Robust Set Of Data Research Articles

“Dead or Alive?” Assessment of the Binary End-of-Event Outcome Indicator for the NEMSIS Public Research Dataset

Objectives The National Emergency Medical Services Information System (NEMSIS) provides a robust set of data to evaluate prehospital care. However, a major limitation is that the vast majority of the records lack a definitive outcome. This study aimed to evaluate the performance of a recently proposed method (“MLB” method) to impute missing end-of-EMS-event outcomes (“dead” or “alive”) for patient care reports in the NEMSIS public research dataset. Methods This study reproduced the recently published method for patient outcome imputation in the NEMSIS database and replicated the results for years 2017 through 2022 (n = 686,075). We performed statistical analyses leveraging an array of established performance metrics for binary classification from the machine learning literature. Evaluation metrics included overall accuracy, true positive rate, true negative rate, balanced accuracy, precision, F1 score, Cohen’s Kappa coefficient, Matthews’ coefficient, Hamming loss, the Jaccard similarity score, and the receiver operating characteristic/area under the curve. Results Extended metrics show consistently good imputation performance from year-to-year but reveal weakness in accurately indicating the minority class: e.g., after adjustments for conflicting labels, “dead” prediction accuracy is 77.7% for 2018 and 61.8% over the six-year NEMSIS sub-sample, even though overall accuracy is 98.8%. Slight over-fitting is also present. Conclusions This study found that the recently published MLB method produced reasonably good “dead” or “alive” indicators. We recommend reporting of True Positive Rate (“dead” prediction accuracy) and True Negative Rate (“alive” prediction accuracy) when applying the imputation method for analyses of NEMSIS data. More attention by EMS clinicians to complete documentation of target NEMSIS elements can further improve the method’s performance.

Historical Control Data of Spontaneous Pathological Findings in C57BL/6J Mice Used in 18-Month Dietary Carcinogenicity Assays.

A retrospective analysis in C57BL6/J mice used in dietary carcinogenicity studies was performed to determine the survival rate, causes of death and incidences of spontaneous non-tumoral and tumoral findings. Data were collected from 1600 mice from control dose groups of sixteen 18-month carcinogenicity assays performed between 2003 and 2021 at the same test facility with similar environmental conditions and experimental procedures. The survival rate was high in both sexes (81%-85%) and the causes of humane euthanasia or death were mainly non-tumoral (chronic ulcerative dermatitis, atrial thrombosis). Benign tumors were more frequent than malignant tumors and females were more affected than males. Pituitary gland adenoma in females, lymphoma, bronchioloalveolar adenoma, and harderian gland adenoma in both sexes were the most common tumors. Systemic amyloidosis, the most frequent non-tumoral lesion, was observed variably across studies without sex predilection. The analysis by cohort (3 time periods of 6 years) showed a tendency toward higher incidences of lymphoma and pituitary gland adenoma and lower incidences of amyloidosis over time. The results presented here provide for the first time a robust set of control historical data in untreated C57BL/6J mice kept for 18 months contributing to build in depth knowledge of this animal model.

An ecological network for large carnivores as a key tool for protecting landscape connectivity in the Carpathians

ABSTRACT The main aim of our study was to delineate a coherent ecological network for large carnivores, using a unified methodology, which would be flexible and serve as a basis for nature and habitat connectivity protection across the Carpathians. We applied a comprehensive methodological approach based on habitat suitability modelling followed by connectivity modelling. For the habitat suitability modelling, we used a robust set of large carnivores occurrence data, which allowed us to build a solid model. From the model, we derived a layer of patches of suitable habitat of required quality and resistance surface to express landscape permeability. Both outputs were crucial for the connectivity modelling. We applied Random Walk and Least-Cost Path models and combined them to determine feasible connections between suitable habitats. With expert discussions and editing/improvement of modelled corridors, we developed a unique ecological network consisting of various components adapted to the heterogeneity of the Carpathian landscape.

Composition-property relationships of BP-1 lunar regolith simulant geopolymers for in-situ resource utilization

In-situ resource utilization (ISRU) to construct environmental protection and critical infrastructure is required for long-term human habitation on the Moon and Mars. Analogous to terrestrial construction materials formed from aluminosilicates, i.e., geopolymers, construction materials formed from lunar aluminosilicate regolith are a promising method to create lunar landing pads and habitats. Building on a previous lateral study across multiple regolith simulants, the effects of composition on one specific regolith simulant, Black Point 1 (BP-1), are investigated with the goal of developing relationships between composition and properties. The effects of overall geopolymer composition, activating solution silica and sodium content, and activating solution water content on seven-day compressive strength, strain to fail, and secant modulus are investigated. Decreasing water content has a positive effect on compressive strength and secant modulus of geopolymers formed at ambient-earth conditions, reaching maximum compressive strength at 75 wt% BP-1. Above a compositional limit of Si/H2O = 0.6, differences in Si and Al coordination result in variations in compressive strength with the elemental composition of the geopolymer. At high solids in formulation, the observed trend in compressive strength is nonmonotonic with varying silicon and aluminum content, emphasizing the need for a mechanistic understanding of the relationship between composition and structure for optimization of geopolymer material properties. This study provides a robust set of data for a widely available lunar regolith simulant that can guide further development of processing strategies for lunar ISRU.

A Systematic Review on Food Baskets Recommended in the Eastern Mediterranean Region

To assist in providing a robust regional set of data and international comparisons, a systematic review was conducted to identify and characterize food baskets (FBs) in the Eastern Mediterranean Region (EMR) countries. Electronic databases of peer-reviewed literature, including PubMed, Scopus, ISI/WOS and Google Scholar, and also, online grey literature, were systematically searched from January 2000 to September 2021. The quality of included studies was assessed using the Joanna Briggs Institute’s (JBI) Critical Appraisal checklists for analytical cross-sectional studies. A total of 20 studies and reports were identified as eligible for inclusion in this systematic review. Linear & goal programming is used in many studies to estimate the FB groups. According to the recent recommendations based on sustainability, less consumption of red meat is proposed, and the poultry group, along with eggs, plays an important role in supplying animal protein in EMR FBs. More than 30 g of legumes has been suggested based on the dietary habits of this area, whereas consumption of more than 30–40 g of oils and fats will not be appropriate for the region. The research results are not comparable due to differences in the tools, protocols, and methods; hence, there is a need for a standardized regional approach.

Dating Agricultural Terraces in the Mediterranean Using Luminescence: Recent Progress and Challenges

Agricultural terraces provide farmers in hilly landscapes with effective ways to increase the area available for crops. They mitigate the risks of soil erosion and promote crop productivity by slowing surface water runoff and retaining moisture. As in other parts of the world, terraces have been constructed and used in the Mediterranean for millennia. The availability of terraced agriculture had important socio-economic, ecological, and environmental implications for past societies. However, the chronology of construction, use, and abandonment of terraces in different regions remains uncertain. A more robust set of chronological data will allow better assessment of whether terrace agriculture was a resilient strategy in the face of past economic or ecological instability and, in turn, inform how terraces could be used to address future agricultural and environmental challenges. In this paper, we review the application of luminescence dating to terrace sediments, the key challenges involved, and the currently published data which include over 250 luminescence ages. We also discuss the use of a multidisciplinary approach involving other geoarchaeological tools (e.g., use of GIS analyses, field-based luminescence readers, and micromorphology) to enhance the ways that past terrace systems can be understood. Terrace systems are inextricably linked to sustainable land use across the Mediterranean. Luminescence dating methods, therefore, have a crucial role to play in understanding the complexities of past and future landscape change.

Validation of a data collection set for the psychiatric, addiction, sleep and chronobiological assessments of patients with depression: A Delphi study for the SoPsy-depression French national cohort

ObjectivesDespite international efforts to identify biomarkers of depression, none has been transferred to clinical practice, neither for diagnosis, evolution, nor therapeutic response. This led us to build a French national cohort (through the clinical and research network named SoPsy within the French biological psychiatry society (AFPBN) and sleep society (SFRMS)), to better identify markers of sleep and biological rhythms and validate more homogeneous subgroups of patients, but also to specify the manifestations and pathogeneses of depressive disorders. Before inclusions, we sought to provide a predefined, standardized, and robust set of data to be collected in all centers. MethodsA Delphi process was performed to achieve consensus through the independent rating of invited experts, the SoPsy-depression co-investigators (n=34). The initial set open for vote included 94 questionnaires targeting adult and child psychiatry, sleep and addiction. ResultsTwo questionnaire rounds were completed with 94% participation in the first round and 100% participation in the second round. The results of the Delphi survey incorporated the consensus opinion of the 32 members who completed both rounds. Nineteen of the 94 questionnaires achieved consensus at the first round and seventy of 75 at the second round. The five remaining questionnaires were submitted to three experts involved in the steering committee during a dedicated meeting. At the end, 24 questionnaires were retained in the mandatory and 26 in the optional questionnaire set. ConclusionsA validated data collection set of questionnaires is now available to assess psychiatry, addiction, sleep and chronobiology dimensions of depressive disorders.

Cryptococcus extracellular vesicles properties and their use as vaccine platforms.

Whereas extracellular vesicle (EV) research has become commonplace in different biomedical fields, this field of research is still in its infancy in mycology. Here we provide a robust set of data regarding the structural and compositional aspects of EVs isolated from the fungal pathogenic species Cryptococcus neoformans, C. deneoformans and C. deuterogattii. Using cutting‐edge methodological approaches including cryogenic electron microscopy and cryogenic electron tomography, proteomics, and flow cytometry, we revisited cryptococcal EV features and suggest a new EV structural model, in which the vesicular lipid bilayer is covered by mannoprotein‐based fibrillar decoration, bearing the capsule polysaccharide as its outer layer. About 10% of the EV population is devoid of fibrillar decoration, adding another aspect to EV diversity. By analysing EV protein cargo from the three species, we characterized the typical Cryptococcus EV proteome. It contains several membrane‐bound protein families, including some Tsh proteins bearing a SUR7/PalI motif. The presence of known protective antigens on the surface of Cryptococcus EVs, resembling the morphology of encapsulated virus structures, suggested their potential as a vaccine. Indeed, mice immunized with EVs obtained from an acapsular C. neoformans mutant strain rendered a strong antibody response in mice and significantly prolonged their survival upon C. neoformans infection.

Evaluation of Landsat-8 TIRS data recalibrations and land surface temperature split-window algorithms over a homogeneous crop area with different phenological land covers

Successive re-calibrations were implemented in Landsat-8 TIRS data since launch. This paper evaluates the performances of both: (1) these re-calibrations, up to the last calibration update announced for TIRS data in the next Landsat Collection 2; and (2) single-channel (SC) corrections and split-window (SW) algorithms to retrieve land surface temperature (LST) from TIRS data. A robust and accurate multi-year (2014–2019) set of reference ground data were used, which included thermal infrared (TIR) radiance measurements taken along transects in a uniform and thermally homogeneous rice paddy area, but also emissivity measurements for the different ground covers at the site through the year. The calibration results showed significant biases at the site for data after the 2014 reprocessing, but negligible biases and root-mean-square differences (RMSDs) <1.5 K were obtained when using the current TIRS data in Collection 1 (i.e., data after the 2017 reprocessing). The last announced calibration update mainly introduced differences in biases, improving slightly the results for band 10 and presenting a calibration response difference between bands. The SC corrections showed negligible LST biases for both bands and the lowest RMSDs (<1.6 K) when using the band 10 data in the current Collection 1, and the bias disappeared for this band after applying the calibration update. Three of the seventeen different SW equations evaluated in the paper showed negligible biases and LST RMSDs lower than or equal to 0.8 K. These three SW algorithms are mainly recommended for users of the current TIRS data in Collection 1; one of them being that proposed to generate a SW LST product in the future Collection 3. Finally, bias differences around 1.4 K were shown in the results of the SW algorithms after applying the calibration update announced for Collection 2.

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19

Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerize chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.

Tastes and volatiles of tropical-type sweet potatoes

For fresh market sweet potato [Ipomea batatas (L.) Lam] quality is defined in terms of attractiveness, sweetness, texture, and flavor composed of taste and odor after cooking. There is a scarcity of information regarding taste and volatiles of tropical type sweet potatoes. The first objective of this research was to provide a robust set of data for carbohydrate type and levels, alcohol insoluble solids (AIS) and volatile organic compounds (VOC) from six cultivars of sweet potato, data which will allow us to have a quantitative selection tool besides crop yield, tuber size and appearance. The second objective was to relate VOC differences among sweet potato cultivars that can be attributed to chemical and physical differences of the pulp before and after cooking. A three harvest study was conducted to identify and determine major carbohydrate content, AIS, starch granule diameter and form, and tastes and odors of six cultivars of sweet potato, which were very sweet ('Gem'), sweet ('Miguela' and Travis'), moderately sweet ('Viola' and 'Martina') and non-sweet ('Ninetynine'). The average AIS contents were 81, 77, 79, 85, 66, and 76% for 'Martina', 'Miguela', 'Ninety-nine', 'Viola', 'Gem' and 'Travis', respectively. Scanning electron micrograph (SEM) indicated that the average starch granule diameters were 12.9, 15.3, 16.9 and 25.2 |jm for 'Martina', 'Miguela', 'Ninety-nine' and 'Viola', respectively. In addition, all of the sweet potato starch granules had both spherical and polygonal granules. The cultivars with greatest change in AIS and maltose content (raw vs. baked) were 'Gem', the sweetest, and 'Travis'. Correlation between sucrose index (SI) and AIS for baked samples was 0.956; for AIS and sweetness, correlation was -0.823. A similar correlation between the AIS and maltose was obtained for the data from the three harvests (-0.8493). Fifty volatile organic compounds of baked sweet potatoes were trapped in cold methylene chloride. The predominant volátiles were hexanal, 3-pente-2-ol, and isoledene. Other sweet aromas detected were 2,4-decadienal, 2-pentyl furan, benzaldehyde, 4-methyl-4-hydroxy-2-pentanone, and benzene acetaldehyde. The great differences in relative concentrations of volátiles among cultivars seem to be related to the synthesis of maltose and the content of AIS.&#x0D;

Cohort profile: SARS-CoV-2/COVID-19 hospitalised patients in Switzerland.

SARS-CoV-2/COVID-19, which emerged in China in late 2019, rapidly spread across the world with several million victims in 213 countries. Switzerland was severely hit by the virus, with 43,000 confirmed cases as of 1 September 2020. In cooperation with the Federal Office of Public Health, we set up a surveillance database in February 2020 to monitor hospitalised patients with COVID-19, in addition to their mandatory reporting system. Patients hospitalised for more than 24 hours with a positive polymerase chain-reaction test, from 20 Swiss hospitals, are included. Data were collected in a customised case report form based on World Health Organisation recommendations and adapted to local needs. Nosocomial infections were defined as infections for which the onset of symptoms was more than 5 days after the patient&rsquo;s admission date. As of 1 September 2020, 3645 patients were included. Most patients were male (2168, 59.5%), and aged between 50 and 89 years (2778, 76.2%), with a median age of 68 (interquartile range 54&ndash;79). Community infections dominated with 3249 (89.0%) reports. Comorbidities were frequently reported, with hypertension (1481, 61.7%), cardiovascular diseases (948, 39.5%) and diabetes (660, 27.5%) being the most frequent in adults; respiratory diseases and asthma (4, 21.1%), haematological and oncological diseases (3, 15.8%) were the most frequent in children. Complications occurred in 2679 (73.4%) episodes, mostly respiratory diseases (2470, 93.2% in adults; 16, 55.2% in children), and renal (681, 25.7%) and cardiac (631, 23.8%) complications for adults. The second and third most frequent complications in children affected the digestive system and the liver (7, 24.1%). A targeted treatment was given in 1299 (35.6%) episodes, mostly with hydroxychloroquine (989, 76.1%). Intensive care units stays were reported in 578 (15.8%) episodes. A total of 527 (14.5%) deaths were registered, all among adults. The surveillance system has been successfully initiated and provides a robust set of data for Switzerland by including about 80% (compared with official statistics) of SARS-CoV-2/COVID-19 hospitalised patients, with similar age and comorbidity distributions. It adds detailed information on the epidemiology, risk factors and clinical course of these cases and, therefore, is a valuable addition to the existing mandatory reporting.

Influence of fifth-order nonlinearities on the statistical fluctuations in emission intensities in a photonic open-cavity complex system

High-order nonlinear optical effects have become an important feature of photonic systems, both ordered and disordered. In this work, a very large and robust set of experimental data obtained from the emission spectra of a trivalent neodymium ion-based random laser, whose action mechanism relies upon gain and disorder, was characterized in detail. Using an effective model, it was possible to describe how the optical nonlinearities of the disordered gain medium affect the statistical behavior of the intensity fluctuations of the random laser for excitations close to and well above the laser threshold. A theoretical framework is presented and, in particular, in the regime well above threshold a nice fit to the experimental data is obtained with a distribution that incorporates nonlinearities up to fifth order, the Izrailev distribution.

An Observational Study of Sepsis in Takeo Province Cambodia: An in-depth examination of pathogens causing severe infections.

The world’s most consequential pathogens occur in regions with the fewest diagnostic resources, leaving the true burden of these diseases largely under-represented. During a prospective observational study of sepsis in Takeo Province Cambodia, we enrolled 200 patients over an 18-month period. By coupling traditional diagnostic methods such as culture, serology, and PCR to Next Generation Sequencing (NGS) and advanced statistical analyses, we successfully identified a pathogenic cause in 46.5% of our cohort. In all, we detected 25 infectious agents in 93 patients, including severe threat pathogens such as Burkholderia pseudomallei and viral pathogens such as Dengue virus. Approximately half of our cohort remained undiagnosed; however, an independent panel of clinical adjudicators determined that 81% of those patients had infectious causes of their hospitalization, further underscoring the difficulty of diagnosing severe infections in resource-limited settings. We garnered greater insight as to the clinical features of severe infection in Cambodia through analysis of a robust set of clinical data.

Deciphering Dynamic Structural and Mechanistic Complexity in Cu/CeO2/ZSM-5 Catalysts for the Reverse Water-Gas Shift Reaction

We have prepared and investigated the structural and mechanistic properties of an array of active Cu/CeO2/ZSM-5 catalysts for the reverse water-gas shift (RWGS) reaction, where the roles of Cu and CeO2 as a promoter were studied under reaction conditions. The concentration of copper was varied between 0 and 10 wt %, while the concentration of CeO2 was varied between 0 and 48 wt % in several permutations of Cu/CeO2/ZSM-5 catalysts. RWGS reactivity trends revealed that all three components of the catalyst system are essential for promoting and sustaining the optimum catalytic activity. The dynamic chemical states of copper and cerium were probed using in situ X-ray absorption spectroscopy (X-ray absorption near-edge structure and extended X-ray absorption fine structure), revealing reduced copper and mostly oxidized ceria in the active state. In situ X-ray diffraction data revealed the presence of copper aggregates, as well as copper within the ZSM-5 lattice. In situ and isotopic switching diffuse reflectance infrared Fourier transform spectroscopy analyses were used to unambiguously establish copper speciation within the ZSM-5 lattice and CeO2 surface sites as participatory in the RWGS mechanism, with surface formate species on CeO2 being a reaction intermediate. Based on a robust set of real-time structural, electronic, and surface chemical data, we proposed a reaction mechanism that involves the participation of all three components of this catalyst system.

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees.

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.

Application of the best-estimate model calibration and prediction through experimental data assimilation methodology to the tests performed on a helium cooled First Wall Mock-up

Abstract Experiments are under progress at the Karlsruhe Institute of Technology (KIT) as part of the general strategy for the development of the DEMO Helium Cooled Pebble Bed (HCPB) design. These experiments are part of the program on the qualification of components and on the validation of system codes. In 2018, thermal-hydraulic investigations have been performed looking into the transient response of a helium-cooled blanket First Wall Mock-Up under Loss Of Flow Accident (LOFA) conditions. The results obtained through these investigations captured the quick temperature rise on the surfaces exposed to heat loads (such as those coming from the plasma), and provided a robust set of data for the validation of system codes such as RELAP5-3D. Then, the present work describes the validation activities performed on the RELAP5-3D code. This validation was performed employing also a Best-Estimate methodology called “Best-Estimate Model Calibration and Prediction through Experimental Data Assimilation methodology”. In a previous work, the same methodology was used to calibrate a RELAP5-3D model of the HELOKA-HP electrical heater under normal operation. In this regard, the present work marks the first application of this methodology to fast transients in the field of fusion reactors.

Automatic detection of involuntary spasticity of major muscles with acoustic and electromyography

Spasticity is a severely debilitating condition where involuntary muscle contraction impedes normal movement, speech and gait. It usually results from motor root damage in patients with history of multiple sclerosis, traumatic brain injury, or stroke. This damage causes misfiring of neurons leading to erratic muscle contraction. Current treatments inhibit muscle activation, however, treatment is given upon onset of the spastic episode, delaying symptom alleviation. Here we developed a sensing device combining surface acoustic myography (sAMG) and surface electromyography (sEMG) for the automatic detection of spasticity. sAMG use acoustic-based sensors to detect pressure waves produced by muscle contraction whereas sEMG detect electrical signals from neuromuscular activity. Merging sEMG and sAMG sensors provided a robust set of spectral data to describe muscle contraction patterns in terms of electromechanical activities. Said patterns were used for calibration using machine-learning algorithms. With established normal patterns, the device can distinguish between voluntary and involuntary muscle movement. In sum, the symbiotic employment of both types of sensors shows potential in early detection of a spastic episode. Being able to detect the onset or to predict a spastic event can provide insight into the underlying physiological conditions and, ultimately, help develop technologies to mitigate its occurrence.

3D Fabric analysis of Venetia Mine, South Africa: Using structural measurements and implicitly-modelled surfaces for improved pit slope design and risk management

The geometry and structural history of high-grade granite-gneiss units, which host the Venetia diatremes in South Africa, have been modelled in 3D using an implicit, rules-based, conditional geometrical technique. The volume contains a pervasive, dominant foliation, which forms the main plane of strength anisotropy in ongoing design and mining of the Venetia pit, which has a planned final depth of approximately 465 m below surface. The well-developed S2 foliation is a continuous feature that was locally re-oriented during D3 and D4 and localised ‘wrapping’ around competent amphibolite/hornblendite lenses or boudins. These local variations have important implications for pit design and operational risk management, particularly where they dip adversely out of the slope, or occur at the base of bench stacks, or in inter-ramp areas. These structures, when undercut by dips steeper than their friction angle, may result in planar sliding, presenting an operational safety risk. Best practice requires early identification of these occurrences, to make design adjustments where warranted. Lithological contacts, which have been transposed into parallelism with the S2 foliation, and a robust set of structural data collected over the last 10 years, are analysed to quantify and represent the variable orientation of S2 in 3D space. This contribution describes several methods, using leading software packages such as Leapfrog Geo™, Micromine™ and Python, to generate representative S2 form surfaces and anisotropy block models, both of which are used for downstream geotechnical engineering analysis. Outputs are also translated into apparent dip “heat” maps that show the angular interaction between S2 foliation and a pit design surface. These methods augment, and lessen the dependence on, typical 2D wedge-shaped design sectors that are commonly employed in pit design. This approach has important safety and economic benefits. As the rock mass fabric is characterised, it supports quick identification of potentially higher-risk areas (e.g. adverse undercut fabric with greater likelihood of slope instability) in current mine plans, which justifies more detailed analysis and/or additional monitoring controls to ensure safe and efficient mining. It also allows rapid mine design optimisation.

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non-cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry.