Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Estimating geographic variation of infection fatality ratios during epidemics.

Objectives: We aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic. Methods: We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs. Results: The estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14. Conclusions: The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics.

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

Meanders as a scaling motif for understanding of floodplain soil microbiome and biogeochemical potential at the watershed scale.

BACKGROUND: Biogeochemical exports from watersheds are modulated by the activity of microorganisms that function over micron scales. Here, we tested the hypothesis that meander-bound regions share a core microbiome and exhibit patterns of metabolic potential that broadly predict biogeochemical processes in floodplain soils along a river corridor. RESULTS: We intensively sampled the microbiomes of floodplain soils located in the upper, middle, and lower reaches of the East River, Colorado. Despite the very high microbial diversity and complexity of the soils, we reconstructed 248 quality draft genomes representative of subspecies. Approximately one third of these bacterial subspecies was detected across all three locations at similar abundance levels, and ~ 15% of species were detected in two consecutive years. Within the meander-bound floodplains, we did not detect systematic patterns of gene abundance based on sampling position relative to the river. However, across meanders, we identified a core floodplain microbiome that is enriched in capacities for aerobic respiration, aerobic CO oxidation, and thiosulfate oxidation with the formation of elemental sulfur. Given this, we conducted a transcriptomic analysis of the middle floodplain. In contrast to predictions made based on the prominence of gene inventories, the most highly transcribed genes were relatively rare amoCAB and nxrAB (for nitrification) genes, followed by genes involved in methanol and formate oxidation, and nitrogen and CO2 fixation. Within all three meanders, low soil organic carbon correlated with high activity of genes involved in methanol, formate, sulfide, hydrogen, and ammonia oxidation, nitrite oxidoreduction, and nitrate and nitrite reduction. Overall, the results emphasize the importance of sulfur, one-carbon and nitrogen compound metabolism in soils of the riparian corridor. CONCLUSIONS: The disparity between the scale of a microbial cell and the scale of a watershed currently limits the development of genomically informed predictive models describing watershed biogeochemical function. Meander-bound floodplains appear to serve as scaling motifs that predict aggregate capacities for biogeochemical transformations, providing a foundation for incorporating riparian soil microbiomes in watershed models. Widely represented genetic capacities did not predict in situ activity at one time point, but rather they define a reservoir of biogeochemical potential available as conditions change. Video abstract.

Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery.

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil-plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red-green-blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.

Skip metastases to lateral cervical lymph nodes in differentiated thyroid cancer: a systematic review.

Papillary thyroid carcinoma is a slow-growing cancer with a generally good prognosis that sometimes have an aggressive behaviour. Metastases to neck lymph nodes is the first step of the diffusion. The central neck compartment is involved most commonly. The ipsilateral lateral neck compartments are usually involved afterwards, and the involvement of the contralateral one is considered a quite rare occurrence. In more rare cases, metastases to lateral neck compartment without central lymph node metastasis (so called "skip metastases") could be observed. Aim of this literature review study is to analyse the average incidence, pattern and risk factors of this occurrence.This study was performed according to PRISMA criteria. A final selection of 13 articles published in English language from 1997 to 2017 was performed. Any research article, review or meta-analysis was taken into consideration. Research was expanded considering the related references of articles.The incidence of skip metastases ranged from 1.6 to 21.8%. Risk factors such as age > 45 years, size < 5 mm and tumor located in the upper pole or isthmus of thyroid gland were found.Due to the frequency of skip metastases in thyroid cancer, a careful preoperative examination of lateral lymph nodes should be necessary.

Anatomical resections are superior to wedge resections for overall survival in patients with Stage 1 typical carcinoids.

OBJECTIVES: Typical carcinoids (TCs) are rare, slow-growing neoplasms, usually characterized by satisfactory surgical outcomes. Due to the rarity of TCs, international guidelines for the management of particular clinical presentations currently do not exist. In particular, non-anatomical resections (wedges) are sometimes advocated for Stage 1 TCs because of their indolent behaviour. The aim of this paper was to evaluate the most effective type of surgery for Stage 1 TCs, using the European Society of Thoracic Surgeons retrospective database of the Neuroendocrine Tumors of the Lung Working Group. METHODS: We analysed the effect of surgical procedure on the survival of patients with Stage 1 TCs. Overall survival (OS) was calculated from the date of intervention. The cumulative incidence of cause-specific death (tumour- and non-tumour-related) was also estimated. The impact of the surgical procedure (i.e. lobectomy vs segmentectomy vs wedge resection) on survival was investigated using the Cox model with shared frailty (for OS, accounting for the within-centre correlation) and the Fine and Gray model (for cause-specific mortality) using the approach based on the multinomial propensity score. Effects were estimated including in the model the logit-transformed propensity scores of segmentectomy and wedge resection as covariates. RESULTS: A total of 876 patients with Stage 1 TCs (569 women, 65%) were included in this study. The median age was 60 years (interquartile range 47-69). At the last follow-up, 66 patients had died: The 5-year OS rate was 94.3% [95% confidence interval (CI) 92.2-95.9]. The 5-year cumulative incidences of tumour- and non-tumour-related deaths were 2.4% (95% CI 1.4-3.9) and 3.9% (95% CI 2.5-5.6%), respectively. The analysis performed using the multinomial propensity score approach confirmed the significantly worse survival of patients treated with a wedge resection compared to those treated with a lobectomy (hazard ratio 2.01, 95% CI 1.09-3.69; P = 0.024). Similar effects of wedge resection are detectable for cause-specific deaths: tumour-related (hazard ratio 2.28, 95% CI 0.86-6.02; P = 0.096) and non-tumour-related (hazard ratio 1.74, 95% CI 0.89-3.40; P = 0.105). CONCLUSIONS: In a large cohort of patients, we were able to demonstrate the superiority of anatomical surgical resection in Stage 1 TCs in terms of OS. This result should therefore be considered for future clinical guidelines for the management of TCs.

Automatic Attribute Profiles.

Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multi-scale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.

Liver hydatidosis: reasoned indications of surgical treatment. Comparison between conservative and radical techniques Retrospective study.

Liver hydatidosis is a focal benign parasitic disease that still cause high rate of morbidity particularly in the Mediterranean area. A retrospective study comparing conservative and radical techniques in a General and Urgent Surgery operative unit was carried out in order to find signs of its surgical treatment. A total of 50 patients, 24 men and 26 women, who experienced a surgical treatment from 2000 to 2011, participated, and through the Fisher's Exact Test characteristics of the cysts, post-operative complications and relapse were compared. As a result, 29 patients undertook conservative surgery, while 21 radical surgery. There was a relationship between characteristics of the cysts and the technique chosen, (p<0.001). Likewise, whenever the diameter was smaller than 10cm or localized in the left lobe a radical technique was used. Conversely, a conservative technique was used when the diameter was bigger than 10 cm or they were localized in the right lobe or complicated by intra-biliary or intra-peritoneal rupture as well as in contiguity with major vascular and biliary structures. In this regard, while the post-operative morbidity was related to the conservative techniques (p<0.004), the relapses tended to not be related to any techniques in our case (p<0.14). In conclusion, in a non hepato-bilio-pancreatic center a radical surgery including liver resection is suggested for left lobe's cysts, while a conservative technique tends to be more effective for right lobe's cysts mainly if complex. Consequently, patients with complicated presentation could explain why conservative treatment causes higher rate of morbidity.