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Despite its prevalence and disease burden, several chasms still exist with regard to the pharmacotherapy of bipolar disorder (BD). Polypharmacy is commonly encountered as a significant proportion of patients remain symptomatic, and the management of the depressive phase of the illness is a particular challenge. Gabapentin and pregabalin have often been prescribed off-label in spite of a paucity of evidence and clinical practice guidelines to support its use. This systematic review aimed to synthesize the available human clinical trials and inform evidence-based pharmacological approaches to BD management. A total of six randomized, controlled trials (RCTs) and 13 open-label trials involving the use of gabapentin and pregabalin in BD patients were reviewed. Overall, the studies show that gabapentin and its related drug pregabalin do not have significant clinical efficacy as either monotherapy or adjunctive therapy for BD. Gabapentin and pregabalin are probably ineffective for acute mania based on the findings of RCT, with only small open-label trials to support its potential adjunctive role. However, its effects on the long-term outcomes of BD remain to be elucidated. The evidence base was significantly limited by the generally small sample sizes and the trials also had heterogeneous designs and generally high risk of bias.The 18F syntheses of tracers for positron emission tomography (PET) typically require several steps, including extraction of [18F]fluoride from H2[18O]O, elution, and drying, prior to nucleophilic substitution reaction, being a laborious and time-consuming process. The elution of [18F]fluoride is commonly achieved by phase transfer catalysts (PTC) in aqueous solution, which makes azeotropic drying indispensable. The ideal PTC is characterized by a slightly basic nature, its capacity to elute [18F]fluoride with anhydrous solvents, and its efficient complex formation with [18F]fluoride during subsequent labeling. Herein, we developed tri-(tert-butanol)-methylammonium iodide (TBMA-I), a quaternary ammonium salt serving as the PTC for 18F-fluorination reactions. IPI-549 mw The favorable elution efficiency of [18F]fluoride using TBMA-I was demonstrated with aprotic and protic solvents, maintaining high 18F-recoveries of 96-99%. 18F-labeling reactions using TBMA-I as PTC were studied with aliphatic 1,3-ditosylpropane and aryl pinacol boronate esters as precursors, providing 18F-labeled products in moderate-to-high radiochemical yields. TBMA-I revealed adequate properties for application to 18F-fluorination reactions and could be used for elution of [18F]fluoride with MeOH, omitting an additional base and azeotropic drying prior to 18F-labeling. We speculate that the tert-alcohol functionality of TBMA-I promotes intermolecular hydrogen bonding, which enhances the elution efficiency and stability of [18F]fluoride during nucleophilic 18F-fluorination.The authors wish to make the following erratum to this paper [...].Text Correction [...].Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt's method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion on the face of a robot. The results revealed that the robot could present different perceptions according to individual preferences. In this study, we considered individual differences to improve the acceptance of the robot by changing the robot's expression according to the emotion of its interacting partner. The emotion was estimated using biological signals, and the robot changed its expression according to three conditions synchronized with the estimated emotion, inversely synchronized, and a funny expression. During the experiment, the participants provided feedback regarding the robot's expression by choosing whether they "like" or "dislike" the expression. We investigated individual differences in the acceptance of the robot expression using the Semantic Differential scale method. In addition, logistic regression was used to create a classification model by considering individual differences based on the biological data and feedback from each participant. We found that the robot expression based on inverse synchronization when the participants felt a negative emotion could result in impression differences among individuals. Then, the robot's expression was determined based on the classification model, and the Semantic Differential scale on the impression of the robot was compared with the three conditions. Overall, we found that the participants were most accepting when the robot expression was calculated using the proposed personalized method.Dynamic calibration was performed in the laboratory on two catching-type drop counter rain gauges manufactured as high-sensitivity and fast response instruments by Ogawa Seiki Co. Ltd. (Japan) and the Chilbolton Rutherford Appleton Laboratory (UK). Adjustment procedures were developed to meet the recommendations of the World Meteorological Organization (WMO) for rainfall intensity measurements at the one-minute time resolution. A dynamic calibration curve was derived for each instrument to provide the drop volume variation as a function of the measured drop releasing frequency. The trueness of measurements was improved using a post-processing adjustment algorithm and made compatible with the WMO recommended maximum admissible error. The impact of dynamic calibration on the rainfall amount measured in the field at the annual and the event scale was calculated for instruments operating at two experimental sites. The rainfall climatology at the site is found to be crucial in determining the magnitude of the measurement bias, with a predominant overestimation at the low to intermediate rainfall intensity range.The paper aims to discuss a case study of sensing analytics and technology in acoustics when applied to reverberation conditions. Reverberation is one of the issues that makes speech in indoor spaces challenging to understand. This problem is particularly critical in large spaces with few absorbing or diffusing surfaces. One of the natural remedies to improve speech intelligibility in such conditions may be achieved through speaking slowly. It is possible to use algorithms that reduce the rate of speech (RoS) in real time. Therefore, the study aims to find recommended values of RoS in the context of STI (speech transmission index) in different acoustic environments. In the experiments, speech intelligibility for six impulse responses recorded in spaces with different STIs is investigated using a sentence test (for the Polish language). Fifteen subjects with normal hearing participated in these tests. The results of the analytical analysis enabled us to propose a curve specifying the maximum RoS values translating into understandable speech under given acoustic conditions. This curve can be used in speech processing control technology as well as compressive reverse acoustic sensing.With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users' performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users' current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.