Incorporating endoscope in middle ear surgery

We conducted a study to evaluate the use of a pediatric rigid otoendoscope for determining the extent of middle ear disease and for assessing ossicular integrity and mobility during tympanoplasty. Our study population was made up of 132 patients who were undergoing surgery for the treatment of chronic suppurative otitis media; of this group, 41 patients underwent otoendoscopy and 91 underwent scutum lowering for purposes of visualization. In the otoendoscopy group, the ossicles were successfully visualized and their mobility assessed in 34 patients; the remaining 7 patients subsequently underwent scutum lowering. A 30° endoscope allowed for complete visualization of the middle ear in almost all of the 34 cases. The mean duration of surgery for the 34 patients in the otoendoscopy group was 62.85 minutes (±15.57), which was significantly shorter than the duration of surgery (71.23 ± 15.65 min) for the 98 patients who underwent scutum lowering (p \u3c 0.005). A total of 50 patients required less than 60 minutes of surgical time-26 of 34 (76.5%) in the endoscopy group and 24 of 98 (24.5%) in the scutum-lowering group. Statistical analysis revealed that the possibility of completing a procedure in less than 60 minutes was 73.65% (±12.56%) when endoscopy was used and 58.62% (±12.60%) when scutum lowering was used-again, a statistically significant difference (p \u3c 0.005). We conclude that incorporation of an angled otoendoscope into middle ear surgery is a worthwhile alternative to scutum lowering

Combining Ability and Heteroses Analysis for Seed Yield and Yield Components in Brassica napus L.

Line × tester analysis of three testers and five lines of Brassica napus L. were used to estimate combining ability and heterosis of plant height, number of primary branches, number of secondary branches, 1000-seed weight and seed yield per plant. Significant mean squares of treatments for yield components and seed yield indicated significant genetic variations among the genotypes including parents and their crosses. Parents Vs crosses mean square indicated, average heterosis was significant for all the traits except plant height. Line × tester mean square was significant for all the traits. High GCA to SCA ratio; indicated the prime importance of additive genetic effects for all traits except seed yield per plant. Significant positive general combining ability (GCA) and specific combining ability (SCA) effects were observed. Most of the crosses had significant positive over better parent heterosis of seed yield, indicating that these hybrids were suitable candidates for improving these traits using combination method. Key words: Combining ability, Heteroses, Line × Tester, Brassica napus L

Data-driven decision-making in COVID-19 response : a survey

COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE

Estimation of Combining Ability for the Development of Hybrid Genotypes in Helianthus annuus L

Plant materials were developed by L×T crossing fashion of nine lines and four testers and their thirty six hybrids were sown in field during 2011 in RCBD design with three replications. Genetic variability, general and specific combining abilities among genotypes was assessed under the research area of department of plant breeding and genetics, university of agriculture, faisalabad, Pakistan. The Line G-93, and G-79 expressed highly significant GCA effects for days to flowering, days to maturity, internodal length, head diameter, %age of filled achenes, 100 achene weight, achene yield per plant and oil contents but they showed best general combiner. Among testers A-85 expressed highly significant GCA effects for days to flowering, days to maturity, 100 achene weight, achene yield per plant and oil contents whereas A-5 exibited best general combiner for days to flowering, days to maturity, internodal length, achene yield per plant and oil contents. The cross G-65×A-85 revealed highest SCA effect for days to 50% flowering and days to maturity, head diameter, 100 achene weight, achene yield per plant and oil contents. The results of analysis of variance were determine among entries for all the traits at significant level (p ? 0.01-0.05). Key words: GCA, SCA, line × tester, oil contents and yield

Quantum Graph Learning : Frontiers and Outlook

Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates the current advances in quantum graph learning (QGL) from three perspectives, i.e., underlying theories, methods, and prospects. We first look at QGL and discuss the mutualism of quantum theory and graph learning, the specificity of graph-structured data, and the bottleneck of graph learning, respectively. A new taxonomy of QGL is presented, i.e., quantum computing on graphs, quantum graph representation, and quantum circuits for graph neural networks. Pitfall traps are then highlighted and explained. This survey aims to provide a brief but insightful introduction to this emerging field, along with a detailed discussion of frontiers and outlook yet to be investigated

The Pakistan risk of myocardial infarction study: A resource for the study of genetic, lifestyle and other determinants of myocardial infarction in south Asia

The burden of coronary heart disease (CHD) is increasing at a greater rate in South Asia than in any other region globally, but there is little direct evidence about its determinants. The Pakistan Risk of Myocardial Infarction Study (PROMIS) is an epidemiological resource to enable reliable study of genetic, lifestyle and other determinants of CHD in South Asia. By March 2009, PROMIS had recruited over 5,000 cases of first-ever confirmed acute myocardial infarction (MI) and over 5,000 matched controls aged 30-80 years. For each participant, information has been recorded on demographic factors, lifestyle, medical and family history, anthropometry, and a 12-lead electrocardiogram. A range of biological samples has been collected and stored, including DNA, plasma, serum and whole blood. During its next stage, the study aims to expand recruitment to achieve a total of about 20,000 cases and about 20,000 controls, and, in subsets of participants, to enrich the resource by collection of monocytes, establishment of lymphoblastoid cell lines, and by resurveying participants. Measurements in progress include profiling of candidate biochemical factors, assay of 45,000 variants in 2,100 candidate genes, and a genomewide association scan of over 650,000 genetic markers. We have established a large epidemiological resource for CHD in South Asia. In parallel with its further expansion and enrichment, the PROMIS resource will be systematically harvested to help identify and evaluate genetic and other determinants of MI in South Asia. Findings from this study should advance scientific understanding and inform regionally appropriate disease prevention and control strategies

Cross network representation matching with outliers

Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network. © 2021 IEEE