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• First physicochemical characterization of Costa Rica independency documents. • First report of Costa Rica independency documents deterioration conditions. • Correlation of the documents' deterioration state with their composition. • Categorization of writing inks based on their deterioration state and composition. • Papers with basic pH and high Pb, and Ca levels are better preserved. This research reports the results of the physicochemical characterization and deterioration condition evaluation of three nineteenth-century manuscripts, which are essential components of the Costa Rican documentary heritage. The documents were characterized by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray fluorescence (XRF), and multispectral photography. The documents' condition was thoroughly evaluated, and the results were transformed into an index to correlate their deterioration state with the document's composition; this was achieved by using correlation plots and heatmaps. The study revealed that the Independence Act of Costa Rica was the most deteriorated document among the three manuscripts from the year 1821. This was attributed to its acidic pH level, high zinc content, and greater exposure to light compared to the other two documents. [ABSTRACT FROM AUTHOR]

Objective: Deterioration index (DI) is a computer-generated score at a specific frequency that represents the overall condition of hospitalized patients using a variety of clinical, laboratory and physiologic data. In this paper, a contrastive transfer learning method is proposed and validated for early prediction of adverse events in hospitalized patients using DI scores. Methods and procedures: An unsupervised contrastive learning (CL) model with a classifier is proposed to predict adverse outcome using a single temporal variable (DI scores). The model is pretrained on an unsupervised fashion with large-scale time series data and fine-tuned with retrospective DI score data. Results: The performance of this model is compared with supervised deep learning models for time series classification. Results show that unsupervised contrastive transfer learning with a classifier outperforms supervised deep learning solutions. Pretraining of the proposed CL model with large-scale time series data and fine-tuning that with DI scores can enhance prediction accuracy. Conclusion: A relationship exists between longitudinal DI scores of a patient and the corresponding outcome. DI scores and contrastive transfer learning can be used to predict and prevent adverse outcomes in hospitalized patients. Clinical impact: This paper successfully developed an unsupervised contrastive transfer learning algorithm for prediction of adverse events in hospitalized patients. The proposed model can be deployed in hospitals as an early warning system for preemptive intervention in hospitalized patients, which can mitigate the likelihood of adverse outcomes.

OBJECTIVES: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN: PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING: The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS: The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861–0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275–0.320) at the first hospital; AUROC 0.875 (0.851–0.902), AUPRC 0.339 (0.281–0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS: Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics. [ABSTRACT FROM AUTHOR]

More frequent global warming events, biological disasters, and anthropogenic activities have caused extensive damage to coral reefs around the world. Coral reefs in the Xisha Islands (also known as the Paracel Islands) have been damaged following rounds of heatwaves and crown-of-thorns starfish (CoTS) outbreaks over recent decades. Based on a comprehensive community survey in 2020, we determined a diagnosis for the present state of six coral regions in the Xisha Islands. The findings suggested that these regions had a total of 213 species of scleractinian corals belonging to 43 genera and 16 families. Living coral coverage across sites was widely divergent and ranged from 0.40% (IQR: 7.74-0.27%) in Panshi Yu to 38.20% (IQR: 43.00-35.90%) in Bei Jiao. Coral bleaching prevalence was 23.90% (IQR: 41.60-13.30%) overall and topped out at 49.30% (IQR: 50.60-48.10%) in Bei Jiao. Five of the coral regions (all but Yongxing Dao) were under threat of CoTS outbreaks. High mortality combined with excellent recruitment rates suggested potential rehabilitation after recent deterioration. We employed a quantifiable Deterioration Index (DI) to evaluate the intensity of deterioration of coral reefs in the Xisha Islands. The results showed that Yongxing Dao and Langhua Jiao had low recent deterioration (DIrecent = 0.05, IQR: 0.07-0.02 and 0.04, IQR: 0.11-0.01, respectively), while Bei Jiao, Yongle Atoll, Yuzhuo Jiao, and Panshi Yu had high recent deterioration (DIrecent > 0.16). Different monitoring sites within the same coral region were heterogeneous with regards to all above indexes. Moreover, we reviewed and discussed potential disturbances that threaten the health of the Xisha Islands' corals. It is crucial to identify severely afflicted areas and find successful methods to better manage coral reef health in this region. [ABSTRACT FROM AUTHOR]

The high salt content in the soil raises special problems regarding the use of soils affected by salinity and alkalinity. In Romania, there is an area of approximately 614,000 ha of land where salinization-alkalization processes occur, causing a low fertility of these soils and at high concentrations, these soils cannot be used for agricultural production. In order to identify the areas occupied by these soils, research was carried out based on satellite images in three localities in Brăila county: Măxineni, Racovi?ă and Tudor, where laboratory determinations such as: soil reaction, content in chlorides, sulfates, magnesium, calcium, sodium and potassium. With the help of remote sensing and satellite images, maps were made regarding the salinity index (SI), Bare Soil index (BSI), Soil Adjusted Vegetation Index (SAVI), Tasseled Cap Transformation Wetness (TCW). Our study demonstrated the ability of remote sensing techniques and geographic information systems to survey, monitor, identify, analyze and classify land degradation manifestations and to detect spatial and temporal variables occurring in the study area. The digital indicators (INDEX) contributed to highlighting the terrain characteristics very effectively helping in clarifying the spatial distribution picture and the quantitative and qualitative evaluation of the types of soil degradation manifestations in the studied area. [ABSTRACT FROM AUTHOR]

This paper aims at proposing and demonstrating the application of a novel distress-based condition assessment approach to evaluate the physical condition of inspectable urban water assets based on the classification of anomalies in terms of severity, intensity and extension using three new deterioration indices. These indices are, firstly, calculated at the asset component level and, then, aggregated at the asset level and at the infrastructure level by weighing the component indices with the respective replacement cost or criticality. A condition grading classification associated with the deterioration indices is proposed. The novel distress-based condition assessment approach is applied to 22 drinking water storage tanks and 17 wastewater pumping stations located in Portugal, that have been specifically inspected for this purpose. Results of deterioration indices are compared with those obtained by the value-based condition assessment approach. Results demonstrate that the proposed approach allows a more reliable assessment of the assets' condition and a more robust establishment of priority rehabilitation or maintenance interventions. [ABSTRACT FROM AUTHOR]

This study aimed to monitor, map and assess the salinity of land in southern Iraq. Where the country suffers greatly from land degradation and desertification problems, especially in the central and southern parts. This study was conducted to monitor the manifestations of salinity in the Najmi area located within the administrative boundaries of the Muthanna Governorate, which has an area of (2066 hectares) and lies between longitudes 44°21'0" to 44°31'0" East and latitude circles 30°11'0" to 32°27'0" North with the help of remote sensing technology through the use of satellite images from the Landsat 8 satellite with field observations. A map of soil units was prepared using Erdas software, Arc GIS for processing, management and analysis of raster and subject data sets. Soil samples were taken from each site for physical and chemical analysis, and the study is summarized as follows: the results indicated when using the spectral index represented by the standard difference index of vegetation cover (NDVI), whose value ranged between (0.47-0.30). It was found that the study area suffers from a lack of vegetation cover by (64%); as for the salinity index values, they ranged between (-0.30-0.49) and therefore the study area suffers from a chemical deterioration represented in the salinity of the soil estimated at (69%); where the values of the dry lands index ranged between (1.37-0.73), and the desertified lands within the study area by (45%); as for the land degradation index, its value ranged from (0.00 to 99.60), and the percentage of degraded lands was estimated at (60%) of the total study lands. [ABSTRACT FROM AUTHOR]

Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53-75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of thermal modification and nano-zinc oxide (nano-ZnO) particle impregnation on the deterioration of Caribbean pine wood under field conditions. Samples were thermally-modified at various temperature levels (control, 180 °C, 200 °C, and 220 °C). Nano-ZnO impregnation was done with an aqueous solution at 1,5 % in an autoclave under two-steps of pressure and vacuum. Unmodified and thermally-modified, non-impregnated and nano-ZnO-impregnated samples were exposed to deterioration for five months in field tests. A deterioration index was used to evaluate the health condition of the samples. The mass loss and occurrence of termite tunnels in percentage were also determined. The nano-ZnO impregnation improved the resistance of unmodified wood to field-deterioration. The thermal modification at 180oC and 200ºC increased the wood deterioration and nano-ZnO impregnation did not improve their resistance. Unmodified and 220 ºC-modified samples had lower mass loss by xylophages than other thermal treatments regardless of the nanoparticle impregnation. The nano-ZnO impregnation decreases the occurrence of termite tunnels in unmodified, 200 ºC and 220 ºC-modified samples.

In this study, the physicochemical properties (total volatile basic nitrogen (TVB-N), pH, and peroxide value) and the volatile flavors of yak meat were systematically evaluated during chilled (0 °C) and controlled freezing-point (− 2 °C) storage. The TVB-N reached 15.21 mg/100 g after 18 days of storage at 0 °C, which exceeded the secondary freshness value according to the Chinese national standard. For storage at − 2 °C, the TVB-N did not exceed 15 mg/100 g until 24 days. Compared with storage at 0 °C, the samples stored at − 2 °C had a slower rate of increase in TVB-N, pH, and peroxide values. The changes in volatile compounds in yak meat during storage at − 2 °C and 0 °C for 24 days were investigated using headspace solid-phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC–MS). The correlations between the changes in the volatile compound contents and meat quality deterioration revealed significant negative correlations (rmin = 0.902, p < 0.05) between some aldehyde flavor components (nonanal, heptanal, benzaldehyde, decanal, and myristal) and TVB-N in the samples stored at controlled freezing-point and chilled temperatures. The decreases in nonanal, benzaldehyde, and myristal contents in yak meat followed zero order reaction kinetics. This result indicated, because of the highly selective and sensitive colorimetric detection method, that volatile compounds can effectively predict the decay in quality of yak meat stored at low temperature in advance. Thus, based on physicochemical and volatile flavor analyses, a new method is proposed to investigate the storage and preservation of yak meat. [ABSTRACT FROM AUTHOR]

Objective: Deterioration index (DI) is a computer-generated score at a specific frequency that represents the overall condition of hospitalized patients using a variety of clinical, laboratory and physiologic data. In this paper, a contrastive transfer learning method is proposed and validated for early prediction of adverse events in hospitalized patients using DI scores., Methods and Procedures: An unsupervised contrastive learning (CL) model with a classifier is proposed to predict adverse outcome using a single temporal variable (DI scores). The model is pretrained on an unsupervised fashion with large-scale time series data and fine-tuned with retrospective DI score data., Results: The performance of this model is compared with supervised deep learning models for time series classification. Results show that unsupervised contrastive transfer learning with a classifier outperforms supervised deep learning solutions. Pretraining of the proposed CL model with large-scale time series data and fine-tuning that with DI scores can enhance prediction accuracy., Conclusion: A relationship exists between longitudinal DI scores of a patient and the corresponding outcome. DI scores and contrastive transfer learning can be used to predict and prevent adverse outcomes in hospitalized patients., Clinical Impact: This paper successfully developed an unsupervised contrastive transfer learning algorithm for prediction of adverse events in hospitalized patients. The proposed model can be deployed in hospitals as an early warning system for preemptive intervention in hospitalized patients, which can mitigate the likelihood of adverse outcomes., (© 2023 The Authors.)

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

Background: Threshold-based early warning systems (EWS) are used to predict adverse events (Aes). Machine learning (ML) algorithms that incorporate all EWS scores prior to an event may perform better in hospitalized patients., Methods: The deterioration index (DI) is a proprietary EWS. A threshold of DI >60 is used to predict a composite AE: all-cause mortality, cardiac arrest, transfer to intensive care, and evaluation by the rapid response team in practice. The DI scores were collected for adult patients (≥18 y-o) hospitalized on medical or surgical services during 8-23-2021 to 3-31-2022 from four different Mayo Clinic sites in the United States. A novel ML model was developed and trained on a retrospective cohort of hospital encounters. DI scores were represented in a high-dimensional space using random convolution kernels to facilitate training of a classifier and the area under the receiver operator characteristics curve (AUC) was calculated. Multiple time intervals prior to an AE were analyzed. A leave-one-out cross-validation protocol was used to evaluate performance across separate clinic sites., Findings: Three different classifiers were trained on 59,617 encounter-derived DI scores in high-dimensional feature space and the AUCs were compared to two threshold models. All three tested classifiers improved the AUC over the threshold approaches from 0.56 and 0.57 to 0.76, 0.85 and 0.94. Time interval analysis of the top performing classifier showed best accuracy in the hour before an event occurred (AUC 0.91), but prediction held up even in the 12 h before an AE (AUC 0.80 at minus 12 h, 0.81 at minus 9 h, 0.85 at minus 6 h, and 0.88 at minus 3 h before an AE). Multisite cross-validation using leave-one-out approach on data from four different clinical sites showed broad generalization performance of the top performing ML model with AUC of 0.91, 0.91, 0.95, and 0.91., Interpretation: A novel ML model that incorporates all the longitudinal DI scores prior to an AE in a hospitalized patient performs better at outcome prediction than the currently used threshold model. The use of clinical data, a generalized ML technique, and successful multisite cross-validation demonstrate the feasibility of our model in clinical implementation., Funding: No funding to report., Competing Interests: BJB is a consultant to Boehringer-Ingelheim and Exact Sciences on unrelated projects. Other authors declare no competing interests., (© 2023 The Author(s).)

The aim of the present work is to point out the role of microorganisms in biodeterioration from two different historical monuments located in Romania: the refectory of the Hurezi monastic complex and Amărăşti church. Mural painting was made in al frescoes technique applied on the brick or on the wooden poles. HOBO LCD data loggers revealed values of temperature between 5 and 24° and relative humidity between 60 and 90% which are favorable for the colonization by bacteria and fungi. Two different genera of moderately halophilic bacteria (Nesterenkonia and Halobacillus) and 5 different genera of fungi (Antrodia, Aspergillus, Alternaria, Penicillium and Ulocladium) had been identified either by molecular techniques or by cultural and mycelium characteristics. They developed a pink and black pigmentation. The main vehicles for spreading of microorganisms are as follows: insects, condensation, convection currents and human activity. Bacterial cells and mycelia have been found both on the surface of the mural paintings, mortars, wooden structure as well as in the fissures and pores. Bacteria producing carotenoids grow on the surface of the substrates but fungi can be located both on the substrate and under it developing in the same time aerial and substrate hyphae. Mycelia produce the peeling and loosing of the pictorial layer. Organic acids act as chelating agents and mycelium as pulling agent. The aesthetical and structural changes of mural painting can be prevented by monitoring the environment and avoiding contamination. If damages took place, understanding their origin and proper treatment will contribute to preserving of cultural heritage. [ABSTRACT FROM AUTHOR]

For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a “yes” or “no” question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil quality deteriorated significantly under cultivation. • NA-based-SQI, as a novel statistical approach, was useful for management. • Soil degradation and resistance indices were used to soil quality evaluation. • The MDS indicator selection approach resulted in more suitable and relevant SQI. Over-increasing population and their needs necessitate sustainable use of soil and the other natural resources. In these circumstances land use changes may occur for supplying the food and settlement requirements of the increased population. Therefore, it is needed to evaluate and monitor quality and degradation of the soils. Recently, identification of appropriate soil quality (SQI) and degradation (SDI) indices for assessing sustainable use of soil resources has received increased attention. However, evaluation of quality, degradation, and resistance indices through various new approaches (e.g., network, NA and factor, FA analysis) particularly under different land uses to determine their susceptibility have been rarely conducted. The present study aimed to i) determine SQI, SDI, and resistance (SRI) indices NA and FA approaches under different land uses; ii) investigate the most influential attributes to soil quality (SQ); and iii) to compare SQIs obtained using two different Integrated (IQI) and Nemoro (NQI) Quality Indices, and indicator selection (Minimum Data Set, MDS and Total Data Set, TDS) approaches. Composite soil samples of cropland, forestland, and rangeland were collected and twenty-one soil physicochemical and fertility indicators were measured. MDS was determined using principal component analysis (PCA). The SDI and SRI were computed and compared. Results revealed that the SDI was -13.8% and -4.6% for the cropland and rangeland, respectively indicating inappropriate soil managerial practices in cropland. SRI was 0.01 and 0.41 for the cropland and rangeland, respectively. The lowest SRI values were obtained for critical level of organic matter (CLOM), organic matter (OM), zinc (Zn) and manganese (Mn) showing that these properties, compared with the others, are more sensitive to land use change. Regression equations between SDI and SRI revealed that these substantial relationships show similar trends indicating that these are useful indices to determine SQ and evaluate the effect of land use change and managerial practices. Soils in cropland had a significantly lower deterioration index (DI) (-66.70) than that of the other land uses. SQI in the cropland was the lowest, especially when screened with the NA method. Furthermore, SQI showed the highest correlation with clay and CLOM. The linear SQI value derived from correlation-based-NA method showed greater max/min ratios than that of the FA. Therefore, the NA-based-SQI, as a novel statistical approach, would be more effective than the SQI-derived-FA for evaluating the effects of land use changes and managerial practices. In general, the mentioned technique is a relatively comprehensive approach integrating different influential soil indicators with various weights showing their contributions to soil quality or degradation process. [ABSTRACT FROM AUTHOR]

The decline in soil productivity amidst efforts to increase crop yield in Sub Saharan Africa (SSA) has made it imperative to assess the current fertilization management approaches. This study was conducted in two agroecological zones (i.e., Guinea Savannah (GS) and Deciduous forest (DF)) of Ghana to evaluate how different fertilization schemes in the long term (>5 years) impacted the soil biochemical properties. Soil samples under four fertilization schemes (inorganic fertilizer only, low-to-medium organic residues only, inorganic fertilizers plus low-to-medium organic residues, and no fertilization) from 20 farmers’ field were sampled from March to April 2015. Soil biochemical quality indicators were determined using standard procedures. Overall, the average chemical and microbial biomass contents for most indicators were significantly higher in DF compared to GS. Relative to the reference sites, soil quality improvement were observed under inorganic fertilization in both agroecologies in contrast to significant soil deterioration (26.5%) under sole organic residue application in GS. Furthermore, the results showed that increased inorganic fertilization rate alone or combination with organic residues improved soil quality relative to the reference. The present results suggest the need to raise the current fertilizer application rates, especially in GS in order to enhance optimum soil productivity.

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer's accuracy (PA) of 86% and a user's accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the "Index of Surface Deterioration" (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status. [ABSTRACT FROM AUTHOR]

More frequent global warming events, biological disasters, and anthropogenic activities have caused extensive damage to coral reefs around the world. Coral reefs in the Xisha Islands (also known as the Paracel Islands) have been damaged following rounds of heatwaves and crown-of-thorns starfish (CoTS) outbreaks over recent decades. Based on a comprehensive community survey in 2020, we determined a diagnosis for the present state of six coral regions in the Xisha Islands. The findings suggested that these regions had a total of 213 species of scleractinian corals belonging to 43 genera and 16 families. Living coral coverage across sites was widely divergent and ranged from 0.40% (IQR: 7.74-0.27%) in Panshi Yu to 38.20% (IQR: 43.00-35.90%) in Bei Jiao. Coral bleaching prevalence was 23.90% (IQR: 41.60-13.30%) overall and topped out at 49.30% (IQR: 50.60-48.10%) in Bei Jiao. Five of the coral regions (all but Yongxing Dao) were under threat of CoTS outbreaks. High mortality combined with excellent recruitment rates suggested potential rehabilitation after recent deterioration. We employed a quantifiable Deterioration Index (DI) to evaluate the intensity of deterioration of coral reefs in the Xisha Islands. The results showed that Yongxing Dao and Langhua Jiao had low recent deterioration (DIrecent = 0.05, IQR: 0.07-0.02 and 0.04, IQR: 0.11-0.01, respectively), while Bei Jiao, Yongle Atoll, Yuzhuo Jiao, and Panshi Yu had high recent deterioration (DIrecent > 0.16). Different monitoring sites within the same coral region were heterogeneous with regards to all above indexes. Moreover, we reviewed and discussed potential disturbances that threaten the health of the Xisha Islands' corals. It is crucial to identify severely afflicted areas and find successful methods to better manage coral reef health in this region., Competing Interests: The authors declare that they have no competing interests., (© 2022 Xiao et al.)

As the demand for food, feed, fiber, and, most recently, bio-energy increases, more land may be converted from native conditions for arable uses. Our objective was to evaluate soil quality under native forest, arable land being used for continuous barley (Hordeum valgare L.) production and land that had been tilled and subsequently enrolled in the Conservation Reserve Program (CRP) for at least 18 years. Several physical, chemical, and biological soil quality indicators were measured and a soil deterioration index (DI) was calculated using forest soil as the reference. Results indicated that most organic matter under forest resided on soil surface and was not mixed with mineral soil due to lack of activities by large soil fauna (e.g., earthworms). Soil samples from disturbed areas had a higher organic matter content, which caused most soil quality indicators to be considered ‘improved’ and resulted in better DIs for agricultural and CRP land than for forest soils. This study emphasized the importance of choosing an appropriate reference point for soil quality assessments, especially when data representing one or more key soil processes are missing. [ABSTRACT FROM PUBLISHER]

The feasibility of near infrared (NIR) spectroscopy was examined for analyzing quality criteria or deterioration induces of soybeans, such as the nitrogen solubility index (NSI), acid value, digestibility and percentage germination, as well as the contents of their major constituents (moisture, oil and total nitrogen). Because all the criteria are closely related to yield and quality of the final product, their rapid analysis is required for process control in the oil milling plant. NIR diffuse-reflectace spectra were measured on ground samples in an InfraAlyzer 500 (Bran+Luebbe Co., Norderstedt, Germany). As for the major constituents, the standard errors of prediction (SEP) were 0.34% (moisture), 0.24% (oil) and 0.067% (total nitrogen). There results show the ability of NIR for analyzing these parameters, as many researchers reported previously. As for the other quality criteria, the SEP values were 2.40 (NSI), 5.58 (digestibility), 0.29 (acid value) and 15.27 (germinatio percentage). Further, NIR analysis for some of these criteria of the extracted residue and defatted soybeans was also examined, and almost the same level of SEP values was obtained. Although it is still necessary to improve the accuracy, we concluded that the NIR method has the feasibility to measure quality criteria for the purpose of process control in the plant in place of the time-consuming chemical analysis that are conventionally used. [ABSTRACT FROM AUTHOR]