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The article presents a study on the capability of S100B to regulate apoptosis in the post-myocardial infarction (MI) myocardium in the U.S. The study intends to investigate the function of S100B and receptor for advanced glycation end products (RAGE). The findings reveal that the interaction of RAGE and its ligand S100 B after myocardial infarction may have a potential function in myocyte apoptosis.

Abstract Background Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. Results We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonlinear distortions, and is completely insensitive to any monotone nonlinear distortions. The performance of the methods is evaluated by performing extensive simulations. In addition, we compare the proposed method with another recent statistical signal detection estimator that uses Fisher's test, based on the Gaussian noise assumption. The results demonstrate that the proposed robust method provides remarkably better robustness properties. Moreover, the performance of the proposed method is preferable also in the standard Gaussian case. We validate the performance of the proposed method on real data on which the method performs very favorably. Conclusion As the time series measured from biological systems are usually short and prone to contain different kinds of non-idealities, we are very optimistic about the multitude of possible applications for our proposed robust statistical periodicity detection method. Availability The presented methods have been implemented in Matlab and in R. Codes are available on request. Supplementary material is available at: http://www.cs.tut.fi/sgn/csb/robustperiodic/.

Background and Objective: Emergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown., Methods: We extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric., Results: Calendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant., Conclusions: Our study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables., (© 2022. The Author(s).)

This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0-9. The raw data collected by the platform are a sequence of IDs from the touched ICs. The system converts the data into bitmaps and their details are increased by interpolating between neighboring samples using the sequential information of IDs. These images of digits written on the platform can be classified, with enough accuracy for practical use, by deep learning. The recognition system was trained and tested with samples from six volunteers using the platform. The real-time number recognition ability of the ClothFace technology is demonstrated to work successfully with a very low error rate. The overall recognition accuracy of the platform is 94.6% and the accuracy for each digit is between 91.1% and 98.3%. As the solution is fully passive and gets all the needed energy from the external RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us.

Previous studies have shown that prolyl oligopeptidase (PREP) negatively regulates autophagy and increases the aggregation of alpha-synuclein (αSyn), linking it to the pathophysiology of Parkinson's disease. Our earlier results have revealed that the potent small molecular PREP inhibitor KYP-2047 is able to increase autophagy and decrease dimerization of αSyn but other PREP inhibitors have not been systematically studied for these two protein-protein interaction mediated biological functions of PREP. In this study, we characterized these effects for 12 known PREP inhibitors with IC 50 -values ranging from 0.2 nM to 1010 nM. We used protein-fragment complementation assay (PCA) to assess αSyn dimerization and Western Blot of microtubule-associated protein light chain 3B II (LC3B-II) and a GFP-LC3-RFP expressing cell line to study autophagy. In addition, we tested selected compounds in a cell-free αSyn aggregation assay, native gel electrophoresis, and determined the compound concentration inside the cell by LC-MS. We found that inhibition of the proteolytic activity of PREP did not predict decreased αSyn dimerization or increased autophagy, and we also confirmed that this result did not simply reflect concentration differences of the compounds inside the cell. Thus, PREP ligands regulate the effect of PREP on autophagy and αSyn aggregation through a conformational stabilization of the enzyme that is not equivalent to inhibiting its proteolytic activity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the current study., (Copyright © 2020 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Objective: Two missense mutations in KCNQ1 , an imprinted gene that encodes the alpha subunit of the voltage-gated potassium channel Kv7.1, cause autosomal dominant growth hormone deficiency and maternally inherited gingival fibromatosis. We evaluated endocrine features, birth size, and subsequent somatic growth of patients with long QT syndrome 1 (LQT1) due to loss-of-function mutations in KCNQ1 ., Design: Medical records of 104 patients with LQT1 in a single tertiary care center between 1995 and 2015 were retrospectively reviewed., Methods: Clinical and endocrine data of the LQT1 patients were included in the analyses., Results: At birth, patients with a maternally inherited mutation ( n  = 52) were shorter than those with paternal inheritance of the mutation ( n  = 29) (birth length, -0.70 ± 1.1 SDS vs. -0.2 ± 1.0 SDS, P  < 0.05). Further analyses showed, however, that only newborns ( n  = 19) of mothers who had received beta blockers during pregnancy were shorter and lighter at birth than those with paternal inheritance of the mutation ( n  = 29) (-0.89 ± 1.0 SDS vs. -0.20 ± 1.0 SDS, P  < 0.05; and 3,173 ± 469 vs. 3,515 ± 466 g, P  < 0.05). Maternal beta blocker treatment during the pregnancy was also associated with lower cord blood TSH levels ( P  = 0.011) and significant catch-up growth during the first year of life (Δ0.08 SDS/month, P  = 0.004). Later, childhood growth of the patients was unremarkable., Conclusion: Loss-of-function mutations in KCNQ1 are not associated with abnormalities in growth, whereas maternal beta blocker use during pregnancy seems to modify prenatal growth of LQT1 patients-a phenomenon followed by catch-up growth after birth.

Introduction: We describe the etiology, MRI findings, and growth patterns in girls who had presented with signs of precocious puberty (PP), i.e., premature breast development or early menarche. Special attention was paid to the diagnostic findings in 6- to 8-year-olds., Materials and Methods: We reviewed the medical records of 149 girls (aged 0.7-10.3 years) who had been evaluated for PP in the Helsinki University Hospital between 2001 and 2014., Results: In 6- to 8-year-old girls, PP was most frequently caused by idiopathic gonadotropin-releasing hormone (GnRH)-dependent PP (60%) and premature thelarche (PT; 39%). The former subgroup grew faster (8.7 ± 2.0 cm/year, n  = 58) than the girls with PT (7.0 ± 1.1 cm/year, n  = 32) ( P  < 0.001), and the best discrimination for GnRH-dependent PP was achieved with a growth velocity cut-off value of 7.0 cm/year (sensitivity 92% and specificity 58%) [area under the curve 0.82, 95% confidence interval (CI) 0.73-0.91, P  < 0.001]. Among asymptomatic and previously healthy 6- to 8-year-old girls with GnRH-dependent PP, one (1.7%, 95% CI 0.3-9.7%) had a pathological brain MRI finding requiring surgical intervention (craniopharyngioma). In girls younger than 3 years, the most frequent cause of breast development was PT, and, in 3- to 6-year-olds, GnRH-dependent PP., Conclusion: In 6- to 8-year-old girls, analysis of growth velocity is helpful in differentiating between PT and GnRH-dependent PP. Although the frequency of clinically relevant intracranial findings in previously healthy, asymptomatic 6- to 8-year-old girls was low, they can present without any signs or symptoms, which favors routine MRI imaging also in this age group.

The aim of this paper is to introduce a new image analysis program "Nanoannotator" particularly developed for analyzing individual nanoparticles in transmission electron microscopy images. This paper describes the usefulness and efficiency of the program when analyzing nanoparticles, and at the same time, we compare it to more conventional nanoparticle analysis techniques. The techniques which we are concentrating here are transmission electron microscopy (TEM) linked with different image analysis methods and X-ray diffraction techniques. The developed program appeared as a good supplement to the field of particle analysis techniques, since the traditional image analysis programs suffer from the inability to separate the individual particles from agglomerates in the TEM images. The program is more efficient, and it offers more detailed morphological information of the particles than the manual technique. However, particle shapes that are very different from spherical proved to be problematic also for the novel program. When compared to X-ray techniques, the main advantage of the small-angle X-ray scattering (SAXS) method is the average data it provides from a very large amount of particles. However, the SAXS method does not provide any data about the shape or appearance of the sample.

In this paper, we study the problem of feature selection in cancer-related machine learning tasks. In particular, we study the accuracy and stability of different feature selection approaches within simplistic machine learning pipelines. Earlier studies have shown that for certain cases, the accuracy of detection can easily reach 100% given enough training data. Here, however, we concentrate on simplifying the classification models with and seek for feature selection approaches that are reliable even with extremely small sample sizes. We show that as much as 50% of features can be discarded without compromising the prediction accuracy. Moreover, we study the model selection problem among the ℓ 1 regularization path of logistic regression classifiers. To this aim, we compare a more traditional cross-validation approach with a recently proposed Bayesian error estimator.

ICAM-5 is a negative regulator of dendritic spine maturation and facilitates the formation of filopodia. Its absence results in improved memory functions, but the mechanisms have remained poorly understood. Activation of NMDA receptors induces ICAM-5 ectodomain cleavage through a matrix metalloproteinase (MMP)-dependent pathway, which promotes spine maturation and synapse formation. Here, we report a novel, ICAM-5-dependent mechanism underlying spine maturation by regulating the dynamics and synaptic distribution of α-actinin. We found that GluN1 and ICAM-5 partially compete for the binding to α-actinin; deletion of the cytoplasmic tail of ICAM-5 or ablation of the gene resulted in increased association of GluN1 with α-actinin, whereas internalization of ICAM-5 peptide perturbed the GluN1/α-actinin interaction. NMDA treatment decreased α-actinin binding to ICAM-5, and increased the binding to GluN1. Proper synaptic distribution of α-actinin requires the ICAM-5 cytoplasmic domain, without which α-actinin tended to accumulate in filopodia, leading to F-actin reorganization. The results indicate that ICAM-5 retards spine maturation by preventing reorganization of the actin cytoskeleton, but NMDA receptor activation is sufficient to relieve the brake and promote the maturation of spines., (© 2015. Published by The Company of Biologists Ltd.)

Determining vesicle localization and association in live microscopy may be challenging due to non-simultaneous imaging of rapidly moving objects with two excitation channels. Besides errors due to movement of objects, imaging may also introduce shifting between the image channels, and traditional colocalization methods cannot handle such situations. Our approach to quantifying the association between tagged proteins is to use an object-based method where the exact match of object locations is not assumed. Point-pattern matching provides a measure of correspondence between two point-sets under various changes between the sets. Thus, it can be used for robust quantitative analysis of vesicle association between image channels. Results for a large set of synthetic images shows that the novel association method based on point-pattern matching demonstrates robust capability to detect association of closely located vesicles in live cell-microscopy where traditional colocalization methods fail to produce results. In addition, the method outperforms compared Iterated Closest Points registration method. Results for fixed and live experimental data shows the association method to perform comparably to traditional methods in colocalization studies for fixed cells and to perform favorably in association studies for live cells.

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.

Background: In bioprocess development, the needs of data analysis include (1) getting overview to existing data sets, (2) identifying primary control parameters, (3) determining a useful control direction, and (4) planning future experiments. In particular, the integration of multiple data sets causes that these needs cannot be properly addressed by regression models that assume linear input-output relationship or unimodality of the response function. Regularized regression and random forests, on the other hand, have several properties that may appear important in this context. They are capable, e.g., in handling small number of samples with respect to the number of variables, feature selection, and the visualization of response surfaces in order to present the prediction results in an illustrative way., Results: In this work, the applicability of regularized regression (Lasso) and random forests (RF) in bioprocess data mining was examined, and their performance was benchmarked against multiple linear regression. As an example, we used data from a culture media optimization study for microbial hydrogen production. All the three methods were capable in providing a significant model when the five variables of the culture media optimization were linearly included in modeling. However, multiple linear regression failed when also the multiplications and squares of the variables were included in modeling. In this case, the modeling was still successful with Lasso (correlation between the observed and predicted yield was 0.69) and RF (0.91)., Conclusion: We found that both regularized regression and random forests were able to produce feasible models, and the latter was efficient in capturing the non-linearity in the data. In this kind of a data mining task of bioprocess data, both methods outperform multiple linear regression.

Motivation: Production and degradation of RNA and proteins are stochastic processes, difficulting the distinction between spurious fluctuations in their numbers and changes in the dynamics of a genetic circuit. An accurate method of change detection is key to analyze plasticity and robustness of stochastic genetic circuits., Results: We use automatic change point detection methods to detect non-spurious changes in the dynamics of delayed stochastic models of gene networks at run time. We test the methods in detecting changes in mean and noise of protein numbers, and in the switching frequency of a genetic switch. We also detect changes, following genes' silencing, in the dynamics of a model of the core gene regulatory network of Saccharomyces cerevisiae with 328 genes. Finally, from images, we determine when RNA molecules tagged with fluorescent proteins are first produced in Escherichia coli. Provided prior knowledge on the time scale of the changes, the methods detect them accurately and are robust to fluctuations in protein and RNA levels., Availability: Simulator: www.cs.tut.fi/~sanchesr/SGN/SGNSim.html, Contact: andre.ribeiro@tut.fi, Supplementary Information: Supplementary data are available at Bioinformatics online.

Purpose: Bioidentification is becoming increasingly important in everyday life. One of the most widespread methods of bioidentification is based on the structure of the iris. Iris photography has several advantages as an identification method: it is relatively simple and effective; it is non-invasive, and it is comparatively inexpensive. However, some medical conditions may change the appearance of the iris. This paper discusses the effects of latanoprost-induced pigmentation changes in iris bioidentification., Methods: The study is based on four extreme cases of latanoprost-induced pigmentation changes. Iris photographs in these patients during treatment are compared with pretreatment photographs. The comparison is carried out with iris recognition software developed by our research group based on the principles of Daugman's well-known IrisCode. The system was evaluated with 595 iris comparisons., Results: Iris photographs showing latanoprost-induced pigmentation changes were correctly matched with pretreatment photographs of the same irises with an error probability similar to that for matching equivalent pairs of photographs in intact eyes., Conclusions: Our results indicate that the pigmentation changes studied do not seem to have a significant effect on the standard identification algorithm.

Matrix metalloproteinase (MMP)-2 and -9 are pivotal in remodeling many tissues. However, their functions and candidate substrates for brain development are poorly characterized. Intercellular adhesion molecule-5 (ICAM-5; Telencephalin) is a neuronal adhesion molecule that regulates dendritic elongation and spine maturation. We find that ICAM-5 is cleaved from hippocampal neurons when the cells are treated with N-methyl-d-aspartic acid (NMDA) or alpha-amino-3-hydroxy-5-methylisoxazole-propionic acid (AMPA). The cleavage is blocked by MMP-2 and -9 inhibitors and small interfering RNAs. Newborn MMP-2- and MMP-9-deficient mice brains contain more full-length ICAM-5 than wild-type mice. NMDA receptor activation disrupts the actin cytoskeletal association of ICAM-5, which promotes its cleavage. ICAM-5 is mainly located in dendritic filopodia and immature thin spines. MMP inhibitors block the NMDA-induced cleavage of ICAM-5 more efficiently in dendritic shafts than in thin spines. ICAM-5 deficiency causes retraction of thin spine heads in response to NMDA stimulation. Soluble ICAM-5 promotes elongation of dendritic filopodia from wild-type neurons, but not from ICAM-5-deficient neurons. Thus, MMPs are important for ICAM-5-mediated dendritic spine development.

Intercellular adhesion molecule-5 (ICAM-5, telencephalin) is a dendrite-expressed membrane glycoprotein of telencephalic neurons in the mammalian brain. By deletion of the cytoplasmic and membrane-spanning domains of ICAM-5, we observed that the membrane distribution of ICAM-5 was determined by the cytoplasmic portion. Therefore we have characterized the intracellular associations of ICAM-5 by using a bacterially expressed glutathione S-transferase (GST) fusion protein encompassing the cytoplasmic part of ICAM-5. One of the main proteins in the neuronal cell line Paju that bound to the ICAM-5 cytodomain was alpha-actinin. ICAM-5 expressed in transfected Paju cells was found in alpha-actinin immunoprecipitates, and ICAM-5 colocalized with alpha-actinin both in Paju cells and in dendritic filopodia and spines of primary hippocampal neurons. We were also able to coprecipitate alpha-actinin from rat brain homogenate. Binding to alpha-actinin appeared to be mediated mainly through the N-terminal region of the ICAM-5 cytodomain, as the ICAM-5(857-861) cytoplasmic peptide (KKGEY) mediated efficient binding to alpha-actinin. Surface plasmon resonance analysis showed that the turnover of the interaction was rapid. In a mutant cell line, Paju-ICAM-5-KK/AA, the distribution was altered, which implies the importance of the lysines in the interaction. Furthermore, we found that the ICAM-5/alpha-actinin interaction is involved in neuritic outgrowth and the ICAM-5(857-861) cytoplasmic peptide induced morphological changes in Paju-ICAM-5 cells. In summary, these results show that the interaction between ICAM-5 and alpha-actinin is mediated through binding of positively charged amino acids near the transmembrane domain of ICAM-5, and this interaction may play an important role in neuronal differentiation.

The receptor for advanced glycation end products (RAGE), a member of the immunoglobulin superfamily, is known to interact with amphoterin. This interaction has been proposed to play a role in neurite outgrowth and process elongation during neurodifferentiation. However, there is as yet no direct evidence of the relevance of this pathway to neurodifferentiation under physiological conditions. In this study we have investigated a possible role of RAGE and amphoterin in the retinoic acid-induced differentiation of neuroblastoma cells. The functional inactivation of RAGE by dominant negative and antisense strategies showed that RAGE is not required for process outgrowth or differentiation, although overexpression of RAGE accelerates the elongation of neuritic processes. Using the antisense strategy, amphoterin was shown to be essential for process outgrowth and differentiation, suggesting that amphoterin may interact with other molecules to exert its effect in this context. Interestingly, the survival of the neuroblastoma cells treated with retinoic acid was partly dependent on the expression of RAGE, and inhibition of RAGE function partially blocked the increase in anti-apoptotic protein Bcl-2 following retinoic acid treatment. Based on these results we propose that a combination therapy using RAGE blockers and retinoic acid may prove as a useful approach for chemotherapy for the treatment of neuroblastoma.

Amphoterin is a protein enhancing process extension and migration in embryonic neurons and in tumor cells through binding to receptor for advanced glycation end products (RAGE), a multiligand transmembrane receptor. S100 proteins, especially S100B, are abundantly expressed in the nervous system and are suggested to function as cytokines with both neurotrophic and neurotoxic effects. However, the cell surface receptor for the cytokine function of S100B has not been identified. Here we show that two S100 family proteins, S100B and S100A1, activate RAGE in concert with amphoterin inducing neurite outgrowth and activation of transcription factor NF-kappaB. Furthermore, activation of RAGE by amphoterin and S100B promotes cell survival through increased expression of the anti-apoptotic protein Bcl-2. However, whereas nanomolar concentrations of S100B induce trophic effects in RAGE-expressing cells, micromolar concentrations of S100B induce apoptosis in an oxidant-dependent manner. Both trophic and toxic effects are specific for cells expressing full-length RAGE since cells expressing a cytoplasmic domain deletion mutant of RAGE are unresponsive to these stimuli. These findings suggest that activation of RAGE by multiple ligands is able to promote trophic effects whereas hyperactivation of RAGE signaling pathways promotes apoptosis. We suggest that RAGE is a signal-transducing receptor for both trophic and toxic effects of S100B.

Amphoterin, a major form of HMG (high mobility group) 1 proteins, is highly expressed in immature and malignant cells. A role in cell motility is suggested by the ability of amphoterin to promote neurite extension through RAGE (receptor of advanced glycation end products), an immunoglobulin superfamily member that communicates with the GTPases Cdc42 and Rac. We show here that cell contact with the laminin matrix induces accumulation of both amphoterin mRNA and protein close to the plasma membrane, which is accompanied by extracellular export of amphoterin. A role for amphoterin in extracellular matrix-dependent cell regulation is further suggested by the finding that specific decrease of amphoterin mRNA and protein, using antisense oligonucleotides transfected into cells, inhibits cell migration to laminin in a transfilter assay whereas the oligonucleotides in the culture medium have no effect. Moreover, affinity-purified anti-amphoterin antibodies inhibit cell migration to laminin, supporting an extracellular role for the endogenous amphoterin in cell motility. The finding that amphoterin expression is more pronounced in cells with a motile phenotype as compared to cells of dense cultures, is consistent with the results of the cell migration assays. Our results strongly suggest that amphoterin is a key player in the migration of immature and transformed cells.