The continued success of any conventional sweet potato breeding program is limited to knowledge of the inheritance of the traits studied, such as flesh color and tuberous root shape, because it is difficult to fractionate color frequencies by visual separation. The objective was to understand the mode of inheritance of these genetic traits. The crossing blocks were established at the Research Institute of Tropical Roots and Tuber Crops (INIVIT-Cuba). Eight parental genotypes of known compatibility were selected, with contrasting phenotypic characteristics to develop seg-regating populations. To express color objectively, the CIE L*a*b* color space was used (L*: lumi-nosity; a* and b*: chromatic coordinates), in addition, four morphometric variables associated with the shape and dimensions of the tuberous roots were evaluated. From 2419 reciprocal cross-es, 2045 botanical seeds and 1764 seedlings were obtained. Incomplete dominance of the white and purple flesh colors over the orange color was observed, as well as transgressive segregation for purple, orange and white flesh colours, and for the shape of the tuberous roots. The results al-lowed to propose a genetic model of biparental crosses to improve the flesh color of I. batatas, as well as a predictive formula of the progenies to be selected.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

Musyakalah is one of the Arabic linguistic styles included under the category of majaz. This style is commonly used in Al-Quran. The Indonesian translation of Al-Quran is a case where many of the figures of speech are translated literally, thereby causing serious semantic problems. Thus, the research problem of this is formulated with the following questions: 1) How many musyakalah ayahs are there in Al-Quran?; 2) How are the musyakalah ayahs translated, literally (harfiyya) or interpretatively (tafsiriyya)?; 3) How many ayahs are translated literally and how many are translated interpretatively?; and 4) Which translated musyakalah ayahs have the potential to raise semantic and theological problems? The corpus in this research consists of all musyakalah ayahs in Al-Quran and their translation to Indonesian published by the Department of Religious Affairs of Indonesia. The research adopted a descriptive-semantic method. The findings of this research are: 1) There are only eleven ayahs in Al-Quran using musyakalah style, namely: Alhasyr ayah 19, Ali Imran ayah 54, Annaml ayah 50, Alanfal ayah 30, Asysyura ayah 40, Albaqarah ayah 15, Almaidah 116, Aljatsiah ayah 34, Attaubah ayah 79, Annisa ayah 142, and Albaqarah 194; 2) The musyakalah ayahs translated literally are: Aljatsiah 34, Almaidah 116, Asysura 40, Annaml 50, and Alhasyr 19, whereas the musyakalah ayahs translated interpretatively are Albaqarah 194, Annisa 142, Attaubah 79, Albaqarah 15, Alanfal 30, and Ali Imran 54; 3) Of the eleven musyakalah ayahs, only Alhasyr ayah 19 that is translated correctly and does not have the potential of creating misinterpretation. Meanwhile, the interpretation of the other four ayahs can potentially cause misinterpretation or are against the basic principles of Islam. The six remaining ayahs are translated interpretatively and thus do not have the potential to be misinterpreted; 4) The findings suggest that musyakalah ayahs are more appropriately translated interpretatively. Therefore, the following is recommended: 1) Considering the different characteristics of Arabic and Indonesian languages, studies on Al-Quran translation into Indonesian should continuously be carried out; 2) In order to avoid misinterpretation, it is better that the translation of musyakalah ayahs uses tafsiriyya (interpretative) approach; 3) The harfiyya (literal) and tafsiriyya (interpretative) approaches should be developed for other styles beside musyakalah..

Classical Arabic poetry is a form of Arabic literature, and almost every piece of Arabic poetry follows one of the 16 meters. The rhythmic framework of the poems or the verses is called meters. The deep learning (DL) model was implemented using TensorFlow in this work using a large dataset of Arabic poetry. The character level encoding was used to convert text to integers for classifying the full-verse and half-verse data. The study evaluates the data without removing diacritics from the text. The train-test-split method with a 70-15-15 split was employed for classification. 15% of the total data was considered unseen test data for all models. The work was conducted with multiple deep learning models, including the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional LSTM (Bi-LSTM). The Bi-LSTM model shows the best accuracy of all the models specified, with 97.53% for full-verse and 95.23% for half-verse without removing diacritic data.

The COVID-19 pandemic spread of the coronavirus across the globe has affected our lives on many different levels. The world we knew before the spread of the virus has become another one. Every country has taken preventive measures, including social distancing, travel restrictions, and curfew, to control the spread of the disease. With these measures implemented, people have shifted to social media platforms in the online sphere, such as Twitter, to maintain connections. In this paper, we describe a coronavirus data set of Arabic tweets collected from January 1, 2020, primarily from hashtags populated from Saudi Arabia. This data set is available to the research community to glean a better understanding of the societal, economical, and political effects of the outbreak and to help policy makers make better decisions for fighting this epidemic.

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

After defeating and pushing aside the Carthaginians, the Romans named the territory of north Africa Mauretania and its population, Mauri. Later on, historians have had to reflect on the origin of the term mauri to designate the population or Mauretania to name the territory. Besides the signification of the Latin/Greek word « mauri » which means « black », it is admitted, as well, that the word Mauri is a transcription, into Latin alphabet, of a punic word meaning « the west » or « the westerners ». If the latter meaning is 2000 years old, the other approbation is relatively modern and suspected of ideological biases. The punic word maġaribis, transcribed mauri/ma’ari/mahauri, really makes sense since it means the west in Punic - the 15 century-long North African lingua franca.

The predominance of English and Latin-based large language models (LLMs) has led to a notable deficit in native Arabic LLMs. This discrepancy is accentuated by the prevalent inclusion of English tokens in existing Arabic models, detracting from their efficacy in processing native Arabic's intricate morphology and syntax. Consequently, there is a theoretical and practical imperative for developing LLMs predominantly focused on Arabic linguistic elements. To address this gap, this paper proposes ArabianGPT, a series of transformer-based models within the ArabianLLM suite designed explicitly for Arabic. These models, including ArabianGPT-0.1B and ArabianGPT-0.3B, vary in size and complexity, aligning with the nuanced linguistic characteristics of Arabic. The AraNizer tokenizer, integral to these models, addresses the unique morphological aspects of Arabic script, ensuring more accurate text processing. Empirical results from fine-tuning the models on tasks like sentiment analysis and summarization demonstrate significant improvements. For sentiment analysis, the fine-tuned ArabianGPT-0.1B model achieved a remarkable accuracy of 95%, a substantial increase from the base model's 56%. Similarly, in summarization tasks, fine-tuned models showed enhanced F1 scores, indicating improved precision and recall in generating concise summaries. Comparative analysis of fine-tuned ArabianGPT models against their base versions across various benchmarks reveals nuanced differences in performance, with fine-tuning positively impacting specific tasks like question answering and summarization. These findings underscore the efficacy of fine-tuning in aligning ArabianGPT models more closely with specific NLP tasks, highlighting the potential of tailored transformer architectures in advancing Arabic NLP.

The new coronavirus outbreak (COVID-19) has swept the world since December 2019 posing a global threat to all countries and communities on the planet. Information about the outbreak has been rapidly spreading on different social media platforms in unprecedented level. As it continues to spread in different countries, people tend to increasingly share information and stay up-to-date with the latest news. It is crucial to capture the discussions and conversations happening on social media to better understand human behavior during pandemics and alter possible strategies to combat the pandemic. In this work, we analyze the Arabic content of Twitter to capture the main discussed topics among Arabic users. We utilize Non-negative Matrix Factorization (NMF) to discover main issues and topics based on a dataset of Arabic tweets from early January to the end of April, and identify the most frequent unigrams, bigrams, and trigrams of the tweets. The final discovered topics are then presented and discussed which can be roughly classified into COVID-19 origin topics, prevention measures in different Arabic countries, prayers and supplications, news and reports, and finally topics related to preventing the spread of the disease such as curfew and quarantine. To our best knowledge, this is the first work addressing the issue of detecting COVID-19 related topics from Arabic tweets.

The review analyzes a possible role of autoimmune processes in the pathogenesis of schizophrenia and evolution of concepts on this issue from its origin to present. Risks of autoimmune processes causing schizophrenia are associated with several factors: an impaired functioning of dopaminergic and glutamatergic systems in the brain, kynurenine pathway disorder with overproduction of quinolinic, anthranilic and kynurenic acids (possibly altering both neurons and T-regulators), increased intestinal permeability, as well as food antigens’ effects, stress and infections with various pathogens at different stages of ontogenesis. An increase in the levels of proinflammatory cytokines and chemokines as well as a decrease in the levels of anti-inflammatory ones also may contribute to schizophrenia risks. Schizophrenia often occurs in those patients having various autoimmune diseases and their first-degree relatives. Cases of schizophrenia resulted from autoimmune pathogenesis (including autoimmune encephalitis caused by autoantibodies against various neuronal antigens) are characterized by quite severe cognitive and psychotic symptoms and less favorable prognosis. This severe course may result from the chronic immune damage of the neuronal receptors such as NMDA, GABA, and others and depend on hyperprolactinemia, induced by antipsychotics, but aggravating autoimmune processes [with 2 tables, 4 figures, bibliography: 99 references].

Since the first confirmed case of COVID-19, information was spreading in large amounts over social media platforms. Information spreading about the COVID-19 pandemic can strongly influence people’s behavior. Therefore, identifying information superspreaders (or influencers) during the COVID-19 pandemic is an im- portant step towards understanding public reactions and information dissemination. In this work, we present an analysis over a large Arabic tweets collected during the COVID-19 pandemic. The presented study con- struct a network from users’ behaviors to identify information superspreaders during the month of March, 2020. We employed both HITS and PageRank algorithms to analyze the influence of information spreading, and compared the ranking of the users. The results show that both HITS and PageRank discovered a similar subset of superspreaders with 40% were found to be verified Twitter accounts.

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

The present investigation deals with the determination of the physio-chemical properties of two commercial grade samples of local gums (Gum Arabic and Natural Rubber Latex (NRL)). The results revealed that the gum samples have high melting point that indicate thermal stability at room and moderate temperatures. The gum samples have about 95 % carbohydrate content and a corresponding high internal energy and can serve as a source of energy. The rheology of the samples revealed shear-thickening characteristics with gum Arabic being thixotropic and pseudo-plastic in nature while NRL was observed to be anti-thixotropic and rheopectic. Further results from the moisture absorption, contact angle and Fourier Transform Infrared Radioscopy (FTIR) analyses gave better insight into their hydroscopic behaviors. Gum Arabic has excellent water absorption capacity with less wettability as it consists mainly of more water-soluble compounds in comparison to Natural Rubber Latex. These insights from the study will enhance wider application of the gums with increased value-addition to the gums and the communities where they (can) thrive.

Nowadays and in the web era, there is a huge amount of information available due to automatically and easily generated websites. This information has many shared opinions from people toward different entities. Over the last decade there was a significant huge interest to determine the sentiment of any type of text especially on twitter. Twitter provides unstructured text that has many misspelling words, slang words and shortened forms. More complexity and challenges have come to the sentiment analysis researchers in the context of twitter. In this paper we conducted a literature review of the recent research of Twitter sentiment analysis on different languages. After that, we investigated challenges related to twitter sentiment analysis of Arabic language. Then we found a new solution for Sentiment analysis of Arabic twitter. So, we designed the main component of the proposed system and presented its algorithm. Finally, we performed many experiments under two kinds of datasets. The system achieves highest accuracy and shows many important results..

Cognitive reserve is the adaptability of cognitive processes in the face of brain aging and pathology. This study aimed to validate the Arabic version of the Cognitive Reserve Index Questionnaire (CRIq) in a healthy Lebanese sample. CRIq assesses cognitive reserve through three domains: education, working activity, and leisure time. Statistical measures, including descriptive and regression analysis along with structural equation modeling, were utilized to investigate the convergent and discriminant validity of CRIq, incorporating fluid intelligence (Gf) and measures of cognitive function, long-term memory encoding and retrieval (Glr), and processing speed (Gs). Results from 174 participants revealed that the activities assessed by CRIq-Arabic were comparable to the original CRIq study, with slight cultural differences. The internal consistency of CRIq-Arabic was good (Cronbach's α = 0.88), indicating reliability. Convergent validity was confirmed, with moderate to high loadings on the cognitive reserve latent construct. Discriminant validity was supported as correlations between cognitive reserve variables and non-target constructs (Gf, Glr, Gs) were less than 1. The findings provide an initial psychometric validation of the CRIq-Arabic. Further research in clinical samples is needed to enhance its utility in neuropsychological practice.

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. In this paper, we propose (PDTS) a punctuation detection approach for segmenting Arabic text, built on top of a multilingual BERT-based model and some generic linguistic rules. Furthermore, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost.

The bonds of England and the UAE date back to over 220 years ago. This article explores the interference of Arabic prepositions in the English used in the United Arab Emirates (UAE), and their occurrences in light of gender and level of education, two important social variables related to linguistic behavior. To do so, participants will translate 20 sentences in Arabic into English as well as fill in 30 gaps in sentences in English with the missing prepositions. We will also experiment how musical intelligence may improve Emiratis’ performance regarding preposition. A study will be carried out to verify if participants from the experimental group, who will receive training on prepositions through music, obtains better results compared to the control group, who will receive training through a more traditional way (by listening to the instructor and repeating).

Chatbots are computer programs that use artificial intelligence to imitate human conversations. Recent advancements in deep learning have shown interest in utilizing language transformers, which do not rely on predefined rules and responses like traditional chatbots. This study provides a comprehensive review of previous research on chatbots that employ deep learning and transfer learning models. Specifically, it examines the current trends in using language transformers with transfer learning techniques to evaluate the ability of Arabic chatbots to understand conversation context and demonstrate natural behavior. The proposed methods explore the use of AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator) transformers, with different variants of these transformers and semantic embedding models. Two datasets were used for evaluation: one with 398 questions and corresponding documents, and another with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions, using confidence and similarity metrics. The experimental results showed that the AraElectra-SQuAD model achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD model consistently outperformed other models, displaying remarkable performance, high confidence, and similarity scores, as well as robustness, highlighting its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the AraElectra-SQuAD model can be further enhanced and applied in various tasks such as chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users. By combining the power of transformer architecture with fine-tuning on SQuAD-like large data, this trend demonstrates its ability to provide accurate and contextually relevant answers to questions in Arabic.

Effective communication is key for healthcare providers to provide optimal care for patients. Pharmacists’ fluency in a patient’s native language is vital to communicate effectively. Additionally, language concordance improves patients’ trust and ensures health equity. In the United States (US), Hispanics are the largest minority group, but only 36% of the pharmacy schools in the US offer Spanish courses in their curriculum. Conversely, Middle Eastern countries have implemented English as the language of instruction in pharmacy schools, though the native language of the patient population is Arabic. The discrepancy between the language of education and the language used by patients might lead to communication problems, thus limiting a pharmacist’s role in practice. This review aims to describe the efforts of pharmacy schools both in the US and Middle Eastern countries to incorporate second language (Spanish and Arabic, respectively) in their curriculum. Spanish language content has scarcely been introduced into the pharmacy curriculum in the US either as didactic elements (elective courses, lab sessions, modules within a course, or co-curricular programs) or as language immersion experiences (rotations and internships nationally or abroad). In Arabic-speaking countries, an Arabic course was introduced to the pharmacy curriculum to enhance students’ communication skills. This review provides an overview of what has been done in various pharmacy programs to prepare students for adequate multilingual speaking. The findings reveal the need for additional strategies to assess the impact of language courses on student performance and patient experience, as well as language competence in pharmacists and pharmacy students.

Breeding for low hydrogen cyanide (HCN) varieties is a major objective of programmes targeting boiled cassava food product. To enhance breeding for low HCN, knowledge on genetic variation and trait heritability is essential. In this study, 64 cassava clones established across four locations and evaluated for HCN using three HCN assessment methods; 1 to 9 scale, 0 ppm to 800 ppm scale and a quantitative assay based on spectrophotometer readings (HCN_Spec). Data were also collected on the weather variables precipitation, relative humidity, and temperature. Significant differences were observed among clones (P < 0.001) and locations (p < 0.001). There were also significant (p < 0. 001) clone by environment interactions. Locations Arua and Serere posted higher HCN scores among clones and were associated with significantly higher (p < 0.001) mean daily temperature (K) and lower relative humidity (%) across 12-hour and 18-hour intervals. Within locations HCN broad sense heritability estimates ranged 0.22 – 0.64 while combined location heritability estimates ranged 0.14 to 0.32. Relationships between the methods were positive and strong (R2 = 0.75 – 0.92). It is expected that the information herein will accelerate efforts towards breeding for low HCN cassava varieties.

An undescribed diterpene, stellerterpenoid A (1) and two undescribed sesquiterpenoids, stellerterpenoids B and C (2-3), together with six known compounds, were isolated from the roots of Stellera chamaejasme L. Their structures were elucidated by extensive spectroscopic data (1D, 2D NMR, IR, UV and HR-ESI-MS). The absolute configuration of 1-3 were elucidated based on ECD calculation. Among of them, stellerterpenoid A was a rare 13, 14 - seco nortigliane diterpenoid and stellerterpenoid B was a guaiacane type sesquiterpenoid with an unusual 1, 2-diketone moiety. The known stelleraguaianone B (5) exhibited moderate activity to suppress NO production in LPS - simulated macrophages with an IC50 value of 24.76 ± 0.4 μM. None of the compounds showed inhibitory influenza virus and tumor activity.

In the realm of the five-category classification endeavor, there has been limited exploration into applied techniques for classifying Arabic text. These methods have primarily leaned on singletask learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

The visual span refers to the number of letters readers can identify in a single fixation without using linguistic skills. Proponents of the visual span hypothesis postulate an influence of early visual processing on reading speed. Given the slowness of reading Arabic texts, the present work aims to study the development of the visual span and its effects on reading speed in the Arabic-speaking context. Thirty-four subjects participated in the study. The trigram task and the rapid serial visual presentation (RSVP) paradigm were used to estimate visual span size and reading speed. In line with our initial assumptions, the results showed a significant effect of grade level on reading speed (F(2,31) = 30.93, p<0.001), visual span size (F(2,31) = 20.57, p<0.001). In good alignment with previous work, our results show that visual span size could explain around 40% of the reading speed variability. Interestingly, our analyses revealed a narrowing of visual span size in our Arabic sample. The results of study 2, suggest that the poor performance in the trigram task is due to poor visual attention capacities in our Arabic readers

This research assesses the classification of Arabic coffee into three primary variations (light, medium, and dark) using simulated data based on actual measurements of color information, antioxidant laboratory tests, and chemical composition tests. Two types of simulated data were generated, with the standard deviation of the measures varied. Multiple classifiers were used for classification. The findings demonstrate the use of color information in accurately distinguishing Arabic coffee types. The different CIE color values, as well as the excellent connection between color information and coffee classes, lead to flawless classification results. However, depending primarily on antioxidant information results in lower classification performance, with variability amongst classifiers due to increasing data complexity. Chemical composition information, on the other hand, possesses outstanding discriminatory strength, allowing faultless classification on its own. Certain characteristics, such as crude protein and crude fiber, show strong correlations and are critical in the classification of coffee types. Based on the findings, it is recommended to create a mobile application that uses image recognition to analyze coffee color while providing chemical composition information. End users, such as consumers, will be able to make informed judgments regarding their coffee preferences owing to this application.

The recent Google’s claim on breakthrough in quantum computing is a gong signal for further analysis of foundational roots of (possible) superiority of some quantum algorithms over the corresponding classical algorithms. This note is a step in this direction. We start with critical analysis of rather common reference to entanglement and quantum nonlocality as the basic sources of quantum superiority. We elevate the role of the Bohr’s principle of complementarity¹ (PCOM) by interpreting the Bell-experiments as statistical tests of this principle. (Our analysis also includes comparison of classical vs genuine quantum entanglements.) After a brief presentation of PCOM and endowing it with the information interpretation, we analyze its computational counterpart. The main implication of PCOM is that by using the quantum representation of probability, one need not compute the joint probability distribution (jpd) for observables involved in the process of computation. Jpd’s calculation is exponentially time consuming. Consequently, classical probabilistic algorithms involving calculation of jpd for n random variables can be over-performed by quantum algorithms (for big values of n). Quantum algorithms are based on quantum probability calculus. It is crucial that the latter modifies the classical formula of total probability (FTP). Probability inference based on the quantum version of FTP leads to constructive interference of probabilities increasing probabilities of some events. We also stress the role the basic feature of the genuine quantum superposition comparing with the classical wave superposition: generation of discrete events in measurements on superposition states. Finally, the problem of superiority of quantum computations is coupled with the quantum measurement problem and linearity of dynamics of the quantum state update.

Plants produce above- and below-ground biomass. However, our understanding of both production and decomposition of below-ground biomass is poor, largely because of the difficulties of accessing study materials. Below-ground organic matter decomposition studies are scanty and especially rare in the tropics. Here, we used a litter bag experiment to quantify the mass loss and nutrients dynamics of decomposing twigs and fine roots from an arbuscular mycorrhizal fungal associated tree, Parashorea chinensis, in a tropical rain forest in Southwest China. Overall, twig litter decomposed 1.9 times faster than fine roots (decay rate (k) twig=0.255, root=0.134). The difference in decomposition rates can be explained by a difference in phosphorus (P) concentration, availability and use by decomposers or C quality. Both materials showed an increase in N concentration, with final measurements still higher than initial levels. This suggests N may not be available due to microbial immobilization. Both carbon and nitrogen dynamics were significantly predicted by mass loss and showed a negative and positive relationship, respectively. Our study results imply that fine roots carbon and nitrogen contribute more to soils organic matter and enlarge the resident time. Therefore, better understanding of carbon cycle requires better understanding of mechanisms governing below ground biomass decomposition.

Uterine fibroid (UF) is a tumour in some parts of the uterus, which introduces health challenges or death due to failed surgery among women globally. This study was designed to ascertain the involvement of micro-nutrients, inflammation, and antioxidant enzymes in the UF development to gain further insights and provide a strategy for managing the disease. One hundred ninety reproductive-aged women were recruited and classified equally into case and control subjects. The supernatant obtained from excised tissues from the fibroid and the normal samples from the adjacent myometrium were assessed for the selected biochemical parameters with standard methods. The levels of vitamin A and sodium between 26-35 years; vitamins D, E, zinc, and selenium between 46-55 years; and vitamin E at 56 years and above significantly decreased (p&lt;0.05). Interleukin-2 (IL-2) level significantly increased (P &lt; 0.05) among the case between 36-45 years. An increase in the activity of glutathione-s-transferase and the reduction in glutathione peroxidase activity and vitamin A level in the uterus between 26-45 years were the most pronounced significant findings (p&lt;0.05) recorded. Prolonged vitamin A deficiency coupled with excess sodium salts facilitating inflammation induced by IL-2 are critical factors for UF development.

The issue of polarization, as opposed to inequality, has been little explored for European countries. in this paper, using harmonized data produced by Luxembourg Income Study Database, observes income trends for 12 European countries, showing an increase in polarization in many of the countries considered. the drivers that led to this concentration of income are also analyzed, noting heterogeneous factors within countries.

The potential of sodium alginate (ALG) and gum arabic (GA) as wall polymers for L-ascorbic acid (AA) encapsulation as a tool for their preservation against the thermo-oxidative degradation was investigated. The influence of such polymers used as wall material on the AA-content, size, encapsulation efficiency, encapsulation yield and thermo-oxidative stability were evaluated. The AA-microparticles were obtained using the spray-drying technique. An experimental Taguchi design was employed to assess the influence of the variables in the encapsulation process. The microparticles morphology and size distribution were characterized by scanning electron microscopy and laser diffraction. The thermal stability of AA microparticles was studied by differential scanning calorimetry and thermogravimetry analysis. This work points out the viability to encapsulate AA using GA and ALG through a spray-drying process. In general, a product yield ranging from 35.1% to 83.2% and an encapsulation efficiency above 90% was reached. Spherical microparticles with a smooth surface were obtained with a mean diameter around 6 μm and 9 μm for the those prepared with GA and ALG, respectively. The thermo-oxidative analysis showed that both polymers allow maintaining AA stable up to 188 °C, which is higher than the traditional processing temperature used in the fish feed industry.

Finding the roots of an equation is a fundamental problem in diverse fields. Optimization problem leads to non-linear equations for calculating the roots of the equation efficiently. Numerical iterative techniques are frequently applied when analytic solution is not available. Hybrid techniques to find roots of an equation is new to this fundamental problem. Recently, some attempts emerged to design hybrid algorithms for efficient solutions. A new heuristic algorithm is designed which is an intuitive approach to hybridization. In this paper, we implement two new algorithms: (1) a new BTsection algorithm blending the Bisection and Trisection algorithm, (2) a hybrid algorithm, that promises to be more efficient than the existing hybrid algorithms. The new hybrid algorithm is a blend of BTsection algorithm and Regula Falsi algorithm. The implementation results validate that the new algorithm, Hybrid4, surpasses the efficiency of existing hybrid algorithms Hybrid1, Hybrid2, and Hybrid3 algorithms. This paper contributes an essential hybrid algorithm to the repertoire of hybrid root finding algorithms.

Ranunculus asiaticus L. is an ornamental geophyte, mainly propagated by rehydrated tuberous roots. Vernalization before planting is a common practice to overcome the natural dormancy of tuberous roots, however little is known about the mechanisms underlying the plant response to low temperature. We investigated the influence of two hybrids, MBO and MDR, and three preparation procedures of tuberous roots, only rehydration (control, C), and rehydration plus vernalization at 3.5 °C for 2 weeks (V2) and for 4 weeks (V4), on plant growth, leaf photosynthesis, flowering and metabolism in plants grown in pot in unheated greenhouse. We reported the responses observed in the aerial part previously in this journal (Fusco et al., 2023). In this paper, we show changes in carbohydrate, amino acids, polyphenols and protein levels throughout the growing cycle, in the different plant stages: pre-planting, vegetative phase and flowering.

Cannabis Sativa L. has been used for a long time to obtain food, fiber and as a medicinal and psychoactive plant. Today the nutraceutical potential of C. Sativa is being increasingly reappraised; however, C. Sativa roots remain poorly studied, despite citations in the scientific literature. In this direction, we identified and quantified the presence of valuable bioactives (namely β-sitosterol, stigmasterol, campesterol, friedelin and epi-friedelanol) in the root extracts of C. Sativa, a finding which might pave the way to the exploitation of the therapeutic potential of C. Sativa in all its parts. To facilitate roots harvesting and processing, aeroponic (AP) and aeroponic elicited cultures (AEP), have been set up and compared to soil-cultivated plant (SP): interestingly a considerable overgrowth of the plants - particularly of roots - and a significant increase (up to 20 fold in the case of β-sitosterol) in the total content of the above roots’ bioactive molecules have been observed in AP and AEP. In conclusion aeroponics, an easy, standardised, free of contaminant cultivation tecchnique, allows an ease harvesting/processing of roots along with a greater production of their secondary bioactive metabolites which could be utilized in the formulation of health promoting and health care products.

The excellent properties of nanomaterials have been confirmed in many fields, but the effects on plants are still unclear. In this study, we have found that bismuth vanadium promoted the growth of taproots and the development of lateral roots, increased the roots activity by regulating the expression of polyamine or hormone-related genes and reducing the accumulation of reactive oxygen species in Arabisdopsis thaliana. Meantime, BV had higher antimicrobial effect than that of graphene oxide (GO). These results showed us a new perspective possibility of nanomaterials assisting the development of plants and crops, which is expected to become one of the ways to solve the problem of controlling and promoting the development of plants and crops.

To elucidate dynamic developmental processes in plants, live tissues and organs have to be visualized frequently and for long time periods. The development of roots is studied in depth at a cellular resolution not only to comprehend the basic processes fundamental to maintenance and pattern formation but also study stress tolerance adaptation in plants. Despite technological advancements, maintaining continuous access to samples and simultaneously preserving their morphological structures and physiological conditions without causing damage presents hindrances in the measurement, visualization and analyses of growing organs including plant roots. We propose a preliminary system which integrates the optical real-time visualization through light microscopy with a liquid culture which enables us to image at the tissue and cellular level horizontally growing Brachypodium roots every few minutes and up to 24 hours. We describe a simple setup which can be used to track the growth of the root as it grows including the root tip growth and osmotic stress dynamics. We demonstrate the system’s capability to scale down the PEG-mediated osmotic stress analysis and collected data on gene expression under osmotic stress.

Assessing carbon stocks in tropical forests is crucial for understanding their role in mitigating climate change. (1) Background: However, existing assessments have underestimated certain factors that can substantially influence carbon dynamics. (2) Methods: This study focuses on one such factor - buttress roots from a tropical forest. (3) Results: Our findings reveal that a significant proportion of trees (69.57%) had 3 to 5 buttress roots per tree. The total biomass of the buttress roots and the aboveground portion of the trees with buttress roots were calculated to be 8.5 tonnes/ha and 10.7 tonnes/ha respectively. The buttress root biomass accounted for 16.18% of the total tree biomass. It was observed that the presence of buttress roots during both the rainy and dry seasons increased the soil organic carbon content by an average of 20.8% in the upslope areas with buttress roots. During the rainy season, the presence of buttress roots did not affect the heavy fraction organic carbon in the soil layers in a statistically significant way. Regardless of the season, the soil respiration rate in the areas without buttress roots was higher than that in the areas with buttress roots. The presence of buttress roots had a positive effect on soil nutrient concentration in all seasons, creating a healthier environment for trees. Tree species with buttress roots had on average 20% higher organic carbon content during both the wet and dry seasons. (4) Conclusions: These findings highlight the importance of considering buttress roots in carbon stock assessments and forest management strategies. By integrating buttress roots into carbon accounting models, we can obtain more accurate estimates of carbon stock potential and develop more effective conservation and restoration strategies for tropical forests.

Suppose G is a graph with vertex-orbits O1,1 O2, . . . ,Ot, and jOi j denotes the cardinallity of Oi. Then OG (x) = åt 2 i=1 xjOi j is called as orbit polynomial. It is well-known that this polynomial 3 has a unique positive zero d in the interval [0, 1]. The aim of this paper is to study the specific 4 properties of this polynomial and then we determine the location of this root for several classes of 5 complex networks to compare with other graphical measures. Besides, we compare the unique 6 positive zero measure with several well-known centrality graph measures.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

In recent times, models like the Transformer have showcased remarkable prowess in tasks related to natural language processing. However, these models tend to be excessively intricate and demand extensive training. Additionally, while the multi-head self-attention mechanism in the Transformer model aims to capture semantic connections between words in a sequence, it encounters limitations when handling short sequences, thereby limiting its effectiveness in 5-polarity Arabic sentiment analysis tasks. The switch-transformer model has recently emerged as a high-performing alternative. Nevertheless, when these models are trained using single-task learning, they often fall short of achieving exceptional performance and struggle to generate robust latent feature representations, especially when working with compact datasets. This challenge is particularly pronounced in the case of the Arabic dialect, which is considered a low-resource language. Given these constraints, this research introduces a novel approach to sentiment analysis in Arabic text. This method leverages multitask learning in tandem with the switch-transformer shared encoder to enhance model adaptability and refine sentence representation. By introducing a mixture of expert (MoE) mechanism that break down the problem into smaller, more manageable sub-problems, the model becomes adept at handling lengthy sequences and intricate input-output relationships, benefiting both five-point and three-polarity Arabic sentiment analysis tasks. This proposed model effectively discerns sentiment in Arabic dialect sentences. The empirical results highlight the outstanding performance of the suggested model, as evidenced in evaluations on the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to the large number of texts and their diversity in all areas of life. In this manuscript, we apply four algorithms to classify tweets written in the Algerian dialect. To extract feelings, we used six features based on three polarities. In the presented work, we manually annotate a corpus of 2,891 texts and create an Algerian lexicon of idioms that contains 1328 annotated words. Our results show that there are improvements gained on the accuracy of the system, where we have achieved a better accuracy of 85.31%.

Arabic is present in the Arab world and beyond. It is used as an official (or co-official) language in the Arab World (this refers to the twenty-two member states of the Arab League). Also, Arabic has the status of a national language in Mali, Niger, and Senegal. Besides, it is spoken in linguistic enclaves in Nigeria, Cyprus, Turkey, Uzbekistan, Iran, and Afghanistan. In addition, it is employed as the liturgical language in Muslim countries and countries with a Muslim minority all over Africa and Asia. As a result of the migration to Western countries since the end of the nineteenth century, Arabs and Arabic has been present in such countries. The presence of Arabs in Western countries raises the following question: Are these Arabs language maintainers or language shifters? The present article is an attempt to answer this question through reviewing a number of studies that have dealt with Arabic language maintenance among the Arabs living in Western countries (namely the United States of America, the United Kingdom, Canada, Australia, New Zealand, Sweden, Greece, and France), deducing from these studies the major trends in Arabic language maintenance among these Arabs, and providing a critique of the studies.

Alfalfa, an important legume forage, is an ideal crop for sustainable agriculture and a potential bioenergy plant. Drought, one of the most common environmental stresses, substantially affects plants’ growth, development and productivity. MicroRNAs (miRNAs) are newly discovered gene expression regulators that have been linked to several plant stress responses. To elucidate the role of miRNAs in drought stress regulation of alfalfa, a high-throughput sequencing approach was used to analyze 12 small RNA libraries comprising of 4 samples, each with 3 biological replicates. We identified 348 known miRNAs, belonging to 80 miRNA families, from the 12 libraries and 281 novel miRNAs using Mireap software. 18 known miRNAs in roots and 12 known miRNAs in leaves were screened out as drought-responsive miRNAs. Except for miR319d and miR157a which were upregulated under drought stress, the expression pattern of drought-responsive miRNAs were different between roots and leaves in alfalfa. This is the first study discovering miR157a, miR1507, miR3512, miR3630, miR5213, miR5294, miR5368 and miR6173 are drought-responsive miRNAs. Target transcripts of drought-responsive miRNAs were computationally predicted. All 447 target genes for the known miRNAs were predicted using an online tool. This study provides a significant insight on understanding drought-responsive mechanisms of alfalfa.

Coulomb’s law and Newton’s law of gravitation are formally identical. Is it a mere coincidence? It seems to us that physics has answered yes so far, at least tacitly. We, on the other hand, do not believe in chance. Since the alternative to the fact that the strong similarity is only the result of chance is to believe that it is not, and in this case there must be a reason for this similarity, we have chosen the path of investigating the possibility that the two fields are not distinct, but have a common root that we will try to find. We propose an electrodynamics of electrically neutral bodies based on the hypothesis that the Coulomb constant in the case of attraction between charges of opposite sign is slightly greater than that of the case of repulsion between charges of the same magnitude as the previous ones, but of the same sign. If this difference were to exist, even small to the point of not being directly measurable in the laboratory, it could still produce macroscopic effects detectable in the case of large aggregates of charges, even if globally electrically neutral, as happens in the astronomical field. We deduce the electromagnetism of such a model and show the results obtained applying the theory to some significant cases such the light deflection in a gravitational field, a planet perihelion precession, the power radiated by merging black holes, the behaviour of antimatter in a gravitational field and, when available, we compare the predictions with the experimental data.

China’s leading development of a complete battery value chain for Electric Vehicles (EV) is restructuring the global automotive sector. The competitive advantage of China's EV battery industry lies on its firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork in an EV battery cluster in South China, this paper identifies what the Chinese EV battery industry’s core competency is and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability to supply lithium-ion batteries vehicle manufacturers (OEMs) with stable and consistent quality as well as competitive prices. This competency is acquired by firms through technological learning at workshop level while taking use of their experiences accumulated in mass production of batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge on battery manufacturing at large scale is also facilitated by the industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings illuminate the likely strategic strengths, weaknesses, and futures of the Chinese EV battery industry in global competition.

In this article, an iterative method of two step is proposed for finding all roots simultaneously of polynomial equations. The order of convergence of the proposed algorithm is 2m, by using any iterative scheme of order m. Numerical tests are performed to confirm the theoretical results and to compare the proposed scheme with existing methods for finding all roots simultaneously.

Globalization has had an impact on the education system in the UAE, where increased use of bilingual curriculum (Arabic-English) is held in high regard. Nevertheless, literacy among Emirati children and teenagers remains low. This study uses a 15-item, open-ended questionnaire completed by Emirati parents and an 8-item, open-ended questionnaire completed by Emirati primary school children from 8 to 11 years of age, and compares the translation of The Little prince into Modern Standard Arabic (MSA) and into the Emirati dialect. The results suggest that not only the Emirati dialect, but any Arabic dialect (in its respective Arab country) could be used in primary schools to motivate children to read in Arabic and bridge the gap between their spoken language (dialect) and formal written Arabic. Not only must an Arab child learn how to read, but also, they have to understand a very formal language system that they are not used to speaking at home, i.e., Modern Standard Arabic (MSA). Having books in their own dialects may make children more interested in reading, as they can understand them more easily.

Salinity substantially affects plant growth and crop productivity worldwide. Plants adopt several biochemical mechanisms including regulation of antioxidant biosynthesis to protect themselves against the toxic effects induced by the stress. One-year-old Pistachio rootstock exhibiting different degrees of salinity tolerance were subjected to sodium chloride induced salt stress to identify genetic diversity among cultivated pistachio rootstock for their antioxidant responses, and to determine the correlation of these enzymes to salinity stress. Leaves and roots were harvested following NaCl-induced stress. Results show that a higher concentration of NaCl treatment induced oxidative stress in the leaf tissue and to a lesser extent in the roots. Both tissues showed an increase in ascorbate peroxidase, superoxide dismutase, catalase, glutathione reductase, peroxidase and malondialdehyde. Responses of antioxidant enzymes were cultivar dependent, as well as temporal and dependent on the salinity level. Linear and quadratic regression model analysis revealed significant correlation of enzyme activities to salinity treatment in both tissues. The variation in salinity tolerance reflected their capabilities in orchestrating antioxidant enzymes at the roots and harmonized across the cell membranes of the leaves. The study provides a better understanding of root and leaf coordination in regulating the antioxidant enzymes to NaCl induced oxidative stress.

Phosphorus (P) is an essential plant macronutrient; however, its availability is often limited in soils. Plants have evolved complex mechanisms for efficient phosphate (Pi) absorption, which are responsive to changes in external and internal Pi concentration, and orchestrated through local and systemic responses. To explore these systemic Pi responses, here, we identified AtMYB44 as a phloem-mobile mRNA, an Arabidopsis homolog of Cucumis sativus MYB44, that is responsive to the Pi-starvation stress. qRT-PCR assays revealed that AtMYB44 was up-regulated and expressed in both shoot and root in response to Pi-starvation stress. The atmyb44 mutant displayed higher shoot and root biomass, compared to wild-type plants, under Pi-starvation conditions. Interestingly, the expression of PHOSPHATE TRANSPORTER1;2 (PHT1;2) and PHT1;4 was enhanced in atmyb44 in response to a Pi-starvation treatment. A split-root assay showed that AtMYB44 expression was systemically regulated, under Pi-starvation conditions and, in atmyb44, systemic controls on PHT1;2 and PHT1;4 expression were moderately disrupted. Heterografting assays confirmed graft transmission of AtMYB44 transcripts, and PHT1;2 and PHT1;4 expression was decreased in heterografted atmyb44 rootstocks. Taken together, our findings support the hypothesis that mobile AtMYB44 mRNA serves as a long-distance Pi response signal, which negatively regulates Pi transport and utilization in Arabidopsis.

In this paper, an iterative procedure to find the solution of a nonlinear structural model is introduced. The model presents different multiplicities where parameters are randomly selected within a solvability region. To achieve this aim, a class of multipoint fixed-point iterative schemes for single roots is modified to find multiple roots, reaching the fourth order of convergence. Complex discrete dynamics techniques are employed to select the members with the most stable performance. The structural problem, as well as some academic problems involving multiple roots, are solved numerically to verify the theoretical analysis, robustness and applicability of the proposed scheme.

Four new unusual pentacyclic triterpenoids (1-4) were isolated from the roots of Jasminum sambac (L.) Ait. Their structures were elucidated by 1D and 2D NMR analysis, single crystal X-ray diffraction and HRESIMS.

This article derives the closed form solution to finding the roots of a quadratic equation in SU3.

Enteric septicemia of catfish, columnaris disease, and streptococcosis, caused by Edwardsiella ictaluri, Flavobacterium columnare, and Streptococcus iniae, respectively, are the most common bacterial diseases of economic significance for the pond-raised channel catfish Ictalurus punctatus industry. Several management practices are used by catfish farmers to prevent large financial losses from these diseases such as the use of commercial antibiotics and other chemicals. In order to discover environmentally benign alternatives, using a rapid bioassay, we evaluated a crude extract from the roots of muscadine Vitis rotundifolia against these fish pathogenic bacteria and determined that the extract was most active against F. columnare. Subsequently, several isolated compounds from the root extract were further evaluated against F. columnare. Among these isolated compounds, (+)-hopeaphenol (2) and (+)-vitisin A (3) were found to be the most active against F. columnare, with 24-h 50% inhibition concentrations of 4.0±0.7 and 7.7±0.6 mg/L, respectively, and minimum inhibitory concentrations of 9.1±0 mg/L for each compound. Efficacy testing of 2 and 3 is necessary to further evaluate the potential for these compounds to be used as antibacterial agents for managing columnaris disease.

Salt marshes are highly dynamic and biologically diverse ecosystems that serve as natural habitats for numerous salt-tolerant plants (halophytes). We investigated the bacterial communities associated with the roots and leaves of plants growing in the coastal salt marshes of the Daphne Bay area in Alabama, United States. We compared external (epiphytic) and internal (endophytic) communities of both leaf and root plant organs. Using 16S rDNA amplicon sequencing methods, we identified 10 bacterial phyla and 59 different amplicon sequence variants (ASVs) at the genus level. Bacterial strains belonging to the phyla Proteobacteria, Bacteroidetes, and Firmicutes were highly abundant in both leaf and root samples. At the genus level, sequences of the genus Pseudomonas were common across all four sample types, with the highest abundance found in the leaf endophytic community. Additionally, Pantoea was found to be dominant in leaf tissue compared to roots. Our study revealed that plant habitat (internal vs. external for leaves and roots) was a determinant of the bacterial community structure. Co-occurrence network analyses enabled us to discern the intricate characteristics of bacterial taxa. Our network analysis revealed varied levels of ASV complexity in the epiphytic networks of roots and leaves compared to the endophytic networks. Overall, this study advances our understanding of the intricate composition of the bacterial microbiota in habitats (epiphytic and endophytic) and organs (leaf and root) of coastal salt marsh plants and suggests that plants might recruit habitat- and organ-specific bacteria to enhance their tolerance to salt stress.

Peanut (Arachis hypogaea) is a crop plant with high economic value, but the epigenetic regulation of its growth and development has only rarely been studied. The peanut histone deacetylase 1 gene (AhHDA1) has been isolated and is known to be ABA- and drought-responsive. In this paper, we investigate the role of AhHDA1 in more detail, focussing on the effect of altered AhHDA1 expression in hairy roots at both the phenotypic and transcriptional levels. Agrobacterium rhizogenes-mediated transformation of A. hypogaea hairy roots was used to analyse how overexpression or RNA interference of AhHDA1 affects this tissue. In both types of transgenic hairy root, RNA sequencing was adopted to identify genes that were differentially expressed, and these genes were assigned to specific metabolic pathways. AhHDA1-overexpressing hairy roots were growth-retarded after 20 d in vitro cultivation, and superoxide anions and hydrogen peroxide accumulated to a greater extent than in control or RNAi groups. Overexpression of AhHDA1 is likely to accelerate flux through various secondary synthetic metabolic pathways in hairy roots, as well as reduce photosynthesis and oxidative phosphorylation. Genes encoding the critical enzymes caffeoyl-CoA O-methyltransferase (Araip.XGB85) and caffeic acid 3-O-methyltransferase (Araip.Z3XZX) in the phenylpropanoid biosynthesis pathway, chalcone synthase (Araip.B8TJ0) and polyketide reductase (Araip.MKZ27) in the flavonoid biosynthesis pathway, and hydroxyisoflavanone synthase (Araip.0P3RJ) and isoflavone 2'-hydroxylase (Araip.S5EJ7) in the isoflavonoid biosynthesis pathway were significantly upregulated by AhHDA1 overexpression, while their expression in AhHDA1-RNAi and control hairy roots remained at a lower level or was unchanged. Our results suggest that alteration of secondary metabolism activities is related to overexpression of AhHDA1, which is mainly reflected in phenylpropanoid, flavonoid and flavonoid biosynthesis. Future studies will focus on the function of AhHDA1 interacting proteins and their action on cell growth and stress responses.

Jalapeño peppers (Capsicum annuum L.) are an important agricultural product worldwide. Despite its high demand in recent years, there are few studies on its production under adverse conditions caused by environmental phenomena. Crops in protected environments such as aeroponics offer greater control of these phenomena and enable efficient use of resources. However, it is necessary to validate the cultivation technique for which new technologies are being used. One of these is the analysis of images captured in the visible and near and far infrared spectrum which involves using non-invasive techniques in order to characterize the growth of a plant and diagnose if it presents any type of stress. This study presents the characterization of vegetative growth and fruiting of jalapeño pepper plants in an aeroponic system, where the root, leaf development parameters and fruits were measured in four jalapeño pepper crops through images of plants captured in the visible (VIS), near infrared (NIR) and far infrared (IR) spectrums. Four crops of thirty jalapeño pepper plants were sown to obtain a total of one hundred and twenty plants which were characterized in the different phases of growth and fruiting. Each of the four jalapeño pepper crops were monitored for sixty days in an aeroponic system in a greenhouse. The first crop was intended to carry out tests to establish the appropriate fertigation times, the next three crops were grown under favorable conditions. Algorithms were developed in Matlab to obtain, over ten image capture sessions, the morphometric and thermal parameters of the roots (perimeter, area, length and average temperature), plants (perimeter, area, height and average temperature), and fruiting. (yield and number of fruits). The statistical analysis was carried out using the ANOVA and Tukey tests considering a value of p ≤ 0.05. The results obtained indicate that there is no significant difference between the characterizations of the four crops. This statement is also supported by the visual analysis of the growth curves parameters of the four crops. In addition, the temperature inside the aeroponic system was contrasted with the ambient temperature and it was verified that the temperature to which the roots are exposed was in the range of 10°C – 20°C. The thermal analysis determined that a plant that presents water stress and is also exposed to high temperatures has an average leaf temperature of 3.7 to 5 °C above the optimal condition for the plant, while a plant with stress at normal temperatures was 1.3 °C higher than the plant without stress.

Memory and emotion are highly vulnerable to psychiatric disorders like post-traumatic stress disorder (PTSD), which has been linked to serotonin (5-HT) metabolism disruptions. In fact, over 90% of the 5-HT precursor tryptophan (Trp) is metabolized via the Trp-kynurenine (KYN) meta-bolic pathway, producing a variety of bioactive molecules. The aadat (kat2) gene encodes mito-chondrial kynurenine aminotransferase (KAT) isotype 2, responsible for kynurenic acid (KYNA) production. Little is known about its role in behavior. In CRISPR/Cas9-induced aadat knockout (kat2−/−) mice, we examined the effects on emotion, memory, motor function, Trp and its metabolite levels, enzyme activities in the plasma and the urine of 8-week-old males compared to wild-type mice. Transgenic mice showed more depressive-like behaviors in the forced swim test, but not in the tail suspension, anxiety, or memory tests. They also had fewer center field and corner entries, shorter walking distances, and fewer jumping counts in the open field test. Plasma metabolite levels are generally consistent with those of urine: KYN, antioxidant KYNs, 5-hydroxyindolacetic acid, and indole-3-acetic acid levels are lower; enzyme activities in KATs, kynureninase, and monoamine oxidase/aldehyde dehydrogenase are lower, but kynurenine 3-monooxygenase is higher; and ox-idative stress and excitotoxicity indices are higher. Transgenic mice show depression-like behavior in a learned helplessness model, emotional indifference, and motor deficits, coupled with a decrease in KYNA, a shift of Trp metabolism toward the KYN-3-HK pathway, and a partial decrease in the gut microbial Trp-indole pathway metabolite. This is the first evidence that deleting the aadat gene causes depression-like behaviors that are unique to despair experience, which appears to be linked to excitatory neurotoxic and oxidative stresses. This may lead to the development of a double-hit preclinical model in experience-based depression, better understanding of these complex condi-tions, and more effective therapeutic strategies by elucidating the relationship between Trp me-tabolism and PTSD pathogenesis.

The proposal that the genetic code was formed on the basis of protein synthesis directed by tRNA dimers is reviewed and updated. The tRNAs paired through the anticodon loops are an indication on the process. Dimers are considered mimics of the ribosomes – structures that hold tRNAs together and facilitate the transferase reaction, and of the translation process – anticodons are at the same time codons for each other. The primitive protein synthesis system gets stabilized when the product peptides are able to bind the producers therewith establishing a self-stimulating production cycle. The chronology of amino acid encoding starts with Glycine and Serine, indicating the metabolic support of the Glycine-Serine C1-assimilation pathway, which is also consistent with evidence on origins of bioenergetics mechanisms. Since it is not possible to reach for substrates simpler than C1 and compounds in the identified pathway are apt for generating the other central metabolic routes, it is considered that protein synthesis is the beginning and center of a succession of sink-effective mechanisms that drive the formation and evolution of the metabolic flow system. Plasticity and diversification of proteins construct the cellular system following the orientation given by the flow and implementing it. Nucleic acid monomers participate in bioenergetics and the polymers are conservative memory systems for the synthesis of proteins. Protoplasmic fission is the final sink-effective mechanism, part of cell reproduction, guaranteeing that proteins don’t accumulate to saturation, which would trigger inhibition.

Part 1 of this 2-paper series identified a wide and deep network of context, generative mechanisms and outcomes responsible for psychosocial development in a performance basketball club. In this – part 2 – study, the stakeholder’s programme theories were tested during a full-season ethnography of the same club. The findings confirm the highly individualised nature of each young person’s journey. Methodologically, immersion in the day-to-day environment generated a fine-grain analysis of the processes involved, including: i) sustained attentional focus; ii) structured and unstructured skill building activities; iii) deliberate and incidental support; and iv) feelings indicating personal growth. Personal development in and through sport is thus shown to be conditional, multi-faceted, time-sensitive and idiosyncratic. The findings of this two-part study are considered to propose a model of psychosocial development in and through sport. This heuristic tool is presented to support sport psychologists, coaches, club administrators and parents to deliberately create and optimise developmental environments.

Background: Given the extensive translation of the Prolapse Quality of Life Questionnaire (P-QoL) into many languages, it is imperative to develop an Arabic version to facilitate study on Pelvic organ health within the Arabian culture. Objective: To investigate, cross culturally adapt and validate Arabic version of the P-QoL. Study Design: Cross-cultural adaptation and psychometric testing. Methods: A total of 90 participants were included in the study. This cross-sectional study went through two phases; during phase I, the P-QoL was translated and adapted from English into Arabic. The Arabic version was psychometrically validated during phase II using the test-retest reliability internal consistency with Cronbach’s alpha coefficient, convergent construct validity (CC) among the four study tools using Spearman’s coefficient (r) and discriminative validity using Mann-Whitney test to find differences between the means of the two samples. Results: A satisfactory level of semantic, conceptual, idiomatic, and content comparability was reached in the cross-cultural adaptation of the Arabic version of the P-QoL questionnaire. The internal consistency was high in terms of psychometric validation, with a Cronbach’s alpha coefficient of 0.971 for the P-QoL questionnaire. The test-retest results showed a high reliability with interclass correlation coefficient (ICC) of P-QoL = 0.987. The convergent construct validity was highly accepted (moderately strong), reflecting a positive correlation between the Arabic version of the P-QoL and the Australian Pelvic Floor Dysfunction Questionnaire (APFD) r = 0.68 (p &lt; 0.001). Similarly, a significant convergent validity of P-QoL Arabic version and Visual Analogue Scale (VAS) r = 0.47 (p &lt; 0.001) was found, as well as the correlation between the APFD and the VAS r = 0.46 (p &lt; 0.001). However, there were no significant correlation between the SF-12, the P-QoL, the APFD, and the VAS. Conclusion: Based on a significant correlation between the Arabic APFD and the VAS, the results reveal good reliability, internal consistency, and construct validity. It is recommended that Arabic-speaking females with pelvic organ prolapse use the Arabic version of the P-QoL. More research is needed to assess the responsiveness of the P-QoL.

Plant roots have significant effects on the soil erosion rates, since they can strongly change the soil detachment capacity (Dc). This study quantifies Dc at different flow rates in soils with Alnus subcordata species, compared to three other species (Brachythecium plumose, Gleditsia caspica and Sambucus ebulus species) in the Saravan forest park and develops regression equations for predicting Dc. Undisturbed samples collected from soils with the four tree species and subjected to five slopes (from 4.3 to 38.3%) and five water discharges (from 0.28 to 0.71 l m−1 s−1) using a hydraulic flume. The results showed that Dc was significantly lower in soils with Alnus subcordata species compared to Brachythecium plumose, Gleditsia caspica and Sambucus ebulus species, as the consequence of the changes in the root characteristics, so that Dc was negatively correlated with root weight density, root length and root biomass. The unit stream power had high accuracy for predicting Dc for all of the studied species. The lowest value of rill erodibility (Kr) was obtained in soils with Alnus subcordata species using regression relationship between the Dc and the shear stress of the soil. This experiment helped to show the importance of plant roots in reducing the soil detachment rates and provided a contribution in understanding the choice of appropriate species for soil conservation.

This research aimed to determine salient factors affecting the decision to become a beginning organic farmer. New and beginning organic farmers have unique characteristics, showcasing their dedication to environmental justice and social justice at the expense of their own businesses. This research employed a multiple-case case study design. We conducted semi-structured interviews with 40 first-generation farmers who operate organic farms in Arkansas, Florida, or Georgia. We analyzed interview transcripts using the qualitative analysis approach of coding. Our results revealed the reasons that people with little practical knowledge start farms. They are inspired by those around them who succeed and encouraged by influential characters in the field who assure them they can do something they love and be profitable. This research showed that first-generation farmers find inspiration and develop values rooted in food justice. Our findings have implications for developing and implementing current and future programmatic activities that aim to enhance beginning farmer training and workforce development. Identifying sources of inspiration will help researchers and service providers target newer and beginning farmers to support a vibrant food system, including a burgeoning market opportunity, developing strong communities around food, and building grassroots solutions.

In this discussion paper, I give an account for non-experts of, arguably, quantum dynamics in the brain, underlying the modeling of affective behavior of humanoid robots in the making. Outreach to the larger audience inevitably leads to abbreviations and simplifications; nonetheless, I try to offer the backgrounds of why it is important to study the virtual aspects of ‘people’ we meet online, what dimensions play a role in assessing such creatures, what humanities, psychology, communication, and computer science provide to help us understand how we become attached to non-existent others. As its capstone for the time being, an approach derived from physics is discussed for a robot to handle emotional ambiguity and vagueness of its user. Two computational models, Silicon and Quantum Coppélia, are discussed for their potential and limitations in explaining human affective behavior while dealing with mediated characters.

Postharvest physiological deterioration (PPD) of fresh cassava roots limits its shelf-life to about 48 hours. There is a demand for simple, cheap, and logistically feasible solutions for extending the shelf life of fresh cassava roots at industrial processes. In this study, three different types of bag materials were tested (woven polypropylene, tarpaulin, and jute as a potential storage solution for cassava roots with different levels of mechanical damages. Microclimate (temperature, humidity, CO2) was monitored to understand the storage conditions for up to 12 days. The results showed that fresh cassava roots could be stored for 8 days, with minimal PPD and starch loss (2.4 %). How-ever, roots with significant mechanical damage (cuts, breakages) had a considerably shorter shelf life in the storage bag, compare to whole roots and roots with retained peduncle (stalk where roots are connected to the main plant). Wetting the roots and type of bag material were not significant factors in determining the shelf life and starch loss. Carbon dioxide concentration in the stores significantly correlated with the starch loss in fresh cassava roots and is proposed as a possible method for continuously and remotely monitoring starch loss in large scale commercial operations and reduce postharvest losses.

We construct a family of two-step optimal fourth order iterative methods for finding single root of non-linear equations. We generalize these methods to simultaneous iterative methods for determining all the distinct as well as multiple roots of single variable non-linear equations. Convergence analysis is present for both cases to show that the order of convergence is four in case of single root finding method and is twelve for simultaneous determination of all roots of non-linear equation. The computational cost, Basin of attraction, efficiency, log of residual and numerical test examples shows, the newly constructed methods are more efficient as compared to the existing methods in literature.

Autophagy is a highly conserved catabolic process in eukaryotic cells. Reactive nitrogen species play roles as inductors and signaling molecules of autophagy. A key mechanism of NO-mediated signaling is S-nitrosylation, a posttranslational modification (PTM) of proteins at cysteine resi-dues. In the present work we analyzed the patterns of protein S-nitrosylation during the induc-tion of autophagy in Triticum aestivum roots. The accumulation of S-nitrosylated proteins in the cells during autophagy induced by KNO2 and antimycin A was visualized using monoclonal an-tibodies by Western blot analysis, and proteins were identified using a standard bottom-up pro-teomics approach. Protein S-nitrosylation is a labile and reversible PTM, and therefore SNO group can be lost during experimental procedures. Subsequent bioinformatic analysis using predictive algorithms and protein-ligand docking showed that identified proteins possess hypothetical S-nitrosylation sites. Analyzing protein-protein interaction networks enabled us to discover the targets that can directly interact with autophagic proteins, and those that can interact with them indirectly via key multifunctional regulatory proteins. In this study, we show that S-nitrosylation is a key mechanism of NO-mediated regulation of autophagy in wheat roots. A combination of in silico predictive algorithms with a mass spectrometry analysis provides a targeted approach for the identification of S-nitrosylated proteins.

The primary goal of this research is to examine the α-amylase inhibitory effect of phytoconstituents found in the roots of the Cyclea peltata plant. The extract will also be used to investigate the effect of root extract on α-amylase inhibition assay. The roots were gathered, processed, and extracted in petroleum ether before being kept at 4ºC. The extract was exposed to a preliminary phytochemical examination. The extract was employed in the α-amylase inhibition experiment at concentrations of 50, 100, 200, 400, 600, 800, and 1000 µg/ml with acarbose as a control. Molecular docking analysis was performed on the phytoconstituents cycleapeltine, cycleadrine, cycleacurine, cycleanorine, cycleahomine chloride, and acarbose on human pancreatic alpha-amylase 1B2Y. A preliminary phytochemical study revealed the presence of alkaloids, saponins, and terpenoids. The tests came back negative for flavonoids, steroids, and tannin. The root extract inhibited α-amylase at 69.42 ± 0.74 % at 1000 µg/ml and acarbose at 94.63 ± 0.57 %. The IC50 value was calculated and found to be 484.08 µg/ml for the extract and 42.47 µg/ml. The docking studies, revealed that cycleacurine (-4.751 Kcal/mol) has a comparable anti-diabetic effect to Acarbose (-6.713 Kcal/mol). Furthermore, the function groups –OH and –NH found in phytoconstituents interacted with the active site of 1B2Y similarly to acarbose. This provides evidence that the function groups –OH and –NH present in the phytoconstituents might inhibit alpha amylase.

Effect of wireworm-damaged lettuce roots on antioxidative defence system (ascorbate-glutathione cycle, photosynthetic pigments) and movement of insect/slug parasitic nematodes towards determined root exudates was studied in a glasshouse experiment. Lettuce seedlings were grown in a substrate soil in the absence/presence of wireworms (Elateridae). Determination of antioxidants and photosynthetic pigments were analysed. Volatile organic compounds (VOC) emitted from lettuce roots were investigated by GC-MS. Herbivore induced root compounds, namely 2,4-nonadienal, glutathione and ascorbic acid, were selected for a chemotaxis assay with nematodes Steinernema feltiae, S. carpocapsae, Heterorhabditis bacteriophora, P. papillosa, and O. myriophilus. Root pests negatively affected photosynthetic pigment contents even before the appearance of visible symptoms, as protective pigments responded to the presence of reactive oxygene species (ROS). Using lettuce as a model plant, we recognised ascorbate-glutathione system as a redox hub in defense response against wireworms and analysed its role in root-exudate mediated chemotaxis of nematodes. Infected plants also demostrated increased levels of volatile 2,4-nonadienal. Entomopathogenic nematodes (EPNs, S. feltiae, S. carpocapsae, and H. bacteriophora) proved to be more mobile than parasitic nematodes O. myriophilus and P. papillosa towards chemotaxis compounds. Among them 2,4 – nonadienal repelled all tested nematodes. Most exudates that are involved in belowground tritrophic interactions remain unknown but an increasing effort is being made in this field of research. Understanding more of these complex interactions would not only allow a better understanding of the rhizosphere but could also offer ecologically sound alternatives in pest management of agricultural systems.

Transformer models have emerged as frontrunners in the field of natural language processing, primarily due to their adept use of self-attention mechanisms to grasp the semantic linkages between words in sequences. Despite their strengths, these models often face challenges in single-task learning scenarios, particularly when it comes to delivering top-notch performance and crafting strong latent feature representations. This challenge is more pronounced in the context of smaller datasets and is particularly acute for under-resourced languages such as Arabic. In light of these challenges, this study introduces a novel methodology for text classification of Arabic texts. This method harnesses the newly developed Reverse Positional Encoding (RPE) technique. It adopts inductive-transfer learning (ITL) framework combined with a switching self-attention shared encoder, thereby increasing the model's adaptability and improving its sentence representation accuracy. The integration of Mixture of Experts (MoE) and Reverse Positional Encoding (RPE) techniques empowers the model to process longer sequences more effectively. This enhancement is notably beneficial for Arabic text classification, adeptly supporting both the intricate five-point and the simpler ternary classification tasks. The empirical evidence points to its outstanding performance, achieving accuracy rates of 87.20% for the HARD dataset, 72.17% for the BRAD dataset, and 86.89% for the LABR dataset, as evidenced by the assessments conducted on these datasets.

Erectile dysfunction (ED) due to diabetes mellitus remains difficult to treat despite advances in pharmacotherapeutic approaches in the field. Therefore, this study investigated the erectogenic effect of the methanolic extract of Maytenus procumbens roots on type 2 diabetes in rats. The fructose-streptozotocin model was used to induce type 2 diabetes-linked ED in male rats. The sexually active male Sprague Dawley rats were randomly divided into two major groups; normal group and high fructose fed group for 120 days. After 120 days, the high fructose fed group rats were given a single intraperitoneal injection of a freshly prepared streptozotocin solution (30 mg/kg). The diabetic ED rats were orally administered with the extract at 250 mg/kg, daily for 28 days. The serum, brain and penile tissues were removed for biochemical analysis and protein expression. Increased testosterone level, mounting frequency, reduced blood glucose level and serum fructosamine content was observed after 28 days of treatment in diabetic rats. Methanolic extract also exhibited an inhibitory effect on arginase, AChE, and ACE activities. The crude extract further downregulated proteins PDE-5, RhoA and increased expression of eNOS in the diabetic ED treated rats. The results obtained indicate that the methanolic extract of Maytenus procumbens roots ameliorates erectile dysfunction in type 2 diabetes-induced erectile dysfunction in rats.

Among the many neglected underutilized species, tuberous Andean root crops like the ahipas (Pachyrhizus ahipa) constitute a promising alternative for increasing diversity in nutrient sources and food security at a regional level. In this study, we present the content of some functional compounds in tuberous roots from several ahipa accessions and the progenies of the interspecific hybrid X207 (P. ahipa × P. tuberosus). A significant objective was to determine protein and free amino acids in the roots to evaluate their food quality as protein supply. The interspecific hybrids have been found to possess the root quality to provide the crop with a higher dry matter content. The high dry matter content of the P. tuberosus Chuin materials is retained in the root quality of the hybrids. Food functional components like carbohydrates, organic acids, and proteins were determined in several ahipa accessions and a stable (non-segregating) progeny of the interspecific hybrid, X207. The X207 roots showed a significantly higher dry matter content and a lower content in soluble sugars, but no significant differences were found in starch content or organic acids compared to the ahipa accessions. About the root mineral contents, Fe and Mn concentrations in X207 were significantly raised compared to the average of ahipa accessions. Among the ahipa and the hybrid, no prominent differences in protein content or protein amino acids were found, being both partially defective in providing sufficient daily intake of some essential amino acids. Root weight, a central component of root yield, was significantly higher in X207, but thorough field studies are required to substantiate the hybrid’s superior yield performance..

With proper processing and utilization, biofortified cassava may contribute to the nutritional status of the consumers, thus, the need for this study. High-quality cassava flour from white- (TME 419) and biofortified (TMS 01/1368) cassava varieties were produced at a commercial processing factory, after which the flour is composite with wheat flour to produce bread. The nutritional composition, physical properties and sensory quality of the composite bread were analyzed using standard methods. Results showed that composite bread from 20% biofortified cassava flour (20-YCF) had a higher value of total β-carotene (0.74 μg/g), moisture (37.83%) and ash (2.29%) contents. The fat (3.72%) and protein (12.83%) contents were higher in 20% white cassava flour (20-WCF) composite bread. The 20-YCF composite bread had the highest loaf volume (3286.2 cm3), elasticity (6.32), chewiness (40.51 N) and gumminess (6.41), 20-WCF composite bread had higher specific volume (3.59 cm3/g) and hardness (176.50 N). The 100% wheat bread had higher cohesiveness (0.10) and loaf weight (932.35 g). A significant negative correlation (r = - 0.98, p≤0.05) exist between bread hardness and protein content. The composite bread compared favourably with the 100% wheat bread in terms of weight and aroma, but, the 100% wheat bread was more acceptable.

Sesquiterpene lactones (STLs) are a large group of terpenoids most commonly found in plants of the Asteraceae family, e. g. in chicory plants, displaying a wide range of interesting biological activities. However, further studies on the biological potential of chicory derived STLs and analogues are challenging as only four of these molecules are commercially available (as analytical standards), and to date there are no published or patented simple extraction-purification processes capable of large scale STLs isolation. In this work we describe a novel three-step large scale extraction and purification method for the simultaneous purification of 11,13-dihydrolactucin (DHLc) and lactucin (Lc) starting from a chicory genotype rich in these STLs and in the corresponding glycosyl and oxalyl conjugated forms. After a small-scale screening on 100 mg of freeze-dried chicory root powder, the best results were achieved with a 17 h water maceration at 30°C. With these conditions we managed to increase the content of DHLc and Lc, at the same time favouring the hydrolysis of their conjugated forms. On a larger scale, the extraction of 750 g of freeze-dried chicory root powder, followed by a liquid-liquid extraction step and a reversed-phase chromatography, allowed the recovery of 642.3 ± 76.3 mg of DHLc and 175.3 ± 32.9 mg of Lc. The two pure STLs were subsequently used in the context of semi-synthesis to generate analogues for biological evaluation as antibacterial agents. In addition, other described chicory STLs that are not commercially available were also synthesized or extracted to serve as analytical standards for the study. In particular, lactucin-oxalate and 11,13-dihydrolactucin-oxalate were synthesized in two steps starting from Lc and DHLc, respectively. On the other hand, 11β,13-dihydrolactucin-glycoside was obtained after a MeOH/H₂O (70/30) extraction, followed by a liquid-liquid extraction step and a reversed-phase chromatography. Together, this work will help facilitate the evaluation of the biological potential of chicory derived STLs and their semisynthetic analogues.

The results of an LA-ICP-MS U-Pb dating and Hf isotope study of zircon crystals separated from small eclogite xenoliths found in Devonian kimberlites within the Prypyat horst, Ukraine, are reported. The studied area is located in the Central Belarusian Suture Zone, which represents a Paleoproterozoic belt extending along the boundary between the Sarmatian and Fennoscandian segments of the East European Platform. Four laser ablation sites on two zircon grains yielded Paleoproterozoic U-Pb ages between 1954 ± 24 Ma and 1735 ± 54 Ma. In contrast, three of four Hf sites revealed negative εHf values and Paleoarchean to Mesoarchean model ages, excluding the possibility that the eclogite xenoliths represent segments of a Paleoproterozoic subducted slab or younger mafic melts crystallized in the subcontinental lithospheric mantle. A single laser ablation Hf spot yielded a positive εHf value (+3) and a Paleoproterozoic model age. Two models for eclogite origin can be proposed. The first foresees the extension of the Archean lower-crustal or lithospheric roots beneath the Central Belarus Suture Zone for over 200 km from the nearest known outcrop of Archean rocks. The second model is that the Central Belarus Suture Zone represents a rifted-out fragment of the Kola-Karelian craton that was accreted to Sarmatia before the actual collision of these two segments of Baltica.

Phospholipase Dα1 (PLDα1) belongs to phospholipases, a large phospholipid hydrolyzing protein family. PLDα1 has a substrate preference for phosphatidylcholine leading to enzymatic production of phosphatidic acid, a lipid second messenger with multiple cellular functions. PLDα1 itself is implicated in biotic and abiotic stress responses. We present here a shot-gun differential proteomic analysis on roots of two pldα1 mutants compared to the Col-0 wild type. Our data suggest new roles of PLDα1 in endomembrane transport, mitochondrial protein import and protein quality control and glucosinolate biosynthesis. Thus, we identified proteins involved in endocytosis, endoplasmic reticulum-Golgi transport and attachment sites of endoplasmic reticulum and plasma membrane (V-type proton ATPases, protein transport protein SEC13 homolog A, vesicle-associated protein 1-2, vacuolar protein sorting-associated protein 29, syntaxin-32, all upregulated in the mutants), mitochondrial import and electron transport chain (mitochondrial import inner membrane translocase subunits TIM23-2 and TIM13, mitochondrial NADH dehydrogenases, ATP synthases, cytochrome c oxidase subunit 6b-1, ADP,ATP carrier protein 2, downregulated in the mutants) and glucosinolate biosynthesis (3-isopropylmalate dehydrogenases 1, 2 and 3, methylthioalkylmalate synthase 1, cytochrome P450 83B1, Glutathione S-transferase F9, indole glucosinolate O-methyltransferase 1, adenylyl-sulfate kinase 1, all upregulated in mutants). Our results suggest broader biological roles of PLDα1 as anticipated so far.

This study assesses the secondary metabolites, minerals, antimicrobial, antioxidant, and anticancer properties of Micromeria fruticosa plant different botanical parts (leaf, stem, root, flower) extracted with various solvents. The plant samples were sequentially obtained using different solvents (n-hexane, ethanol and water) through steeping. Then, each of the extracts was further analyzed by using gas chromatography–mass spectrometry (GC-MS). Moreover, the extracts were bio-assayed to test their antioxidant, antibacterial, and anti-cancer activities. Quali-quantitative analysis of M. fruticosa crude extracts revealed the occurrence of 27 secondary metabolites were identified including mainly monoterpenes, sesquterpenes, and fatty acids, with varying quantities. Some of the major bioactive compounds included, Menthone (5.42-30.05%), Oleamide (3.40-32.20%), Pulegone (10.66-64.1%), and Menthol (3.61-100.0%), which were detected mostly in all plant parts with significant quantities. Several antioxidant minerals, mainly, Fe, Zn, and Mn, were detected with the highest amounts in the Micromeria water extracts. Results from antimicrobial assays showed that the water extract of leaves exhibited the highest DPPH scavenging activity (89.73%) followed by the water extract of flowers (80.07%) at a concentration of 100 μg/mL. The water extract of stems showed greater antimicrobial activity against all the tested gram negative and positive bacteria (Methicillin-resistant Staphylococcus aureus, Staphylococcus aureus, Escherichia coli, and Shigella sonnie). The leaves ethanolic and stem aqueous extracts had a strong antimicrobial activity against E. coli. and C. albicans. Flower aqueous extract demonstrated the highest cytostatic effect on the colon cell line by reducing viability up to 30.4%, followed by the leaf ethanol extract with 38.6% cell viability reduction at 1000 µg/mL. In conclusion, extraction solvents influenced the recovery of phytocompounds and the highest pharmacological activities of the different extracts could be correlated to the presence of additional bioactive compounds. Our results suggest that M. fruticosa plant is a promising source of natural products with antioxidant, anti-microbial and anti-cancer properties for potential nutraceutical, therapeutic, and functional food applications. , , , , , ,

Nowadays, brain signal processing is performed rapidly in various brain-computer interface (BCI) applications. Most researchers focus on developing new methods for the future or improving the basic implemented models to identify the optimum standalone feature set. Our research focuses on four ideas. One of them introduces future communication models, and the others are for improving old models or methods. These are: 1) new communication imagery model instead of speech imager using the mental task: Due to speech imagery is very difficult, and it is impossible to imagine sound for all of the characters in all of the languages. Our research introduces a new mental task model for all languages that call Lip-sync imagery. This model can use for all characters in all languages. This paper implemented two lip-sync for two sounds, characters or letters. 2) New combination Signals: Selecting an inopportune frequency domain can lead to inefficient feature extraction. Therefore, domain selection is so important for processing. This combination of limited frequency ranges proposes a preliminary for creating Fragmentary Continuous frequency. For the first model, two s intervals of 4 Hz as filter banks were examined and tested. The primary purpose is to identify the combination of filter banks with 4Hz (scale of each filter bank) from the 4Hz to 40Hz frequency domain as new combination signals (8Hz) to obtain well and efficient features using increasing distinctive patterns and decreasing similar patterns of brain activities.3) new supplement bond graph classifier for SVM classifier: When SVM linear uses in very noisy, the performance is decreased. But we introduce a new bond graph linear classifier to supplement SVM linear in noisy data. 4) a deep formula recognition model: it converts the data of the first layer into a formula model (formula extraction model). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. The output of the last layer is the coefficients selected by different functions in different layers. Finally, the classifier extracts the root interval of the formulas, and the diagnosis does based on the root interval. For all of the ideas achieved the results of implementing methods. The results are between 55% to 98%. Less result is 55% for the deep detection formula, and the highest result is 98% for new combination signals.

Background: The number of young Japanese Brazilians, who are return migrants with Japanese ancestral roots, is increasing rapidly in Japan. However, the characteristics of their mental health and the relation between mental health and a complex ethnic identity remains unclear. Methods: This cross-sectional study compared 25 Japanese-Brazilian high school students with 62 Japanese high school students living in the same area. Research using self-report questionnaires on mental health, help-seeking behavior tendencies, and ethnic identity was conducted. The Japanese-Brazilian group was also divided into high and low ethnic identity groups, and their mental health conditions were compared. Results: The Japanese-Brazilian group had significantly poorer mental health conditions and lower ethnic identities than the Japanese group and were less likely to seek help from family members and close relatives. Among the Japanese Brazilians, those with low ethnic identity had significantly poorer mental health than those with high ethnic identity. Conclusions: Young Japanese Brazilians may face conflicts of ethnic identity that can disturb their mental health. To build an inclusive society, the establishment of community services to support mental health and to help return migrants develop their ethnic identity is essential.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p &lt; 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.

Milk serves as a crucial source of natural bioactive compounds essential for human nutrition and health. The gastrointestinal digestion of milk proteins releases various biologically active peptides that positively impact human health and enhance physiological performance. With the growing production of cheese, Greek yogurt, and other dairy products, the volume of unutilized acid whey is significantly increasing. Acid whey, containing almost all whey proteins and approximately one-quarter of casein, presents a valuable raw material for generating peptides with potential health benefits. These peptides exhibit properties such as antioxidant, antimicrobial, anti-inflammatory, anticarcinogenic, antihypertensive, antithrombotic, opioid, mineral-binding, and growth-stimulating activities, contributing to improved human immunity and the treatment of chronic diseases. Enzymatic hydrolysis of milk proteins has been developed as a method to produce these peptides, facilitating their use in food fortification. To incorporate whey into functional beverages, it undergoes fermentation by lactic acid bacteria with proteolytic activity. To specifically enrich these beverages with peptides exhibiting targeted bioactivities, it is essential to select lactic acid bacteria strains possessing the requisite protease profiles. Additionally, the sensory properties of the final product can be enhanced using starter cultures with diverse metabolic capabilities, even in the absence of added flavoring agents. Given the imperative to develop waste-free technologies and enhance the profitability of the dairy industry, there is a growing need to efficiently utilize and transform acid whey into a broad spectrum of valuable products and food additives. This approach is justified not only by the nutritional benefits of the bioactive components in whey but also by the commercial potential of safe and health-enhancing products.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The impact of interaction SS with MJO on rainfall along Java Island to East Nusa Tenggara has been studied using daily rainfall observation data from 2140 weather observation stations, which are also equipped with GSMaP, OLR, and ERA5 data. There were 61 SS events found, 17 during active MJO and 44 in inactive MJO. Rainfall increases more in the western study area when the active MJO and more in the eastern study area when the inactive MJO is due to SS. SS reduces rainfall in the Jakarta region, regardless of the activity or inactivity of the MJO. SS causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the IMC, SS predominates over MJO in supporting increased water vapor transport. Large-scale synoptic circulations like the monsoon, SS, and MJO can interact with local land-sea wind circulation to produce spatiotemporal variability in rainfall on Java Island. Rainfall mostly increases in the afternoon and decreases in the morning when SS occurs, whether there is MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by lowering CIN and raising VIMFC.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

Pyrops candelaria is one of the common pests of fruit trees, but the research on the pathogenic microorganisms it may carry is very limited. Therefore, it is essential to reveal the pathogenic microbes it carries and their potential hazards. This study found a new virus from the transcriptome of P. candelaria, which was first reported in P. candelaria and named as PyCaV (Pyrops candelaria associated virus). RACE and bioinformatics assay revealed that the full-length of PyCaV is 10855 bp with the polyA tail, containing a single open reading frame (ORF) encoding a polyprotein consisting of 3171 amino acid (aa). The virus has a typical Iflavirus structure, including two rhv domains, a RNA helicase domain (HEL), a 3C cysteine protease domain (Pro), and a RNA-dependent RNA polymerase domain (RdRp). Further phylogenetic analysis revealed that this virus belongs to family Iflaviridae and sequence alignements analysis suggedted PyCaV is new member in unassigned genus of family Iflaviridae. Further in-depth analysis of virus-infection-part showed that PyCaV is distributing in the whole longan lanternfly, including its head, chest, and abdomen, but more PyCaV was identified in the chest. The distribution of PyCaV in different parts of P. candelaria was further explored, which showed that more PyCaV were detected in its piercing-sucking mouthparts and chest viscera. Statistical analysis showed that the PyCaV infection was affected by time and locations.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

COX II containing a dual core CuA active site is one of the three core subunits of mitochondrial Cco, which plays a significant role in the physiological process. In this report, the full-length cDNA of COXⅡ gene was cloned from Sitophilus zeamais, which had an ORF of 684 bp encoding 227 amino acids residues. The predicted COXⅡ protein had a molecular mass of 26.2 kDa with pI value of 6.37, and multiple sequence alignment and phylogenetic analysis indicated that Sitophilus zeamais COXⅡ had high sequence identity 78.51% with the COXⅡ of other insect species, especially similarity to sitophilus oryzae. This gene was subcloned into the prokaryotic expression vector pET-32a, and induced by IPTG in E.coli Transetta (DE3) expression system. Finally the COXⅡ with 6-His tag was purified using affinity chromatography with Ni²⁺-NTA agarose. WB showed the recombinant COXⅡ was about 44 kD, and the concentration of fusion protein was 50μg/mL. UV-spectrophotometer and infrared spectrometer analysis showed that recombinant COXⅡ could catalyze the oxidation of substrate Cytc, and influenced by AITC. It was found that AITC could form a hydrophobic region with COXⅡ protein via molecular docking, besides, a sulfur atom of AITC structure could form a length of 2.9 Å hydrogen bond with Leu-31. These results will provide valuable information for elucidating the role of COXⅡ in Sitophilus zeamais responses to AITC, meanwhile, it will helpful to carry out a point mutation in AITC binding sites for the future research.

In this paper, we extend the KdVSKR equations in (1+1)-dimension and (2+1)-dimension to fractional KdVSKR equations with modified Riemann-Liouville derivative. The (2+1)-dimensional KdVSKR equation, which is a recent extension of (1+1)-dimensional KdVSKR equation, can model the resonances of solitons in shallow water. By means of the Hirota bilinear method, finite symmetry group method and consistent Riccati expansion method, many new interaction solutions have been derived. Soliton-cnoidal interaction solution of the (1+1)-dimensional fractional KdVSKR equation has been derived for the first time. For the (2+1)-dimensional frac-tional KdVSKR equation, two-wave interaction solutions and three-wave interaction solutions, including dark-soliton-sine interaction solution, bright-soliton-elliptic interaction solution, and lump-hyperbolic-sine interaction solution, have been derived. Impact of the fractional order on the shapes of these solutions has been illustrated by figures. Three-wave interaction solutions of fractional systems have not been reported in the existing references. The research idea in this paper can be applied to other fractional differential equations.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.