Sample Acquisition Research Articles

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

ABSTRACT The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (C ab), maximum carboxylation rate (V cmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and C ab were reliably retrieved in most cases, whereas V cmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.

Using UAVs to collect filtered water samples for mineral exploration: Will it take off?

The advent of unmanned aerial vehicle (UAV) assisted surface water sampling and ongoing technological advances in sampling and data acquisition, offers many opportunities to conduct high-quality hydrogeochemical surveys with low cost, high efficiency, and reduced human interactions. Hydrogeochemical mineral exploration is one area that could greatly benefit from a UAV sampling revolution, with survey sites often located in highly remote areas with limited existing infrastructure. Currently, a lack of point source filtration and complicated physiochemical data acquisition hinder mainstream UAV deployment in the context of hydrogeochemical studies. The aim of this paper is to provide guidance on effective UAV sampling methods and physiochemical data collection for use in surface water hydrogeochemical mineral exploration. To date, case study surveys have utilized sampling systems where sampled waters are filtered after collection or analyzed for ‘total’ (unfiltered) concentrations. This paper details a methodology for point-source filtration of water samples using a UAV system to recover filter sample aliquots for the determination of ‘dissolved’ (<0.45 μm) trace element concentrations and compares UAV methods to conventional sampling strategies. This study systematically compares the quality of analytical data collected from lakes, ponds, and rivers in the Long Lake area of southern Ontario, using conventional manual sampling (from a boat or canoe) and a series of UAV-based sampling methodologies. The waters sampled within the study area are highly meteoric and show evidence of solute input from water-rock interaction with local country rocks. The results of this study show that in general, conventional sampling methodologies are statistically comparable to samples collected using UAVs. However, there is some evidence of element variation related to lake stratification, with dissolved Cu concentrations higher in samples collected at depth compared to those from the surface. Similarly, samples filtered after collection typically have lower concentrations of Fe and Mn, potentially resulting from precipitation before filtration. An enclosed sampling system offered from peristaltic pumping with in-line filtration removes the potential for contamination from the surrounding environment and from the UAV itself.

Stromal tumor infiltrating lymphocytes in hormone receptor positive/HER2 negative metastatic breast cancer

The immune landscape of hormone receptor positive, HER2-negative metastatic breast cancer (HR+/HER2- mBC), the most common subtype of BC, remains understudied. This is mainly due to reduced sample acquisition opportunities from metastases as compared to primary tumors. In this study, we explored stromal tumor-infiltrating lymphocytes (sTIL) in metastatic samples collected through our post-mortem tissue donation program UPTIDER (NCT04531696). sTIL were scored as a continuous parameter according to international guidelines on 427 metastases and 38 primary untreated tumors acquired from 20 patients with HR+/HER2- mBC. ER-status was evaluated on 362 metastases with a cut-off for positivity set at 1% according to ASCO/CAP guidelines. Our analyses show that 54% and 15% of metastases had sTIL levels >1% and >5% respectively. sTIL levels tended to be lower in metastases as compared to their respective primary tumor (Estimate: -2.83, 95%CI: -5.77-0.11, p:0.07). sTIL levels were lower in metastases from invasive lobular carcinoma than in metastases from invasive breast carcinoma of no special type (Estimate: -1.67, 95%CI: -2.35--0.98, p:<0.001). A loss of ER expression was observed in 14% of all metastases, yet a negative ER-status was not significantly associated with increased sTIL levels. Finally, sTIL levels were significantly higher in lung and axillary lymph node metastases compared to all metastases. While these analyses were conducted on multiple metastases obtained at the end of life after several lines of treatment, the data provides novel and valuable insights into the state of immune infiltration in patients with metastatic HR+/HER2- BC.

ASMI: An automated, low-cost indenter for soft matter

The automated soft matter indenter (ASMI) is a platform for rapidly performing mechanical characterization of samples with elastic moduli in the range 7 kPa to 67 MPa with a sample acquisition time between 1 and 10 min. It is a low-cost system based upon open-source software, a modified mill, and an educational force sensor with a total bill of materials <$500. This system tests batches of up to 96 samples based on a standard well-plate sample holder without requiring any human intervention. Using the ASMI, users can obtain mechanical data in a programmable manner that enables high-throughput workflows, precisely testing time-dependent phenomena, and integration with other processing steps for closed-loop optimization.

The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans.

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000-4000 cm-1) and preprocessing (Standardization, Savitzky-Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90-180-270-360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models.

Comparison of Different Negative-Sample Acquisition Strategies Considering Sample Representation Forms for Debris Flow Susceptibility Mapping

The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models.

Development of an intelligent geographic information system for mountain roads monitoring: Ground data collection and analysis

This study examines the development of an intelligent geographic information system for mountain road monitoring. To make well-informed and timely management decisions in the planning, construction, and maintenance of mountain roads, and to anticipate potential critical situations and their effects on road conditions, it is imperative to employ modern methodologies. This study involves data collection from a specific section of a mountain road situated near Almaty, Kazakhstan. We gathered data on the road section through visual assessment, measurements of the coefficient of adhesion, and the acquisition of material samples from the structural layers of the roadbed. Based on the visual assessment and surface adhesion measurements, it became evident that the deformation of the studied road section, which includes a network of cracks and "non-standard" transverse cracks, is attributed to mountain mass shifts and water flows from slopes. Laboratory analysis of the collected samples revealed that excessive water saturation could result in cracks during freeze-thaw cycles, while a low resistance coefficient would lead to softening during humid conditions. An Intelligent Geographic Information System (IGIS) will incorporate the collected data alongside open remote sensing and weather data. The objective is to utilize this integrated system in the future for forecasting road conditions and maintenance requirements for similar roads in mountainous regions of the area. The measurements are carried out according to the state standards. That makes the system useful for government management purposes.

Landsat image classification using a deep learning model and multiple-source training samples

ABSTRACT Public release of the entire Landsat data archive and cloud-based geocomputation platforms have greatly facilitated land-cover mapping worldwide. The acquisition of training samples presents a significant challenge in mapping. In this study, we have developed an approach based on transfer learning to creatively use existing global land-cover products and training samples to resolve the training sample issue. We pretrained a deep neural network (DNN) model using low-quality samples automatically generated from existing global land-cover products. The pretrained model was then fine-tuned using high-quality training samples gathered from the training samples shared by authors of existing studies. We implemented this approach to generate a land-cover map for Beijing City, China, in 2015. Using the fine-tuned DNN model, we achieved an overall accuracy (OA) of 86.4% and a kappa coefficient of 0.796, based on independent validation samples. The accuracy was the same and even surpassed that of existing land-cover maps for Beijing. The fine-tuned DNN outperformed the random forest (RF) (OA = 70.2%, kappa = 0.588) and support vector machine (SVM) models (OA = 68.7%, kappa = 0.555) using the same training samples, showing that the need for training samples in land-cover classification can be met by combining deep learning models, existing global land-cover products, and recycled training samples.

Prussian-Blue-Nanozyme-Enhanced Simultaneous Immunochromatographic Control of Two Relevant Bacterial Pathogens in Milk

Salmonella typhimurium and Listeria monocytogenes are relevant foodborne bacterial pathogens which may cause serious intoxications and infectious diseases in humans. In this study, a sensitive immunochromatographic analysis (ICA) for the simultaneous detection of these two pathogens was developed. For this, test strips containing two test zones with specific monoclonal antibodies (MAb) against lipopolysaccharides of S. typhimurium and L. monocytogenes and one control zone with secondary antibodies were designed, and the double-assay conditions were optimized to ensure high analytical parameters. Prussian blue nanoparticles (PBNPs) were used as nanozyme labels and were conjugated with specific MAbs to perform a sandwich format of the ICA. Peroxidase-mimic properties of PBNPs allowed for the catalytic amplification of the colorimetric signal on test strips, enhancing the assay sensitivity. The limits of detection (LODs) of Salmonella and Listeria cells were 2 × 102 and 7 × 103 cells/mL, respectively. LODs were 100-fold less than those achieved due to the ICA based on the traditional gold label. The developed double ICA was approbated for the detection of bacteria in cow milk samples, which were processed by simple dilution by buffer before the assay. For S. typhimurium and L. monocytogenes, the recoveries from milk were 86.3 ± 9.8 and 118.2 ± 10.5% and correlated well with those estimated by the enzyme-linked immunosorbent assay as a reference method. The proposed approach was characterized by high specificity: no cross-reactivity with other bacteria strains was observed. The assay satisfies the requirements for rapid tests: a full cycle from sample acquisition to result assessment in less than half an hour. The developed ICA has a high application potential for the multiplex detection of other foodborne pathogens.

Autonomous Planetary Liquid Sampler (APLS) for In Situ Sample Acquisition and Handling from Liquid Environments.

Many natural and artificial liquid environments, such as rivers, oceans, lakes, water storage tanks, aquariums, and urban water distribution systems, are difficult to access. As a result, technology is needed to enable autonomous liquid sampling to monitor water quality and ecosystems. Existing in situ sample acquisition and handling systems for liquid environments are currently limited to a single use and are semi-autonomous, relying on an operator. Liquid sampling systems should be robust and light and withstand long-term operation in remote locations. The system components involved in liquid sampling should be sterilisable to ensure reusability. Here, we introduce a prototype of a liquid sampler that can be used in various liquid environments and may be valuable for the scientific characterisation of different natural, remote, and planetary settings. The Autonomous Planetary Liquid Sampler (APLS) is equipped with pre-programmed, fully autonomous extraction, cleaning, and sterilisation functionalities. It can operate in temperatures between -10 °C and 60 °C and pressure of up to 0.24 MPa (~24 m depth below mean sea level on Earth). As part of the control experiment, we demonstrate its safe and robust autonomous operation in a laboratory environment using a liquid media with Bacillus subtilis. A typical sampling procedure required 28 s to extract 250 mL of liquid, 5 s to fill the MilliQ water, 25 s for circulation within the system for cleaning and disposal, and 200 s to raise the system temperature from ~30 °C ambient laboratory temperature to 150 °C. The temperature is then maintained for another 3.2 h to sterilise the critical parts, allowing a setup reset for a new experiment. In the future, the liquid sampler will be combined with various existing analytical instruments to characterise the liquid solution and enable the autonomous, systematic monitoring of liquid environments on Earth.

Rapid analysis of spilled petroleum oils by direct analysis in real time time-of-flight mass spectrometry with hydrophobic paper sample collection

BackgroundOil spills are widespread and can cause devastating environmental consequences. Rapid oil identification is critical to find the origin of the spill, monitor the environment, and lead to informed mitigation measures. The current standard methods in oil spill identification are precise and reliable, but require extensive sample preparation, long instrument runs, and time-consuming data processing. Direct analysis in real time time-of-flight mass spectrometry (DART-ToF MS) has been employed to screen for spilled petroleum oils, with results obtained in mere hours. The present study introduced an innovative, simple, and fast oil sampling method using hydrophobic filter paper and demonstrated its compatibility with DART-ToF MS analysis. Motor oils, jet fuels, marine diesels, crude oils, intermediate fuel oils, heavy fuel oils, and diluted bitumen were collected using the filter paper sampling method. Classification models were constructed from the spectral data by heat map inspection followed by principal component analysis (PCA) and discriminant analysis of principal components (DAPC). Oil slicks and weathered oil slicks were prepared from five oil types, and samples from each slick were collected using filter paper.ResultsThe filter paper technique allowed for effective oil sampling and data acquisition by DART-ToF MS for diluted source oils, oil slicks and weathered oil slicks. Classification via the constructed DAPC models indicated that the DART-ToF MS instrument in tandem with filter paper sampling and multivariate statistics can accurately identify common oil types, with significant improvement of sample collection and turnaround time.ConclusionsThe promising classification results, simple sample collection, and rapid data analysis illustrate the potential use of hydrophobic filter paper and DART-ToF MS as tools in managing large scale oil spill emergency situations.

In situ study on articular cartilage degeneration in simulated microgravity by HOF-ATR-FTIR spectroscopy

Fourier transform infrared spectroscopy (FTIRS) can provide rich information on the composition and content of samples, enabling the detection of subtle changes in tissue composition and structure. This study represents the first application of FTIRS to investigate cartilage under microgravity. Simulated microgravity cartilage model was firstly established by tail-suspension (TS) for 7, 14 and 21 days, which would be compared to control samples. A self-developed hollow optical fiber attenuated total reflection (HOF-ATR) probe coupled with a FTIR spectrometer was used for the spectral acquisition of cartilage samples in situ, and one-way analysis of variance (ANOVA) was employed to analyze the changes in the contents of cartilage matrix at different stages. The results indicate that cartilage degenerates in microgravity, the collagen content gradually decreases with the TS time, and the structure of collagen fibers changes. The trends of proteoglycan content and collagen integrity show an initial decrease followed by an increase, ultimately significantly decreasing. The findings provide the basis for the cartilage degeneration in microgravity with TS time, which must be of real significance for space science and health detection.

Battery-less long-range wireless fluidic sensing system using flexible additive manufacturing ambient energy harvester and microfluidics

Fluid sensing has been an important but missing part of the massive Internet-of-Things sensor networks due to challenges including excessive manufacturing time/cost, finite wireless interrogation range, limited immunity to ambient clutter, and excessive required power for autonomous microfluidics operability. Here, we proposed an additive manufacturing flexible system as a solution to those challenges while enabling fluid analysis from controlled labs to virtually everywhere. Energy harvesting provides all required power for the actuation of the micro-pump enabling battery-less liquid sample acquisition. Energy sources including ultra-high-frequency radio frequency identification and hand-held devices like two-way talk radio are harvested simultaneously to support energy requirements for periodic monitoring every 6.6 min and on-demand monitoring within 4.63 s. Backscattering topologies are used to significantly extend the reading range while increasing the immunity to interferences and reducing the cost to the reader. A new additive manufacturing process is proposed to reduce fabrication time and cost while enabling massive scalability of flexible microfluidics. The good flexibility makes the system suitable for working toward future wearable applications. Prototypes of a sweat sensing system are demonstrated and successfully interrogated at 3 m with more than 15 dB signal-to-noise ratio using only a 14 dBm transmitter equivalent isotropic radiated power.

Identification of modal parameters of soil specimen based on impact force

This study used vibration testing signals of soil samples under external loading to identify modal parameters (including natural frequencies and damping ratios) with different compaction degrees. Based on these parameters, a novel approach was proposed for reliable roadbed vibration compaction control and compaction process optimization. The experimental section utilized six soil samples with varying compaction degrees as experimental subjects, using the hammering method as the excitation mode. Subsequently, the frequency response function and modal parameters of the sample system were obtained through the acquisition, analysis, and parameter identification of samples’ acceleration signals. Firstly, samples with compaction degrees ranging from 88 % to 97 % primarily exhibited three modes, with the second modal frequency response displaying the weakest amplitude, and the fundamental mode being the dominant one. Additionally, parameter identification results revealed that the fundamental modal frequency exhibited a significant negative exponential growth with increasing compaction degree, while the second and third modal frequencies showed significant linear growth. Furthermore, the average damping ratio also demonstrated a tendency toward linear change with increasing compaction degree. Finally, the feasibility of modal parameters being actively used in practical engineering is discussed. Consequently, this study aimed to propose an indicator system for accurately assessing the bearing level of compacted soils from a modal dynamics perspective and to integrate modal dynamic indicators with density-class indicators into further optimization design work on road compaction processes.

Collaborative robotics to enable ultra-high-throughput IR-MALDESI

Over the last 5 years, IR-MALDESI-MS (Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry) has been demonstrated for use in a range of high-throughput biochemical and cellular assays with remarkable sample acquisition rates up to 22 Hz for a single 384-well assay plate. With such high single plate acquisition rates, the rate limiting step becomes how fast subsequent plates can be presented to the MS for analysis. To make this transfer as fast as possible while maintaining safe operation in a laboratory environment, we developed a collaborative robotic plate transfer system (CRPTS) that combines a 6-axis robot with dual plate grippers, a 7th axis conveyor stage, and a 420-plate capacity sample loading window. As a demonstration of the throughput and flexibility of CRPTS, we performed a biochemical assay that monitored the oxidation of tris(2-carboxyethyl)phosphine (TCEP) to screen for nuisance compounds. Using continuous and step motion scan profiles, we analyzed 158,799 compounds contained in 448 assay plates over the course of 12.5 h (Z-Factor=0.87) and 17.5 h (Z-factor=0.99), respectively. Extrapolating these results enables the screening of a million compounds within 6–7 working days.

Profil Anisometropia pada Orang Dewasa di Rumah Sakit Ibnu Sina Makassar

Anisometropia is an interocular asymmetric refractive condition and is caused by a significant difference in diopters between the right and left eyes. Anisometropia occurs when there is a difference in the refractive power of the two eyes. Classification of several levels of difference, namely 1) difference in refraction between the two eyes less than 1.5 D, where both eyes can still be used together with good fusion and stereoscopic, 2) difference in refraction between the two eyes 1.5 D to 3.0 D (the presence of cylindrical differences is more significant than spherical), and 3) the difference in refraction is more than 3.0 D. The high incidence of Anisometropia can be caused by a lack of community initiative in seeking health assistance to treat Anisometropia, or even prevent Anisometropia from getting worse. The aim of this study was to determine the characteristics of Anisometropia in adults at Ibnu Sina Hospital Makassar. This research is descriptive in nature. The population of this study was all Anisometropia patients registered at Ibnu Sina Hospital Makassar so that the research sample was obtained by total sampling, namely the entire study population of 20 patients. Sample acquisition was adjusted to the inclusion criteria, namely Anisometropia patients with complete medical record data. The data was processed using the univariate method so that the research results showed that the majority of Anisometropia patients at the eye clinic at Ibnu Sina Hospital Makassar were on average 18-25 years old (45%) and mostly occurred in women (60%) and most were students (40%). %) The incidence of Anisometropia is probably caused by excessive use of cellphone or laptop gadgets and at very close range.

LUNGBANK: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer.

LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets. LUNGBANK functions not merely as a repository but as a sophisticated platform crucial for advancing lung cancer research, poised to facilitate significant discoveries. LUNGBANK was meticulously designed to optimize every stage of biospecimen handling, from collection and storage to processing. Rigorous standard operating procedures and stringent quality control measures guarantee the integrity of collected biospecimens. Advanced data management protocols facilitate the efficient integration and analysis of various datasets, enhancing the depth and breadth of research possibilities in lung cancer. LUNGBANK has amassed a comprehensive collection of biospecimens essential for unraveling the intricate molecular mechanisms of lung cancer. The integration of state-of-the-art technologies ensures the acquisition of top-tier data, fostering breakthroughs in translational and histological research. Moreover, the establishment of patient-derived systems by LUNGBANK underscores its pivotal role in personalized medicine approaches. The establishment of LUNGBANK marks a significant milestone in addressing the critical challenges of lung cancer research. By providing researchers with high-quality biospecimens and advanced research tools, LUNGBANK not only supports Project LUNGMARK's objectives but also contributes extensively to the broader landscape of personalized medicine. It promises to enhance our understanding of lung cancer initiation, progression, and therapeutic interventions tailored to individual patient needs, thereby advancing the field towards more effective diagnostic and therapeutic strategies.

Evaluating the efficacy of sampling acquisition paths for mapping vegetation structure using terrestrial mobile laser scanning.

Mobile Laser Scanning (MLS) is rapidly increasing in popularity for the capture and quantification of vegetation structure in natural landscapes. However, understanding the optimal implementation for forest capture in the field is still limited, particularly regarding the choice in acquisition path and length followed during data capture. How variable is the characterisation of vegetation structure when using different acquisition paths, and is this difference likely to be significant for most users? In this study we compared four acquisition path designs commonly cited in the literature to determine the importance of this choice for users when capturing vegetation structure. MLS point clouds were systematically captured to repeatedly survey study plots in a closed-canopy forest ecosystem in south-eastern Australia. Digital elevation models, canopy height models, and vertical voxel occupancy profiles were derived to illustrate the sensitivity of the path's length and configuration to variability in the quantification of vegetation structure. We found strong agreement between the acquisition path designs that increased as path length increased. No significant differences were found in digital elevation models derived at 75% and 100% path lengths. However, differences were observed in two and four of the 25% and 50% path length plots respectively. Significant differences in canopy height model data were only found in one plot at the 50% path length. Mean differences to the reference digital elevation models was 0.09 m across all plots, and 0.4 m for the canopy height models. Voxel occupancy profiles showed the greatest agreement between the understory and canopy where the mean difference between acquisition paths across all plots was 5.72%. Our findings have significant implications for the use of MLS from an operational perspective, as they illustrate the variability and reliability of their use in natural forested landscapes. Users can choose to balance their choice in acquisition path design with path length to meet requirements such as efficiency or ease of data capture in the field.

An Observatory to monitor range extension of the Mediterranean monk seal based on its eDNA traces: collecting data and delivering results in the "Open Science" era.

The monk seal is the most endangered pinniped in the world and the only one found in the Mediterranean, where its distribution and abundance have suffered a drastic decline in the last few decades. Data on its status are scattered due to both its rarity and evasiveness and records are biased towards occasional, mostly coastal encounters. Nowadays, molecular techniques allow us to detect and quantify minute amounts of DNA traces released into the environment (eDNA) by any organism. A species-specific molecular assay is now available for detecting the recent presence of the monk seal in the water column through the analysis of sea-water samples collected from the sea surface. The project "Spot the Monk" uses this non-invasive detection tool to monitor monk seal occurrence in Mediterranean waters by means of eDNA analysis. The simplicity in the acquisition of samples together with the need to collect samples in multiple points simultaneously made the project well suited to the involvement of the general public. Up to today, about 350 samples have been collected and analysed in the central-western Mediterranean by researchers and a multifarious range of citizen scientists - from recreational sailing organisations, both amateur and competitive sportsmen, to fishermen. This work announces the launch of an open-source Observatory (https://www.spot-the-monk-observatory.com/) where the project outcomes are publicly accessible as soon as they are produced. Embracing the principles of Open Science, we believe that such an approach can contribute to filling the knowledge gap about the distribution of this charismatic species in our seas, providing, at the same time, a proof of concept on how data collected by a variety of actors can be returned to the scientific and non-scientific communities in an innovative format for immediate consultation.

Orthologs of NOX5 and EC-SOD/SOD3: dNox and dSod3 Impact Egg Hardening Process and Egg Laying in Reproductive Function of Drosophila melanogaster.

The occurrence of ovarian dysfunction is often due to the imbalance between the formation of reactive oxygen species (ROS) and the ineffectiveness of the antioxidative defense mechanisms. Primary sources of ROS are respiratory electron transfer and the activity of NADPH oxidases (NOX) while superoxide dismutases (SOD) are the main key regulators that control the levels of ROS and reactive nitrogen species intra- and extracellularly. Because of their central role SODs are the subject of research on human ovarian dysfunction but sample acquisition is low. The high degree of cellular and molecular similarity between Drosophila melanogaster ovaries and human ovaries provides this model organism with the best conditions for analyzing the role of ROS during ovarian function. In this study we clarify the localization of the ROS-producing enzyme dNox within the ovaries of Drosophila melanogaster and by a tissue-specific knockdown we show that dNox-derived ROS are involved in the chorion hardening process. Furthermore, we analyze the dSod3 localization and show that reduced activity of dSod3 impacts egg-laying behavior but not the chorion hardening process.