Risk Factors for Severity and Mortality of Patients Hospitalized for COVID-19 during the 3rd Wave of the Epidemic-Sao Tome and Principe

Rationale: In the literature, some risk factors for severity and mortality from COVID-19 have been indicated. However, these factors can change, depending on the characteristics of the population and health services. In this sense, longitudinal studies can be useful for understanding local realities and subsidizing health actions based on these realities. Objective: To analyze the risk factors for severity and death in hospitalized patients with COVID-19. Methods: A retrospective cohort of patients with COVID-19 hospitalized from August 1 to October 16, 2021 (3rd wave of the pandemic), notified by the Department of Epidemiological Surveillance of Sao Tome and Principe. We employed measures of strength of associations for the analysis of exposure risk factors. Results: We analyzed 110 hospitalized patients (31.8% severe-critical and 68.2% non-severe). The risk factors for severe forms of COVID-19 were: being aged ≥60 years (RR = 3.3), being male (RR = 2), having comorbidities (RR = 2) and the risk increases to 10-fold for multicomorbidities, with emphasis on obesity, neoplasia, skin-muscle-surgical infection, dementia and to some degree CVD. 62.9% of patients with severe forms of the disease were not vaccinated. Risk factors for death among hospitalized and severe/critical cases, respectively, were having comorbidities (RR = 8 and 2.4) multicomorbidities (RR = 10 and 2.8 for those with 2 comorbidities and RR = 33.3 and 4 for those with 3 or 4 comorbidities), especially diabetes, dementia, neoplasia, cutaneous-muscular infection, and obesity. Although CVD was not associated with risk factors for death, these were the most frequently found among the severely hospitalized and deaths. In addition, important risk factors associated with death were not using corticoids (RR = 3.3, 230-fold risk) and not using anticoagulants-heparin (RR = 1.3, 30% risk) more compared to the severe cases that did use them. Most of the patients who died (63.2%) were not vaccinated. Moreover, having only 1 dose of the vaccine was a risk factor 1.9 times more for death among all hospitalized patients, but in the severe cases, there was no association between the variable vaccination and death. Among those hospitalized with 2 doses, it was a 0.5-fold protective factor among those hospitalized. The Delta variant of Sarscov-2 was the one found among severe cases and deaths investigated by genetic sequencing, with more exuberant clinical features compared to the other 2 previous vaccinations. Conclusion: Being elderly, male and presenting comorbidities, mainly multicomorbidities were the main characteristics associated with severity of COVID-19. On the other hand, comorbidities, and even worse, multicomorbidities, hospitalization for respiratory failure, lowered level of consciousness, no use of corticoid and no use of anticoagulation in critically ill patients, and not having at least 2 doses of vaccine for covid-19, were characteristics associated with death by COVID-19. These results will help inform healthcare providers so that the best interventions can be implemented to improve outcomes for patients with COVID-19. Public health interventions must be carefully tailored and implemented in these susceptible groups to reduce the risk of mortality in patients with COVID-19 and then the risk of major complications. Intensive and regular follow-up is needed to detect early occurrences of clinical conditions.

Operating in Tandem Chinese experts in Sao Tome and Principe cooperate with local farmers to raise medical standards in the breeding industry

Santomean pig farmer Simao Vicente was hopeful when he came to ask Zou Rui for help. His pig was suffering from hernia, and Zou, a 42-year-old Chinese agricultural expert working in Sao Tomé and Príncipe, was the only person on the island who could provide emergency surgery.

A generalized nonlinear three-dimensional Hoek‒Brown failure criterion

To understand the strengths of rocks under complex stress states,a generalized nonlinear threedimensional(3D)Hoek‒Brown failure(NGHB)criterion was proposed in this study.This criterion shares the same parameters with the generalized HB(GHB)criterion and inherits the parameter advantages of GHB.Two new parameters,b,and n,were introduced into the NGHB criterion that primarily controls the deviatoric plane shape of the NGHB criterion under triaxial tension and compression,respectively.The NGHB criterion can consider the influence of intermediate principal stress(IPS),where the deviatoric plane shape satisfies the smoothness requirements,while the HB criterion not.This criterion can degenerate into the two modified 3D HB criteria,the Priest criterion under triaxial compression condition and the HB criterion under triaxial compression and tension condition.This criterion was verified using true triaxial test data for different parameters,six types of rocks,and two kinds of in situ rock masses.For comparison,three existing 3D HB criteria were selected for performance comparison research.The result showed that the NGHB criterion gave better prediction performance than other criteria.The prediction errors of the strength of six types of rocks and two kinds of in situ rock masses were in the range of 2.0724%-3.5091%and 1.0144%-3.2321%,respectively.The proposed criterion lays a preliminary theoretical foundation for prediction of engineering rock mass strength under complex in situ stress conditions.

Systemic modulation of skeletal mineralization by magnesium implant promoting fracture healing: Radiological exploration enhanced with PCA-based machine learning in a rat femoral model

The clinical application of magnesium(Mg)and its alloys for bone fractures has been well supported by in vitro and in vivo trials.However,there were studies indicating negative effects of high dose Mg intake and sustained local release of Mg ions on bone metabolism or repair,which should not be ignored when developing Mg-based implants.Thus,it remains necessary to assess the biological effects of Mg implants in animal models relevant to clinical treatment modalities.The primary purpose of this study was to validate the beneficial effects of intramedullary Mg implants on the healing outcome of femoral fractures in a modified rat model.In addition,the mineralization parameters at multiple anatomical sites were evaluated,to investigate their association with healing outcome and potential clinical applications.Compared to the control group without Mg implantation,postoperative imaging at week 12 demonstrated better healing outcomes in the Mg group,with more stable unions in 3D analysis and high-mineralized bridging in 2D evaluation.The bone tissue mineral density(TMD)was higher in the Mg group at the non-operated femur and lumbar vertebra,while no differences between groups were identified regarding the bone tissue volume(TV),TMD and bone mineral content(BMC)in humerus.In the surgical femur,the Mg group presented higher TMD,but lower TV and BMC in the distal metaphyseal region,as well as reduced BMC at the osteotomy site.Principal component analysis(PCA)-based machine learning revealed that by selecting clinically relevant parameters,radiological markers could be constructed for differentiation of healing outcomes,with better performance than 2D scoring.The study provides insights and preclinical evidence for the rational investigation of bioactive materials,the identification of potential adverse effects,and the promotion of diagnostic capabilities for fracture healing.

Photobiomodulation inhibits the expression of chondroitin sulfate proteoglycans after spinal cord injury via the Sox9 pathway

Both glial cells and glia scar greatly affect the development of spinal cord injury and have become hot spots in research on spinal cord injury treatment.The cellular deposition of dense extracellular matrix proteins such as chondroitin sulfate proteoglycans inside and around the glial scar is known to affect axonal growth and be a major obstacle to autogenous repair.These proteins are thus candidate targets for spinal cord injury therapy.Our previous studies demonstrated that 810 nm photo biomodulation inhibited the formation of chondroitin sulfate proteoglycans after spinal cord injury and greatly improved motor function in model animals.However,the specific mechanism and potential targets involved remain to be clarified.In this study,to investigate the therapeutic effect of photo biomodulation,we established a mouse model of spinal cord injury by T9 clamping and irradiated the injury site at a power density of 50 mW/cm~2 for 50 minutes once a day for 7 consecutive days.We found that photobiomodulation greatly restored motor function in mice and down regulated chondroitin sulfate proteoglycan expression in the injured spinal cord.Bioinformatics analysis revealed that photobiomodulation inhibited the expression of proteoglycan-related genes induced by spinal cord injury,and versican,a type of proteoglycan,was one of the most markedly changed molecules.Immunofluorescence staining showed that after spinal cord injury,versican was present in astrocytes in spinal cord tissue.The expression of versican in primary astrocytes cultured in vitro increased after inflammation induction,whereas photobiomodulation inhibited the expression of ve rsican.Furthermore,we found that the increased levels of p-Smad3,p-P38 and p-Erk in inflammatory astrocytes were reduced after photobiomodulation treatment and after delivery of inhibitors including FR 180204,(E)-SIS3,and SB 202190.This suggests that Sma d 3/Sox9 and MAP K/Sox9 pathways may be involved in the effects of photobiomodulation.In summary,our findings show that photobiomodulation modulates the expression of chondroitin sulfate proteoglycans,and versican is one of the key target molecules of photo biomodulation.MAPK/Sox9 and Smad3/Sox9 pathways may play a role in the effects of photo biomodulation on chondroitin sulfate proteoglycan accumulation after spinal cord injury.

Three-dimensional stress variation characteristics in deep hard rock of CJPL-Ⅱ project based on in-situ monitoring

In deep hard rock excavation, stress plays a pivotal role in inducing stress-controlled failure. While the impact of excavation-induced stress disturbance on rock failure and tunnel stability has undergone comprehensive examination through laboratory tests and numerical simulations, its validation through insitu stress tests remains unexplored. This study analyzes the three-dimensional stress changes in the surrounding rock at various depths, monitored during the excavation of B2 Lab in China Jinping Underground Laboratory Phase Ⅱ(CJPL-Ⅱ). The investigation delves into the three-dimensional stress variation characteristics in deep hard rock, encompassing stress components and principal stress. The results indicate changes in both the magnitude and direction of the principal stress during tunnel excavation. To quantitatively describe the degree of stress disturbance, a series of stress evaluation indexes are established based on the distances between stress tensors, including the stress disturbance index(SDI), the principal stress magnitude disturbance index(SDIm), and the principal stress direction disturbance index(SDId). The SDI indicates the greatest stress disturbance in the surrounding rock is 4.5 m from the tunnel wall in B2 Lab. SDIm shows that the principal stress magnitude disturbance peaks at2.5 m from the tunnel wall. SDId reveals that the largest change in principal stress direction does not necessarily occur near the tunnel wall but at a specific depth from it. The established relationship between SDI and the depth of the excavation damaged zone(EDZ) can serve as a criterion for determining the depth of the EDZ in deep hard rock engineering. Additionally, it provides a reference for future construction and support considerations.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Regulation effects of water and nitrogen on yield,water,and nitrogen use efficiency of wolfberry

Wolfberry(Lycium barbarum L.)is important for health care and ecological protection.However,it faces problems of low productivity and resource utilization during planting.Exploring reasonable models for water and nitrogen management is important for solving these problems.Based on field trials in 2021 and 2022,this study analyzed the effects of controlling soil water and nitrogen application levels on wolfberry height,stem diameter,crown width,yield,and water(WUE)and nitrogen use efficiency(NUE).The upper and lower limits of soil water were controlled by the percentage of soil water content to field water capacity(θ_(f)),and four water levels,i.e.,adequate irrigation(W0,75%-85%θ_(f)),mild water deficit(W1,65%-75%θ_(f)),moderate water deficit(W2,55%-65%θ_(f)),and severe water deficit(W3,45%-55%θ_(f))were used,and three nitrogen application levels,i.e.,no nitrogen(N0,0 kg/hm^(2)),low nitrogen(N1,150 kg/hm^(2)),medium nitrogen(N2,300 kg/hm^(2)),and high nitrogen(N3,450 kg/hm^(2))were implied.The results showed that irrigation and nitrogen application significantly affected plant height,stem diameter,and crown width of wolfberry at different growth stages(P<0.01),and their maximum values were observed in W1N2,W0N2,and W1N3 treatments.Dry weight per plant and yield of wolfberry first increased and then decreased with increasing nitrogen application under the same water treatment.Dry weight per hundred grains and dry weight percentage increased with increasing nitrogen application under W0 treatment.However,under other water treatments,the values first increased and then decreased with increasing nitrogen application.Yield and its component of wolfberry first increased and then decreased as water deficit increased under the same nitrogen treatment.Irrigation water use efficiency(IWUE,8.46 kg/(hm^(2)·mm)),WUE(6.83 kg/(hm^(2)·mm)),partial factor productivity of nitrogen(PFPN,2.56 kg/kg),and NUE(14.29 kg/kg)reached their highest values in W2N2,W1N2,W1N2,and W1N1 treatments.Results of principal component analysis(PCA)showed that yield,WUE,and NUE were better in W1N2 treatment,making it a suitable water and nitrogen management mode for the irrigation area of the Yellow River in the Gansu Province,China and similar planting areas.

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Analysis of strength characteristics of loess before and after freezing using a hollow cylinder torsional shear apparatus

This paper aims to comprehensively analyze the influence of the principal stress angle rotation and intermediate principal stress on loess's strength and deformation characteristics. A hollow cylinder torsional shear apparatus was utilized to conduct tests on remolded samples under both normal and frozen conditions to investigate the mechanical properties and deformation behavior of loess under complex stress conditions. The results indicate significant differences in the internal changes of soil particles, unfrozen water, and relative positions in soil samples under normal and frozen conditions, leading to noticeable variations in strength and strain development.In frozen state, loess experiences primarily compressive failure with a slow growth of cracks, while at normal temperature, it predominantly exhibits shear failure. With the increase in the principal stress angle, the deformation patterns of the soil samples under different conditions become essentially consistent, gradually transitioning from compression to extension, accompanied by a reduction in axial strength. The gradual increase in the principal stress axis angle(α) reduces the strength of the generalized shear stress and shear strain curves.Under an increasing α, frozen soil exhibits strain-hardening characteristics, with the maximum shear strength occurring at α = 45°. The intermediate principal stress coefficient(b) also significantly impacts the strength of frozen soil, with an increasing b resulting in a gradual decrease in generalized shear stress strength. This study provides a reference for comprehensively exploring the mechanical properties of soil under traffic load and a reliable theoretical basis for the design and maintenance of roadbeds.

PCA-LSTM:An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning

Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods,the generalization and accuracy of the identification process are low.To address these problems,an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed.Firstly,ground-shaking characteristics were analyzed and groundshaking the data was annotated using Baker’smethod.Secondly,the Principal Component Analysis(PCA)method was used to extract the most relevant features related to impulsive ground-shaking.Thirdly,a Long Short-Term Memory network(LSTM)was constructed,and the extracted features were used as the input for training.Finally,the identification results for the Artificial Neural Network(ANN),Convolutional Neural Network(CNN),LSTM,and PCA-LSTMmodels were compared and analyzed.The experimental results showed that the proposed method improved the accuracy of pulsed ground-shaking identification by>8.358%and identification speed by>26.168%,compared to other benchmark models ground-shaking.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

Nonholonomic Theory of Principal-direction Orthonormal Basis for a Layer of Surfaces

In order to carry out tensor analysis in a neighborhood of a reference surface,the principal-direction orthogonal basis accompanying with Lame s coefficients or general curvilinear coordinate systems are widely used.A novel kind of field theory termed as the nonholonomic theory of the Principal-Direction Orthonormal Basis(PDOB)is presented systematically in the present paper,in which the formal Christoffel symbols are related directly to the principal and geodesic curvatures with respect to the principal directions of the surface.Furthermore,a systematic and simple way to determine the curvatures of the surface are presented with some examples.It provides a way to recognize qualitatively the bending property of a surface.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Effects of Using Municipal Solid Waste Incineration Tailings as Lightweight Aggregate on the Mechanical Properties of Specified Density Concrete

Municipal solid waste incineration tailings were used as lightweight aggregate(MSWIT-LA)in the preparation of specified density concrete to study the effects on compressive strength,axial compressive strength,flexural strength,microhardness,total number of pores,pore area,and pore spacing.The results showed that the internal curing and morphological effects induced by an appropriate quantity of MSWIT-LA improved the compressive response of specified density concrete specimens,whereas an excessive quantity of MSWIT-LA significantly reduced their mechanical properties.An analysis of pore structure indicated that the addition of MSWIT-LA increased the total quantity of pores and promoted cement hydration,resulting in a denser microstructure than that of ordinary concrete.The results of a principal component analysis showed that the mechanical response of specified density concrete prepared with 25%MSWIT-LA was superior to that of an equivalent ordinary concrete.It was therefore concluded that MSWIT-LA can be feasibly applied to achieve excellent specified density concrete properties while utilising municipal solid waste incineration tailings to protect the environment and alleviate shortages of sand and gravel resources.

Mechanism of principal stress rotation and deformation failure behavior induced by excavation in roadways

The failure modes of rock after roadway excavation are diverse and complex.A comprehensive investigation of the internal stress field and the rotation behavior of the stress axis in roadways is essential for elucidating the mechanism of roadway failure.This study aimed to examine the spatial relationship between roadways and stress fields.The law of stress axis rotation under three-dimensional(3D)stress has been extensively studied.A stress model of roadways in the spatial stress field was established,and the far-field stress state at different spatial positions of the roadways was analyzed.A mechanical model of roadways under a 3D stress state was established using far-field stress solutions as boundary conditions.The distribution of principal stressesσ1,σ2 andσ3 around the roadways and the variation of the stress principal axis were solved.It was found that the stability boundary of the stress principal axis exhibits hysteresis when compared with that of the principal stress magnitudes.A numerical analysis model for spatial roadways was established to validate the distribution of principal stress and the mechanism of principal axis rotation.Research has demonstrated that the stress axis undergoes varying degrees of spatial rotation in different orientations and radial depths.Based on the distribution of principal stress and the rotation law of the stress principal axis,the entire evolution mechanism of the two stress adjustments to form the final failure form after roadway excavation has been revealed.The on-site detection results also corroborate the findings presented in this paper.The results provide a basis for the analysis of the failure mechanism under a 3D stress state.

Retrospective and prospective review of the generalized nonlinear strength theory for geomaterials

Strength theory is the basic theory for calculating and designing the strength of engineering materials in civil,hydraulic,mechanical,aerospace,military,and other engineering disciplines.Therefore,the comprehensive study of the generalized nonlinear strength theory(GNST)of geomaterials has significance for the construction of engineering rock strength.This paper reviews the GNST of geomaterials to demonstrate the research status of nonlinear strength characteristics of geomaterials under complex stress paths.First,it systematically summarizes the research progress of GNST(classical and empirical criteria).Then,the latest research the authors conducted over the past five years on the GNST is introduced,and a generalized three-dimensional(3D)nonlinear Hoek‒Brown(HB)criterion(NGHB criterion)is proposed for practical applications.This criterion can be degenerated into the existing three modified HB criteria and has a better prediction performance.The strength prediction errors for six rocks and two in-situ rock masses are 2.0724%-3.5091%and 1.0144%-3.2321%,respectively.Finally,the development and outlook of the GNST are expounded,and a new topic about the building strength index of rock mass and determining the strength of in-situ engineering rock mass is proposed.The summarization of the GNST provides theoretical traceability and optimization for constructing in-situ engineering rock mass strength.

Dip2a regulates stress susceptibility in the basolateral amygdala

Dysregulation of neurotransmitter metabolism in the central nervous system contributes to mood disorders such as depression, anxiety, and post–traumatic stress disorder. Monoamines and amino acids are important types of neurotransmitters. Our previous results have shown that disco-interacting protein 2 homolog A(Dip2a) knockout mice exhibit brain development disorders and abnormal amino acid metabolism in serum. This suggests that DIP2A is involved in the metabolism of amino acid–associated neurotransmitters. Therefore, we performed targeted neurotransmitter metabolomics analysis and found that Dip2a deficiency caused abnormal metabolism of tryptophan and thyroxine in the basolateral amygdala and medial prefrontal cortex. In addition, acute restraint stress induced a decrease in 5-hydroxytryptamine in the basolateral amygdala. Additionally, Dip2a was abundantly expressed in excitatory neurons of the basolateral amygdala, and deletion of Dip2a in these neurons resulted in hopelessness-like behavior in the tail suspension test. Altogether, these findings demonstrate that DIP2A in the basolateral amygdala may be involved in the regulation of stress susceptibility. This provides critical evidence implicating a role of DIP2A in affective disorders.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.