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High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning. Single-cell RNA sequencing data analysis is limited by noise and high dimensionality. Here, authors present scLENS, a tool that automates accurate signal detection without manual input, particularly in complex datasets. [ABSTRACT FROM AUTHOR]

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method. In infectious disease models, epidemiological parameters are typically estimated assuming a exponential distributions of latent and infectious periods. Here, the authors show that adapting models to incorporate Gamma distributions produces less biased estimates when applied to the example of the early COVID-19 pandemic in South Korea. [ABSTRACT FROM AUTHOR]

Background: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder. Methods: We collected a sample of 800 responses from the EMBRAIN survey system. Based on the responses, seven items were grouped based on the similarity of their response using exploratory factor analysis (EFA). The most representative item within each group was selected by using eXtreme Gradient Boosting (XGBoost). Results: Based on the selected three key items, maintenance of sleep, interference with daily function, and concerns about sleep problems, we developed a data-driven shortened questionnaire of ISI, ISI-3 m (machine learning). ISI-3 m achieved the highest coefficient of determination ( R 2 = 0.910 ) for the ISI score prediction task and the accuracy of 0.965, precision of 0.841, and recall of 0.838 for the multiclass-classification task, outperforming four previous versions of the shortened ISI. Conclusion: As ISI-3 m is a highly accurate shortened version of the ISI, it allows clinicians to efficiently screen for insomnia and observe variations in the condition throughout the treatment process. Furthermore, the framework based on the combination of EFA and XGBoost developed in this study can be utilized to develop data-driven shortened versions of the other questionnaires. [ABSTRACT FROM AUTHOR]

Ultrasensitive transcriptional switches enable sharp transitions between transcriptional on and off states and are essential for cells to respond to environmental cues with high fidelity. However, conventional switches, which rely on direct repressor-DNA binding, are extremely noise-sensitive, leading to unintended changes in gene expression. Here, through model simulations and analysis, we discovered that an alternative design combining three indirect transcriptional repression mechanisms, sequestration, blocking, and displacement, can generate a noise-resilient ultrasensitive switch. Although sequestration alone can generate an ultrasensitive switch, it remains sensitive to noise because the unintended transcriptional state induced by noise persists for long periods. However, by jointly utilizing blocking and displacement, these noise-induced transitions can be rapidly restored to the original transcriptional state. Because this transcriptional switch is effective in noisy cellular contexts, it goes beyond previous synthetic transcriptional switches, making it particularly valuable for robust synthetic system design. Our findings also provide insights into the evolution of robust ultrasensitive switches in cells. Specifically, the concurrent use of seemingly redundant indirect repression mechanisms in diverse biological systems appears to be a strategy to achieve noise-resilience of ultrasensitive switches. [ABSTRACT FROM AUTHOR]

In mammals, the circadian clock consists of transcriptional and translational feedback loops through DNA cis-elements such as E-box and RRE. The E-box-mediated core feedback loop is interlocked with the RRE-mediated feedback loop, but biological significance of the RRE-mediated loop has been elusive. In this study, we established mutant cells and mice deficient for rhythmic transcription of Bmal1 gene by deleting its upstream RRE elements and hence disrupted the RRE-mediated feedback loop. We observed apparently normal circadian rhythms in the mutant cells and mice, but a combination of mathematical modeling and experiments revealed that the circadian period and amplitude of the mutants were more susceptible to disturbance of CRY1 protein rhythm. Our findings demonstrate that the RRE-mediated feedback regulation of Bmal1 underpins the E-box-mediated rhythm in cooperation with CRY1-dependent posttranslational regulation of BMAL1 protein, thereby conferring the perturbation-resistant oscillation and chronologically-organized output of the circadian clock. The mammalian circadian clock is composed of clock genes forming transcriptional feedback loops. Here, the authors identify a key role of the secondary feedback loop that is interlocked with the core loop to establish a perturbation-resilient clock system. [ABSTRACT FROM AUTHOR]

Long-term behaviors of biochemical reaction networks (BRNs) are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity, and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives symbolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity. Hong, Kim and colleagues develop a method for analytically deriving the stationary distributions of stochastic biochemical reaction networks using network structure transformation. They provide this method in a user-friendly computational package, CASTANET, available open-source. [ABSTRACT FROM AUTHOR]

Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea–hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes. [ABSTRACT FROM AUTHOR]

Aqueous extracts of 18-day embryonic chicken brains, 15-day embryonic and adult rat brains and human brain tumors, as well as control histologically-normal adult human brain taken from around brain tumors or around arteriovenous malformations each stimulated the growth of cultured chick astrocytes. Eight mitogenic fractions were separated reproducibly by Bio-Gel P-10 molecular seive chromatography. They had apparent molecular weights (M.W.) of 24, 17, 12, 9, 5, 2.8, 1.4 and 1.2 kD. The activity of each fraction was concentration dependent. The fractions did not appear to be artifactually derived by proteolysis from a larger mitogen since (i) protease inhibitors were added at the time of homogenization to prevent degradation, (ii) protease treatment did not produce large quantities of the lower molecular weight fractions, (iii) incubation of brain extracts for up to four hours at 30° C did not alter the activity of the various mitogenic fractions and (iv) addition of albumin to inhibit protease activity similarly did not change the profile of the factors. In contrast, treatment with protease reduced the activity of all the factors although those with M.W. of 5 and 1.2 kD were inactivated more slowly than the others. The various fractions were stable when rechromatographed. This suggested they were not chance aggregates derived artifactually during extraction but rather might have physiological and pathological roles. The activities of each mitogenic fraction were significantly higher in brain extracts from embryonic rats than in those from adult rats. In brain extracts of rat and chicken embryos the fractions of lower M.W. 5 kD to 1.2 kD were relatively abundent. In contrast in brain extracts from adult rats the predominant mitogenic fractions had apparent M.W. of 24,17 and 12 kD. In histologically normal adult human brain taken from around the tumors or around arteriovenous malformations the 5 kD fraction was present in small amounts and the fractions of lower molecular weight were present in very small amounts. In human glial brain tumors there was a preponderance of the 5 kD activity and more of the 2.8 and 1.4 kD activity fractions than in histologically normal adult human brain. But there was relatively less activity in the 24 and 17 kD fractions. The growth factor profile of human meningiomas was quite different from that of histologically normal human brain or human glial brain tumors. The fraction from meningiomas that was most mitogenic for astrocytes had a molecular weight of 12 kD. Although the higher molecular weight factors may represent already described growth factors derived from brain, the lower molecular weight factors have not been reported previously. [ABSTRACT FROM AUTHOR]

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care. [ABSTRACT FROM AUTHOR]

Circadian clocks play a pivotal role in orchestrating numerous physiological and developmental events. Waveform shapes of the oscillations of protein abundances can be informative about the underlying biochemical processes of circadian clocks. We derive a mathematical framework where waveforms do reveal hidden biochemical mechanisms of circadian timekeeping. We find that the cost of synthesizing proteins with particular waveforms can be substantially reduced by rhythmic protein half-lives over time, as supported by previous plant and mammalian data, as well as our own seedling experiment. We also find that previously enigmatic, cyclic expression of positive arm components within the mammalian and insect clocks allows both a broad range of peak time differences between protein waveforms and the symmetries of the waveforms about the peak times. Such various peak-time differences may facilitate tissue-specific or developmental stage-specific multicellular processes. Our waveform-guided approach can be extended to various biological oscillators, including cell-cycle and synthetic genetic oscillators. Hang-Hyun Jo et al. derive a mathematical framework for analyzing circadian clock waveforms. Using data from plants and animals, they find that waveforms of clock protein profiles provide important information about the biochemical mechanisms of circadian timekeeping. [ABSTRACT FROM AUTHOR]