Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach.

The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique "Structure from Motion" (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an [Formula: see text] of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao's Q, Coefficient of Variation, Berger-Parker index, and Simpson's D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao's Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems.

The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.

The Spectral Species Concept in Living Color.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.

Pathology reports represent a primary source of information for cancer registries. Hospitals routinely process high volumes of free-text reports, a valuable source of information regarding cancer diagnosis for improving clinical care and supporting research. Information extraction and coding of textual unstructured data is typically a manual, labour-intensive process. There is a need to develop automated approaches to extract meaningful information from such texts in a reliable and accurate way. In this scenario, Natural Language Processing (NLP) algorithms offer a unique opportunity to automatically encode the unstructured reports into structured data, thus representing a potential powerful alternative to expensive manual processing. However, notwithstanding the increasing interest in this area, there is still limited availability of NLP approaches for pathology reports in languages other than English, including Italian, to date. The aim of our work was to develop an automated algorithm based on NLP techniques, able to identify and classify the morphological content of pathology reports in the Italian language with micro-averaged performance scores higher than 95%. Specifically, a novel, domain-specific classifier that uses linguistic rules was developed and tested on 27,239 pathology reports from a single Italian oncological centre, following the International Classification of Diseases for Oncology morphology classification standard (ICD-O-M). The proposed classification algorithm achieved successful results with a micro-F1 score of 98.14% on 9594 pathology reports in the test dataset. This algorithm relies on rules defined on data from a single hospital that is specifically dedicated to cancer, but it is based on general processing steps which can be applied to different datasets. Further research will be important to demonstrate the generalizability of the proposed approach on a larger corpus from different hospitals.

Response of a comprehensive cancer center to the COVID-19 pandemic: the experience of the Fondazione IRCCS-Istituto Nazionale dei Tumori di Milano.

BACKGROUND: The rapid spread of coronavirus disease (COVID-19) is affecting many countries. While healthcare systems need to cope with the need to treat a large number of people with different degrees of respiratory failure, actions to preserve aliquots of the healthcare system to guarantee treatment to patients are mandatory. METHODS: In order to protect the Fondazione IRCCS-Istituto Nazionale dei Tumori di Milano from the spread of COVID-19, a number of to-hospital and within-hospital filters were applied. Among others, a triage process to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity in patients with cancer was developed consisting of high-resolution low-dose computed tomography (CT) scan followed by reverse transcription polymerase chain reaction (RT-PCR) detection of SARS-CoV-2 in nose-throat swabs whenever CT was suggestive of lung infection. To serve symptomatic patients who were already admitted to the hospital or in need of hospitalization while waiting for RT-PCR laboratory confirmation of infection, a COVID-19 surveillance zone was set up. RESULTS: A total of 301 patients were screened between March 6 and April 3, 2020. Of these, 47 were hospitalized, 53 needed a differential diagnosis to continue with their cancer treatment, and 201 were about to undergo surgery. RT-PCR was positive in 13 of 40 hospitalized patients (32%), 14 of 52 day hospital patients (27%), and 6 of 201 surgical patients (3%). CONCLUSION: Applying filters to protect our comprehensive cancer center from COVID-19 spread contributed to guaranteeing cancer care during the COVID-19 crisis in Milan. A surveillance area and surgical triage allowed us to protect the hospital from as many as 33 patients infected with SARS-CoV-2.

Understanding Deviations from Clinical Practice Guidelines in Adult Soft Tissue Sarcoma.

In recent years we have witnessed the increasing adoption of clinical practice guidelines (CPGs) as decision support tools that guide medical treatment. As CPGs gain popularity, it has become evident that physicians frequently deviate from CPG recommendations, both erroneously and due to sound medical rationale. In this study we developed a methodology to computationally identify these deviation cases and understand their movitation. This was achieved using an integrated approach consisting of natural language processing, data modeling, and comparison methods to characterize deviations from CPG recommendations for 1431 adult soft tissue sarcoma patients. The results show that 48.9% of patient treatment programs deviate from CPG recommendations, with the largest deviation type being overtreatment, followed by differences in drug treatments. Interestingly, we identified over a dozen potential reasons for these deviations, with those directly related to the patients' cancer status being most abundant. These findings can be used to modify CPGs, increase adherence to CPG recommendations, reduce treatment cost, and potentially impact sarcoma care. Our approach can be applied to additional diseases that are subject to high deviation levels from CPGs.

A breast cancer clinical registry in an Italian comprehensive cancer center: an instrument for descriptive, clinical, and experimental research.

In clinical research, many potentially useful variables are available via the routine activity of cancer center-based clinical registries (CCCR). We present the experience of the breast cancer clinical registry at Fondazione IRCCS "Istituto Nazionale dei Tumori" to give an example of how a CCCR can be planned, implemented, and used. Five criteria were taken into consideration while planning our CCCR: (a) available clinical and administrative databases ought to be exploited to the maximum extent; (b) open source software should be used; (c) a Web-based interface must be designed; (d) CCCR data must be compatible with population-based cancer registry data; (e) CCCR must be an open system, able to be connected with other data repositories. The amount of work needed for the implementation of a CCCR is inversely linked with the amount of available coded data: the fewer data are available in the input databases as coded variables, the more work will be necessary, for information technology staff, text mining analysis, and registrars (for collecting data from clinical records). A cancer registry in a comprehensive cancer center can be used for several research aspects, such as estimate of the number of cases needed for clinical studies, assessment of biobank specimens with specific characteristics, evaluation of clinical practice and adhesion to clinical guidelines, comparative studies between clinical and population sets of patients, studies on cancer prognosis, and studies on cancer survivorship.

ICT and mobile health to improve clinical process delivery. a research project for therapy management process innovation.

The volume and the complexity of clinical and administrative information make Information and Communication Technologies (ICTs) essential for running and innovating healthcare. This paper tells about a project aimed to design, develop and implement a set of organizational models, acknowledged procedures and ICT tools (Mobile & Wireless solutions and Automatic Identification and Data Capture technologies) to improve actual support, safety, reliability and traceability of a specific therapy management (stem cells). The value of the project is to design a solution based on mobile and identification technology in tight collaboration with physicians and actors involved in the process to ensure usability and effectivenes in process management.

Analyzing the "CareGap": assessing gaps in adherence to clinical guidelines in adult soft tissue sarcoma.

Clinical decision support systems (CDSSs) are gaining popularity as tools that assist physicians in optimizing medical care. These systems typically comply with evidence-based medicine and are designed with input from domain experts. Nonetheless, deviations from CDSS recommendations are abundant across a broad spectrum of disorders, raising the question as to why this phenomenon exists. Here, we analyze this gap in adherence to a clinical guidelines-based CDSS by examining the physician treatment decisions for 1329 adult soft tissue sarcoma patients in northern Italy using patient-specific parameters. Dubbing this analysis "CareGap", we find that deviations correlate strongly with certain disease features such as local versus metastatic clinical presentation. We also notice that deviations from the guideline-based CDSS suggestions occur more frequently for patients with shorter survival time. Such observations can direct physicians' attention to distinct patient cohorts that are prone to higher deviation levels from clinical practice guidelines. This illustrates the value of CareGap analysis in assessing quality of care for subsets of patients within a larger pathology.

Evicase: an evidence-based case structuring approach for personalized healthcare.

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

Automatic detection of inconsistencies between free text and coded data in Sarcoma discharge letters.

Discordance between data stored in Electronic Health Records (EHR) may have a harmful effect on patient care. Automatic identification of such situations is an important yet challenging task, especially when the discordance involves information stored in free text fields. Here we present a method to automatically detect inconsistencies between data stored in free text and related coded fields. Using EHR data we train an ensemble of classifiers to predict the value of coded fields from the free text fields. Cases in which the classifiers predict with high confidence a code different from the clinicians' choice are marked as potential inconsistencies. Experimental results over discharge letters of sarcoma patients, verified by a domain expert, demonstrate the validity of our method.

The evolution of hospital information systems and the role of electronic patient records: from the Italian scenario to a real case.

Health care organizations can gain great value from Information and Communication Technologies (ICT), yet, although there is growing awareness of the potential benefits associated with their use, results often fall far short of expectations. Each year, the "ICT in Health Care" Observatory--part of the Politecnico di Milano School of Management--outlines a profile of the role of ICT in the Italian health care industry, investigating current projects in terms of their impact on processes and organizations, implementation state of the art, governance models, and prospective pathways. The 2009 collaborative research process outlines the need for a change in the way health care CIOs approach technological and organizational evolutions. ICT departments lack vision, governance mechanisms, skilled resources, and top management commitment. This has led to a series of distortions in the innovation of Hospital Information Systems (HISs) and ICT departments themselves. Currently they are too concerned with day-to-day operations and delay comprehensive initiatives capable of leading to effective ICT-driven innovations. The paper points out the problems that health care organizations are tackling and how they are trying to solve them. The case of the Italian National Cancer Institute in Milan provides a valuable example of how a health care organization is developing its HIS.