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Caspase-1 activated in inflammasomes triggers a programmed necrosis called pyroptosis, which is mediated by gasdermin D (GSDMD). However, GSDMD-deficient cells are still susceptible to caspase-1-mediated cell death. Therefore, here, we investigate the mechanism of caspase-1-initiated cell death in GSDMD-deficient cells. Inflammasome stimuli induce apoptosis accompanied by caspase-3 activation in GSDMD-deficient macrophages, which largely relies on caspase-1. Chemical dimerization of caspase-1 induces pyroptosis in GSDMD-sufficient cells, but apoptosis in GSDMD-deficient cells. Caspase-1-induced apoptosis involves the Bid-caspase-9-caspase-3 axis, which can be followed by GSDME-dependent secondary necrosis/pyroptosis. However, Bid ablation does not completely abolish the cell death, suggesting the existence of an additional mechanism. Furthermore, cortical neurons and mast cells exhibit little or low GSDMD expression and undergo apoptosis after oxygen glucose deprivation and nigericin stimulation, respectively, in a caspase-1- and Bid-dependent manner. This study clarifies the molecular mechanism and biological roles of caspase-1-induced apoptosis in GSDMD-low/null cell types. In inflammasomes, caspase-1 activation leads to pyroptosis mediated by gasdermin D, but cells lacking gasdermin-D still initiate caspase-dependent cell death. Here, Tsuchiya et al. show that these cells undergo Bid- and caspase-3-dependent apoptosis.

BackgroundPlasma technology is an emerging sector in agriculture. The effect of low-frequency glow discharge (LFGD) plasma at medium pressure (10 torr) on maize morpho-physiological and agronomical behavior was investigated in the current studies. The LFGD plasma act as a secondary messenger to improve maize production. This cutting-edge plasma technology can be used in agriculture to boost agronomic possibilities.Materials and methodsMaize seeds were treated with LFGD Ar + Air gas plasma for 30 s, 60 s, 90 s, and 120 s. The gas ratio of Ar + Air was 1:99. Plasma was produced with a high voltage (1–6 kV) and low (3–5 kHz) frequency power supply across the electrodes. The internal pressure was maintained at ~ 10 torrs with a vacuum pump in the plasma chamber. Inside the plasma production chamber, the gas flow rate was maintained at 1 L/min.ResultsEffect of LFGD Ar + Air plasma on seed germination, and growth parameters including, shoot length, root length, fresh weight, dry weight, plant height, stem diameter, and chlorophyll were measured and in comparison with the control the parameter scores increased by 4.89%, 3.18%, 1.77%, 5.53%, 1.90%, 5.16%, 1.90%, 1.98%, respectively. The SEM image of the seeds surface demonstrated remarkable changes caused by plasma treatment. In roots, APX and SOD activities improved by only 0.022% and 0.64%, whereas, in shoots their activities showed a 0.014% and 0.25% increment compared to control. Further, H2O2, soluble protein, and sugar content increased by 0.12%, 0.33%, 2.50% and 1.15%, 1.41%, 2.99%, 1.16% in shoots and roots, respectively, while NO showed no significant changes in plants. Interestingly, notable improvement were found in nutritional properties (protein 0.32%, fat 0.96%, fiber 0.22%, ash 0.31%, grain iron 1.77%, shoots iron 7.61%, and manganese 6.25%), while the moisture content was reduced by 0.93% which might be useful in prolonged seed storage and the long life viability of the seeds. However, zinc (Zn) content in maize seedlings from plasma-treated seeds showed no significant change.ConclusionThe present study revealed that LFGD Ar + Air gas plasma is associated with the elevation of ROS in leaves and roots, which in turn improves the seed germination rate, agronomic traits, growth, enzymatic activity, and nutritional supplement in maize.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not only limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of publications over the past two decades, further indicates the consistent improvements, as well as breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG) based BCI system is been deliberated. Secondly, a considerable number of popular BCI applications are reviewed in terms of its electrophysiological control signals, feature extraction, classification algorithms as well as the performance evaluation metrics. Finally, the challenges to the recent BCI system are discussed, and the possible solutions to mitigate the issues are recommended.

Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high ( 56 Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours. Mutational signatures induced by ionising radiation remain largely unexplored. Here in TP53 mutant mice, the authors characterise the genomic landscape of tumours induced by high- and low-energy radiation.

Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/ .

Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.

Hypertension (HT) and diabetes mellitus (DM) are two major noncommunicable diseases (NCDs) with a high rate of comorbidity that utilize similar health system resources. Limited evidence exists, however, on the level of readiness of health facilities in Bangladesh to manage HT and integrated HT-DM care. The objective of this study was an assessment of facility readiness for HT and integrated HT-DM management and to identify key factors influencing levels of readiness. The study involved the analysis of data from 382 health facilities at or above sub-district level from the most recent nationally representative dataset, the 2017 Bangladesh Health Facility Survey (BHFS). Readiness for HT and integrated HT-DM services was assessed based on composite scores as constructed from the WHO-Service Availability and Readiness Assessment (SARA) indicators. Negative binomial regression models were applied to determine the factors associated with facility readiness. Facilities showed low readiness scores for both HT and integrated HT-DM care, with mean scores of 3.72 (out of 8) and 6.71 (out of 16), respectively. Although most facilities have basic equipment like BP apparatuses (98.7%) and stethoscopes (98.8%), a huge gap is observed in the training of staff (20.5%) and availability of guidelines for management (16.6%) of HT, diagnostic tools for DM, and essential medicines including ACE inhibitors (7.3%), thiazide diuretics (12.0%), and metformin (49.4%). Significant determinants of HT readiness included type of facility, client feedback system, and the number of HT care providers, while for integrated HT-DM readiness, important predictors were a type of facility, treatment-only service provision, client feedback mechanism, and structure of user-fees. In Bangladesh, health facilities are still not adequately ready to deliver integrated HT-DM services, illustrating deficits in human resources, clinical protocols, diagnostics, and availability of medicines at an overall system level. There are several areas that need improvement-the need for strengthening integrated training, free availability of medicines, client feedback systems, and collaboration between the public-private-NGO sectors. The results, though based on 2017 data, remain important in capturing system readiness prior to the reform and serve as a nationally representative baseline to assess the upspring of HT and NCD program improvements in Bangladesh.

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

Early marriage, defined as marriage under the age of 18, is widely recognized as a human rights violation with deleterious consequences on women's health and well-being. It persists as a significant global public health issue, predominantly being practiced in South Asian countries. In Countries like Bangladesh, this practice contributes to an increase in early pregnancies among women of reproductive age, further exacerbating adverse maternal and child health outcomes. While certain predictors of early marriage are recognized, additional investigation is warranted due to diverse socio-economic and demographic circumstances. This study, therefore, aimed to identify the prevalence and determinants of early marriage among women in Bangladesh. This study included a total weighted sample of 18,228 married women aged 18 to 49 years, extracted from the most recent nationally representative Bangladesh Demography and Health Survey (2017-18). We estimated the survey weighted pooled prevalence of early marriage among women and stratified by their different characteristics. We used multilevel mixed-effect binary logistic regression model and estimated the odds ratios (ORs) with their 95% confidence intervals (CIs) to identify the individual-, household-, and community-level factors associated with early marriage practice. All analyses were performed by Stata software version 18. Overall, 74.27% [95% CI: 73.15, 75.35] women got married before reaching the age of 18 years. Early marriage was more prevalent in Rajshahi (82.69%), Rangpur (81.35%), and Khulna division (79.32%). Women with higher education (OR = 0.10, 95% CI: 0.08, 0.12), husband's higher education (OR = 0.57, 95% CI: 0.48, 0.67), and non-Muslim women (OR = 0.46, 95% CI: 0.40, 0.52) were associated with a lower likelihood of experiencing early marriage. Compared to those household heads aged ≤35 years, the likelihood of early marriage among women was lower for those household heads aged 36-55 years (OR = 0.84, 95% CI: 0.76, 0.93) and >55 years (OR = 0.78, 95% CI: 0.69-0.88). Women aged 18-24 years (OR = 1.24, 95% CI: 1.10, 1.40), husbands with agricultural occupation (OR = 1.22, 95% CI:1.06, 1.41), middle wealth index level (OR = 1.14, 95% CI: 1.02, 1.28), family size five or more (OR = 1.21, 95% CI: 1.11, 1.31), and rural residence (OR = 1.17, 95% CI: 1.04, 1.30) were more likely to experience early marriage. This study underscores the alarming prevalence of early marriage among women in Bangladesh, with three-fourths experiencing early marriage, particularly in specific regions. Notably, women education and older household heads were significantly associated with a reduced likelihood of early marriage. Our findings suggest that culturally sensitive interventions should focus on empowering older household heads, alongside initiatives to increase awareness among younger household heads, and enhance education, particularly in rural and impoverished households. These efforts could potentially alter socio-cultural practices and reduce early marriage in Bangladesh.

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.