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Soybeans play a key role in global food security. U.S. soybean yields, which comprise [Formula: see text] of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they  are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods-adopted by most of the laboratories to make recommendations-is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN's increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible., (© 2022. The Author(s).)

Background: In 2020, the COVID-19 pandemic affected healthcare systems throughout the world, including the management of patients and compliance rates of quality indicators., Objective: To measure the impact in Israel of the COVID-19 pandemic on the indicator-relevant caseload and compliance rates of the quality indicators reported by medical services providers within the Israeli National Program for Quality Indicators (NPQI)., Methods: Data was collected from the reports made to the NPQI by participating hospitals and medical service providers. The indicator results for the number of cases and compliance rates for 2019 were compared to those from 2020. We assessed and compared the results of the quality indicators in general hospitals, geriatric hospitals and departments, psychiatric hospitals and departments, emergency medical services (EMS), and Mother and Baby health centers., Results: We found a decrease in measurable cases in 2020 relative to 2019, especially in geriatric hospitals. In most indicators, compliance rates rose in 2020. Few indicators had lower compliance rates associated with COVID-19 pandemic regulations., Conclusions and Policy Implications: Routine medical activity decreased in Israel in 2020 in comparison to 2019, as reflected by a decrease in cases, but compliance rates were better in most indicators. The results of our study imply that the functioning of healthcare quality measurement programs should not be interrupted during a pandemic. This not only allows measuring of the healthcare system's performance during a crisis, but also may assist in maintaining a high level of healthcare quality., (© 2022. The Author(s).)

Background: It is accepted that a woman's lifetime risk of developing breast cancer after menopause is reduced by early full term pregnancy and multiparity. This phenomenon is thought to be associated with the development and differentiation of the breast during pregnancy., Methods: In order to understand the underlying molecular mechanisms of pregnancy induced breast cancer protection, we profiled and compared the transcriptomes of normal breast tissue biopsies from 71 parous (P) and 42 nulliparous (NP) healthy postmenopausal women using Affymetrix Human Genome U133 Plus 2.0 arrays. To validate the results, we performed real time PCR and immunohistochemistry., Results: We identified 305 differentially expressed probesets (208 distinct genes). Of these, 267 probesets were up- and 38 down-regulated in parous breast samples; bioinformatics analysis using gene ontology enrichment revealed that up-regulated genes in the parous breast represented biological processes involving differentiation and development, anchoring of epithelial cells to the basement membrane, hemidesmosome and cell-substrate junction assembly, mRNA and RNA metabolic processes and RNA splicing machinery. The down-regulated genes represented biological processes that comprised cell proliferation, regulation of IGF-like growth factor receptor signaling, somatic stem cell maintenance, muscle cell differentiation and apoptosis., Conclusions: This study suggests that the differentiation of the breast imprints a genomic signature that is centered in the mRNA processing reactome. These findings indicate that pregnancy may induce a safeguard mechanism at post-transcriptional level that maintains the fidelity of the transcriptional process.

Background: India has 2.0 million estimated tuberculosis (TB) cases per annum with an estimated 280,000 TB-related deaths per year. Understanding when in the course of TB treatment patients die is important for determining the type of intervention to be offered and crucially when this intervention should be given. The objectives of the current study were to determine in a large cohort of TB patients in India:- i) treatment outcomes including the number who died while on treatment, ii) the month of death and iii) characteristics associated with "early" death, occurring in the initial 8 weeks of treatment., Methods: This was a retrospective study in 16 selected Designated Microscopy Centres (DMCs) in Hyderabad, Krishna and Adilabad districts of Andhra Pradesh, South India. A review was performed of treatment cards and medical records of all TB patients (adults and children) registered and placed on standardized anti-tuberculosis treatment from January 2005 to September 2009., Results: There were 8,240 TB patients (5183 males) of whom 492 (6%) were known to have died during treatment. Case-fatality was higher in those previously treated (12%) and lower in those with extra-pulmonary TB (2%). There was an even distribution of deaths during anti-tuberculosis treatment, with 28% of all patients dying in the first 8 weeks of treatment. Increasing age and new as compared to recurrent TB disease were significantly associated with "early death"., Conclusion: In this large cohort of TB patients, deaths occurred with an even frequency throughout anti-TB treatment. Reasons may relate to i) the treatment of the disease itself, raising concerns about drug adherence, quality of anti-tuberculosis drugs or the presence of undetected drug resistance and ii) co-morbidities, such as HIV/AIDS and diabetes mellitus, which are known to influence mortality. More research in this area from prospective and retrospective studies is needed.

Background: A large number of animal and plant genomes have been completely sequenced over the last decade and are now publicly available. Although genomes can be rapidly sequenced, identifying protein-coding genes still remains a problematic task. Availability of protein sequence data allows direct confirmation of protein-coding genes. Mass spectrometry has recently emerged as a powerful tool for proteomic studies. Protein identification using mass spectrometry is usually carried out by searching against databases of known proteins or transcripts. This approach generally does not allow identification of proteins that have not yet been predicted or whose transcripts have not been identified., Results: We searched 3,967 mass spectra from 16 LC-MS/MS runs of Anopheles gambiae salivary gland homogenates against the Anopheles gambiae genome database. This allowed us to validate 23 known transcripts and 50 novel transcripts. In addition, a novel gene was identified on the basis of peptides that matched a genomic region where no gene was known and no transcript had been predicted. The amino termini of proteins encoded by two predicted transcripts were confirmed based on N-terminally acetylated peptides sequenced by tandem mass spectrometry. Finally, six sequence polymorphisms could be annotated based on experimentally obtained peptide sequences., Conclusion: The peptide sequences from this study were mapped onto the genomic sequence using the distributed annotation system available at Ensembl and can be visualized in the context of all other existing annotations. The strategy described in this paper can be used to correct and confirm genome annotations and permit discovery of novel proteins in a high-throughput manner by mass spectrometry.

Background: The explosion in biological information creates the need for databases that are easy to develop, easy to maintain and can be easily manipulated by annotators who are most likely to be biologists. However, deployment of scalable and extensible databases is not an easy task and generally requires substantial expertise in database development., Results: BioBuilder is a Zope-based software tool that was developed to facilitate intuitive creation of protein databases. Protein data can be entered and annotated through web forms along with the flexibility to add customized annotation features to protein entries. A built-in review system permits a global team of scientists to coordinate their annotation efforts. We have already used BioBuilder to develop Human Protein Reference Database http://www.hprd.org, a comprehensive annotated repository of the human proteome. The data can be exported in the extensible markup language (XML) format, which is rapidly becoming as the standard format for data exchange., Conclusions: As the proteomic data for several organisms begins to accumulate, BioBuilder will prove to be an invaluable platform for functional annotation and development of customizable protein centric databases. BioBuilder is open source and is available under the terms of LGPL.