Analysis of Variability of Functionals of Recombinant Protein Production Trajectories Based on Limited Data

Making statistical inference on quantities defining various characteristics of a temporally measured biochemical process and analyzing its variability across different experimental conditions is a core challenge in various branches of science. This problem is particularly difficult when the amount of data that can be collected is limited in terms of both the number of replicates and the number of time points per process trajectory. We propose a method for analyzing the variability of smooth functionals of the growth or production trajectories associated with such processes across different experimental conditions. Our modeling approach is based on a spline representation of the mean trajectories. We also develop a bootstrap-based inference procedure for the parameters while accounting for possible multiple comparisons. This methodology is applied to study two types of quantities-the "time to harvest" and "maximal productivity"-in the context of an experiment on the production of recombinant proteins. We complement the findings with extensive numerical experiments comparing the effectiveness of different types of bootstrap procedures for various tests of hypotheses. These numerical experiments convincingly demonstrate that the proposed method yields reliable inference on complex characteristics of the processes even in a data-limited environment where more traditional methods for statistical inference are typically not reliable

Emotion Dynamics of Public Opinions on Twitter

[EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). 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The decision of targeted, systematic or combined biopsy in a biopsy naïve patient for the diagnosis of prostate cancer, can be made on the basis of multiparametric magnetic resonance imaging

Introduction: The current trend to implement multiparametric magnetic resonance imaging (mpMRI)-guided targeted biopsy (TB) as primary biopsy for the diagnosis of suspected prostate cancer and to avoid systematic biopsy (SB) is growing. However, concern remains regarding missing clinically significant (Cs) cancer on the normal mpMRI areas of the prostate. Therefore, we compared the normal and abnormal areas from mpMRI at the same prostate biopsy, using simultaneous SB and TB technique. Methods: A prospective, comparative effectiveness study included 134 patients initially referred for primary biopsy (from October 2017 to June 2018); 100 men were selected, mean age 68 years, with a median level of prostate specific antigen of 7.6, with average prostate volume of 52 cm3 (T3 disease and prostate imaging reporting and data system (PI-RADS) score &lt; 3 were excluded). All underwent six cores TB (median), from an average of two lesions on mpMRI and also eight cores SB (median) from normal mpMRI areas of the prostate after informed consent. Results: The combined (SB + TB) biopsy cancer detection rate was 67%, 51% having Cs disease. For Cs cancer, 35 patients were detected by both techniques. TB missed four Cs cancer (95% confidence interval (CI), p &lt; 0.0001). Fewer men in the TB group than in the SB group were found to have clinically insignificant (Ci) cancer (95% CI, p &lt; 0.0001). No Cs cancer diagnosis was missed on TB from PI-RADS 5 lesion. Overall, 4% Cs cancers were missed on TB and avoided over diagnosis of 9% Ci cancer. Conclusions: Cognitive TB didn’t miss any Cs cancer from PI-RADS 5 lesion found on mpMRI. Only doing Cognitive TB on PI-RADS 5 lesion would save time, reduce workload and will be cost effective both for Urology and Pathology. PI-RADS 3 and 4 lesions on mpMRI will benefit from adding systematic samples. Level of evidence: 4 Oxford Centre for Evidence-Based Medicine (CEBM). </jats:sec