Tree-ring based summer temperature variability since 1790 CE in the Hindu Kush region of northern Pakistan.

The Hindu Kush high-altitude regions of Pakistan are currently experiencing severe consequences as a result of global warming. In this sense, increasing soil erosion and the quick melting of glaciers are two particularly evident effects. In such a scenario, understanding long-term temperature changes is crucial for making accurate forecasts about how the Hindu Kush region may experience regional temperature changes in the future. In this study, the climate tree-ring width (TRW) analysis designated a positive and significant correlation (r = 0.622, p < 0.001) between the TRW chronology and the June to September (summer) mean maximum temperature (MMT). Using the tree-ring width of Pinus wallichiana A. B. Jackson, we reconstructed summer temperatures in the Hindu Kush region from 1790 CE. Statistical analysis showed that the reconstruction model has explained 38.7% of the climate variance during the instrumental period of 1967 to 2018 CE. Five extremely warm summer periods (≥ 4 years; before the instrumental period 1967-2018 CE) of 1804-1830, 1839-1862, 1876-1879, 1905-1910, 1923-1935 CE, and six cold summer periods of 1790-1803, 1832-1838, 1863-1875, 1880-1904, 1911-1922, and 1936-1945 CE have been observed during the past 229 years. Individually, the year 1856 CE experienced severe warmth (31.85 °C), whereas 1794 CE was relatively cooler (29.60 °C). The spectral multi-taper method (MTM) shows significant (p < 0.05) cycles, which take place about every 9.3, 5.7, 4.2, and 3.6 years. In particular, the 9.3-year cycle, which closely aligns with the 11-year solar activity cycle, suggests a potential correlation between solar activity and local temperature fluctuations. Moreover, our reconstruction demonstrates a significant degree of consistency when compared to actual climate data and regional temperature reconstruction series, reporting a strong logic of trust in the reliability and accuracy of our findings. This evidence reaffirms that our reconstruction shows significant and dependable regional temperature signals, notably being representative for the Hindu Kush region.

Cytogenetic Bioindication in Root Meristems for Vitality Assessment of Trees.

Our method describes how to collect forest tree root tips in the field, to store them for transfer to the lab, to pretreat root tips in order to arrest cells in metaphase, fix root tips to preserve specific morphological organizations, to stain fixed root tips by Feulgen's Reaction in order to increase contrast, and to prepare the root meristem for analyzing mitotic stages and chromosomal aberrations via light microscopy. We further describe how to classify chromosomal abnormalities and quantify them via aberration indices.

Tree-ring widths of Pinus tabulaeformis Carr reveal variability of winter half-year precipitation on the north-south transition zone in central China over the past 220 years.

Long-term, high-resolution regional drought records contribute to understanding the impacts of drought on environmental and social systems in central China. Here, we develop a regional tree-ring width chronology of Pinus tabulaeformis Carr from the northern slope of Funiu Mountains on the north-south transition zone in central China. Monthly correlation analyses showed that temperature and humidity in current May and June are main limiting factors on tree growth. Despite that, the highest correlation with tree growth was found to be precipitation from previous December to current June (PreDJ, 0.718, p < 0.001), which was chosen for reconstruction. The reconstructed PreDJ revealed six drought periods and five wet periods over the past 220 years, and the recent dry spell would likely to continue. Spectral analyses indicated that the reconstructed PreDJ was closely related to the El Nino-Southern Oscillation (ENSO, 2-7a) and 35a climatic oscillation of Bruckner, and was also affected by the Quasi-Biennial Oscillation (QBO). Wavelet analyses showed that the quasi-cycle of 2-7a persisted over the past 220 years and strengthened after the 1980s, and the QBO signals appeared from the 1860s to 1970s and wear off thereafter, and 35a cycle only appeared during 1820-1920. Spatial analysis found that the reconstructed PreDJ had good spatial representation of precipitation in the central-eastern China. Therefore, the results of this study provide reliable information for understanding long-term drought impacts on environmental conditions and socioeconomic development in central China.

Twelve newly assembled jasmine chloroplast genomes: unveiling genomic diversity, phylogenetic relationships and evolutionary patterns among Oleaceae and Jasminum species.

BACKGROUND: Jasmine (Jasminum), renowned for its ornamental value and captivating fragrance, has given rise to numerous species and accessions. However, limited knowledge exists regarding the evolutionary relationships among various Jasminum species. RESULTS: In the present study, we sequenced seven distinct Jasminum species, resulting in the assembly of twelve high-quality complete chloroplast (cp) genomes. Our findings revealed that the size of the 12 cp genomes ranged from 159 to 165 kb and encoded 134-135 genes, including 86-88 protein-coding genes, 38-40 tRNA genes, and 8 rRNA genes. J. nudiflorum exhibited a larger genome size compared to other species, mainly attributed to the elevated number of forward repeats (FRs). Despite the typically conservative nature of chloroplasts, variations in the presence or absence of accD have been observed within J. sambac. The calculation of nucleotide diversity (Pi) values for 19 cp genomes indicated that potential mutation hotspots were more likely to be located in LSC regions than in other regions, particularly in genes ycf2, rbcL, atpE, ndhK, and ndhC (Pi > 0.2). Ka/Ks values revealed strong selection pressure on the genes rps2, atpA, rpoA, rpoC1, and rpl33 when comparing J. sambac with the three most closely related species (J. auriculatum, J. multiflorum, and J. dichotomum). Additionally, SNP identification, along with the results of Structure, PCA, and phylogenetic tree analyses, divided the Jasminum cp genomes into six groups. Notably, J. polyanthum showed gene flow signals from both the G5 group (J. nudiflorum) and the G3 group (J. tortuosum and J. fluminense). Phylogenetic tree analysis reflected that most species from the same genus clustered together with robust support in Oleaceae, strongly supporting the monophyletic nature of cp genomes within the genus Jasminum. CONCLUSION: Overall, this study provides comprehensive insights into the genomic composition, variation, and phylogenetic relationships among various Jasminum species. These findings enhance our understanding of the genetic diversity and evolutionary history of Jasminum.

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Root and rhizosphere contribution to the net soil COS exchange.

Background and aims: Partitioning the measured net ecosystem carbon dioxide (CO2) exchange into gross primary productivity (GPP) and ecosystem respiration remains a challenge, which scientists try to tackle by using the properties of the trace gas carbonyl sulfide (COS). Its similar pathway into and within the leaf makes it a potential photosynthesis proxy. The application of COS as an effective proxy depends, among other things, on a robust inventory of potential COS sinks and sources within ecosystems. While the soil received some attention during the last couple of years, the role of plant roots is mostly unknown. In our study, we investigated the effects of live roots on the soil COS exchange. Methods: An experimental setup was devised to measure the soil and the belowground plant parts of young beech trees observed over the course of 9 months. Results: During the growing season, COS emissions were significantly lower when roots were present compared to chambers only containing soil, while prior to the growing season, with photosynthetically inactive trees, the presence of roots increased COS emissions. The difference in the COS flux between root-influenced and uninfluenced soil was fairly constant within each month, with diurnal variations in the COS flux driven primarily by soil temperature changes rather than the presence or absence of roots. Conclusion: While the mechanisms by which roots influence the COS exchange are largely unknown, their contribution to the overall ground surface COS exchange should not be neglected when quantifying the soil COS exchange. Supplementary Information: The online version contains supplementary material available at 10.1007/s11104-023-06438-0.

Modeling habitat suitability for the lesser-known populations of endangered mountain nyala (Tragelaphus buxtoni) in the Arsi and Ahmar Mountains, Ethiopia.

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal.

PREMISE: Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. METHODS: Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. RESULTS: Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. CONCLUSIONS: Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Diet and lifestyle behaviours simultaneously act on frailty: it is time to move the threshold of frailty prevention and control forward.

BACKGROUND: To analyse the association among the simultaneous effects of dietary intake, daily life behavioural factors, and frailty outcomes in older Chinese women, we predicted the probability of maintaining physical robustness under a combination of different variables. METHODS: The Fried frailty criterion was used to determine the three groups of "frailty", "pre-frailty", and "robust", and a national epidemiological survey was performed. The three-classification decision tree model was fitted, and the comprehensive performance of the model was evaluated to predict the probability of occurrence of different outcomes. RESULTS: Among the 1,044 participants, 15.9% were frailty and 50.29% were pre-frailty; the overall prevalence first increased and then decreased with age, reaching a peak at 70-74 years of age. Through univariate analysis, filtering, and embedded screening, eight significant variables were identified: staple food, spices, exercise (frequency, intensity, and time), work frequency, self-feeling, and family emotions. In the three-classification decision tree, the values of each evaluation index of Model 3 were relatively average; the accuracy, recall, specificity, precision, and F1 score range were between 75% and 84%, and the AUC was also greater than 0.800, indicating excellent performance and the best interpretability of the results. Model 3 takes exercise time as the root node and contains 6 variables and 10 types, suggesting the impact of the comprehensive effect of these variables on robust and non-robust populations (the predicted probability range is 6.67-93.33%). CONCLUSION: The combined effect of these factors (no exercise or less than 0.5 h of exercise per day, occasional exercise, exercise at low intensity, feeling more tired at work, and eating too many staple foods (> 450 g per day) are more detrimental to maintaining robustness.

Phylogenetic network classes through the lens of expanding covers.

It was recently shown that a large class of phylogenetic networks, the 'labellable' networks, is in bijection with the set of 'expanding' covers of finite sets. In this paper, we show how several prominent classes of phylogenetic networks can be characterised purely in terms of properties of their associated covers. These classes include the tree-based, tree-child, orchard, tree-sibling, and normal networks. In the opposite direction, we give an example of how a restriction on the set of expanding covers can define a new class of networks, which we call 'spinal' phylogenetic networks.

TrieDedup: a fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing.

BACKGROUND: High-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, 'N's. PCR duplicates introduced in library preparation are conventionally removed in genomics studies, and several deduplication tools have been developed for this purpose. Two identical reads may appear different due to ambiguous bases and the existing tools cannot address 'N's correctly or efficiently. RESULTS: Here we proposed and implemented TrieDedup, which uses the trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base 'N's, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage. CONCLUSIONS: The TrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment, and repertoire diversity analysis of large-scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors.

Procedure to identify fortified foods in the Dutch branded food database.

Introduction: Information on fortified foods is needed for multiple purposes, including food consumption research and dietary advice. Branded food databases are a valuable source of food label data. European labeling legislation prescribes that food fortification should be indicated in the ingredient list, and nutrient values should be declared under certain conditions. This creates the potential to identify fortified foods in branded food databases, though it is not straightforward and labor-intensive. The aim of our study was to develop an automated approach to identify fortified foods in the Dutch branded food database called LEDA. Methods: An automated procedure, based on a stepwise approach conforming with European labeling legislation, using a list of rules and search terms, was developed to identify fortified foods. Fortification with calcium, folic acid, vitamin B12, and zinc was studied as an example. The results of a random stratified sample with fortified and not-fortified foods were validated by two experts. Results: The automated approach resulted in identifying 1,817 foods fortified with one or more of the selected nutrients in the LEDA dataset (0.94%). The proportions of fortified foods per nutrient were below 0.7%. The classification of fortified/non-fortified foods matched manual validation by experts for the majority of the foods in the sample, i.e., sensitivity and specificity indicating the probability of correctly identifying fortified and non-fortified foods was high (>94.0%). Conclusion: The automated approach is capable of easily and quickly identifying fortified foods in the Dutch branded food database with high accuracy, although some improvements to the automated procedure could be made. In addition, the completeness, correctness, and consistency of the LEDA database can be improved. To fully benefit from this automated approach, it needs to be expanded to cover all micronutrients that may be added to foods.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

Insights into population adaptation and biodiversity of lactic acid bacteria in challenged date palm leaves silaging, using MALDI-TOF MS.

The study focused on isolating indigenous Qatari lactic acid bacteria (LAB) from various challenged date palm tree leaf silages to construct a comprehensive strain collection, useful to study the diversity of these strains following their adaptation to the uncommon silage. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) was employed for strain identification and differentiation. The diversity of LAB populations and strains was assessed through principal component analysis (PCA) and dendrogram analyses. A total of 88 LAB isolates were obtained from silages of fresh palm leaves, silage of mixed leaves and dairy feed, along with fresh palm tree leaves, and dairy feed, adapted to local harsh environments. These isolates were categorized according to the new classification of 2020, belonging to genera of Pediococcus, Lactiplantibacillus plantarum, Lacticaseibacillus paracasei, Companilactobacillus farciminis, Limosilactobacillus oris, Limosilactobacillus vaginalis, Lactiplantibacillus pentosus and Lactobacillus johnsonii. Pediococcus was the most prevalent genus, falling mostly within the species Pediococcus lolii. MALDI-TOF MS protein profiles, PCA, and dendrogram analyses successfully grouped the LAB isolates into five distinctive clusters based on the protein's similarities. The high diversity of the indigenous LAB in spontaneous palm leaf silages demonstrated their adaptation and mutualistic interactions, forming robust consortia that ensure the quality of the silage. The straightforward, quick, and accurate identification of LAB in this silage using MALDI-TOF MS presents a valuable approach for formulating LAB consortia for silaging harsh agricultural by-products.

High risk, high gain? Trade-offs between growth and resistance to extreme events differ in northern red oak (Quercus rubra L.).

Information about the resistance and adaptive potential of tree species and provenances is needed to select suitable planting material in times of rapidly changing climate conditions. In this study, we evaluate growth responses to climatic fluctuations and extreme events for 12 provenances of northern red oak (Quercus rubra L.) that were tested across three trial sites with distinct environmental conditions in Germany. Six provenances each were sourced from the natural distribution in North America and from introduced stands in Germany. We collected increment cores of 16 trees per provenance and site. Dendroecological methods were used to compare provenance performance and establish climate-growth relationships to identify the main growth limiting factors. To evaluate the provenance response to extreme drought and frost events, three site-specific drought years were selected according to the Standardized Precipitation Evapotranspiration Index (SPEI) and 2010 as a year with an extreme late frost event. Resistance indices for these years were calculated and assessed in relation to overall growth performance. We observed a high variation in growth and in the climate sensitivity between sites depending on the prevailing climatic conditions, as well as a high intra-specific variation. Overall, summer drought and low temperatures in the early growing season appear to constrain the growth of red oak. The resistance of provenances within sites and extreme years showed considerable rank changes and interaction effects. We did not find a trade-off between growth and resistance to late frost, namely, fast growing provenances had a high frost hardiness. Further, there was no evidence for a trade-off between growth and drought hardiness. Still, responses to drought or late frost differ between provenances, pointing to dissimilar adaptive strategies. Provenances from introduced (i.e. German) stands represent suitable seed sources, as they combine a higher growth and frost hardiness compared to their North American counterparts. Drought hardiness was slightly higher in the slow-growing provenances. The results provide a better understanding of the variable adaptive strategies between provenances and help to select suitable planting material for adaptive forest management.

First characterization of major histocompatibility complex class II DRB3 diversity in cattle breeds raised in Egypt.

Genes encoding bovine leukocyte antigen (BoLA) enable the immune system to identify pathogens. Therefore, these genes have been used as genetic markers for infectious and autoimmune diseases as well as for immunological traits in cattle. Although BoLA polymorphisms have been reported in various cattle breeds worldwide, they have not been studied in cattle populations in Egypt. In this study, we characterized BoLA-DRB3 in two local Egyptian populations and one foreign population using polymerase chain reaction-sequence-based typing (PCR-SBT) method. Fifty-four previously reported BoLA-DRB3 alleles and eight new alleles (BoLA-DRB3*005:08, *015:07, *016:03, *017:04, *020:02:02, *021:03, *164:01, and *165:01) were identified. Alignment analysis of the eight new alleles revealed 90.7-98.9 %, and 83.1-97.8 % nucleotide and amino acid identities, respectively, with the BoLA-DRB3 cDNA clone NR-1. Interestingly, BoLA-DRB3 in Egyptian cattle showed a high degree of allelic diversity in native (na = 28, hE > 0.95), mixed (na = 61, hE > 0.96), and Holstein (na = 18, hE > 0.88) populations. BoLA-DRB3*002:01 (14.3 %), BoLA-DRB3*001:01 (8.5 %), and BoLA-DRB3*015:01 (20.2 %) were the most frequent alleles in native, mixed, and Holstein populations, respectively, indicating that the genetic profiles differed in each population. Based on the allele frequencies of BoLA-DRB3, genetic variation among Egyptian, Asian, African, and American breeds was examined using Nei's distances and principal component analysis. The results suggested that native and mixed cattle populations were most closely associated with African breeds in terms of their gene pool, whereas Holstein cattle were more distinct from the other breeds and were closely related to Holstein cattle populations from other countries.

Dissimilarity in radial growth and response to drought of Korshinsk peashrub (Caragana korshinskii Kom.) under different management practices in the western Loess Plateau.

Quantitative assessment of tree responses to the local environment can help provide scientific guidance for planted forest management. However, research on the climate-growth relationship of Korshinsk peashrub (Caragana korshinskii Kom.) under different land preparation and post-management (irrigation) conditions is still insufficient. In this study, we collected 223 tree-ring samples from Korshinsk peashrubs using dendroecological methods and systematically quantified the relationships between shrub growth and climatic factors under different management practices in the western Loess Plateau of China. Our findings demonstrated that drought stress caused by scarce precipitation from April to August was the primary factor limiting the growth of Korshinsk peashrubs in the northern and southern mountains of Lanzhou. The "climwin" climate model results showed a weak correlation between natural Korshinsk peashrub growth and drought stress, whereas planted Korshinsk peashrub under rain-fed conditions in the southern mountain was significantly (p<0.05) limited by drought stress from April to August. Moreover, planted Korshinsk peashrub growth under irrigated conditions in the northern mountain was limited only by drought stress in January. Drought model explained 28.9%, 38.3%, and 9.80% of the radial growth variation in Xiguoyuan (XGY), Shuibaozhan (SBZ), and Zhichagou (ZCG) sites, respectively. Artificial supplementary irrigation alleviated the limitation of drought on planted forest growth, which may be implemented for Korshinsk peashrubs planted on sunny slopes, while planted Korshinsk peashrubs under natural rain-fed conditions can be planted on shady slopes through rainwater harvesting and conservation measures such as horizontal ditches and planting holes.

Unraveling the Life Cycle of Nyssopsora cedrelae: A Study of Rust Diseases on Aralia elata and Toona sinensis.

Rust disease poses a major threat to global agriculture and forestry. It is caused by types of Pucciniales, which often require alternate hosts for their life cycles. Nyssopsora cedrelae was previously identified as a rust pathogen on Toona sinensis in East and Southeast Asia. Although this species had been reported to be autoecious, completing its life cycle solely on T. sinensis, we hypothesized that it has a heteroecious life cycle, requiring an alternate host, since the spermogonial and aecial stages on Aralia elata, a plant native to East Asia, are frequently observed around the same area where N. cedrelae causes rust disease on T. sinensis. Upon collecting rust samples from both A. elata and T. sinensis, we confirmed that the rust species from both tree species exhibited matching internal transcribed spacer (ITS), large subunit (LSU) rDNA, and cytochrome oxidase subunit III (CO3) mtDNA sequences. Through cross-inoculations, we verified that aeciospores from A. elata produced a uredinial stage on T. sinensis. This study is the first report to clarify A. elata as an alternate host for N. cedrelae, thus providing initial evidence that the Nyssopsora species exhibits a heteroecious life cycle.

Effect of Arbuscular Mycorrhizal Fungi and Rock Phosphate on Growth, Physiology, and Biochemistry of Carob under Water Stress and after Rehydration in Vermicompost-Amended Soil.

In the Mediterranean region, reforestation programs record failures following successive drought periods. The use of different plant-growth-promoting amendments and the understanding of drought-induced physiological and biochemical responses of carob will contribute to the reforestation program's success. In this study, the effects of arbuscular-mycorrhizal-fungi (AMF), vermicompost (VC), and rock phosphate (RP) on carob seedlings under drought stress (DS) and recovery (REC) conditions were evaluated. A greenhouse experiment was conducted with carob seedlings grown in the presence of AMF, VC, and RP, applied alone or in combination under well-watered (WW), DS (by stopping irrigation for 12 days), and recovery (REC) conditions. The obtained results indicated that the triple combination (AMF + VC + RP) presented the highest improvement in water potential, photosynthetic pigment content, stomatal conductance, and chlorophyll fluorescence compared to the controls under DS and after REC. In addition, this combination resulted in improved tolerance of carob seedlings to DS and a high potential for rapid recovery after rehydration due to a high accumulation of sugars, proteins, and antioxidant enzymes. In summary, the results underline the importance of inoculating carob with AMF in combination with (in)-organic amendments in improving its tolerance to DS and its recovery performances.

Ocean to Tree: Leveraging Single-Molecule RNA-Seq to Repair Genome Gene Models and Improve Phylogenomic Analysis of Gene and Species Evolution.

Understanding gene evolution across genomes and organisms, including ctenophores, can provide unexpected biological insights. It enables powerful integrative approaches that leverage sequence diversity to advance biomedicine. Sequencing and bioinformatic tools can be inexpensive and user-friendly, but numerous options and coding can intimidate new users. Distinct challenges exist in working with data from diverse species but may go unrecognized by researchers accustomed to gold-standard genomes. Here, we provide a high-level workflow and detailed pipeline to enable animal collection, single-molecule sequencing, and phylogenomic analysis of gene and species evolution. As a demonstration, we focus on (1) PacBio RNA-seq of the genome-sequenced ctenophore Mnemiopsis leidyi, (2) diversity and evolution of the mechanosensitive ion channel Piezo in genetic models and basal-branching animals, and (3) associated challenges and solutions to working with diverse species and genomes, including gene model updating and repair using single-molecule RNA-seq. We provide a Python Jupyter Notebook version of our pipeline (GitHub Repository: Ctenophore-Ocean-To-Tree-2023 https://github.com/000generic/Ctenophore-Ocean-To-Tree-2023 ) that can be run for free in the Google Colab cloud to replicate our findings or modified for specific or greater use. Our protocol enables users to design new sequencing projects in ctenophores, marine invertebrates, or other novel organisms. It provides a simple, comprehensive platform that can ease new user entry into running their evolutionary sequence analyses.