Enhancing English Language Vocabulary Learning among Indigenous Learners through Google Translate

The emergence of new technologies has brought massive changes to teaching and learning processes. In recent years, mobile phones have evolved into effective teaching tools; when used practically, they could improve learning outcomes. The potential of mobile phones as a learning platform has led to a proliferation of research into their effectiveness. This paper aims to examine the effectiveness of the Google Translate mobile application (hereinafter “app”) in improving indigenous learners’ English language vocabulary. Fifteen Iban participants with low English language proficiency from rural schools were chosen through purposive sampling. The data were collected by comparing scores in the pre-test and post-test. In addition, the data were triangulated through structured interviews. Key findings indicated that almost all participants achieved high scores in the post-test. The interviews also revealed that all participants affirmed that Google Translate supports their English language proficiency, and only one participant was unsure of its effectiveness. Thus, the findings of this study imply that Google Translate could be an effective teaching tool to enhance learners’ English language vocabulary. Future research could examine the effectiveness of the app in teaching vocabulary in different contexts

Privacy-preserving continual learning methods for medical image classification: a comparative analysis

BackgroundThe implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution.MethodsWe evaluated twelve privacy-preserving non-storage continual learning algorithms based deep learning models for classifying retinal diseases from public optical coherence tomography (OCT) images, in a class-incremental learning scenario. The OCT dataset comprises 108,309 OCT images. Its classes include normal (47.21%), drusen (7.96%), choroidal neovascularization (CNV) (34.35%), and diabetic macular edema (DME) (10.48%). Each class consisted of 250 testing images. For continuous training, the first task involved CNV and normal classes, the second task focused on DME class, and the third task included drusen class. All selected algorithms were further experimented with different training sequence combinations. The final model's average class accuracy was measured. The performance of the joint model obtained through retraining and the original finetune model without continual learning algorithms were compared. Additionally, a publicly available medical dataset for colon cancer detection based on histology slides was selected as a proof of concept, while the CIFAR10 dataset was included as the continual learning benchmark.ResultsAmong the continual learning algorithms, Brain-inspired-replay (BIR) outperformed the others in the continual learning-based classification of retinal diseases from OCT images, achieving an accuracy of 62.00% (95% confidence interval: 59.36-64.64%), with consistent top performance observed in different training sequences. For colon cancer histology classification, Efficient Feature Transformations (EFT) attained the highest accuracy of 66.82% (95% confidence interval: 64.23-69.42%). In comparison, the joint model achieved accuracies of 90.76% and 89.28%, respectively. The finetune model demonstrated catastrophic forgetting in both datasets.ConclusionAlthough the joint retraining model exhibited superior performance, continual learning holds promise in mitigating catastrophic forgetting and facilitating continual model updates while preserving privacy in healthcare deep learning models. Thus, it presents a highly promising solution for the long-term clinical deployment of such models

Comparative transcriptome profiling of the fertile and sterile flower buds of a dominant genic male sterile line in sesame (Sesamum indicum L.)

Expressions and annotations of the 1502 differentially expressed unigenes in sesame. (XLSX 338 kb

Stavudine exposure results in developmental abnormalities by causing DNA damage, inhibiting cell proliferation and inducing apoptosis in mouse embryos

Stavudine is an anti-AIDS drug widely used to prevent HIV transmission from pregnant mothers to the fetuses in underdeveloped countries for its low price. However, there is still a controversy on whether stavudine affects embryo development. In the current study, embryotoxicity of stavudine was evaluated using cultured mouse embryos with the concentrations: 5, 10, 15 μM and vehicle control. The data indicated that the effect of stavudine was dose-dependent at early neurogenesis. Stavudine exposure reduced somite numbers, yolk sac diameter, crown-rump length, and increased the rate of embryonic degeneration compared with the control. We chose the lowest but clearly toxic concentration: 5 μM to investigate the molecular mechanisms of the damage. At the molecular level, stavudine produced DNA damage, increased the levels of the phospho-CHK1 and cleaved-caspase-3, and decreased the expression level of proliferating cell nuclear antigen. These changes indicated that stavudine caused a coordinated DNA damage response, inhibited cell proliferation, and induced apoptosis in the embryos. Collectively these results suggest that stavudine exposure disturbs the embryonic development, and its use in pregnant mothers should be re-examined

Large language models approach expert-level clinical knowledge and reasoning in ophthalmology:A head-to-head cross-sectional study

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.5 and GPT-4 on 347 ophthalmology questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64–90%), ophthalmology trainees (median 59%, range 57–63%), and unspecialised junior doctors (median 43%, range 41–44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types (p > 0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher (p < 0.05). LLMs are approaching expert-level knowledge and reasoning skills in ophthalmology. In view of the comparable or superior performance to trainee-grade ophthalmologists and unspecialised junior doctors, state-of-the-art LLMs such as GPT-4 may provide useful medical advice and assistance where access to expert ophthalmologists is limited. Clinical benchmarks provide useful assays of LLM capabilities in healthcare before clinical trials can be designed and conducted

Disability Weight of Clonorchis sinensis Infection: Captured from Community Study and Model Simulation

Clonorchiasis is caused by eating incompletely cooked fishery product which carries the larval of Clonorchis sinensis. Millions of people are estimated to suffer in Southeast Asia. However, it is still among the most neglected tropical diseases due to the lack of clear evaluation, of which no disease burden available is one important reason. Our study is the first attempt to estimate the disability of C. sinensis infection, which reflects the average loss of life value due to some conditions and is crucial for calculating disease burden in terms of disability-adjusted life years (DALYs). After obtaining the probability and disability of single sequelae caused by C. sinensis infection through community investigation and literatures reviewing respectively, the overall disability was captured through model simulation. It was showed the overall disability of the male was higher than that of the female, positive correlation occurred between disability and infection intensity, and gallstone took the major attributable proportion. Thus, C. sinensis infection can cause apparent disability. The disability captured here may promote the further studies and benefit the final estimation of disease burden, which will promote health awareness and implementation of intervention

Towards clinical AI fairness: A translational perspective

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness

Dense infraspecific sampling reveals cryptic differentiation in the enigmatic hemiparasitic love vine Cassytha filiformis (Lauraceae)

Species delimitation remains a challenge worldwide, especially in highly diverse tropical and subtropical regions. Here, we use an integrative approach that combines morphology, phylogenomics, and species distribution modeling (SDM) to clarify the cryptic differentiation within the enigmatic hemiparasitic love vine Cassytha filiformis (Lauraceae) in China and adjacent regions. We generated complete plastid genomes and nuclear ribosomal sequences for diverse samples from across the species range and compared results with previously published plastid data, recovering two well-supported monophyletic clades. Further, the analysis revealed significant differences in two morphological characters and SDM, indicating distinct environmental factors influencing their distributions. Fossil-calibrated analyses to estimate the origins and diversification patterns for the cryptic species gave divergence age estimates corresponding to the Oligo-Miocene; a period of new ecological opportunities associated with the prevailing East Asian monsoon. Multivariate analyses support the conclusion that southern China and adjacent regions have a different, previously unknown, cryptic lineage of C. filiformis. Our study highlights the importance of using multivariate approach to characterize plant species, as well as the significant role that past climatic changes have played in driving speciation in parasitic plants in tropical and subtropical zones.</p

BnaMPK3 Is a Key Regulator of Defense Responses to the Devastating Plant Pathogen Sclerotinia sclerotiorum in Oilseed Rape

The disease caused by Sclerotinia sclerotiorum has traditionally been difficult to control, resulting in tremendous economic losses in oilseed rape (Brassica napus). Identification of important genes in the defense responses is critical for molecular breeding, an important strategy for controlling the disease. Here, we report that a B. napus mitogen-activated protein kinase gene, BnaMPK3, plays an important role in the defense against S. sclerotiorum in oilseed rape. BnaMPK3 is highly expressed in the stems, flowers and leaves, and its product is localized in the nucleus. Furthermore, BnaMPK3 is highly responsive to infection by S. sclerotiorum and treatment with jasmonic acid (JA) or the biosynthesis precursor of ethylene (ET), but not to treatment with salicylic acid (SA) or abscisic acid. Moreover, overexpression (OE) of BnaMPK3 in B. napus and Nicotiana benthamiana results in significantly enhanced resistance to S. sclerotiorum, whereas resistance is diminished in RNAi transgenic plants. After S. sclerotiorum infection, defense responses associated with ET, JA, and SA signaling are intensified in the BnaMPK3-OE plants but weakened in the BnaMPK3-RNAi plants when compared to those in the wild type plants; by contrast the level of both H2O2 accumulation and cell death exhibits a reverse pattern. The candidate gene association analyses show that the BnaMPK3-encoding BnaA06g18440D locus is a cause of variation in the resistance to S. sclerotiorum in natural B. napus population. These results suggest that BnaMPK3 is a key regulator of multiple defense responses to S. sclerotiorum, which may guide the resistance improvement of oilseed rape and related economic crops

Evasion of anti-growth signaling: a key step in tumorigenesis and potential target for treatment and prophylaxis by natural compounds

The evasion of anti-growth signaling is an important characteristic of cancer cells. In order to continue to proliferate, cancer cells must somehow uncouple themselves from the many signals that exist to slow down cell growth. Here, we define the anti-growth signaling process, and review several important pathways involved in growth signaling: p53, phosphatase and tensin homolog (PTEN), retinoblastoma protein (Rb), Hippo, growth differentiation factor 15 (GDF15), AT-rich interactive domain 1A (ARID1A), Notch, insulin-like growth factor (IGF), and Krüppel-like factor 5 (KLF5) pathways. Aberrations in these processes in cancer cells involve mutations and thus the suppression of genes that prevent growth, as well as mutation and activation of genes involved in driving cell growth. Using these pathways as examples, we prioritize molecular targets that might be leveraged to promote anti-growth signaling in cancer cells. Interestingly, naturally-occurring phytochemicals found in human diets (either singly or as mixtures) may promote anti-growth signaling, and do so without the potentially adverse effects associated with synthetic chemicals. We review examples of naturally-occurring phytochemicals that may be applied to prevent cancer by antagonizing growth signaling, and propose one phytochemical for each pathway. These are: epigallocatechin-3-gallate (EGCG) for the Rb pathway, luteolin for p53, curcumin for PTEN, porphyrins for Hippo, genistein for GDF15, resveratrol for ARID1A, withaferin A for Notch and diguelin for the IGF1-receptor pathway. The coordination of anti-growth signaling and natural compound studies will provide insight into the future application of these compounds in the clinical setting