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Soil pollution caused by metal(loid)s is increasingly serious and poses unexpected risks to terrestrial organisms. Establishing soil quality standards is essential for assessing ecological risks of metal(loid)s and protecting soil ecosystems. However, the limited availability of metal(loid) ecotoxicological data has hampered the development of soil quality standards due to financial and practical constraints on toxicity testing. This study collected 77 normalization equations and 58 cross-species extrapolation equations to calculate the normalized EC10 (the added concentration causing a 10 % inhibition effect) of metal(loid)s under a representative scenario. A set of quantitative ion character-activity relationship (QICAR) models were then constructed using normalized EC10 and nine critical ionic characters (AR, AR/AW, BP, MP, Z/r2, Z/r, Xm, σp, and |Log(KOH)|). Subsequently, these QICAR models were employed to predict ecotoxicological EC10 of 17 metal(loid)s to 12 soil species and coupled with species sensitivity distribution (SSD) to determine Predicted No Effect Concentration (PNEC). The results demonstrated the coupled QICAR-SSD model could effectively derive terrestrial PNEC for data-poor metal(loid)s, with errors between the predicted PNEC and reported soil standards (excluding soil background levels) from different countries mostly <0.3 orders of magnitude. Finally, soil ecological criteria (SEC) for 17 metal(loid)s were calculated using an added risk approach based on PNEC and national soil background concentration. Overall, the coupled model proposed here can provide a valuable supplement to the development of soil quality standards for numerous metal(loid)s in soil components.

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Gastrochilus is an orchid genus containing 73 species of mainly epiphytic on trees or rocks in mountain forests of tropical and subtropical Asia. Previous phylogenetic analyses and morphological assessments have failed to produce a well-resolved phylogeny at the infrageneric level. In the present study, a new infrageneric classification of Gastrochilus is proposed based on thoroughly morphological and phylogenetic analyses based on 52 species. Our phylogenetic analysis divided the genus into six sections including three new sections, G. sect. Pseudodistichi, G. sect. Brachycaules and G. sect. Acinacifolii. We also reinstate G. suavis to the specific rank. Furthermore, two new species, G. armeniacus Jun Y. Zhang, B. Xu & Yue H. Cheng and G. minjiangensis Jun Y. Zhang, B. Xu & Yue H. Cheng, are described and illustrated. A key to six sections of the genus is presented.

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Background: Collecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. Methods: CHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. Results: For non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. Conclusion: The tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.

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This dataset presents a comprehensive collection of handwritten Grantha characters, comprising numbers and vowels, gathered from participants spanning diverse age groups. Utilizing standard A4 sheets, participants were instructed to handwrite Grantha characters. The Grantha script encompasses 10 numbers and 34 vowels. The Grantha Character dataset comprises 44 distinct characters of numbers and vowels. A dataset comprising 133 handwritten samples for each number and 133 for each vowel was collected. These samples underwent digitization and preprocessing steps, including segmentation, resizing, and grayscale conversion. The final dataset consists of 5852 images, comprising 1330 samples for numbers and 4522 samples for vowels. The data is provided in both image and CSV formats, accompanied by corresponding labels. facilitating its utilization in machine learning model development. With limited datasets available for the Grantha script, this contribution addresses a significant gap by providing a benchmark dataset for Grantha numeral and vowel recognition. Moreover, this novel dataset serves as a fundamental resource for commencing machine learning research in Indian languages that have historical connections to the Grantha script.

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Pyoverdines are high affinity siderophores produced by most Pseudomonas with a wide role in microbial interspecies interactions. They are primarily composed of a conserved chromophore moiety, an acyl side chain and a peptide backbone which may be highly variable among strains. Upon ferric iron sequestration, pyoverdines are internalized through specialized receptors. The peptide precursor of pyoverdine, termed ferribactin, is synthesized by a set of non-ribosomal peptide synthetase (NRPS) enzymes and further modified by tailoring enzymes. While PvdL, the NRPS responsible for the synthesis of the peptide moiety that derives into the chromophore is conserved, the NRPSs for the peptide backbone are different across fluorescent Pseudomonas. Although the variation of pyoverdine is a widely recognized characteristic within the genus, the evolutionary events associated with the diversity and distribution of this trait remain mostly unknown. This study analyzed the NRPSs clusters for the biosynthesis of the peptide backbone of ferribactin in the genomes of a representative subset of strains of the Pseudomonas fluorescens complex. Bioinformatic analysis of the specificity of adenylation domains of the NRPSs allowed the prediction of 30 different pyoverdine variants. Phylogenetic reconstruction and mapping of the NRPS clusters pinpointed two different general levels of modifications. In the first level, a complete replacement of the set of NRPRs by horizontal transfer occurs. In the second level, the original set of NRPSs is modified through different mechanisms, including partial substitution of the NRPS genes by horizontal transfer, adenylation domain specificity change or NRPS accessory domain gain/loss.

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The Pegon script is an Arabic-based writing system used for Javanese, Sundanese, Madurese, and Indonesian languages. Due to various reasons, this script is now mainly found among collectors and private Islamic boarding schools (pesantren), creating a need for its preservation. One preservation method is digitization through transcription into machine-encoded text, known as OCR (Optical Character Recognition). No published literature exists on OCR systems for this specific script. This research explores the OCR of Pegon typed manuscripts, introducing novel synthesized and real annotated datasets for this task. These datasets evaluate proposed OCR methods, especially those adapted from existing Arabic OCR systems. Results show that deep learning techniques outperform conventional ones, which fail to detect Pegon text. The proposed system uses YOLOv5 for line segmentation and a CTC-CRNN architecture for line text recognition, achieving an F1-score of 0.94 for segmentation and a CER of 0.03 for recognition.

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Automatic narrative text analysis is gaining traction as artificial intelligence-based computational linguistic tools such as named entity recognition systems and natural language processing (NLP) toolkits become more prevalent. Character identification is the first stage in narrative text analysis; however, it is difficult due to the diversity of appearances and distinctive characteristics among regions. Further challenging analyses, such as role classification, emotion and personality profiling, and character network development, require successful character identification initially, which is crucial. Because there are so many annotated English datasets, computational linguistic tools are mostly focused on English literature. However, there are restricted tools for analyzing Balinese story texts because of a scarcity of low-resource language datasets. The study presents the first annotated Balinese story texts dataset for narrative text analyses, consisting of four sub-datasets for character identification, alias clustering (named entity linking, alias resolution), and character classification. The dataset is a compilation of 120 manually annotated Balinese stories from books and public websites, spanning multiple genres such as folk tales, fairy tales, fables, and mythology. Two Balinese native speakers, including an expert in sociolinguistics and macrolinguistics, annotated the dataset using predetermined guidelines set by an expert. The inter-annotator agreement (IAA) score is calculated using Cohen's Kappa Coefficient, Jaccard Similarity Coefficient, Mean F1-score to measure the level of agreement between annotators and dataset consistency and its reliability. The first subdataset consists of 89,917 annotated words with five labels referring to the Balinese-character named entities. Each character entity's appearance in 6,634 sentences is further annotated in the second subdataset. These two sub-datasets can be used for character identification purposes at the word and sentence level. The list of character groups which are groups of various aliases for each character entity has been annotated in the third subdataset for alias clustering purposes. The third subdataset contains 930-character groups from 120 story texts with each story text containing an average of 7-to-8-character groups. In the fourth subdataset, 848-character groups-of the 930-character groups in the third subdataset-have been categorized as protagonists and antagonists. The protagonists (66.16 %) make up most character groups, with the antagonists (33.84 %) making up the rest of the groups. The fourth subdataset can be used for computing-based classification of characters into two roles between protagonist and antagonist. These datasets have the potential to improve research in narrative text analyses, especially in the areas of computational linguistic tools and advanced machine learning (ML) and deep learning (DL) models in low resource languages. It can also be used for further research including character network development, character relationship extraction, and character classification beyond protagonist and antagonist.

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Developing Deep Learning Optical Character Recognition is an active area of research, where models based on deep neural networks are trained on data to eventually extract text within an image. Even though many advances are currently being made in this area in general, the Arabic OCR domain notably lacks a dataset for ancient manuscripts. Here, we fill this gap by providing both the image and textual ground truth for a collection of ancient Arabic manuscripts. This scarce dataset is collected from the central library of the Islamic University of Madinah, and it encompasses rich text spanning different geographies across centuries. Specifically, eight ancient books with a total of forty pages, both images and text, transcribed by the experts, are present in this dataset. Particularly, this dataset holds a significant value due to the unavailability of such data publicly, which conspicuously contributes to the deep learning models development/augmenting, validation, testing, and generalization by researchers and practitioners, both for the tasks of Arabic OCR and Arabic text correction.

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Recently, oriented external electric fields (OEEFs) have earned much attention due to the possibility of tuning the properties of electronic systems. From a theoretical perspective, one can resort to electronic structure calculations to understand how the direction and strength of OEEFs affect the properties of electronic systems. However, for multi-reference (MR) systems, calculations employing the popular Kohn-Sham density functional theory with the traditional semilocal and hybrid exchange-correlation energy functionals can yield erroneous results. Owing to its decent compromise between accuracy and efficiency for MR systems at the nanoscale (i.e., MR nanosystems), in this study, thermally assisted occupation density functional theory (TAO-DFT) is adopted to explore the electronic properties of n-acenes (n = 2-10), containing n linearly fused benzene rings, in OEEFs, where the OEEFs of various electric field strengths are applied along the long axes of n-acenes. According to our TAO-DFT calculations, the ground states of n-acenes in OEEFs are singlets for all the cases examined. The effect of OEEFs is shown to be significant on the vertical ionization potentials and vertical electron affinities of ground-state n-acenes with odd-number fused benzene rings. Moreover, the MR character of ground-state n-acenes in OEEFs increases with the increase in the acene length and/or the electric field strength.

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Research suggests that character attributes may enable youth to envision and perform civic actions that benefit society, but few studies have examined the longitudinal associations between character attributes and civic actions. As a response to this gap, this study investigated how specific character attributes (purpose, future mindedness, humility, and moral courage) may be differently linked to various civic actions (community service, political activities, social activism) cross-sectionally and longitudinally. Survey data were collected from 521 adolescents (Mage = 14.44, SD = 1.92, 58.5% girls, 60.8% White). Structural equation models demonstrated that purpose and future mindedness were positively associated with all three civic actions at Time 1, but not at Time 2. Humility was not associated with any Time 1 civic actions, but was negatively associated with Time 2 political activities. Moral courage was positively associated with Time 1 social activism and Time 2 political activities. Results illuminate the short-term interplay between character attributes and civic actions and point to internal resources that may promote different forms of youth civic action.

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With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors, and complexity of character interactions. Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy. To address the dataset complexity and multi-targeting problems, feature extraction is optimized, and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of the YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students-raising their hands, reading, writing, playing on their cell phones, looking down, and leaning on the table-in high-definition images. And the accuracy and robustness of this network is verified. Compared with small-object detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network demonstrates good detection performance on low-resolution small objects, complex datasets with numerous targets, occlusion, and overlapping students.

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Long non-coding RNAs (lncRNAs) are well known for their oncogenic or anti-oncogenic roles in cancer development. AGAP2-AS1, a new lncRNA, has been extensively demonstrated as an oncogenic lncRNA in various cancers. Abundant experimental results have proved the aberrantly high level of AGAP2-AS1 in a great number of malignancies, such as glioma, colorectal, lung, ovarian, prostate, breast, cholangiocarcinoma, bladder, colon and pancreatic cancers. Importantly, the biological functions of AGAP2-AS1 have been extensively demonstrated. It could promote the proliferation, migration and invasion of cancer cells. Simultaneously, the clinical significances of AGAP2-AS1 were also illustrated. AGAP2-AS1 was exceptionally overexpressed in various cancer tissues. Clinical studies disclosed that the abnormal overexpression of AGAP2-AS1 was tightly connected with overall survival (OS), lymph nodes metastasis (LNM), clinical stage, tumor infiltration, high histological grade (HG), serous subtype and PFI times. However, to date, the biological actions and clinical significances of AGAP2-AS1 have not been systematically reviewed in human cancers. In the present review, the authors overviewed the biological actions, potential mechanisms and clinical features of AGAP2-AS1 according to the previous studies. In summary, AGAP2-AS1, as a vital oncogenic gene, is a promising biomarker and potential target for carcinoma prognosis and therapy.

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This study builds upon our prior researches and seeks to investigate and clarify the influences of various characteristics of hydrogen bonds (H-bonds) and charge transfer (CT) interactions, which were detected within the inhibitor binding pockets (labeled as the QM models I-IV) of MraYAA-capuramycin, MraYAA-carbacaprazamycin, MraYAA-3'-hydroxymureidomycin A, and MraYAA-muraymycin D2 complexes by QTAIM and NBO analyses from DFT QM/MM MD calculations, on the 17O chemical shielding (CS) and electric field gradient (EFG) tensors of carboxylate (Oδ), carbonyl (C═O), and hydroxyl (O-H) oxygens in these models. The 17O CS and EFG tensors of these three types of oxygens in QM models I-IV were calculated at the M06-2X/6-31G** level by including the solvent effects using the polarizable continuum model. From the computed 17O CS and EFG tensors in these models, it was found that the nuclear shielding, σiso, for carboxylate or carbonyl oxygen increases (shielding effect) as the H-bond length decreases and the percentage p-character of nOδ/nC═O lone pair partner in the CT interaction enhances. In contrast, the σiso (17O-H) decreases (deshielding effect) with a reduction in the H-bond length as well as with an enhancement in percentage s-character of the nOH lone pair/σ*O-H antibond. By reducing the H-bond length or by increasing p-character of the nOδ/nC═O lone pair, the 17Oδ/17O═C quadrupole coupling constant smoothly decreases, while the 17Oδ/17O═C asymmetry parameter smoothly increases. Moreover, these calculated parameters are in a good agreement with the experimental values. The information garnered here is valuable particularly for further understanding of empirical correlations between 17O NMR spectroscopic and H-bonding characteristics in the protein-ligand complexes.

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Teachers play a crucial role in guiding learners through life's challenges. They face educational and socio-economic shifts while striving to teach for a better future. Our study focuses on equipping future teachers, called pre-service teachers, with resilience-building resources during their teacher training program. A potential antecedent to resilience, which facilitates cognitive strategies and attentional processes, is cognitive flexibility. We first tested whether cognitive flexibility predicts resilience. Next, relying on the tenets of positive psychology, which studies resilience, we developed and tested the effectiveness of character strengths intervention in enhancing pre-service teachers' resilience and cognitive flexibility. An individual's belief in their ability to change personal resources could influence how an intervention manifests. Hence, we studied how mindset impacts the intervention's effect on cognitive flexibility and, thus, resilience. We adopted a multi-method approach, guided by Polk's theory of resilience, to test out objectives. Using a cross-sectional design, study one (n = 273) found that cognitive flexibility significantly predicted resilience. Study two (N = 193; nexp = 133, ncont = 60) was a multi-site field experiment. We found that intervention significantly enhanced resilience (experimental group M = 29.62, control group M = 28.33) and cognitive flexibility (experimental group M = 54.42, control group M = 52.01). Further, a growth mindset, was found to moderate the indirect effect of character strengths intervention on resilience via cognitive flexibility. The study contributes to theoretical and practical advancements in resilience. Taken together, the findings highlight the cognitive-affective-behavioural makeup of resilience and, importantly, the role of cognitive flexibility. The intervention can be seamlessly integrated into teacher training curricula for a resilient future.

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Introduction: This study aims to investigate whether exercise adherence and positive mental character significantly affect subjective well-being among Chinese college students during the COVID-19 pandemic and whether positive mental character plays a mediating role. Methods: The study employed questionnaires, including the Exercise Adherence Scale, the Positive Mental Character Scale, and the Subjective Well-Being Scale, which were administered to students across seven universities in Henan Province, China. A total of 1,001 participants were analyzed in the final sample. Data were analyzed using SPSS 21.0 for descriptive statistics, independent samples T-test, correlation, and regression analyses. Furthermore, structural equation model with AMOS was conducted to examine the potential mediating effect of positive mental characteristics on the relationship between exercise adherence and subjective well-being. Results: The results indicated significant differences in exercise adherence, positive mental character, and subjective well-being between male and female participants, with males scoring higher in all three domains. Among Chinese university students during the COVID-19 pandemic, there was a significant correlation among exercise adherence, positive mental character, and subjective well-being. Exercise adherence was found to have a significant and positive impact on both positive mental character and subjective well-being. Additionally, it was found that positive mental character had a significant positive association with subjective well-being. The mediating role of positive mental character in the relationship between exercise adherence and subjective well-being was partially supported. Discussion: Exercise adherence among Chinese college students had a significant positive association on both positive mental character and subjective well-being throughout the COVID-19 pandemic. The exercise adherence can directly or indirectly enhance subjective well-being through its association with positive mental character. Therefore, positive mental and subjective well-being can be enhanced by consistent physical activity even during a pandemic.

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Self-interest bias describes an observer's tendency to judge moral transgression leniently when they benefit from it. However, what factors moderate the self-interest bias is an open empirical question. Here, we investigated to what extent hypocrisy moderates the self-interest bias. Preregistered Study 1a (N = 194) and replication in Study 1b (N = 193) demonstrated that observers' interest impacts moral character judgments of hypocritical transgressors. This effect was explained by observers' goal attainment due to transgression (Study 2, N = 713) and agreement to aid observers' or ingroup interests (Study 3, N = 634). Importantly, transgressors' hypocrisy moderated the impact of observers' interests in moral character judgments (Studies 2 & 3). In summary, when judging hypocritical transgressors, peoples' moral character judgments tend to be biased by their or their group's interests. However, in comparison to non-hypocritical transgressors, this impact is less pronounced.

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Readers of Chinese characters need to recognize how they are formed in order to identify them correctly. However, our understanding of the cognitive processing of characters in working memory is limited. In Experiment 1, using the character N-back task paradigm, electrophysiological data were recorded from 26 participants to investigate the effects of the visual feature of radicals on neural activity during the character recognition, updating and maintenance in the N-back task. Results showed that compound characters required longer response times than single-component characters. For the event-related potentials (ERPs), the compound character condition had more negative N2pc and lower P300 amplitudes than the single-component character condition. In Experiment 2, data from 26 participants were used to analyse the effect of the phonological feature of radicals on neural activity during the character recognition, updating and maintenance in the N-back task. Results showed that there was a larger P200 in the irregular character condition than in the regular character condition, but there was no difference between the regular and the irregular characters in the N2pc, P300 and slow wave (SW) components. The visual feature and the phonological feature of the radicals may have different effects on the character processing. This study reveals the neural effects of Chinese character radicals on cognitive processing in a working memory task and provides behavioural and electrophysiological evidence for a theoretical model of verbal working memory subprocesses.

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Pashtu is one of the most widely spoken languages in south-east Asia. Pashtu Numerics recognition poses challenges due to its cursive nature. Despite this, employing a machine learning-based optical character recognition (OCR) model can be an effective way to tackle this issue. The main aim of the study is to propose an optimized machine learning model which can efficiently identify Pashtu numerics from 0-9. The methodology includes data organizing into different directories each representing labels. After that, the data is preprocessed i.e., images are resized to 32 × 32 images, then they are normalized by dividing their pixel value by 255, and the data is reshaped for model input. The dataset was split in the ratio of 80:20. After this, optimized hyperparameters were selected for LSTM and CNN models with the help of trial-and-error technique. Models were evaluated by accuracy and loss graphs, classification report, and confusion matrix. The results indicate that the proposed LSTM model slightly outperforms the proposed CNN model with a macro-average of precision: 0.9877, recall: 0.9876, F1 score: 0.9876. Both models demonstrate remarkable performance in accurately recognizing Pashtu numerics, achieving an accuracy level of nearly 98%. Notably, the LSTM model exhibits a marginal advantage over the CNN model in this regard.

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Gymnosperms originated in the Middle Devonian and have experienced a long evolutionary history with pulses of speciation and extinction, which resulted in the four morphologically distinct extant groups, i.e., cycads, Ginkgo, conifers and gnetophytes. For over a century, the systematic relationships within the extant gymnosperms have been debated because different authors emphasized different characters. Recent phylogenomic studies of gymnosperms have given a consistent topology, which aligns well with extant gymnosperms classified into three classes, five subclasses, eight orders, and 13 families. Here, we review the historical opinions of systematics of gymnosperms with special reference to several problematic taxa and reconsider the evolution of some key morphological characters previously emphasized by taxonomists within a phylogenomic context. We conclude that (1) cycads contain two families, i.e., the Cycadaceae and the Zamiaceae; (2) Ginkgo is sister to cycads but not to conifers, with the similarities between Ginkgo and conifers being the result of parallel evolution including a monopodial growth pattern, pycnoxylic wood in long shoots, and the compound female cones, and the reproductive similarities between Ginkgo and cycads are either synapomorphic or plesiomorphic, e.g., the boat-shaped pollen, the branched pollen tube, and the flagellate sperms; (3) conifers are paraphyletic with gnetophytes nested within them, thus gnetophytes are derived conifers, and our newly delimited coniferophytes are equivalent to the Pinopsida and include three subclasses, i.e., Pinidae, Gnetidae, and Cupressidae; (4) fleshy cones of conifers originated multiple times, the Podocarpaceae are sister to the Araucariaceae, the Cephalotaxaceae and the Taxaceae comprise a small clade, which is sister to the Cupressaceae; (5) the Cephalotaxaceae are distinct from the Taxaceae, because the former family possesses typical female cones and the fleshy part of the seed is derived from the fleshiness of integument, while the latter family has reduced female cones and preserves no traces of the seed scale complexes.

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This work reports synthesis of a germylene based donor-acceptor molecule and its thermal excitation to a triplet state by coordination with a Lewis acid. Products have been characterized by single crystal X-ray diffraction, EPR spectroscopy, and SQUID measurement, in conjunction with DFT calculation. The singlet-triplet energy gap of the donor-acceptor molecule is dramatically reduced from -18.8 to -7.2 kcal/mol by the coordination with B(C6F5)3 (BCF), which enables an intramolecular single electron transfer from one germylene moiety to another upon heating, forming an intramolecular radical ion pair with diradical character. The work provides an approach to the formation of thermally populated open-shell species of heavier main group elements.