Travel surveillance uncovers dengue virus dynamics and introductions in the Caribbean.

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

Monkeypox virus genomic accordion strategies.

The 2023 monkeypox (mpox) epidemic was caused by a subclade IIb descendant of a monkeypox virus (MPXV) lineage traced back to Nigeria in 1971. Person-to-person transmission appears higher than for clade I or subclade IIa MPXV, possibly caused by genomic changes in subclade IIb MPXV. Key genomic changes could occur in the genome's low-complexity regions (LCRs), which are challenging to sequence and are often dismissed as uninformative. Here, using a combination of highly sensitive techniques, we determine a high-quality MPXV genome sequence of a representative of the current epidemic with LCRs resolved at unprecedented accuracy. This reveals significant variation in short tandem repeats within LCRs. We demonstrate that LCR entropy in the MPXV genome is significantly higher than that of single-nucleotide polymorphisms (SNPs) and that LCRs are not randomly distributed. In silico analyses indicate that expression, translation, stability, or function of MPXV orthologous poxvirus genes (OPGs), including OPG153, OPG204, and OPG208, could be affected in a manner consistent with the established "genomic accordion" evolutionary strategies of orthopoxviruses. We posit that genomic studies focusing on phenotypic MPXV differences should consider LCR variability.

Factors Associated with SARS-CoV-2 Infection in Fully Vaccinated Nursing Home Residents and Workers.

Persons living or working in nursing homes faced a higher risk of SARS-CoV-2 infections during the pandemic, resulting in heightened morbidity and mortality among older adults despite robust vaccination efforts. This prospective study evaluated the humoral and cellular immunity in fully vaccinated residents and workers from two nursing homes in Madrid, Spain, from 2020 to 2021. Measurements of IgG levels were conducted in August 2020 (pre-vaccination) and June and September 2021 (post-vaccination), alongside assessments of neutralizing antibodies and cellular responses in September 2021 among the most vulnerable individuals. Follow-up extended until February 2022 to identify risk factors for SARS-CoV-2 infection or mortality, involving 267 residents (mean age 87.6 years, 81.3% women) and 302 workers (mean age 50.7 years, 82.1% women). Residents exhibited a significantly higher likelihood of experiencing COVID-19 before June 2021 compared with nursing staff (OR [95% CI], 7.2 [3.0 to 17.2], p < 0.01). Participants with a history of previous COVID-19 infection showed more significant increases in IgG levels in August 2020, June 2021 and September 2021, alongside an increased proportion of neutralizing antibodies in the most vulnerable individuals. However, IgG decay remained the same between June and September 2021 based on the previous COVID-19 status. During the Omicron variant wave, residents and staff showed a similar rate of SARS-CoV-2 infection. Notably, preceding clinical or immunological factors before receiving three vaccination doses did not demonstrate associations with COVID-19 infection or overall mortality in our participant cohort.

Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors.

The epidemiology of sexually transmitted infections (STIs) is complex due to the coexistence of various pathogens, the variety of transmission modes derived from sexual orientations and behaviors at different ages and genders, and sexual contact hotspots resulting in network transmission. There is also a growing proportion of recreational drug users engaged in high-risk sexual activities, as well as pharmacological self-protection routines fostering non-condom practices. The frequency of asymptomatic patients makes it difficult to develop a comprehensive approach to STI epidemiology. Modeling approaches are required to deal with such complexity. Membrane computing is a natural computing methodology for the virtual reproduction of epidemics under the influence of deterministic and stochastic events with an unprecedented level of granularity. The application of the LOIMOS program to STI epidemiology illustrates the possibility of using it to shape appropriate interventions. Under the conditions of our basic landscape, including sexual hotspots of individuals with various risk behaviors, an increase in condom use reduces STIs in a larger proportion of heterosexuals than in same-gender sexual contacts and is much more efficient for reducing Neisseria gonorrhoeae than Chlamydia and lymphogranuloma venereum infections. Amelioration from diagnostic STI screening could be instrumental in reducing N. gonorrhoeae infections, particularly in men having sex with men (MSM), and Chlamydia trachomatis infections in the heterosexual population; however, screening was less effective in decreasing lymphogranuloma venereum infections in MSM. The influence of STI epidemiology of sexual contacts between different age groups (<35 and ≥35 years) and in bisexual populations was also submitted for simulation.IMPORTANCEThe epidemiology of sexually transmitted infections (STIs) is complex and significantly influences sexual and reproductive health worldwide. Gender, age, sexual orientation, sexual behavior (including recreational drug use and physical and pharmacological protection practices), the structure of sexual contact networks, and the limited application or efficiency of diagnostic screening procedures create variable landscapes in different countries. Modeling techniques are required to deal with such complexity. We propose the use of a simulation technology based on membrane computing, mimicking in silico STI epidemics under various local conditions with an unprecedented level of detail. This approach allows us to evaluate the relative weight of the various epidemic drivers in various populations at risk and the possible outcomes of interventions in particular epidemiological landscapes.

Rapid expansion of lymphogranuloma venereum infections with fast diversification and spread of Chlamydia trachomatis L genovariants.

IMPORTANCE: Numerous international organizations, including the World Health Organization, have been drawing attention to the global increase in sexually transmitted infections. Twenty years ago, lymphogranuloma venereum (LGV) was mainly considered a tropical disease; in recent decades, however, LGV has been increasingly present in high-income countries. This increase has been linked to men who have sex with men who participate in highly interconnected sexual networks, leading to a rapid spread of LGV. This study focuses on the spread of LGV, presenting the largest time series of LGV prevalence in Spain, which includes more than a thousand diagnosed cases in one large city. The number of LGV cases diagnosed was analyzed over time, and a selection of strains was subjected to molecular genotyping. The results indicate that the LGV epidemic is gradually evolving toward an increasingly complex diversification due to the selection of successful genovariants that have emerged by mutation and recombination events, suggesting that we are moving toward an unpredictable scenario.

Free PCR virus detection via few-layer bismuthene and tetrahedral DNA nanostructured assemblies.

In this work we describe a highly sensitive method based on a biocatalyzed electrochemiluminescence approach. The system combines, for the first time, the use of few-layer bismuthene (FLB) as a platform for the oriented immobilization of tetrahedral DNA nanostructures (TDNs) specifically designed and synthetized to detect a specific SARS-CoV-2 gene sequence. In one of its vertices, these TDNs contain a DNA capture probe of the open reading frame 1 ab (ORF1ab) of the virus, available for the biorecognition of the target DNA/RNA. At the other three vertices, there are thiol groups that enable the stable anchoring/binding to the FLB surface. This novel geometry/approach enables not only the binding of the TDNs to surfaces, but also the orientation of the capture probe in a direction normal to the bismuthine surface so that it is readily accessible for binding/recognition of the specific SARS-CoV-2 sequence. The analytical signal is based on the anodic electrochemiluminescence (ECL) intensity of luminol which, in turn, arises as a result of the reaction with H2O2, generated by the enzymatic reaction of glucose oxidation, catalyzed by the biocatalytic label avidin-glucose oxidase conjugate (Av-GOx), which acts as co-reactant in the electrochemiluminescent reaction. The method exhibits a limit of detection (LOD) of 4.31 aM and a wide linear range from 14.4 aM to 1.00 µM, and its applicability was confirmed by detecting SARS-CoV-2 in nasopharyngeal samples from COVID-19 patients without the need of any amplification process.

Transmitted drug resistance to antiretroviral drugs in Spain during the period 2019-2021.

To evaluate the prevalence of transmitted drug resistance (TDR) to nucleoside and nonnucleoside reverse transcriptase inhibitors (NRTI, NNRTI), protease inhibitors (PI), and integrase strand transfer inhibitors (INSTI) in Spain during the period 2019-2021, as well as to evaluate transmitted clinically relevant resistance (TCRR) to antiretroviral drugs. Reverse transcriptase (RT), protease (Pro), and Integrase (IN) sequences from 1824 PLWH (people living with HIV) were studied. To evaluate TDR we investigated the prevalence of surveillance drug resistance mutations (SDRM). To evaluate TCRR (any resistance level ≥ 3), and for HIV subtyping we used the Stanford v.9.4.1 HIVDB Algorithm and an in-depth phylogenetic analysis. The prevalence of NRTI SDRMs was 3.8% (95% CI, 2.8%-4.6%), 6.1% (95% CI, 5.0%-7.3%) for NNRTI, 0.9% (95% CI, 0.5%-1.4%) for PI, and 0.2% (95% CI, 0.0%-0.9%) for INSTI. The prevalence of TCRR to NRTI was 2.1% (95% CI, 1.5%-2.9%), 11.8% for NNRTI, (95% CI, 10.3%-13.5%), 0.2% (95% CI, 0.1%-0.6%) for PI, and 2.5% (95% CI, 1.5%-4.1%) for INSTI. Most of the patients were infected by subtype B (79.8%), while the majority of non-Bs were CRF02_AG (n = 109, 6%). The prevalence of INSTI and PI resistance in Spain during the period 2019-2021 is low, while NRTI resistance is moderate, and NNRTI resistance is the highest. Our results support the use of integrase inhibitors as first-line treatment in Spain. Our findings highlight the importance of ongoing surveillance of TDR to antiretroviral drugs in PLWH particularly with regard to first-line antiretroviral therapy.

Principal Component Analysis Applied to Digital Pulse Shape Analysis for Isotope Discrimination.

Digital pulse shape analysis (DPSA) techniques are becoming increasingly important for the study of nuclear reactions since the development of fast digitizers. These techniques allow us to obtain the (A, Z) values of the reaction products impinging on the new generation solid-state detectors. In this paper, we present a computationally efficient method to discriminate isotopes with similar energy levels, with the aim of enabling the edge-computing paradigm in future field-programmable gate-array-based acquisition systems. The discrimination of isotope pairs with analogous energy levels has been a topic of interest in the literature, leading to various solutions based on statistical features or convolutional neural networks. Leveraging a valuable dataset obtained from experiments conducted by researchers in the FAZIA Collaboration at the CIME cyclotron in GANIL laboratories, we aim to establish a comparative analysis regarding selectivity and computational efficiency, as this dataset has been employed in several prior publications. Specifically, this work presents an approach to discriminate between pairs of isotopes with similar energies, namely, 12,13C, 36,40Ar, and 80,84Kr, using principal component analysis (PCA) for data preprocessing. Consequently, a linear and cubic machine learning (ML) support vector machine (SVM) classification model was trained and tested, achieving a high identification capability, especially in the cubic one. These results offer improved computational efficiency compared to the previously reported methodologies.

The imperative for quality control programs in Monkeypox virus DNA testing by PCR: CIBERINFEC quality control.

To evaluate molecular assays for Mpox diagnosis available in various clinical microbiology services in Spain through a quality control (QC) approach. A total of 14 centers from across Spain participated in the study. The Reference Laboratory dispatched eight serum samples and eight nucleic acid extracts to each participating center. Some samples were spiked with Mpox or Vaccinia virus to mimic positive samples for Mpox or other orthopox viruses. Participating centers provided information on the results obtained, as well as the laboratory methods used. Among the 14 participating centers seven different commercial assays were employed, with the most commonly used kit being LightMix Modular Orthopox/Monkeypox (Mpox) Virus (Roche®). Of the 12 centers conducting Mpox determinations, concordance ranged from 62.5% (n = 1) to 100% (n = 11) for eluates and from 75.0% (n = 1) to 100% (n = 10) for serum. Among the 10 centers performing Orthopoxvirus determinations, a 100% concordance was observed for eluates, while for serum, concordance ranged from 87.5% (n = 6) to 100% (n = 4). Repeatedly, 6 different centers reported a false negative in serum samples for Orthopoxvirus diagnosis, particularly in a sample with borderline Ct = 39. Conversely, one center, using the TaqMan™ Mpox Virus Microbe Detection Assay (Thermo Fisher), reported false positives in Mpox diagnosis for samples spiked with vaccinia virus due to cross-reactions. We observed a positive correlation of various diagnostic assays for Mpox used by the participating centers with the reference values. Our results highlight the significance of standardization, validation, and ongoing QC in the microbiological diagnosis of infectious diseases, which might be particularly relevant for emerging viruses.

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Travel surveillance uncovers dengue virus dynamics and introductions in the Caribbean.

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

Description of mpox reinfection by whole genome sequencing.

Several possible mpox reinfections have been reported, however, the debate on whether these are confirmed reinfections remains open. A 30-year-old male living with HIV and a history of single-dose mpox vaccination, first diagnosed with mpox in September 2022, presented with genital ulcers in March 2023, testing positive for mpox virus. Real-time polymerase chain reaction revealed the presence of viral DNA with cycle threshold values of 24 and 25, respectively. Whole genome sequencing and phylogenetic approach allowed us to classify these viruses as Clade IIb lineage B.1 and Clade IIb lineage B.1.4, respectively. Twelve nucleotide differences were identified. The observed difference was higher than the estimate of mutations/genome/year described. These data confirm that mpox reinfection is possible and reinforces current vaccination campaigns.

Impact of storage time in dried blood samples (DBS) and dried plasma samples (DPS) for point-of-care hepatitis C virus (HCV) RNA quantification and HCV core antigen detection.

The scale-up of hepatitis C virus (HCV) diagnosis and treatment requires affordable and simple tools to improve access to care, especially in low- and middle-income settings with limited infrastructure or high-risk populations. Dried blood and plasma samples (DBS and DPS) are useful alternative for hepatitis C detection in settings lacking adequate infrastructure. We evaluated the performance of DBS and DPS vs plasma in a point-of-care HCV RNA quantitative assay (Xpert HCV Viral Load-Cepheid), and compared HCV core antigen (HCVcAg) detection by the Architect HCV core antigen assay (Abbott) in DBS vs serum. The dried samples were stored at room temperature for different storage times to reproduce the time from sampling to testing in settings with centralized diagnosis or when testing mobile populations. HCV RNA quantification in DBS and DPS presented 100% sensitivity and specificity and a high correlation for up to 3 months of storage. HCV viremia showed a mean decrease of 0.5 log10 IU/mL (DBS) and 0.3 log10 IU/mL (DPS) for storage times up to 1 month. Architect HCVcAg detection presented high sensitivity/specificity (96%/100%) in DBS tested immediately after sampling, decreasing to 86% sensitivity after 7 days of storage. However, sensitivity increased when an optimized cut-off was applied for each storage time. We conclude that DBS and DPS are suitable samples for HCV RNA detection and quantification, being DPS more reliable for shorter storage times. DBS can be also used for HCVcAg qualitative detection and the sensitivity can be increased when adjusting the cut-off values. IMPORTANCE Hepatitis C infection remains a global burden despite the effectiveness of antivirals. In the WHO roadmap to accomplish HCV elimination by 2030, HCV diagnosis is one of the main targets. However, identifying patients in resource-limited settings and high-risk populations with limited access to healthcare remains a challenge and requires innovative approaches that allow decentralized testing. The significance of our research is in verifying the good performance of dried samples for HCV diagnosis using two different diagnostics assays and considering the effect of room temperature storage in this sample format. We confirmed dried samples are an interesting alternative for HCV screening and reflex testing in resource-limited settings or high-risk populations.

Aerial monitoring of atmospheric particulate matter produced by open-pit mining using low-cost airborne sensors.

Mining is an economic activity that entails the production and displacement of significant amounts of atmospheric particulate matter (PM) during operations involving intense earthcrushing or earthmoving. As high concentrations of PM may have adverse effects on human health, it is necessary to monitor and control the fugitive emissions of this pollutant. This paper presents an innovative methodology for the online monitoring of PM10 concentrations in air using a low-cost sensor (LCS, <300 USD) onboard an unmanned aerial vehicle. After comprehensive calibration, the LCS was horizontally flown over seven different areas of the large Riotinto copper mine (Huelva, Spain) at different heights to study the PM10 distribution at different longitudes and altitudes. The flights covered areas of zero activity, intense mining, drilling, ore loading, waste discharge, open stockpiling, and mineral processing. In the zero-activity area, the resuspension of PM10 was very low, with a weak wind speed (3.6 m/s). In the intense-mining area, unhealthy concentrations of PM10 (>51 µgPM10/m3) could be released, and the PM10 can reach surrounding populations through long-distance transport driven by several processes being performed simultaneously. Strong dilution was also observed at high altitudes (> 50 m). Mean concentrations were found to be 22-89 µgPM10/m3, with peaks ranging from 86 to 284 µgPM10/m3. This study demonstrates the potential applicability of airborne LCSs in the high-resolution online monitoring of PM in mining, thus supporting environmental managers during decision-making against fugitive emissions in a cost-effective manner.

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

BACKGROUND: Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation workflow. To extend the standard validation workflow we propose doing a pilot test with the next characteristics. First, the assumption of perfect ground truth (100% sensitive and specific) is unrealistic, as high intra and inter-observer variability have been reported. To address this, we propose using Bayesian latent class models (BLCA) to estimate accuracy during the pilot. Additionally, assessing only the performance of a model without considering its applicability and acceptance by physicians is insufficient if we hope to integrate AI systems into day-to-day clinical practice. Therefore, we propose employing explainable artificial intelligence (XAI) methods during the pilot test to involve physicians and evaluate how well a Deep Learning model is accepted and how helpful it is for routine decisions as well as analyze its limitations by assessing the etiology. This study aims to apply the proposed pilot to test a deep Convolutional Neural Network (CNN)-based model for identifying consolidation in pediatric chest-X-ray (CXR) images already validated using the standard workflow. METHODS: For the standard validation workflow, a total of 5856 public CXRs and 950 private CXRs were used to train and validate the performance of the CNN model. The performance of the model was estimated assuming a perfect ground truth. For the pilot test proposed in this article, a total of 190 pediatric chest-X-ray (CXRs) images were used to test the CNN model support decision tool (SDT). The performance of the model on the pilot test was estimated using extensions of the two-test Bayesian Latent-Class model (BLCA). The sensitivity, specificity, and accuracy of the model were also assessed. The clinical characteristics of the patients were compared according to the model performance. The adequacy and applicability of the SDT was tested using XAI techniques. The adequacy of the SDT was assessed by asking two senior physicians the agreement rate with the SDT. The applicability was tested by asking three medical residents before and after using the SDT and the agreement between experts was calculated using the kappa index. RESULTS: The CRXs of the pilot test were labeled by the panel of experts into consolidation (124/176, 70.4%) and no-consolidation/other infiltrates (52/176, 29.5%). A total of 31/176 (17.6%) discrepancies were found between the model and the panel of experts with a kappa index of 0.6. The sensitivity and specificity reached a median of 90.9 (95% Credible Interval (CrI), 81.2-99.9) and 77.7 (95% CrI, 63.3-98.1), respectively. The senior physicians reported a high agreement rate (70%) with the system in identifying logical consolidation patterns. The three medical residents reached a higher agreement using SDT than alone with experts (0.66±0.1 vs. 0.75±0.2). CONCLUSIONS: Through the pilot test, we have successfully verified that the deep learning model was underestimated when a perfect ground truth was considered. Furthermore, by conducting adequacy and applicability tests, we can ensure that the model is able to identify logical patterns within the CXRs and that augmenting clinicians with automated preliminary read assistants could accelerate their workflows and enhance accuracy in identifying consolidation in pediatric CXR images.

Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility.

The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals remain uninfected despite high-risk exposures. To understand lipid and metabolite profiles related with COVID-19 susceptibility and disease progression. We collected samples from an exceptional group of unvaccinated healthcare workers heavily exposed to SARS-CoV-2 but not infected ('non-susceptible') and subjects who became infected during the follow-up ('susceptible'), including non-hospitalized and hospitalized patients with different disease severity providing samples at early disease stages. Then, we analyzed their plasma metabolomic profiles using mass spectrometry coupled with liquid and gas chromatography. We show specific lipids profiles and metabolites that could explain SARS-CoV-2 susceptibility and COVID-19 severity. More importantly, non-susceptible individuals show a unique lipidomic pattern characterized by the upregulation of most lipids, especially ceramides and sphingomyelin, which could be interpreted as markers of low susceptibility to SARS-CoV-2 infection. This study strengthens the findings of other researchers about the importance of studying lipid profiles as relevant markers of SARS-CoV-2 pathogenesis.

Robust expression of the TRPC1 channel associated with photoreceptor loss in the rat retina.

Baseline intracellular calcium levels are significantly higher in neuronal and glial cells of rat retinas with retinitis pigmentosa (RP). Although this situation could initiate multiple detrimental pathways that lead to cell death, we considered the possibility of TRPC1 being involved in maintaining calcium homeostasis in the retina by acting as a component of store-operated calcium (SOC) channels with special relevance during photoreceptor degeneration. In this study, we examined by Western blot the expression of TRPC1 in healthy control rat retinas (Sprague-Dawley, SD) and retinas with RP (P23H-1 rats). We also analyzed its specific cellular distribution by immunofluorescence to recognize changes during neurodegeneration and to determine whether its presence is consistent with high basal calcium levels and cellular survival in degenerating retinas. We found that TRPC1 immunostaining was widely distributed across the retina in both rat strains, SD and P23H, and its expression levels significantly increased in the retinas with advanced degeneration compared to the age-control SD rats. In the outer retina, TRPC1 immunoreactivity was distributed in pigment epithelium cells, the photoreceptor inner segments of older animals, and the outer plexiform layer. In the inner retina, TRPC1 labeling was detected in horizontal cells, specific somata of bipolar and amacrine cells, and cellular processes in all the strata of the inner plexiform layer. Somata and processes were also highly immunoreactive in the ganglion cell layer and astrocytes in the nerve fiber layer in all animals. In the P23H rat retinas, the TRPC1 distribution pattern changed according to advancing photoreceptor degeneration and the gliosis reaction, with TRPC1 immunoreactive Müller cells mainly in advanced stages of disease. The cellular TRPC1 immunoreactivity found in this work suggests different mechanisms of activation of these channels depending on the cell type. Furthermore, the results support the idea that photoreceptor loss due to RP is associated with robust TRPC1 protein expression in the rat inner retina and raise the possibility of TRPC1 channels contributing to maintain high basal calcium levels during neurodegeneration and/or maintenance processes of the inner retina.

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.

Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings.