SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

5'-Nucleotidase is dispensable for the growth of Salmonella Typhimurium but inhibits the bactericidal activity of macrophage extracellular traps.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a zoonotic pathogen that causes severe gastroenteritis. The 5'-nucleotidases of pathogens can dephosphorylate adenosine phosphates, boost adenosine levels and suppress the pro-inflammatory immune response. In our previous study, an extracellular nuclease, 5'-nucleotidase, was identified in the extracellular proteins of S. Typhimurium. However, the nuclease activity and the function of the 5'-nucleotidase of S. Typhimurium have not been explored. In the present study, deletion of the 5'-nucleotidase gene is dispensable for S. Typhimurium growth, even under environmental stress. Fluorescence microscopy revealed that the 5'-nucleotidase mutant induced more macrophage extracellular traps (METs) than the wild type did. Furthermore, recombinant 5'-nucleotidase protein (r5Nuc) could degrade λDNA, and the nuclease activity of r5Nuc was optimum at 37 °C and pH 6.0-7.0. The Mg2+ enhanced the nuclease activity of r5Nuc, whereas Zn2+ inhibited it. Meanwhile, deletion of the 5'-nucleotidase gene increased the bactericidal activity of METs, and r5Nuc could degrade METs and inhibit the bactericidal activity of METs. In conclusion, S. Typhimurium growth was independent of 5'-nucleotidase, but the nuclease activity of 5'-nucleotidase assisted S. Typhimurium to evade macrophage-mediated extracellular killing through degrading METs.

Interaction of alginate with nano-hydroxyapatite-collagen using strontium provides suitable osteogenic platform.

BACKGROUND: Hydrogels based on organic/inorganic composites have been at the center of attention for the fabrication of engineered bone constructs. The establishment of a straightforward 3D microenvironment is critical to maintaining cell-to-cell interaction and cellular function, leading to appropriate regeneration. Ionic cross-linkers, Ca2+, Ba2+, and Sr2+, were used for the fabrication of Alginate-Nanohydroxyapatite-Collagen (Alg-nHA-Col) microspheres, and osteogenic properties of human osteoblasts were examined in in vitro and in vivo conditions after 21 days. RESULTS: Physicochemical properties of hydrogels illustrated that microspheres cross-linked with Sr2+ had reduced swelling, enhanced stability, and mechanical strength, as compared to the other groups. Human MG-63 osteoblasts inside Sr2+ cross-linked microspheres exhibited enhanced viability and osteogenic capacity indicated by mineralization and the increase of relevant proteins related to bone formation. PCR (Polymerase Chain Reaction) array analysis of the Wnt (Wingless-related integration site) signaling pathway revealed that Sr2+ cross-linked microspheres appropriately induced various signaling transduction pathways in human osteoblasts leading to osteogenic activity and dynamic growth. Transplantation of Sr2+ cross-linked microspheres with rat osteoblasts into cranium with critical size defect in the rat model accelerated bone formation analyzed with micro-CT and histological examination. CONCLUSION: Sr2+ cross-linked Alg-nHA-Col hydrogel can promote functionality and dynamic growth of osteoblasts.

Growth patterns in early childhood and cardiovascular structure and function at 4 years old: A prospective cohort study.

BACKGROUND AND AIMS: Childhood overweight and obesity are lifetime risk factors for cardiovascular disease but the relationship between dynamic body mass index (BMI) change and cardiovascular structure and function in early childhood remains unclear. METHODS AND RESULTS: This cohort study consisted 525 participants with 6 distinct representative growth patterns to examine the associations between BMI growth patterns and subsequent cardiovascular structure and function at age 4. BMIs were obtained at birth, 2 and 4 years old. Cardiovascular assessments were performed, including blood pressure (BP), cardiac geometric parameters, left ventricular (LV) function, speckle-tracking, integrated backscatter analysis and carotid intima-media thickness. Compared to the stable normal BMI pattern, children with the stable overweight (OW) pattern had significantly greater LV anatomic parameters in fully adjusted models. Children with the catch-up (CU) pattern revealed a uniform trend and had poorer strain. LV diameters and integrated backscatter signals were larger for those with BMI gain and lose pattern. Children with BMI lose pattern showed improved tendency involving LV mass index and BP. Both OW and CU patterns were associated with high systolic BP [odds ratio (95% CI): OW: 3.67 (1.08, 12.47); CU: 4.24 (1.75, 10.28)]. Compared to static BMI measurements at birth, 2 and 4 years old, dynamic BMI growth patterns were more predictive of cardiovascular structure and function at 4. CONCLUSIONS: Children with overweight-related BMI growth patterns in early childhood experienced undesirable cardiovascular functional or structural changes as early as 4 years old, indicating that early intervention is needed and potentially beneficial.

The potential of spring distraction to dynamically correct complex spinal deformities in the growing child.

PURPOSE: Current treatment of progressive early onset scoliosis involves growth-friendly instrumentation if conservative treatment fails. These implants guide growth by passive sliding or repeated lengthenings. None of these techniques provide dynamic correction after implantation. We developed the spring distraction system (SDS), by using one or multiple compressed springs positioned around a standard sliding rod, to provide active continuous distraction of the spine to stimulate growth and further correction. The purpose of this study was to determine feasibility and proof of concept of the SDS. METHODS: We developed a versatile, dynamic spring distraction system for patients who would benefit from active continuous distraction. This prospective case series evaluates four patients with exceptional and progressive congenital spine deformities. RESULTS: Four patients had a mean age of 6.8 years at surgery with a mean follow-up of 36 months (range 25-45). The mean progressive thoracic lordosis, which was the reason for initiating surgical treatment in two patients, changed from 32° lordosis preoperatively to 1° kyphosis post-operatively. During follow-up, this further improved to 32° thoracic kyphosis. In the two other patients, with cervicothorcacic scoliosis, the main coronal curve improved from 79° pre-operatively to 56° post-operatively and further improved to 42°. The mean T1-S1 spine growth during follow-up for all patients was 1.3 cm/year. There was one reoperation because of skin problems and no device-failures. CONCLUSION: These early results show the feasibility and the proof of concept of spring-based distraction as a dynamic growth-enhancing system with the potential of further correction of the deformity after implantation.

Dynamic growth models for Caragana korshinskii shrub biomass in China.

The Caragana korshinskii shrub is a widely distributed plant found in arid regions and plays an important role in ecological environment protection. Accurate estimations of shrub biomass are particularly important for natural resource management decision making. 114 individual C. korshinskii shrubs from three regions were collected in northwest China in this study. With regions as fixed (dummy variables) and random effects, the nonlinear least square (NLS) regression approach, nonlinear fixed effects (NLFE) approach and nonlinear mixed effects (NLME) approach were developed to predict dynamic growth of total, aboveground, stem, foliage, and root biomass values of C. korshinskii shrub based on logistic function. Results revealed that both NLFE and NLME models performed better than NLS, which indicated that regions were important factors influenced shrub biomass dynamic growth. Additionally, NLME models had a smaller Bayesian information criterion (BIC) than NLFE models. For NLME models, the random effects of regions mainly influenced the growth rate and asymptotic value of the dynamic growth curve, and there was no significant influence on the values associated with the curve shape. Moreover, the modified NLME models with heteroscedasticity exhibited extremely significant differences (p < 0.0001) when compared to NLME models by the likelihood ratio (LR) test. The NLME models were proved to be an efficient approach for considering the random effects on shrub biomass dynamic growth and accounted for the heteroscedasticity of shrub biomass data.

Two-stage identification of SNP effects on dynamic poplar growth.

This project proposes an approach to identify significant single nucleotide polymorphism (SNP) effects, both additive and dominant, on the dynamic growth of poplar in diameter and height. The annual changes in yearly phenotypes based on regular observation periods are considered to represent multiple responses. In total 156,362 candidate SNPs are studied, and the phenotypes of 64 poplar trees are recorded. To address this ultrahigh dimensionality issue, this paper adopts a two-stage approach. First, the conventional genome-wide association studies (GWAS) and the distance correlation sure independence screening (DC-SIS) methods (Li et al., 2012) were combined to reduce the model dimensions at the sample size; second, a grouped penalized regression was applied to further refine the model and choose the final sparse SNPs. The multiple response issue was also carefully addressed. The SNP effects on the dynamic diameter and height growth patterns of poplar were systematically analyzed. In addition, a series of intensive simulation studies was performed to validate the proposed approach.

High Frequency Electromagnetic Radiation Stimulates Neuronal Growth and Hippocampal Synaptic Transmission.

Terahertz waves lie within the rotation and oscillation energy levels of biomolecules, and can directly couple with biomolecules to excite nonlinear resonance effects, thus causing conformational or configuration changes in biomolecules. Based on this mechanism, we investigated the effect pattern of 0.138 THz radiation on the dynamic growth of neurons and synaptic transmission efficiency, while explaining the phenomenon at a more microscopic level. We found that cumulative 0.138 THz radiation not only did not cause neuronal death, but that it promoted the dynamic growth of neuronal cytosol and protrusions. Additionally, there was a cumulative effect of terahertz radiation on the promotion of neuronal growth. Furthermore, in electrophysiological terms, 0.138 THz waves improved synaptic transmission efficiency in the hippocampal CA1 region, and this was a slow and continuous process. This is consistent with the morphological results. This phenomenon can continue for more than 10 min after terahertz radiation ends, and these phenomena were associated with an increase in dendritic spine density. In summary, our study shows that 0.138 THz waves can modulate dynamic neuronal growth and synaptic transmission. Therefore, 0.138 terahertz waves may become a novel neuromodulation technique for modulating neuron structure and function.

The laws and effects of terahertz wave interactions with neurons.

Introduction: Terahertz waves lie within the energy range of hydrogen bonding and van der Waals forces. They can couple directly with proteins to excite non-linear resonance effects in proteins, and thus affect the structure of neurons. However, it remains unclear which terahertz radiation protocols modulate the structure of neurons. Furthermore, guidelines and methods for selecting terahertz radiation parameters are lacking. Methods: In this study, the propagation and thermal effects of 0.3-3 THz wave interactions with neurons were modelled, and the field strength and temperature variations were used as evaluation criteria. On this basis, we experimentally investigated the effects of cumulative radiation from terahertz waves on neuron structure. Results: The results show that the frequency and power of terahertz waves are the main factors influencing field strength and temperature in neurons, and that there is a positive correlation between them. Appropriate reductions in radiation power can mitigate the rise in temperature in the neurons, and can also be used in the form of pulsed waves, limiting the duration of a single radiation to the millisecond level. Short bursts of cumulative radiation can also be used. Broadband trace terahertz (0.1-2 THz, maximum radiated power 100 µW) with short duration cumulative radiation (3 min/day, 3 days) does not cause neuronal death. This radiation protocol can also promote the growth of neuronal cytosomes and protrusions. Discussion: This paper provides guidelines and methods for terahertz radiation parameter selection in the study of terahertz neurobiological effects. Additionally, it verifies that the short-duration cumulative radiation can modulate the structure of neurons.

Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought.

In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.

A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks.

Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results: In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions: We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.

Combination of polyglycerol sebacate coated with collagen for vascular engineering.

Introduction: Here, we monitored the cytocompatibility of scaffolds consisting of poly (glycerol sebacate) (PGS) coated with collagen (Col) for endothelial cell activity after 72 hours. Methods: Human endothelial cells were allocated into Control, PGS, and PGS+Col groups. Scaffolds were characterized using FTIR and HNMR spectroscopy. Contact angel analysis and SEM were used to study wettability, surface morphology, and cell attachment. Cell survival was assessed using LDH leakage assay. Levels of Tie-1, Tie-2, VE-Cadherin, and VEGFR-2 were measured using western blotting and real-time PCR. Results: FTIR and HNMR analyses revealed the proper blending in PGS+Col group. SEM imaging exhibited a flat surface in the PGS group while thin Col fibers were detected in PGS+Col surface. The addition of Col to the PGS reduced the contract angle values from 97.3˚ to 81.1˚. Compared to PGS substrate alone, in PGS+Col group, cells appropriately attached to the surface. PGS and PGS+Col did not alter the leakage of LDH to the supernatant compared to control cells, showing the cytocopatiblity of PGS-based scaffolds. SOD and NO levels were increased significantly in PGS (p<0.05) and PGS+Col groups (p<0.001), respectively. We found that PGS+Col decreased Tie-1 content in endothelial cells whereas protein levels of Tie-2 and VE-Cadherin and expression of VEGFR-2 remained unchanged compared to PGS and control groups. Conclusion: Simultaneous application of Col and PGS can stimulate normal endothleial cell morphology without the alteration of tyrosine kinases receptors and cadherin.

Static-Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction.

Accurate prediction of the tumor's future imaging features can provide its complete growth evolution and more detailed clinical parameters. The existing longitudinal models tend to lose detailed growth information and make it difficult to model the complete tumor development process. In this paper, we propose the Static-Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction (SDC-Transformer). To extract the static high-level features of tumors in each period, and to further explore the dynamic growth associations and expansion trend of tumors between different periods. Aiming at the insensitivity to local pixel information of the Transformer, we propose the Local Adaptive Transformer Module to facilitate a strongly coupled status of feature images, which ensures the characterization of tumor complex growth trends. Faced with the dynamic changes brought about by tumor growth, we introduce the Dynamic Growth Estimation Module to predict the future growth trend of the tumor. As a core part of SDC-Transformer, we design the Enhanced Deformable Convolution to enrich the sampling space of tumor growth pixels. And a novel Cascade Self-Attention is performed under multi-growth imaging to obtain dynamic growth relationships between periods and use dual cascade operations to predict the tumor's future expansion trajectories and growth contours. Our SDC-Transformer is rigorously trained and tested on longitudinal tumor data composed of the National Lung Screening Trial (NLST) and collaborative Shanxi Provincial People's Hospital. The RMSE, Dice, Recall, and Specificity of the longitudinal prediction results reach 11.32, 89.31%, 90.57%, and 89.64%, respectively. This result shows that our proposed SDC-Transformer model can achieve accurate longitudinal prediction of tumors, which will help physicians to establish specific treatment plans and accurately diagnose lung cancer. The code will be released soon.

Time-sequential detection of quantitative trait loci and candidate genes underlying the dynamic growth of Salix suchowensis.

Elucidating the genetic factors underlying long-term biological processes remains challenging since the relevant genes and their effects may vary across different developmental stages. In this study, we carried out a large-scale field trial of the progeny of an F1 full-sib pedigree of Salix suchowensis and measured plant height and ground diameter periodically over a time course of 240 days. With the obtained data, we characterized plant growth rhythms and performed time-sequential analyses of quantitative trait loci underlying the dynamic growth of the plants. The dynamic mapping of quantitative trait loci revealed that stem height and ground diameter were under the control of four quantitative trait loci, and the effects of these quantitative trait loci varied greatly throughout the growth process, in which two quantitative trait loci were found to exert a pleiotropic effect determining the correlation between stem height and ground diameter. The analysis of candidate genes in the target genetic intervals showed that the pleiotropic effect of the two quantitative trait loci arises from the colocalization of genes with independent effects on stem height and ground diameter. Further examination of the expression patterns of the candidate genes indicated that height and circumference growth involve different activities of leaf and cambium tissues. This study provides unprecedented information to help us understand the dynamic growth of plants and presents an applicable strategy for elucidating the genetic mechanism underlying a long-term biological process by using plant growth as an example.

Application of gas sensors for modelling the dynamic growth of Pseudomonas in pork stored at different temperatures.

Pseudomonas have a faster growth rate over other bacteria in chilled meat under aerobic conditions. A non-destructive method for modelling the dynamic growth of Pseudomonas in pork stored at different temperatures using gas sensors was presented in our work. Based on selected gas sensor data, the first-order kinetic equations (Gompertz and Logistic Functions) combined with the secondary model (Square-root Function) effectively simulated Pseudomonas growth in pork at different temperatures with R2 and RMSE values of 0.71-0.97 and 0.27-0.84, respectively. Additionally, these models showed high accuracy with correlation coefficients greater than 0.90, in addition to several individual accuracy values. Furthermore, HS-SPME/GC-MS results demonstrated the presence of identified key volatiles in samples inoculated with Pseudomonas, including three amine compounds (mercaptamine, 1-octanamine and 1-heptadecanamine), phenol and indole. Our work showed that gas sensors are a rapid, easy and non-destructive method with acceptable feasibility in modelling the dynamic growth of spoilage microorganisms in meat.

Prediction in the Dynamics and Spoilage of Shewanella putrefaciens in Bigeye Tuna (Thunnus obesus) by Gas Sensors Stored at Different Refrigeration Temperatures.

Shewanella putrefaciens have a faster growth rate and strong spoilage potential at low temperatures for aquatic products. This study developed a nondestructive method for predicting the kinetic growth and spoilage of S. putrefaciens in bigeye tuna during cold storage at 4, 7 and 10 °C by electronic nose. According to the responses of electronic nose sensor P30/2, the fitted primary kinetic models (Gompertz and logistic models) and secondary model (square root function model) were able to better simulate the dynamic growth of S. putrefaciens, with high R2 and low RMSE values in the range of 0.96-0.99 and 0.021-0.061, respectively. A partial least squares (PLS) regression model based on both electronic nose sensor response values and electrical conductivity (EC) values predicted spoilage of S. putrefaciens in bigeye tuna more accurately than the PLS model based on sensor signal values only. In addition, SPME/GC-MS analysis suggested that 1-octen-3-ol, 2-nonanone, 2-heptanone, dimethyl disulfide and methylamine, N, N-dimethyl- are the key VOCs of tuna inoculated with S. putrefaciens.

Upregulated effects of miR-7 in methicillin-resistant Staphylococcus aureus.

The aim of the study was to investigate the characteristic function of the upregulated effects of miR-7 in methicillin-resistant Staphylococcus aureus (MRSA). After separating the MRSA in clinic, the expression of miR-7 mRNA was tested by reverse transcription polymerase chain reaction. The overexpression, inhibition of miR-7, and control group were established by plasmid in vitro. Following transfection of the bacterial strain, the effect of ß-lactam antibiotics in minimum inhibitory concentration (MIC) was observed using the microporous dilution method, and antibacterial effects in vitro were observed using the dynamic growth curve method. The expression of miR-7 in sensitive MRSA was upregulated distinctly, with significant difference (P<0.05). MIC and the number of bacteria in the miR-7 overexpression group significantly increased while the inhibition group decreased prominently, with significant difference (P<0.05). The control and null plasmid groups revealed no significant difference. In conclusion, miR-7 upregulated the antimicrobial activity of MRSA, and the intervention of its expression may become a possible antibacterial target.

Dynamic Growth of Pinhole-Free Conformal CH3NH3PbI3 Film for Perovskite Solar Cells.

Two-step dipping is one of the popular low temperature solution methods to prepare organic-inorganic halide perovskite (CH3NH3PbI3) films for solar cells. However, pinholes in perovskite films fabricated by the static growth method (SGM) result in low power conversion efficiency (PCE) in the resulting solar cells. In this work, the static dipping process is changed into a dynamic dipping process by controlled stirring PbI2 substrates in CH3NH3I isopropanol solution. The dynamic growth method (DGM) produces more nuclei and decreases the pinholes during the nucleation and growth of perovskite crystals. The compact perovskite films with free pinholes are obtained by DGM, which present that the big perovskite particles with a size of 350 nm are surrounded by small perovskite particles with a size of 50 nm. The surface coverage of the perovskite film is up to nearly 100%. Such high quality perovskite film not only eliminated pinholes, resulting in reduced charge recombination of the solar cells, but also improves the light harvesting efficiency. As a result, the PCE of the perovskite solar cells is increased from 11% for SGM to 13% for DGM.