Effect of seasonality on the leaf phenology and physiology of Byrsonima species (Malpighiaceae)

Introduction: Defined seasonality in savanna species can stimulate physiological responses that maximize photosynthetic metabolism and productivity. However, those physiological responses are also linked to the phenological status of the whole plant, including leaf phenophases. Objective: To study how physiological traits influence phenophase timing among congeneric and co-occurring savanna species. Methods: We evaluated the leaf phenology and physiological traits of populations of Byrsonima intermedia, B. coccolobifolia, and B. verbascifolia. Physiological measurements were performed at the onset of the dry and rainy seasons and again late in the season. Results: B. intermedia and B. coccolobifolia were classified as brevideciduous and B. verbascifolia as evergreen. The maximum quantum yield for B. intermedia and B. coccolobifolia were lowest during the dry season. At the onset of the dry period, the highest chloroplastidic pigment levels were observed, which decreased as the season advanced, total chlorophyll/carotenoid ratios were lowest, and carotenoid contents were highest. We detected low starch content values at the start of the rainy season, coinciding with the resumption of plant growth. Two months into this season, the leaves were at their peak structural and functional maturity, with high water-soluble polysaccharide values and photosynthetic rates, and were storing large amounts of starch. Conclusions: Physiological and leaf phenological strategies of the Byrsonima species were related to drought resistance and acclimatization to the seasonality of savanna water resources. The oscillations of the parameters quantified during the year indicated a strong relationship with water seasonality and with the phenological status of the leaves.

Introducción: La marcada estacionalidad en las especies de sabana puede estimular respuestas fisiológicas que maximicen el metabolismo fotosintético y la productividad. Sin embargo, esas respuestas fisiológicas están vinculadas al estado fenológico de toda la planta, incluidas las fenofases de las hojas. Objetivo: Estudiar cómo los rasgos fisiológicos influyen en el tiempo de la fenofase entre especies de sabana congenéricas y concurrentes. Métodos: Evaluamos la fenología y características fisiológicas de poblaciones de Byrsonima intermedia, B. coccolobifolia y B. verbascifolia. Las mediciones fisiológicas se realizaron al inicio de la estación seca y lluviosa, y de nuevo al final de la estación. Resultados: B. intermedia y B. coccolobifolia se clasificaron como brevicaducifolias y B. verbascifolia como perennifolias. El rendimiento cuántico máximo para B. intermedia y B. coccolobifolia fueron más bajos durante la época seca. Al inicio del período seco, se observaron niveles de pigmentos cloroplastídicos más altos, aunque los niveles de clorofila disminuyeron a medida que avanzaba la estación seca, las proporciones clorofila/carotenoides totales fueron más bajas y los contenidos de carotenoides más altos. Detectamos valores bajos de contenido de almidón al inicio de la época lluviosa, que coincide con la reanudación del crecimiento de la planta. A dos meses de esta época, las hojas estaban en su máxima madurez estructural y funcional, con altos valores de polisacáridos solubles en agua y tasas fotosintéticas, y almacenaban grandes cantidades de almidón. Conclusiones: Las estrategias fisiológicas y fenológicas de las hojas de las especies de Byrsonima estaban relacionadas con la resistencia a la sequía y la aclimatación a la estacionalidad de los recursos hídricos de la sabana. Las oscilaciones de los parámetros cuantificados durante el año indicaron una fuerte relación con la estacionalidad hídrica y con los estados fenológicos de las hojas.

The relevance of oral food challenge in a patient allergic to peanut and tree nuts.

Peanut allergy is one of the most common food allergies in childhood. In vitro cross-sensitization between peanut and tree nuts (TN) is high, but only a subgroup of patients allergic to peanut will have a concomitant allergy to one or several TN. In this article, the authors report a case of a 12-year-old boy who experienced 1 episode of lips and mouth itching, generalized urticarial, and eyelid angioedema 20 minutes after ingestion of peanut at 4 years of age. The immunoallergological study revealed the presence of a concomitant allergy to peanut, pistachio, and cashew confirmed with medically supervised oral food challenges (OFC) in a child who had never eaten these TN. The mechanism of IgE-mediated hypersensitivity was demonstrated by positive skin prick tests (SPT) with commercial extracts, although the specific IgE (sIgE) for these foods was negative. As described in the literature, we concluded that serum peanut and TN sIgE measurements have lower sensitivity than SPT to assess IgE sensitization, and OFC is the gold standard for accurate diagnosis of food allergy. We highlight the relevance of excluding or confirming TN allergy in a peanut-allergic patient who had never ingested certain TN, and of knowing the clinical relevant cross-reactivity patterns between TN, pistachio/cashew, and walnut/pecan, that could reduce the need for OFC in clinical practice, reducing allergy rates and financial and health burdens of food allergy.

Multi-omic characterisation as a tool to improve knowledge, valorisation and conservation of wild fruit genetic resources: the case of Arbutus unedo L.

The valorisation and conservation of plant genetic resources (PGRs) and wild fruit PGRs are critical to ensure the maintenance of genetic and cultural heritage and to promote new perspectives on resource use. New strategies to characterize PGRs are needed, and the omics approach can provide information that is still largely unknown. The Strawberry tree (Arbutus unedo L.) is an underutilized, drought and fire-resistant species distributed in the Mediterranean area and its berries have large ethnobotanical use. Although their phenolic profile and antioxidant capacity are known, they are not well characterised, particularly from a proteomic perspective. The aim of this work is the characterisation of two ecotypes of A. unedo (Campania and Sicily) from a molecular viewpoint to valorise and encourage the preservation of this wild fruit. Samples were collected from two different geographical areas to assess whether different geographical conditions could influence the characteristics of leaves and fruits at the three stages of ripening (green, veraison, red). Proteomic analysis identified 904 proteins, of which 122 showed significance along the ripening. Some of these differentially abundant proteins, such as chalcone synthase, show a marked increase during ripening. The protein functional classes with the highest representation are involved in protein and amino acid metabolism, glycolysis and in secondary metabolism. From a proteomic perspective, there are no differences between the fruits from the two regions compared by the ripening stage. However, the pedoclimatic metabolic imprinting allowed the observation of good diversity in the metabolomic profiles between the two ecotypes, especially for anthocyanins, 4 times more abundant in the Sicilian veraisoned fruit than in the Campania one, and catechins, with double the abundance in the Campania ecotype compared to the Sicilian ecotype in the green phase, but more abundant (3x) in the Sicilian veraisoned fruit. Phenolic compounds show a 20% greater abundance in the Campania green arbutus fruit than in the Sicilian one, values that then equalise as ripening progresses. Multi-omic characterisation enhanced the knowledge on a wild fruit plant species which shows specific adaptations and responses to the environment to be considered when addressing the issue of local agrobiodiversity.

Study on the effect of magnesium on leaf metabolites, growth and quality of tea tree.

Magnesium (Mg) is one of the essential elements for the growth of tea trees. In this study, we investigated changes in metabolites, photosynthetic fluorescence parameters and quality indexes of tea leaves under different concentrations of magnesium treatment, and the results showed that there were no significant differences in the quantity and total content of metabolites in tea leaves under different Mg concentrations. The results of volcano map analysis showed that the content of 235 metabolites in tea leaves showed an increasing trend and the content of 243 metabolites showed a decreasing trend with the increase of Mg concentration. The results of the combined analysis of the OPLS-DA model and bubble map showed that 45 characteristic metabolites were screened at different concentrations of Mg. Among these, the content of 24 characteristic metabolites showed an increasing trend and 21 characteristic metabolites showed a decreasing trend with the increase of Mg concentrations. The results of KEEG pathway enrichment showed that 24 characteristic metabolites with a upward trend were significantly enriched in saccharides metabolism, nucleic acid metabolism and vitamin metabolism, while the 21 characteristic metabolites with a downward trend were enriched in the synthesis of plant secondary metabolites, phenylpropanoid biosynthesis, biosynthesis of terpenoids, synthesis and metabolism of alkaloids, and synthesis and metabolism of amino acids. It can be inferred that Mg regulation was beneficial to enhance the photosynthetic capacity of tea trees, improve the accumulation and metabolism of carbohydrate substances in tea trees, and thus promoted the growth of tea trees, but was not conducive to the synthesis of secondary metabolites and amino acids related to tea quality. The results of photosynthetic fluorescence parameters and quality indexes of the tea tree confirmed the conclusion predicted by metabolomics. This study provided a reference for regulating of the growth and quality of tea trees with Mg fertilizer in tea plantations.

Syntactic theory of mathematical expressions.

Mathematical expressions consist of recursive combinations of numbers, variables, and operators. According to theoretical linguists, the syntactic mechanisms of natural language also provide a basis for mathematics. To date, however, no theoretically rigorous investigation has been conducted to support such arguments. Therefore, this study uses a methodology based on theoretical linguistics to analyze the syntactic properties of mathematical expressions. Through a review of recent behavioral and neuroimaging studies on mathematical syntax, we report several inconsistencies with theoretical linguistics, such as the use of ternary structures. To address these, we propose that a syntactic category called Applicative plays a central role in analyzing mathematical expressions with seemingly ternary structures by combining binary structures. Besides basic arithmetic expressions, we also examine algebraic equations and complex expressions such as integral and differential calculi. This study is the first attempt at building a comprehensive framework for analyzing the syntactic structures of mathematical expressions.

Effects of pruning on tea tree growth, tea quality, and rhizosphere soil microbial community.

Pruning is an important agronomic measure in tea plantation management. This study analyzed the effects of pruning on tea tree growth, tea quality, rhizosphere soil physicochemical indexes, microbial communities, and metabolic pathways. The results showed that pruning was beneficial for promoting tea tree growth and increasing tea yield, but not for the synthesis and accumulation of quality-related compounds in tea leaves. After pruning, organic matter, available phosphorus content and catalase, acid phosphatase, and sucrase activities in rhizosphere soil were significantly higher than those in unpruned tea trees, while total phosphorus, total potassium, and available nitrogen content were significantly lower than those in unpruned tea trees. The results of microbial community analysis of tea rhizosphere soil showed that the key changed characteristic microorganisms after pruning were Haliangium, Acidicaldus, Reyranella, Acidobacterium, Aquicella, and Granulicella, and the key changed characteristic microbial metabolic pathways were ko00072, ko00473, ko00750, ko01055, ko00521, and ko02040. Furthermore, the results found that pruning promoted Haliangium, Acidicaldus, and Reyranella abundances, ko00072, ko00473, and ko00750, respectively, microbial metabolic pathways in tea trees rhizosphere soil, and reduced Acidobacterium, Granulicella, and Aquicella abundance, ko01055, ko00521, and ko02040, respectively, microbial metabolic pathways, thereby increasing the activities of soil catalase, acid phosphatase, and sucrase, improving soil organic matter decomposition efficiency and available phosphorus content, and promoting tea yield, but not synthesis and accumulation of quality-related compounds in tea leaves. This study provides an important theoretical reference for the management of agronomic measures in tea plantations. IMPORTANCE Pruning is an important agronomic measure in tea cultivation and management. We found that pruning was beneficial to increase tea yield, but it would reduce tea quality, especially the content of polyphenols, theanine, flavonoids, and free amino acids in tea leaves was reduced. The reason for this phenomenon was that pruning promotes the enrichment of special functional microorganisms and the enhancement of special metabolic pathways in the soil, leading to changes in the nutrient cycle in the soil.

Probability Distribution of Tree Age for the Simple Birth-Death Process, with Applications to Distributions of Number of Ancestral Lineages and Divergence Times for Pairs of Taxa in a Yule Tree.

In this contribution, a general expression is derived for the probability density of the time to the most recent common ancestor (TMRCA) of a simple birth-death tree, a widely used stochastic null-model of biological speciation and extinction, conditioned on the constant birth and death rates and number of extant lineages. This density is contrasted with a previous result which was obtained using a uniform prior for the time of origin. The new distribution is applied to two problems of phylogenetic interest. First, that of the probability of the number of taxa existing at any time in the past in a tree of a known number of extant species, and given birth and death rates, and second, that of determining the TMRCA of two randomly selected taxa in an unobserved tree that is produced by a simple birth-only, or Yule, process. In the latter case, it is assumed that only the rate of bifurcation (speciation) and the size, or number of tips, are known. This is shown to lead to a closed-form analytical expression for the probability distribution of this parameter, which is arrived at based on the known mathematical form of the age distribution of Yule trees of a given size and branching rate, which is derived here de novo, and a similar distribution which additionally is conditioned on tree age. The new distribution is the exact Yule prior for divergence times of pairs of taxa under the stated conditions and is potentially useful in statistical (Bayesian) inference studies of phylogenies.

Tolerability and Safety of Sublingual Immunotherapy in Patients with Tree Pollen Allergy in Daily Practice-An Open, Prospective, Non-Interventional Study.

To investigate the tolerability and safety of two sublingual tree pollen extracts approved in 2018, a non-interventional study (NIS) was performed. This NIS was an 8-month observational study conducted at 84 sites throughout Germany. Study participants received either a sublingual liquid allergen extract of birch pollen (SBPE) or a liquid allergen extract consisting of a mixture of birch, hazel, and alder tree pollen (STPE). Data from 432 patients were analyzed for the occurrence of adverse events and patient compliance. At least one local reaction occurred in 69 (22.2%) patients, whereas systemic reactions were only observed in 27 (6.3%) patients. STPE-treated patients developed systemic reactions more frequently than SBPE-treated patients (SBPE: 9 (4.3%) vs. STPE: 18 (8.0%)). Only one patient developed a systemic grade III reaction. Severe systemic grade IV reactions were not observed. A total of 348 (98.6%) of the patients who completed all visits were satisfied or very satisfied with the sublingual immunotherapy (SLIT), and 322 (71%) patients completed all visits. Both investigated products were well tolerated by the patients and demonstrated a good safety profile. AEs were observed less frequently than in the preceding clinical phase III trial, and no new safety concerns were identified.

Hepatic Involvement across the Metabolic Syndrome Spectrum: Non-Invasive Assessment and Risk Prediction Using Machine Learning.

Metabolic-dysfunction-associated steatotic liver disease (MASLD) and metabolic syndrome (MetS) are inextricably linked conditions, both of which are experiencing an upward trend in prevalence, thereby exerting a substantial clinical and economic burden. The presence of MetS should prompt the search for metabolic-associated liver disease. Liver fibrosis is the main predictor of liver-related morbidity and mortality. Non-invasive tests (NIT) such as the Fibrosis-4 index (FIB4), aspartate aminotransferase-to-platelet ratio index (APRI), aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), hepatic steatosis index (HIS), transient elastography (TE), and combined scores (AGILE3+, AGILE4) facilitate the detection of liver fibrosis or steatosis. Our study enrolled 217 patients with suspected MASLD, 109 of whom were diagnosed with MetS. We implemented clinical and biological evaluations complemented by transient elastography (TE) to discern the most robust predictors for liver disease manifestation patterns. Patients with MetS had significantly higher values of FIB4, APRI, HSI, liver stiffness, and steatosis parameters measured by TE, as well as AGILE3+ and AGILE4 scores. Machine-learning algorithms enhanced our evaluation. A two-step cluster algorithm yielded three clusters with reliable model quality. Cluster 1 contained patients without significant fibrosis or steatosis, while clusters 2 and 3 showed a higher prevalence of significant liver fibrosis or at least moderate steatosis as measured by TE. A decision tree algorithm identified age, BMI, liver enzyme levels, and metabolic syndrome characteristics as significant factors in predicting cluster membership with an overall accuracy of 89.4%. Combining NITs improves the accuracy of detecting patterns of liver involvement in patients with suspected MASLD.

A Cardinality Estimator in Complex Database Systems Based on TreeLSTM.

Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.

Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks.

Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium's broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments.

Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches.

OBJECTIVE: To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults. METHOD: For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF). RESULTS: Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status. CONCLUSION: The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.

Host suitability of summer cover crops and peach rootstocks to the peach root-knot nematode, Meloidogyne floridensis.

Greenhouse experiments were conducted to determine the host suitability of ten summer cover crops and two peach rootstocks to M. floridensis by inoculating them with 10,000 M. floridensis eggs. Brown top millet and sunn hemp were non-hosts as they did not support nematode reproduction. Buckwheat, cowpea, pearl millet, Japanese millet, and sunflower supported more than 25,000 eggs/pot indicating these crops are good hosts to M. floridensis. The crops that supported poor nematode reproduction were sesame, grain sorghum, and sorghum-sudangrass with the reproduction ranging from 219 to 7,750 eggs/pot. In addition to having many galls on the roots, the peach rootstock Guardian® had 10,100 eggs on the roots and 450 second-stage juveniles in the pot indicating Guardian® is a good host to M. floridensis. Although the nematode reproduction on MP-29 rootstock was relatively lower, the presence of many large galls on the roots indicates MP-29 is susceptible to M. floridensis. Results from the current study suggest that the employment of non-host cover crops and poor-host rootstocks could aid in effective nematode management programs for peaches.

Topography modulates climate sensitivity of multidecadal trends of holm oak decline.

Forest decline events have increased worldwide over the last decades being holm oak (Quercus ilex L.) one of the tree species with the most worrying trends across Europe. Since this is one of the tree species with the southernmost distribution within the European continent, its vulnerability to climate change is a phenomenon of enormous ecological importance. Previous research identified drought and soil pathogens as the main causes behind holm oak decline. However, despite tree health loss is a multifactorial phenomenon where abiotic and biotic factors interact in time and space, there are some abiotic factors whose influence has been commonly overlooked. Here, we evaluate how land use (forests versus savannas), topography, and climate extremes jointly determine the spatiotemporal patterns of holm oak defoliation trends over almost three decades (1987-2014) in Spain, where holm oak represents the 25% of the national forested area. We found an increasing defoliation trend in 119 out of the total 134 holm oak plots evaluated, being this defoliation trend significantly higher in forests compared with savannas. Moreover, we have detected that the interaction between topography (which covariates with the land use) and summer precipitation anomalies explains trends of holm oak decline across the Mediterranean region. While a higher occurrence of dry summers increases defoliation trends in steeper terrains where forests dominate, an inverse relationship was found in flatter terrains where savannas are mainly located. These opposite relationships suggest different causal mechanisms behind decline. Whereas hydric stress is likely to occur in steeper terrains where soil water holding capacity is limited, soil waterlogging usually occurs in flatter terrains what increases tree vulnerability to soil pathogens. Our results contribute to the growing evidence of the influence of local topography on forest resilience and could assist in the identification of potential tree decline hotspots and its main causes over the Mediterranean region.

Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient.

Oxygen and hydrogen isotopes of cellulose in plant biology are commonly used to infer environmental conditions, often from time series measurements of tree rings. However, the covariation (or the lack thereof) between δ18 O and δ2 H in plant cellulose is still poorly understood. We compared plant water, and leaf and branch cellulose from dominant tree species across an aridity gradient in Northern Australia, to examine how δ18 O and δ2 H relate to each other and to mean annual precipitation (MAP). We identified a decline in covariation from xylem to leaf water, and onwards from leaf to branch wood cellulose. Covariation in leaf water isotopic enrichment (Δ) was partially preserved in leaf cellulose but not branch wood cellulose. Furthermore, whilst δ2 H was well-correlated between leaf and branch, there was an offset in δ18 O between organs that increased with decreasing MAP. Our findings strongly suggest that postphotosynthetic isotope exchange with water is more apparent for oxygen isotopes, whereas variable kinetic and nonequilibrium isotope effects add complexity to interpreting metabolic-induced δ2 H patterns. Varying oxygen isotope exchange in wood and leaf cellulose must be accounted for when δ18 O is used to reconstruct climatic scenarios. Conversely, comparing δ2 H and δ18 O patterns may reveal environmentally induced shifts in metabolism.

Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications.

Bioethanol has gained popularity in recent decades as an ecofriendly alternative to fossil fuels due to increasing concerns about global climate change. However, economically viable ethanol fermentation remains a challenge. High-temperature fermentation can reduce production costs, but Saccharomyces cerevisiae yeast strains normally ferment poorly under high temperatures. In this study, we present a machine learning (ML) approach to optimize bioethanol production in S. cerevisiae by fine-tuning the promoter activities of three endogenous genes. We created 216 combinatorial strains of S. cerevisiae by replacing native promoters with five promoters of varying strengths to regulate ethanol production. Promoter replacement resulted in a 63% improvement in ethanol production at 30 °C. We created an ML-guided workflow by utilizing XGBoost to train high-performance models based on promoter strengths and cellular metabolite concentrations obtained from ethanol production of 216 combinatorial strains at 30 °C. This strategy was then applied to optimize ethanol production at 40 °C, where we selected 31 strains for experimental fermentation. This reduced experimental load led to a 7.4% increase in ethanol production in the second round of the ML-guided workflow. Our study offers a comprehensive library of promoter strength modifications for key ethanol production enzymes, showcasing how machine learning can guide yeast strain optimization and make bioethanol production more cost-effective and efficient. Furthermore, we demonstrate that metabolic engineering processes can be accelerated and optimized through this approach.

Drought survival in conifer species is related to the time required to cross the stomatal safety margin.

The regulation of water loss and the spread of xylem embolism have mostly been considered separately. The development of an integrated approach taking into account the temporal dynamics and relative contributions of these mechanisms to plant drought responses is urgently needed. Do conifer species native to mesic and xeric environments display different hydraulic strategies and temporal sequences under drought? A dry-down experiment was performed on seedlings of four conifer species differing in embolism resistance, from drought-sensitive to extremely drought-resistant species. A set of traits related to drought survival was measured, including turgor loss point, stomatal closure, minimum leaf conductance and xylem embolism resistance. All species reached full stomatal closure before the onset of embolism, with all but the most drought-sensitive species presenting large stomatal safety margins, demonstrating that highly drought-resistant species do not keep their stomata open under drought conditions. Plant dry-down time to death was significantly influenced by the xylem embolism threshold, stomatal safety margin, and minimum leaf conductance, and was best explained by the newly introduced stomatal margin retention index (SMRIΨ50) which reflects the time required to cross the stomatal safety margin. The SMRIΨ50 may become a key tool for the characterization of interspecific drought survival variability in trees.

Use of tree-based machine learning methods to screen affinitive peptides based on docking data.

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees(CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model(C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4%). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

Decision trees and labeling of low noise pavements as support for noise action plans.

Low noise pavements (LNPs) are a market driven trend to mitigate the high road traffic noise exposure levels. Their improvement towards acoustic efficiency and durability over time is a challenge since these factors can conflict with road primary functions, such as safety. LNPs are not always the most cost-effective solution in health effects prevention. Whilst Green Public Procurement (GPP) highlighted the importance of reducing rolling noise emissions by introducing new regulations for new-layed LNPs, the fixed minimum requirements are not exhaustive. Generally, limits are set following the Close ProXimity method, which is only source oriented. This method does not consider real traffic flows and it is not aimed at evaluating citizens' disturbance. This work presents strategy tools that could assist policymakers in choosing LNPs, when truly effective, over other mitigations. The approach includes a variety of indicators that would allow for comparing different facets of noise assessment. The proposed methodology does not require additional efforts from stakeholders because the measurements required for the estimation of the indicators must already be carried out for both verification of legal limits and GPP. The strategy tools are a decisional tree to support the evaluation of the applicability of a LNP before its approval, and an evaluation flowchart applicable after its laying to evaluate its efficiency. Finally, a first LNP labeling approach, based on the same set of indicators, is proposed. As a case study, these tools are applied to measurements performed before and after the laying of twelve LNPs part of the LIFE NEREiDE project.

scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data.

BACKGROUND: Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types. METHODS: In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells. RESULTS: We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence (p < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration (p = 0.009) and the cell cycle (p < 0.001) than other methods in additional validation on the bulk RNAseq dataset. CONCLUSIONS: Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells.