Explore the vast range of titles available.

Key messageWing loading, diaspore type (round-winged vs. single-winged), and aerodynamic motion (autogyro vs. floater) influence the terminal velocity of wind-dispersed diaspores and falling patterns.The dispersal ability of diaspores dispersed by wind can be reflected in the terminal velocity of the diaspore. Therefore, we measured the terminal velocity of wind-dispersed diaspores in 17 major forest and urban tree species in South Korea and tracked falling diaspore patterns up to the achievement of terminal velocity using the video camera recording method. In addition, the morphological characteristics of the diaspores were measured, and their effect on diaspore terminal velocity tested. Chamaecyparis obtusa (2.66 m s−1) had the highest terminal velocity, whereas Picea abies (0.61 m s−1) had the lowest terminal velocity. Falling diaspores achieved terminal velocity through (1) the oscillating falling pattern of floater diaspores, with a constant descent velocity with minor increases and decreases; (2) the decelerating falling pattern of single-winged diaspores, with accelerating descent velocity followed by rapid deceleration to terminal velocity; or (3) the accelerating falling pattern of round-winged autogyro diaspores, with descent velocity increasing steadily up to terminal velocity. The terminal velocities of single-winged diaspores were significantly lower than those of round-winged diaspores. Although there were cases of similar terminal velocity between species in the same genera (e.g., Abies, Pinus), there were large intraspecific differences in terminal velocity within the same genus due to morphological differences (e.g., Acer). The measured terminal velocities could be applied in simulations for diaspore dispersal distances for forestry tree species. The present study explored the relationship between diaspore morphological characteristics and terminal velocity, and is the first to report the dispersal ability of wind-dispersed seeds of major tree species in East Asia. The findings of the present study can be adopted as key input variables in seed dispersal modeling and facilitate the establishment of natural regeneration plans and conservation of endangered species in the wake of climate change.

Natural regeneration in forest management, which relies on artificial planting, is considered a desirable alternative to reforestation. However, there are large uncertainties regarding the natural regeneration processes, such as seed production, seed dispersal, and seedling establishment. Among these processes, seed dispersal by wind must be modeled accurately to minimize the risks of natural regeneration. This study aimed to (1) review the main mechanisms of seed dispersal models, their characteristics, and their applications and (2) suggest prospects for seed dispersal models to increase the predictability of natural regeneration. With improving computing and observation systems, the modeling technique for seed dispersal by wind has continued to progress steadily from a simple empirical model to the Eulerian-Lagrangian model. Mechanistic modeling approaches with a dispersal kernel have been widely used and have attempted to be directly incorporated into spatial models. Despite the rapid development of various wind-dispersal models, only a few studies have considered their application in natural regeneration. We identified the potential attributes of seed dispersal modeling that cause high uncertainties and poor simulation results in natural regeneration scenarios: topography, pre-processing of wind data, and various inherent complexities in seed dispersal processes. We suggest that seed dispersal models can be further improved by incorporating (1) seed abscission mechanisms by wind, (2) spatiotemporally complex wind environments, (3) collisions with the canopy or ground during seed flight, and (4) secondary dispersal, long-distance dispersal, and seed predation. Interdisciplinary research linking climatology, biophysics, and forestry would help improve the prediction of seed dispersal and its impact on natural regeneration.

While research on the effectiveness of the MMPI-2 validity scales has been extensive, past studies have mostly relied on one research design: the \"simulation\" study. In this design, participants are instructed to feign psychopathology by overreporting symptoms or feign defensiveness by presenting oneself in a positive light. Their response set is compared to profiles from participants given standard instructions. This research design accounted for 22 of the 23 studies published since 1991, as reviewed by Baer in his 2002 meta-analysis; and all 68 of the studies reviewed by Rogers in his 2003 meta-analysis. Using this design, researchers have placed their confidence in the overall usefulness of the MMPI-2 validity scales. Despite the popularity of this method, researchers do not know the extent to which experimental feigners resemble genuine feigners. This puts the generalizability of any findings into question. Another design administers the MMPI-2 to a large group of individuals and selects out a subset of participants who are later discovered to have misrepresented themselves. These feigned profiles are compared to honest profiles. This \"known-groups\" design is believed to be more generalizable because these feigners are genuine feigners. The present study used the \"known-groups\" design with the Q-sort (a measure known for its high reliability) as the independent measure of feigning. 104 outpatients from a community mental health clinic completed the Minnesota Multiphasic Personality Inventory 2nd edition. An expert interpreted the MMPI-2 profiles and, blind to the validity scales, created a Q-sort profile. The therapists of the participants provided behavioral correlates by describing the participants using another Q-sort. Therefore, this study gave us two types of data: (1) description of a person by interpretation of an MMPI-2, and (2) description of a person by someone who knew that person on an intimate level. The two Q-sorts were correlated providing an index of how accurately the MMPI-2 matched a therapist's experience of that individual. There were four hypotheses in the study. Hypothesis 1. This examined the degree to which a score on a validity scale could detect genuine dishonest responding. It was predicted that MMPI-2 profiles with low Q-correlations would have high validity scales scores. This was indeed the case for the scales measuring underreporting which appeared to be functioning intact as predicted by previous research. However, the F scales performed very poorly, contrary to the literature. This suggested that established cutoffs scores for the F scales, which were firmly established using a simulated design, may not work when applied to a genuine population. Hypothesis 2. This examined the degree to which a score on a validity scale could predict the type and level of faking. Last among the Q-sort items was the critical item #100 \"Overall, this individual is psychologically healthy and well-adjusted.\" rated on a Likert scale of 1 to 7. The difference between the expert-interpreted score on this item and the therapist-rated score indexed the degree to which the participant faked. Correlations showed that L + K, K and S were best at predicting a faked profile. Scales L, Fb, and Fp performed worst. The results also suggested that low scores on the underreporting scales could be used to detect overreporting. Hypothesis 3. This examined whether fakers from past simulation studies resembled genuine fakers. The MMPI-2 profiles of those deemed to be genuine fakers were compared to previously published MMPI-2 profiles of participants who were instructed to fake. MMPI-2 profiles from past simulation studies produced more exaggerated response styles when compared to genuine fakers. In other words, simulation studies may not be the optimal method to study MMPI-2 validity scales. Hypothesis 4. This examined whether the validity scales, when used holistically, were useful in determining whether a profile was answered honestly. The examiner completed an additional Q-sort using the participant's full MMPI-2 profile with access to all of the validity scales and correlated the new Q-sort with the therapist's Q-sort rating, and compared to previous Q-correlations obtained from MMPI-2 when blind to the validity scales. The information of the validity scales increased the correlations between the interpretation and the therapist's Q-sort, suggesting that knowledge of the validity scales, when applied holistically, added to the accuracy of an MMPI-2 interpretation.

Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the feasibility of the automated classification of headache disorders. The self-report data of 2162 patients were analyzed. Headache disorders were merged into five major entities. The patients were divided into training (n = 1286) and test (n = 876) cohorts. We trained a stacked classifier model with four layers of XGBoost classifiers. The first layer classified between migraine and others, the second layer classified between tension-type headache (TTH) and others, and the third layer classified between trigeminal autonomic cephalalgia (TAC) and others, and the fourth layer classified between epicranial and thunderclap headaches. Each layer selected different features from the self-reports by using least absolute shrinkage and selection operator. In the test cohort, our stacked classifier obtained accuracy of 81%, sensitivity of 88%, 69%, 65%, 53%, and 51%, and specificity of 95%, 55%, 46%, 48%, and 51% for migraine, TTH, TAC, epicranial headache, and thunderclap headaches, respectively. We showed that a machine-learning based approach is applicable in analyzing patient-reported questionnaires. Our result could serve as a baseline for future studies in headache research.

Small humanin-like peptide 2 (SHLP2) is a mitochondrial-derived peptide implicated in several biological processes such as aging and oxidative stress. However, its functional role in the regulation of energy homeostasis remains unclear, and its corresponding receptor is not identified. Hereby, we demonstrate that both systemic and intracerebroventricular (ICV) administrations of SHLP2 protected the male mice from high-fat diet (HFD)-induced obesity and improved insulin sensitivity. In addition, the activation of pro-opiomelanocortin (POMC) neurons by SHLP2 in the arcuate nucleus of the hypothalamus (ARC) is involved in the suppression of food intake and the promotion of thermogenesis. Through high-throughput structural complementation screening, we discovered that SHLP2 binds to and activates chemokine receptor 7 (CXCR7). Taken together, our study not only reveals the therapeutic potential of SHLP2 in metabolic disorders but also provides important mechanistic insights into how it exerts its effects on energy homeostasis. SHLP2 is a mitochondrial-derived peptide that plays an important role in energy homeostasis. Here, the authors show SHLP2’s protective effect against obesity and its mechanisms of action by binding to CXCR7 and activating hypothalamic neurons that regulate food intake, energy expenditure, and glucose homeostasis.

Background Group II introns are mobile genetic elements that can insert at specific target sequences, however, their origins are often challenging to reconstruct because of rapid sequence decay following invasion and spread into different sites. To advance understanding of group II intron spread, we studied the intron-rich mitochondrial genome (mitogenome) in the unicellular red alga, Porphyridium . Results Analysis of mitogenomes in three closely related species in this genus revealed they were 3–6-fold larger in size (56–132 kbp) than in other red algae, that have genomes of size 21–43 kbp. This discrepancy is explained by two factors, group II intron invasion and expansion of repeated sequences in large intergenic regions. Phylogenetic analysis demonstrates that many mitogenome group II intron families are specific to Porphyridium , whereas others are closely related to sequences in fungi and in the red alga-derived plastids of stramenopiles. Network analysis of intron-encoded proteins (IEPs) shows a clear link between plastid and mitochondrial IEPs in distantly related species, with both groups associated with prokaryotic sequences. Conclusion Our analysis of group II introns in Porphyridium mitogenomes demonstrates the dynamic nature of group II intron evolution, strongly supports the lateral movement of group II introns among diverse eukaryotes, and reveals their ability to proliferate, once integrated in mitochondrial DNA.

Background The red algae (Rhodophyta) diverged from the green algae and plants (Viridiplantae) over one billion years ago within the kingdom Archaeplastida. These photosynthetic lineages provide an ideal model to study plastid genome reduction in deep time. To this end, we assembled a large dataset of the plastid genomes that were available, including 48 from the red algae (17 complete and three partial genomes produced for this analysis) to elucidate the evolutionary history of these organelles. Results We found extreme conservation of plastid genome architecture in the major lineages of the multicellular Florideophyceae red algae. Only three minor structural types were detected in this group, which are explained by recombination events of the duplicated rDNA operons. A similar high level of structural conservation (although with different gene content) was found in seed plants. Three major plastid genome architectures were identified in representatives of 46 orders of angiosperms and three orders of gymnosperms. Conclusions Our results provide a comprehensive account of plastid gene loss and rearrangement events involving genome architecture within Archaeplastida and lead to one over-arching conclusion: from an ancestral pool of highly rearranged plastid genomes in red and green algae, the aquatic (Florideophyceae) and terrestrial (seed plants) multicellular lineages display high conservation in plastid genome architecture. This phenomenon correlates with, and could be explained by, the independent and widely divergent (separated by >400 million years) origins of complex sexual cycles and reproductive structures that led to the rapid diversification of these lineages.

Eukaryotic photosynthetic organelles, plastids, are the powerhouses of many aquatic and terrestrial ecosystems. The canonical plastid in algae and plants originated >1 Ga and therefore offers limited insights into the initial stages of organelle evolution. To address this issue, we focus here on the photosynthetic amoeba Paulinella micropora strain KR01 (hereafter, KR01) that underwent a more recent (∼124 Ma) primary endosymbiosis, resulting in a photosynthetic organelle termed the chromatophore. Analysis of genomic and transcriptomic data resulted in a high-quality draft assembly of size 707 Mb and 32,361 predicted gene models. A total of 291 chromatophore-targeted proteins were predicted in silico, 208 of which comprise the ancestral organelle proteome in photosynthetic Paulinella species with functions, among others, in nucleotide metabolism and oxidative stress response. Gene coexpression analysis identified networks containing known high light stress response genes as well as a variety of genes of unknown function (“dark” genes). We characterized diurnally rhythmic genes in this species and found that over 49% are dark. It was recently hypothesized that large double-stranded DNA viruses may have driven gene transfer to the nucleus in Paulinella and facilitated endosymbiosis. Our analyses do not support this idea, but rather suggest that these viruses in the KR01 and closely related P. micropora MYN1 genomes resulted from a more recent invasion.

Coastal bottom dissolved oxygen (DO) depletion poses a serious threat to marine ecosystems and aquaculture, and hypoxic events in the semi-enclosed Jinhae Bay, Korea, repeatedly cause large-scale damage to fish farms. Accurate DO prediction models are therefore crucial for ecosystem management and loss mitigation. This study analyzes how different tidal input representations affect the performance of data-driven DO prediction models in a tide-dominated coastal environment. Using time-series data of oceanographic and meteorological variables from nearby observation sites, we develop an long short-term memory (LSTM)-based neural network ensemble model with four experimental configurations. These include not only water level but also tidal envelope, tidal-intensity proxy, and temporal differences in water level and DO (Δtide, ΔDO) as additional inputs. Compared with the baseline configuration, the full tide-informed input case reduced the 72 h mean root mean square error (RMSE) from 1.16 to 1.12 and increased the Pearson correlation coefficient from 0.873 to 0.883. It also improved the representation of intraday variability and prediction stability. These results show that tide-derived variables help the model more effectively capture tidal-phase-locked DO fluctuations, while temporal-difference inputs further strengthen short-term variability and sensitivity to DO changes. These results indicate that properly representing tidal forcing is essential for learning the temporal structure and variability of coastal bottom DO.