Detection accuracy of new well sites using Landsat time series data: a case study in the Alberta Oil Sands Region

Forest change features related to resource exploration and extraction are important in the Alberta Oil Sands Region (AOSR), where, for example, 2486 oil and gas well sites were established in a 5000 km² area on three leases in the period 1984–2011. A newly established well site is typically readily identified visually in Landsat multispectral and high spatial resolution imagery, but poses an automated detection and mapping challenge over larger areas and long time periods relative to other major disturbance features. In this study, Landsat time series image composites from the national Composite-2-Change (C2C) change detection protocol were used in a comparison to randomly sampled, independently-generated well site reference data. The highest accuracy reported was approximately 83%, with relatively low errors of omission (13%) and high errors of commission (up to 37%). Future research will incorporate well site disturbance object characteristics in this type of regionally-sensitive forest change analysis.     

Drinking pattern as a predictor of cardiovascular harm: A longitudinal study using alternative drinking pattern measures

Background: This study compared measures of drinking pattern at baseline, and subsequent cardiovascular harm in a longitudinal study.

Method: In Winnipeg, Manitoba, Canada, a community sample of 1154 adult men and women was interviewed at baseline in 1990 and 1991, then followed with all‐cause surveillance. Cox proportional hazards regressions were used to assess the “time to event” for morbidity or mortality from coronary heart disease (CHD), hypertension, or other cardiovascular disease. Surveillance was through a 10‐year series of documented physician visits, hospital discharges and deaths, classified by diagnosis. Drinking pattern was defined as either ?8 drinks (80–120 g of alcohol or more) at a sitting in the past 12 months, a report of feeling the effects, or ?5 usual drinks/day.

Results: There were 104 individuals with CHD events in the data. When ?8 drinks at a sitting was the predictor, there were significant hazards for CHD among both men and women [hazard ratio (HR) = 2.32 and 1.07; p = 0.004 and 0.04], and marginally significant hazards for hypertension among men (HR = 1.40; p = 0.08). When feeling the effects or ?5 usual drinks/day were the predictors, there were no significant hazards of drinking pattern.

Conclusion: Eight or more drinks was a stronger predictor of cardiovascular harm in these data than were feeling the effects or ?5 usual drinks.     

Flat foot functional evaluation using pattern recognition of ground reaction data.

OBJECTIVE: Main purpose of this study was to apply quantitative gait analysis and statistical pattern recognition as clinical decision-making aids in flat foot diagnosis and post-surgery monitoring. DESIGN: Statistical pattern recognition techniques were applied to discriminate between normal and flat foot populations through ground reaction force measurements; ground reaction forces time course was assumed as a sensible index of the foot function. BACKGROUND: Gait analysis is becoming recognized as an important clinical tool in orthopaedics, in pre-surgery planning, post-surgery monitoring and in a posteriori evaluation of different treatment techniques. Statistical pattern recognition techniques have been utilized with success in this field to identify the most significant variables of selected motor functions in different pathologies, and to design classification rules and quantitative evaluation scores. METHODS: Ground reaction forces were recorded during free speed barefoot walks on 28 healthy subjects, and 28 symptomatic flexible flat foot children selected for surgical intervention. A new feature selection algorithm, based on heuristic optimization, was applied to select the most discriminant ground reaction forces time samples. A two-stage pattern recognition system, composed by three linear feature extractors, one for each ground reaction force component, and a linear classifier, was designed to classify the feet of each subject using the selected features. The output of the classifier was used to define a functional score. RESULTS: The classifier assigned the ground reaction force patterns performed by each subject into the right class with an estimated error of 15%, corresponding to an assignment error for each subject's foot of 9%. The most discriminant ground reaction forces time samples selected are in full agreement with the pathophysiology of the symptomatic flexible flat foot. The obtained score was utilized to monitor the 1 and 2 years post-operative functional recovery of two differently treated subgroups of 32 flexible flat foot subjects. CONCLUSIONS: Statistical pattern recognition techniques are promising tools for clinical gait analysis; the obtained score provides important functional information that could be used as a further aid in the clinical evaluation of flat foot and different surgical treatment techniques. RELEVANCE: Symptomatic flexible flat foot surgical decision making is frequently difficult because of the lack of objective criteria to assess functional abnormalities of the foot/ankle complex. Gait analysis and statistical pattern recognition can give us parameters with which to characterize "functional" flat foot. Moreover, we can objectively follow up the recovery of the foot/ankle complex function after surgical treatment.     

A rapid enzymatic procedure for "fingerprinting" bacteria by using pattern recognition of two-dimensional fluorescence data

We describe a rapid fluorescence technique for bacterial "fingerprinting," based on the differences in enzyme content and activity of various bacteria. Bacterial cells are incubated with a mixture of carefully chosen fluorogenic enzyme substrates to produce fluorophores with unique emission and excitation properties. The resulting two-dimensional fluorescence spectrum of the product mixture produces a pattern characteristic of the bacterium. A Fourier-transform-based pattern recognition algorithm is used for spectral matching and for differentiating visually similar spectra. This sensitive technique is potentially applicable to differentiation and identification of bacteria in clinical laboratories.     

CT-超声实时影像虚拟导航引导穿刺活检的实验研究

目的 利用体模验证CT-超声实时影像虚拟导航(real-time virtual navigation system,RVS)引导活检的可行性及准确性.方法 体外模型内含30个红色球形胶块,胶块CT可见但超声不可见.两名不同经验操作者针对每个胶块以RVS引导与常规超声引导两种方法各活检一次,记录活检是否成功及检出的目标胶条长度.结果 两名操作者无论采用RVS引导还是常规超声引导,其活检成功率及检出的目标胶条长度差异均无统计学意义;RVS引导法及常规超声引导法活检成功率分别为96.7％(58/60)及43.3％(26/60),检出目标胶条长度的中位数分别为7.5(6～9)mm及0(0～5)mm.RVS引导法活检成功率及检出目标胶条的长度均高于常规超声引导法,差异有统计学意义(P＜0.05).结论 RVS引导活检超声不可见目标是可行的,且具较高的准确性,有望为介入性超声提供一种新的引导方法.     

Combined experimental and computational study of Al2O3 catalyzed transamidation of secondary amides with amines

Amides are the most extensively used substances in both synthetic organic and bioorganic chemistry. Unfortunately, the traditional synthesis of amides suffers from some important drawbacks, including low atom efficiency, high catalyst loading, separation of products from the reaction mixture and production of byproducts. Al₂O₃ is an amphoteric catalyst that activates the carbonyl carbon of the secondary amide group and helps the C–N cleavage of the reactant amide group by attacking the N–H hydrogen. By using the concepts of amphoteric properties of Al₂O₃, amides were synthesized from secondary amides and amines in the presence of triethylamine solvent. Several aliphatic and aromatic amines were used for the transamidation of N-methylbenzamide in the presence of the Al₂O₃ catalyst. Moreover, using the Gaussian09 software at the DFT level, HUMO, LUMO and the intrinsic reaction coordinates (IRCs) have also been calculated to find out the transition state of the reaction and energy. In this study, five successful compounds were synthesized by the transamidation of secondary amides with amines using a reusable Al₂O₃ catalyst. The catalyst was reused several times with no significant loss in its catalytic activity. The products were purified by recrystallization and column chromatography techniques. This catalytic method is effective for the simultaneous activation of the carbonyl group and N–H bond by using the Al₂O₃ catalyst.

Amides are the most extensively used substances in both synthetic organic and bioorganic chemistry.     

Bis-indolylation of aldehydes and ketones using silica-supported FeCl3: molecular docking studies of bisindoles by targeting SARS-CoV-2 main protease binding sites

We report herein an operationally simple, efficient and versatile procedure for the synthesis of bis-indolylmethanes via the reaction of indoles with aldehydes or ketones in the presence of silica-supported ferric chloride under grindstone conditions. The prepared supported catalyst was characterized by SEM and EDX spectroscopy. The present protocol has several advantages such as shorter reaction time, high yield, avoidance of using harmful organic solvents during the reaction and tolerance of a wide range of functional groups. Molecular docking studies targeted toward the binding site of SARS-CoV-2 main protease (3CL^pro or M^pro) enzymes were investigated with the synthesized bis-indoles. Our study revealed that some of the synthesized compounds have potentiality to inhibit the SARS-CoV-2 M^pro enzyme by interacting with key amino acid residues of the active sites via hydrophobic as well as hydrogen bonding interactions.

Silica supported FeCl₃ catalyzed simple protocol for the synthesis of bis-indolylmethanes was explored via grindstone chemistry. Synthesized compounds were screened virtually as inhibitor by targeting the binding site of SARS-CoV-2 main protease enzyme.     

Choice making in Rett syndrome: a descriptive study using video data

Purpose: To describe the choice-making abilities of girls and women with Rett syndrome.

Method: Females with Rett syndrome registered with the Australian Rett Syndrome Database with a pathogenic MECP2 mutation were included in this study. Video clips showing choice making in 64 females at a median age of 11.6 years (range 2.3–35.6 years) were analysed. Video clips were coded for the location and nature of the choice-making interaction, and the actions of the communication partner and female with Rett syndrome.

Results: The majority (82.8%, 53/64) of females made a choice, most using eye gaze. Just under half (24/53) used one modality to communicate their choice, 52.8% used two modalities and one used three modalities. Of those who made a choice, 50% did so within 8?s. The length of time to make a choice did not appear to vary with age. During choice making, 57.8% (37/64) of communication partners used language and gestures, 39.1% (25/64) used only language and two used language, gestures and symbols within the interaction.

Conclusions: The provision of adequate time allowing for a response and observation for the use of multiple modalities could promote effective choice making in females with Rett syndrome.

• Implications for Rehabilitation

• The provision of adequate time allowing for a response will promote effective choice making in girls and women with Rett syndrome.

• Although almost all girls and women with Rett syndrome used eye gaze to indicate their choice, communication partners also need to recognise and respond to other communication modalities that are sometimes used like body movements.

     

Preparing a large data set for analysis: using the minimum data set to study perineal dermatitis

AIM: The aim of this paper is to present a practical example of preparing a large set of Minimum Data Set records for analysis, operationalizing Minimum Data Set items that defined risk factors for perineal dermatitis, our outcome variable. BACKGROUND: Research with nursing home elders remains a vital need as 'baby boomers' age. Conducting research in nursing homes is a daunting task. The Minimum Data Set is a standardized instrument used to assess many aspects of a nursing home resident's functional capability. United States Federal Regulations require a Minimum Data Set assessment of all nursing home residents. These large data would be a useful resource for research studies, but need to be extensively refined for use in most statistical analyses. Although fairly comprehensive, the Minimum Data Set does not provide direct measures of all clinical outcomes and variables of interest. METHOD: Perineal dermatitis is not directly measured in the Minimum Data Set. Additional information from prescribers' (physician and nurse) orders was used to identify cases of perineal dermatitis. The following steps were followed to produce Minimum Data Set records appropriate for analysis: (1) identification of a subset of Minimum Data Set records specific to the research, (2) identification of perineal dermatitis cases from the prescribers' orders, (3) merging of the perineal dermatitis cases with the Minimum Data Set data set, (4) identification of Minimum Data Set items used to operationalize the variables in our model of perineal dermatitis, (5) determination of the appropriate way to aggregate individual Minimum Data Set items into composite measures of the variables, (6) refinement of these composites using item analysis and (7) assessment of the distribution of the composite variables and need for transformations to use in statistical analysis. RESULTS: Cases of perineal dermatitis were successfully identified and composites were created that operationalized a model of perineal dermatitis. CONCLUSION: Following these steps resulted in a data set where data analysis could be pursued with confidence. Incorporating other sources of data, such as prescribers' orders, extends the usefulness of the Minimum Data Set for research use.     

Differentiation of breast masses using 3-D sonographic and echo-enhancer-based evaluation of the vascular pattern: initial experiences

To evaluate the potential of combined 3-D B-mode and color Doppler (CD) data sets in the differentiation of breast masses, in 50 patients with histologically proven solid breast lesions, 3-D datasets were acquired. A 3-D display was created and volume calculation of tumors, their periphery and vasculature was performed. Time-intensity curves of enhancement after administration of a contrast agent were analyzed. Volumetry of tumor vasculature yielded no significant differences between malignant and benign tumors regarding vascularization of the center (2.60 vs. 2.88%) and periphery (6.66 vs. 3.78%). Only the mean values for the rise time in the center of the tumor, fibroadenoma (FA): 5.7 s and ductal invasive carcinoma (DIC): 15.8s; p = 0.05, and the time to peak in the periphery, FA: 21.0 s and DIC: 31.6 s; p = 0.03, differed significantly. The 3-D ultrasound (US) technique was of no additional value in differentiating breast masses. The calculation of time-intensity curves after administration of a contrast agent may be helpful in differentiating FA and DIC.     

Retrospective cohort study of three-wall orbital resection for treatment of endocrine orbitopathy using 3D tomographic data and biomechanical modeling

BackgroundSurgical treatment of endocrine orbitopathy can be performed by way of resecting orbital walls, which effectively releases superfluous tissue from the surgically enlarged orbital space allowing the eyeballs to move back. Existing approaches aim to select an optimal surgical strategy based on statistical correlations between the extent of the surgical procedure and the resulting bulbus displacement but do not provide an individual surgery plan or predict surgery outcome.MethodsIn this retrospective study, we performed a quantitative analysis of pre- and post-surgery 3D tomographic data of six patients and applied explorative biomechanical modeling of orbital mechanics to dissect factors influencing patient-specific outcome.FindingsOur experimental results showed a large variability of the backward eyeball displacement in dependency on the amount of orbital volume flow, which could partially be described by computational simulation. Our detailed analysis revealed that patients with regular fat tissue show a good correlation between bulbus displacement and relative volume of decompressed tissue, which, in turn, correlates with decrease in hydrostatic pressure. In contrast, patients with fibrotic tissue exhibit significantly reduced and computationally less predictable eyeball translation in response to surgical tissue decompression.InterpretationBased on the results of this study we see a great potential for quantitative planning of surgical exophthalmos treatment using 3D biomechanical modeling. Conventional approaches to planning of soft tissue interventions consider, however, only the patient's 3D anatomy and widely disregard individual tissue properties. Further investigations are required to establish reliable procedures for assessment of individual tissue properties and incorporating them into patient-specific models of orbital mechanics.     

Breast tissue classification using diagnostic ultrasound and pattern recognition techniques: I. Methods of pattern recognition

This paper discusses the application of statistical pattern recognition techniques to problems in diagnostic ultrasound. Using our own system as an example, we describe the concepts and specific methods that we have applied to a problem involving the computer-aided classification of breast tissue in vivo. Topics include feature generation, feature selection and classification, as well as a method which estimates the probability of error on classifying future data. An accompanying paper applies these methods to the classification of backscattered RF signals from normal and diseased breast tissue.     

3D pattern of brain abnormalities in Williams syndrome visualized using tensor-based morphometry

Williams syndrome (WS) is a neurodevelopmental disorder associated with deletion of approximately 20 contiguous genes in chromosome band 7q11.23. Individuals with WS exhibit mild to moderate mental retardation, but are relatively more proficient in specific language and musical abilities. We used tensor-based morphometry (TBM) to visualize the complex pattern of gray/white matter reductions in WS, based on fluid registration of structural brain images. METHODS: 3D T1-weighted brain MRIs of 41 WS subjects (age [mean+/-SD]: 29.2+/-9.2 years; 23F/18M) and 39 age-matched healthy controls (age: 27.5+/-7.4 years; 23F/16M) were fluidly registered to a minimum deformation target. Fine-scale volumetric differences were mapped between diagnostic groups. Local regions were identified where regional structure volumes were associated with diagnosis, and with intelligence quotient (IQ) scores. Brain asymmetry was also mapped and compared between diagnostic groups. RESULTS: WS subjects exhibited widely distributed brain volume reductions (approximately 10-15% reduction; P<0.0002, permutation test). After adjusting for total brain volume, the frontal lobes, anterior cingulate, superior temporal gyrus, amygdala, fusiform gyrus and cerebellum were found to be relatively preserved in WS, but parietal and occipital lobes, thalamus and basal ganglia, and midbrain were disproportionally decreased in volume (P<0.0002). These regional volumes also correlated positively with performance IQ in adult WS subjects (age > or = 30 years, P = 0.038). CONCLUSION: TBM facilitates 3D visualization of brain volume reductions in WS. Reduced parietal/occipital volumes may be associated with visuospatial deficits in WS. By contrast, frontal lobes, amygdala, and cingulate gyrus are relatively preserved or even enlarged, consistent with unusual affect regulation and language production in WS.     

Logistic regression and Bayesian networks to study outcomes using large data sets

BACKGROUND: In nursing research, the interest in using large health care databases to predict nursing sensitive outcomes is growing rapidly. Traditionally, one of the most frequently used methods is logistic regression (LR), which, although powerful and familiar, has several limitations when used in the analysis of large databases. As a result, innovative approaches are required. APPROACH: To (a) introduce an innovative/alternative data analysis approach (Bayesian network), (b) discuss the constraints of LR and the complementary advantages of Bayesian networks (BNs) in working with large and multidimensional health care data, and (c) provide a fundamental understanding of the use of BNs in the nursing/health care domain. RESULTS: Studies have shown that BNs have several advantages over LR in analyzing complex and large data: (a) statistical assumptions, such as linearity and additivity, are relaxed; (b) handling of a larger number of predictors and identification of interactions among predictors is less complex; and (c) the discovery of structure, pattern, and knowledge, for example, of unknown, complex, and nonlinear relationships, in data is facilitated. CONCLUSION: Outcome studies, such as those undertaken by nurse researchers, may benefit from the examination and use of innovative approaches such as BNs to the analysis of very large and complex health care data sets.     

Prediction of deficiency-excess pattern in Japanese Kampo medicine: Multi-centre data collection

ObjectiveThe purpose of the present study was to compare important patient questionnaire items by creating a random forest model for predicting deficiency-excess pattern diagnosis in six Kampo specialty clinics.DesignA multi-centre prospective observational study.SettingParticipants who visited six Kampo specialty clinics in Japan from 2012 to 2015.Main outcome measureDeficiency-excess pattern diagnosis made by board-certified Kampo experts.MethodsTo predict the deficiency-excess pattern diagnosis by Kampo experts, we used 153 items as independent variables, namely, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We extracted the 30 most important items in each clinic’s random forest model and selected items that were common among the clinics. We integrated participating clinics’ data to construct a prediction model in the same manner. We calculated the discriminant ratio using this prediction model for the total six clinics’ data and each clinic’s independent data.ResultsFifteen items were commonly listed in top 30 items in each random forest model. The discriminant ratio of the total six clinics’ data was 82.3%; moreover, with the exception of one clinic, the independent discriminant ratio of each clinic was approximately 80% each.ConclusionsWe identified common important items in diagnosing a deficiency-excess pattern among six Japanese Kampo clinics. We constructed the integrated prediction model of deficiency-excess pattern.