A Survey Paper on Sequence Pattern Mining with Incremental Approach

Sequential pattern mining finds frequently occurring patterns ordered by time. The problem was first introduced by Agrawal and Srikant [1]. An example of a sequential pattern is “A customer who purchased a new Ford Explorer two years ago, is likely to respond favourably to a trade-in option now”. Let X be the clause “purchased a new Ford Explorer” and Y be the clause “responds favourably to a trade-in”. Then notice that the pattern XY above, is different from pattern YX which states that “A customer who responded favourably to a trade-in two years ago, will purchase a Ford Explorer now”. The order in which X and Y appear is important, and hence XY and YX are mined as two separate patterns.Sequential pattern mining is widely applicable since many types of data have a time component to them. For example, it can be used in the medical domain to help determine a correct diagnosis from the sequence of symptoms experienced; over customer data to help target repeat customers; and with web-log data to better structure a company’s website for easier access to the most popular links[2]

Anaesthetic Management of Cataract Surgery in Patient with Joubert Syndrome

Joubert syndrome is a rare autosomal recessive disorder of the cerebellum that occurs In 1 of 100000 live births. The syndrome is characterized by malformations of the cerebellum and brainstem, hypotonia, developmental delay, hypertonia or apnea attacks or atypical eye movement. Cognitive changes are mild to severe, and can range to the extent of mental retardation. These patients may be sensitive to respiratory depression caused by anaesthetics, so the anaesthetic management of these patients needs more attention. The case is here presented of the anaesthesia management of a 27-years old female with Joubert syndrome who underwent general anaesthesia for surgery to a cataract

Development and Presentation of a Lesson on Mental Health for High School Students During the 2021 COVID-19 Pandemic

With the support of the HRSA, the Health Careers Opportunity Program (HCOP) was established to increase the number of students from underrepresented backgrounds to pursue careers in healthcare. HCOP combines the efforts of high school, undergraduate, and medical students in creating an original project to serve a need within the community. In this HCOP project, we aim to address the issue of mental health with high school students at two New Jersey high schools: Pennsauken and Williamtown

Understanding and Controlling Sialylation in a CHO Fc-Fusion Process.

A Chinese hamster ovary (CHO) bioprocess, where the product is a sialylated Fc-fusion protein, was operated at pilot and manufacturing scale and significant variation of sialylation level was observed. In order to more tightly control glycosylation profiles, we sought to identify the cause of variability. Untargeted metabolomics and transcriptomics methods were applied to select samples from the large scale runs. Lower sialylation was correlated with elevated mannose levels, a shift in glucose metabolism, and increased oxidative stress response. Using a 5-L scale model operated with a reduced dissolved oxygen set point, we were able to reproduce the phenotypic profiles observed at manufacturing scale including lower sialylation, higher lactate and lower ammonia levels. Targeted transcriptomics and metabolomics confirmed that reduced oxygen levels resulted in increased mannose levels, a shift towards glycolysis, and increased oxidative stress response similar to the manufacturing scale. Finally, we propose a biological mechanism linking large scale operation and sialylation variation. Oxidative stress results from gas transfer limitations at large scale and the presence of oxygen dead-zones inducing upregulation of glycolysis and mannose biosynthesis, and downregulation of hexosamine biosynthesis and acetyl-CoA formation. The lower flux through the hexosamine pathway and reduced intracellular pools of acetyl-CoA led to reduced formation of N-acetylglucosamine and N-acetylneuraminic acid, both key building blocks of N-glycan structures. This study reports for the first time a link between oxidative stress and mammalian protein sialyation. In this study, process, analytical, metabolomic, and transcriptomic data at manufacturing, pilot, and laboratory scales were taken together to develop a systems level understanding of the process and identify oxygen limitation as the root cause of glycosylation variability

Reduced DO level impacts 5-L cell culture gene expression.

<p>5-L bioreactors were operated under control (50% DO, blue) and low DO (20% gray, 15% yellow, and 10% shifted to 20% DO on day 5 green) conditions. Gene expression, relative to the control (50% DO) is shown for oxidative stress (A) and glucose metabolism (B) markers. Replicate bioreactors were used for control (n = 3), and 15% DO (n = 2) conditions. Statistical differences were determined using a student t-test, * indicates p<0.05 and ** indicates p<0.01.</p

N-Glycan distribution for 5-L treatments.

<p>N-Glycan distribution for 5-L treatments.</p

Proposed biological mechanism for manufacturing and low oxygen laboratory scale bioreactors.

<p>Reduced oxygen levels trigger an oxidative stress response and shift in glucose metabolism, resulting in upregulation of glycolysis and mannose synthesis, and downregulation of the hexosamine pathway and acetyl-CoA formation. This metabolic shift results in reduced GlcNAc levels, as well as levels of metabolites formed from GlcNAc including UDP-GlcNAc and CMP-NANA, triggering a reduction in terminal sialylation of N-glycans. Metabolite or gene expression levels directly measures as increased (green arrows) or decreased (red arrows) are shown. Abbreviations are as follows: CMP, cytidine monophosphate; CMP-SAT, CMP sialic acid transporter; CoA, coenzyme A; CTP, cytidine triphosphate; Frc-6-P, fructose-6-phosphate; Glc-6-P, glucose-6-phosphate; GlcN, glucosamine; GlcNAc, N-acetyl glucosamine; Gln, glutamine; Glu, glutamate; GTP, guanosine triphosphate; Man, mannose; Man-6-P, mannose-6-phosphate; ManNAc, N-acetyl mannosamine; MK, mannosekinase; NANA, N-acetylneuraminic acid; NH₃, ammonia; PDH, pyruvate dehydrogenase; PDK, pyruvate dehydrogenase kinase; PFK, phosphofructokinase; PMI, mannose phosphate isomerase; UDP, uridine diphosphate; UMP, uridine monophosphate; UTP, uridine triphosphate.</p

N-Glycan Structures.

<p>The N-glycan assay method quantifies the percent distribution of N-glycan species. Five key structures were quantified in samples generated from 5-L bioreactors were operated under normal (50%) and low (15%) DO conditions. G0F, G1F and G2F are unsialylated, S1G1F is monosialylated, and S2G2F is disialylated.</p

Links between glucose metabolism and NANA.

<p>Untargeted metabolomics & glucose consumption calculations were performed on samples ranging from 50-L to 5000-L scale. A. Relative mannose levels, a metabolite not provided in the cell culture media, were compared across runs with normal (red) and low (blue) NANA levels. Error bars represent the mannose range associated with each time point. B. Maximum specific glucose consumption was calculated for each run and found to be significantly inversely correlated to day 10 NANA (p<0.001). C. Residuals of maximum specific glucose consumption were calculated for each run and found to be significantly inversely correlated to day 10 NANA (p<0.0001).</p