Exploration of culturable bacterial associates of aphids and their interactions with entomopathogens.

Aphids shelter several bacteria that benefit them in various ways. The associates having an obligatory relationship are non-culturable, while a few of facultative associates are culturable in insect cell lines, axenic media or standard microbiology media. In the present investigation, isolation, and characterization of the culturable bacterial associates of various aphid species, viz., Rhopalosiphum maidis, Rhopalosiphum padi, Sitobion avenae, Schizaphis graminum, and Lipaphis erysimi pseudobrassicae were carried out. A total of 42 isolates were isolated using different growth media, followed by their morphological, biochemical, and molecular characterization. The isolated culturable bacterial associates were found to belong to the genera Acinetobacter, Bacillus, Brevundimonas, Cytobacillus, Fictibacillus, Planococcus, Priestia, Pseudomonas, Staphylococcus, Sutcliffiella, and Tumebacillus which were grouped under seven families of four different orders of phyla Bacillota (Firmicutes) and Pseudomonata (Proteobacteria). Symbiont-entomopathogen interaction study was also conducted, in which the quantification of colony forming units of culturable bacterial associates of entomopathogenic fungal-treated aphids led us to the assumption that the bacterial load in aphid body can be altered by the application of entomopathogens. Whereas, the mycelial growth of entomopathogens Akanthomyces lecanii and Metarhizium anisopliae was found uninhibited by the bacterial associates obtained from Sitobion avenae and Rhopalosiphum padi. Analyzing persistent aphid microflora and their interactions with entomopathogens enhances our understanding of aphid resistance. It also fosters the development of innovative solutions for agricultural pest management, highlighting the intricate dynamics of symbiotic relationships in pest management strategies.

Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning Approach Using Clinical Note Annotations.

Background: Lower back pain is a common weakening condition that affects a large population. It is a leading cause of disability and lost productivity, and the associated medical costs and lost wages place a substantial burden on individuals and society. Recent advances in artificial intelligence and natural language processing have opened new opportunities for the identification and management of risk factors for lower back pain. In this paper, we propose and train a deep learning model on a data set of clinical notes that have been annotated with relevant risk factors, and we evaluate the model's performance in identifying risk factors in new clinical notes. Objective: The primary objective is to develop a novel deep learning approach to detect risk factors for underlying disease in patients presenting with lower back pain in clinical encounter notes. The secondary objective is to propose solutions to potential challenges of using deep learning and natural language processing techniques for identifying risk factors in electronic medical record free text and make practical recommendations for future research in this area. Methods: We manually annotated clinical notes for the presence of six risk factors for severe underlying disease in patients presenting with lower back pain. Data were highly imbalanced, with only 12% (n=296) of the annotated notes having at least one risk factor. To address imbalanced data, a combination of semantic textual similarity and regular expressions was used to further capture notes for annotation. Further analysis was conducted to study the impact of downsampling, binary formulation of multi-label classification, and unsupervised pretraining on classification performance. Results: Of 2749 labeled clinical notes, 347 exhibited at least one risk factor, while 2402 exhibited none. The initial analysis shows that downsampling the training set to equalize the ratio of clinical notes with and without risk factors improved the macro-area under the receiver operating characteristic curve (AUROC) by 2%. The Bidirectional Encoder Representations from Transformers (BERT) model improved the macro-AUROC by 15% over the traditional machine learning baseline. In experiment 2, the proposed BERT-convolutional neural network (CNN) model for longer texts improved (4% macro-AUROC) over the BERT baseline, and the multitask models are more stable for minority classes. In experiment 3, domain adaptation of BERTCNN using masked language modeling improved the macro-AUROC by 2%. Conclusions: Primary care clinical notes are likely to require manipulation to perform meaningful free-text analysis. The application of BERT models for multi-label classification on downsampled annotated clinical notes is useful in detecting risk factors suggesting an indication for imaging for patients with lower back pain.

Synergistic impact of nanomaterials and plant probiotics in agriculture: A tale of two-way strategy for long-term sustainability.

Modern agriculture is primarily focused on the massive production of cereals and other food-based crops in a sustainable manner in order to fulfill the food demands of an ever-increasing global population. However, intensive agricultural practices, rampant use of agrochemicals, and other environmental factors result in soil fertility degradation, environmental pollution, disruption of soil biodiversity, pest resistance, and a decline in crop yields. Thus, experts are shifting their focus to other eco-friendly and safer methods of fertilization in order to ensure agricultural sustainability. Indeed, the importance of plant growth-promoting microorganisms, also determined as "plant probiotics (PPs)," has gained widespread recognition, and their usage as biofertilizers is being actively promoted as a means of mitigating the harmful effects of agrochemicals. As bio-elicitors, PPs promote plant growth and colonize soil or plant tissues when administered in soil, seeds, or plant surface and are used as an alternative means to avoid heavy use of agrochemicals. In the past few years, the use of nanotechnology has also brought a revolution in agriculture due to the application of various nanomaterials (NMs) or nano-based fertilizers to increase crop productivity. Given the beneficial properties of PPs and NMs, these two can be used in tandem to maximize benefits. However, the use of combinations of NMs and PPs, or their synergistic use, is in its infancy but has exhibited better crop-modulating effects in terms of improvement in crop productivity, mitigation of environmental stress (drought, salinity, etc.), restoration of soil fertility, and strengthening of the bioeconomy. In addition, a proper assessment of nanomaterials is necessary before their application, and a safer dose of NMs should be applicable without showing any toxic impact on the environment and soil microbial communities. The combo of NMs and PPs can also be encapsulated within a suitable carrier, and this method aids in the controlled and targeted delivery of entrapped components and also increases the shelf life of PPs. However, this review highlights the functional annotation of the combined impact of NMs and PPs on sustainable agricultural production in an eco-friendly manner.

Bi4TaO8Cl as a New Class of Layered Perovskite Oxyhalide Materials for Piezopotential Driven Efficient Seawater Splitting.

Piezocatalytic water splitting is an emerging approach to generate "green hydrogen" that can address several drawbacks of photocatalytic and electrocatalytic approaches. However, existing piezocatalysts are few and with minimal structural flexibility for engineering properties. Moreover, the scope of utilizing unprocessed water is yet unknown and may widely differ from competing techniques due to the constantly varying nature of surface potential. Herein, we present Bi4TaO8Cl as a representative of a class of layered perovskite oxyhalide piezocatalysts with high hydrogen production efficiency and exciting tailorable features including the layer number, multiple cation-anion combination options, etc. In the absence of any cocatalyst and scavenger, an ultrahigh production rate is achievable (1.5 mmol g-1 h-1), along with simultaneous generation of value-added H2O2. The production rate using seawater is somewhat less yet appreciably superior to photocatalytic H2 production by most oxides as well as piezocatalysts and has been illustrated using a double-layer model for further development.

Impact of blue green algae (BGA) technology: an empirical evidence from northwestern Indo-Gangetic Plains.

The focus of the current study is to assess the impact of blue green algae (BGA) technology on farming practices and economic conditions of farming households. The states of Punjab, Uttar Pradesh and Haryana considered as major rice-growing states of India were chosen for the primary survey. It was observed that use of BGA resulted in 25.2% of urea reduction with an overall 3.8% increase in the yield and a marginal decrease in per acre cultivation cost. Tobit model has been used to identify the determinants of increasing cropping area under BGA. Factors such as age, education, operated land holdings and leased-in land have a positive and significant impact on area under BGA. The study also provided the comparative analysis on yield of paddy, urea consumption and income with and without BGA application. It was observed that farmers earned about 3% greater income along with 41.1% reduction in dosage of urea while reaping 1% higher yield of paddy.