Results Of Standard Tests Research Articles

Low-cost system for multispectral image acquisition and its applicability to analysis of the physiological potential of soybean seeds

The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds.

Damage resistance and open-hole strength of thin veneer laminates: Adopting design and testing principles from fibre-reinforced polymers

Bulky, thick solid wooden parts are typically used despite the fact that such cross-sections do not yield the optimal load transfer. This study investigates thin quasi-isotropic [90/45/0/−45]s Beech veneer laminates subjected to a range of mechanical tests derived from fibre-reinforced counterparts. The results of standard tension, open-hole tension and compact tension tests in combination with digital image correlation and cross-sectional microscopy reveal that Beech veneer laminates can be tested and analysed in the same manner as fibre-reinforced laminates, leading to consistent values of strength and damage resistance as a result of reliable and repeatable damage patterns. It is found that modulus (5.5 GPa), tensile strength (70 MPa) and open-hole strength as well as translaminar fracture energy (5 – 14 kJ/m2) are one order of magnitude lower compared to fibre-reinforced polymer composites.

Specific refraction-index increments of oxygenated hemoglobin from thalassemia-minor patients are not significantly different than those from healthy individuals.

The mass and concentration of hemoglobin per erythrocyte are important hematological parameters. Measuring these parameters from intact erythrocytes requires the value of specific refraction-index increment (RII) of oxygenated hemoglobin, which diverges in the literature. Refractive indices of hemoglobin solutions are measured directly by digital holographic microscopy on a microfluidic channel filled with hemoglobin solutions prepared by hemolysis of fresh human erythrocytes and refractive-index standards sequentially. Hemoglobin extracted from thalassemic patients shows 3-4% higher RII than that from healthy volunteers, but the difference is not significant in comparison to inter-subject variations within each group. The quantified RIIs are applied to quantify mean corpuscular hemoglobin mass of blood from 37 human subjects, and results are in accord with standard clinical test results.

A population-wide gene-environment interaction study on how genes, schools, and residential areas shape achievement

A child’s environment is thought to be composed of different levels that interact with their individual genetic propensities. However, studies have not tested this theory comprehensively across multiple environmental levels. Here, we quantify the contributions of child, parent, school, neighbourhood, district, and municipality factors to achievement, and investigate interactions between polygenic indices for educational attainment (EA-PGI) and environmental levels. We link population-wide administrative data on children’s standardised test results, schools and residential identifiers to the Norwegian Mother, Father, and Child Cohort Study (MoBa), which includes >23,000 genotyped parent-child trios. We test for gene-environment interactions using multilevel models with interactions between EA-PGI and random effects for school and residential environments (thus remaining agnostic to specific features of environments). We use parent EA-PGI to control for gene-environment correlation. We found an interaction between students’ EA-PGI and schools suggesting compensation: higher-performing schools can raise overall achievement without leaving children with lower EA-PGI behind. Differences between schools matter more for students with lower EA-PGI, explaining 4 versus 2% of the variance in achievement for students 2 SD below versus 2 SD above the mean EA-PGI. Neighbourhood, district, and municipality variation contribute little to achievement (<2% of the variance collectively), and do not interact with children’s individual EA-PGI. Policy to reduce social inequality in achievement in Norway should focus on tackling unequal support across schools for children with difficulties.

Serum Sirtuin-1, HMGB1-TLR4, NF-KB and IL-6 Levels in Alzheimer's: The Relation Between Neuroinflammatory Pathway and Severity of Dementia.

In this study, serum Sirtuin-1(SIRT-1), High Mobility Group Box 1 (HMGB1), Toll-Like Receptor-4 (TLR4), Nuclear Factor Kappa B (NF-kB), Interleukin-6 (IL-6), Amyloid βeta-42 (Aβ- 42), and p-tau181 levels in patients diagnosed with AD according to NINCS-ADRA criteria were studied. We investigated the inflammatory pathways that lead to progressive neuronal loss and highlight their possible relationship with dementia severity in the systemic circulation. Patients over 60 years of age were grouped according to their Standard Mini Mental Test results, MRI, and/or Fludeoxyglucose positron emission tomography or according to their CT findings as Control n:20; AD n:32; Vascular Dementia (VD) n:17; AD + VD; n = 21. Complete blood count, Glucose, Vitamin B12, Folic Acid, Enzymes, Urea, Creatinine, Electrolytes, Bilirubin, and Thyroid Function tests were evaluated. ELISA was used for the analysis of serum SIRT1, HMGB1, TLR4, NF-kB, IL-6, Aβ-42, and p-tau181 levels. Levels of serum Aβ-42, SIRT1, HMGB1, and IL-6 were significantly higher (p< 0.001, p< 0.01, p< 0.001, and p< 0.001, respectively), and TLR4 levels were significantly lower (p< 0.001) in the dementia group than in the control group. No significant difference was observed between dementia and control groups for serum NF-kB and p-tau181 levels. Our results show that the levels of the Aβ42, SIRT 1, HMGB1, and TLR4 pathways are altered in AD and VD. SIRT 1 activity plays an important role in the inflammatory pathway of dementia development, particularly in AD.

Finite Element Analysis of Hybrid Fiber Reinforced Engineered Cementitious Composite Link Slabs

Finite element (FE) analysis of hybrid fiber reinforced engineered cementitious composite (hybrid-ECC) link slabs is conducted under static loading. First, the material model of hybrid-ECC is developed and validated with standard material test results. Numerical analyses are then conductedto investigate the materials’ effects on the structural performances of link slabs built by using hybrid-ECCs with different volume fractions of polyethylene (PE) and steel (ST) fibers. The FE model of the link slab is validated against test results. Analysis results suggest the hybrid-ECC with volume fractions of 1.25% PE and 0.75% ST fiber is suitable for link slab applications.

Integrated Genotoxicity Testing of three anti-infective drugs using the TGx-DDI transcriptomic biomarker and high-throughput CometChip® assay in TK6 cells.

Genotoxicity testing relies on the detection of gene mutations and chromosome damage and has been used in the genetic safety assessment of drugs and chemicals for decades. However, the results of standard genotoxicity tests are often difficult to interpret due to lack of mode of action information. The TGx-DDI transcriptomic biomarker provides mechanistic information on the DNA damage-inducing (DDI) capability of chemicals to aid in the interpretation of positive in vitro genotoxicity data. The CometChip® assay was developed to assess DNA strand breaks in a higher-throughput format. We paired the TGx-DDI biomarker with the CometChip® assay in TK6 cells to evaluate three model agents: nitrofurantoin (NIT), metronidazole (MTZ), and novobiocin (NOV). TGx-DDI was analyzed by two independent labs and technologies (nCounter® and TempO-Seq®). Although these anti-infective drugs are, or have been, used in human and/or veterinary medicine, the standard genotoxicity testing battery showed significant genetic safety findings. Specifically, NIT is a mutagen and causes chromosome damage, and MTZ and NOV cause chromosome damage in conventional in vitro tests. Herein, the TGx-DDI biomarker classified NIT and MTZ as non-DDI at all concentrations tested, suggesting that NIT’s mutagenic activity is bacterial specific and that the observed chromosome damage by MTZ might be a consequence of in vitro test conditions. In contrast, NOV was classified as DDI at the second highest concentration tested, which is in line with the fact that NOV is a bacterial DNA-gyrase inhibitor that also affects topoisomerase II at high concentrations. The lack of DNA damage for NIT and MTZ was confirmed by the CometChip® results, which were negative for all three drugs except at overtly cytotoxic concentrations. This case study demonstrates the utility of combining the TGx-DDI biomarker and CometChip® to resolve conflicting genotoxicity data and provides further validation to support the reproducibility of the biomarker.

Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties.

This study presents a novel method for predicting the undrained shear strength (cu) using artificial intelligence technology. The cu value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The group method of data handling (GMDH)-type neural network (NN) was utilized for the prediction of cu. The GMDH-type NN models were designed with various combinations of input parameters. In the prediction, the effective stress (σv’), standard penetration test result (NSPT), liquid limit (LL), plastic limit (PL), and plasticity index (PI) were used as input parameters in the design of the prediction models. In addition, the GMDH-type NN models were compared with the most commonly used method (i.e., linear regression) and other regression models such as random forest (RF) and support vector regression (SVR) models as comparative methods. In order to evaluate each model, the correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) were calculated for different input parameter combinations. The most effective model, the GMDH-type NN with input parameters (e.g., σv’, NSPT, LL, PL, PI), had a higher correlation coefficient (R2 = 0.83) and lower error rates (MAE = 14.64 and RMSE = 22.74) than other methods used in the prediction of cu value. Furthermore, the impact of input variables on the model output was investigated using the SHAP (SHApley Additive ExPlanations) technique based on the extreme gradient boosting (XGBoost) ensemble learning algorithm. The results demonstrated that using the GMDH-type NN is an efficient method in obtaining a new empirical mathematical model to provide a reliable prediction of the undrained shear strength of soils.

Chinese (Mandarin) translation of the incremental shuttle walk test and its validity and reliability: A cross-sectional study.

To date, there are no published validated Chinese versions of the incremental shuttle walk test (ISWT) instructions despite its wide clinical applications. Translation of the Chinese ISWT instruction is done in an ad-hoc manner within the Chinese-speaking populations, affecting the test's reliability and validity since translation can differ significantly between individuals. This warrants the need for psychometric testing of such translation. To develop a Chinese (Mandarin) version of the ISWT instructions (ISWT-CHN) that is conceptually equivalent to the original English version (ISWT-ENG) and establish its reliability and validity. Forward and backward translations from the ISWT-ENG were done to generate the ISWT-CHN. Face and content validity was determined during the translation process. Intra-rater and inter-rater reliability of the ISWT-CHN, construct and criterion validity were established by analysing the ISWT and the gold standard cardiopulmonary exercise test results. The Item-Content validity index (I-CVI), Scale-level-Content validity index (S-CVI), and content validity ratio (CVR) of the ISWT-CHN were 1.0. Intra-class Correlation Coefficient (ICC) for inter-rater reliability between two raters were excellent (, 95% CI 0.97-1.0, ; m, m). The intra-rater reliability of both Raters A (, 95% CI 0.53-0.98, ; m, m) and B (, 95% CI 0.76-0.96, ; m, m) were good. In a sample of 32 healthy participants, both ISWT-CHN and ISWT-ENG instruction results showed low-positive correlations with the VO determined from the cardiopulmonary exercise test (r = 0.439, ; , ). There is a very high correlation between ISWT-ENG and ISWT-CHN results with no statistically significant differences (, ). The construct and criterion validity of the ISWT-CHN were established. This study developed the ISWT-CHN and showed that it is a valid and reliable measure conceptually comparable to the ISWT-ENG. It will benefit the determination of functional exercise capacity in Chinese-speaking populations. •This study is aimed to develop a Chinese (Mandarin) version of the ISWT instructions.•The ISWT Chinese translation is valid and reliable that is conceptually comparable to the original English instruction.•The translated ISWT-Chinese instruction will enable the use of ISWT among the Chinese-speaking populations.

Maximizing information obtained from standardized fire tests using a Bayesian framework

Current practice is mostly focused on prescriptive design approaches where the performance of the structure in case of fire is assessed based on its performance in standardized fire tests. Those tests indicate whether the structural member can withstand standardized ISO 834 fire exposure for a certain code specified time. This method however does not provide an explicit safety level. This issue is enlarged even more by the fact that the ISO 834 fire exposure does not represent a natural fire exposure, but a pseudo-worst-case exposure, making the correlation between the standardized fire test results and real-life behaviour of structural members exposed to fire questionable. However, there has been a century-old tradition of standardized fire tests with a lot of experience and infrastructure based on it. For that reason, here, a methodology is presented to obtain more information on the behaviour of structural members exposed to a natural fire from the standardized fire test results by using a Bayesian framework. As an example structure, a simply supported concrete slab is considered. Its failure during the standardized fire test is modelled and the parameters affecting the time when it fails (i.e., parameters affecting the nominal fire resistance time) are determined. The model is then used in a Markov chain Monte Carlo procedure to update parameter distributions based on the measured fire resistance time. Using these updated distributions, a full probabilistic calculation of the performance of the slab considering a natural fire exposure is then conducted to assess the failure probability.

Sulfide Variation in the Coeur Rochester Silver Deposit: Use of Geologic Block Modeling in the Prediction and Management of Mine Waste

Abstract A critical issue in mine waste management is the management and mitigation of acid rock drainage and metal leaching particularly from waste rock. This requires good understanding and characterization of materials most likely to generate acid pH waters and generate leachable metals and metalloids, as well as the presence of minerals most likely to neutralize acidic pH such as carbonates and certain silicates. For low-sulfidation epithermal deposits in silica-rich volcanic rocks, neutralization minerals are rare. Consequently, determination of acid generation is almost entirely a function of sulfide content of the rocks. By understanding the distribution of sulfide minerals within such a deposit, an initial mine waste management plan can be developed. The Rochester mine currently exploits a low-sulfidation epithermal gold-silver deposit and is located on the southern flank of the Humboldt Range in west-central Nevada. The Rochester mine is a conventional open-pit mining operation with cyanide heap leaching and Merrill Crowe processing. Silver and lesser gold mineralization occur associated with sulfide minerals hosted in Permian-Triassic Koipato Group rhyolitic units with extensive quartz-sericite-pyrite alteration. Gangue mineralogy of the deposit is dominated by slow or unreactive silicate minerals with little or no carbonate. Silver-bearing mineralization was controlled by primary hypogene sulfides and secondary supergene oxidation processes, and most of the mineralized material mined to date has been fully or partially oxidized. Oxidation mobilizes and depletes all forms of sulfur from primary sulfide minerals. Due to the low sulfate content and low content of neutralizing gangue phases, the waste rock classification can be based on total sulfur content. Based on the analysis of pulp samples collected from within the mine area, a sulfur block model was developed and compared to the results of standard acid-base accounting and net acid generation tests. This comparison demonstrates the benefit of the block model in identifying zones of potentially acid-generating material in the mined waste. Application of this information to mine planning has allowed more efficient management of acid-generating mine waste by allowing the operator to develop areas of the waste rock facility to receive the waste in a timely fashion. Such an approach is based on good mine geologic and mineralogical characterization and the use of a representative and comprehensive geologic block model.

The Evaluation of Functional Abilities Using the Modified Fullerton Functional Fitness Test Is a Valuable Accessory in Diagnosing Men with Heart Failure

The assessment of functional abilities reflects the ability to perform everyday life activities that require specific endurance and physical fitness. The Fullerton functional fitness test (FFFT) seems to be the most appropriate for assessing physical fitness in heart failure (HF) patients. The study group consisted of 30 consecutive patients hospitalized for the routine assessment of HF with a reduced ejection fraction (HFrEF). They formed the study group, and 24 healthy subjects formed the control group. Each patient underwent a cardiopulmonary exercise test (CPET), transthoracic echocardiography and FFFT modified by adding the measurement of the handgrip force of the dominant limb with the digital dynamometer. The HF patients had significantly lower peak oxygen uptake (peakVO2), maximal minute ventilation, and higher ventilatory equivalent (VE/VCO2). The concentrations of B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) were significantly higher in the study group. The results of all the FFFT items were significantly worse in the study group. FFFT parameters, together with the assessment of the strength of the handgrip, strongly correlated with the results of standard tests in HF. FFFT is an effective and safe tool for the functional evaluation of patients with HFrEF. Simple muscle strength measurement with a hand-held dynamometer can become a convenient and practical indicator of muscle strength in HF patients.

Lateral response evaluation of single piles based on pressuremeter test results (using Foxta) versus standard penetration test results (using Lpile)

Lateral response evaluation of single piles based on pressuremeter test results (using Foxta) versus standard penetration test results (using Lpile)

Inaccurate recording of routinely collected data items influences identification of COVID-19 patients

BackgroundDuring the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients. MethodsThe following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled ‘COVID-19′ if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled ‘non-COVID-19′ if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F1 score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences. ResultsThe total dataset consisted of 402 patients: 196 ‘COVID-19′ and 206 ‘non-COVID-19′ patients. F1 scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F1 scores ranged between 0.73 and 0.99. F1 scores showed no clear pattern in variability between the two time periods. ConclusionsOur study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse.

The Study of Optimal Performance Improvement Method for Aircraft of Various Variants within the Same Type

In this paper, we studied the optimal method of improving performance for aircraft having various variants within the same type. The study defined configuration of an entire fleet of aircraft being subject to a performance improvement program. And selected the most complicated aircraft configuration among them as a Standard Aircraft for modification by according to the proposed Aircraft Selection Process for developing an optimal Aircraft Performance Improvement Process. Based on the selected the Standard Aircraft, drew a system integration design result and carried out Evaluation Test and obtained Airworthiness Certification. Created the database with the design data of the Standard Aircraft, Evaluation Test, and Airworthiness Certification results, and applied it to variants of aircraft to complete the performance improvement program with optimized schedules and costs. By applying the proposed method to IFF performance improvement program, drew optimal system integration design and completed the program with minimized schedule.

Global System for Atmospheric Modeling: Model Description and Preliminary Results

AbstractThe extension of a cloud‐resolving model, the System for Atmospheric Modeling (SAM), to global domains is described. The resulting global model, gSAM, is formulated on a latitude‐longitude grid. It uses an anelastic dynamical core with a single reference profile (as in SAM), but its governing equations differ somewhat from other anelastic models. For quasihydrostatic flows, they are isomorphic to the primitive equations (PE) in pressure coordinates but with the globally uniform reference pressure playing the role of actual pressure. As a result, gSAM can exactly maintain steady zonally symmetric baroclinic flows that have been specified in pressure coordinates, produces accurate simulations when initialized or nudged with global reanalyses, and has a natural energy conservation equation despite the drawbacks of using the anelastic system to model global scales. gSAM employs a novel treatment of topography using a type of immersed boundary method, the Quasi‐Solid Body Method, where the instantaneous flow velocity is forced to stagnate in grid cells inside a prescribed terrain. The results of several standard tests designed to evaluate the accuracy of global models with and without topography as well as results from real Earth simulations are presented.

Liquefaction assessment of gravelly soils: the role of in situ and laboratory geotechnical tests through the case study of the Sulmona basin (Central Italy)

Even though liquefaction in gravelly soil is well documented in many earthquakes since 1891, most of the “simplified procedures” and national buildings codes still consider only sandy soil liquefaction in seismic hazard assessment. In this study, 109 sites of gravel liquefaction related to 27 historical earthquakes from 1891 to 2020 are reported, with a wide range of moment magnitudes, Mw , (5.3 to 9.2) and focal depths (5.4 to 33 km), highlighting the potential for liquefaction of gravelly soils even during moderate earthquakes. Although gravels are often thought to have hydraulic conductivities high enough to preclude liquefaction, gravels that have liquefied are generally well-graded sandy gravels. The sand content is typically 30% or more so that the hydraulic conductivity is governed by the sand size making them clearly liquefiable. Even for gravels with lower sand contents, a low permeability surface layer has often been observed that could restrict drainage and produce excess pore pressure during strong shaking.The epicentral distance calculated for gravel liquefaction sites plotted vs the magnitude of the earthquake event shows a pattern which closely follows similar curves provided in the literature for sandy soils. However, field observations of liquefaction in gravelly soils are less frequent in the historical record.In addition to examining gravel liquefaction sites in general, this paper provides a case history illustrating the difficulties of liquefaction assessment in gravels at a site in Santa Rufina (Sulmona basin, Central Italy). This site, characterised by high vertical and lateral stratigraphic variability, was selected for gravel liquefaction assessment using a combination of in-situ tests, laboratory geotechnical analysis, and geological studies. We found that, even if SPT- and DPT- based in-situ methods provide conflicting results, the availability of a borehole log, along with standard laboratory test results, proved to be fundamental to achieving a reliable assessment of the liquefaction hazard.

Liquefaction assessment of silty sands: Experimental characterization and numerical calibration

The main goal of this paper is to assess the reliability of a nonlinear hysteretic model to capture the behavior of saturated silty sand mixtures under cyclic loading conditions. The parameters governing the liquefaction triggering are identified using the results of standard laboratory tests (monotonic and cyclic consolidated undrained triaxial tests as well as resonant column tests) undertaken for reconstituted soil samples. Consequently, the uncertainty related with collected samples is reduced. The numerical results, obtained after calibration of the constitutive model, are in good agreement with the experimental ones and they are analyzed through the effects of relative density, fine content, and confining pressure in the liquefaction resistance of silty sands.

Detecting autism from picture book narratives using deep neural utterance embeddings.

Deficits in the ability to use language in social contexts, including storytelling skills, are observed across the autism spectrum. Development in machine-learning approaches may contribute to clinical psychology and psychiatry, given its potential to support decisions concerning the diagnosis and treatment of psychiatric conditions and disorders. To evaluate the usefulness of deep neural networks for detecting autism spectrum disorder (ASD) from textual utterances, specifically from narrations produced by individuals with ASD. We examined two text encoders: Embeddings from Language Models (ELMo) and Universal Sentence Encoder (USE), and three classification algorithms: XGBoost, support vector machines, and dense neural network layer. We aimed to classify 25 participants with ASD and 25 participants with typical development (TD) based on their narrations produced during the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) picture book task. The results of computational approaches were compared with the results of standardized testing and classifications made by two psychiatrists (raters). The raters were asked to read utterances produced by a participant (without an examiner's statements and additional information) and assign a participant to one of the two groups: ASD or with typical development (TD). The computer-based models had higher sensitivity, specificity, positive predictive values and negative predictive values than the raters, and lower than the two standardized instruments: ADOS-2 and Social Communication Questionnaire (SCQ). Our findings lay the groundwork for future studies involving deep neural network-based text representation models as tools for augmenting the ASD diagnosis or screening. Both ELMo and USE text encoders provided promising specificities, sensitivities, positive predictive values and negative predictive values. Our results indicate the usefulness of page-level embeddings for utterance representation in ADOS-2 picture book task. What is already known on this subject Deficits in the use of language in social contexts, and narrative ability in particular, are observed across the autism spectrum. Most research on narrative skills has applied hand-coding methods. Hitherto, machine-learning methods were used mostly for image recognition problems and data from screening questionnaires for ASD classification. Detection of mental and developmental disorders from textual input is an emerging field for machine and deep-learning methods. What this paper adds to existing knowledge This study explored the ability of several types of deep neural network-based text representation models to detect ASD. Both ELMo and USE provided the most promising values of specificity, sensitivity, positive predictive values and negative predictive values. What are the potential or actual clinical implications of this work? Competitive accuracy, repeatability, speed and ease of operation are all advantages of computerized methods. They allow for objective and quantitative assessment of narrative ability and complex language skills. Deep neural network-based text representation models could in the future support clinicians and augment the decision-making process related to ASD diagnosis, screening and intervention planning.

Construction of an Assisted Model Based on Natural Language Processing for Automatic Early Diagnosis of Autoimmune Encephalitis.

IntroductionEarly diagnosis and etiological treatment can effectively improve the prognosis of patients with autoimmune encephalitis (AE). However, anti-neuronal antibody tests which provide the definitive diagnosis require time and are not always abnormal. By using natural language processing (NLP) technology, our study proposes an assisted diagnostic method for early clinical diagnosis of AE and compares its sensitivity with that of previously established criteria.MethodsOur model is based on the text classification model trained by the history of present illness (HPI) in electronic medical records (EMRs) that present a definite pathological diagnosis of AE or infectious encephalitis (IE). The definitive diagnosis of IE was based on the results of traditional etiological examinations. The definitive diagnosis of AE was based on the results of neuronal antibodies, and the diagnostic criteria of definite autoimmune limbic encephalitis proposed by Graus et al. used as the reference standard for antibody-negative AE. First, we automatically recognized and extracted symptoms for all HPI texts in EMRs by training a dataset of 552 cases. Second, four text classification models trained by a dataset of 199 cases were established for differential diagnosis of AE and IE based on a post-structuring text dataset of every HPI, which was completed using symptoms in English language after the process of normalization of synonyms. The optimal model was identified by evaluating and comparing the performance of the four models. Finally, combined with three typical symptoms and the results of standard paraclinical tests such as cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), or electroencephalogram (EEG) proposed from Graus criteria, an assisted early diagnostic model for AE was established on the basis of the text classification model with the best performance.ResultsThe comparison results for the four models applied to the independent testing dataset showed the naïve Bayesian classifier with bag of words achieved the best performance, with an area under the receiver operating characteristic curve of 0.85, accuracy of 84.5% (95% confidence interval [CI] 74.0–92.0%), sensitivity of 86.7% (95% CI 69.3–96.2%), and specificity of 82.9% (95% CI 67.9–92.8%), respectively. Compared with the diagnostic criteria proposed previously, the early diagnostic sensitivity for possible AE using the assisted diagnostic model based on the independent testing dataset was improved from 73.3% (95% CI 54.1–87.7%) to 86.7% (95% CI 69.3–96.2%).ConclusionsThe assisted diagnostic model could effectively increase the early diagnostic sensitivity for AE compared to previous diagnostic criteria, assist physicians in establishing the diagnosis of AE automatically after inputting the HPI and the results of standard paraclinical tests according to their narrative habits for describing symptoms, avoiding misdiagnosis and allowing for prompt initiation of specific treatment.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40120-022-00355-7.