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Objective: Recent studies suggest that boys enter puberty at a younger age, and the incidence of male central precocious puberty (CPP) is increasing. In this study, we explore the incidence of male CPP and identify key clinical and auxological indicators for organic CPP (OCPP)., Design: A retrospective registry-based study., Methods: The medical records of 43 boys treated with CPP at the Helsinki University Hospital between 1985 and 2014 were reviewed. Clinical, auxological, and endocrine data of the CPP patients were included in the analyses., Results: Based on brain MRI, 26% of patients had OCPP. Between 2010 and 2014, the CPP incidence in boys was 0.34 per 10 000 (95% CI 0.20-0.60). Between 1990 and 2014, the male CPP incidence increased (incidence rate ratio [IRR] 1.10, P = .001). This increase was driven by rising idiopathic CPP (ICPP) incidence (IRR 1.11, 95% CI 1.05-1.19, P < .001), while OCPP incidence remained stable (P = .41). Compared with the patients with ICPP, the patients with OCPP were younger (P = .006), were shorter (P = .003), and had higher basal serum testosterone levels (P = .038). Combining 2 to 4 of these readily available clinical cues resulted in good to excellent (all, area under the curve 0.84-0.97, P < .001) overall performance, differentiating organic etiology from idiopathic., Conclusions: The estimated incidence of CPP in boys was 0.34 per 10 000, with 26% of cases associated with intracranial pathology. The increase in CPP incidence was driven by rising ICPP rates. Patients with OCPP were characterized by shorter stature, younger age, and higher basal testosterone levels, providing valuable cues for differentiation in addition to brain MRI. Utilizing multiple cues could guide diagnostic decision-making., Competing Interests: Conflict of interest: The authors have no conflict of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Endocrinology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

A combined pretreatment based on alkaline deep eutectic solvent (DES) of K 2 CO 3 and glycerol and sequential acid fractionation was developed to extract reactive lignin from wheat straw biomass. This process exhibited excellent purification performance in lignin isolation, and the lignin fractionated at low pH displayed high reactivity, having hydroxyl and carboxyl groups up to 9.60 and 2.52 mmol/g, respectively. Silica was selectively separated and removed during the precipitation stage, avoiding the "silica interference". Moreover, DES-lignin nanospheres created by self-assembly using lignin fractions obtained by acid precipitation possessed a high zeta potential, large particle size and high content of hydrophilic groups. Overall, the findings related to the dissociation mechanism and fractionation of reactive lignin during alkaline DES pretreatment and the acid sequence precipitation are crucial for facilitating lignin valorization in high-added value products., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

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.

Synaptic adhesion molecules are key components in development of the brain, and in the formation of neuronal circuits, as they are central in the assembly and maturation of chemical synapses. Several families of neuronal adhesion molecules have been identified such as the neuronal cell adhesion molecules, neurexins and neuroligins, and in particular recently several leucine-rich repeat proteins, e.g., Netrin G-ligands, SLITRKs, and LRRTMs. The LRRTMs form a family of four proteins. They have been implicated in excitatory glutamatergic synapse function and were specifically characterized as ligands for neurexins in excitatory synapse formation and maintenance. In addition, LRRTM3 and LRRTM4 have been found to be ligands for heparan sulfate proteoglycans, including glypican. We report here the crystal structure of a thermostabilized mouse LRRTM2, with a Tm 30 °C higher than that of the wild-type protein. We localized the neurexin binding site to the concave surface based on protein engineering, sequence conservation, and prior information about the interaction of the ligand with neurexins, which allowed us to propose a tentative model for the LRRTM-neurexin interaction complex. We also determined affinities of the thermostabilized LRRTM2 and wild-type LRRTM1 and LRRTM2 for neurexin-β1 with and without Ca(2+). Cell culture studies and binding experiments show that the engineered protein is functional and capable of forming synapselike contacts. The structural and functional data presented here provide the first structure of an LRRTM protein and allow us to propose a model for the molecular mechanism of LRRTM function in the synaptic adhesion.