Correlation between neural response telemetry measurements and fitting levels.

INTRODUCTION: The neural response telemetry (NRT) is a standard procedure in cochlear implantation mostly used to determine the functionality of implanted device and to check auditory nerve responds to the stimulus. Correlation between NRT measurements and subjective threshold (T) and maximum comfort (C) levels has been reported but results are inconsistent, and it is still not clear which of the NRT measurements could be the most useful in predicting fitting levels. PURPOSE: In our study we aimed to investigate which NRT measurement corresponds better to fitting levels. Impedance (IMP), Evoked Action Potential (ECAP) threshold and amplitude growth function (AGF) slope values were included in the study. Also, we tried to identify cochlear area at which the connection between NRT measurements and fitting levels would be the most pronounced. MATERIALS AND METHODS: Thirty-one children implanted with Cochlear device were included in this retrospective study. IMP, ECAP thresholds and AGF were obtained intra-operatively and 12 months after surgery at electrodes 5, 11 and 19 as representative for each part of cochlea. Subjective T and C levels were obtained 12 months after the surgery during cochlear fitting. RESULTS: ECAP thresholds obtained 12 months after surgery showed statistically significant correlation to both T and C levels at all 3 selected electrodes. IMP correlated with C levels while AGF showed tendency to correlate with T levels. However, these correlations were not statistically significant for all electrodes. CONCLUSION: ECAP threshold measurements correlated to T and C values better than AGF slope and IMP. Measurements obtained twelve months after surgery seems to be more predictive of T and C values compared to intra-operative measurements. The best correlation between ECAP threshold and T and C values was found at electrode 11 suggesting NRT measurements at mid-portion cochlear region to be the most useful in predicting fitting levels.

Age is not just a number: How incorrect ageing impacts close-kin mark-recapture estimates of population size.

Population size is a key parameter for the conservation of animal species. Close-kin mark-recapture (CKMR) relies on the observed frequency and type of kinship among individuals sampled from the population to estimate population size. Knowledge of the age of the individuals, or a surrogate thereof, is essential for inference with acceptable precision. One common approach, particularly in fish studies, is to measure animal length and infer age using an assumed age-length relationship (a 'growth curve'). We used simulation to test the effect of misspecifying the length measurement error and the growth curve on population size estimation. Simulated populations represented two fictional shark species, one with a relatively simple life history and the other with a more complex life history based on the grey reef shark (Carcharhinus amblyrhynchos). We estimated sex-specific adult abundance, which we assumed to be constant in time. We observed small median biases in these estimates ranging from 1.35% to 2.79% when specifying the correct measurement error standard deviation and growth curve. CI coverage was adequate whenever the growth curve was correctly specified. Introducing error via misspecified growth curves resulted in changes in the magnitude of the estimated adult population, where underestimating age negatively biased the abundance estimates. Over- and underestimating the standard deviation of length measurement error did not introduce a bias and had negligible effect on the variance in the estimates. Our findings show that assuming an incorrect standard deviation of length measurement error has little effect on estimation, but having an accurate growth curve is crucial for CKMR whenever ageing is based on length measurements. If ageing could be biased, researchers should be cautious when interpreting CKMR results and consider the potential biases arising from inaccurate age inference.

Comparison of nonlinear functions to describe lactation curves for cumulative milk production in buffalo.

The aim of this study was to examine the suitability of different growth functions (linear, sinusoidal, Gompertz, Schumacher and Richards) to fit cumulative milk production data from buffalo cows. Cumulative milk production at each day in milk was calculated from two published datasets reporting (i) fortnightly test-day milk yield records of the first lactation of Murrah buffalo that had calved during 1977-2012 and (ii) the first lactation records of Jaffarabadi buffalo collected from history-cum-pedigree registers for each quinquennium between 1991 and 2010. Each function was fitted to the lactation curves using nonlinear regression procedures. The Richards and sinusoidal equations provided the smallest root mean square error values, Akaike's and Bayesian information criteria and, therefore, the best fit for the cumulative lactation curves for milk yield. The Richards equation appeared to provide the most accurate estimate of the cumulative milk production at peak milk yield. Sinusoidal and flexible classical growth functions are appropriate to describe cumulative milk production curves and estimate lactation traits in buffalo.

Quantifying the effects of plant density on soybean lodging resistance and growth dynamics in maize-soybean strip intercropping.

Shading-induced soybean stem lodging is a prevalent concern in the maize (Zea mays L.)-soybean (Glycine max L. Merr.) strip intercropping system, leading to a substantial decline in yield. Nevertheless, the associations between soybean growth, stem lodging, and yield formation in this scenario remain unclear. To investigate this, the logistic and beta growth models were utilized to analyze the growth process of soybean organs (stems, leaves, branches, and pods) and the accumulation of carbohydrates (lignin, cellulose, and sucrose) at three planting densities (8.5, 10, and 12.5 plants m-2) in both strip intercropping and skip strip monoculture systems. The results indicate that shading stress caused by maize in the intercropping system reduced lignin and cellulose accumulation in soybean stems, thus decelerating soybean organ growth compared to monoculture. Furthermore, intercropped soybean at higher planting density (PD3) exhibited a 28% reduction in the maximum dry matter growth rate (cm) and a 11% decrease in the time taken to reach the maximum dry matter growth rate (te) compared to the lower planting density (PD1). Additionally, a 29% decrease in the maximum accumulation rate (cmax) of sucrose, lignin, and cellulose was observed, along with a 13% decrease in the continuous accumulation time (tc) of these carbohydrates in intercropped soybean at PD3. Interspecific and intraspecific shading stress led to a preferential allocation of assimilates into soybean stems, enhancing plant height during the initial stage, while at later stages, a greater proportion of sucrose was allocated to leaves. Consequently, this hindered the conversion of sucrose into lignin and cellulose within the stems, ultimately resulting in a reduction in the lodging resistance index (LRI). Overall, this study provides valuable insights into the effects of shading stress on soybean growth and yield. It also emphasizes how optimizing planting density in intercropping systems can effectively alleviate shading stress and enhance crop productivity.

The evolution of the labor curve and its implications for clinical practice: the relationship between cervical dilation, station, and time during labor.

The assessment of labor progress is germane to every woman in labor. Two labor disorders-arrest of dilation and arrest of descent-are the primary indications for surgery in close to 50% of all intrapartum cesarean deliveries and are often contributing indications for cesarean deliveries for fetal heart rate abnormalities. Beginning in 1954, the assessment of labor progress was transformed by Friedman. He published a series of seminal works describing the relationship between cervical dilation, station of the presenting part, and time. He proposed nomenclature for the classification of labor disorders. Generations of obstetricians used this terminology and normal labor curves to determine expected rates of dilation and fetal descent and to decide when intervention was required. The analysis of labor progress presents many mathematical challenges. Clinical measurements of dilation and station are imprecise and prone to variation, especially for inexperienced observers. Many interrelated factors influence how the cervix dilates and how the fetus descends. There is substantial variability in when data collection begins and in the frequency of examinations. Statistical methods to account for these issues have advanced considerably in recent decades. In parallel, there is growing recognition among clinicians of the limitations of using time alone to assess progress in cervical dilation in labor. There is wide variation in the patterns of dilation over time and most labors do not follow an average dilation curve. Reliable assessment of labor progression is important because uncertainty leads to both over-use and under-use of cesarean delivery and neither of these extremes are desirable. This review traces the evolution of labor curves, describes how limitations are being addressed to reduce uncertainty and to improve the assessment of labor progression using modern statistical techniques and multi-dimensional data, and discusses the implications for obstetrical practice.

Constructing two-level nonlinear mixed-effects crown width models for Moso bamboo in China.

Bamboo crown width (CW) is a reliable index for evaluating growth, yield, health and vitality of bamboo, and light capture ability and carbon fixation efficiency of bamboo forests. Based on statistical results produced from fitting the eight basic growth functions using data from 1374 Phyllostachys pubescens in Yixing, Jiangsu Province, China, this study identified the most suitable function (logistic function) to construct a two-level mixed effects (NLME) CW model with the forest block and sample plot-level effects included as random effects in the model. Four methods for selecting sample bamboos per sample plot (largest bamboo, medium-sized bamboo, smallest bamboo, and randomly selected bamboos) and eight sample sizes (1-8 selected bamboos per sample plot) were evaluated to calibrate our NLME CW model. Using diameter at breast height (DBH), height to crown base (HCB), arithmetic mean diameter at breast height (MDBH), and height (H) as predictor variables, the model produced the best fit statistics (Max R2, min RMSE, and TRE). This model was further improved by introducing random effects at two levels. The results showed a positive correlation of CW with HCB and DBH and a negative correlation with H. The smallest two bamboo poles per sample plot used to estimate the random effects of the NLME model provided a satisfactory compromise regarding measurement cost, model efficiency, and prediction accuracy. The presented NLME CW model may guide effective management and carbon estimation of bamboo forests.

Immediate effects of individualized music listening on behavioral and psychological symptoms of dementia: A randomized controlled trial.

OBJECTIVES: Evidence suggests that individualized music listening (IML) can effectively reduce the behavioral and psychological symptoms of dementia (BPSD). So far, however, studies have been primarily based on questionnaire measures of BPSD completed by proxy. We therefore investigate effects of IML on BPSD based on systematic observation. We address the methodological limitations of previous observational studies by using a validated instrument, time-based sampling, and longitudinal analytical methods. METHODS: We compared BPSD of nursing home residents with dementia in an IML intervention group (IG; n = 44) and a control group (CG; n = 46) in a randomized controlled trial (DRKS00013793; ISRCTN59052178). Trained raters observed 18 BPSD in 15 four-minute intervals before, during and after an IML session. We used t-tests to compare BPSD in the IG and CG before, during and after the session and piecewise latent curve modelling to compare BPSD trajectories across 1 hour. RESULTS: BPSD were observed less frequently in the IG than in the CG during, but not before or after the session. Likewise, whereas the CG experienced a stable trajectory of BPSD, the IG experienced a u-shaped trajectory characterized by stability before the session, a decrease during the session, and increase after the session. There was significant interindividual variability in baseline BPSD and in the pre- and post-session slopes. DISCUSSION: Our results provide additional evidence that IML effectively reduces BPSD, although the effect is short-lived. As IML rarely has negative side effects, is highly accepted and easily implemented, IML should be integrated into the everyday care routines for people with dementia. CLINICAL TRIAL REGISTRATION: German Clinical Trials Register DRKS00013793; ISRCTN registry, ISRCTN59052178.

Field irrigation using magnetized brackish water affects the growth and water consumption of Haloxylon ammodendron seedlings in an arid area.

Freshwater resources in arid areas are scarce, while there are abundant brackish water reserves that have great application potential for the irrigation of desert plants. However, brackish water irrigation will lead to soil salinization, which will inhibit plant growth. Magnetized water is a new technology that makes the use of brackish water feasible. The present study assessed the effects of irrigation using three water types (fresh, brackish, and magnetized brackish water) and five irrigation amounts (W1, 81 mm; W2, 108 mm; W3, 135 mm; W4, 162mm; and W5, 189 mm) on soil salinity and Haloxylon ammodendron seedling growth. Compared with fresh water, brackish water irrigation inhibited the growth of H. ammodendron and reduced water consumption. Irrigation with magnetized brackish water effectively improved the effect of soil salt leaching, promoted the growth and water absorption of H. ammodendron roots, and stimulated the growth of plant height, basal diameter, shoot length, and crown width. Based on the principal component analysis, the first three treatments of H. ammodendron comprehensive growth state were FW4, FW3, and MBW4, respectively. This showed that magnetized brackish water combined with an appropriate irrigation amount was helpful to optimize the growth of H. ammodendron seedlings on the basis of fresh water saving. Therefore, magnetized brackish water irrigation is an effective strategy for ensuring the establishment and growth of H. ammodendron seedlings in arid and water-deficient areas.

Statistical Methods Revisited for Estimating Daily Milk Yields: How Well do They Work?

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of "2.0" for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.

Otolith Weight as an Estimator of the Age of Seriola lalandi Valenciennes, 1833 (Carangidae), in the Southeastern Pacific.

The age and growth of fish populations is a critical issue for stock assessment, population dynamics and fishery management. Spawner biomass, mortality, growth, recruitment and age at maturity can be misconstrued if the age estimator is inaccurate. Age can be estimated by annuli count, but this requires expertise and is expensive. Otolith weight (OW) is a good indicator of how long a fish has lived, because OW increases during an individual's life. Seriola lalandi is a migratory fish and is an important resource for local fishermen in northern Chile. Aspects of its biology, i.e., age and growth, remain unknown, at least for the population annually arriving in northern Chile. Fish of a known age (n = 105; from 5.5 to 25.7 cm in FL) from yellowtail aquaculture facilities at Universidad de Antofagasta allowed us to estimate the growth rate of OW, and fish obtained from local fishermen (n = 71; from 37.9 to 109 cm in FL) allowed us to estimate the age and growth of S. lalandi from the analysis of OW, without the need for calibration. The following four models were fitted with the known ages and fork lengths: the von Bertalanffy growth function, the Gompertz and logistic models and the seasonalized von Bertalanffy growth function. The latter model showed the best adjustment (according to the Akaike information criterion) with the following parameters: L∞, 98.58 cm.; K, 0.59; t0, 0.07; ts, 0.84; and C, 0.97.

Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations.

BACKGROUND: With the spread of COVID-19, the time-series prediction of COVID-19 has become a research hotspot. Unlike previous epidemics, COVID-19 has a new pattern of long-time series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with short-time series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms. METHODS: A dynamical model with only infected humans and removed humans was established. Then the process of COVID-19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a non-linear variable and NO2 concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions. RESULTS: The experiments and analysis showed the R2 of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. CONCLUSION: The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID-19 with local fluctuations and multiple peaks, which can provide valuable assistant decision-making information.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

Trinucleotide k-circular codes I: Theory.

A code X is (⩾k)-circular if every concatenation of words from X that admits, when read on a circle, more than one partition into words from X, must contain at least k+1 words. In other words, the reading frame retrieval is guaranteed for any concatenation of up to k words from X. A code that is (⩾k)-circular for all integers k is said to be circular. Any code is (⩾0)-circular and it turns out that a code of trinucleotides is circular as soon as it is (⩾4)-circular. A code is k-circular if it is (⩾k)-circular and not (⩾k+1)-circular. Due to the explosive combinatorics of trinucleotide k-circular codes, we developed three classes of algorithms based on: (i) the smallest directed cycles (directed girth) in graphs; (ii) the eigenvalues of matrices; and (iii) the files that incrementally save partial results. These different approaches also allow us to verify the computational results obtained. We determine here the growth functions of trinucleotide k-circular codes, k varying between 0 and 4, in the general case and in various particular cases: minimum, minimal, maximum, self-complementary k-, (k,k,k)- and self-complementary (k,k,k)-circular.

System dynamics kinetic model for predicting biogas production in anaerobic condition: Preliminary assessment.

INTRODUCTION: This preliminary assessment of a grey-box model, was predicated on system dynamics principles and developed using Vensim® DSS software. The purpose is to predict biogas production under anaerobic conditions for energy utilization at the design stage. OBJECTIVE: To describe the process of a developed system dynamics model to predict biogas production under anaerobic conditions. METHODS: This method involves two-stage kinetics of the biogas production process in anaerobic conditions using the first-order and Gompertz functions. The model is depicted in two parts: causal loop diagram and stock-flow diagram. The causal loop diagram describes the anaerobic digestion process a substrate undergoes for the production of biogas, while stock-flow diagram depicts basic building blocks of the dynamic behavior of an anaerobic digestion process. Primary data is from a laboratory-scale experiment of biogas production using vegetal wastes, while the secondary one is from the literature on studies using similar substrates. RESULTS: Primary and secondary data are used to validate and stimulate the developed model. The kinetic model shows the substrate being reduced exponentially with increasing time; consumption of substrate and production of methane and carbon dioxide follows exponential growth and decay pattern, with carbon dioxide production starting early compared to methane, and was produced at a rate faster due to the strong and resilient characteristics of fermentative microorganisms. DISCUSSION: Comparing data from empirical and model simulation shows some close relationship, though not too perfectly. Both results reflect signs of inhibitions occurring within the substrates in the digester under anaerobic conditions explaining the low methane yield or instability.

A Fast Approximate Method for Predicting the Behavior of Auditory Nerve Fibers and the Evoked Compound Action Potential (ECAP) Signal.

BACKGROUND: The goal of the current research is to develop a model based on computer simulations which describes both the behavior of the auditory nerve fibers and the cochlear implant system as a rehabilitation device. METHODS: The approximate method was proposed as a low error and fast tool for predicting the behavior of auditory nerve fibers as well as the evoked compound action potential (ECAP) signal. In accurate methods every fiber is simulated; whereas, in approximate method information related to the response of every fiber and its characteristics such as the activation threshold of cochlear fibers are saved and interpolated to predict the behavior of a set of nerve fibers. RESULTS: The approximate model can predict and analyze different stimulation techniques. Although precision is reduced to <1.66% of the accurate method, the required execution time for simulation is reduced by more than 98%. CONCLUSION: The amplitudes of the ECAP signal and the growth function were investigated by changing the parameters of the approximate model including geometrical parameters, electrical, and temporal parameters. In practice, an audiologist can tune the stimulation parameters to reach an effective restoration of the acoustic signal.

Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.

Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure.

Amplitude Growth Functions of Auditory Nerve Responses to Electric Pulse Stimulation With Varied Interphase Gaps in Cochlear Implant Users With Ipsilateral Residual Hearing.

Amplitude growth functions (AGFs) of electrically evoked compound action potentials (eCAPs) with varying interphase gaps (IPGs) were measured in cochlear implant users with ipsilateral residual hearing (electric-acoustic stimulation [EAS]). It was hypothesized that IPG effects on AGFs provide an objective measure to estimate neural health. This hypothesis was tested in EAS users, as residual low-frequency hearing might imply survival of hair cells and hence better neural health in apical compared to basal cochlear regions. A total of 16 MED-EL EAS subjects participated, as well as a control group of 16 deaf cochlear implant users. The IPG effect on the AGF characteristics of slope, threshold, dynamic range, and stimulus level at 50% maximum eCAP amplitude (level50%) was investigated. AGF threshold and level50% were significantly affected by the IPG in both EAS and control group. The magnitude of AGF characteristics correlated with electrode impedance and electrode-modiolus distance (EMD) in both groups. In contrast, the change of the AGF characteristics with increasing IPG was independent of these electrode-specific measures. The IPG effect on the AGF level50% in both groups, as well as on the threshold in EAS users, correlated with the duration of hearing loss, which is a predictor of neural health. In EAS users, a significantly different IPG effect on level50% was found between apical and medial electrodes. This outcome is consistent with our hypothesis that the influence of IPG effects on AGF characteristics provides a sensitive measurement and may indicate better neural health in the apex compared to the medial cochlear region in EAS users.

Horizontal visibility graph of a random restricted growth sequence.

We study the distributional properties of horizontal visibility graphs associated with random restrictive growth sequences and random set partitions of size n. Our main results are formulas expressing the expected degree of graph nodes in terms of simple explicit functions of a finite collection of Stirling and Bernoulli numbers.

Relationship between Vessel Formation and Seasonal Changes in Leaf Area of Evergreen and Deciduous Species with Different Vessel Arrangements.

To discuss the diversity of morphological traits and life strategies of trees, the functional relationship between leaf expansion and vessel formation must be clarified. We compared the temporal relationship among tree species with different leaf habits and vessel arrangements. Twigs, leaves, and trunk core samples were periodically acquired from 35 sample trees of nine species in a temperate forest in Japan. We quantitatively estimated leaf expansion using a nonlinear regression model and observed thin sections of twigs and trunks with a light microscope. Almost all of the first-formed vessels in twigs, which formed adjacent to the annual ring border, were lignified with a leaf area between 0% and 70% of the maximum in all species. The first-formed vessels in trunks lignified between 0% and 95% of the maximum leaf area in ring-porous deciduous Quercus serrata and ring-(radial-)porous evergreen Castanopsis cuspidate. Their lignification occurred earlier than in diffuse-porous deciduous Liquidambar styraciflua, diffuse-porous evergreen Cinnamomum camphora and Symplocos prunifolia, and radial-porous evergreen Quercus glauca and Quercus myrsinifolia. The timing varied in semi-ring-porous deciduous Acanthopanax sciadophylloides and diffuse-porous evergreen Ilex pedunculosa. The observed differences in the timing of vessel formation after leaf appearance were reflected in their differing vessel porosities and were connected to the different life strategies among tree species.