Extreme Deviations Research Articles

Double pass solar air heater with aerofoil and bio-inspired fins: A numerical analysis of thermohydraulic performance

In the present study, a numerical flow analysis is performed on a double pass solar air heater (DPSAH) with a bottom plate surface roughened using aerofoil fins to study its thermohydraulic performance. The experimentally validated DPSAH model was studied considering factors like different fin shapes, diurnal variation of solar radiation, absorber plate material, and the flow conditions at different relative height ratios (h/H) between 0.17 and 0.50 and fin length ratio (l/H) of 0.29. Using the RNG k-ε turbulence model, the relative Nusselt number (Nu/Nus), relative friction factor (f/fs), and thermohydraulic performance factor (THPF) were found for Reynolds number between 3000 and 21000 with an increment of 3000. The maximum enhancements of 4.23 and 2.51 times were achieved in the Nu/Nus and THPF values, respectively, for a Re value of 3000 in a 2-row setup with h/H of 0.50. The DPSAH with bio-inspired fins performed better than optimized aerofoil fins for majority of the tested range even with similar increase in effective heat transfer area. The values predicted using correlations developed in this study and numerically obtained values of the Nusselt number and friction factor matched closely with an extreme deviation of 5 % in f values.

Normative Modeling of Thalamic Nuclear Volumes and Characterization of Lateralized Volume Alterations in Alzheimer’s Disease Versus Schizophrenia

BackgroundThalamic nuclei facilitate a wide range of complex behaviors, emotions, and cognition and have been implicated in neuropsychiatric disorders including Alzheimer’s disease (AD) and schizophrenia (SCZ). The aim of this work was to establish novel normative models of thalamic nuclear volumes and their laterality indices and investigate their changes in SCZ and AD. MethodsVolumes of bilateral whole thalami and 10 thalamic nuclei were generated from T1 magnetic resonance imaging data using a state-of-the-art novel segmentation method in healthy control participants (n = 2374) and participants with early mild cognitive impairment (n = 211), late mild cognitive impairment (n = 113), AD (n = 88), and SCZ (n = 168). Normative models for each nucleus were generated from healthy control participants while controlling for sex, intracranial volume, and site. Extreme z-score deviations (|z| > 1.96) and z-score distributions were compared across phenotypes. z Scores were associated with clinical descriptors. ResultsIncreased infranormal and decreased supranormal z scores were observed in SCZ and AD. z Score shifts representing reduced volumes were observed in most nuclei in SCZ and AD, with strong overlap in the bilateral pulvinar, medial dorsal, and centromedian nuclei. Shifts were larger in AD, with evidence of a left-sided preference in early mild cognitive impairment while a predilection for right thalamic nuclei was observed in SCZ. The right medial dorsal nucleus was associated with disorganized thought and daily auditory verbal hallucinations. ConclusionsIn AD, thalamic nuclei are more severely and symmetrically affected while in SCZ, the right thalamic nuclei are more affected. We highlight the right medial dorsal nucleus, which may mediate multiple symptoms of SCZ and is affected early in the disease course.

Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals’ transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.

Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.

Frontal pole-precuneus connectivity is associated with a discrepancy between self-rated and observer-rated depression severity in mood disorders: a resting-state functional magnetic resonance imaging study.

Discrepancies in self-rated and observer-rated depression severity may underlie the basis for biological heterogeneity in depressive disorders and be an important predictor of outcomes and indicators to optimize intervention strategies. However, the neural mechanisms underlying this discrepancy have been understudied. This study aimed to examine the brain networks that represent the neural basis of the discrepancy between self-rated and observer-rated depression severity using resting-state functional MRI. To examine the discrepancy between self-rated and observer-rated depression severity, self- and observer-ratings discrepancy (SOD) was defined, and the higher and lower SOD groups were selected from depressed patients as participants showing extreme deviation. Resting-state functional MRI analysis was performed to examine regions with significant differences in functional connectivity in the two groups. The results showed that, in the higher SOD group compared to the lower SOD group, there was increased functional connectivity between the frontal pole and precuneus, both of which are subregions of the default mode network that have been reported to be associated with ruminative and self-referential thinking. These results provide insight into the association of brain circuitry with discrepancies between self- and observer-rated depression severity and may lead to more treatment-oriented diagnostic reclassification in the future.

Dystocia due to lateral deviation of foetal head and its management in a Punganur cattle

Dystocia due to lateral deviation of head and neck is one of the most common types of ruminant dystocia. In one such case, a 7-year- old Punganur cow was reported and successfully managed. The cow was straining and showing symptoms of labor pain and respiratory distress. The cervix was fully dilated, and the birth passage was dry. Foetus was in anterior presentation, dorso-sacral position with extreme left deviation of head and neck. When attempts made by extraction to expel out the foetus, Punganur calf was delivered. The cow was provided with proper postoperative care and management to facilitate the recovery of the uterus and restore normal functioning.

On current limitations of online eye-tracking to study the visual processing of source code

Context:Eye-tracking is an increasingly popular instrument to study how programmers process and comprehend source code. While most studies are conducted in controlled environments with lab-grade hardware, it would be desirable to simplify and scale participation in experiments for users sitting remotely, leveraging home equipment. Objective:This study investigates the possibility of performing eye-tracking studies remotely using open-source algorithms and consumer-grade webcams. It establishes the technology’s current limitations and evaluates the quality of the data collected by it. We conclude by recommending ways forward to address the shortcomings and make remote code-reading studies in support of eye-tracking feasible in the future. Method:We gathered eye-gaze data remotely from 40 participants performing a code reading experiment on a purpose-built web application. The utilized eye-tracker worked client-side and used ridge regression to generate x- and y-coordinates in real-time predicting the participants’ on-screen gaze points without the need to collect and save video footage. We processed and analysed the collected data according to common practices for isolating eye-movement events and deriving metrics used in software engineering eye-tracking studies. In response to the lack of an algorithm explicitly developed for detecting oculomotor fixation events in low-frequency webcam data, we also introduced a dispersion threshold algorithm for that purpose. The quality of the collected data was subsequently assessed to determine the adequacy and validity of the methodology for eye-tracking. Results:The collected data was found to be of varying quality despite extensive calibration and graphical user guidance. We present our results highlighting both the negative and positive observations from which the community hopefully can learn. Both accuracy and precision were low and ultimately deemed insufficient for drawing valid conclusions in a high-precision empirical study. We nonetheless contribute to identifying critical limitations to be addressed in future research. Apart from the overall challenge of vastly diverse equipment, setup, and configuration, we found two main problems with the current webcam eye-tracking technology. The first was the absence of a validated algorithm to isolate fixations in low-frequency data, compromising the assurance of the accuracy of the data derived from it. The second problem was the lack of algorithmic support for head movements when predicting gaze location. Unsupervised participants do not always keep their heads still, even if instructed to do so. Consequently, we frequently observed spatial shifts that corrupted many collected datasets. Three encouraging observations resulted from the study. Even when shifted, gaze points were consistently dispersed in patterns resembling both the shape and size of the stimuli without extreme deviations. We could also distinguish recognizable reading patterns. Linearity was significantly different when participants were reading source code compared to natural text, and we could detect the expected left-to-right and top-to-bottom reading directions for participants reading natural text snippets. Conclusion:The accuracy and precision levels were not sufficient for a word-by-word analysis of code reading but could be adequate for a broader, coarse-grained precision study. Additionally we identified two main issues compromising the collected data validity and contributed a fixation detection algorithm to approach one of these issues. With suitable solutions to the identified issues, remote eye-tracking studies with webcams on code reading could eventually be feasible.

Characterisation of extreme precipitation changes in the upper reaches of the Yangtze River, China

ABSTRACT The paper analyses the spatial and temporal characteristics of extreme precipitation in the upper reaches of the Yangtze River through extreme precipitation indicators based on the trend method, the Mann–Kendall trend test, and the rescaled extreme deviation extreme deviation using daily precipitation data from 1961 to 2021. The following conclusions were obtained: The overall precipitation in the upper reaches of the Yangtze River is reduced, and the number of rainy days is reduced. The frequency of extreme precipitation is generally reduced, but the spatial difference in the intensity of extreme precipitation is greater, which makes the occurrence of extreme precipitation more concentrated and more destructive. Extreme precipitation indicators showed relatively large fluctuations after 2000, especially in terms of extreme precipitation intensity. The frequency of extreme precipitation in the upper reaches of the Yangtze River is the highest in the main stream of the Yangtze River Basin and the Wujiang River Basin, the intensity of extreme precipitation is in the Jialing River and the Wujiang River Basin, and the accumulation of extreme precipitation is the highest in the Jialing River and the Wujiang River Basin, whereas the maximum value of the station extreme precipitation intensity and frequency is in the Minjiang River Basin.

Experimental study of penetration of pre-cut free surface rock materials based on orthogonal tests and response surface testing methods

The parameters of the pre-cut kerf as the free surface on rock material have an important influence on the rock-breaking force of the disc cutter of TBM. The rock-breaking force of the disc cutter can be simplified as the normal penetration load of the metal indenter. In order to study the effect of the pre-cut free surface parameters on the penetration load, orthogonal experiments and response surface tests were carried out on sandstone and granite samples, and extreme deviation analysis and analysis of variance (ANOVA) were performed. The results showed that: (1) the order of priority of the factors affecting the penetration load of the disc cutter was Kerf depth > Kerf spacing > Kerf angle. (2) The fitting degree of the regression equation for the predicted values of the penetration loads of the sandstone and granite samples was R2 (Sandstone) = 0.9810 and R2 (Granite) = 0.9995, respectively, indicating that there was a good fit between the measured and predicted values of the penetration load of the two samples.

Role of Singles Amplitudes in ADC(2) and CC2 for Low-Lying Electronically Excited States.

The closely related second-order methods CC2 and ADC(2) usually perform very similarly for single excitations of organic molecules. However, as rationalized in this work, significant deviations between these two methods can arise if the ground state and a low-lying singly excited state arise from a strong coupling between their leading configurations. Such a configuration mixing is partially accounted for in CC2 through the ground-state singles amplitudes but is omitted in ADC(2). This can cause unusual deviations between the results obtained with these methods. In this work, we study how severe this effect can become at the example of two solvatochromic dyes: the negatively solvatochromic betaine dye N1-tBu and the positively solvatochromic bithiophene P1. These two dyes allow one to study the limits of both small and somewhat larger excitation energies and configuration mixing by tuning the S0 → S1 transition energy through the polarity of the environment. Higher-level calculations at the CC3 level provide information on the accuracy of ADC(2) and CC2 in these cases. The most extreme deviation between ADC(2) and CC2 is found for N1-tBu in vacuum, where the ADC(2) result is 0.45 eV below that of CC2. In this case, the methodical error of CC2 with respect to CC3 is only 0.05 eV. With increasing excitation energy in polar solvents, the CC2-ADC(2) deviation decreases and reaches a value of only 0.15 eV. For P1, which has larger excitation energies, these effects are reversed due to the opposite solvatochromism but also smaller in magnitude: the deviation increases from 0.08 eV in vacuum to 0.16 eV in the so-called conductor limit of the continuum solvation model. Although for these two dyes larger deviations are observed for smaller excitation energies, the extent of configuration mixing does not generally correlate with only the size of excitation energy. For example, s-triazine (0.15 eV), formamide (0.19 eV), and formaldehyde (0.23 eV) also show large deviations between CC2 and ADC(2) despite their much higher excitation energies compared to those of N1-tBu and P1.

Engineering Approach to Assessing the Vulnerability of Water Abstraction

Variability in stream flow/discharge results in serious problems for engineers and difficulties in characterizing water systems under future climatic conditions. The management of water security in the engineering domain requires approaches aimed at minimizing the detrimental effects of the hydrological behavior of natural systems. Abstraction facilities must be strengthened to ensure sustainable supply and water security over time and at different scales. Several approaches and methodologies have been developed to translate water security into a framework that provides information on how to improve it. In this study, a scalar range idea is used to evaluate the sensitivity of a water resource system and cause–effect linkages define the vulnerability indicator as management-relevant information to address water security. This intuitively relates the extreme deviations of a particular streamflow to the average system response related to a particular hazard indicator. This determines the current stress in the operation of the abstraction facilities based on historical hydrometeorological changes, which is the basis for assessing future operational conditions and risks. This study uses streamflow extremes and averages as hazard-relevant indicators of water supply security. The results of the two case studies show that the applied approach fully appreciates the internal properties of water resource systems that affect the sensitivity/vulnerability of streamflow, as well as the derived streamflow vulnerability index and function. The obtained results were used to assess the vulnerability of water intake as well as the choice of safety factors and design parameters in accordance with the forecasted average annual and seasonal climate factors.

Probabilistic Design Method for Aircraft Thermal Protective Layers Based on Surrogate Models

In this study, a probabilistic method was proposed for an aircraft’s thermal protective layers. The uncertainties of material properties, geometric dimensions, and incoming flow environments were considered for the design inputs. To accelerate the design efficiency, Latin hypercube sampling and surrogate models were built based on finite element method calculations to enhance the simulation efficiency. Thus, the Monte Carlo method can be implemented with such a fast simulation method and produce a massive number of samples for the uncertainty quantification and sensitivity analysis, exploring their impact on the back temperature of the thermal protection layer. Compared to the deterministic method with the extreme deviation design, the probabilistic design yields a weight reduction of 15.61%. This indicates that probabilistic design is an efficient approach to enhance the performance of aircraft and reduce the overall weight of the aircraft. The general goal of this study is to provide a new design method for the coating film of thermal protection systems by considering multiple sources of uncertainties.

Multi-Objective Optimization of Low-Alloy Hot-Rolled Strip Cooling Process Based on Gray Correlation Analysis

The residual stress in low-alloy hot-rolled strips seriously affects the use and processing of products. Reducing residual stress is important for improving the product quality of hot-rolled strips. In this paper, the changes in grain size and residual stress of hot-rolled strips under different cooling processes were investigated via thermal simulation experiments and electron backscatter diffraction. It was found that the optimum cooling process solution for single-objective optimization of grain size was a final rolling temperature of 875 °C, a laminar cooling speed of 50 °C/s, and a coiling temperature of 550 °C. When single-objective optimization of residual stress was carried out, the optimal cooling process scheme was 900 °C for final rolling temperature, 20 °C/s for laminar cooling speed, and 625 °C for coiling temperature. The significance of the effect of cooling processes on grain size and residual stress was analyzed based on the extreme deviation of the effect of each cooling process on grain size and residual stress in orthogonal experiments. The results show that the coiling temperature was the most influential factor on grain size and residual stress among the cooling process parameters. The difference was that grain size increased with increasing coiling temperature, and residual stress decreased with increasing coiling temperature. Using both grain size and residual stress as evaluation indicators, a multi-objective optimization of the cooling process for hot-rolled strips was carried out via the gray correlation analysis method. The optimized solution was 875 °C final rolling temperature, 30 °C/s laminar cooling speed, and 625 °C coiling temperature. At this time, the grain size was 4.8 μm, and the KAM (Kernel Average Misorientation) was 0.40°. The grain size under the actual production process scheme was 4.4 μm with a KAM of 0.78°. Compared to the actual production process solution, the multi-objective optimization solution showed little change in grain size, with only a 9% increase and a 49% reduction in KAM. The optimization scheme in this paper could significantly reduce the level of residual stresses while ensuring the fine grain size of hot-rolled strips, thus improving the overall quality of hot-rolled strips.

An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

Extreme deviations from the normative model reveal cortical heterogeneity and associations with negative symptom severity in first-episode psychosis from the OPTiMiSE and GAP studies

There is currently no quantifiable method to predict long-term clinical outcomes in patients presenting with a first episode of psychosis. A major barrier to developing useful markers for this is biological heterogeneity, where many different pathological mechanisms may underly the same set of symptoms in different individuals. Normative modelling has been used to quantify this heterogeneity in established psychotic disorders by identifying regions of the cortex which are thinner than expected based on a normative healthy population range. These brain atypicalities are measured at the individual level and therefore potentially useful in a clinical setting. However, it is still unclear whether alterations in individual brain structure can be detected at the time of the first psychotic episode, and whether they are associated with subsequent clinical outcomes. We applied normative modelling of cortical thickness to a sample of first-episode psychosis patients, with the aim of quantifying heterogeneity and to use any pattern of cortical atypicality to predict symptoms and response to antipsychotic medication at timepoints from baseline up to 95 weeks (median follow-ups = 4). T1-weighted brain magnetic resonance images from the GAP and OPTiMiSE samples were processed with Freesurfer V6.0.0 yielding 148 cortical thickness features. An existing normative model of cortical thickness (n = 37,126) was adapted to integrate data from each clinical site and account for effects of gender and site. Our test sample consisted of control participants (n = 149, mean age = 26, SD = 6.7) and patient data (n = 295, mean age = 26, SD = 6.7), this sample was used for estimating deviations from the normative model and subsequent statistical analysis. For each individual, the 148 cortical thickness features were mapped to centiles of the normative distribution and converted to z-scores reflecting the distance from the population mean. Individual cortical thickness metrics of +/– 2.6 standard deviations from the mean were considered extreme deviations from the norm. We found that no more than 6.4% of psychosis patients had extreme deviations in a single brain region (regional overlap) demonstrating a high degree of heterogeneity. Mann-Whitney U tests were run on z-scores for each region and significantly lower z-scores were observed in FEP patients in the frontal, temporal, parietal and occipital lobes. Finally, linear mixed-effects modelling showed that negative deviations in cortical thickness in parietal and temporal regions at baseline are related to more severe negative symptoms over the medium-term. This study shows that even at the early stage of symptom onset normative modelling provides a framework to identify individualised cortical markers which can be used for early personalised intervention and stratification.

Systematic Review and Meta-Analysis of the Index of Orthognathic Functional Treatment Need for Detecting Subjects with Great Need for Orthognathic Surgery.

Index of Orthognathic Functional Treatment Need (IOFTN) Identifies patients with greatest need for orthognathic surgery. This systematic review looks into the available evidence on prevalence of patients with great treatment need (IOFTN 4&5) who had orthognathic surgery in previous studies. Literature search of the major electronic databases was carried out for articles published between 2014 and September 2023, looking for "Index of Orthognathic Functional Treatment Need","Orthognathic Surgery","IOFTN", "Epidemiology" and "Dentofacial Deformity". The prevalence of patients identified with IOFTN grades of 4&5 was calculated. A random-effect model was employed and the Forest and Galbraith plots were produced. The pooled prevalence estimate for subjects with IOFTN scores of 4&5 was calculated. The heterogeneity among studies was assessed with the I2 statistics. In total, 14 retrospective studies conducted between 2015 and 2019 in 7 countries (UK, New Zealand, Iran, Pakistan, Turkey, Malaysia, Japan) were included, reporting on 1339 orthognathic patients. The sample sizes ranged from 30 to 200. Only 7 studies reported on the subcategories of the IOFTN for grades 1, 2&3. The prevalence of orthognathic patients with IOFTN scores of 4 & 5 ranged from 79% to 100%. The heterogeneity I2 statistics was high (76%, random effects model) for 14 studies, but after removing 3 outlier studies reduced to zero. The pooled prevalence estimate for subjects with IOFTN scores of 4&5 was 93% (95% CI, 0.91-0.94%). According to this meta-analysis, IOFTN successfully identified 93% of patients who underwent orthognathic surgery with a great need for treatment (Grade 4 & 5). To enhance the identification of patients with well-compensated malocclusions, previous orthodontic treatment, or extreme occlusal deviations (e.g., proclination or retroclination of incisors), it would be advantageous to employ additional means alongside IOFTN for assessing orthognathic need in this group.

Impact valuation of droughts in soybean and maize production: the case of Argentina

PurposeIn Argentina, soy and maize represent 28% of the total country exports, affecting the balance of payments, international reserves accumulation and sovereign credit risk. In the past 10 years, three extreme and moderate droughts have affected the agricultural areas, causing significant losses in soybean and maize production. This study aims to estimate the economic impact generated by different drought levels for soy and maize production areas through a financial perspective that allows the estimation of the cash flow and income losses.Design/methodology/approachBy analyzing the extreme deviations in yields during dry periods, the losses generated by droughts were valuated among 183 departments nationwide.FindingsThe aggregated results indicated a total loss of US$24.170m, representing 57.45% of the international reserves of the Argentinean Central Bank in 2021. This estimate shows the magnitude of the climate impact on the Argentinean economy, indicating that severe droughts have macroeconomic impacts, with the external sector as the main transmission channel in an economy with historic restrictions on the balance of payments, international reserve accumulation and sovereign credit risk.Originality/valueThis study analyses the macroeconomic impact of drought on Argentinean soybean and maize production.

Reanalysis of a μ opioid receptor crystal structure reveals a covalent adduct with BU72

BackgroundThe first crystal structure of the active μ opioid receptor (μOR) exhibited several unexplained features. The ligand BU72 exhibited many extreme deviations from ideal geometry, along with unexplained electron density. I previously showed that inverting the benzylic configuration resolved these problems, establishing revised stereochemistry of BU72 and its analog BU74. However, another problem remains unresolved: additional unexplained electron density contacts both BU72 and a histidine residue in the N-terminus, revealing the presence of an as-yet unidentified atom.ResultsThese short contacts and uninterrupted density are inconsistent with non-covalent interactions. Therefore, BU72 and μOR form a covalent adduct, rather than representing two separate entities as in the original model. A subsequently proposed magnesium complex is inconsistent with multiple lines of evidence. However, oxygen fits the unexplained density well. While the structure I propose is tentative, similar adducts have been reported previously in the presence of reactive oxygen species. Moreover, known sources of reactive oxygen species were present: HEPES buffer, nickel ions, and a sequence motif that forms redox-active nickel complexes. This motif contacts the unexplained density. The adduct exhibits severe strain, and the tethered N-terminus forms contacts with adjacent residues. These forces, along with the nanobody used as a G protein substitute, would be expected to influence the receptor conformation. Consistent with this, the intracellular end of the structure differs markedly from subsequent structures of active μOR bound to Gi protein.ConclusionsLater Gi-bound structures are likely to be more accurate templates for ligand docking and modelling of active G protein-bound μOR. The possibility of reactions like this should be considered in the choice of protein truncation sites and purification conditions, and in the interpretation of excess or unexplained density.

Asphalt pavement surface repair area detection based on smartphone sensors

Asphalt pavement repair areas affect pavement performance and service levels. It is necessary to distinguish the repair areas from normal sections. Based on vehicle vibration signals, this study identified ten pavement repair areas and divided them into four cases by factors including length and form in conjunction with the driving approach. Additionally, time domain analysis, frequency analysis, and probability distribution analysis were used to form the characteristics of the repair cases as well as the normal sections. It was found that the maximum value, extreme deviation, standard deviation in the time domain, maximum amplitude in the frequency domain, and peak of the probability density curve would serve as judgment indexes. A framework for identifying the repair areas was also established based on the five indexes. By validation, the overall accuracy can reach 95.0%, demonstrating a strong generalization capability.

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BackgroundThe high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume. MethodsWe enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors. ResultsIn contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146–0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800–0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination. ConclusionsCapturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.