Animals have sophisticated homeostatic controls. While mammalian body temperature fluctuates throughout the day, small ectotherms, such as Drosophila achieve a body temperature rhythm (BTR) through their preference of environmental temperature. Here, we demonstrate that pigment dispersing factor (PDF) neurons play an important role in setting preferred temperature before dawn. We show that small lateral ventral neurons (sLNvs), a subset of PDF neurons, activate the dorsal neurons 2 (DN2s), the main circadian clock cells that regulate temperature preference rhythm (TPR). The number of temporal contacts between sLNvs and DN2s peak before dawn. Our data suggest that the thermosensory anterior cells (ACs) likely contact sLNvs via serotonin signaling. Together, the ACs-sLNs-DN2s neural circuit regulates the proper setting of temperature preference before dawn. Given that sLNvs are important for sleep and that BTR and sleep have a close temporal relationship, our data highlight a possible neuronal interaction between body temperature and sleep regulation.DOI: http://dx.doi.org/10.7554/eLife.23206.001

Five grip spans (45 to 65 mm) were tested to evaluate the effects of handle grip span and user's hand size on maximum grip strength, individual finger force and subjective ratings of comfort using a computerised digital dynamometer with independent finger force sensors. Forty-six males participated and were assigned into three hand size groups (small, medium, large) according to their hands' length. In general, results showed the 55- and 50-mm grip spans were rated as the most comfortable sizes and showed the largest grip strength (433.6 N and 430.8 N, respectively), whereas the 65-mm grip span handle was rated as the least comfortable size and the least grip strength. With regard to the interaction effect of grip span and hand size, small and medium-hand participants rated the best preference for the 50- to 55-mm grip spans and the least for the 65-mm grip span, whereas large-hand participants rated the 55- to 60-mm grip spans as the most preferred and the 45-mm grip span as the least preferred. Normalised grip span (NGS) ratios (29% and 27%) are the ratios of user's hand length to handle grip span. The NGS ratios were obtained and applied for suggesting handle grip spans in order to maximise subjective comfort as well as gripping force according to the users' hand sizes. In the analysis of individual finger force, the middle finger force showed the highest contribution (37.5%) to the total finger force, followed by the ring (28.7%), index (20.2%) and little (13.6%) finger. In addition, each finger was observed to have a different optimal grip span for exerting the maximum force, resulting in a bow-contoured shaped handle (the grip span of the handle at the centre is larger than the handle at the end) for two-handle hand tools. Thus, the grip spans for two-handle hand tools may be designed according to the users' hand/finger anthropometrics to maximise subjective ratings and performance based on this study. Results obtained in this study will provide guidelines for hand tool designers and manufacturers for designing grip spans of two-handle tools, which can maximise handle comfort and performance.

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Interpreting gene expression profiles often involves statistical analysis of large numbers of differentially expressed genes, isoforms, and alternative splicing events at either static or dynamic spectrums. Reduced sequencing costs have made feasible dense time-series analysis of gene expression using RNA-seq; however, statistical methods in the context of temporal RNA-seq data are poorly developed. Here we will review current methods for identifying temporal changes in gene expression using RNA-seq, which are limited to static pairwise comparisons of time points and which fail to account for temporal dependencies in gene expression patterns. We also review recently developed very few number of temporal dynamic RNA-seq specific methods. Application and development of RNA-specific temporal dynamic methods have been continuously under the development, yet, it is still in infancy. We fully cover microarray specific temporal methods and transcriptome studies in initial digital technology (e.g., SAGE) between traditional microarray and new RNA-seq.