Online tree reconstruction and forest inventory on a mobile robotic system

Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing of 3D data. In this work, we present a real-time mapping and analysis system that enables online generation of forest inventories using mobile laser scanners that can be mounted e.g. on mobile robots. Given incrementally created and locally accurate submaps—data payloads—our approach extracts tree candidates using a custom, Voronoi-inspired clustering algorithm. Tree candidates are reconstructed using an algorithm based on the Hough transform, which enables robust modeling of the tree stem. Further, we explicitly incorporate the incremental nature of the data collection by consistently updating the database using a pose graph LiDAR SLAM system. This enables us to refine our estimates of the tree traits if an area is revisited later during a mission. We demonstrate competitive accuracy to TLS or manual measurements using laser scanners that we mounted on backpacks or mobile robots operating in conifer, broad-leaf and mixed forests. Our results achieve RMSE of 1.93 cm, a bias of 0.65 cm and a standard deviation of 1.81 cm (averaged across these sequences)—with no post-processing required after the mission is complete

DigiForests: a longitudinal LIDAR dataset for forestry robotics

Forests are vital to our ecosystems, acting as carbon sinks, climate stabilizers, biodiversity centers, and wood sources. Due to their scale, monitoring and managing forests takes a lot of work. Forestry robotics offers the potential for enabling efficient and sustainable foresting practices through automation. Despite increasing interest in this field, the scarcity of robotics datasets and benchmarks in forest environments is hampering progress in this domain. In this paper, we present a real-world, longitudinal dataset for forestry robotics that enables the development and comparison of approaches for various relevant applications, ranging from semantic interpretation to estimating traits relevant to forestry management. The dataset consists of multiple recordings of the same plots in a forest in Switzerland during three different growth periods. We recorded the data with a mobile 3D LiDAR scanning setup. Additionally, we provide semantic annotations of trees, shrubs, and ground, instance-level annotations of trees, as well as more fine-grained annotations of tree stems and crowns. Furthermore, we provide reference field measurements of traits relevant to forestry management for a subset of the trees. Together with the data, we also provide open-source baseline panoptic segmentation and tree trait estimation approaches to enable the community to bootstrap further research and simplify comparisons in this domain

Operational Multi-Modal Distance Metric Learning to Image Reclamation

Distance learning is an eminent technique that improves the search for images based on content. Although widely studied, most DML approaches generally recognize a modalization training framework that teaches a metric distance or a combination of distances in which several types of characteristics are simply interconnected. DML methods of that type suffer some critical limitations (a) Some feature types can significantly overwhelm others with the DML assignment, due to different attributes, and (b) the distance learning standard in the combined metric properties can be consumed using the feature attribute approach combined. In this article we refer to these the restrictions are reviewed online- multimodal distance metric training scheme (OMDML), which explores a dual duplication online learning scheme. (c) learn to optimize the distance metric in each owner space separately; and (d) learn find the optimal combination of different types of characteristics. To overestimate the cost of DML in sophisticated areas, we offer a low level OMDML algorithm that not only reduces estimated costs, but also guarantees high accuracy. We are here carried out exhaustive experiments to estimate the performance of the algorithms proposed for the restoration of multimedia images.  </jats:p

The Use of Angiotensin Converting Enzyme Inhibitors and Angiotensin Receptor Blockers and the Risk of Developing Venous Thromboembolism in Patients with Atherosclerotic Disease.

Abstract Abstract 1078 Poster Board I-100 Objectives: Arterial and venous thrombosis may share common pathophysiology involving the activation of platelets and inflammatory mediators. A growing body of evidence suggests prothrombotic effect of renin angiotensin system (RAS) including vascular inflammation and platelet activation. We hypothesized that the use of angiotensin converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs), therefore, plays a role in protecting against venous thromboembolism (VTE) in patients with history of atherosclerosis. Whether ACEIs and ARBs actually prevents VTE has never been studied in a clinical setting. Methods: We conducted a retrospective study, reviewing 596 consecutive patients admitted to Albert Einstein Medical Center (AEMC), Philadelphia with a diagnosis of either myocardial infarction or ischemic stroke during September 2007 to January 2009. Patients were followed up to maximum of 30 months. Patients who had been treated with anticoagulation therapy before or after the first visit at AEMC were excluded. The occurrence of VTE during the follow up period, risk factors for VTE on admission, and use of ACEIs and ARBs during the follow up period were recorded. Results: The mean age of the entire study population was 68.1 years. 52.0% of the patients were female and 76.5% were African American. The overall incidence of VTE was 13.4% (n=80); and 68.8% (n= 410) were on RAS inhibitors [either ACEIs only (n=348, 58.4%) or ARBs only (n=89, 14.9%) or both (n=27, 4.5%)]. Among patients on RAS inhibitors, 11.0 % (45/410) developed a VTE, compared with 18.8% (35/186) in the nonuser group [OR (Odd ratio), 0.53; 95% CI (confidence interval), 0.33 – 0.86; P=0.01]. Even after controlling for factors related to VTE (smoking, history of cancer, and immobilization, hormone use) and diabetes, the use of RAS inhibitors was still associated with lower risk of developing VTE [OR, 0.55; 95% CI, 0.34 – 0.90; P=0.02]. Although statitistically not significant due to small sample size, the OR of VTE for ACEI only users were 0.66 [CI, 0.37-1.16, p=0.15], whereas the OR for ARB only users was 0.75 [CI, 0.30-1.85, p=0.53]. Interestingly, Among patients using both ACEI and ARB, no one developed VTE (0/27), compared with 10.9% (38/348) in ACEI only users and 7.9% (7/89) in ARB only users. Conclusions: The use of RAS inhibitors appears to be associated with a reduction in the risk of VTE. This possible antithrombotic effect of antagonizing RAS warrants further prospective clinical investigation. Disclosures: No relevant conflicts of interest to declare. </jats:sec

Four-Fold Increased Mortality from Sars-Cov-2 Infection in Patients with Hematologic Versus Non-Hematologic Malignancies Treated at the Largest Tertiary COVID-19 Center in Chicago/Rush University Medical Center (March 1, 2020-December 31,2020)

Abstract N.K.Y., P.C., &amp; P.R.Y. contributed equally to this study Introduction: Many studies have concluded that active cancer patients infected with SARS-CoV-2 have a more complicated infection course and worse outcomes compared to the general patient population hospitalized with COVID-19. However, little evidence exists whether having a history of cancer plays a significant role in these observations. Patients with hematologic malignancy (HM) might have worse prognosis among all cancer patients but the reason remains unclear. Our objective is to evaluate outcomes and severity of COVID-19 in patients with Hematological Malignancy (HM) versus Solid-tumors (ST) in different clinical settings and also compare these outcomes within the group of patients with hematological malignancies. Methods: This retrospective study examines risk factors and outcomes of COVID-19 in patients with a history of cancer and laboratory-confirmed COVID-19 diagnosis between March 1 st, 2020, and December 31 st, 2020, at Rush University Medical Center, one of the largest COVID-19 tertiary care hospitals in Chicago. Baseline characteristics, malignancy type and types of cancer treatment within the last 30 days were recorded. Measures of COVID-19 severity included hospital admission versus outpatient care, use of oxygen, intensive care unit (ICU) admission, and mechanical ventilation. The primary outcome was death. Statistical analysis was conducted using optimal discriminant analysis, a non-parametric exact machine-learning algorithm which identifies the relationship between independent and dependent variables that maximizes model predictive accuracy adjusted to remove the effect of chance. Analysis was performed separately for each attribute using the entire sample ("training" analysis), then one-sample jackknife analysis was conducted to estimate cross-generalizability of findings using the model to classify an independent random sample. Results: 378 total patients with a history of cancer tested positive for COVID-19 within the time frame of the study. Of these, 294 (78%) patients had ST malignancy and 84 (22%) patients had HM. Characteristics and outcomes are summarized in Table 1. ST patients were marginally older than HM patients (p&amp;lt;0.025). A significantly greater proportion of HM patients were male (p&amp;lt;0.0023). HM and ST patients did not differ with respect to percentage receiving active cancer treatment (p&amp;lt;0.81). Compared to ST patients, more HM patients had received corticosteroids in the 30 days prior to COVID-19 diagnosis (p&amp;lt;0.017), had higher rates of hospitalization (p&amp;lt;0.0013) and ICU requirement (p&amp;lt;0.0001) with a significantly longer length of ICU stay (p&amp;lt;0.0036). Compared to ST patients, HM patients also required oxygen (p&amp;lt;0.002) and mechanical ventilation (p&amp;lt;0.0005) more often and had a 3.88-fold statistically higher death rate (OR 3.88 [95% CI 1.62-9.29] p&amp;lt;0.003). Patients with HM are categorized by disease subtype and summarized in Table 2. The case fatality rate from COVID-19 was 33.3% for patients with myeloproliferative neoplasms/myelodysplastic syndromes (MPN/MDS), 21.4% for patients with chronic lymphocytic leukemia (CLL), 13.6% for patients with non-Hodgkin lymphoma, 10.5% for patients with plasma cell neoplasms, and 4.5% for patients with acute leukemia. When looking at outcomes, CLL had the highest percentage of patients requiring hospital admission, oxygen, and ICU admission, and MPN/MDS had the highest percentage of patients requiring mechanical ventilation. Conclusions: Patients with hematologic malignancies had more severe COVID-19 illness and hospitalization rates and a 3.88-fold higher rate of death than patients with solid tumors. The comparable proportion of patients on anti-cancer therapy despite differences in survival suggests that being on anti-cancer therapy is less important than the underlying diagnosis of HM versus ST as a determinant of poor outcomes. Clinicians should closely monitor and initiate early COVID-19 treatments for all patients with HM and COVID-19. Because HM are highly heterogenous group of cancers, it is important to look at subtypes in greater detail. Numerous patient-level, disease-specific, and therapy-related factors may impact outcomes of COVID-19 among patients with HM, and we are currently analyzing additional data to better understand the factors which make this disease group more susceptible to severe infection. Figure 1 Figure 1. Disclosures Kuzel: Sanofi-Genzyme Genomic Health Tempus laboratories Bristol Meyers Squibb: Honoraria; Genomic Health: Membership on an entity's Board of Directors or advisory committees; Exelixis: Membership on an entity's Board of Directors or advisory committees; Cardinal Health: Membership on an entity's Board of Directors or advisory committees; Abbvie: Other; Curio Science: Membership on an entity's Board of Directors or advisory committees; AmerisourceBergen Corp: Membership on an entity's Board of Directors or advisory committees; CVS: Membership on an entity's Board of Directors or advisory committees; Tempus Laboratories: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; Merck: Other: Data Monitoring Committee Membership; Amgen: Other: Data Monitoring Committee Membership; SeaGen: Other: Data Monitoring Committee Membership; Medpace: Other: Data Monitoring Committee Membership. </jats:sec

Baseline Global Longitudinal Strain Predictive of Anthracycline-Induced Cardiotoxicity

Abstract Background: Anthracycline-induced cardiotoxicity is a major source of morbidity and mortality in long-term cancer survivors. Impaired GLS predicts decreased left ventricular ejection fraction (LVEF) in patients receiving anthracyclines, but knowledge regarding the clinical utility of baseline GLS in patients at low-risk of anthracycline-induced cardiotoxicity is limited. Objectives: The purpose of this study was to investigate whether baseline echocardiographic assessment of global longitudinal strain (GLS) before treatment with anthracyclines is predictive of cardiotoxicity in a broad cohort of patients with normal baseline LVEF..Methods: Study participants comprised 188 patients at a single institution who underwent baseline 2-dimensional (2D) speckle-tracking echocardiography before treatment with anthracyclines and at least one follow-up echocardiogram 3 months after chemotherapy initiation. Patients with a baseline LVEF &lt;55% were excluded from the analysis. The primary endpoint, cardiotoxicity, was defined as an absolute decline in LVEF &gt;10% from baseline and an overall reduced LVEF &lt;50%. Potential and known risk factors were evaluated using univariable and multivariable Cox proportional hazards regression analysis. Results: Twenty-three patients (12.23%) developed cardiotoxicity. Among patients with cardiotoxicity, the mean GLS was -17.51% ± 2.77%. The optimal cutoff point for cardiotoxicity was -18.05%. The sensitivity was 0.70 and specificity was 0.70. The area under ROC curve was 0.70. After adjustment for cardiovascular and cancer therapy related risk factors, GLS or impaired baseline GLS ≥-18% was predictive of cardiotoxicity (adjusted hazards ratio 1.17, 95% confidence interval 1.00, 1.36; p=0.044 for GLS, or hazards ratio 3.54; 95% confidence interval 1.34, 9.35; p = 0.011 for impaired GLS), along with history of tobacco use, pre-chemotherapy systolic blood pressure, and cumulative anthracycline dose. Conclusions: Baseline GLS or impaired baseline GLS was predictive of cardiotoxicity before anthracycline treatment in a cohort of cancer patients with a normal baseline LVEF. This data supports the implementation of strain-protocol echocardiography in cardio-oncology practice for identifying and monitoring patients who are at elevated risk of anthracycline-induced cardiomyopathy.</jats:p