Circular RNAs (circRNAs) are a type of newly identified non-coding RNAs through high-throughput deep sequencing, which play important roles in miRNA function and transcriptional controlling in human, animals, and plants. To date, there is no report in wheat seedlings regarding the circRNAs identification and roles in the dehydration stress response. In present study, the total RNA was extracted from leaves of wheat seedlings under dehydration-stressed and well-watered conditions, respectively. Then, the circRNAs enriched library based deep sequencing was performed and the circRNAs were identified using bioinformatics tools. Around 88 circRNAs candidates were isolated in wheat seedlings leaves while 62 were differentially expressed in dehydration-stressed seedlings compared to well-watered control. Among the dehydration responsive circRNAs, six were found to act as 26 corresponding miRNAs sponges in wheat. Sixteen circRNAs including the 6 miRNAs sponges and other 10 randomly selected ones were further validated to be circular by real-time PCR assay, and 14 displayed consistent regulation patterns with the transcriptome sequencing results. After Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the targeted mRNAs functions, the circRNAs were predicted to be involved in dehydration responsive process, such as photosynthesis, porphyrin, and chlorophyll metabolism, oxidative phosphorylation, amino acid biosynthesis, and metabolism, as well as plant hormone signal transduction, involving auxin, brassinosteroid, and salicylic acid. Herein, we revealed a possible connection between the regulations of circRNAs with the expressions of functional genes in wheat leaves associated with dehydration resistance.

This study investigated the impact of indoor illuminance and correlated color temperature (CCT) on healthy adults' cognitive performance, subjective mood, and alertness during daytime office hours and differences in time-of-day effects. A 2(illuminance) × 2(CCT) × 2(morning vs. afternoon) mixed design (N = 60) was employed. Participants felt less sleepy in the bright light exposure. The low "cool" lighting induced the least positive mood. The effects of illuminance and CCT on subjective feelings were not time-of-day dependent. The results demonstrated the slowest responses in inhibition, working memory, and recognition of facial expression tasks in the low "warm" lighting. The effect on long-term memory was most

a b s t r a c tSleep onset is associated with marked changes in behavioral, physiological, and subjective phenomena. In daily life though subjective experience is the main criterion in terms of which we identify it. But very few studies have focused on these experiences. This study seeks to identify the subjective variables that reflect sleep onset. Twenty young subjects took an afternoon nap in the laboratory while polysomnographic recordings were made. They were awakened four times in order to assess subjective experiences that correlate with the (1) appearance of slow eye movement, (2) initiation of stage 1 sleep, (3) initiation of stage 2 sleep, and (4) 5 min after the start of stage 2 sleep. A logistic regression identified control over and logic of thought as the two variables that predict the perception of having fallen asleep. For sleep perception, these two variables accurately classified 91.7% of the cases; for the waking state, 84.1%.

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AimTo explore the diagnostic models of Crohn’s disease (CD), Intestinal tuberculosis (ITB) and the differential diagnostic model between CD and ITB by analyzing serum proteome profiles.MethodsSerum proteome profiles from 30 CD patients, 21 ITB patients and 30 healthy controls (HCs) were analyzed by using weak cationic magnetic beads combined with MALDI-TOF-MS technique to detect the differentially expressed proteins of serum samples. Three groups were made and compared accordingly: group of CD patients and HCs, group of ITB patients and HCs, group of CD patients and ITB patients. Wilcoxon rank sum test was used to screen the ten most differentiated protein peaks (P < 0.05). Genetic algorithm combining with support vector machine (SVM) was utilized to establish the optimal diagnostic models for CD, ITB and the optimal differential diagnostic model between CD and ITB. The predictive effects of these models were evaluated by Leave one out (LOO) cross validation method.ResultsThere were 236 protein peaks differently expressed between group of CD patients and HCs, 305 protein peaks differently expressed between group of ITB patients and HCs, 332 protein peaks differently expressed between group of CD patients and ITB patients. Ten most differentially expressed peaks were screened out between three groups respectively (P < 0.05) to establish diagnostic models and differential diagnostic model. A diagnostic model comprising of four protein peaks (M/Z 4964, 3029, 2833, 2900) can well distinguish CD patients and HCs, with a specificity and sensitivity of 96.7% and 96.7% respectively. A diagnostic model comprising four protein peaks (M/Z 3030, 2105, 2545, 4210) can well distinguish ITB patients and HCs, with a specificity and sensitivity of 93.3% and 95.2% respectively. A differential diagnostic model comprising three potential biomarkers protein peaks (M/Z 4267, 4223, 1541) can well distinguish CD patients and ITB patients, with a specificity and sensitivity of 76.2% and 80.0% respectively. Among the eleven protein peaks from the diagnostic models and differential diagnostic model, two have been successfully purified and identified, Those two peaks were M/Z 2900 from the diagnostic model between CD and HCs and M/Z 1541 from the differential diagnostic model between CD and ITB. M/Z 2900 was identified as appetite peptide, M/Z 1541 was identified as Lysyl oxidase-like 2 (LOXL-2).ConclusionThe differently expressed protein peaks analyzed by serum proteome with weak cationic magnetic beads combined MALDI-TOF-MS technique can effectively distinguish CD patients and HCs, ITB patients and HCs, CD patients and ITB patients. The diagnostic model between CD patients and HCs consisting of four protein peaks (M/Z 4964, 3029, 2833, 2900), the diagnostic model between ITB patients and HCs comprising four protein peaks (M/Z 3030, 2105, 2545, 4210) and the differential diagnostic model between CD patients and ITB patients comprising three protein peaks (M/Z 4267, 4223, 1541) had high specificity and sensitivity and can contribute to diag...