ResearchPad - multivariate-analysis https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Crystal structure of <i>Thermus thermophilus</i> methylenetetrahydrofolate dehydrogenase and determinants of thermostability]]> https://www.researchpad.co/article/elastic_article_13865 The elucidation of mechanisms behind the thermostability of proteins is extremely important both from the theoretical and applied perspective. Here we report the crystal structure of methylenetetrahydrofolate dehydrogenase (MTHFD) from Thermus thermophilus HB8, a thermophilic model organism. Molecular dynamics trajectory analysis of this protein at different temperatures (303 K, 333 K and 363 K) was compared with homologous proteins from the less temperature resistant organism Thermoplasma acidophilum and the mesophilic organism Acinetobacter baumannii using several data reduction techniques like principal component analysis (PCA), residue interaction network (RIN) analysis and rotamer analysis. These methods enabled the determination of important residues for the thermostability of this enzyme. The description of rotamer distributions by Gini coefficients and Kullback–Leibler (KL) divergence both revealed significant correlations with temperature. The emerging view seems to indicate that a static salt bridge/charged residue network plays a fundamental role in the temperature resistance of Thermus thermophilus MTHFD by enhancing both electrostatic interactions and entropic energy dispersion. Furthermore, this analysis uncovered a relationship between residue mutations and evolutionary pressure acting on thermophilic organisms and thus could be of use for the design of future thermostable enzymes.

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<![CDATA[A descriptive cross sectional study comparing barriers and determinants of physical activity of Sri Lankan middle aged and older adults]]> https://www.researchpad.co/article/elastic_article_7830 Benefits of physical activities are numerous. Barriers for physical exercise may differ among middle aged and older adults. Therefore, identifying and comparing the barriers for participating in regular physical exercises among middle aged and older adults will be useful in designing age specific physical exercise programmes.MethodsThis descriptive cross sectional study was carried out among 206 Sri Lankan adults in the age range of 40–84 years in the Colombo North region of Sri Lanka using culturally validated questionnaires to determine and compare the barriers and factors associated with regular physical activity participation. Majority were males (56%) and 54% were < 60 years. People in the age range of 40–59 years were considered as middle age and ≥ 60 years as older adults. Bivariate analysis and multivariate analysis was carried out to determine the significant factors that are associated with regular physical activity participation.ResultsLack of free time (52%), feeling too lazy (26%) and bad weather (29%) were the main barriers for the participants. In < 60 years, high level of income (p = 0.008) and in ≥ 60 years, being a male (p = 0.016), having a high level of education (P = 0.002) and a high BMI (p = 0.002) had a significant negative association with the level of physical activities.ConclusionsContrary to findings from surveys in several developed countries, this study showed that having a high level of education and being a male were strongly related with lack of physical activity participation. ]]> <![CDATA[Time-lapse imaging of HeLa spheroids in soft agar culture provides virtual inner proliferative activity]]> https://www.researchpad.co/article/Nceafa1bd-f75c-4e08-9c15-587118f668b1

Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher’s own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher’s own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher’s own samples, helping to bridge the gap between experiment and simulation.

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<![CDATA[Spatiotemporal characteristics and driving forces of construction land expansion in Yangtze River economic belt, China]]> https://www.researchpad.co/article/N8b3a4cad-34cf-4f1c-a2ed-3b8bbfbaa3a5

With rapid economic and population growth, construction land expansion in Yangtze River economic belt in China becomes substantial, carrying significant social and economic implications. This research uses Expansion Speed Index and Expansion Intensity Index to examine spatiotemporal characteristics of construction land expansion in the Yangtze River economic belt from 2000 to 2017. Based on a STIRPAT model, driving forces of construction land expansion are measured by Principal Component Analysis and Ordinary Least Square regression. The results show that: (1) there is a clear expansion pattern regarding the time sequence in provinces/cities of the Yangtze River economic belt, with rapid expansion in the initial stage, moderate expansion in the middle stage and rapid expansion in the later stage. (2) Spatial analysis demonstrates first expansion in the lower reaches in the early stage, rapid expansion of the upper reaches in the middle and later stage, and steady expansion of the middle reaches throughout the research period. (3)There are statistical significant correlations between construction land expansion and GDP, social fixed asset investments, population at the end of the year, population urbanization rate, per capita road area, and number of scientific and technological professionals as well as secondary and tertiary industry values. Of these factors, GDP, social fixed asset investments, population urbanization rate and second industry value are important common driving forces of construction land expansion in this region. The research findings have significant policy implications particularly on coordinated development of urban agglomerations and sustainable industry upgrading when construction land expansion is concerned.

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<![CDATA[Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008–2016)]]> https://www.researchpad.co/article/Nfe4e2064-ca0a-4d6d-a8b7-4f75eb296e9a

Introduction

In order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016.

Methods

Dengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman’s rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors.

Results

We found the minimum temperature suitable for dengue transmission was ≥18°C, and as 97.91% of cases occurred when the minimum temperature was above 18 °C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60.

Conclusion

Our model was accurate and timely, with consideration of interactive effects between meteorological factors.

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<![CDATA[A novel nonsense variant in SUPT20H gene associated with Rheumatoid Arthritis identified by Whole Exome Sequencing of multiplex families]]> https://www.researchpad.co/article/5c8acceed5eed0c48499036b

The triggering and development of Rheumatoid Arthritis (RA) is conditioned by environmental and genetic factors. Despite the identification of more than one hundred genetic variants associated with the disease, not all the cases can be explained. Here, we performed Whole Exome Sequencing in 9 multiplex families (N = 30) to identify rare variants susceptible to play a role in the disease pathogenesis. We pre-selected 77 genes which carried rare variants with a complete segregation with RA in the studied families. Follow-up linkage and association analyses with pVAAST highlighted significant RA association of 43 genes (p-value < 0.05 after 106 permutations) and pinpointed their most likely causal variant. We re-sequenced the 10 most significant likely causal variants (p-value ≤ 3.78*10−3 after 106 permutations) in the extended pedigrees and 9 additional multiplex families (N = 110). Only one SNV in SUPT20H: c.73A>T (p.Lys25*), presented a complete segregation with RA in an extended pedigree with early-onset cases. In summary, we identified in this study a new variant associated with RA in SUPT20H gene. This gene belongs to several biological pathways like macro-autophagy and monocyte/macrophage differentiation, which contribute to RA pathogenesis. In addition, these results showed that analyzing rare variants using a family-based approach is a strategy that allows to identify RA risk loci, even with a small dataset.

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<![CDATA[Validation of the Spanish-language Cardiff Anomalous Perception Scale]]> https://www.researchpad.co/article/5c897783d5eed0c4847d2ec2

The Cardiff Anomalous Perceptions Scale (CAPS) is a psychometric measure of hallucinatory experience. It has been widely used in English and used in initial studies in Spanish but a full validation study has not yet been published. We report a validation study of the Spanish-language CAPS, conducted in both Spain and Colombia to cover both European and Latin American Spanish. The Spanish-language version of the CAPS was produced through back translation with slight modifications made for local dialects. In Spain, 329 non-clinical participants completed the CAPS along with 40 patients with psychosis. In Colombia, 190 non-clinical participants completed the CAPS along with 21 patients with psychosis. Participants completed other psychometric scales measuring psychosis-like experience to additionally test convergent and divergent validity. The Spanish-language CAPS was found to have good internal reliability. Test-retest reliability was slightly below the cut-off, although could only be tested in the Spanish non-clinical sample. The scale showed solid construct validity and a principal components analysis broadly replicated previously reported three component factor structures for the CAPS.

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<![CDATA[Structure and variability of delay activity in premotor cortex]]> https://www.researchpad.co/article/5c990204d5eed0c484b9749c

Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an ‘initial condition’ which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial. We examined population-level responses in macaque premotor cortex (PMd) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations. We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach. Further, we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space. We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces (BMIs). We found that decoding of angle between reaches is dependent on reach distance, while decoding of arc-length is independent. Thus, it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them. Lastly, we show that in contrast to the common notion that direction can better be decoded than distance, their decoding capabilities are comparable. These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs.

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<![CDATA[Normalization of large-scale behavioural data collected from zebrafish]]> https://www.researchpad.co/article/5c706738d5eed0c4847c6c63

Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often complex in structure and are subjected to systematic biases, which require new approaches for analysis and normalization. This study addressed the normalization need by establishing an approach based on linear-regression modeling. The model was established using a dataset of visual motor response (VMR) obtained from several strains of wild-type (WT) zebrafish collected at multiple stages of development. The VMR is a locomotor response triggered by drastic light change, and is commonly measured repeatedly from multiple larvae arrayed in 96-well plates. This assay is subjected to several systematic variations. For example, the light emitted by the machine varies slightly from well to well. In addition to the light-intensity variation, biological replication also created batch-batch variation. These systematic variations may result in differences in the VMR and must be normalized. Our normalization approach explicitly modeled the effect of these systematic variations on VMR. It also normalized the activity profiles of different conditions to a common baseline. Our approach is versatile, as it can incorporate different normalization needs as separate factors. The versatility was demonstrated by an integrated normalization of three factors: light-intensity variation, batch-batch variation and baseline. After normalization, new biological insights were revealed from the data. For example, we found larvae of TL strain at 6 days post-fertilization (dpf) responded to light onset much stronger than the 9-dpf larvae, whereas previous analysis without normalization shows that their responses were relatively comparable. By removing systematic variations, our model-based normalization can facilitate downstream statistical comparisons and aid detecting true biological differences in high-throughput studies of neurobehaviour.

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<![CDATA[Rivaroxaban administration after acute ischemic stroke: The RELAXED study]]> https://www.researchpad.co/article/5c6dca1cd5eed0c48452a7cf

The efficacy of early anticoagulation in acute stroke with nonvalvular atrial fibrillation (NVAF) remains unclear. We performed a study to evaluate the risk of recurrent ischemic stroke (IS) and major bleeding in acute IS patients with NVAF who started rivaroxaban. This observational study evaluated patients with NVAF and acute IS/transient ischemic attack (TIA) in the middle cerebral arterial territory who started rivaroxaban within 30 days after the index IS/TIA. The primary endpoints were recurrent IS and major bleeding within 90 days after the index IS/TIA. The relationship between the endpoints and the time to start rivaroxaban was evaluated by correlation analysis using cerebral infarct volume, determined by diffusion-weighted magnetic resonance images within 48 hours of onset of the index IS/TIA. Of 1309 patients analyzed, recurrent IS occurred in 30 (2.3%) and major bleeding in 11 (0.8%) patients. Among patients with known infarct size (N = 1207), those with small (<4.0 cm3), medium (≥4.0 and <22.5 cm3), and large (≥22.5 cm3) infarcts started rivaroxaban a median of 2.9, 2.9, and 5.8 days, respectively, after the index IS/TIA. Recurrent IS was significantly less frequent when starting rivaroxaban ≤14 days versus ≥15 days after IS (2.0% versus 6.8%, P = 0.0034). Incidences of recurrent IS and major bleeding in whom rivaroxaban was started <3 days (N = 584) after IS were also low: 1.5% and 0.7%, respectively. Initiation of rivaroxaban administration in acute IS or TIA was associated with a low recurrence of IS (2.3%), and a low incidence of major bleeding events (0.8%) for 90 days after the index stroke. For the prevention of recurrent attacks in acute IS patients with NVAF, it is feasible to start the administration of rivaroxaban within 14 days of onset. Rivaroxaban started within 3 days of onset may be a feasible treatment option for patients with a small or medium-sized infarction.

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<![CDATA[Haralick texture feature analysis for quantifying radiation response heterogeneity in murine models observed using Raman spectroscopic mapping]]> https://www.researchpad.co/article/5c706766d5eed0c4847c6fbd

Tumour heterogeneity plays a large role in the response of tumour tissues to radiation therapy. Inherent biological, physical, and even dose deposition heterogeneity all play a role in the resultant observed response. We here implement the use of Haralick textural analysis to quantify the observed glycogen production response, as observed via Raman spectroscopic mapping, of tumours irradiated within a murine model. While an array of over 20 Haralick features have been proposed, we here concentrate on five of the most prominent features: homogeneity, local homogeneity, contrast, entropy, and correlation. We show that these Haralick features can be used to quantify the inherent heterogeneity of the Raman spectroscopic maps of tumour response to radiation. Furthermore, our results indicate that Haralick-calculated textural features show a statistically significant dose dependent variation in response heterogeneity, specifically, in glycogen production in tumours irradiated with clinically relevant doses of ionizing radiation. These results indicate that Haralick textural analysis provides a quantitative methodology for understanding the response of murine tumours to radiation therapy. Future work in this area can, for example, utilize the Haralick textural features for understanding the heterogeneity of radiation response as measured by biopsied patient tumour samples, which remains the standard of patient tumour investigation.

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<![CDATA[Facial cues to age perception using three-dimensional analysis]]> https://www.researchpad.co/article/5c6dc9add5eed0c484529fdc

To clarify cues for age perception, the three-dimensional head and face forms of Japanese women were analyzed. It is known that age-related transformations are mainly caused by changes in soft tissue during adulthood. A homologous polygon model was created by fitting template meshes to each study participant to obtain three-dimensional data for analyzing whole head and face forms. Using principal component analysis of the vertices coordinates of these models, 26 principal components were extracted (contribution ratios >0.5%), which accounted for more than 90% of the total variance. Among the principal components, five had a significant correlation with the perceived ages of the participants (p < 0.05). Transformations with these principal components in the age-related direction produced aged faces. Moreover, the older the perceived age, the larger the ratio of age-manifesting participants, namely participants who had one or more age-related principal component score greater than +1.0 σ in the age-related direction. Therefore, these five principal components were regarded as aging factors. A cluster analysis of the five aging factors revealed that all of the participants fell into one of four groups, meaning that specific combinations of factors could be used as cues for age perception in each group. These results suggest that Japanese women can be classified into four groups according to age-related transformations of soft tissue in the face.

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<![CDATA[The risk factors for diabetic peripheral neuropathy: A meta-analysis]]> https://www.researchpad.co/article/5c76fe3ad5eed0c484e5b744

Diabetic peripheral neuropathy (DPN), the most common chronic complication of diabetes, has become an important public health crisis worldwide. Given that DPN is extremely difficult to treat, determining its risk factors and controlling it at an early stage is critical to preventing its serious consequences and the burden of social disease. Current studies suggest that the risk factors for diabetic peripheral neuropathy are the duration of diabetes, age, glycosylated hemoglobin A1c (HbA1c), diabetic retinopathy (DR), smoking, and body mass Index (BMI). However, most of the aforementioned studies are cross-sectional, and the sample sizes are very limited, so the strength of causal reasoning is relatively low. The current study systematically evaluated DPN’s influencing factors in patients with type 2 diabetes using evidence-based medicine. Overall, 16 included studies (14 cross-sectional studies and 2 case-control studies including 12,116 cases) that conformed to the present criteria were included in the final analysis. The results suggested that the duration of diabetes (MD 2.5, 95% CI 1.71~3.29), age (MD 4.00, 95% CI 3.05~4.95), HbA1c (MD 0.48, 95% CI 0.33~0.64), and DR (OR 2.34, 95% CI 1.74~3.16) are associated with significantly increased risks of DPN among diabetic patients, while BMI, smoking, total triglyceride (TG), and total cholesterol (TC) did not indicate any risks of increasing DPN. The findings provide a scientific basis for a further understanding of the causes of type 2 diabetes complicated with peripheral neuropathy and the improvement of preventive strategies. The next step is to conduct further high-quality prospective cohort studies to validate this paper’s findings.

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<![CDATA[Physical assessment, spectroscopic and chemometric analysis of starch-based foils with selected functional additives]]> https://www.researchpad.co/article/5c6dc9cdd5eed0c48452a1f6

The paper presents the results of studies related to the impact of functional additives in the form of polylactide (PLA), polyvinyl alcohol (PVA), and keratin hydrolysate (K) on the physical characteristics of biopolymer foils. TPS granulate was obtained using a TS-45 single-screw extruder with L/D = 16. Foil was produced with the use of an L/D = 36 extruder with film-blowing section. The impact of the quantity and type of the functional additives on the processing efficiency and energy consumption of granulate extrusion, as well as the physical characteristics of the foil produced: thickness, basis weight, and colour were determined. By measuring the FTIR spectra it was determined the type and origin of the respective functional groups. It was observed that foils produced from granulates with the addition of 3% PVA were characterised by the lowest thickness and basis weight. Addition of 2 and 3% of PLA increased thickness and basis weight of starch-based foils significantly. Increasing the content of keratin in SG/K samples resulted in a decrease of brightness and intensify the yellow tint of foils, especially when 2 and 3% of keratin was used. In terms of the other samples, it was observed that the colour remained almost unchanged irrespective of the percentage content of the additive used. Infrared analyses conducted on foil containing PVA, PLA, and K revealed a change in spectra intensity in the frequency range associated with–OH groups originating from the forming free, intra- and intermolecular hydrogen bonds. Based on an analysis of the respective bands within the IR range it was also concluded that considerable structural changes took place with respect to the glycosidic bonds of starch itself. The application of the mentioned additives had a significant structural impact on the produced starch-based foils. Furthermore, the conducted UV-Vis analyses revealed a substantial increase in absorbance and a related reduction of the permeability (colour change) of the obtained materials in the range of ultraviolet and visible light.

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<![CDATA[Sensitivity analysis for reproducible candidate values of model parameters in signaling hub model]]> https://www.researchpad.co/article/5c6c75bbd5eed0c4843d00a1

Mathematical models for signaling pathways are helpful for understanding molecular mechanism in the pathways and predicting dynamic behavior of the signal activity. To analyze the robustness of such models, local sensitivity analysis has been implemented. However, such analysis primarily focuses on only a certain parameter set, even though diverse parameter sets that can recapitulate experiments may exist. In this study, we performed sensitivity analysis that investigates the features in a system considering the reproducible and multiple candidate values of the model parameters to experiments. The results showed that although different reproducible model parameter values have absolute differences with respect to sensitivity strengths, specific trends of some relative sensitivity strengths exist between reactions regardless of parameter values. It is suggested that (i) network structure considerably influences the relative sensitivity strength and (ii) one might be able to predict relative sensitivity strengths specified in the parameter sets employing only one of the reproducible parameter sets.

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<![CDATA[Risk factors for small bowel bleeding in an overt gastrointestinal bleeding presentation after negative upper and lower endoscopy]]> https://www.researchpad.co/article/5c76fe6dd5eed0c484e5ba31

Introduction

A small bowel source is suspected when evaluation of overt gastrointestinal (GI) bleeding with upper and lower endoscopy is negative. Video capsule endoscopy (VCE) is the recommended next diagnostic test for small bowel bleeding sources. However, clinical or endoscopic predictive factors for small bowel bleeding in the setting of an overt bleeding presentation are unknown. We aimed to define predictive factors for positive VCE among individuals presenting with overt bleeding and a suspected small bowel source.

Methods

We included consecutive inpatient VCE performed between September 1, 2012 to September 1, 2015 for melena or hematochezia at two tertiary centers. All patients had EGD and colonoscopy performed prior to VCE. Patient demographics, medication use, and endoscopic findings were retrospectively recorded. VCE findings were graded based on the P0-P2 grading system. The primary outcome of interest was a positive (P2) VCE. The secondary outcome of interest was the performance of a therapeutic intervention. Data were analyzed with the Fisher exact test for dichotomous variables and logistic regression.

Results

Two hundred forty-three VCE were reviewed, and 117 were included in the final analysis. A positive VCE (P2) was identified in 35 (29.9%) cases. In univariate analysis, a positive VCE was inversely associated with presence of diverticula on preceding colonoscopy (OR: 0.44, 95% CI: 0.2–0.99), while identification of blood on terminal ileal examination was associated with a positive VCE (OR: 5.18, 95% CI: 1.51–17.76). In multivariate analysis, only blood identified on terminal ileal examination remained a significant risk factor for positive VCE (OR: 6.13, 95% CI: 1.57–23.81). Blood on terminal ileal examination was also predictive of therapeutic intervention in both univariate (OR: 4.46, 95% CI: 1.3–15.2) and multivariate analysis (OR: 5.04, 95% CI: 1.25–20.32).

Conclusion

Among patients presenting with overt bleeding but negative upper and lower endoscopy, the presence of blood on examination of the terminal ileum is strongly associated with a small bowel bleeding source as well as with small bowel therapeutic intervention. Presence of diverticula on colonoscopy is inversely associated with a positive VCE and therapeutic intervention in univariate analysis.

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<![CDATA[Building geochemically based quantitative analogies from soil classification systems using different compositional datasets]]> https://www.researchpad.co/article/5c75abe2d5eed0c484d07e1f

Soil heterogeneity is a major contributor to the uncertainty in near-surface biogeochemical modeling. We sought to overcome this limitation by exploring the development of a new classification analogy concept for transcribing the largely qualitative criteria in the pedomorphologically based, soil taxonomic classification systems to quantitative physicochemical descriptions. We collected soil horizons classified under the Alfisols taxonomic Order in the U.S. National Resource Conservation Service (NRCS) soil classification system and quantified their properties via physical and chemical characterizations. Using multivariate statistical modeling modified for compositional data analysis (CoDA), we developed quantitative analogies by partitioning the characterization data up into three different compositions: Water-extracted (WE), Mehlich-III extracted (ME), and particle-size distribution (PSD) compositions. Afterwards, statistical tests were performed to determine the level of discrimination at different taxonomic and location-specific designations. The analogies showed different abilities to discriminate among the samples. Overall, analogies made up from the WE composition more accurately classified the samples than the other compositions, particularly at the Great Group and thermal regime designations. This work points to the potential to quantitatively discriminate taxonomically different soil types characterized by varying compositional datasets.

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<![CDATA[Heat stress modifies the lactational performances and the urinary metabolomic profile related to gastrointestinal microbiota of dairy goats]]> https://www.researchpad.co/article/5c6730b8d5eed0c484f37f98

The aim of the study is to identify the candidate biomarkers of heat stress (HS) in the urine of lactating dairy goats through the application of proton Nuclear Magnetic Resonance (1H NMR)-based metabolomic analysis. Dairy does (n = 16) in mid-lactation were submitted to thermal neutral (TN; indoors; 15 to 20°C; 40 to 45% humidity) or HS (climatic chamber; 37°C day, 30°C night; 40% humidity) conditions according to a crossover design (2 periods of 21 days). Thermophysiological traits and lactational performances were recorded and milk composition analyzed during each period. Urine samples were collected at day 15 of each period for 1H NMR spectroscopy analysis. Principal component analysis (PCA) and partial least square—discriminant analysis (PLS-DA) assessment with cross validation were used to identify the goat urinary metabolome from the Human Metabolome Data Base. HS increased rectal temperature (1.2°C), respiratory rate (3.5-fold) and water intake (74%), but decreased feed intake (35%) and body weight (5%) of the lactating does. No differences were detected in milk yield, but HS decreased the milk contents of fat (9%), protein (16%) and lactose (5%). Metabolomics allowed separating TN and HS urinary clusters by PLS-DA. Most discriminating metabolites were hippurate and other phenylalanine (Phe) derivative compounds, which increased in HS vs. TN does. The greater excretion of these gut-derived toxic compounds indicated that HS induced a harmful gastrointestinal microbiota overgrowth, which should have sequestered aromatic amino acids for their metabolism and decreased the synthesis of neurotransmitters and thyroid hormones, with a negative impact on milk yield and composition. In conclusion, HS markedly changed the thermophysiological traits and lactational performances of dairy goats, which were translated into their urinary metabolomic profile through the presence of gut-derived toxic compounds. Hippurate and other Phe-derivative compounds are suggested as urinary biomarkers to detect heat-stressed dairy animals in practice.

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<![CDATA[Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning]]> https://www.researchpad.co/article/5c5ca307d5eed0c48441f020

Background

Exhaled aerosols from lungs have unique patterns, and their variation can be correlated to the underlying lung structure and associated abnormities. However, it is challenging to characterize such aerosol patterns and differentiate their difference because of their complexity. This challenge is even greater for small airway diseases, where the disturbance signals are weak.

Objectives and methods

The objective of this study is exploiting different feature extraction algorithms to develop a practical classifier to diagnose obstructive lung diseases using exhaled aerosol images. These include proper orthogonal decomposition (POD), principal component analysis (PCA), dynamic mode decomposition (DMD), and DMD with control (DMDC). Aerosol images were generated via physiology-based simulations in one normal and four diseased airway models in G7-9 bronchioles. The image data were classified using both the support vector machine (SVM) and random forest (RF) algorithms. The effectiveness of different features was evaluated by classification accuracy and misclassification rate.

Findings

Results show a significantly higher performance using dynamic feature extractions (DMD and DMDC) than static algorithms (POD and PCA). Adding the control variables to DMD further improved classification accuracy. Comparing the classification methods, RF persistently outperformed SVM for all types of features considered. While the performance of RF constantly increased with the number of features retained, the performance of SVM peaked at 50 and decreased thereafter. The 5-class classification accuracy was 94.8% using the DMDC-RF model and 93.0% using the DMD-RF model, both of which were higher than 87.0% in the previous study that used fractal dimension features.

Conclusion

Considering that disease progression is inherently a dynamic process, DMD(C)-based feature extraction preserves temporal information and is preferred over POD and PCA. Compared with hand-crafted features like fractals, feature extraction by DMD and DMDC is automatic and more accurate.

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<![CDATA[Predicting neurological recovery with Canonical Autocorrelation Embeddings]]> https://www.researchpad.co/article/5c58d654d5eed0c484031bb6

Early prediction of the potential for neurological recovery after resuscitation from cardiac arrest is difficult but important. Currently, no clinical finding or combination of findings are sufficient to accurately predict or preclude favorable recovery of comatose patients in the first 24 to 48 hours after resuscitation. Thus, life-sustaining therapy is often continued for several days in patients whose irrecoverable injury is not yet recognized. Conversely, early withdrawal of life-sustaining therapy increases mortality among patients who otherwise might have gone on to recover. In this work, we present Canonical Autocorrelation Analysis (CAA) and Canonical Autocorrelation Embeddings (CAE), novel methods suitable for identifying complex patterns in high-resolution multivariate data often collected in highly monitored clinical environments such as intensive care units. CAE embeds sets of datapoints onto a space that characterizes their latent correlation structures and allows direct comparison of these structures through the use of a distance metric. The methodology may be particularly suitable when the unit of analysis is not just an individual datapoint but a dataset, as for instance in patients for whom physiological measures are recorded over time, and where changes of correlation patterns in these datasets are informative for the task at hand.

We present a proof of concept to illustrate the potential utility of CAE by applying it to characterize electroencephalographic recordings from 80 comatose survivors of cardiac arrest, aiming to identify patients who will survive to hospital discharge with favorable functional recovery. Our results show that with very low probability of making a Type 1 error, we are able to identify 32.5% of patients who are likely to have a good neurological outcome, some of whom have otherwise unfavorable clinical characteristics. Importantly, some of these had 5% predicted chance of favorable recovery based on initial illness severity measures alone. Providing this information to support clinical decision-making could motivate the continuation of life-sustaining therapies for these patients.

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