raw data supporting the conclusions of this article and code used for analysis will be made available by the authors on reasonable request pending appropriate study approvals and data transfer agreements between participating institutions.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/brainsci14070669/s1, Table S1: Inclusion/exclusion criteria for each data source; Table S2: Deficit in words recalled for each clinical condition relative to matched controls. Refs. [61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100] are cited in the Supplementary Materials.Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.This research was funded by the Psychological Health/Traumatic Brain Injury Research Program Long-Term Impact of Military Relevant Brain Injury Consortium (LIMBIC), Grant/Award Numbers: W81XWH18PH, TBIRPLIMBIC under Awards Numbers: W81XWH1920067 and W81XWH1320095; US Department of Defense, Grant/Award Number: AZ150145; US Department of Veterans Affairs, Grant/Award Numbers: I01 CX002097, I01 CX002096, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX001135, I01 RX002076, I01 RX001880, I01 RX002172, I01 RX002173, I01 RX002171, I01 RX002174, I01 RX002170, 1I01 RX003444; National Institutes of Health (NIH), Grant/Award Number(s): RF1NS115268, RF1NS128961, U01NS086625, U01MH124639, P50MH115846, R01MH113827, R25MH080663, K08MH068540, R01NS100973, R01EB006841, P20GM103472, RO1MH083553, T32MH019535, R01 HD061504, RO1MH083553, R01AG050595, R01AG076838, R01AG060470, R01AG064955, P01AG055367, K23MH095661, R01MH094524, R01MH121246, T32MH019535, R01NS124585, R01NS122827, R61NS120249, R01NS122184, U54EB020403, R01MH116147, R56AG058854, P41EB015922, R01MH111671, P41RR14075, M01RR01066, R01EB006841, R01EB005846, R01 EB000840, RC1MH089257, U24 RR021992, and NCRR 5 month-RR001066 (MGH General Clinical Research Center); National Institute of Mental Health (NIMH), Grant/Award Number: 1P20RR021938; Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III, Grant/Award Numbers: PI15-00852, PI18-00945, JR19-00024, PI17-00481, PI20-00721; Sara Borrell contract, Grant/Award Number: CD19-00149; German Research Foundation DFG grant FOR2107, Grant/Award Numbers: JA 1890/7-1, JA 1890/7-2, NE2254/1-2, NE2254/2-1, NE2254/3-1, NE2254/4-1, KI588/14-1, KI588/14-2, DA1151/5-1, DA1151/5-2, SFB-TRR58, Projects C09 and Z02; European Union, NextGenerationEU, Grant/Award Numbers: PMP21/00051, PI19/01024; Structural Funds; Seventh Framework Program; H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking: Project PRISM-2, Grant/Award Number: 101034377; Project AIMS-2-TRIALS, Grant/Award Number: 777394; Horizon Europe; NSF, Grant/Award Number: 2112455; Madrid Regional Government, Grant/Award Number: B2017/BMD-3740 AGES-CM-2; Dalhousie Medical Research Foundation; Research Nova Scotia, Grant/Award Number: RNS-NHIG-2021-1931; NJ Commission on TBI Research Grants, Grant/Award Numbers: CBIR11PJT020, CBIR13IRG026; Department of Psychology, University of Oslo; Sunnaas Rehabilitation Hospital, Grant/Award Number: HF F32NS119285; Canadian Institutes of Health Research, Grant/Award Number: 166098; Neurological Foundation of New Zealand, Grant/Award Number: 2232 PRG; Canterbury Medical Research Foundation, University of Otago; Biogen US Investigator-initiated grant; Italian Ministry of Health, Grant/Award Number: RF-2019-12370182 and Ricerca Corrente RC 23; National Institute on Aging; National Health and Medical Research Council, Investigator Grant/Award Number: APP1176426; PA Health Research, Grant/Award Number: 4100077082; La Caixa Foundation, Grant/Award Number: 100010434, fellowship code: LCF/BQ/PR22/11920017; Research Council of Norway, Grant/Award Number: 248238; Health Research Council of New Zealand Sir Charles Hercus Early Career Development, Grant/Award Numbers: 17/039 and 14-440; Health Research Council of New Zealand, Grant/Award Numbers: 20/538 and 14/440; Research and Education Trust Pacific Radiology, Grant/Award Number: MRIJDA; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2018076; Norwegian ExtraFoundation for Health and Rehabilitation, Grant/Award Numbers: 2015/FO5146; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2015044; Stiftelsen K.G. Jebsen, Grant/Award Number: SKGJ MED-02; The Liaison Committee between Central Norway Regional Health Authority (RHA) and the Norwegian University of Science and Technology (NTNU), Grant/Award Number: 2020/39645; National Health and Medical Research Council, Grant/Award Number: APP1020526; Brain Foundation; Wicking Trust; Collie Trust; Sidney and Fiona Myer Family Foundation; U.S. Army Medical Research and Materiel Command (USAMRMC), Grant/Award Number: 13129004; Department of Energy, Grant/Award Number: DE-FG02-99ER62764; Mind Research Network; National Association for Research in Schizophrenia and Affective Disorders, Young Investigator Award; Blowitz Ridgeway and Essel Foundations; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster; NOW ZonMw TOP, Grant/Award Number: 91211021; UCLA Easton Clinic for Brain Health; UCLA Brain Injury Research Center; Stan and Patty Silver; Clinical and Translational Research Center, Grant/Award Numbers: UL1RR033176, UL1TR000124; Mount Sinai Institute for NeuroAIDS Disparities; VA Rehab SPIRE; CDMRP PRAP; VA RR&D, Grant/Award Number: IK2RX002922; Veski Fellowship; Femino Foundation grant; Fundación Familia Alonso; Fundación Alicia Koplowitz; CIBERSAM, Madrid Regional Government, Grant/Award Numbers: B2017/BMD-3740 AGES-CM-2, 2019R1C1C1002457, 21-BR-03-01, 2020M3E5D9079910, 21-BR-03-01; Deutsche Forschungsgemeinschaft (DFG), Grant/Award Numbers: NE2254/1-2, NE2254/2-1, NE2254/3-1, NE2254/4-1