Toxicity of the Diatom Genus Pseudo-nitzschia (Bacillariophyceae): Insights from Toxicity Tests and Genetic Screening in the Northern Adriatic Sea

Diatoms of the genus Pseudo-nitzschia H.Peragallo are known to produce domoic acid (DA), a toxin involved in amnesic shellfish poisoning (ASP). Strains of the same species are often classified as both toxic and nontoxic, and it is largely unknown whether this difference is also genetic. In the Northern Adriatic Sea, there are virtually no cases of ASP, but DA occasionally occurs in shellfish samples. So far, three species—P. delicatissima (Cleve) Heiden, P. multistriata (H. Takano) H. Takano, and P. calliantha Lundholm, Moestrup, & Hasle—have been identified as producers of DA in the Adriatic Sea. By means of enzme-linked immunosorbent assay (ELISA), high-performance liquid chromatography with UV and visible spectrum detection (HPLC-UV/VIS), and liquid chromatography with tandem mass spectrometry (LC-MS/MS), we reconfirmed the presence of DA in P. multistriata and P. delicatissima and detect for the first time in the Adriatic Sea DA in P. galaxiae Lundholm, & Moestrup. Furthermore, we attempted to answer the question of the distribution of DA production among Pseudo-nitzschia species and strains by sequencing the internal transcribed spacer (ITS) phylogenetic marker and the dabA DA biosynthesis gene and coupling this with toxicity data. Results show that all subclades of the Pseudo-nitzschia genus contain toxic species and that toxicity appears to be strain dependent, often with geographic partitioning. Amplification of dabA was successful only in toxic strains of P. multistriata and the presence of the genetic architecture for DA production in non-toxic strains was thus not confirmed.

Federated Learning for 5G Radio Spectrum Sensing

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

Monoclonal Antibodies against Nucleocapsid Protein of SARS-CoV-2 Variants for Detection of COVID-19

Mitigation strategies of the coronavirus disease 2019 (COVID-19) pandemic have been greatly hindered by the continuous emergence of SARS-CoV-2 variants. New sensitive, rapid diagnostic tests for the wide-spectrum detection of viral variants are needed. We generated a panel of 41 monoclonal antibodies against the SARS-CoV-2 nucleocapsid protein (NP) by using mice hybridoma techniques. Of these mAbs, nine exhibited high binding activities and were applied in latex-based lateral flow immunoassays (LFIAs). The LFIAs utilizing NP-mAb-7 and -40 had the best sensitivity and lowest limit of detection: 8 pg for purified NP and 625 TCID50/mL for the authentic virus (hCoV-19/Taiwan/4/2020). The specificity tests showed that the NP-mAb-40/7 LFIA strips did not cross-react with five human coronavirus strains or 20 other common respiratory pathogens. Importantly, we found that 10 NP mutants, including alpha (B.1.1.7), beta (B.1.351), gamma (P.1), and delta (B.1.617.2) variants, could be detected by NP-mAb-40/7 LFIA strips. A clinical study (n = 60) of the NP-mAb-40/7 LFIA strips demonstrated a specificity of 100% and sensitivity of 90% in infected individuals with cycle threshold (Ct) values < 29.5. These anti-NP mAbs have strong potential for use in the clinical detection of SARS-CoV-2 infection, whether the virus is wild-type or a variant of concern.

Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

Signal modulation recognition is widely utilized in the field of spectrum detection, channel estimation, and interference recognition. With the development of artificial intelligence, substantial advances in signal recognition utilizing deep learning approaches have been achieved. However, a huge amount of data is required for deep learning. With increasing focus on privacy and security, barriers between data sources are sometimes difficult to break. This limits the data and renders them weak, so that deep learning is not sufficient. Federated learning can be a viable way of solving this challenge. In this article, we will examine the recognition of signal modulation based on federated learning with differential privacy, and the results show that the recognition rate is acceptable while data protection and security are being met.

Artefact-removal algorithms for Fourier domain quantum optical coherence tomography

Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomography and is able to provide a twofold axial resolution increase and immunity to resolution-degrading dispersion. The main drawback of Q-OCT are artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. Whereas there are very practical and successful methods for artefact removal in Time-domain Q-OCT, no such scheme has been devised for Fourier-domain Q-OCT (Fd-Q-OCT), although the latter modality—through joint spectrum detection—outputs a lot of useful information on both the system and the imaged object. Here, we propose two algorithms which process a Fd-Q-OCT joint spectrum into an artefact-free A-scan. We present the theoretical background of these algorithms and show their performance on computer-generated data. The limitations of both algorithms with regards to the experimental system and the imaged object are discussed.

Spectrum sensing based on adaptive sampling of received signal

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

Broadening INPP5E phenotypic spectrum: detection of rare variants in syndromic and non-syndromic IRD

Pathogenic variants in INPP5E cause Joubert syndrome (JBTS), a ciliopathy with retinal involvement. However, despite sporadic cases in large cohort sequencing studies, a clear association with non-syndromic inherited retinal degenerations (IRDs) has not been made. We validate this association by reporting 16 non-syndromic IRD patients from ten families with bi-allelic mutations in INPP5E. Additional two patients showed early onset IRD with limited JBTS features. Detailed phenotypic description for all probands is presented. We report 14 rare INPP5E variants, 12 of which have not been reported in previous studies. We present tertiary protein modeling and analyze all INPP5E variants for deleteriousness and phenotypic correlation. We observe that the combined impact of INPP5E variants in JBTS and non-syndromic IRD patients does not reveal a clear genotype–phenotype correlation, suggesting the involvement of genetic modifiers. Our study cements the wide phenotypic spectrum of INPP5E disease, adding proof that sequence defects in this gene can lead to early-onset non-syndromic IRD.

Control and Signal Acquisition System of Broad-Spectrum Micro-Near-Infrared Spectrometer Based on Dual Single Detector

Based on the scanning grating mirror developed by us, this paper presents a method for precise control of the scanning grating mirror and high-speed spectrum data acquisition. In addition, a system circuit of the scanning grating mirror control and a spectrum signal acquisition system were designed and manufactured. The final results of the experiment show that the control system successfully allowed the precise control of the swing of the scanning grating mirror and the acquisition system successfully carried out the high-speed acquisition and transmission of the spectrum and angle data. The spectrum detection range of the NIR spectrometer was 80–2532 nm. The overall resolution of the spectrum was better than 12 nm.