Extensive Monitoring Research Articles

A Luminous Red Supergiant and Dusty Long-period Variable Progenitor for SN 2023ixf

We analyze pre-explosion near- and mid-infrared (IR) imaging of the site of SN 2023ixf in the nearby spiral galaxy M101 and characterize the candidate progenitor star. The star displays compelling evidence of variability with a possible period of ≈1000 days and an amplitude of Δm ≈ 0.6 mag in extensive monitoring with the Spitzer Space Telescope since 2004, likely indicative of radial pulsations. Variability consistent with this period is also seen in the near-IR J and K s bands between 2010 and 2023, up to just 10 days before the explosion. Beyond the periodic variability, we do not find evidence for any IR-bright pre-supernova outbursts in this time period. The IR brightness ( mag) and color (J − K s = 1.6 mag) of the star suggest a luminous and dusty red supergiant. Modeling of the phase-averaged spectral energy distribution (SED) yields constraints on the stellar temperature ( K) and luminosity (). This places the candidate among the most luminous Type II supernova progenitors with direct imaging constraints, with the caveat that many of these rely only on optical measurements. Comparison with stellar evolution models gives an initial mass of M init = 17 ± 4 M ⊙. We estimate the pre-supernova mass-loss rate of the star between 3 and 19 yr before explosion from the SED modeling at to 3 × 10−4 M ⊙ yr−1 for an assumed wind velocity of v w = 10 km s−1, perhaps pointing to enhanced mass loss in a pulsation-driven wind.

Occurrence of resistance in field populations of Erysiphe necator to QoI, azanaphthalene and aryl-phenyl-ketone fungicides in Serbia

The objective of this study was to investigate whether the sensitivity of E. necator populations to several important fungicide groups has changed in Serbia. Field trials, bioassays, and molecular tests were conducted from 2019 to 2022. Samples were collected from commercial, experimental and extensive vineyards. Low efficacy of QoIs, azanaphthalenes and aryl-phenyl-ketones was observed in the field trials. A total of 110 isolates were analyzed by qPCR for the presence of the G143A mutation. The mutation was positively detected in 61.8% of the isolates, therefore isolates containing >95% of the G143A allele were QoI-resistant in bioassays. Contrary, QoI-sensitive isolates contained <5% of the G143A allele. Of the 54 isolates tested in bioassays, 46 grew significantly at the recommended concentration, whereas 36 were able to grow at a fourfold higher concentration of quinoxyfen. Proquinazid showed more significant inhibition than quinoxyfen, suggesting that there is no cross-resistance between these two members of the azanaphthalene group. Unsatisfactory effect of metrafenone and pyriofenone at recommended concentrations was observed in 29 of 41 isolates. These isolates grew readily at four times higher concentrations of metrafenone and pyriofenone, indicating the cross-resistance between them. Overall, the results of this study suggest that resistance to QoIs has developed and is widespread in commercial vineyards, requiring growers to exclude them completely from powdery mildew management. The performance of proquinazid in the field was not affected, making it suitable for powdery mildew control. Special attention and extensive monitoring should be directed to metrafenone and pyriofenone, as cross-resistance has been found at certain sites where they have been used intensively over the past decade.

Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise

Abstract. Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.

Case study of an active landslide at the flank of a water reservoir and its response during earthquakes

Slow-moving active landslides are a widespread phenomenon and often cause major damage to infrastructure. Normally, such slow slides do not cause fatalities, but this can change during extreme events such as a heavy rain or a strong earthquake. In particular their response to earthquakes is only partially understood, while documented field observation of such cases are rare. Considering their mobile pre-seismic state, one would expect active slides to be highly susceptible to an acceleration during earthquakes. When such a landslide affects a flank of a water reservoir, the consequences of its collapse can be devastating due to subsequent tsunami waves overtopping the dam. In this article, a case study of a deep-seated slow-moving landslide with a volume of about 5 million m3 at the flank of a water reservoir in the Swiss Alps is presented. Extensive field investigations and monitoring allow a profound understanding of the kinematics of the landslides and its dependency on the hydrological conditions. Together with laboratory ring shear tests, this forms the basis for quantifying rate effects in the shear zone over a very wide velocity range and allows formulating and calibrating an accurate mechanical landslide model. The material point method is used to investigate potential landslide scenarios during different earthquakes. The simulations show rather small co-seismic displacements and even for pessimistic scenarios, a threat from tsunami waves seems unlikely. This implies that slow-moving active landslides can be less susceptible to co-seismic acceleration than stable slopes due to their ductile behaviour.

Advancing Water Quality Research: K-Nearest Neighbor Coupled with the Improved Grey Wolf Optimizer Algorithm Model Unveils New Possibilities for Dry Residue Prediction

Monitoring stations have been established to combat water pollution, improve the ecosystem, promote human health, and facilitate drinking water production. However, continuous and extensive monitoring of water is costly and time-consuming, resulting in limited datasets and hindering water management research. This study focuses on developing an optimized K-nearest neighbor (KNN) model using the improved grey wolf optimization (I-GWO) algorithm to predict dry residue quantities. The model incorporates 20 physical and chemical parameters derived from a dataset of 400 samples. Cross-validation is employed to assess model performance, optimize parameters, and mitigate the risk of overfitting. Four folds are created, and each fold is optimized using 11 distance metrics and their corresponding weighting functions to determine the best model configuration. Among the evaluated models, the Jaccard distance metric with inverse squared weighting function consistently demonstrates the best performance in terms of statistical errors and coefficients for each fold. By averaging predictions from the models in the four folds, an estimation of the overall model performance is obtained. The resulting model exhibits high efficiency, with remarkably low errors reflected in the values of R, R2, R2ADJ, RMSE, and EPM, which are reported as 0.9979, 0.9958, 0.9956, 41.2639, and 3.1061, respectively. This study reveals a compelling non-linear correlation between physico-chemical water attributes and the content of dry tailings, indicating the ability to accurately predict dry tailing quantities. By employing the proposed methodology to enhance water quality models, it becomes possible to overcome limitations in water quality management and significantly improve the precision of predictions regarding critical water parameters.

Water Safety Plan, Monochloramine Disinfection and Extensive Environmental Sampling Effectively Control Legionella and Other Waterborne Pathogens in Nosocomial Settings: The Ten-Year Experience of an Italian Hospital.

Legionella contamination control is crucial in healthcare settings where patients suffer an increased risk of disease and fatal outcome. To ensure an effective management of this health hazard, the accurate application of a hospital-specific Water Safety Plan (WSP), the choice of a suitable water disinfection system and an extensive monitoring program are required. Here, the ten-year experience of an Italian hospital is reported: since its commissioning, Legionellosis risk management has been entrusted to a multi-disciplinary Working Group, applying the principles of the World Health Organization's WSP. The disinfection strategy to prevent Legionella and other waterborne pathogens relies on the treatment of domestic hot water with a system ensuring the in situ production and dosage of monochloramine. An average of 250 samples/year were collected and analyzed to allow an accurate assessment of the microbiological status of water network. With the aim of increasing the monitoring sensitivity, in addition to the standard culture method, an optimized MALDI-ToF MS-based strategy was applied, allowing the identification of Legionella species and other relevant opportunistic pathogens. Data collected so far confirmed the effectiveness of this multidisciplinary approach: the fraction of positive samples never overcame 1% on a yearly basis and Legionnaires' Disease cases never occurred.

Structural Behaviors of Large-Section Mine Tunnels Undercrossing Existing Metro Lines

To meet the operational requirements of a new metro line in Guangzhou, China, a complex tunneling project was undertaken in a business area surrounded by high-rise buildings and underground utility pipelines. Two large-section mine tunnels, with a maximum excavation span of 15.6 m, were constructed, crossing beneath the existing metro line 7 in close proximity. To ensure construction safety, extensive field monitoring was conducted, investigating the deformation and stress characteristics of the newly built tunnel lining structures and settlement of existing tunnels during various construction stages. The findings indicate that the structural integrity of the newly built tunnels may be compromised by the ground pressure resulting from deep-hole grouting, which is aimed at protecting the existing metro line above. The primary lining of the new tunnels is particularly susceptible to stress at the arch waist, where it experiences significantly higher stress compared to the arch crown and arch foot. While constructing the secondary lining before removing temporary diaphragm supports can be advantageous in controlling ground settlement, the stress levels within the secondary lining can fluctuate significantly once the temporary supports are removed. Once the tunnels are completed as closed loops, the stress conditions in both the primary and secondary lining tend to stabilize. The secondary lining primarily undergoes flexural stress, with internal force diagrams roughly symmetrical to the cross-section center line, and the most vulnerable position being at the left arch waist or invert. This investigation provides valuable insights for the design and construction of similar engineering projects in the future.

Diatoms. A viable tool for quantifying surface water pollution

The current work focused on the water quality of the Bega and Timis rivers measured by two methods. One of them is the method by which the water was analyzed from a physical-chemical point of view, and the other aimed at quantifying the degree of water pollution by biological methods. In this sense, diatoms were used. In the study, samples of water and diatoms were taken using specific techniques from the Bega and Timis rivers, starting from near the area of their formation until near the border with Serbia. Four sampling points were chosen on the Timis River plus one sampling point on the Surgani River, which is a tributary of the Timis, and six sampling points on the Bega River. The points were chosen specifically, being located downstream of the major sources of pollution. In order to locate the points, information was collected from the studies carried out as part of an extensive monitoring that spanned several years. The abundance and diversity of diatom species were used to calculate the Biological Diatom Index (BDI) and the Saprobity Index (SI). These two indices provide valuable information about organic or heavy metal pollution of water bodies.

Occurrence of Goniozus legneri (Hymenoptera: Bethylidae) and its association to lepidopteran pests in Northern cultivated oasis of Mendoza province, Argentina

Goniozus legneri Gordh (Hymenoptera: Bethylidae) is a larval ectoparasitoid of lepidopterans, with a worldwide history as a biocontrol agent of various agricultural pests. By means of extensive monitoring, this wasp was found in four distant districts of Mendoza province parasitizing three lepidopteran pests: Lobesia botrana (Den. et Shiff.) in grapevine, Ectomyelois ceratoniae (Zeller) in walnut and “espinillo” (Vachellia caven (Molina) Seigler et Ebinger) and Cydia pomonella (L.) in walnut. To corroborate the parasitoid species identification and to analyze its population viability in this region, studies of male genitalia and sexual crossings between wild and laboratory-reared wasps were conducted. Further, we report for the first time the natural host-parasitoid interaction of L. botrana with this parasitoid in vineyards from Argentina.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.

Graft dysfunction in compassionate use of genetically engineered pig-to-human cardiac xenotransplantation: a case report

A genetically engineered pig cardiac xenotransplantation was done on Jan 7, 2022, in a non-ambulatory male patient, aged 57 years, with end-stage heart failure, and on veno-arterial extracorporeal membrane oxygenation support, who was ineligible for an allograft. This report details our current understanding of factors important to the xenotransplantation outcome. Physiological and biochemical parameters critical for the care of all heart transplant recipients were collected in extensive clinical monitoring in an intensive care unit. To ascertain the cause of xenograft dysfunction, we did extensive immunological and histopathological studies, including electron microscopy and quantification of porcine cytomegalovirus or porcine roseolovirus (PCMV/PRV) in the xenograft, recipient cells, and tissue by DNA PCR and RNA transcription. We performed intravenous immunoglobulin (IVIG) binding to donor cells and single-cell RNA sequencing of peripheral blood mononuclear cells. After successful xenotransplantation, the graft functioned well on echocardiography and sustained cardiovascular and other organ systems functions until postoperative day 47 when diastolic heart failure occurred. At postoperative day 50, the endomyocardial biopsy revealed damaged capillaries with interstitial oedema, red cell extravasation, rare thrombotic microangiopathy, and complement deposition. Increased anti-pig xenoantibodies, mainly IgG, were detected after IVIG administration for hypogammaglobulinaemia and during the first plasma exchange. Endomyocardial biopsy on postoperative day 56 showed fibrotic changes consistent with progressive myocardial stiffness. Microbial cell-free DNA testing indicated increasing titres of PCMV/PRV cell-free DNA. Post-mortem single-cell RNA sequencing showed overlapping causes. Hyperacute rejection was avoided. We identified potential mediators of the observed endothelial injury. First, widespread endothelial injury indicates antibody-mediated rejection. Second, IVIG bound strongly to donor endothelium, possibly causing immune activation. Finally, reactivation and replication of latent PCMV/PRV in the xenograft possibly initiated a damaging inflammatory response. The findings point to specific measures to improve xenotransplant outcomes in the future. The University of Maryland School of Medicine, and the University of Maryland Medical Center.

Long-term static and dynamic monitoring to failure scenarios assessment in steel truss railway bridges: A case study

The latest studies on failures in steel truss-type bridges found that they are highly vulnerable to damage and thus prone to potential local or total collapse. Many authors recommend monitoring the critical elements in the existing steel truss-type bridges in real time to anticipate any failures in local members. Although mechanical strain is the most frequently used variable for this purpose, this method also happens to be the most expensive monitoring strategy. This paper describes a case study of failure scenarios assessment in a steel truss-type railway bridge after extensive long-term monitoring conducted by the authors, based on measuring vertical deflections and modal frequencies. The structure has both an isostatic and a hyperstatic configuration, and was assessed by means of a combination of: (i) long-term monitoring results, and (ii) a finite element analysis to simulate several failure scenarios. A sensitivity study of the different failure scenarios has been carried out, identifying those that can be detected. The results are used to define practical recommendations for failure detection by measuring vertical deflections and modal frequencies.

Deucravacitinib: The First FDA-Approved Oral TYK2 Inhibitor for Moderate to Severe Plaque Psoriasis.

The objective of this study was to review the safety and efficacy of deucravacitinib, a tyrosine kinase 2 (TYK2) inhibitor for moderate to severe plaque psoriasis. Literature was reviewed from MEDLINE and Clinicaltrials.gov up to December 2022 using the terms "deucravacitinib" and "BMS-986165." Relevant articles in English relating to the pharmacodynamics, pharmacokinetics, efficacy, and safety of deucravacitinib were included. A total of 6 trial results were included. Deucravacitinib showed clinical efficacy across all the phase II and III clinical trials. Excluding the long-term extension study, there were 2248 subjects across all studies, with 63.2% of patients receiving deucravacitinib 6 mg daily. Of these subjects, the average proportion achieving a PASI 75 (a reduction of greater than 75% in the Psoriasis Area and Severity Index) at week 16 was 65.1%. Patients receiving deucravacitinib 6 mg once daily had a higher rate of achieving both PASI 75 response and a Static Physician's Global Assessment (sPGA) score of 0 or 1, compared with oral apremilast 30 mg twice daily. The safety profile of deucravacitinib includes mild adverse events (AEs), most commonly nasopharyngitis, with serious AEs reported ranging from 1.35% to 9.5%. While many available therapies for moderate to severe plaque psoriasis rely on an injectable dosage form or extensive monitoring, deucravacitinib can potentially reduce patient medication-related burden. This review summarizes the efficacy and safety of oral deucravacitinib for the treatment of severe plaque psoriasis. Deucravacitinib shows a consistent efficacy and safety profile as the first oral TYK2 inhibitor approved for adult patients with moderate to severe plaque psoriasis who are eligible for systemic therapy or phototherapy treatment.

A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas.

Industrial and Environmental Disaster Risk Assessment for Hazardous Materials in Balikpapan City, East Kalimantan, Indonesia

Industrial disasters may cause massive public health problems, as they create large environmental damage and major loss of life. Balikpapan City has experienced a large oil spill which caused an industrial and environmental disaster: five fishermen died, various public health problems arose, and damage to environmental wildlife and ecosystems occurred. The objective of this study was to evaluate the industrial and environmental disaster risk related to hazardous material (hazmat) in Balikpapan. The research method involved the use of a semi-quantitative risk analysis with an originally organized checklist, a risk matrix, and a Geographic Information System (GIS) analysis. The results suggested that the risk score in Balikpapan’s City is 32 (MEDIUM; the dominant types of hazmat were flammables and corrosives). The major sectors contributing to the risk appeared to be the mining, energy, and oil and gas industries, with a medium risk (average risk score 33), while one clean water industry showed a low risk (risk score 24) using the checklist and risk matrix. According to the results from the GIS mapping, the areas t the highest risks appeared to be located within Balikpapan City coastal regions. The industries and the government of Balikpapan must be prepared for industrial and environmental disasters by educating competent major hazmat responders and ensuring a large spills response equipment, extensive environmental monitoring and measurement, procedures to deal with major fire and explosions and adequate disaster communication and coordination.

Intelligent dual-drive DNA nanosensor for ultrasensitive detection of prostate cancer-related circulating microRNA-200c

Rapid and accurate detection of the dynamic changes of prostate cancer-related miRNAs in peripheral blood is of great significance for prostate monitoring. Herein, we constructed an intelligent dual-drive DNA nanosensor (DD-DNS) for molecularly determination of microRNA-200c (miR-200c), a potential monitoring marker for diagnosis and treatment of prostate cancer. Unlike common detection technologies, the most distinctive feature of DD-DNS is the ingenious combination of enzyme-catalyzed chain amplification reaction and non-enzyme-catalyzed chain replacement reaction to realize the complementation of performance and improvement of detection capability. This improvement can significantly improve the detection sensitivity and environmental adaptability, which is more conducive to the detection of actual samples. In the presence of miR-200c, miR-200c activates the polymerase/nickase cycle reaction, resulting in exponential levels of cycle primers being produced. As a programmed process, cyclic primers catalyze the hairpin assembly reaction to achieve a surge in fluorescence signals. As a novel amplification strategy, the detection limit of this method can reach 5.5 fM, which fully meets the requirements of clinical quantitative analysis. Thus, miRNA detection technology based on DD-DNS shows superior detection performance and stability, which is conducive to carrying out extensive disease screening and treatment monitoring in a variety of complex situations.

Prioritization of micropollutants based on removal effort in drinking water purification treatment

Abstract A main focus of water managers with regard to micropollutants is the protection of aquatic ecology. However, micropollutants also have the potential to affect the production of clean drinking water. In this paper, we propose to consider the removal effort when assessing micropollutants with an ‘Effort Index’ (EI). Assessments using the EI show which micropollutants need more extensive monitoring or abatement because of their difficulty to be removed using low-effort water purification treatment techniques. For water containing mixtures of micropollutants, the averaged EI values can indicate overall water quality. Data on the removal by different purification treatment techniques are not necessarily available for all micropollutants. Therefore, a set of data-driven indicative removal rules is derived to quantify the relation between micropollutant properties and different drinking water treatment techniques. The indicative removal rules provide a rough indication of removability. As an illustration, the water quality of the river Rhine is evaluated between 2000 and 2018. The EI value shows that the Rhine contains increasingly more difficult-to-remove micropollutants. In total, 18 of those are labeled as particularly difficult-to-remove chemicals. These micropollutants are suggested as candidates for abatement to lower the required effort in drinking water production.

Machine learning-based soft-sensor development for road quality classification

Vibrations in road vehicles cause several harmful effects, health problems can occur for the passengers, and mechanical damage can occur to the vehicle components. Given the health, safety, and financial issues that arise, keeping the road network in good condition and detecting road defects as early as possible requires an extensive monitoring system. Related to this, our study presents the development of hardware and software for a low-cost, multi-sensor road quality monitoring system for passenger vehicles. The developed monitoring system can classify road sections according to their quality parameters into four classes. In order to detect vibrations in the vehicle, accelerometers and gyroscope sensors are installed at several points. Then, a machine learning-based soft-sensor development is introduced. Besides noise filtering, each data point is resampled by spatial frequency to reduce the velocity dependence. Subsequently, a decision tree-based classification model is trained using features from the power spectrum and principal component analysis. The classification algorithm is validated and tested with measurement data in a real-world environment. In addition to reviewing the accuracy of the model, we examine the correlation of the data measured in the cabin and on the suspension to see how much additional information is provided by the sensor on the axle.

Optical assessment of the spatial variation in total soil carbon using laser-induced breakdown spectroscopy

Soil carbon storage is a substantial factor in the global carbon cycle. Carbon sequestration in agricultural soils, and the assessment and validation of soil carbon storage, are crucial for the mitigation of agricultural greenhouse gas emissions and for steering towards sustainable farming practices. Enforcement and verification of carbon sequestration policies, methods, and models require extensive soil carbon monitoring capability. However, current conventional laboratory-based methods for soil carbon estimation are laborious, expensive, and time-consuming. In this work, we have developed a compact, robust, and field-capable experimental device based on laser-induced breakdown spectroscopy (LIBS) for the rapid assessment of total soil carbon content and its spatial distribution in mineral soils. The carbon content quantification was performed using a spectral line of carbon at a wavelength of 193.1 nm emitted from the laser-induced plasma plume. The LIBS measurements were performed on soil samples collected from 28 different locations and various depths (up to 1 m) of a test field cultivated with a forage legume (red clover - Trifolium pratense, L.) and grass (Timothy - Phleum pratense, L.) mixture in eastern Finland. A calibration model was established based on a limited and randomly chosen sample set and validated by comparing soil carbon estimates obtained from various locations in the test field using the dry combustion (LECO) method. Further, we demonstrate here the usefulness of LIBS methodology for mapping three-dimensional carbon distribution at the test field. We emphasize here that the calibration model can be generalized to other sample areas under similar soil type with a relative error of less than 10 % and possesses potential for fast on-site determination of spatial variation in total soil carbon, reducing substantially the need of time-consuming sample processing in laboratory. Therefore, LIBS enables frequent and extensive spatial and temporal soil carbon mapping and has the potential to become part of the future carbon monitoring network.

The role of MIBG scan in the diagnosis of pheochromocytoma.

e15064 Background: Pheochromocytoma is an uncommon adrenal medulla tumor. Metaiodobenzylguanidine (MIBG) scintigraphy is the current gold standard functional imaging method for evaluating pheochromocytoma, due to its excellent 100% specificity, 80% sensitivity, and general availability. In addition to establishing an extension evaluation and monitoring medication efficacy, MIBG scintigraphy is essential to confirm the diagnosis of suspected pheochromocytoma, especially when there is a tumor-like adrenal mass whose etiology is to be determined and biopsy is contraindicated given the risk of catecholamine discharge with hypertensive flare and the risk of bleeding. Our aims are to determine the efficacy of scintigraphy in the diagnosis of pheochromocytoma by examining the epidemiological characteristic and uptake patterns of the disease. Methods: In a retrospective analysis from January 2010 to December 2020, we examined pheochromocytoma patients medical records who had been referred for MIBG scintigraphy to the regional western Algerian Nuclear Medicine department at the University Hospital Center of Tlemcen. Prior to a progressive intravenous injection of 40–80 MBq of l-MIBG, patients underwent preparation (discontinuation of interfering drugs with I-MIBG especially antihypertensive treatment, thyroid blocking, and hydration). Imaging acquisition was carried out 48 and 72 hours later. Results: Fifty patients with probable pheochromocytoma (sex ratio = 0,78; mean age = 43 [17-74] years) completed I-MIBG scintigraphy prior to adrenalectomy. The MIBG scan has a high specificity and positive predictive value (100%). It confirms the presence of pheochromocytoma in 21 cases (42%), even if the metanephrine is normal in a patient with a non-secreting pheochromocytoma. In 18 cases, the main pattern is a single, homogenous, and intense intra-abdominal uptake. For the three remaining patients, a voluminous and heterogeneous uptake with a photopenic center that was associated to a necrotic adrenal mass larger than 10 cm in size, seen on abdominal Computerized Tomography (CT). In this retrospective analysis, no cases of metastatic or multiple diseases were documented. The sensitivity and negative predictive value are 84% and 86%, respectively. In 29 patients (58%), the scan reveals no abnormalities in uptake. This is attributed, in part, to 30% false positive biology (sampling conditions of urinary and/or plasmatic metanephrines and/or normetanephrines), 20% false positive CT imaging (differential diagnosis of adrenal mass: benign or malignant adrenal nodule), and 8% false negative MIBG scintigraphy (subcentimetric, cystic and necrotic lesion). Conclusions: Due to its exceptional specificity, robust sensitivity, and whole-body staging, MIBG scintigraphy—along with biology and morphology imaging—is a crucial noninvasive diagnostic method for the management of pheochromocytoma prior to adrenalectomy.