[Dynamic Response of Aerobic Denitrification Bacteria to Water Quality in Baiyangdian Lake Under Different Hydrological Scenarios].

In order to explore the evolution law and driving mechanism of aerobic denitrification bacteria in Baiyangdian Lake under different hydrological scenarios, based on water quality survey and high-throughput sequencing technology, this study conducted a water quality factor analysis and aerobic denitrification bacteria α-diversity analysis, species composition, and network analysis. The results showed that the water body of Baiyangdian Lake was weakly alkaline, with the highest T and the lowest DO in the rainy season and the lowest T and the highest DO in the freezing season. There were significant differences between NH4+-N, NO2--N, NO3--N, TN, permanganate index, Fe, and Mn in Baiyangdian water under different hydrological scenarios (P < 0.01), and there was no significant difference in TP under different hydrological scenarios (P > 0.05). The largest category in water bodies under different hydrological scenarios was Proteobacteria, and the genera with a higher relative abundance were Magnetospirillum, Aeromonas, Pseudomonas, Azospirillum, and Bradyrhizobium. In addition, within the aerobic denitrifying bacteria community, there were significant differences in α-diversity (P < 0.001), with the highest abundance of microbial communities occurring during the freezing period, and the highest diversity and evenness of microbial communities during the dry and freezing periods. According to the RDA and Mantel analyses, the water quality driving factors of flora were different under different hydrological scenarios. The water quality driving factors of flora in the dry season were pH, NO3--N, NO2--N, and permanganate index; the driving factors of flora in the rainy season were pH, T, DO, NO2--N, and TP; the driving factors of flora in the normal season were NO2--N, Fe, and permanganate index; and the driving factors of flora in the freezing season were NO3--N and NONO2--N. Network analysis showed that there were temporal differences in species related to water quality driving factors. The genera related to water quality driving factors during the dry season were Magnetospirillum, Aeromonas, and Azoarcus, whereas the genera related to the rainy season were Magnetospirillum, Pseudomonas, and Aeromonas. The genera related to the normal season were Magnetospirillum, Pseudomonas, and Limnohabitans, and the genera related to the freezing period were Magnetospirillum, Azoarcus, and Pseudomonas. The relationship between key water quality factors (mainly T, DO, NO3--N, and permanganate index) and aerobic denitrification flora in different hydrological scenarios was gradually changing with time. In conclusion, the study on the evolution characteristics of aerobic denitrification bacteria in Baiyangdian Lake under different hydrological scenarios and the driving mechanism of environmental factors could provide a basis for understanding the evolution mechanism of aerobic denitrification bacteria in the natural environment.

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis.

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

[Spectral Characteristics and Sources of Dissolved Organic Matter in Inflow Rivers of Baiyangdian Lake Water in Summer Flood Season].

DOM is the largest reservoir of organic carbon in the world, and it plays a crucial role in the biogeochemical cycles of natural water bodies. A river is a transition area connecting source water and receiving water that controls the DOM exchange between them. Therefore, in this study, ultraviolet visible spectroscopy (UV-vis) and three-dimensional fluorescence spectroscopy (EEMs) combined with parallel factor analysis (PARAFAC) were used to analyze the spectral characteristics and sources of dissolved organic matter in the Fuhe River, Xiaobai River, Baigouyin River, and Puhe River of Baiyangdian. The results showed that a245 and a355 in the Fuhe River and Xiaobai River were significantly higher than those in the Baigouyin River and Puhe River. E2/E3 showed that the DOM relative molecular mass of the inflow river water body was Puhe River > Baigouyin River > Fuhe River > Xiaobai River. Three components, tyrosine-like (C1), terrigenous humus (C2), and tryptophan-like (C3), were determined using three-dimensional fluorescence through PARAFAC. There was no difference among the fluorescence components (P>0.05), but there were differences among the C2 and C3 components (P<0.05). The proportion of easily degradable protein-like components (C1+C3) was higher than that of humus-like components (C2). The autogeny index BIX was greater than 1, and the humification index HIX was less than 4, indicating that the autogeny characteristics of the river bodies were obvious, and the humification degree was weak. The FI index was the highest (1.96±0.25), and the HIX index was the lowest (0.46±0.08), and the self-generated source characteristics gradually strengthened along the direction of the river entering the lake, indicating that the water body of the Fuhe River showed higher endogenous and autogenic characteristics. Based on the correlation analysis of fluorescence components and characteristic parameters of DOM, the correlations between the Fuhe River and Xiaobaihe River and between the Baigouyin River and Puhe River bodies were similar. The correlation between fluorescence components of DOM and water quality parameters of each lake was significantly different, and it was strongly correlated with nitrogen and phosphorus in water. According to multiple linear regression analysis, there was no significant difference among C1 components, but there was a significant difference between C2 and C3 components. In summary, the carbon cycle process of Baiyangdian Lake was further understood through the study on the DOM spectral characteristics and sources of the inflow river waters in the summer flood season.

Lumbar and neck injuries of occupants in different reclining postures.

PURPOSE: With the increasing level of automation in automobiles, the advent of autonomous vehicles has reduced the tendency of drivers and passengers to focus on the task of driving. The increasing automation in automobiles reduced the drivers' and passengers' focus on driving, which allowed occupants to choose a more relaxed and comfortable sitting position. Meanwhile, the occupant's sitting position went from a frontal, upright position to a more relaxed and reclined one, which resulted in the existing restraint systems can not to keep occupants safe and secure. This study aimed to determine the effects of different reclining states on occupants' lumbar and neck injuries. METHODS: This is an original research on the field of automotive safety engineering. Occupants in different initial seating positions (25°, 35°, 45°, and 55°) were adapted to changes in seat back angle and restraint systems and placed in the same frontal impact environment. Neck injury indexes, lumbar axial compression force and acceleration, as well as occupant dynamic response during the impact, were compared in different seating positions. The injury response and kinematic characteristics of occupants in different reclining positions were analyzed by the control variable method. RESULTS: As the sitting angle increased, the occupant's head acceleration decreased, and the forward-lean angle decreased. Occupants in the standard sitting position had the greatest neck injury, with an Nij of 0.95, and were susceptible to abbreviated injury scale 2+ cervical medullary injuries. As the seatback angle increased, the geometric position of the lumbar spine tended to be horizontal, and the impact load transmitted greater forces to the lumbar spine. The occupant's lumbar injury was greatest in the lying position, with a peak axial compression force on the lumbar region of 5.5 KN, which was 2.3 KN greater than in the standard sitting position. CONCLUSION: The study of occupant lumbar and neck injuries based on different recline states can provide a theoretical basis for optimizing lumbar evaluation indexes, which is conducive to the understanding of the lumbar injury mechanism and the comprehensive consideration of occupant safety protection.

Mechanochemical Synthesis of Cuprous Complexes for X-ray Scintillation and Imaging.

Cuprous complex scintillators show promise for X-ray detection with abundant raw materials, diverse luminescent mechanisms, and adjustable structures. However, their synthesis typically requires a significant amount of organic solvents, which conflict with green chemistry principles. Herein, we present the synthesis of two high-performance cuprous complex scintillators using a simple mechanochemical method for the first time, namely [CuI(PPh3)2R] (R = 4-phenylpyridine hydroiodide (PH, Cu-1) and 4-(4-bromophenyl)pyridine hydroiodide (PH-Br, Cu-2). Both materials demonstrated remarkable scintillation performances, exhibiting radioluminescence (RL) intensities 1.52 times (Cu-1) and 2.52 times (Cu-2) greater than those of Bi4Ge3O12 (BGO), respectively. Compared to Cu-1, the enhanced RL performance of Cu-2 can be ascribed to its elevated quantum yield of 51.54%, significantly surpassing that of Cu-1 at 37.75%. This excellent luminescent performance is derived from the introduction of PH-Br, providing a more diverse array of intermolecular interactions that effectively constrain molecular vibration and rotation, further suppressing the nonradiative transition process. Furthermore, Cu-2 powder can be prepared into scintillator film with excellent X-ray imaging capabilities. This work establishes a pathway for the rapid, eco-friendly, and cost-effective synthesis of high-performance cuprous complex scintillators.

A fluorescent aptasensor for highly sensitive and selective detection of carcinoembryonic antigen based on upconversion nanoparticles and WS2 nanosheets.

A highly sensitive fluorescent aptasensor for carcinoembryonic antigen (CEA) was developed by employing upconversion nanoparticles (UCNPs) as an energy donor and WS2 nanosheets as an energy acceptor, respectively. Polyacrylic acid (PAA) modified NaYF4:Yb/Er UCNPs and an amine modified CEA aptamer were linked together by a covalent bond. Owing to the physical adsorption between WS2 nanosheets and the CEA aptamer, the UCNPs-aptamer was close to WS2 nanosheets, resulting in upconversion fluorescence energy transfer from UCNPs to WS2 nanosheets, and the UCNP fluorescence was quenched. With the introduction of CEA into the UCNPs-aptamer complex system, the aptamer preferentially bound to CEA resulting in a change in spatial conformation which caused UCNPs to depart from WS2 nanosheets. As a result, the energy transfer was inhibited and the fluorescence of UCNPs was observed again, and the degree of fluorescence recovery was linearly related to the concentration of CEA in a range of 0.05-10 ng mL-1 with a limit of detection of 0.008 ng mL-1. Furthermore, the aptasensor based on UCNPs and WS2 nanosheets could be competent for detecting CEA in human serum, which suggests the great application potential of the proposed aptasensor in clinical diagnosis.

Polyoxometalate/s-triazine hybrid heterostructures with ultrafast photochromic properties.

As an emerging class of hybrid complexes, donor-acceptor (D-A) hybrid heterostructures, which combine the advantages of both organic and inorganic photoactive components, provide excellent platforms for the fabrication of photochromic materials with enhanced photo-responsive performances. Herein, four novel hybrid heterostructures, namely H3TPT·(PW12O40)·2NMP (1), (H1.5TPT)2·(PW12O40) (2), (H3TPT)2·(SiW12O40)·2Cl·2MeCN (3), and H3TPT·(HPMo12O40)·Cl·3NMP (4) (TPT is tri(4-pyridyl)-s-triazine, NMP is N-methylpyrrolidone), have been synthesized and characterized. Benefitting from the strong interactions (anion-π interactions) and matching electron energy levels between the donors and acceptors, some of them exhibited ultrafast photochromic behaviour even up to 1 second. Furthermore, based on experimental and theoretical calculations, the plausible PIET process and structure-activity relationship have been discussed in detail.

Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems.

High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm's performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.

A model-driven approach for fast modeling of three-dimensional laser point cloud in large substation.

Using point cloud to reconstruct the 3D model of a substation is crucial for smart grid operation. Its main objective is to swiftly capture equipment point cloud data and align each device's model within the large and noisy point cloud scene of the substation. However, substation reconstruction needs improvement due to the low efficiency of traditional noise-resistant clustering methods and challenges in accurately classifying similar-looking electrical equipment. This paper proposes an automatic modeling framework for large-scale substation point cloud scenes. Firstly, we reduce the substation scene's dimensionality to improve clustering efficiency and establish relationships between data dimensions using a re-clustering algorithm. Next, a neural network is developed to identify various device types within clusters, even with limited subdivisions. Finally, a model library is employed to register standard models onto the target device's point cloud, obtaining device types and orientations. Real substation data processing demonstrates the ability to rapidly extract devices from complex and noisy point cloud scenes, effectively avoiding missegmentation issues. The automatic modeling approach achieves a precise substation calculation rate of 92.86%.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology.

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

A novel hermit crab optimization algorithm.

High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells.

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

A deep memory bare-bones particle swarm optimization algorithm for single-objective optimization problems.

A deep memory bare-bones particle swarm optimization algorithm (DMBBPSO) for single-objective optimization problems is proposed in this paper. The DMBBPSO is able to perform high-precision local search while maintaining a large global search, thus providing a reliable solution to high-dimensional complex optimization problems. Normally, maintaining high accuracy while conducting global searches is an important challenge for single-objective optimizers. Traditional particle swarms optimizers can rapidly lose the diversity during iterations and are unable to perform global searches efficiently, and thus are more likely to be trapped by local optima. To address this problem, the DMBBPSO combines multiple memory storage mechanism (MMSM) and a layer-by-layer activation strategy (LAS). The MMSM catalyzes a set of deep memories to increase the diversity of the particle swarm. For every single particle, both of the personal best position and deep memories will be used in the evaluation process. The LAS enables the particle swarm to avoid premature convergence while enhancing local search capabilities. The collaboration between MMSM and LAS enhances the diversity of the particle swarm, which in turn enhances the robustness of the DMBBPSO. To investigate the optimization ability of the DMBBPSO for single-objective optimization problems, The CEC2017 benchmark functions are used in experiments. Five state-of-the-art evolutionary algorithms are used in the control group. Finally, experimental results demonstrate that the DMBBPSO can provide high precision results for single-objective optimization problems.

3D hierarchical ZnCo2S4@Ni(OH)2 nanowire arrays with excellent flexible energy storage and electrocatalytic performance.

It is essential for energy storage and conversion systems to construct electrodes and electrocatalysts with superior performance. In this work, ZnCo2S4@Ni(OH)2 nanowire arrays are synthesized on nickel foam by hydrothermal methods. As a supercapacitor electrode, the ZnCo2S4@Ni(OH)2 structure exhibits a specific capacitance of 1,263.0C g-1 at 1 A g-1. The as-fabricated ZnCo2S4@Ni(OH)2//active carbon device can achieve a maximum energy density of 115.4 Wh kg-1 at a power density of 5,400 W kg-1. As electrocatalysts, the ZnCo2S4@Ni(OH)2 structure delivers outstanding performance for oxygen evolution reaction (an overpotential of 256.3 mV at 50 mA cm-2), hydrogen evolution reaction (141.7 mV at 10 mA cm-2), overall water splitting (the cell voltage of 1.53 V at 50 mA cm-2), and a high stability for 13 h.

Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles.

To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values.

A twinning bare bones particle swarm optimization algorithm.

A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

Donor-Acceptor Hybrid Heterostructures: An Emerging Class of Photoactive Materials with Inorganic and Organic Semiconductive Components.

Just as the heterojunctions in physics, donor-acceptor (D-A) heterostructures are an emerging class of photoactive materials fabricated from two semiconductive components at the molecular level. Among them, D-A hybrid heterostructures from organic and inorganic semiconductive components have attracted extensive attention in the past decades due to their combined advantages of high stability for the inorganic semiconductors and modifiability for the organic semiconductors, which are particularly beneficial to efficiently achieve photoinduced charge separation and transfer upon irradiations. In this review, by analogy with the heterojunctions in physics, a definition of the D-A heterostructures and their general design and synthetic strategies are given. Meanwhile, the D-A hybrid heterostructures are focused on and their recent advances in potential applications of photochromism, photomodulated luminescence, and photocatalysis summarized.

Photochromic Polyoxometalate/Perylenediimide Donor-Acceptor Hybrid Crystals with Interesting Luminescent Properties.

The self-assembly of electron-deficient protonated N, N'-dipyridyltetrachloroperylenediimide (4Cl-DPPDI) and electron-rich polyoxometalate acids HnXM12O40 (POMs; X = P or Si; M = W or Mo) resulted in four isomorphous donor-acceptor hybrid crystals 1-4 with segregated POM anions and one-dimensional racemic hydrogen-bonded 4Cl-DPPDI networks as electron-donor and -acceptor components, respectively. Because of the compact contacts between the POM anions and 4Cl-DPPDI tectons induced by anion-π interactions, besides enhanced photochromism, these four unique isostructural hybrids exhibited unusual room-temperature phosphorescence (RTP) emissions. More interestingly, owing to the facial compact contacts of two racemic 4Cl-DPPDI tectons induced by lone pair-π-assisted π-π interactions, they also showed unprecedented photon upconversion by triplet-triplet annihilation (TTA).

Photochromic and Room Temperature Phosphorescent Donor-Acceptor Hybrid Crystals Regulated by Core-Substituted Naphthalenediimides.

Donor-acceptor (D-A) hybrid crystals are an emerging kind of crystalline hybrid material composed of semiconductive inorganic donors and organic acceptors. Except for the intrinsic photochromism, recently we have reported that the anion-π polyoxometalate (POM)/naphthalenediimide (NDI) hybrid crystals could produce an interesting room temperature phosphorescence (RTP) quantum yield up to 7.2%. Herein, we extended into core-substituted NDIs and anticipated the regulation of their photochromic and RTP properties. Thus, two hybrid crystals, namely (H4BDMPy-Br2NDI)·(NMP)4·(HPW12O40) (1) and (H4BDMPy-I2NDI)·(HPW12O40) (2) (H2BDMPy-Br2NDI: N,N'-bis(3,5-dimethylpyrazolyl)-2,6-dibromo-1,4,5,8-naphthalenediimide and H2BDMPy-I2NDI: N,N'-bis(3,5-dimethylpyrazolyl)-2,6-diiodide-1,4,5,8-naphthalenediimide), have been synthesized from phosphotungstic anions (PW12O403-) and Br or I core-substituted NDIs. Compared to the core-unsubstituted analogues (H4BDMPy-NDI)·(NMP)4·(HPW12O40) (3), 2 with photosensitive iodine substituents is more sensitive to light, which can become discolored under natural light. As a result of the heavy-atom effect, hybrid 1 exhibits remarkable RTP with the quantum yield up to 10.2% and a lifetime of 1.14 ms.

[Systematic review and Meta analysis on the effectiveness and safety of tuina in treatment of functional constipation].

OBJECTIVE: To review systematically the effectiveness and safety of tuina (Chinese massage)in treatment of functional constipation. METHODS: The articles on functional constipation treated with tuina were collected by computer retrieval from 7 databases from the date of establishment to March 28, 2020, including Chinese biomedical literature database (SinoMed), China journal full-text database (CNKI), full-text database of Wanfang academic journals (Wanfang), VIP Chinese science and technology journal database(VIP), PubMed, Dutch medical literature database (EMbase) and the Cochrane Library. After data extraction and quality evaluation of the included articles, Meta analysis was conducted with RevMan5.3 software. RESULTS: A total of 16 articles were included, with 1424 cases involved. Meta analysis results showed: ①The total effective rate in the treatment group was higher than that in the control group (RR=1.28, 95%CI: 1.16-1.42, P<0.000 01). ②The effective rate for the symptoms of functional constipation in traditional Chinese medicine in the treatment group was higher than that in the control group (RR=1.38, 95%CI :1.25-1.52, Z=6.31, P<0.000 01). ③Adverse reactions in the treatment group in the treatment of functional constipation were less than those in the control group (RR=0.10, 95%CI: 0.02-0.49, Z=2.81, P=0.005).④The effective rate of functional constipation treated on the base of syndrome differentiation in the treatment group was higher than that of the control group (RR=1.50, 95%CI: 1.08-2.10, Z=2.39, P=0.02).⑤The improvements in fecal characteristics, defecation time and defecation frequency of the patients with functional constipation in the treatment group were better than those in the control group (P<0.05). CONCLUSION: Tuina therapy presents a certain advantages on its curative effect on functional constipation, has less adverse reactions and relieves the relevant symptoms of functional constipation. But more randomized controlled trials with high quality and large sample are required to provide further verification of its effect.