http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Diabetes Mellitus and the Development of Lumbar Canal Stenosis: Is There Any Relevance?
Kakadiya Ghanshyam,Saindane Kalpesh,Soni Yogesh,Gohil Kushal,Shakya Akash,Attar Mohhamad Umair 대한척추외과학회 2022 Asian Spine Journal Vol.16 No.3
Study Design: Retrospective study.Purpose: To assess the relationship between the severity of lumbar canal stenosis (LCS) and type-II diabetes mellitus (DM).Overview of Literature: DM is a multiorgan disorder that has an effect on all types of connective tissues. LCS is a narrowing of the spinal canal with nerve root impingement that causes neurological claudication and radiculopathy. Identification of the risk factors of LCS is key in the prevention of its onset or progression.Methods: LCS patients were divided into three groups as per DM status: group A without DM (n=150); group B patients with well-controlled DM; and group C patients with uncontrolled DM. Groups B and C were subdivided into group B1: patients with DM with a duration of ≤10 years (n=76), group B2: DM with duration of >10 years (n=68), group-C1 DM duration ≤10 years (n=56), and group C2 DM duration >10 years (n=48). The severity of LCS was evaluated using the Swiss Spinal Stenosis Scale (SSSS) and Modified Oswestry Disability score (MODS). Operated patients ligamentum flavum sent for histological staining and quantitative immunofluorescence analysis.Results: The demographic data of groups did not show any difference except in age. There was no difference between the mean SSSS and MODS of groups A and B1. Groups B2, C1, and C2 had higher average SSSS and MODS than group A (p<0.05). Groups B2 and C2 had higher SSSS and MODS than groups B1 and C1. Group C1 and C2 had higher scores than groups B1 and B2 (p<0.05). The severity of LCS was significantly related to the duration of DM in groups B and C (p<0.05). Uncontrolled and longer duration of DM had significant elastin fibers loss and also higher rate of disk apoptosis, high matrix aggrecan fragmentation, and high disk glycosaminoglycan content.Conclusions: Longer duration and uncontrolled diabetes were risk factors for LCS and directly correlate with the severity of LCS.
Sethi Benu,Jain Monika,Chowdhary Manish,Soni Yogesh,Bhatia Yukti,Sahai Vikram,Mishra Saroj The Korean Society for Biotechnology and Bioengine 2002 Biotechnology and Bioprocess Engineering Vol.7 No.1
The cloning and expression of $\beta-glucosidase$ II, encoded by the gene ${\beta}glu2$, from thermotolerant yeast Pichia etchellsii into Escherichia coli is described. Cloning of the 7.3 kb BamHI/SalI yeast insert containing ${\beta}glu2$ in pUC18, which allowed for reverse orientation of the insert, resulted in better enzyme expression. Transformation of this plasmid into E. coli JM109 resulted in accumulation of the enzyme in periplasmic space. At $50^{\circ}C$, the highest hydrolytic activity of 1686 IU/g protein was obtained on sophorose. Batch and fed-batch techniques were employed for enzyme production in a 14 L bioreactor. Exponential feeding rates were determined from mass balance equations and these were employed to control specific growth rate and in turn maximize cell growth and enzyme production. Media optimization coupled with this strategy resulted in increased enzyme units of 1.2 kU/L at a stabilized growth rate of $0.14\;h^{-l}$. Increased enzyme production in bioreactor was accompanied by formation of inclusion bodies.
Saroj Mishra,Benu Sethi,Monika Jain,Manish Chowdhary,Yogesh Soni,Yukti Bhatia,Vikram Sahai 한국생물공학회 2002 Biotechnology and Bioprocess Engineering Vol.7 No.1
The cloning and expression of β-glucosidase II, encoded by the gene bglu2, from thermo- tolerant yeast Pichia etchellsii into Escherichia coli is described. Cloning of the 7.3 kb BamHI/SalI yeast insert containing bglu2 in pUC18, which allowed for reverse orientation of the insert, resulted in better enzyme expression. Transformation of this plasmid into E. coli JM109 resulted in accumulation of the enzyme in periplasmic space. At 50oC, the highest hydrolytic activity of 1686 IU/g protein was obtained on sophorose. Batch and fed-batch techniques were employed for enzyme production in a 14 L bioreactor. Exponential feeding rates were determined from mass balance equations and these were employed to control specific growth rate and in turn maximize cell growth and enzyme production. Media optimization coupled with this strategy resulted in increased enzyme units of 1.2 kU/L at a stabilized growth rate of 0.14 h-1. Increased enzyme production in bioreactor was accompanied by formation of inclusion bodies.
Rathore Divya,Divyanth L. G.,Reddy Kaamala Lalith Sai,Chawla Yogesh,Buragohain Mridula,Soni Peeyush,Machavaram Rajendra,Hussain Syed Zameer,Ray Hena,Ghosh Alokesh 한국농업기계학회 2023 바이오시스템공학 Vol.48 No.2
Purpose The process of robotic harvesting has revolutionized the agricultural industry, allowing for more effi cient and costeff ective fruit picking. Developing algorithms for accurate fruit detection is essential for vision-based robotic harvesting of apples. Although deep-learning techniques are popularly used for apple detection, the development of robust models that can accord information about the fruit’s occlusion condition is important to plan a suitable strategy for end-eff ector manipulation. Apples on the tree experience occlusions due to leaves, stems (branches), trellis wire, or other fruits during robotic harvesting. Methods A novel two-stage deep-learning-based approach is proposed and successfully demonstrated for detecting ontree apples and identifying their occlusion condition. In the fi rst stage, the system employs a cutting-edge YOLOv7 model, meticulously trained on a custom Kashmiri apple orchard image dataset. The second stage of the approach utilize the powerful Effi cientNet-B0 model; the system is able to classify the apples into four distinct categories based on their occlusion condition, namely, non-occluded, leaf-occluded, stem/wire-occluded, and apple-occluded apples. Results The YOLOv7 model achieved an average precision of 0.902 and an F1-score of 0.905 on a test set for detecting apples. The size of the trained weights and detection speed were observed to be 284 MB and 0.128 s per image. The classifi cation model produced an overall accuracy of 92.22% with F1-scores of 94.64%, 90.91%, 86.87%, and 90.25% for nonoccluded, leaf-occluded, stem/wire-occluded, and apple-occluded apple classes, respectively. Conclusion This study proposes a novel two-stage model for the simultaneous detection of on-tree apples and classify them based on occlusion conditions, which could improve the eff ectiveness of autonomous apple harvesting and avoid potential damage to the end-eff ector due to the objects causing the occlusion.