Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability.

We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems

Additive effect of cerebral atrophy on cognition in dementia-free elderly with cerebrovascular disease

Objective: To explore the additive effect of neurodegenerative diseases, measured by atrophy, on neurocognitive function in Asian dementia-free elderly with cerebrovascular disease (CeVD). Methods: The present study employed a cross-sectional design and was conducted between 2010 and 2015 among community-dwelling elderly participants recruited into the study. Eligible participants were evaluated with an extensive neuropsychological battery and neuroimaging. The weighted CeVD burden scale comprising markers of both small- and large-vessel diseases was applied, with a score of ≥2, indicating significant CeVD burden. Cortical atrophy (CA) and medial temporal atrophy (MTA) were graded using the global cortical atrophy scale and Schelten's scale, respectively. Global and domain-specific (attention, executive function, language, visuomotor speed, visuoconstruction, visual memory, and verbal memory) neurocognitive performance was measured using a locally validated neuropsychological battery (Vascular Dementia Battery, VDB). Results: A total of 819 dementia-free participants were included in the analysis. Among none-mild CeVD subjects, there was no significant difference in the global cognitive performance across atrophy groups (no atrophy, CA, and CA+MTA). However, in moderate-severe CeVD subjects, CA+MTA showed significantly worse global cognitive performance compared with those with CA alone (mean difference=-0.35, 95% CI -0.60 to -0.11, p=0.002) and those without atrophy (mean difference=-0.46, 95% CI -0.74 to -0.19, p<0.001, p<0.001). In domain-specific cognitive performance, subjects with CA+MTA performed worse than other groups in visual memory (p=0.005), executive function (p=0.001) and visuomotor speed (p<0.001) in moderate-severe CeVD but not in none-mild CeVD. Conclusions and relevance: Atrophy and moderate-severe CeVD burden showed an additive effect on global and domain-specific cognitive performance. This study highlights the importance of investigating the mechanisms of clinico-pathological interactions between neurodegenerative processes and vascular damage, particularly in the pre-dementia stage

Quantum Physics and Human Language

Human languages employ constructions that tacitly assume specific properties of the limited range of phenomena they evolved to describe. These assumed properties are true features of that limited context, but may not be general or precise properties of all the physical situations allowed by fundamental physics. In brief, human languages contain `excess baggage' that must be qualified, discarded, or otherwise reformed to give a clear account in the context of fundamental physics of even the everyday phenomena that the languages evolved to describe. The surest route to clarity is to express the constructions of human languages in the language of fundamental physical theory, not the other way around. These ideas are illustrated by an analysis of the verb `to happen' and the word `reality' in special relativity and the modern quantum mechanics of closed systems.Comment: Contribution to the festschrift for G.C. Ghirardi on his 70th Birthday, minor correction

40 days and 40 nights: Clinical characteristics of major trauma and orthopaedic injury comparing the incubation and lockdown phases of COVID-19 infection

Aims The first death in the UK caused by COVID-19 occurred on 5 March 2020. We aim to describe the clinical characteristics and outcomes of major trauma and orthopaedic patients admitted in the early COVID-19 era. Methods A prospective trauma registry was reviewed at a Level 1 Major Trauma Centre. We divided patients into Group A, 40 days prior to 5 March 2020, and into Group B, 40 days after. Results A total of 657 consecutive trauma and orthopaedic patients were identified with a mean age of 55 years (8 to 98; standard deviation (SD) 22.52) and 393 (59.8%) were males. In all, 344 (approximately 50%) of admissions were major trauma. Group A had 421 patients, decreasing to 236 patients in Group B (36%). Mechanism of injury (MOI) was commonly a fall in 351 (52.4%) patients, but road traffic accidents (RTAs) increased from 56 (13.3%) in group A to 51 (21.6%) in group B (p = 0.030). ICU admissions decreased from 26 (6.2%) in group A to 5 (2.1%) in group B. Overall, 39 patients tested positive for COVID-19 with mean age of 73 years (28 to 98; SD 17.99) and 22 (56.4%) males. Common symptoms were dyspnoea, dry cough, and pyrexia. Of these patients, 27 (69.2%) were nosocomial infections and two (5.1%) of these patients required intensive care unit (ICU) admission with 8/39 mortality (20.5%). Of the patients who died, 50% were older and had underlying comorbidities (hypertension and cardiovascular disease, dementia, arthritis). Conclusion Trauma admissions decreased in the lockdown phase with an increased incidence of RTAs. Nosocomial infection was common in 27 (69.2%) of those with COVID-19. Symptoms and comorbidities were consistent with previous reports with noted inclusion of dementia and arthritis. The mortality rate of trauma and COVID-19 was 20.5%, mainly in octogenarians, and COVID-19 surgical mortality was 15.4%

Assessing intraspecific wood density variations of Syzgium sp. in tropical forest of Southwest Sabah

Wood density (WD) is a critical determinant of estimating forest above-ground biomass (AGB) and carbon stock. Thus, heterogeneity in WD on individuals within species trees needs to be scrutinized, and acquisition of fixed WD value is essential to estimate carbon stock with confidence. This study investigated intraspecific variation in WD of Syzgium sp., also known as “Jambu” or “Kelat”. It is the most occurring species in study areas, and is regarded as an economically important species. Firstly, one half-diameter drilling from bark-to-pith measurement was taken per tree using Rinntech Resistograph R650-ED at breast height. Meanwhile, 5.15 mm-diameter core was sampled at 1.30 m above-ground, with DeWalt DCF899HP2 20V impact wrench 950 Nm and Haglöf increment borer. WD was estimated for each core sample using a dimensional method. Drilling resistance (DR) profiles were processed using DECOM 2.38m1 Scientific (c), and several independent variables were extracted from the resistogram. All resistogram-derived variables were positively correlated with field WD (R: 0.2 – 0.70). In addition, variability on WD in Syzgium sp. population is predominantly explained by the Resistograph amplitude, expressed as mean raw scale of adjusted DR (DRadj.RawSC) in a regression model. Given that intraspecific variation in WD is a crucial conjecture in forest AGB estimation, it is recommended to analyze with larger samples, and in-depth exploration on Resistograph-based variables is deemed to improve the accuracy of WD prediction models

Galactomannan testing of bronchoalveolar lavage fluid is useful for diagnosis of invasive pulmonary aspergillosis in hematology patients

<p>Abstract</p> <p>Background</p> <p>Invasive pulmonary aspergillosis (IPA) is a major cause of morbidity and mortality in patients with hematological malignancies in the setting of profound neutropenia and/or hematopoietic stem cell transplantation. Early diagnosis and therapy has been shown to improve outcomes, but reaching a definitive diagnosis quickly can be problematic. Recently, galactomannan testing of bronchoalveolar lavage (BAL) fluid has been investigated as a diagnostic test for IPA, but widespread experience and consensus on optical density (OD) cut-offs remain lacking.</p> <p>Methods</p> <p>We performed a prospective case-control study to determine an optimal BAL galactomannan OD cutoff for IPA in at-risk patients with hematological diagnoses. Cases were subjects with hematological diagnoses who met established definitions for proven or probable IPA. There were two control groups: subjects with hematological diagnoses who did not meet definitions for proven or probable IPA and subjects with non-hematological diagnoses who had no evidence of aspergillosis. Following bronchoscopy and BAL, galactomannan testing was performed using the Platelia <it>Aspergillus </it>seroassay in accordance with the manufacturer's instructions.</p> <p>Results</p> <p>There were 10 cases and 52 controls. Cases had higher BAL fluid galactomannan OD indices (median 4.1, range 1.1-7.7) compared with controls (median 0.3, range 0.1-1.1). ROC analysis demonstrated an optimum OD index cutoff of 1.1, with high specificity (98.1%) and sensitivity (100%) for diagnosing IPA.</p> <p>Conclusions</p> <p>Our results also support BAL galactomannan testing as a reasonably safe test with higher sensitivity compared to serum galactomannan testing in at-risk patients with hematological diseases. A higher OD cutoff is necessary to avoid over-diagnosis of IPA, and a standardized method of collection should be established before results can be compared between centers.</p

Vertical accuracy comparison of multi-source Digital Elevation Model (DEM) with Airborne Light Detection and Ranging (LiDAR)

Digital Elevation Model (DEM) is a digital representation of ground surface topography or terrain. There are many freely available DEM data with a spatial resolution of 30 m to 90 m. Nevertheless, their vertical accuracy may vary, depending on the vegetation cover and terrain characteristics. This study examined the vertical accuracy of open-access global DEMs (ALOS PALSAR, ASTER GDEM3, SRTM, TanDEM-X) and fused DEM (EarthEnvDEM90, MERIT DEM). Their performances were assessed using a Digital Terrain Model (DTM) generated using airborne LiDAR data that had an outstanding absolute vertical accuracy (mean error (ME) = 0.24 m; root mean square error (RMSEz) = 1.20 m). Height differences between the global DEMs and the LiDAR DTM were calculated and examined their performances by forested vs. non-forested, slope, and elevation classes. The results showed the MERIT DEM was superior to other DEMs in most of the testing methods. It outperformed other DEMs with an RMSEz value of 3.02 m in the forested areas, followed by ALOS PALSAR (9.29 m), EarthEnv-DEM90 (9.40 m), SRTM (9.80 m), TanDEM-X (10.41 m), and ASTER GDEM3 (12.57 m). The MERIT DEM also had the best accuracy in the higher elevation areas. Overall, the ASTER GDEM3 had the worst accuracies, with relatively large over-estimations compared to other DEMs. Despite its low spatial resolution, the MERIT DEM was the best for representing terrain elevation for applications over a large area