Abstract Objectives To investigate the influence of maternal level of thyroid-stimulating hormone (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) one by one or in combination on incidence of gestational hypertension and preeclampsia. Methods The study included pregnant women (n=107) hospitalized in the period from July 1, 2020 to October 10, 2021 at the Department of Pathology of Pregnancy of the University Clinic of Obstetrics and Gynecology, University Clinical Center Sarajevo (UCCS) (Bosnia and Herzegovina), due to hypertensive disorder in pregnancy without symptoms of impaired thyroid function. In all patients fulfilling inclusion criteria TSH, FT3, and FT4 using electrochemiluminescence immunoassay (ECLIA, Roche Diagnostics, Basel, Switzerland) were checked. There were two groups of patients: one with gestational hypertension (G1) and the other with preeclampsia (G2). The programs SPSS for Windows 25.0, SPSS Inc, Chicago, IL, USA and Microsoft Excel 11, Microsoft Corporation, Redmond, WA, USA were used for statistical analysis using nonparametric Mann-Whitney U test because the distribution of the data was not normal. The result was considered statistically significant if p<0.05. Results Gestational age at delivery (G2 36.86 ± 3.79 vs. G1 38.94 ± 2.15; p=0.002) and birth weight (G2 2,841.36 ± 1,006.39 vs. G2 3,290.73 ± 745.6; p=0,032) were significantly different between the investigated groups. The difference between the peak systolic (p=0.002), peak diastolic blood pressure (p=0.007), TSH (p=0.044), and FT3 (p=0.045) were statistically significant. Impaired thyroid function was observed more often in G2 than in G1. Conclusions Thyroid function was more often affected adversely in pregnancies complicated with preeclampsia than with gestational hypertension. Based on the results of our study it might be prudent to check thyroid hormones in all asymptomatic pregnancies with preeclampsia or gestational hypertension. These findings need confirmation in larger better designed prospective studies.

Being used in key features, such as sensing and intelligent path planning, Artificial Intelligence (AI) has become an inevitable part of automated vehicles (AVs). However, their usage in the automotive industry always comes with a “label” that questions their impact on the overall AV safety. This paper focuses on the safe deployment of AI-based AVs. Among the various ways for ensuring the safety of AI-based AVs is to monitor the safe execution of the system responsible for automated driving (i.e., Automated Driving System (ADS)) at runtime (i.e., runtime monitoring). Most of the research done in the past years focused on verifying whether the path or trajectory generated by the ADS does not immediately collide with objects on the road. However, as we will show in this paper, there are other unsafe situations that do not immediately result in a collision but the monitor should check for them. To build our case, we have looked into the National Highway Traffic Safety Administration (NHTSA) database of 5.9 million police-reported light-vehicle accidents and categorized these accidents into five main categories of unsafe vehicle operations. Furthermore, we have performed a high-level evaluation of the runtime monitoring approach proposed in [1], by estimating what percentage of the total population of 5.9 million of unsafe operations the approach would be able to detect. Lastly, we have performed the same evaluation on other existing runtime monitoring approaches to make a basic comparison of their diagnostic capabilities.

The paper shows that the information of the first just noticeable difference (JND) point position can significantly improve the performance of the objective peak signal-to-noise ratio (PSNR) measure in assessing the quality of JPEG compressed images. The degree of improvement depends on the choice of the first JND point position prediction model. Also, the paper shows that simple features derived from the gradient magnitude (spatial information and spatial frequency) of the original uncompressed image can be used for reliable position prediction. The analysis was conducted on two publicly available JND subject-rated image datasets MCL-JCI and JND-Pano. Among others, the linear correlation coefficient is used as an objective measurement parameter in prediction and in image quality assessment analysis. The prediction based on spatial frequency provided the best results, with over 95% of agreement with ground truth JND points position. This simple picture-wise prediction model has significantly improved the performance of conventional PSNR measure, with over 90% of agreement with subjective scores in image quality assessment. The PSNR performance is most enhanced by using a deep learning approach, where the correlation with subjective test results is close to 92%.

The expression pattern of Connexins (Cx) 37, 40, 43, 45 and Pannexin 1 (Pnx1) was analyzed immunohistochemically, as well as semi-quantitatively and quantitatively in histological sections of developing 8th- to 12th-week human eyes and postnatal healthy eye, in retinoblastoma and different uveal melanomas. Expressions of both Cx37 and Cx43 increased during development but diminished in the postnatal period, being higher in the retina than in the choroid. Cx37 was highly expressed in the choroid of retinoblastoma, and Cx43 in epitheloid melanoma, while they were both increasingly expressed in mixoid melanoma. In contrast, mild retinal Cx40 expression during development increased to strong in postnatal period, while it was significantly higher in the choroid of mixoid melanoma. Cx45 showed significantly higher expression in the developing retina compared to other samples, while it became low postnatally and in all types of melanoma. Pnx1 was increasingly expressed in developing choroid but became lower in the postnatal eye. It was strongly expressed in epithelial and spindle melanoma, and particularly in retinoblastoma. Our results indicate importance of Cx37 and Cx40 expression in normal and pathological vascularization, and Cx43 expression in inflammatory response. Whereas Cx45 is involved in early stages of eye development, Pnx1might influence cell metabolism. Additionally, Cx43 might be a potential biomarker of tumor prognosis.

Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale datasets. However, most of these datasets are collected from 2.4G WiFi devices and through similar channel environments. Meanwhile, they only provided receiving data collected by the specific equipment. This paper utilizes software radio peripheral as a dataset generating platform. Therefore, the user can customize the parameters of the dataset, such as frequency band, modulation mode, antenna gain, and so on. In addition, the proposed dataset is generated through various and complex channel environments, which aims to better characterize the radio frequency signals in the real world. We collect the dataset at transmitters and receivers to simulate a real-world RFF dataset based on the long-term evolution (LTE). Furthermore, we verify the dataset and confirm its reliability. The dataset and reproducible code of this paper can be downloaded from GitHub link: https://github.com/njuptzsp/XSRPdataset.

this study aims to analyze the impact of data selection to train machine learning models and forecast Bitcoin prices. Specifically, we train elastic net regularization models using two datasets with almost identical total observations. One dataset emphasizes years of observations (depth) over total variables, while the second one emphasizes the number of variables (width) over years of data. Our results suggest that the dataset with more extended historical time series and fewer variables provides a lower forecasting error than the dataset with shorter time series and more variables. Our results may be helpful to practitioners looking to identify data selection strategies to train ML-based forecasting models.

Standardized monitoring of BCR::ABL1 mRNA levels is essential for the management of chronic myeloid leukemia (CML) patients. From 2016 to 2021 the European Treatment and Outcome Study for CML (EUTOS) explored the use of secondary, lyophilized cell-based BCR::ABL1 reference panels traceable to the World Health Organization primary reference material to standardize and validate local laboratory tests. Panels were used to assign and validate conversion factors (CFs) to the International Scale and assess the ability of laboratories to assess deep molecular response (DMR). The study also explored aspects of internal quality control. The percentage of EUTOS reference laboratories (n = 50) with CFs validated as optimal or satisfactory increased from 67.5% to 97.6% and 36.4% to 91.7% for ABL1 and GUSB, respectively, during the study period and 98% of laboratories were able to detect MR4.5 in most samples. Laboratories with unvalidated CFs had a higher coefficient of variation for BCR::ABL1IS and some laboratories had a limit of blank greater than zero which could affect the accurate reporting of DMR. Our study indicates that secondary reference panels can be used effectively to obtain and validate CFs in a manner equivalent to sample exchange and can also be used to monitor additional aspects of quality assurance.

Physics‐informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics‐informed neural network models suffer from several issues and can fail to provide accurate solutions in many scenarios. We discuss a few of these challenges and the techniques, such as the use of Fourier transform, that can be used to resolve these issues. This paper proposes and develops a physics‐informed neural network model that combines the residuals of the strong form and the potential energy, yielding many loss terms contributing to the definition of the loss function to be minimized. Hence, we propose using the coefficient of variation weighting scheme to dynamically and adaptively assign the weight for each loss term in the loss function. The developed PINN model is standalone and meshfree. In other words, it can accurately capture the mechanical response without requiring any labeled data. Although the framework can be used for many solid mechanics problems, we focus on three‐dimensional (3D) hyperelasticity, where we consider two hyperelastic models. Once the model is trained, the response can be obtained almost instantly at any point in the physical domain, given its spatial coordinates. We demonstrate the framework's performance by solving different problems with various boundary conditions.

COVID-19 is an illness caused by severe acute respiratory syndrome coronavirus 2. Due to its rapid spread, in March 2020 the World Health Organization (WHO) declared pandemic. Since the outbreak of pandemic many governments, scientists, and institutions started to work on new vaccines and finding of new and repurposing drugs. Drug repurposing is an excellent option for discovery of already used drugs, effective against COVID-19, lowering the cost of production, and shortening the period of delivery, especially when preclinical safety studies have already been performed. There are many approved drugs that showed significant results against COVID-19, like ivermectin and hydrochloroquine, including alternative treatment options against COVID-19, utilizing herbal medicine. This article summarized 11 repurposing drugs, their positive and negative health implications, along with traditional herbal alternatives, that harvest strong potential in efficient treatments options against COVID-19, with small or no significant side effects. Out of 11 repurposing drugs, four drugs are in status of emergency approval, most of them being in phase IV clinical trials. The first repurposing drug approved for clinical usage is remdesivir, whereas chloroquine and hydrochloroquine approval for emergency use was revoked by FDA for COVID-19 treatment in June 2020.

The aim of this study is to investigate the environmental risk of long-term metallurgical waste disposal. The investigated site was used for the open storage of lead and zinc waste materials originating from a lead smelter and refinery. Even after remediation was performed, the soil in the close vicinity of the metallurgical waste deposit was heavily loaded with heavy metals and arsenic. The pollutants were bound in various compounds in the form of sulfides, oxides, and chlorides, as well as complex minerals, impacting the pH values of the investigated soil, such that they varied between 2.8 for sample 6 and 7.34 for sample 8. In order to assess the environmental risk, some eight soil samples were analyzed by determining the total metal concentration by acid digestion and chemical fractionation of heavy metals using the BCR sequential extraction method. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine six elements (As, Cd, Cu, Pb, Zn, and Ni). Total concentrations of the elements in the tested soil samples were in the range of 3870.4–52,306.18 mg/kg for As, 2.19–49.84 mg/kg for Cd, 268.03–986.66 mg/kg for Cu, 7.34–114.67 mg/kg for Ni, 1223.13–30,339.74 mg/kg for Pb, and 58.21–8212.99 mg/kg for Zn. The ratio between the mean concentrations of the tested metals was determined in this order: As > Pb > Zn > Cu > Ni > Cd. The BCR results showed that Pb (50.7%), Zn (49.2%), and Cd (34.7%) had the highest concentrations in mobile fractions in the soil compared to the other metals. The contamination factor was very high for Pb (0.09–33.54), As (0.004–195.8), and Zn (0.14–16.06). According to the calculated index of potential environmental risk, it was confirmed that the mobility of Pb and As have a great impact on the environment.

Solid-phase microextraction (SPME)-direct mass spectrometry (MS) has proven to be an efficient tool for the rapid screening and quantitation of target compounds at trace levels. However, it is challenging to perform screening using both positive and negative modes in one analytical run without compromising scanning speed and detection sensitivity. To take advantage of the special geometry of a coated blade spray (CBS) blade, which consists of two flat sides coated with the same SPME coating, we developed a CBS-MS method that enables desorption and ionization to be performed in positive ionization mode on one side of a coated blade and negative ionization mode on the other side of the same blade. By simply flipping the blade 180°, MS analysis in both ionization modes on different sides can be completed in 40 s. Combining this approach with an automated Concept 96-blade-based SPME system allowed analysis for one sample in positive and negative modes to be completed in less than 1 min. The workflow was optimized by using a biocompatible polyacrylonitrile as an undercoating layer and a binder of polyacrylonitrile/hydrophilic-lipophilic balance (HLB) particles, which enabled the rapid analysis of 20 drugs of abuse in saliva samples in both positive and negative modes. The proposed method provided low limits of quantification (between 0.005 and 10 ng/mL), with calibration linear correlation coefficients ⩾ 0.9925, accuracy between 72% and 126%, and relative precision < 15% for three validation points.

Given the doping profiles available in different CMOS technologies, different single-photon avalanche diode (SPAD) structures could be designed. A good insight into the effect of various doping profiles on the electric field distribution within the device is crucial for optimizing the photodetection performance. In this paper, we present an experimental and simulation characterization of the photon detection probability (PDP) for two reach-through SPADs with different doping profiles, and study the effect of the electric field distribution on the PDP performance. We use a comprehensive model to evaluate the PDP up to an excess bias voltage of 13.2V. In addition, it is shown that the SPAD with a thicker high-field region, despite having the lower maximum value of the electric field, shows higher carrier avalanche triggering probabilities and, consequently, a higher PDP (67% at 13.2 V excess bias and a wavelength of 642 nm). The PDP at the wavelength of the absolute transmission maximum of the isolation and passivation stack at 665 nm is even 84% at 13.2 V excess bias. The presented results and discussions can offer a better insight to the designer to achieve higher PDP for other SPAD structures by optimizing the electric field profile using doping modifications.

RNA-binding proteins contain intrinsically disordered regions whose functions in RNA recognition are poorly understood. The RNA chaperone Hfq is a homohexamer that contains six flexible C-terminal domains (CTDs). The effect of the CTDs on Hfq’s integrity and RNA binding has been challenging to study because of their sequence identity and inherent disorder. We used native mass spectrometry (nMS) coupled with surface-induced dissociation (SID) and molecular dynamics (MD) simulations to disentangle the arrangement of the CTDs and their impact on the stability of E. coli Hfq with and without RNA. The results show that the CTDs stabilize the Hfq hexamer through multiple interactions with the core and between CTDs. RNA binding perturbs this network of CTD interactions, destabilizing the Hfq ring. This destabilization is partially compensated by binding of RNAs that contact multiple surfaces of Hfq. By contrast, binding of short RNAs that only contact one or two subunits results in net destabilization of the complex. Together, the results show that a network of intrinsically disordered interactions integrate RNA contacts with the six subunits of Hfq. We propose that this CTD network raises the selectivity of RNA binding. Significance Statement Hfq is a protein hexamer necessary for gene regulation by non-coding RNA in bacteria, during infection or under stress. In the cell, Hfq must distinguish its RNA partners from many similar nucleic acids. Mass spectrometry dissociation patterns, together with molecular dynamics simulations, showed that flexible extensions of each Hfq subunit form a dense network that interconnects the entire hexamer. This network is disrupted by RNA binding, but the lost interactions are compensated by RNAs that contact multiple Hfq subunits. By measuring interactions that are too irregular to be counted by other methods, mass spectrometry shows how flexible protein extensions help chaperones like Hfq recognize their RNA partners in the messy interior of the cell.