Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation and Detection for Molecular MIMO Communications in the Internet of Bio-Nano Things

For the Internet of Bio-Nano Things (IoBNT) applications demanding high transmission rates, a well-modeled Molecular Communication (MC) channel is essential. The existing studies proposing multiple-input and multiple-output (MIMO) models for MC, however, often make the unrealistic assumption of using ideal receivers with perfect absorption. Hence, this paper proposes a molecular MIMO channel model with spherical transmitters and partially-absorbing ligand receptor-based receivers underpinned by four unique parameters. For the non-analytical nature of the MIMO channel, we use a supervised learning algorithm to estimate the number of molecules in the reception space. We evaluate the root mean square error (RMSE) of our solution, which returns consistent results. The estimation is used for ligand-receptor binding statistics, in which the intersymbol inference (ISI) and molecular interference are considered. We also propose two techniques based on convolutional and recurrent neural networks (CNN & RNN) as alternatives to the generic threshold-based detection. Our detectors outperform the threshold-based technique; specifically, the CNN-based method improves the mean bit error rate (BER) performance three times.
Initial Experiences of Selective RET Inhibitor Selpercatinib in Adults with Metastatic Differentiated Thyroid Carcinoma and Medullary Thyroid Carcinoma: Real-World Case Series in Korea

Simple Summary Recently, selpercatinib, a highly selective inhibitor of RET receptor tyrosine kinase, has been used for RET-altered thyroid cancer. However, real-world data of its effectiveness and safety in various clinical situations are lacking. We present four cases of patients with advanced thyroid cancer who were treated with selpercatinib with variable clinical situations. Selpercatinib showed good efficacy in all four patients without significant side effects. Despite facing varying clinical obstacles of the real world, selpercatinib safely proved remarkable therapeutic efficacy, although drug safety and durability through long-term use should be further validated. Abstract Recently, selpercatinib, a highly selective inhibitor of RET receptor tyrosine kinase, has been used for RET-altered thyroid cancer. We present four cases of patients with advanced thyroid cancer who were treated with selpercatinib. The first patient was a 63-year-old male with advanced medullary thyroid cancer (MTC) treated with vandetanib. Six months ago, he had an intracranial hemorrhage and swallowing difficulty. He started selpercatinib with percutaneous endoscopic gastrostomy (PEG). For 11 months, a partial response (PR) was observed stably with PEG administration without any more cardiovascular events. The second patient was a 67-year-old female with advanced MTC treated with vandetatib. After selpercatinib treatment, a PR was observed for most metastatic sites, including choroidal metastasis. The third patient was a 32-year-old female with advanced papillary thyroid cancer (PTC) without history of systematic treatment. For six months, a PR was observed at her metastatic site with manageable adverse events. The last patient was a 59-year-old female with advanced PTC treated with lenvatinib. She suffered from a panic disorder and pleural pain due to metastasis during lenvatinib treatment. After selpercatinib treatment, her pain and panic symptoms were improved. Facing varying clinical obstacles of the real world, selpercatinib safely proved remarkable therapeutic efficacy regardless of previous treatment or metastatic site.
Geolocation Patterns, Wi-Fi Connectivity Rates, and Psychiatric Symptoms Among Urban Homeless Youth: Mixed Methods Study Using Self-report and Smartphone Data

Background Despite significant research done on youth experiencing homelessness, few studies have examined movement patterns and digital habits in this population. Examining these digital behaviors may provide useful data to design new digital health intervention models for youth experiencing homelessness. Specifically, passive data collection (data collected without extra steps for a user) may provide insights into lived experience and user needs without putting an additional burden on youth experiencing homelessness to inform digital health intervention design. Objective The objective of this study was to explore patterns of mobile phone Wi-Fi usage and GPS location movement among youth experiencing homelessness. Additionally, we further examined the relationship between usage and location as correlated with depression and posttraumatic stress disorder (PTSD) symptoms. Methods A total of 35 adolescent and young adult participants were recruited from the general community of youth experiencing homelessness for a mobile intervention study that included installing a sensor data acquisition app (Purple Robot) for up to 6 months. Of these participants, 19 had sufficient passive data to conduct analyses. At baseline, participants completed self-reported measures for depression (Patient Health Questionnaire-9 [PHQ-9]) and PTSD (PTSD Checklist for DSM-5 [PCL-5]). Behavioral features were developed and extracted from phone location and usage data. Results Almost all participants (18/19, 95%) used private networks for most of their noncellular connectivity. Greater Wi-Fi usage was associated with a higher PCL-5 score (P=.006). Greater location entropy, representing the amount of variability in time spent across identified clusters, was also associated with higher severity in both PCL-5 (P=.007) and PHQ-9 (P=.045) scores. Conclusions Location and Wi-Fi usage both demonstrated associations with PTSD symptoms, while only location was associated with depression symptom severity. While further research needs to be conducted to establish the consistency of these findings, they suggest that the digital patterns of youth experiencing homelessness offer insights that could be used to tailor digital interventions.
Multi-Agent-Based Controller for Microgrids: An Overview and Case Study

A microgrid can be defined as a grid of interconnected distributed energy resources, loads and energy storage systems. In microgrid systems containing renewable energy resources, the coordinated operation of distributed generation units is important to ensure the stability of the microgrid. A microgrid needs a successful control scheme to achieve its design goals. Undesirable situations such as distorted voltage profile and frequency fluctuations are significantly reduced by installing the appropriate hardware such as energy storage systems, and control strategies. The multi-agent system is one of the approaches used to control microgrids. The application of multi-agent systems in electric power systems is becoming popular because of their inherent benefits such as autonomy, responsiveness, and social ability. This study provides an overview of the agent concept and multi-agent systems, as well as reviews of recent research studies on multi-agent systems’ application in microgrid control systems. In addition, a multi-agent-based controller and energy management system design is proposed for the DC microgrid in the study. The designed microgrid is composed of a photovoltaic system consisting of 30 series-connected PV modules, a wind turbine, a synchronous generator, a battery-based energy storage system, critical and non-critical DC loads, the grid and the control system. The microgrid is controlled by the designed multi-agent-based controller. The proposed multi-agent-based controller has a distributed generation agent, battery agent, load agent and grid agent. The roles of each agent and communication among the agents are designed properly and coordinated to achieve control goals, which basically are the DC bus voltage quality and system stability. The designed microgrid and proposed multi-agent-based controller are tested for two different scenarios, and the performance of the controller has been verified with MATLAB/Simulink simulations. The simulation results show that the proposed controller provides constant DC voltage for any operation condition. Additionally, the system stability is ensured with the proposed controller for variable renewable generation and variable load conditions.
Application of Minimal Physiologically-Based Pharmacokinetic Model to Simulate Lung and Trachea Exposure of Pyronaridine and Artesunate in Hamsters

A fixed-dose combination of pyronaridine and artesunate, one of the artemisinin-based combination therapies, has been used as a potent antimalarial treatment regimen. Recently, several studies have reported the antiviral effects of both drugs against severe acute respiratory syndrome coronavirus two (SARS-CoV-2). However, there are limited data on the pharmacokinetics (PKs), lung, and trachea exposures that could be correlated with the antiviral effects of pyronaridine and artesunate. The purpose of this study was to evaluate the pharmacokinetics, lung, and trachea distribution of pyronaridine, artesunate, and dihydroartemisinin (an active metabolite of artesunate) using a minimal physiologically-based pharmacokinetic (PBPK) model. The major target tissues for evaluating dose metrics are blood, lung, and trachea, and the nontarget tissues were lumped together into the rest of the body. The predictive performance of the minimal PBPK model was evaluated using visual inspection between observations and model predictions, (average) fold error, and sensitivity analysis. The developed PBPK models were applied for the multiple-dosing simulation of daily oral pyronaridine and artesunate. A steady state was reached about three to four days after the first dosing of pyronaridine and an accumulation ratio was calculated to be 1.8. However, the accumulation ratio of artesunate and dihydroartemisinin could not be calculated since the steady state of both compounds was not achieved by daily multiple dosing. The elimination half-life of pyronaridine and artesunate was estimated to be 19.8 and 0.4 h, respectively. Pyronaridine was extensively distributed to the lung and trachea with the lung-to-blood and trachea-to-blood concentration ratios (=Cavg,tissue/Cavg,blood) of 25.83 and 12.41 at the steady state, respectively. Also, the lung-to-blood and trachea-to-blood AUC ratios for artesunate (dihydroartemisinin) were calculated to be 3.34 (1.51) and 0.34 (0.15). The results of this study could provide a scientific basis for interpreting the dose–exposure–response relationship of pyronaridine and artesunate for COVID-19 drug repurposing.
Deconvolution with the l 1 norm

Given a wavelet w and a noisy trace t = s * w + n, an approximation s of the spike train s can be obtained using the l 1 norm. This extraction has the advantage of preserving isolated spikes in s. On some types of data the spike train s can represent s as a sparse series of spikes, which may be sampled at a rate higher than the sample rate of the data trace t. The extracted spike train s may be qualitatively much different than those commonly extracted using the l 2 norm.The l 1 norm can also be used to extract a wavelet w from a trace t when a spike train s is known. This wavelet extraction can be constrained to give a smooth wavelet which integrates to zero and goes to zero at the ends.Given a trace t and an initial approximation for either s or w, it is possible to alternately extract spike trains and wavelets to improve the representation of trace t.Although special algorithms have been developed to solve l 1 problems, all of the calculations can be performed using a general linear programming system. Proper weighting procedures allow these methods to be used on ungained data.
Ultrasound Guided Parasternal Block for Perioperative Analgesia in Cardiac Surgery: A Prospective Study

Ultrasound guided parasternal block is a regional anaesthesia technique targeting the anterior branches of intercostal nerves, which supply the anterior thoracic wall. The aim of this prospective study is to assess the efficacy of parasternal block to manage postoperative analgesia and reduce opioid consumption in patients undergoing cardiac surgery throughout sternotomy. A total of 126 consecutive patients were allocated to two different groups, receiving (Parasternal group) or not (Control group) preoperative ultrasound guided bilateral parasternal block with 20 mL of 0.5% ropivacaine per side. The following data were recorded: postoperative pain expressed by a 0–10 numeric rating scale (NRS), intraoperative fentanyl consumption, postoperative morphine consumption, time to extubation and perioperative pulmonary performance at incentive spirometry. Postoperative NRS was not significantly different between Parasternal and Control groups with a median (IQR) of 2 (0–4.5) vs. 3 (0–6) upon awakening (p = 0.07); 0 (0–3) vs. 2 (0–4) at 6 h (p = 0.46); 0 (0–2) vs. 0 (0–2) at 12 h (p = 0.57). Postoperative morphine consumption was similar among groups. However, intraoperative fentanyl consumption was significantly lower in the Parasternal group [406.3 ± 81.6 mcg vs. 864.3 ± 154.4, (p < 0.001)]. Parasternal group showed shorter times to extubation [(191 ± 58 min vs. 305 ± 72 min, (p)] and better performance at incentive spirometer with a median (IQR) of 2 raised balls (1–2) vs. 1 (1–2) after awakening (p = 0.04). Ultrasound guided parasternal block provided an optimal perioperative analgesia with a significant reduction in intraoperative opioid consumption, time to extubation and a better postoperative performance at spirometry when compared to the Control group.
Total Synthesis of Lineaflavones A, C, D, and Analogues

The first total synthesis of lineaflavones A, C, D, and their analogues has been accomplished. The key synthetic steps include aldol/oxa-Michael/dehydration sequence reactions to assemble the tricyclic core, Claisen rearrangement and Schenck ene reaction to construct the key intermediate, and selective substitution or elimination of tertiary allylic alcohol to obtain natural compounds. In addition, we also explored five new routes to synthesize fifty-three natural product analogues, which can contribute to a systematic structure–activity relationship during biological evaluation.
Ornstein Uhlenbeck Cache Obliviousness Neural Congestion Control in Wireless Network for IOT Data Transmission

– Wireless Network is one of the Internet-of-Things (IoT) prototypes that come up with monitoring services, therefore, influencing the life of human beings. To ensure efficiency and robustness, Quality-of-Service (QoS) is of the predominant point at issue. Congestion in wireless networks will moreover minimize the anticipated QoS of the related applications. Motivated by this, a novel method called, Ornstein– Uhlenbeck Transition and Cache Obliviousness Neural Adaptive (OUT-CONA) to improve congestion control of wireless mesh networks is presented. Adaptive actor-critic deep reinforcement learning scheme on Ornstein–Uhlenbeck State Transition scheduling model to address handovers during data transmission for IoT-enabled Wireless Networks is first designed. Here, by employing the Ornstein–Uhlenbeck state transition scheduling model, both the advantages of the Gauss and Markov Processes are exploited, therefore reducing the energy consumption involved while performing the transition. Next, in the OUT-CONA method, LSTM is imposed for learning the current state representation. The LSTM with the current state representation achieves the objective of controlling congestion with cache obliviousness. The Cache Obliviousness-based Congestion method is utilized for congestion control with obliviousness caching using coherent shielding among organized as well as disorganized data. Furthermore, the performance of the OUT-CONA method is evaluated and compares the results with the performances of conventional techniques, adaptive aggregation as well as hybrid deep learning. The evaluation of the OUT-CONA congestion control method attains better network using lesser misclassification rate, consumption of energy, delay as well as higher goodput using conventional methods in Wireless Mesh Networks.
Depth Separable-CNN for Improved Spectral Super-Resolution

Hyperspectral Image reconstruction from RGB images is a low-cost and convenient alternative to acquiring hyperspectral images directly. The challenge in estimating the spectral response function and using it for generating the hyperspectral image data is addressed effectively by the use of Deep convolutional networks for the task. Deep networks, however, involve expensive convolutions. To address this problem, we have adapted deep convolution networks into Deep Separable convolution networks. We propose an isotropic network which consists of a cascade of blocks implementing depth separable convolution constructed by a series combination of a spatial convolution followed by convolution along the spectral domain. The two sub-blocks are termed as intra-channel sub-block and intra-pixel sub-block respectively. Intra-channel sub-block helps in capturing spatial correlation and intra-pixel sub-block captures spectral correlations. The explicit use of spectral-domain convolution results in a more direct approach to interpolating information across bands and hence helps in obtaining more accurate Hyperspectral data from RGB images. In this study we have conducted experiments on three benchmark datasets to show that deep separable-CNN achieves better accuracy. These result and the ablation study demonstrate the superiority of our technique.
Dynamic systems with delays under the smith predictor methodology

This contribution presents a solution to the dead time problem that is known as "Smith Predictor. " This solution allows us to use already known techniques for the design of controllers for systems without delay and adapt them to systems with delay. "As a design goal, it aims to achieve that the response of the system with delay, has the same dynamic characteristics of the system without delay, for example, that has the same response to the step input, but displaced in time the value of the delay.
Efficacy and Accuracy of Maxillary Arch Expansion with Clear Aligner Treatment

The aim of this work was to evaluate the efficacy and accuracy of maxillary arch transverse expansion using the Invisalign® clear aligner system without auxiliaries other than Invisalign attachments. Knowing the accuracy of a movement through a clear aligner system allows the clinician to plan the treatment with greater precision and to achieve the expected result faster. The study group included 28 patients with a mean age of 17 ± 3.2 years. The treatment protocol for all the selected patients included the application of the Invisalign® clear aligner system without auxiliaries, except for the Invisalign® attachments; in no case were tooth extraction or interproximal enamel reduction (IPR) performed. Linear measurements of the expansion were assessed before treatment (T0), at the end of treatment (T1), and on final virtual models by ClinCheck® (TC). A paired t-test was used to compare T0-T1 and T1-TC differences. A paired t-test was applied, and one normality was validated with the Shapiro–Wilks test. If normality was not met, the nonparametric test (Mann–Whitney U test) was applied. The level of significance was set at 5%. Statistically significant differences were found for all measurements at T0-T1. The results showed an average accuracy of efficacy of 70.88%. The differences in predictability between the various vestibular measurements (intercanine, inter-premolar, and intermolar) were not statistically significant, while they were for gingival measurements. The overall accuracy of the expansion treatment was 70%, regardless of tooth type.
Phase Transitions in Asymmetric Nuclear Matter

We study the thermodynamics of an asymmetric system with arbitrary proton fraction. The system is described by a mean-field theory, with terms for the surface and Coulombian contributions to the interaction energy. Consistently with the binary nature of the system under study, we impose baryon number and isospin conservation and investigate the coexistence surface (binodal). The chemical instability, as well as the mechanical instability, are taken into account in the construction of the meta-stability region as a function of the proton fraction and temperature. The effect of including surface and Coulombian terms is also discussed.
Editorial: Moving beyond the molecular mechanisms of malignant pleural mesothelioma: Cues for novel biomarkers and drug targets

COPYRIGHT © 2023 Cavallari, Cerciello, Giovannetti and Urso. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 06 March 2023 DOI 10.3389/fonc.2023. 1163144
Circularly Polarized Cross-Dipole Antenna for UHF RFID Readers Applicated in the Warehouse Environment

In this paper, we proposed a broadband circularly polarized antenna with wide beamwidth. The main structure of the proposed antenna is a combined cross-polarized dipole antenna, four metal arms, a single coaxial cable and two feed structures with 90° phase delay. In order to attain a wide beamwidth antenna performance that can be used in a warehouse environment, we innovatively bent the metal arms and added a metal plane at a specific location to gain maximum beamwidth and the highest achievable gain. Especially, the application of metal bending arms not only reduce the space size of antenna, but also possess good axial ratio characteristic. The measurement illustrates that the antenna prototype achieves S11 < −10 dB impedance bandwidth (IBW) of 40% (820 – 1230 MHz), a 3-dB axial ratio bandwidth (ARBW) of 7.3% (895 – 970 MHz), and a maximum half-power beamwidth (HPBW) of 104° (@915 MHz). In addition, the good radiation characteristics of a gain of 6.33 dBic and an average radiation efficiency of 76.3% were measured in the operating bandwidth, which makes the proposed antenna a good candidate for the hasty logistics radio frequency identification (RFID) system.
Solid-phase reactions and crystallographic structures in Zr/Si systems

Solid‐phase reactions and crystallographic structures in the interfacial region of Zr/(100)Si systems, which is closely related to the specific contact resistivity, have been investigated by x‐ray diffraction, Auger electron spectroscopy and cross‐sectional high‐resolution transmission electron microscopy. As‐grown Zr films are in an amorphous phase including crystallites. The Zr film on silicon annealed at a temperature of 420 °C for 30 min has a bilayer structure. The upper layer is a crystalline α‐Zr layer with a (0001) fibrous structure and the lower layer is an amorphous zirconium silicide layer. The formation of the amorphous silicide layer is considered to result from the diffusion of Si atoms from the substrate into the Zr film and to play a major role in a low contact resistivity of 4×10−8 Ω cm2 achieved in this system. By annealing above 560 °C, which brings about an increase in contact resistivity, the crystalline Zr layer with the fibrous structure is changed to an amorphous silicide throughou.