Establishment of laurel forest vegetation in adjacent secondary forest in central Japan

Distributions of lucidophyllous species are limited due to the fragmentation of laurel forest. On Komayama Hill in central Japan, we evaluated the colonization of typical lucidophyllous vascular plants from a 350-year-old laurel forest into adjacent abandoned secondary forest for conservation and restoration purposes. A total of 14 consecutive subplots were established along the vegetation border between the two forests (length, 30 m; width, 5 m), extending 70 m into the secondary forest; 18 quadrats of old-growth forest were surveyed. Edge effects of old-growth forest were found to play an important role in re-establishing lucidophyllous saplings and seedlings in the secondary forest. In particular, the abundances of the four dominant canopy species of the old-growth forest significantly decreased with increasing distance. Hence, they are expected to colonize further into the secondary forest and, ultimately, to dominate the canopy. However, the number of lucidophyllous species did not change with distance. Species such as Ficus nipponica, Damnacanthus indicus, Ilex integra, and Lemmaphyllum microphyllum were near-completely or completely limited to the old-growth forest. They are known to be negatively affected by forest fragmentation and were observed to be struggling to colonize the exterior of the old-growth forest even after 60 years of abandonment. Their absence highlighted the limited colonization capacities of some old-growth forest species and underlined the time required for habitat restoration following human disturbance. We conclude that it is important to consider the population dynamics of dominant canopy species and the colonization of these interior species when assessing the habitat expansion of lucidophyllous species and hence the restoration of degraded lands.     

Multiple Hypotheses in the Analysis of a Crossover Trial

Crossover trials are used in a variety of fields, such as medicine, biology, psychology, and some commercial goods investigations. The aim of this paper is to extend a methodology for multiple comparisons to the problem of testing in crossover trials with two treatments. These two treatments are given in two orderings, treatment A first or treatment B first. We perform inference on the effect of one treatment relative to the effect of the other, without assuming that these effects are independent of treatment ordering, using techniques from order-restricted inference and multiple comparisons, and compare to some existing multiple comparison tests.     

Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare, and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose subgroup-based adaptive (SUBA), designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization, and a design based on a probit regression. In simulation studies, we find that SUBA compares favorably against the alternatives.     

Strategies for Genomic and Proteomic Profiling of Cancers

Omics-based technology platforms have made new kinds of cancer profiling tests feasible. There are several valuable examples in clinical practice, and many more under development. A concerted, transparent process of discovery with lock-down of candidate assays and classifiers and clear specification of intended clinical use is essential. The Institute of Medicine has now proposed a three-stage scheme of confirming and validating analytical findings, validating performance on clinical specimens, and demonstrating explicit clinical utility for an approvable test (Micheel et al., Evolution of translational omics: lessons learned and path forward, 2012).     

Dynamic light scattering: a practical guide and applications in biomedical sciences

Dynamic light scattering (DLS), also known as photon correlation spectroscopy (PCS), is a very powerful tool for studying the diffusion behaviour of macromolecules in solution. The diffusion coefficient, and hence the hydrodynamic radii calculated from it, depends on the size and shape of macromolecules. In this review, we provide evidence of the usefulness of DLS to study the homogeneity of proteins, nucleic acids, and complexes of protein–protein or protein–nucleic acid preparations, as well as to study protein–small molecule interactions. Further, we provide examples of DLS’s application both as a complementary method to analytical ultracentrifugation studies and as a screening tool to validate solution scattering models using determined hydrodynamic radii.     

Enhanced sampling simulations to construct free-energy landscape of protein–partner substrate interaction

Molecular dynamics (MD) simulations using all-atom and explicit solvent models provide valuable information on the detailed behavior of protein–partner substrate binding at the atomic level. As the power of computational resources increase, MD simulations are being used more widely and easily. However, it is still difficult to investigate the thermodynamic properties of protein–partner substrate binding and protein folding with conventional MD simulations. Enhanced sampling methods have been developed to sample conformations that reflect equilibrium conditions in a more efficient manner than conventional MD simulations, thereby allowing the construction of accurate free-energy landscapes. In this review, we discuss these enhanced sampling methods using a series of case-by-case examples. In particular, we review enhanced sampling methods conforming to trivial trajectory parallelization, virtual-system coupled multicanonical MD, and adaptive lambda square dynamics. These methods have been recently developed based on the existing method of multicanonical MD simulation. Their applications are reviewed with an emphasis on describing their practical implementation. In our concluding remarks we explore extensions of the enhanced sampling methods that may allow for even more efficient sampling.     

Structural aspects of nucleic acid-sensing Toll-like receptors

Invading pathogens elicit potent immune responses in cells through interactions between structurally conserved molecules derived from the pathogens and specialized innate immune receptors such as the Toll-like receptors (TLRs). Nucleic acid is one of the principal TLR ligands. Nucleic acid-sensing TLRs recognize an array of nucleic acids, including double-stranded RNA, single-stranded RNA, and DNAs with specific sequence motifs. Although ligand-induced dimerization is commonly observed followed by TLR activation, both the specific recognition mechanisms and the ligand–receptor interactions vary among different TLRs. In this review, we highlight our current understanding of how these receptors recognize their cognate ligands based on the recent advances in structural biology.     

Glucose‐based microbial production of the hormone melatonin in yeast Saccharomyces cerevisiae

Melatonin is a natural mammalian hormone that plays an important role in regulating the circadian cycle in humans. It is a clinically effective drug exhibiting positive effects as a sleep aid and a powerful antioxidant used as a dietary supplement. Commercial melatonin production is predominantly performed by complex chemical synthesis. In this study, we demonstrate microbial production of melatonin and related compounds, such as serotonin and N‐acetylserotonin. We generated Saccharomyces cerevisiae strains that comprise heterologous genes encoding one or more variants of an L‐tryptophan hydroxylase, a 5‐hydroxy‐L‐tryptophan decarboxylase, a serotonin acetyltransferase, an acetylserotonin O‐methyltransferase, and means for providing the cofactor tetrahydrobiopterin via heterologous biosynthesis and recycling pathways. We thereby achieved de novo melatonin biosynthesis from glucose. We furthermore accomplished increased product titers by altering expression levels of selected pathway enzymes and boosting co‐factor supply. The final yeast strain produced melatonin at a titer of 14.50 ± 0.57 mg L^?1 in a 76h fermentation using simulated fed‐batch medium with glucose as sole carbon source. Our study lays the basis for further developing a yeast cell factory for biological production of melatonin.