An Integrated High-density Linkage Map of Soybean with RFLP, SSR, STS, and AFLP Markers Using A Single F2 Population

Soybean [Glycine max (L.) Merrill] is the most important leguminous crop in the world due to its high contents of high-quality protein and oil for human and animal consumption as well as for industrial uses. An accurate and saturated genetic linkage map of soybean is an essential tool for studies on modern soybean genomics. In order to update the linkage map of a F2 population derived from a cross between Misuzudaizu and Moshidou Gong 503 and to make it more informative and useful to the soybean genome research community, a total of 318 AFLP, 121 SSR, 108 RFLP, and 126 STS markers were newly developed and integrated into the framework of the previously described linkage map. The updated genetic map is composed of 509 RFLP, 318 SSR, 318 AFLP, 97 AFLP-derived STS, 29 BAC-end or EST-derived STS, 1 RAPD, and five morphological markers, covering a map distance of 3080 cM (Kosambi function) in 20 linkage groups (LGs). To our knowledge, this is presently the densest linkage map developed from a single F2 population in soybean. The average intermarker distance was reduced to 2.41 from 5.78 cM in the earlier version of the linkage map. Most SSR and RFLP markers were relatively evenly distributed among different LGs in contrast to the moderately clustered AFLP markers. The number of gaps of more than 25 cM was reduced to 6 from 19 in the earlier version of the linkage map. The coverage of the linkage map was extended since 17 markers were mapped beyond the distal ends of the previous linkage map. In particular, 17 markers were tagged in a 5.7 cM interval between CE47M5a and Satt100 on LG C2, where several important QTLs were clustered. This newly updated soybean linkage map will enable to streamline positional cloning of agronomically important trait locus genes, and promote the development of physical maps, genome sequencing, and other genomic research activities

Severity of eczema and mental health problems in Japanese schoolchildren: The ToMMo Child Health Study

Background: The association between eczema and mental health problems in schoolchildren has been underexplored. We aimed to investigate this association with the validated questionnaires. Methods: Of 46,648 invited children, we analyzed 9954 (21.3%) in the 2nd to the 8th grades from the ToMMo Child Health Study conducted in 2014 and 2015, a cross-sectional survey in Miyagi Prefecture, Japan. We defined eczema status as “normal,” “mild/moderate,” or “severe,” based on the presence of persistent flexural eczema and sleep disturbance, according to the International Study of Asthma and Allergies in Childhood (ISAAC) Eczema Symptom Questionnaire. Clinical ranges of Strengths and Difficulties Questionnaire (SDQ) total difficulties scores and four SDQ subcategories of emotional symptoms, conduct problems, hyperactivity/inattention, and peer problems were defined as scores ≥16, ≥5, ≥5, ≥7, and ≥5, respectively. Results: The mean SDQ total difficulties score significantly increased as eczema status worsened (all P ≤ 0.004 for trend). The OR of scores in the clinical range for SDQ total difficulties were 1.51 (95% CI, 1.31–1.74) for mild/moderate eczema and 2.63 (95% CI, 1.91–3.63) for severe eczema (P < 0.001 for trend), adjusted for sex, school grade, current wheeze, and disaster-related factors, using normal eczema as a reference. The association between severity of eczema and four SDQ subcategories showed a similar trend (all P ≤ 0.017 for trend). Conclusions: We found a significant association between severity of eczema and mental health problems. The presence of eczema was associated with four SDQ subcategories. Keywords: Atopic dermatitis, Cross-sectional survey, ISAAC, Sleep disorders, Strengths and difficulties questionnair

Thermoresponsive Colloidal Crystallization Using Adsorption of Ionic Surfactants

Thermoresponsive Colloidal Crystallization Using Adsorption of Ionic Surfactant

Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods

Abstract We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin B6 responsiveness by selected phenotype variables. We analyzed the existing data from our intervention study with 17 persons. First, we focused on signs and biomarkers that have been identified as candidates for vitamin B6 responsiveness indicators. Second, we conducted hypothesis testing among these selected variables and their combinations. Finally, we further investigated the results by conducting cluster analyses with two different algorithms, affinity propagation and k-medoids. Statistically significant variables for vitamin B6 responsiveness, including combination of hypersensitivity to sound and clumsiness, and plasma glutamine level, were included. As an a priori variable, the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS) scores was also included. The affinity propagation analysis showed good classification of three potential vitamin B6-responsive persons with ASD. The k-medoids analysis also showed good classification. To our knowledge, this is the first study to attempt to identify subgroup of persons with ASD who show specific treatment responsiveness using selected phenotype variables. We applied machine learning methods to further investigate these variables’ ability to identify this subgroup of ASD, even when only a small sample size was available