Effect of steady-state aerobic exercise intensity and duration on the relationship between reserves of heart rate and oxygen uptake
Background The percentages of heart rate (%HRR) or oxygen uptake (%V̇O2R) reserve are used interchangeably for prescribing aerobic exercise intensity due to their assumed 1:1 relationship, although its validity is debated. This study aimed to assess if %HRR and %V̇O2R show a 1:1 relationship during steady-state exercise (SSE) and if exercise intensity and duration affect their relationship. Methods Eight physically active males (age 22.6 ± 1.2 years) were enrolled. Pre-exercise and maximal HR and V̇O2 were assessed on the first day. In the following 4 days, different SSEs were performed (running) combining the following randomly assigned durations and intensities: 15 min, 45 min, 60% HRR, 80% HRR. Post-exercise maximal HR and V̇O2 were assessed after each SSE. Using pre-exercise and post-exercise maximal values, the average HR and V̇O2 of the last 5 min of each SSE were converted into percentages of the reserves (%RES), which were computed in a 3-way RM-ANOVA (α = 0.05) to assess if they were affected by the prescription parameter (HRR or V̇O2R), exercise intensity (60% or 80% HRR), and duration (15 or 45 min). Results The %RES values were not affected by the prescription parameter (p = 0.056) or its interactions with intensity (p = 0.319) or duration and intensity (p = 0.117), while parameter and duration interaction was significant (p = 0.009). %HRRs and %V̇O2Rs did not differ in the 15-min SSEs (mean difference [MD] = 0.7 percentage points, p = 0.717), whereas %HRR was higher than %V̇O2R in the 45-min SSEs (MD = 6.7 percentage points, p = 0.009). Conclusion SSE duration affects the %HRR-%V̇O2R relationship, with %HRRs higher than %V̇O2Rs in SSEs of longer duration.
Ferri Marini et al. (2022) Effect of steady-state aerobic exercise intensity and duration on the relationship between reserves of heart rate and oxygen uptake. PeerJ 10:e13190 https://doi.org/10.7717/peerj.13190
Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods
The tremendous boost in next generation sequencing and in the “omics” technologies makes it possible to characterize the human gut microbiome—the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn’s disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.
Bakir-Gungor et al. (2022) Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods. PeerJ 10:e13205 https://doi.org/10.7717/peerj.13205
A DNA barcode survey of insect biodiversity in Pakistan
Although Pakistan has rich biodiversity, many groups are poorly known, particularly insects. To address this gap, we employed DNA barcoding to survey its insect diversity. Specimens obtained through diverse collecting methods at 1,858 sites across Pakistan from 2010–2019 were examined for sequence variation in the 658 bp barcode region of the cytochrome c oxidase 1 (COI) gene. Sequences from nearly 49,000 specimens were assigned to 6,590 Barcode Index Numbers (BINs), a proxy for species, and most (88%) also possessed a representative image on the Barcode of Life Data System (BOLD). By coupling morphological inspections with barcode matches on BOLD, every BIN was assigned to an order (19) and most (99.8%) were placed to a family (362). However, just 40% of the BINs were assigned to a genus (1,375) and 21% to a species (1,364). Five orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera) accounted for 92% of the specimens and BINs. More than half of the BINs (59%) are so far only known from Pakistan, but others have also been reported from Bangladesh (13%), India (12%), and China (8%). Representing the first DNA barcode survey of the insect fauna in any South Asian country, this study provides the foundation for a complete inventory of the insect fauna in Pakistan while also contributing to the global DNA barcode reference library.
Ashfaq et al. (2022) A DNA barcode survey of insect biodiversity in Pakistan. PeerJ 10:e13267 https://doi.org/10.7717/peerj.13267
Spatial variability of microzooplankton grazing on phytoplankton in coastal southern Florida, USA
Microzooplankton are considered the primary consumers of phytoplankton in marine environments. Microzooplankton grazing rates on phytoplankton have been studied across the globe, but there are still large regions of the ocean that are understudied, such as sub-tropical coastal oceans. One of these regions is the coastal area around south Florida, USA. We measured microzooplankton grazing rates in two distinct environments around south Florida; the oligotrophic Florida Keys and the mesotrophic outflow from the Everglades. For 2-years from January 2018 to January 2020, we set up 55 dilution and light-dark bottle experiments at five stations to estimate the microzooplankton community grazing rate, instantaneous phytoplankton growth rate, and primary production. Our results suggest that microzooplankton are consuming a higher proportion of the primary production near the Everglades outflow compared to the Florida Keys. We also found that changes in phytoplankton growth rates are disconnected from changes in the microzooplankton grazing rates in the Florida Keys. Overall, the data from the Everglades outflow is what would be expected based on global patterns, but factors other than microzooplankton grazing are more important in controlling phytoplankton biomass in the Florida Keys.
Millette et al. (2022) Spatial variability of microzooplankton grazing on phytoplankton in coastal southern Florida, USA. PeerJ 10:e13291 https://doi.org/10.7717/peerj.13291
Association between laryngopharyngeal reflux and obstructive sleep apnea in adults
Background Obstructive sleep apnea syndrome (OSAS) and laryngopharyngeal reflux (LPR) have been found to coexist in the population. OSAS and LPR also share obesity as an important risk factor. However, the relationship between LPR and OSAS remains unclear. This study aimed to correlate LPR as measured by the Reflux Symptom Index (RSI) and the Reflux Finding Score (RFS) with OSAS. Methods This cross-sectional study included sixty-four subjects who underwent anamnesis to complete the RSI and the Epworth Sleeping Scale (ESS). The subjects were then divided into the OSAS and non-OSAS groups based on the Apnea-Hypopnea Index (AHI) obtained through a polysomnography examination. Both groups underwent a flexible fiberoptic nasopharyngolaryngoscopy examination to determine the RFS. LPR was identified based on the RSI and RFS. Results The mean BMI of the OSAS group significantly was higher than the non-OSAS group (p < 0.05). Most of the subjects in the OSAS group exhibited mild-moderate OSAS (AHI 10–29), and severe OSAS occurred in only seven subjects. The mean RSI and RFS values in the OSAS group did not differ significantly from the non-OSAS group (p = 0.34 and p = 0.36, respectively). The proportion of LPR between the mild-moderate OSAS group, the severe OSAS group, and the non-OSAS group did not differ significantly (p = 1.00). RSI and RFS did not significantly correlate with AHI. Based on RSI, the proportion of LPR between the ESS (+) and ESS (-) groups did not significantly differ (adjusted p = 0.062). The proportion of LPR based on RFS was almost equal between the ESS (+) and ESS (-) groups (p = 0.817). Conclusions The BMI of the OSAS group was significantly higher than the non-OSAS group. There was no significant difference in RSI and RFS between the OSAS and non-OSAS groups. There was no significant correlation between RSI and AHI, or between RFS and AHI. There was no significant difference in the proportion of RSI between the ESS (+) and the ESS (-) groups.
Tamin et al. (2022) Association between laryngopharyngeal reflux and obstructive sleep apnea in adults. PeerJ 10:e13303 https://doi.org/10.7717/peerj.13303
Do patterns of insect mortality in temperate and tropical zones have broader implications for insect ecology and pest management?
Background Understanding how biotic and abiotic factors affect insect mortality is crucial for both fundamental knowledge of population ecology and for successful pest management. However, because these factors are difficult to quantify and interpret, patterns and dynamics of insect mortality remain unclear, especially comparative mortality across climate zones. Life table analysis provides robust information for quantifying population mortality and population parameters. Methods In this study, we estimated cause-of-death probabilities and irreplaceable mortality (the portion of mortality that cannot be replaced by another cause or combination of causes) using a Multiple Decrement Life Table (MDLT) analysis of 268 insect life tables from 107 peer-reviewed journal articles. In particular, we analyzed insect mortality between temperate and tropical climate zones. Results Surprisingly, our results suggest that non-natural enemy factors (abiotic) were the major source of insect mortality in both temperate and tropical zones. In addition, we observed that irreplaceable mortality from predators in tropical zones was 3.7-fold greater than in temperate zones. In contrast, irreplaceable mortality from parasitoids and pathogens was low and not different between temperate and tropical zones. Surprisingly, we did not observe differences in natural enemy and non-natural enemy factors based on whether the insect species was native or non-native. We suggest that characterizing predation should be a high priority in tropical conditions. Furthermore, because mortality from parasitoids was low in both tropical and temperate zones, this mortality needs to be better understood, especially as it relates to biological control and integrated pest management.
Pinto et al. (2022) Do patterns of insect mortality in temperate and tropical zones have broader implications for insect ecology and pest management?. PeerJ 10:e13340 https://doi.org/10.7717/peerj.13340
Effects of cigarette smoking on metabolic activity of lung cancer on baseline 18F-FDG PET/CT
Background Never-smokers with lung cancer usually have a higher survival rate than that of smokers. The high metabolic activity of lung cancer on 18F-2-Fluoro-2-deoxyglucose (18F-FDG) PET/CT generally indicates a poor outcome. However, there is a lack of reports on the association between cigarette smoking and 18F-FDG metabolic activity in patients with lung cancer. In this study, we aimed to investigate the effects of cigarette smoking on metabolic activity of lung cancer on 18F-FDG PET/CT. Materials and Methods A total of 338 patients (230 males, 108 females; mean age: 66.3, range 34–86) with pathologically diagnosed lung cancer were enrolled from September 2019 to April 2021. All patients underwent baseline 18F-FDG PET/CT and the maximum standard uptake value (SUVmax) of the primary tumor (pSUVmax), lymph node (nSUVmax) and distant metastasis (mSUVmax) were measured. The associations between cigarette smoking status, clinical stage, pathological subtypes and metabolic parameters on 18F-FDG PET/CT were analyzed. Results Of the 338 patients, cigarette smoking was identified in 153 patients (45.3%) and the remaining 185 (54.7%) were never-smokers. Smoking was found more frequently in males, squamous cell carcinoma (SCC) and stage III–IV diseases. The pSUVmax in smokers was significantly higher than that in never-smokers (t = 3.386, P < 0.001), but the nSUVmax and mSUVmax revealed no statistically significant differences (t = 0.399, P = 0.690 and t = 0.057, P = 0.955; respectively). With the increase of cumulative smoking dose, pSUVmax increased significantly (r = 0.217, P < 0.001). In addition, the pSUVmax in patients with stage III–IV was significantly higher than that in stage I–II (t = 8.509, P < 0.001). Smokers showed a higher pSUVmax than never-smokers for patients with stage I–II (t = 3.106, P = 0.002), but not in stage III–IV (t = 0.493, P = 0.622). The pSUVmax was significantly different among patients with different pathological subtypes of lung cancer (F = 11.45, P < 0.001), while only the adenocarcinoma (ADC) and SCC groups showed a difference in pSUVmax (t = 6.667, P < 0.001). Smokers with ADC showed a higher pSUVmax when compared to never-smokers, but not in SCC. There were no significant differences of pSUVmax between smokers and never-smokers at stage I–II ADC or SCC and stage III–IV ADC or SCC. Conclusions This study demonstrated a close association between cigarette smoking and the metabolic activity of lung cancer and suggests that smoking may be a potential risk factor of higher pSUVmax in early lung cancer on 18F-FDG PET/CT.
Jiang et al. (2022) Effects of cigarette smoking on metabolic activity of lung cancer on baseline 18F-FDG PET/CT. PeerJ 10:e13352 https://doi.org/10.7717/peerj.13352
Excito-repellency of Myristica fragrans Houtt. and Curcuma longa L. extracts from Southern Thailand against Aedes aegypti (L.)
The development of local plant extracts as a mosquito repellent is environmentally safe, inexpensive, and easily accessible for residents. In this study, three extracts from two local plants, Myristica fragrans Houtt. (flesh and mace) and Curcuma longa L. (rhizome) from southern Thailand, were investigated for their inherent repellent activity using the excito-repellency (ER) assay system against insectary-colonized Aedes aegypti (L.) (Diptera: Culicidae). The escape responses of mosquitoes exposed to concentrations of 0.5% to 5.0% (w/v) were measured to determine the contact irritant and non-contact repellent properties of each extract. Both the flesh and mace extracts of M. fragrans had relatively limited contact irritants (28.1% and 34.6% escape) and non-contact repellent (16.7% and 18.3% escape) activities against Ae. aegypti, respectively. The C. longa rhizome extract produced higher escape responses in the non-contact (42.6% escape) and contact (41.4% escape) trials at concentrations of 5.0% and 1.0%, respectively. GC-MS analysis found diethyl malate (56.5%) and elemicin (11.7%) to be the main components of the flesh and mace extracts, respectively, while ar-turmerone (24.6%), β-turmerone (15.2%), α-turmerone (10.5%) were the primary constituents of the rhizome extract. Overall, our results indicate that both M. fragrans extracts primarily caused Ae. aegypti escape through contact irritation. For C. longa, lower concentrations (0.5% and 1.0%) exhibited contact irritancy, but higher concentrations (2.5% and 5.0%) exhibited non-contact repellency against Ae. aegypti. Although they had limited efficacy, further experiments (e.g., mixing with other plant-based compounds) could enhance the ER of both local plant extracts. Additional evaluation of these extracts against other mosquito species and the ER of their chemical components, either alone or in combination, would also be beneficial for the development of green repellents. Our findings emphasize the possibility of utilizing plant-based mosquito repellent as an alternative personal protection method for future mosquito control programs.
Bhoopong et al. (2022) Excito-repellency of Myristica fragrans Houtt. and Curcuma longa L. extracts from Southern Thailand against Aedes aegypti (L.). PeerJ 10:e13357 https://doi.org/10.7717/peerj.13357
RFPDR: a random forest approach for plant disease resistance protein prediction
Background Plant innate immunity relies on a broad repertoire of receptor proteins that can detect pathogens and trigger an effective defense response. Bioinformatic tools based on conserved domain and sequence similarity are within the most popular strategies for protein identification and characterization. However, the multi-domain nature, high sequence diversity and complex evolutionary history of disease resistance (DR) proteins make their prediction a real challenge. Here we present RFPDR, which pioneers the application of Random Forest (RF) for Plant DR protein prediction. Methods A recently published collection of experimentally validated DR proteins was used as a positive dataset, while 10x10 nested datasets, ranging from 400-4,000 non-DR proteins, were used as negative datasets. A total of 9,631 features were extracted from each protein sequence, and included in a full dimension (FD) RFPDR model. Sequence selection was performed, to generate a reduced-dimension (RD) RFPDR model. Model performances were evaluated using an 80/20 (training/testing) partition, with 10-cross fold validation, and compared to baseline, sequence-based and state-of-the-art strategies. To gain some insights into the underlying biology, the most discriminatory sequence-based features in the RF classifier were identified. Results and Discussion RD-RFPDR showed to be sensitive (86.4 ± 4.0%) and specific (96.9 ± 1.5%) for identifying DR proteins, while robust to data imbalance. Its high performance and robustness, added to the fact that RD-RFPDR provides valuable information related to DR proteins underlying properties, make RD-RFPDR an interesting approach for DR protein prediction, complementing the state-of-the-art strategies.
Simón et al. (2022) RFPDR: a random forest approach for plant disease resistance protein prediction. PeerJ 10:e11683 https://doi.org/10.7717/peerj.11683
Species limits and recent diversification of Cerradomys (Sigmodontinae: Oryzomyini) during the Pleistocene
Cerradomys is a genus of the tribe Oryzomyini with eight species currently recognized, and a controversial taxonomy. These species are mainly distributed in the South America dry diagonal, but some species extend into Atlantic Forest, reaching the coastal sandy plains known as Restingas. This study aimed to address species limits and patterns of diversification of Cerradomys species. For this purpose, we performed cytogenetic and molecular analyses (phylogeny, coalescent species delimitation, barcoding, and divergence times estimation) using multiple mitochondrial and nuclear markers on a comprehensive sampling, representing all nominal taxa reported so far. Chromosomal information was a robust marker recognizing eight Cerradomys species. Reciprocal monophyly was recovered for all the species, except for C. subflavus. These results together with coalescent analyses recovered eight species as the most congruent species delimitation scenario for the genus (mean Ctax: 0.72). Divergence time estimates revealed that Cerradomys’ diversification occurred about 1.32 million years ago (Mya) during the Pleistocene. Although our results conservatively support the eight Cerradomys species described so far, different lines of evidence suggest that C. langguthi and C. subflavus could potentially be species-complexes. We discussed this scenario in the light of multiple evolutionary processes within and between species and populations, since Cerradomys comprises a species group with recent diversification affected by Pleistocene climatic changes and by the complex biogeographic history of South America dry diagonal. This work supports that the diversity of Cerradomys is underestimated and reiterates that interdisciplinary approaches are mandatory to identify small rodent species properly, and to unhide cryptic species.
Di-Nizo et al. (2022) Species limits and recent diversification of Cerradomys (Sigmodontinae: Oryzomyini) during the Pleistocene. PeerJ 10:e13011 https://doi.org/10.7717/peerj.13011