Hasil untuk "Botany"

G. Berg, D. Rybakova, M. Grube et al.

Vipul Bundake, Veena Khilnani, Archana Kale et al.

A pot experiment of maize was carried during summer seasons of March–July, 2023 and 2024 at the experimental field of Rashtriya Chemicals and Fertilizers, Mumbai, India, to assess the impact of multi nano micronutrients formulation (NM) on maize growth. The experiment was structured using a Completely Randomized Block Design with 12 treatments, including control with only water, Recommended Dose of Fertilizer (RDF), and different concentrations of NM having zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron (B) ranging from 20 mg to 0.15 mg 15 kg-1 of soil, as well as commercial micronutrients and micronutrient salts. Results revealed that application of 100% RDF+0.312 mg (T9) and 0.156 mg (T10) of nano micronutrients with drenching recorded better results of nutrient uptake (NU), apparent recovery (ANR) and agronomic efficiency (ARE). The NU (kg ha-1) of nitrogen (120.368), potassium (101.422), Cu (0.114), Fe (1.235), Mn (0.107) and Zn (6.069) was higher in T9 when compared to 100% RDF. The ANR was 9154.19% higher in T10 and 158.28% higher for Nitrogen(N), Phosphorus (P), and Potassium compared to 100% RDF. The protein and chlorophyll content were better in T9 and T10 of nano micronutrients respectively. The applications of T9 and T10 was found to be most effective in NU, ARE, ANR, protein content and chlorophyll content. Higher nutrient content in soil was found in treatment with lower concentrations. Overall, lower concentration of nano micronutrients appeared to be more effective for all traits.

Jazmín Vanessa Pérez-Pazos, Deimer Fuentes-Cassiani, Sol-Mara Regino et al.

The production of high-quality cassava planting material is a key strategy for mitigating the spread of pests and diseases. To promote the adoption of such strategies by farmers, it is essential to strengthen local capacities through knowledge transfer and the incorporation of innovative technologies, such as tunnels for rapid propagation (TxRPs), which have been successfully implemented in various international contexts. This study appraised the performance of four industrial cassava (Manihot esculenta Crantz) varieties—Corpoica Tai, Corpoica Belloti, Corpoica Ropain, and Corpoica Sinuana—under tunnel conditions at two locations on the Caribbean coast of Colombia. Planting material consisted of mini-cuttings (7–9 months old) with three buds. Five successive harvest cycles were assessed by measuring key growth parameters, including plant height, leaf number, SPAD (Soil Plant Analysis Development) chlorophyll index, leaf area, and biomass (dry weight and nutrient content). Environmental conditions within the tunnels, such as temperature and humidity, were regulated to promote rapid sprouting and accelerated growth of the cuttings. However, sprouting, vigor, and overall growth performance varied by variety. All four cassava varieties produced high-quality cuttings (>20 mm in diameter and >6 leaves), suitable for further seedling propagation. Cutting vigor increased across cycles, with productivity rising from over 60 cuttings/m² in the first cycle to more than 180 cuttings/m² by the fifth. Substrate mixtures enhanced both physical and chemical soil properties, depending on the source (CRT or CBL). The addition of coco peat or sand effectively minimized environmental impacts by preventing substrate compaction. The findings demonstrate the potential of tunnel-based systems to accelerate the production of high-quality cassava planting material, supporting improved productivity and sustainability in cassava cultivation for both farmers and industry stakeholders.

Karola Maul, S. Robbert Gradstein, Dietmar Quandt et al.

Abstract The evolutionary history underlying gradients in species richness is still subject to discussions and understanding the past niche evolution might be crucial in estimating the potential of taxa to adapt to changing environmental conditions. In this study we intend to contribute to elucidation of the evolutionary history of liverwort species richness distributions along elevational gradients at a global scale. For this purpose, we linked a comprehensive data set of genus occurrences on mountains worldwide with a time-calibrated phylogeny of liverworts and estimated mean diversification rates (DivElev) and mean ages (AgeElev) of the respective genera per elevational band. In addition, we reconstructed the ancestral temperature preferences of the genera. We found that diversification rates increase linearly with temperature, and hence decrease with elevation. This pattern is mainly driven by epiphytic genera. In contrast, overall genus age is highest at intermediate elevations where liverwort species richness peaks and decreases towards both ends of the elevational and thermal gradient. Our results further indicate that the ancestral lineages from which the extant liverwort genera descended had a preference for cool and humid habitats. We conclude that the extant liverwort species diversity accumulated over long time under these climatic conditions, which are today prevailing at mid-elevations of the world’s mountains. Subsequently, liverworts expanded their ranges from these temperate areas towards warm (with high diversification rates) and cold regions (with low diversification rates), located in contemporaneous (tropical) lowlands and high mountains, respectively. The conserved preference for temperate climates shared by the majority of liverwort lineages gives reason to the assumption that they will not be able to cope with the conditions induced by rapid climate warming, whereas the current low-elevation radiation may be less affected by climate change.

Vassana Supapongsri, Ananya Sahathippayakul, Wanchat Sirisarn et al.

Two novel species of Penicillium, comprising P. chanthaburiense sp. nov. and P. buranakarlianum sp. nov., were isolated from mangrove sediment collected at the King Rama IX International Mangrove Botanical Garden in Chanthaburi Province, Thailand. Morphological characterization combined with multilocus phylogenetic analyses of the internal transcribed spacer (ITS), β-tubulin (TUB), calmodulin (CaM), and RNA polymerase II second largest subunit (RPB2) genes placed P. chanthaburiense sp. nov. as a new member of section Exilicaulis, series Erubescentia, while P. buranakarlianum sp. nov. was assigned to section Lanata-Divaricata, series Janthinella. In addition, this study reports the first record of P. danzhouense from mangrove sediment in Thailand. Diagnostic morphological and molecular features distinguishing these taxa from their closest phylogenetic relatives are presented. These findings enrich the taxonomic framework of Penicillium and contribute to the understanding of fungal biodiversity in mangrove ecosystems. Furthermore, P. chanthaburiense sp. nov. exhibited antibacterial activity against several clinically relevant Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus (MRSA), highlighting the potential of mangrove-derived Penicillium species in antimicrobial research.

Jinli Liu, Haimin Liao, Shasha Chen et al.

Salt stress limits plant growth and yield. Though nitrogen fertilizer can alleviate salt damage, the effects of salt and nitrate on the stress resistance gene OsAAI1 are unclear. This study examined the Salt stress sensitivity of OsAAI1 transgenic lines and nitrate's role. Results showed OsAAI1 expression decreased with Salt and increased with nitrate. Under salt stress, the mutant OsAAI1 (osaai1) had significantly higher plant height, root length and number, and lower ROS accumulation than ZH 11, while OsAAI1 overexpression (OE 19) showed opposite trends. OE 19 also had lower antioxidant enzyme activities and higher MDA content. Analyses of topology, biomass distribution and connectivity of root scans after 30 and 50 days of salt stress treatment showed that osaai1 was able to sustain root growth and development under salt stress conditions, whereas OE 19 was more damaged. Exogenous salt stress tests confirmed these findings. Notably, nitrate application enhanced OsAAI1 is salt tolerance, improving root growth and increasing ROS scavenging enzyme activities. Under KNO₃ induction, high-concentration KNO₃ restores the root phenotype in OE 19. In conclusion, overexpression of OsAAI1 was more sensitive to salt, and OsAAI1 regulated ROS homeostasis through the nitrate pathway to enhance its tolerance to salt stress.

Herve Goeau, Alexis Joly, Pierre Bonnet et al.

The LifeCLEFs plant identification task provides a testbed for a system-oriented evaluation of plant identification about 500 species trees and herbaceous plants. Seven types of image content are considered: scan and scan-like pictures of leaf, and 6 kinds of detailed views with unconstrained conditions, directly photographed on the plant: flower, fruit, stem & bark, branch, leaf and entire view. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of ten groups from six countries and with a total of twenty seven submitted runs, involving distinct and original methods, this fourth year task confirms Image & Multimedia Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification.

Tin Lai, Farnaz Farid, Yueyang Kuan et al.

Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.

Jiakai Lin, Jinchang Zhang, Ge Jin et al.

Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.

Herve Goeau, Pierre Bonnet, Alexis Joly

The 2017-th edition of the LifeCLEF plant identification challenge is an important milestone towards automated plant identification systems working at the scale of continental floras with 10.000 plant species living mainly in Europe and North America illustrated by a total of 1.1M images. Nowadays, such ambitious systems are enabled thanks to the conjunction of the dazzling recent progress in image classification with deep learning and several outstanding international initiatives, such as the Encyclopedia of Life (EOL), aggregating the visual knowledge on plant species coming from the main national botany institutes. However, despite all these efforts the majority of the plant species still remain without pictures or are poorly illustrated. Outside the institutional channels, a much larger number of plant pictures are available and spread on the web through botanist blogs, plant lovers web-pages, image hosting websites and on-line plant retailers. The LifeCLEF 2017 plant challenge presented in this paper aimed at evaluating to what extent a large noisy training dataset collected through the web and containing a lot of labelling errors can compete with a smaller but trusted training dataset checked by experts. To fairly compare both training strategies, the test dataset was created from a third data source, i.e. the Pl@ntNet mobile application that collects millions of plant image queries all over the world. This paper presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.

Selene Cerna, Sara Si-Moussi, Wilfried Thuiller et al.

Foundation models have demonstrated a remarkable ability to learn rich, transferable representations across diverse modalities such as images, text, and audio. In modern machine learning pipelines, these representations often replace raw data as the primary input for downstream tasks. In this paper, we address the challenge of adapting a pre-trained foundation model to inject domain-specific knowledge, without retraining from scratch or incurring significant computational costs. To this end, we introduce BotaCLIP, a lightweight multimodal contrastive framework that adapts a pre-trained Earth Observation foundation model (DOFA) by aligning high-resolution aerial imagery with botanical relevés. Unlike generic embeddings, BotaCLIP internalizes ecological structure through contrastive learning with a regularization strategy that mitigates catastrophic forgetting. Once trained, the resulting embeddings serve as transferable representations for downstream predictors. Motivated by real-world applications in biodiversity modeling, we evaluated BotaCLIP representations in three ecological tasks: plant presence prediction, butterfly occurrence modeling, and soil trophic group abundance estimation. The results showed consistent improvements over those derived from DOFA and supervised baselines. More broadly, this work illustrates how domain-aware adaptation of foundation models can inject expert knowledge into data-scarce settings, enabling frugal representation learning.

MUNEEBA, Abdul KHALIQ, Faran MUHAMMAD et al.

Salinity poses a significant constraint to cereal productivity particularly in arid and semiarid regions. The application of allelochemical has shown promising results in mitigating the intensity of abiotic stresses. A pot experiment was conducted to assess the efficacy of different concentrations of aqueous allelopathic extract derived from moringa leaves in mitigating the adverse impacts of salinity on the germination and growth of maize cultivars via seed priming. The study involved three variables: two cultivars of maize, ‘Pioneer 30Y87’ (salt tolerant) and ‘Pioneer 30T60’ (salt sensitive) e seed priming with moringa leaf extract (MLE) at varying concentrations of 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, 3.0%, and hydro-priming as control; and different salinity levels of 0, 6, and 12 dS m-1. Salinity had a negative impact on the germination process, leading to delayed and suboptimal growth of seedlings. Additionally, salinity reduced the synthesis of photosynthetic pigments (20-50%), photosynthesis, transpiration, internal carbon, and stomatal conductance. Further, MLE also improved the antioxidant activities (catalase: CAT and peroxidase: POD) by 22-56% which reduced the hydrogen peroxide production. Moreover, ‘P-30Y87’ exhibited favorable performance in terms of better germination, growth, photosynthesis and antioxidant activities.  The application of moringa leaf extract (3%) resulted in a more notable hermetic effect in elevating salinity stress thereby enhancing germination, growth, photosynthesis and antioxidant activities. In the conclusion, application of MLE (3%) is a promising approach to mitigate the adverse impacts of salinity by improving germination, growth, photosynthesis and antioxidant activities.

Fayomi Omotola Michael, Olasan Joseph Olalekan, Aguoru Celestine Uzoma et al.

Aim. This study was purposed to investigate the effects of zinc oxide nanoparticles (ZnO NPs) on the growth and yield performance of two soybean (Glycine max L.) varieties, TGX1904-6F and TGX1951-3F, under controlled experimental conditions. Methods. Zinc oxide nanoparticles were synthesized and characterized following standard protocols, and their effects were evaluated across five treatment levels (20, 40, 60, 80, and 100 ppm) in a completely randomized design with five replicates. Growth parameters, including plant height, leaf morphology, stem diameter, and branch number, were assessed alongside phenological and yield traits such as days to flowering, flower production, pod metrics, and seed weights. The results revealed significant improvements in plant growth and yield metrics at intermediate ZnO NP concentrations, with enhancements observed in plant height, branch number, pod weight, and seed yield. Specifically, 60 ppm ZnO NP treatment resulted in the highest branch production, while 40 and 80 ppm treatments significantly promoted floral and pod development. Conversely, higher concentrations (100 ppm) exhibited inhibitory effects on plant height and leaf morphology, suggesting potential toxicity at elevated ZnO NP levels. Statistical analyses, including one-way ANOVA and Pearson’s correlation, confirmed significant treatment effects (P ≤ 0.05) on growth and yield parameters, highlighting the critical role of dose optimization. Conclusions. The findings underscore the potential of ZnO NPs as a novel agricultural supplement to enhance soybean productivity while emphasizing the need for balanced application to mitigate toxicity risks. This study contributes valuable insights into sustainable farming practices, leveraging nanotechnology to optimize crop performance and address global food security challenges.

Giesta Maria Olmedo Machado, André Luís de Gasper

Abstract Parablechnum cordatum (Blechnaceae) exhibits significant morphological variability, with many species considered as its synonyms, and is treated as a species complex by several authors. In this study, we offer a comprehensive revision of the taxon and an updated circumscription of Parablechnum usterianum, a species frequently confused with P. cordatum, leading to a refined understanding of species delimitation. Our analysis, based on morphological and spore micro- and ultrastructural examination of Brazilian specimens within the complex, recognizes three distinct species: Parablechnum cordatum s.s., Parablechnum brasiliense, and Parablechnum regnellianum, the latter being treated as a synonym but now resurrected with a new combination. Here, we present a key to the species, along with detailed descriptions, diagnostic images, distribution maps, lectotypifications, synonymies, and nomenclatural clarifications.

Kouki Taniyama

Let $α$ be a map from the set of all knot types ${\mathcal K}$ to a set $X$. Let $β$ be a map from ${\mathcal K}$ to a set $Y$. We define the relation between $α$ and $β$ to be the image of a map $(α,β)$ from ${\mathcal K}$ to $X\times Y$ sending an element $K$ of ${\mathcal K}$ to $(α(K),β(K))$. We determine the relations between $α$ and $β$ for certain $α$ and $β$ such as crossing number, unknotting number, bridge number, braid index, genus and canonical genus. This is a study of geography problem in knot theory.

Giulio Facchini, Marcello Budroni, Gabor Schuszter et al.

Phyllotactic patterns, i.e. regular arrangements of leaves or seeds around a plant stem, are fascinating examples of complex structures encountered in Nature. In botany, their symmetries develop when a new primordium periodically grows in the largest gap left between the previous primordium and the apex. Experiments using ferrofluid droplets have also shown that phyllotactic patterns can spontaneously form when identical elements repulsing each other are periodically released at a given distance from an injection center and are advected radially at a constant speed. A central issue in phyllotaxis is to understand whether other self-organized mechanisms can generate such patterns. Here, we show that phyllotactic structures also develop in the large class of spatial symmetry-breaking systems giving spotted patterns with an intrinsic wavelength, in the case of radial growth. We evidence this experimentally on chemical precipitation patterns, and numerically on two different models describing reaction-driven phase transitions and spatial Turing patterns, respectively. A generalized method for the construction of this new family of phyllotactic structures is presented, which paves the way to discover them in large classes of systems ranging from spinodal decomposition, chemical, biological or optical Turing structures, and Liesegang patterns, to name a few.

Brian E. Mapes

The conceptual landscape of convection has two simple gateways: optimal function and random form. Optimal convection adjusts toward a univariate ideal called neutrality. Convection form involves elements (parcels, bubbles, drafts) whose most parsimonious assumption is random. Between these gates lies a wilderness of realizable flow configurations. The only simple principle is natural selection by fitness, a scalar whose gradient is a local direction in an abstract configuration space. Random or high-entropy patterns occupy most of configuration space and occur spontaneously. With time, convection can discover less facile but more efficient (organized) configurations, by sequential selection. Here two data exercises explore that self-organization process, in shallow and deep moist convection. For shallow convection, causal network postulates are explored in a large set of cyclic-domain large-eddy simulations (LES; the Cloud Botany set). When an evolutionary pathway (mainly layer deepening in these simulations) leads to precipitation, mesoscale patterns blossom rapidly. For deep convection, expanding rings of conditional cell probability around prior cells are estimated from satellite imagery over South America and the South Pacific. In a Monte Carlo model iterating such a conditional probability kernel, hundreds of hourly cells take days to discover a self-sustaining squall configuration the kernel affords. Larger-scale implications include overshoots (redefinition of neutrality) and tens-of-hours timescales to both adjustment and noise (indeterminacy). If functional organization can be inferred from horizontal patterns, the abundance of horizontal texture information in satellite cloud imagery could find quantitative value.

W. Peng, Yu-Jie Liu, Na Wu et al.

Mengmeng Zhang, Jiaolong Wang, Lei Zhu et al.

Zanthoxylum bungeanum Maxim. (Rutaceae) is a popular food additive and traditional Chinese herbal medicine commonly named HuaJiao in China. This plant is widely distributed in Asian countries. The aim of this paper is to provide a systematic review on the traditional usages, botany, phytochemistry, pharmacology, pharmacokinetics, and toxicology of this plant. Furthermore, the possible development and perspectives for future research on this plant are also discussed. To date, over 140 compounds have been isolated and identified from Z. bungeanum, including alkaloids, terpenoids, flavonoids, and free fatty acids. The extracts and compounds have been shown to possess wide-ranging biological activity, such as anti-inflammatory and analgesic effects, antioxidant and anti-tumor effects, antibacterial and antifungal effects, as well as regulatory effects on the gastrointestinal system and nervous system, and other effects. As a traditional herbal medicine, Z. bungeanum has been widely used to treat many diseases, especially digestive disorders, toothache, stomach ache, and diarrhea. Many traditional usages of this plant have been validated by present investigations. However, further research elucidating the structure-function relationship among chemical compounds, understanding the mechanism of unique sensation, as well as exploring new clinical effects and establishing criteria for quality control for Z. bungeanum should be further studied.

Guoqi Yao, Hua Zhang, Bingying Leng et al.

Abstract Background The structural basis of chloroplast and the regulation of chloroplast biogenesis remain largely unknown in maize. Gene mutations in these pathways have been linked to the abnormal leaf color phenotype observed in some mutants. Large scale structure variants (SVs) are crucial for genome evolution, but few validated SVs have been reported in maize and little is known about their functions though they are abundant in maize genomes. Results In this research, a spontaneous maize mutant, pale green leaf-shandong (pgl-sd), was studied. Genetic analysis showed that the phenotype of pale green leaf was controlled by a recessive Mendel factor mapped to a 156.8-kb interval on the chromosome 1 delineated by molecular markers gy546 and gy548. There were 7 annotated genes in this interval. Reverse transcription quantitative PCR analysis, SV prediction, and de novo assembly of pgl-sd genome revealed that a 137.8-kb deletion, which was verified by Sanger sequencing, might cause the pgl-sd phenotype. This deletion contained 5 annotated genes, three of which, including Zm00001eb031870 , Zm00001eb031890 and Zm00001eb031900, were possibly related to the chloroplast development. Zm00001eb031870, encoding a Degradation of Periplasmic Proteins (Deg) homolog, and Zm00001eb031900, putatively encoding a plastid pyruvate dehydrogenase complex E1 component subunit beta (ptPDC-E1-β), might be the major causative genes for the pgl-sd mutant phenotype. Plastid Degs play roles in protecting the vital photosynthetic machinery and ptPDCs provide acetyl-CoA and NADH for fatty acid biosynthesis in plastids, which were different from functions of other isolated maize leaf color associated genes. The other two genes in the deletion were possibly associated with DNA repair and disease resistance, respectively. The pgl-sd mutation decreased contents of chlorophyll a, chlorophyll b, carotenoids by 37.2%, 22.1%, and 59.8%, respectively, and led to abnormal chloroplast. RNA-seq revealed that the transcription of several other genes involved in the structure and function of chloroplast was affected in the mutant. Conclusions It was identified that a 137.8-kb deletion causes the pgl-sd phenotype. Three genes in this deletion were possibly related to the chloroplast development, which may play roles different from that of other isolated maize leaf color associated genes.