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Recent advances in molecular approaches and DNA sequencing have greatly progressed the field of ecology and allowed for the study of complex communities in unprecedented detail. Next generation sequencing (NGS) can reveal powerful insights into the diversity, composition, and dynamics of cryptic organisms, but results may be sensitive to a number of technical factors, including molecular practices used to generate amplicons, sequencing technology, and data processing. Despite the popularity of some techniques over others, explicit tests of the relative benefits they convey in molecular ecology studies remain scarce. Here we tested the effects of PCR replication, sequencing depth, and sequencing platform on ecological inference drawn from environmental samples of soil fungi. We sequenced replicates of three soil samples taken from pine biomes in North America represented by pools of either one, two, four, eight, or sixteen PCR replicates with both 454 pyrosequencing and Illumina MiSeq. Increasing the number of pooled PCR replicates had no detectable effect on measures of α- and β-diversity. Pseudo-β-diversity - which we define as dissimilarity between re-sequenced replicates of the same sample - decreased markedly with increasing sampling depth. The total richness recovered with Illumina was significantly higher than with 454, but measures of α- and β-diversity between a larger set of fungal samples sequenced on both platforms were highly correlated. Our results suggest that molecular ecology studies will benefit more from investing in robust sequencing technologies than from replicating PCRs. This study also demonstrates the potential for continuous integration of older datasets with newer technology.

As the global burden of dementia increases, the absence of treatment underscores the need for identification of factors that may improve cognitive reserve-the ability to stave off cognitive decline in old age. The beneficial association between musical instrument engagement and episodic memory has been identified in children, young adults, and older adults. Yet, previous studies in musical instrument engagement have rarely examined the potential for adolescence and adulthood exposures to independently improve cognition, nor have they been linked with the rate of memory decline over time in older adults. We investigated whether adolescent musical instrument engagement and continued musical instrument engagement over the adult life course were separately associated with higher episodic memory, as well as rate of decline in a large longitudinal cohort. Data were from a prospective cohort of high school graduates from 1957. High school music engagement (HSME) was ascertained through graduate yearbooks and assessed as membership in musical performance groups. A questionnaire was used to assess musical engagement through adulthood (MEA) at ages 35, 55, and 65. The episodic memory score was composed of immediate and delayed recall task scores, and was assessed when participants were aged approximately 65 and 72 years old among 5,718 individuals. Linear mixed models were used to assess the association between music, and memory performance and decline over time. Of high school graduates who participated in the study, 38.1% played music in high school, and 21.1% played music in adulthood. While musical engagement was more common in those who played in childhood, 40% of those who played continuously as an adult did not play in high school. High HSME (B = 0.348, p = 0.049) and continuous MEA (B = 0.424, p = 0.012) were associated with higher memory scores at age 65 after covariate adjustment. When examining memory decline, the benefits of high HSME decreased over time (B = -0.435, p = 0.048), while the rate of decline did not differ between MEA groups. Exploratory models revealed differential benefits for HSME and immediate recall, and MEA and delayed recall. This study provides further evidence that musical engagement in childhood or adulthood is associated with non-musical cognitive reserve. These two exposures may act differentially in different domains of episodic memory. Further work is needed to determine the relationship between musicianship and the rate of cognitive decline.

Identifying the ecological processes that structure communities and the consequences for ecosystem function is a central goal of ecology. The recognition that fungi, bacteria, and viruses control key ecosystem functions has made microbial communities a major focus of this field. Because many ecological processes are apparent only at particular spatial or temporal scales, a complete understanding of the linkages between microbial community, environment, and function requires analysis across a wide range of scales. Here, we map the biological and functional geography of soil fungi from local to continental scales and show that the principal ecological processes controlling community structure and function operate at different scales. Similar to plants or animals, most soil fungi are endemic to particular bioregions, suggesting that factors operating at large spatial scales, like dispersal limitation or climate, are the first-order determinants of fungal community structure in nature. By contrast, soil extracellular enzyme activity is highly convergent across bioregions and widely differing fungal communities. Instead, soil enzyme activity is correlated with local soil environment and distribution of fungal traits within the community. The lack of structure–function relationships for soil fungal communities at continental scales indicates a high degree of functional redundancy among fungal communities in global biogeochemical cycles.

In recent years, bumblebees have become a prominent insect model organism for a variety of biological disciplines, particularly to investigate learning behaviors as well as visual performance. Understanding these behaviors and their underlying neurobiological principles requires a clear understanding of brain anatomy. Furthermore, to be able to compare neuronal branching patterns across individuals, a common framework is required, which has led to the development of 3D standard brain atlases in most of the neurobiological insect model species. Yet, no bumblebee 3D standard brain atlas has been generated. Here we present a brain atlas for the buff-tailed bumblebee Bombus terrestris using micro-computed tomography (micro-CT) scans as a source for the raw data sets, rather than traditional confocal microscopy, to produce the first ever micro-CT-based insect brain atlas. We illustrate the advantages of the micro-CT technique, namely, identical native resolution in the three cardinal planes and 3D structure being better preserved. Our Bombus terrestris brain atlas consists of 30 neuropils reconstructed from ten individual worker bees, with micro-CT allowing us to segment neuropils completely intact, including the lamina, which is a tissue structure often damaged when dissecting for immunolabeling. Our brain atlas can serve as a platform to facilitate future neuroscience studies in bumblebees and illustrates the advantages of micro-CT for specific applications in insect neuroanatomy.

Ecologists have long acknowledged the importance of seed banks; yet, despite the fact that many plants rely on mycorrhizal fungi for survival and growth, the structure of ectomycorrhizal (ECM) fungal spore banks remains poorly understood. The primary goal of this study was to assess the geographic structure in pine‐associated ECM fungal spore banks across the North American continent. Soils were collected from 19 plots in forests across North America. Fresh soils were pyrosequenced for fungal internal transcribed spacer (ITS) amplicons. Adjacent soil cores were dried and bioassayed with pine seedlings, and colonized roots were pyrosequenced to detect resistant propagules of ECM fungi. The results showed that ECM spore banks correlated strongly with biogeographic location, but not with the identity of congeneric plant hosts. Minimal community overlap was found between resident ECM fungi vs those in spore banks, and spore bank assemblages were relatively simple and dominated by Rhizopogon, Wilcoxina, Cenococcum, Thelephora, Tuber, Laccaria and Suillus. Similar to plant seed banks, ECM fungal spore banks are, in general, depauperate, and represent a small and rare subset of the mature forest soil fungal community. Yet, they may be extremely important in fungal colonization after large‐scale disturbances such as clear cuts and forest fires.

Objectives. We sought to test the hypothesis that providing help to others predicts a reduced association between stress and mortality. Methods. We examined data from participants (n = 846) in a study in the Detroit, Michigan, area. Participants completed baseline interviews that assessed past-year stressful events and whether the participant had provided tangible assistance to friends or family members. Participant mortality and time to death was monitored for 5 years by way of newspaper obituaries and monthly state death-record tapes. Results. When we adjusted for age, baseline health and functioning, and key psychosocial variables, Cox proportional hazard models for mortality revealed a significant interaction between helping behavior and stressful events (hazard ratio [HR] = 0.58; P < .05; 95% confidence interval [CI] = 0.35, 0.98). Specifically, stress did not predict mortality risk among individuals who provided help to others in the past year (HR = 0.96; 95% CI = 0.79, 1.18), but stress did predict mortality among those who did not provide help to others (HR = 1.30; P < .05; 95% CI = 1.05, 1.62). Conclusions. Helping others predicted reduced mortality specifically by buffering the association between stress and mortality.

Natural killer T (NKT) cells detect lipids presented by CD1d. Most studies focus on type I NKT cells that express semi-invariant αβ T cell receptors (TCR) and recognize α-galactosylceramides. However, CD1d also presents structurally distinct lipids to NKT cells expressing diverse TCRs (type II NKT cells), but our knowledge of the antigens for type II NKT cells is limited. An early study identified a nonlipidic NKT cell agonist, phenyl pentamethyldihydrobenzofuransulfonate (PPBF), which is notable for its similarity to common sulfa drugs, but its mechanism of NKT cell activation remained unknown. Here, we demonstrate that a range of pentamethylbenzofuransulfonates (PBFs), including PPBF, activate polyclonal type II NKT cells from human donors. Whereas these sulfa drug–like molecules might have acted pharmacologically on cells, here we demonstrate direct contact between TCRs and PBF-treated CD1d complexes. Further, PBF-treated CD1d tetramers identified type II NKT cell populations expressing αβTCRs and γδTCRs, including those with variable and joining region gene usage (TRAV12-1–TRAJ6) that was conserved across donors. By trapping a CD1d–type II NKT TCR complex for direct mass-spectrometric analysis, we detected molecules that allow the binding of CD1d to TCRs, finding that both selected PBF family members and short-chain sphingomyelin lipids are present in these complexes. Furthermore, the combination of PPBF and short-chain sphingomyelin enhances CD1d tetramer staining of PPBF-reactive T cell lines over either molecule alone. This study demonstrates that nonlipidic small molecules, which resemble sulfa drugs implicated in systemic hypersensitivity and drug allergy reactions, are targeted by a polyclonal population of type II NKT cells in a CD1d-restricted manner.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by flares ranging from mild to life-threatening. Severe flares and complications can require hospitalizations, which account for most of the direct costs of SLE care. This study investigates two machine learning approaches in predicting SLE hospitalizations using longitudinal data from 925 patients enrolled in a multicenter electronic health record (EHR)-based lupus cohort. Our first Differential approach accounts for the time dependencies in sequential data by introducing additional lagged variables between consecutive time steps. We next evaluate the performance of LSTM, a state-of-the-art deep learning model designed for time series. Our experimental results demonstrate that both methods can effectively predict lupus hospitalizations, but each has its strengths and limitations. Specifically, the Differential approach can be integrated into any non-temporal machine learning algorithms and is preferred for tasks with short observation periods. On the contrary, the LSTM model is desirable for studies utilizing long observation intervals attributing to its capability in capturing long-term dependencies embedded in the longitudinal data. Furthermore, the Differential approach has more options in handling class imbalance in the underlying data and delivers stable performance across different prognostic horizons. LSTM, on the other hand, demands more class-balanced training data and outperforms the Differential approach when there are sufficient positive samples facilitating model training. Capitalizing on our experimental results, we further study the optimal length of patient monitoring periods for different prediction horizons.