Sustained release of vascular endothelial growth factor A and basic fibroblast growth factor from nanofiber membranes reduces oxygen/glucose deprivation-induced injury to neurovascular units

Upregulation of vascular endothelial growth factor A/basic fibroblast growth factor(VEGFA/b FGF)expression in the penumbra of cerebral ischemia can increase vascular volume,reduce lesion volume,and enhance neural cell proliferation and differentiation,thereby exerting neuroprotective effects.However,the beneficial effects of endogenous VEGFA/b FGF are limited as their expression is only transiently increased.In this study,we generated multilayered nanofiber membranes loaded with VEGFA/b FGF using layer-by-layer self-assembly and electrospinning techniques.We found that a membrane containing 10 layers had an ideal ultrastructure and could efficiently and stably release growth factors for more than 1 month.This 10-layered nanofiber membrane promoted brain microvascular endothelial cell tube formation and proliferation,inhibited neuronal apoptosis,upregulated the expression of tight junction proteins,and improved the viability of various cellular components of neurovascular units under conditions of oxygen/glucose deprivation.Furthermore,this nanofiber membrane decreased the expression of Janus kinase-2/signal transducer and activator of transcription-3(JAK2/STAT3),Bax/Bcl-2,and cleaved caspase-3.Therefore,this nanofiber membrane exhibits a neuroprotective effect on oxygen/glucose-deprived neurovascular units by inhibiting the JAK2/STAT3 pathway.

Risk Factors for Mortality in Acute Kidney Injury in Intensive Care Units in Togo

Context: Acute kidney injury (AKI) in intensive care unit (ICU) is common and associated with very high mortality. In Togo, a tropical country with limited resources and only one nephrology department in the north, acute kidney injury seems to be a real tragedy with high mortality. Aims: to determine risk factors for mortality in acute kidney injury in the intensive care units. Methods and Material: We made a multicentric cross sectional study during 6 months in the four referral centers in northern Togo. Univariate and multivariate logistic regression was used to identify factors associated with mortality. Data were analyzed using RStudio 2023.04.1. Results: A total of 12.6% of patients admitted to intensive care had presented with AKI. The mean age was 49.6 ± 17.9. The sex ratio (M/F) was 2.1. Community-acquired AKI was in the majority (67.7%). Oligo anuria was the most frequent functional sign (38.4%). In our series, 81.6% of patients were in KDIGO stages 2 to 3. AKI was organic in 56.2% of cases. Mortality was 44.3%. In multivariate analysis, the main factors predictive of death were: respiratory distress (OR = 2.36;p Conclusions: Acute kidney injury in intensive care is common in northern Togo, and mortality is high. Identification of associated factors should help anticipate prognosis.

One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units

Artificial neural networks(ANN) have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit(1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 p J/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies(> 90%) within a large range of dropout probabilities up to40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.

Hounsfield units in assessing bone mineral density in ankylosing spondylitis patients with cervical fracture-dislocation

BACKGROUND Cervical spine fracture-dislocations in patients with ankylosing spondylitis(AS)are mostly unstable and require surgery.However,osteoporosis,one of the comorbidities for AS,could lead to detrimental prognoses.There are few accurate assessments of bone mineral density in AS patients.AIM To analyze Hounsfield units(HUs)for assessing bone mineral density in AS patients with cervical fracture-dislocation.METHODS The HUs from C2 to C7 of 51 patients obtained from computed tomography(CT)scans and three-dimensional reconstruction of the cervical spine were independently assessed by two trained spinal surgeons and statistically analyzed.Inter-reader reliability and agreement were assessed by interclass correlation coefficient.RESULTS The HUs decreased gradually from C2 to C7.The mean values of the left and right levels were significantly higher than those in the middle.Among the 51 patients,25 patients(49.02%)may be diagnosed with osteoporosis,and 16 patients(31.37%)may be diagnosed with osteopenia.CONCLUSION The HUs obtained by cervical spine CT are feasible for assessing bone mineral density with excellent agreement in AS patients with cervical fracture-dislocation.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

Improvement of the nutritional support management system for patients in intensive care units

BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.

Epidemiology of carbapenem-resistant Acinetobacter baumannii colonization in neonatal intensive care units:A systematic review and meta-analysis

BACKGROUND The rising prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)in neonatal intensive care units(NICUs)represents an escalating challenge in healthcare settings,particularly in managing hospital-acquired infections(HAIs).Studies across various World Health Organization regions have documented a significant incidence of CRAB-related HAIs,with rates as high as 41.7 cases per 1000 patients in ICUs,accounting for 13.6%of all HAIs.These infections pose a doubled mortality risk compared to infections with carbapenem-susceptible Acinetobacter baumannii.A particularly concerning aspect of CRAB colonization is its asymptomatic nature,enabling its transmission through healthcare workers(HCWs)or the NICU environment to vulnerable neonates with developing immune systems.AIM To explore the prevalence of CRAB colonization in NICUs,focusing on neonates,healthcare workers,and the environmental samples,to enhance epidemiological understanding and inform targeted interventions.METHODS We conducted according to PRISMA 2020 checklist guidelines,a comprehensive literature search across multiple databases including MEDLINE(Ovid),EMBASE(Ovid),Global Health(Ovid),Web of Science,and Global Index Me-dicus.Studies were selected based on predetermined criteria,primarily involving neonates,HCWs,and environmental swabs,using culture or molecular methods to detect CRAB colonization.We excluded studies that did not specifically focus on NICUs,were duplicates,or lacked necessary data.The study selection and quality assessment were conducted independently by two reviewers.Data extraction involved collecting comprehensive details about each study.Our statistical analysis used a random-effects model to calculate the pooled prevalence and confidence intervals,stratifying results by regional location.We assessed study heterogeneity using Cochran's Q statistic and I²statistic,with regression tests employed to evaluate potential publication bias.RESULTS We analyzed 737 records from five databases,ultimately including 13 studies from ten countries.For neonates,the pooled prevalence was 4.8%(95%CI:1.1%to 10.5%)with the highest rates observed in South-East Asia(10.5%;95%CI:2.4%to 23.3%).Among HCWs,a single Indian study reported a 3.3%prevalence.Environmental samples showed a prevalence of 2.3%(95%CI:0%to 9.3%),with the highest rates in South-East Asia(10%;95%CI:4.2%to 17.7%).Significant heterogeneity was found across studies,and no publication bias was detected.CONCLUSION This systematic review highlights a significant prevalence of CRAB colonization in neonates across various regions,particularly in South-East Asia,contrasting with lower rates in high-income countries.The study reveals a gap in research on HCWs colonization,with only a single study from India reporting moderate prevalence.Environmental samples indicate moderate levels of CRAB contamination,again higher in South-East Asia.These findings underscore the need for more extensive and focused research on CRAB colonization in NICUs,including exploring the roles of HCWs and the environment in transmission,understanding antimicrobial resistance patterns,and developing effective prevention measures.

Temperature Trends and Accumulation of Chill Hours, Chill Units, and Chill Portions in South Carolina

There is considerable concern about the potential impact of climate change on agriculture, such as the accumulation of chilling hours needed to break the dormancy of many perennial plants, like fruit trees. Therefore, this study aimed to determine if there had been a significant change in air temperatures and chill hours, chill units, and chill portion accumulation in South Carolina over the last two decades. Two decades of daily maximum (Tmax) and minimum (Tmin) air temperature records were obtained from weather stations in thirty-one counties in South Carolina. Hourly temperature data, reconstructed from the daily data, were used to calculate the daily and annual chill hours, chill units, and chill portions accumulation using four different chill models for each location and year. The chill models included the T(t) °C model, the 0°C °C model, the Utah model, and the Dynamic model. For each county, regression analyses were conducted to evaluate the historical trends. Despite year-to-year variability, the tendency was a statistically significant (α = 0.05) increase in air temperature, averaging 0.089°C per year for 20 out of 31 counties in South Carolina. The other 11 counties had no significant change in temperature. The average temperature increase in the 31 counties was 0.072°C per year. The temperature increase resulted in a decrease in annual chill accumulation during the fall to spring, averaging 17.7 chill hours, 8.6 chill hours, 17.0 chill units, and 0.40 chill portions per year calculated with the T(t) °C, 0°C °C, Utah, and Dynamic models, respectively. However, whether this decrease in chill values was statistically significant or not depended on the chill model used. This study did not investigate the cause of the observed historical trends in temperature and chill accumulation. Still, if the trends continue, they could significantly impact the future of the temperate fruit tree industry in the state.

Improved Units of Measure in Rotational Mechanics

The SI system of units in rotational mechanics yields correct numerical results, but it produces physically incorrect units of measure in many cases. SI units also violate the principle of general covariance—the general rule for defining continuous coordinates and units in mathematics and mathematical physics. After 30+ years of wrestling with these problems, the ultimate authority on units of measure has declared that Newton–meter and Joule are not equivalent in rotational mechanics, as they are in the rest of physics. This article proposes a simple modification to SI units called “Nonstandard International units” (“NI units”) until a better name is agreed upon. NI units yield correct numerical results and physically correct units of measure, and they satisfy the principle of general covariance. The main obstacle to the adoption of NI units is the consensus among users that the radius of rotation should have the unit meter because the radius can be measured with a ruler. NI units assigned to radius should have units meter/radian because the radius is a conversion factor between angular size and circumferential length, as in arclength = rθ. To manage the social consensus behind SI units, the author recommends retaining SI units as they are, and informing users who want correct units that NI units solve the technical problems of SI units.

A Deep Learning-Based Teaching Design for High School Geography Units:Taking the Example of Landforms of the Humanistic Education Edition

The traditional teaching methods of one-way cultivation of students can no longer meet the requirements of talent cultivation at this stage.The issue of how to promote students from passive acceptance to the independent cognitive understanding stage(i.e.deep learning)has become the focus of geography teaching.Therefore,under the guidance of deep learning theory,this paper takes the“landforms”knowledge unit of the Humanistic Education Edition as an example,improves the classroom teaching means through the unit teaching mode,reconstructs the“landforms”teaching unit,and explores the specific teaching of high school geography unit based on deep learning.This study provides a good example and guidelines for high school geography teaching and learning.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.Plant height (H),stem diameter (SD),leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.However,the combined effect of temperature and light on maize growth is rarely considered in crop growth models.Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H,SD,LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.Either the accumulative growing degree-days (AGDD),helio thermal units (HTU),photothermal units (PTU) or photoperiod thermal units (PPTU,first proposed here) was used as a single driving factor in the models;or AGDD was combined with either accumulative actual solar hours (ASS),accumulative photoperiod response (APR,first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.The model performances were evaluated using seven statistical indicators and a global performance index.The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.Among the four single factor-driven models,the overall performance of the Mlog_(PTU)Model was the best,followed by the Mlog_(AGDD)Model.The Mlog_(PPTU)Model was better than the Mlog_(AGDD)Model in simulating SD and LAI.Among the 10 models,the overall performance of the Mlog_(AGDD–APR)Model was the best,followed by the Mlog_(AGDD–ASS)Model.Specifically,the Mlog_(AGDD–APR)Model performed the best in simulating H and LAI,while the Mlog_(AGDD–ADL)and Mlog_(AGDD–ASS)models performed the best in simulating SD and DM,respectively.In conclusion,the modified logistic growth equations with AGDD and either APR,ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

Investigation of the Interaction Mechanism between Lignin Structural Units and Enzyme

The effect of lignin structural units on enzymatic hydrolysis of lignocellulosic biomass was investigated,especially the inhibitory role of lignin in non-productive adsorption with enzymes.Milled wood lignin(MWL)was isolated from different hardwoods of poplar,eucalyptus and acacia.The isolated lignin samples were characterized by elemental analysis,gel permeation chromatography,nitrobenzene oxidation and fourier infrared spectroscopy.The mechanism of lignin structural units on enzymatic hydrolysis of cellulose was studied by quartz crystal microbalance(QCM).The results showed that different structural units of lignin had different adsorption capacity for enzymes.The results of nitrobenzene oxidation indicated that the S/G ratio(S:syringyl-like lignin structures;G:guaiacyl-like lignin structures)of lignin of poplar was 0.99,that of eucalyptus was 1.92 and that of acacia was 1.34.According to the results of QCM,the adsorption capacity of the three lignin films was as follows:Poplar MWL(S/G ratio 0.99)<Acacia MWL(S/G ratio 1.34)<Eucalyptus MWL(S/G ratio 1.92).Eucalyptus MWL with higher degree of condensation and S/G ratio showed stronger affinity to enzymes and more non-productive adsorption with enzymes,resulting in less adsorption between enzymes and cellulose,and lower enzymatic hydrolysis efficiency.

Prediction of the instability probability for rainfall induced landslides:the effect of morphological differences in geomorphology within mapping units

Slope units is an effective mapping unit for rainfall landslides prediction at regional scale.At present,slope units extracted by hydrology and morphological method report very different morphological feature and boundaries.In order to investigate the effect of morphological difference on the prediction performance,this paper presents a general landslide probability analysis model for slope units.Monte Carlo method was used to describe the spatial uncertainties of soil mechanical parameters within slope units,and random search technique was performed to obtain the minimum safety factor;transient hydrological processes simulation was used to provide key hydrological parameters required by the model,thereby achieving landslide prediction driven by quantitative precipitation estimation and forecasting data.The prediction performance of conventional slope units(CSUs)and homogeneous slope units(HSUs)were analyzed in three case studies from Fengjie County,China.The results indicate that the mean missing alarm rate of CSUs and HSUs are 31.4% and 10.6%,respectively.Receiver Operating Characteristics(ROC)analysis also reveals that HSUs is capable of improving the overall prediction performance,and may be used further for rainfall-induced landslide prediction at regional scale.

Assessment of Monitor Units and Gamma Pass Rate for 6 MV and Flattening Filter Free (FFF) Beams in Volumetric Modulated Arc Therapy (VMAT)

Background: In linear accelerators, the treatment field’s uniform intensity is achieved by including a flattening filter in the beam. However, to produce more conformal dose distributions, contemporary radiotherapy practice now frequently uses fluence and aperture modifying techniques, such as volumetric modulated arc therapy. In these circumstances, the flattening filter in the beam manufacturing process is no longer required. It is therefore necessary to compare the monitor units of 6 MV and flattening filter free plans and how it influences the gamma pass rates to determine which is best for treating cervical cancer with pelvic lymph node metastasis. Methods: VMAT plans for fifteen patients with cervical cancer with pathological pelvic lymph node metastasis were included in this study. Each patient had two VMAT plans using conventional 6 MV beam with flattening filter and one with flattening filter free beam (FFF). The VMAT plans were made using two arcs, and then recalculated to give the planned dose distribution to the detectors in a Delta4 phantom. The VMAT plans were irradiated on the Delta4 phantom using an Elekta linear accelerator (6 MV). Results: The mean monitor unit for the 6 MV plans was 506.3 MU and a standard deviation of 48.6 while that of the FFF plans had a mean MU of 701.5 with a standard deviation of 87.6. The total monitor units (MUs) for the FFF plans were significantly greater than the 6 MV plans (p = 6.1 × 10-5). Conclusion: Flattening filter free (FFF) plans require more numbers of monitor units in comparison to conventional 6 MV filtered beams for external radiation of cervical cancer with pelvic lymph nodes involvement.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Type Synthesis of Fully Decoupled Three Translational Parallel Mechanism with Closed-Loop Units and High Stifness

In order to solve the problem of weak stifness of the existing fully decoupled parallel mechanism, a new synthesis method of fully decoupled three translational (3T) parallel mechanisms (PMs) with closed-loop units and high stifness is proposed based on screw theory. Firstly, a new criterion for the full decoupled of PMs is presented that the reciprocal product of the transmission wrench screw matrix and the output twist screw matrix of PMs is a diagonal matrix, and all elements on the main diagonal are nonzero constants. The forms of the transmission wrench screws are determined by the criterion. Secondly, the forms of the actuated and unactuated screws can be obtained according to their relationships with the transmission wrench screws. The basic decoupled limbs are generated by combination of the above actuated and unactuated screws. Finally, a closed-loop units construction method is investigated to apply the decoupled mechanisms in a better way on the high stifness occasion. The closed-loop units are constructed in the basic decoupled limbs to generate a high-stifness fully decoupled 3T PM. Kinematic and stifness analyses show that the Jacobian matrix is a diagonal matrix, and the stifness is obviously higher than that of the coupling mechanisms, which verifes the correctness of the proposed synthesis method. The mechanism synthesized by this method has a good application prospect in vehicle durability test platform.

Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

The trajectories of physical growth in 4 months postnatal corrected age among preterm infants discharged from neonatal intensive care units and associated factors: A prospective study

Objectives Growth retardation is a risk for premature infants.In addition to demographic and perinatal factors,preterm infants’physical growth may be affected by neonatal intensive care unit(NICU)stress,maternal postpartum depression,and mother-infant interaction.This study aimed to investigate the trajectories of physical growth in 4 months corrected age among preterm infants discharged from the NICU and the impactors on these trajectories.Methods A prospective study was conducted among 318 preterm infants from September 2019 to April 2021 in Shanghai,China.Latent growth modeling was applied to identify the weight,length,and head circumference growth trajectories in 4 months corrected age and explore the effects of demographic and medical characteristics,infant stress during NICU stay,maternal postpartum depression,and mother-infant interaction on each trajectory.Results Unconditional latent growth models showed curve trajectories with increasingly slower growth in weight,length,and head circumference until 4 months of corrected age.Conditional latent growth models showed that a longer length of stay in the NICU and more skin punctures were negatively associated with weight at 40 weeks corrected gestational age(β=−0.43 and−0.19,respectively,P<0.05).The maternal postpartum depression between 40 weeks corrected gestational age and 1 month corrected postnatal age was associated with a lower growth rate of length(β=−0.17,P=0.040),while between 2 and 3 months corrected postnatal age,there were lower growth rates of weight and head circumference(β=−0.15 and−0.19,respectively,P<0.05).The mother-infant interaction scores between 40 weeks corrected gestational age and 1 month corrected postnatal age negatively predicted the growth rate of weight(β=−0.19,P=0.020).Conclusion The physical growth trajectories of preterm infants discharged from the NICU were influenced by infant stress during the NICU stay,maternal postpartum depression and mother-infant interaction.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.