Natural grazing by horses and cattle promotes bird diversity in a restored European alluvial grassland
Context A challenge in grassland conservation is to maintain both the openness and the heterogeneity of the habitat to support the diversity of their animal communities, including birds—a taxon that is known to be sensitive to disturbance. An increasingly used management tool in European grassland conservation, especially in rewilding projects, is grazing by large herbivores such as horses and cattle. These grazers are believed to create and maintain patchy landscapes that promote diversity and richness of other species, but their influence on birds is often debated by conservationists, who raise concerns about the impact of disturbance by the grazers. Objectives Our aim was to examine the relationship between the abundance and species richness of birds across four foraging guilds and the area utilization patterns of Highland cattle and Konik horses in an alluvial grassland in France. We also aimed to examine the influence of land cover and season on the spatial distribution, including abundance and species richness, of different bird guilds present in the grazed area. Methods We used GPS-collars on all grazers and recorded their positions on an hourly basis over a study period of 1.5 years, assessing patterns of area usage. We counted birds weekly along three transects to describe their distribution within the grazed area and carried out land-cover surveys to describe the habitat. To assess how species richness and abundance of birds of different guilds were related to grazer density, season, and habitat characteristics, we used GAMM models in a spatially explicit framework. We also compared bird numbers at our main study site with a nearby non-grazed control area. Results The number of birds in the grazed area was about twice the number in the non-grazed control area. Within the grazed area, the abundance of open-area foraging birds increased with increasing grazer density. The number of woodland-foraging birds was also positively correlated with grazer density but less so than open-area foraging birds. The number of individuals in the aerial and wetland bird guilds was not correlated with the density of grazers. Most bird species and individuals were observed on open landscapes scattered with woody patches and waterbodies, and on areas with moderate grazer density. Conclusions Low-intensity grazing represents a potentially important management tool in creating heterogeneity in alluvial grasslands, thereby promoting suitable habitat for a diverse assemblage of bird species.
Lovász et al. (2024) Natural grazing by horses and cattle promotes bird diversity in a restored European alluvial grassland. PeerJ 12:e17777 https://doi.org/10.7717/peerj.17777
Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review
Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; “Artificial Intelligence”, “Machine Learning”, “Neural Networks”, “Virtual Reality”, “Digital Dentistry”, “CAD/CAM”, and “Restorative Dentistry”. Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners’ CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field.
Yeslam et al. (2024) Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review. PeerJ 12:e17793 https://doi.org/10.7717/peerj.17793
Acute clomipramine exposure elicits dose-dependent surfacing behavior in adult zebrafish (Danio rerio)
Chronic treatment with clomipramine, a tricyclic antidepressant drug, reduces symptoms of obsessive-compulsive disorder (OCD) and can influence the activity of the hypothalamic-pituitary-adrenal axis. However, little is known regarding the effects of acute clomipramine on the immediate expression of stress responses. Serotonergic drugs can elicit surfacing, a behavioral profile potentially related to toxicity in fish, although surfacing has not yet been observed after clomipramine exposure. The present study investigated the impact of acute exposure to clomipramine on basal and stress-induced behaviors in the novel tank test and cortisol levels in mixed-sex, wild-type, adult zebrafish (Danio rerio). The findings show clomipramine-exposed groups (regardless of stress exposure) spent much more time in the top of the novel tank and had significantly less overall motor activity in the behavioral task compared to the fish not exposed to the drug. Then, the dose-dependent effects of acute clomipramine on activity in the surface of the novel tank (top third of the top half) were investigated further. Clomipramine dose-dependently increased surface-dwelling and elicited a dose-dependent hypoactivity in overall motor behavior. There were no statistically significant differences in whole-body cortisol levels in either experiment. Like other serotonin-acting drugs, clomipramine strongly elicited surface-dwelling and depressed motor behavior in adult zebrafish. Additional testing is needed to elucidate whether surfacing represents a toxic state and how serotonin regulates surfacing.
Shafiq et al. (2024) Acute clomipramine exposure elicits dose-dependent surfacing behavior in adult zebrafish (Danio rerio). PeerJ 12:e17803 https://doi.org/10.7717/peerj.17803
Bioprospecting of soil-borne microorganisms and chemical dereplication of their anti-microbial constituents with the aid of UPLC-QTOF-MS and molecular networking approach
Due to the emergence of drug-resistant microorganisms, the search for broad-spectrum antimicrobial compounds has become extremely crucial. Natural sources like plants and soils have been explored for diverse metabolites with antimicrobial properties. This study aimed to identify microorganisms from agricultural soils exhibiting antimicrobial effects against known human pathogens, and to highlight the chemical space of the responsible compounds through the computational metabolomics-based bioprospecting approach. Herein, bacteria were extracted from soil samples and their antimicrobial potential was measured via the agar well diffusion method. Methanolic extracts from the active bacteria were analyzed using the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique, and the subsequent data was further analyzed through molecular networking approach which aided in identification of potential anti-microbial compounds. Furthermore, 16S rRNA gene sequencing enabled identification of the active bacterial isolates, where isolate 1 and 2 were identified as strains of Bacillus pumilus, whilst isolate 3 was found to be Bacillus subtilis. Interestingly, isolate 3 (Bacillus subtilis) displayed wide-ranging antimicrobial activity against the tested human pathogens. Molecular networking revealed the presence of Diketopiperazine compounds such as cyclo (D-Pro-D-Leu), cyclo (L-Tyr-L-Pro), cyclo (L-Pro-D-Phe), and cyclo (L-Pro-L-Val), alongside Surfactin C, Surfactin B, Pumilacidin E, and Isarrin D in the Bacillus strains as the main anti-microbial compounds. The application of the molecular networking approach represents an innovation in the field of bio-guided bioprospection of microorganisms and has proved to be an effective and feasible towards unearthing potent antimicrobial compounds. Additionally, the (computational metabolomics-based) approach accelerates the discovery of bioactive compounds and isolation of strains which offer a promising avenue for discovering new clinical antimicrobials. Finally, soil microbial flora could serve an alternative source of anti-microbial compounds which can assist in the fight against emergence of multi-drug resistance bacterial pathogens.
Khwathisi et al. (2024) Bioprospecting of soil-borne microorganisms and chemical dereplication of their anti-microbial constituents with the aid of UPLC-QTOF-MS and molecular networking approach. PeerJ 12:e17364 https://doi.org/10.7717/peerj.17364
Automated identification and segmentation of urine spots based on deep-learning
Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of micturition behavior in rodents. However, the traditional VSA method relies on manual judgment, introduces subjective errors, faces difficulty in obtaining appearance time of each urine spot, and struggles with quantitative analysis of overlapping spots. To address these challenges, we developed a deep learning-based approach for the automatic identification and segmentation of urine spots. Our system employs a target detection network to efficiently detect each urine spot and utilizes an instance segmentation network to achieve precise segmentation of overlapping urine spots. Compared with the traditional VSA method, our system achieves automated detection of urine spot area of micturition in rodents, greatly reducing subjective errors. It accurately determines the urination time of each spot and effectively quantifies the overlapping spots. This study enables high-throughput and precise urine spot detection, providing important technical support for the analysis of urination behavior and the study of the neural mechanism underlying urination.
Fan et al. (2024) Automated identification and segmentation of urine spots based on deep-learning. PeerJ 12:e17398 https://doi.org/10.7717/peerj.17398
Intraspecific variation in the pterosaur Rhamphorhynchus muensteri—implications for flight and socio-sexual signaling
Pterosaurs were the first powered flying vertebrates, with a fossil record that stretches back to about 230 million years before present. Most species are only known from one to three specimens, which are most often fragmentary. However, Rhamphorhynchus muensteri is known from numerous excellent specimens, including multiple specimens with soft tissue preservation. As such, Rhamphorhynchus muensteri is one of the only pterosaurs amenable to analysis for intraspecific variation. It has been previously predicted that elements directly involved in the flight apparatus, such as those of the forelimb, will be more highly constrained in their proportions than other parts of the skeleton. We investigated the degree of variation seen in elements and body parts of Rhamphorhynchus, which represents the best model system among pterosaurs for testing these expectations of intraspecific variation. We recover evidence for high levels of constraint throughout the appendicular and axial elements (head, neck, torso, tail, forelimbs, hindlimbs), suggesting that all were important for flight. We further find that tail variation increases among the largest specimens, suggesting reduced constraint and/or stronger sexual selection on the tail in more mature individuals.
Habib et al. (2024) Intraspecific variation in the pterosaur Rhamphorhynchus muensteri—implications for flight and socio-sexual signaling. PeerJ 12:e17524 https://doi.org/10.7717/peerj.17524
The prognostic role of lymphocyte-to-monocyte ratio in patients with resectable pancreatic cancer: a systematic review and meta-analysis
Objectives This systematic review and meta-analysis examined whether the lymphocyte-to-monocyte ratio (LMR) can serve as an indicator for predicting the prognosis of patients with resectable pancreatic cancer. Patients and Methods This meta-analysis was registered with PROSPERO: CRD42023461260. A systematic literature search was conducted in the PubMed, Embase, Cochrane, and Web of Science databases up to September 2023 to assess whether LMR can predict the prognosis of patients with resectable pancreatic cancer. The outcomes measured included subgroup analyses of overall survival (OS) with hazard ratios (HR) and confidence intervals of geographical region, patient population, and LMR threshold. A sensitivity analysis was also performed for OS and HR and confidence intervals were calculated for recurrence-free survival (RFS). Results A total of 14 eligible articles, comprising 4,019 patients, were included in the comprehensive analysis. The results of this comprehensive analysis indicate that LMR is a robust predictor of OS, demonstrating strong prognostic significance (HR = 0.55, 95% CI [0.44–0.69], I2 = 79%, P < 0.00001). This predictive significance extended to various types of pancreatic cancer, such as pancreatic ductal adenocarcinoma (HR = 0.73, 95% CI [0.57–0.93], I2 = 46%, P = 0.01), pancreatic neuroendocrine neoplasms (HR = 0.81, 95% CI [0.66–0.99], P = 0.04) and other subtypes (HR = 0.40, 95% CI [0.22–0.72], I2 = 89%, P < 0.00001), but not to pancreatic head cancer (HR = 0.46, 95% CI [0.16–1.13], I2 = 59%, P = 0.12). LMR retained its predictive value across different regions, including Asia (HR = 0.62, 95% CI [0.47–0.76], I2 = 68%, P < 0.0001), Europe (HR = 0.78, 95% CI [0.67–0.91], I2 = 0%, P = 0.002), and the Americas (HR = 0.14, 95% CI [0.08–0.24], I2 = 0%, P < 0.00001). Notably, both LMR cut-off values greater than or equal to three (HR = 0.62, 95% CI [0.47–0.82], I2 = 67%, P = 0.0009) and less than three (HR = 0.47, 95% CI [0.32–0.69], I2 = 85%, P = 0.0001) exhibited prognostic significance. The sensitivity analysis for OS confirmed the strong predictive value of LMR, whereas LMR did not exhibit predictive significance for RFS (HR = 0.35, 95% CI [0.09–1.32], I2 = 95%, P = 0.12). In both subgroups categorized by Newcastle-Ottawa Scale (NOS) scores of ≥7 (HR = 0.66, 95% CI [0.54–0.80], I2 = 53%, P = 0.04) and <7 (HR = 0.41, CI [0.23–0.72], I2 = 89%, P < 0.00001), LMR was demonstrated to have predictive value. Conclusion Despite the observed heterogeneity and potential biases in the included studies, the findings of this study suggest that LMR may serve as a valuable predictor of OS in patients with resectable pancreatic cancer.
Li et al. (2024) The prognostic role of lymphocyte-to-monocyte ratio in patients with resectable pancreatic cancer: a systematic review and meta-analysis. PeerJ 12:e17585 https://doi.org/10.7717/peerj.17585
A multi-ethnic proteomic profiling analysis in Alzheimer’s disease identifies the disparities in dysregulation of proteins and pathogenesis
Background Alzheimer’s disease (AD) is the most common type of dementia that affects the elderly population. Lately, blood-based proteomics have been intensively sought in the discovery of AD biomarkers studies due to the capability to link external environmental factors with the development of AD. Demographic differences have been shown to affect the expression of the proteins in different populations which play a vital role in the degeneration of cognitive function. Method In this study, a proteomic study focused on Malaysian Chinese and Malay prospects was conducted. Differentially expressed proteins (DEPs) in AD patients and normal controls for Chinese and Malays were identified. Functional enrichment analysis was conducted to further interpret the biological functions and pathways of the DEPs. In addition, a survey investigating behavioural practices among Chinese and Malay participants was conducted to support the results from the proteomic analysis. Result The variation of dysregulated proteins identified in Chinese and Malay samples suggested the disparities of pathways involved in this pathological condition for each respective ethnicity. Functional enrichment analysis supported this assumption in understanding the protein-protein interactions of the identified protein signatures and indicate that differentially expressed proteins identified from the Chinese group were significantly enriched with the functional terms related to Aβ/tau protein-related processes, oxidative stress and inflammation whereas neuroinflammation was associated with the Malay group. Besides that, a significant difference in sweet drinks/food intake habits between these two groups implies a relationship between sugar levels and the dysregulation of protein APOA4 in the Malay group. Additional meta-analysis further supported the dysregulation of proteins TF, AHSG, A1BG, APOA4 and C4A among AD groups. Conclusion These findings serve as a preliminary understanding in the molecular and demographic studies of AD in a multi-ethnic population.
Tan et al. (2024) A multi-ethnic proteomic profiling analysis in Alzheimer’s disease identifies the disparities in dysregulation of proteins and pathogenesis. PeerJ 12:e17643 https://doi.org/10.7717/peerj.17643
Classification of major species in the sericite–Artemisia desert grassland using hyperspectral images and spectral feature identification
Background The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring. Methods Ground hyperspectral images of a sericite–Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land. Results The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%). Conclusions The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite–Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.
Liu et al. (2024) Classification of major species in the sericite–Artemisia desert grassland using hyperspectral images and spectral feature identification. PeerJ 12:e17663 https://doi.org/10.7717/peerj.17663
Associations between SNPs and vegetation indices: unraveling molecular insights for enhanced cultivation of tea plant (Camellia sinensis (L.) O. Kuntze)
Background Breeding programs for nutrient-efficient tea plant varieties could be advanced by the combination of genotyping and phenotyping technologies. This study was aimed to search functional SNPs in key genes related to the nitrogen-assimilation in the collection of tea plant Camellia sinensis (L.) Kuntze. In addition, the objective of this study was to reveal efficient vegetation indices for phenotyping of nitrogen deficiency response in tea collection. Methods The study was conducted on the tea plant collection of Camellia sinensis (L.) Kuntze of Western Caucasus grown without nitrogen fertilizers. Phenotypic data was collected by measuring the spectral reflectance of leaves in the 350–1100 nm range calculated as vegetation indices by the portable hyperspectral spectrometer Ci710s. Single nucleotide polymorphisms were identified in 30 key genes related to nitrogen assimilation and tea quality. For this, pooled amplicon sequencing, SNPs annotation and effect prediction with SnpEFF tool were used. Further, a linear regression model was applied to reveal associations between the functional SNPs and the efficient vegetation indices. Results PCA and regression analysis revealed significant vegetation indices with high R2 values (more than 0.5) and the most reliable indices to select ND-tolerant genotypes were established: ZMI, CNDVI, RENDVI, VREI1, GM2, GM1, PRI, and Ctr2, VREI3, VREI2. The largest SNPs frequency was observed in several genes, namely F3’5’Hb, UFGTa, UFGTb, 4Cl, and AMT1.2. SNPs in NRT2.4, PIP, AlaDC, DFRa, and GS1.2 were inherent in ND-susceptible genotypes. Additionally, SNPs in AlaAT1, MYB4, and WRKY57, were led to alterations in protein structure and were observed in ND-susceptible tea genotypes. Associations were revealed between flavanol reflectance index (FRI) and SNPs in ASNb and PIP, that change the amino acids. In addition, two SNPs in 4Cl were associated with water band index (WBI). Conclusions The results will be useful to identify tolerant and susceptible tea genotypes under nitrogen deficiency. Revealed missense SNPs and associations with vegetation indices improve our understanding of nitrogen effect on tea quality. The findings in our study would provide new insights into the genetic basis of tea quality variation under the N-deficiency and facilitate the identification of elite genes to enhance tea quality.
Kuzmina et al. (2024) Associations between SNPs and vegetation indices: unraveling molecular insights for enhanced cultivation of tea plant (Camellia sinensis (L.) O. Kuntze). PeerJ 12:e17689 https://doi.org/10.7717/peerj.17689