Exploring the Potential of Biochar Derived from Chinese Herbal Medicine Residue for Efficient Removal of Norfloxacin.

One-step carbonization was explored to prepare biochar using the residue of a traditional Chinese herbal medicine, Atropa belladonna L. (ABL), as the raw material. The resulting biochar, known as ABLB4, was evaluated for its potential as a sustainable material for norfloxacin (NOR) adsorption in water. Subsequently, a comprehensive analysis of adsorption isotherms, kinetics, and thermodynamics was conducted through batch adsorption experiments. The maximum calculated NOR adsorption capacity was 252.0 mg/g at 298 K, and the spontaneous and exothermic adsorption of NOR on ABLB4 could be better suited to a pseudo-first-order kinetic model and Langmuir model. The adsorption process observed is influenced by pore diffusion, π-π interaction, electrostatic interaction, and hydrogen bonding between ABLB4 and NOR molecules. Moreover, the utilization of response surface modeling (RSM) facilitated the optimization of the removal efficiency of NOR, yielding a maximum removal rate of 97.4% at a temperature of 304.8 K, an initial concentration of 67.1 mg/L, and a pH of 7.4. Furthermore, the biochar demonstrated favorable economic advantages, with a payback of 852.5 USD/t. More importantly, even after undergoing five cycles, ABLB4 exhibited a consistently high NOR removal rate, indicating its significant potential for application in NOR adsorption.

Oxidative Cascade Iodocyclization of 1,n-Dienes: Synthesis of Iodinated Benzo[b]azepine and Benzo[b]azocine Derivatives.

An oxidative cascade iodocyclization of 1,7- or 1,8-dienes has been realized under mild conditions. By employing I2 as an iodine source, this protocol provides a concise and efficient approach to a great deal of biologically significant iodinated benzo[b]azepine and benzo[b]azocine derivatives in moderate to good yields. The gram-scale synthesis and further transformation of products render the approach practical and attractive. Radical trapping and deuterium-labeling experiments help to understand the mechanism.

Unlocking the potential of Chinese herbal medicine residue-derived biochar as an efficient adsorbent for high-performance tetracycline removal.

This study employed hydrothermal carbonization (HTC) in conjunction with ZnCl2 activation and pyrolysis to produce biochar from one traditional Chinese medicine astragali radix (AR) residue. The resultant biochar was evaluated as a sustainable adsorbent for tetracycline (TC) elimination from water. The adsorption performance of TC on two micropore-rich AR biochars, AR@ZnCl2 (1370 m2 g-1) and HAR@ZnCl2 (1896 m2 g-1), was comprehensively evaluated using adsorption isotherms, kinetics, and thermodynamics. By virtue of pore diffusion, π-π interaction, electrostatic attraction, and hydrogen bonding, the prepared AR biochar showed exceptional adsorption properties for TC. Notably, the maximum adsorption capacity (930.3 mg g-1) of TC on HAR@ZnCl2 can be achieved when the adsorbent dosage is 0.5 g L-1 and C0 is 500 mg L-1 at 323 K. The TC adsorption on HAR@ZnCl2 took place spontaneously. Furthermore, the impact of competitive ions behavior is insignificant when coexisting ion concentrations fall within the 10-100 mg L-1 range. Additionally, the produced biochar illustrated good economic benefits, with a payback of 701 $ t-1. More importantly, even after ten cycles, HAR@ZnCl2 still presented great TC removal efficiency (above 77%), suggesting a good application prosperity. In summary, the effectiveness and sustainability of AR biochar, a biowaste-derived product, were demonstrated in its ability to remove antibiotics from water, showing great potential in wastewater treatment application.

Hydrocarbon adsorption mechanism of modern automobile engines and methods of reducing hydrocarbon emissions during cold start process: A review.

With the global emphasis on environmental protection and increasingly stringent emission regulations for internal combustion engines, there is an urgent need to overcome the problem of large hydrocarbon (HC) emissions caused by unstable engine cold starts. Synergistic engine pre-treatment (reducing hydrocarbon production) as well as after-treatment devices (adsorbing and oxidizing hydrocarbons) is the fundamental solution to emissions. In this paper, the improvement of hydrocarbon emissions is summarized from two aspects: pre-treatment and after-treatment. The pre-treatment for engine cold start mainly focuses on summarizing the intake control, fuel, and engine timing parameters. The after-treatment mainly focuses on summarizing different types of adsorbents and modifications (mainly including different molecular sieve structures and sizes, preparation conditions, silicon aluminum ratio, ion exchange modification, and heterogeneity, etc.), adsorptive catalysts (mainly including optimization of catalytic performance and structure), and catalytic devices (mainly including coupling with thermal management equipment and HC trap devices). In this paper, a SWOT (strength, weakness, opportunity, and threat) analysis of pre-treatment and after-treatment measures is conducted. Researchers can obtain relevant research results and seek new research directions and approaches for controlling cold start HC emissions.

Fine-Regional Role of the Claustrum in Anxiety and Higher Sensitivity to Cocaine in Adolescent Cocaine-Exposed Male Mice during Adulthood.

Adolescent cocaine exposure (ACE) induces anxiety and higher sensitivity to substances abuse during adulthood. Here, we show that the claustrum is crucial for controlling these psychiatric problems in male mice. In anxiety-like behavioral tests, the CaMKII-positive neurons in the median portion of the claustrum (MClaustrum) were triggered, and local suppression of these neurons reduced the anxiety-like behavior in ACE mice during adulthood. In contrast, the CaMKII-positive neurons in the anterior portion of the claustrum (AClaustrum) were more activated in response to subthreshold dose of cocaine induced conditioned place preference (CPP), and local suppression of these neurons blocked the acquisition of cocaine CPP in ACE mice during adulthood. Our findings for the first time identified the fine-regional role of the claustrum in regulating the anxiety and susceptibility to cocaine in ACE mice during adulthood, extending our understanding of the claustrum in substance use disorder.

Paternal methamphetamine exposure induces higher sensitivity to methamphetamine in male offspring through driving ADRB1 on CaMKII-positive neurons in mPFC.

Paternal abuse of drugs, such as methamphetamine (METH), elevates the risk of developing addiction in subsequent generations, however, its underlying molecular mechanism remains poorly understood. Male adult mice (F0) were exposed to METH for 30 days, followed by mating with naïve female mice to create the first-generation mice (F1). When growing to adulthood, F1 were subjected to conditioned place preference (CPP) test. Subthreshold dose of METH (sd-METH), insufficient to induce CPP normally, were used in F1. Selective antagonist (betaxolol) for ß1-adrenergic receptor (ADRB1) or its knocking-down virus were administrated into mPFC to regulate ADRB1 function and expression on CaMKII-positive neurons. METH-sired male F1 acquired sd-METH-induced CPP, indicating that paternal METH exposure induce higher sensitivity to METH in male F1. Compared with saline (SAL)-sired male F1, CaMKII-positive neuronal activity was normal without sd-METH, but strongly evoked after sd-METH treatment in METH-sired male F1 during adulthood. METH-sired male F1 had higher ADRB1 levels without sd-METH, which was kept at higher levels after sd-METH treatment in mPFC. Either inhibiting ADRB1 function with betaxolol, or knocking-down ADRB1 level on CaMKII-positive neurons (ADRB1CaMKII) with virus transfection efficiently suppressed sd-METH -evoked mPFC activation, and ultimately blocked sd-METH-induced CPP in METH-sired male F1. In the process, the p-ERK1/2 and ΔFosB may be potential subsequent signals of mPFC ADRB1CaMKII. The mPFC ADRB1CaMKII mediates paternal METH exposure-induced higher sensitivity to drug addiction in male offspring, raising a promising pharmacological target for predicting or treating transgenerational addiction.

Effect of Different Ratios of Gasoline-Ethanol Blend Fuels on Combustion Enhancement and Emission Reduction in Electronic Fuel Injection Engine.

The severity of engine emissions for the environment and human health cannot be ignored. This article optimizes the combustion and emission of gasoline-cassava bioethanol fuel blends in electronic fuel injection engines using response surface methodology to achieve the goal of reducing carbon and pollutant emissions. The experiment investigated the effects of different gasoline-cassava bioethanol mixing ratios (G100, G90E10, G80E20, and G70E30) on engine performance, including torque, brake specific fuel consumption, power, total hydrocarbons, nitrogen oxides, and carbon monoxide emissions. The results show that the gasoline-cassava bioethanol fuel blend is not as good as G100 in terms of braking power, torque, and brake specific fuel consumption, but better than G100 in terms of carbon monoxide emissions and total hydrocarbon emissions. Then, the optimization objective function was determined, and the combustion and emission characteristics were optimized using the response surface methodology method. The optimization results indicate that the response surface methodology method can determine the interaction between design variables such as brake specific fuel consumption, nitrogen oxides, and total hydrocarbon emissions and find the best solution. In this experiment, the independent variables of the best solution were 72.9 N·m torque, 30% G70E30 mixing rate, and 2000 rpm speed, corresponding to brake specific fuel consumption at 313 g/(kW·h), nitrogen oxide emissions at 2.85 × 103 ppm, and total hydrocarbon emissions at 166 ppm. The findings of this study indicate that by optimizing the gasoline-cassava bioethanol mixture ratio, lower emission levels can be achieved in electronic fuel injection engines, thereby promoting the sustainable development of renewable energy and reducing pollutant emissions.

Utilization of renewable biodiesel blends with different proportions for the improvements of performance and emission characteristics of a diesel engine.

This work investigated and compared the impact on performance and emission characteristics of diesel engine fueled with five different proportions of biodiesel blends. Firstly, the three-dimensional simulation software CONVERGE was used to create a 3D simulation model of in-cylinder combustion for a diesel engine. Secondly, the experimental data of cylinder pressure and NOx emissions at 50% and 100% loads were employed to verify the simulation model. Finally, the combustion processes of blends with proportions of 0%, 5%, 10%, 15%, and 20% biodiesel were simulated and compared by using the model. The study showed that the brake thermal efficiencies (BTEs) of biodiesel blends with 5%, 10%, 15%, and 20% of biodiesel were increased by 1.24%, 1.89%, 3.13%, and 3.82% at 50% load, respectively, compared with pure diesel. In addition, the soot emissions were decreased by 1.20%, 2.64%, 3.88%, and 4.65%, respectively. However, as the proportion of biodiesel in the biodiesel blends increased, the brake specific fuel consumption (BSFC) and NOx emissions increased. At 50% load, the BSFCs of biodiesel blends with 5%, 10%, 15%, and 20% of biodiesel increased by 0.61%, 1.34%, 1.42%, and 2.17%, respectively, compared with pure diesel. Additionally, the brake powers (BPs) were decreased by 0.64%, 1.31%, 1.88%, and 2.62% at 100% load, respectively.

3D Honeycomb Fe/MXene Derived from Prussian Blue Microcubes with a Tunable Structure for Efficient Low-Frequency and Flexible Electromagnetic Absorbers.

The unique layered structure and high conductivity of MXene materials make them highly promising for microwave absorption. However, the finite loss mechanism and severe agglomeration present challenging obstacles for ideal microwave absorbers, which could be effectively improved by constructing a three-dimensional (3D) porous structure. This study reports a 3D honeycomb MXene using a straightforward template method. The 3D MXene framework offers ample cavities to anchor the Prussian blue microcubes and their derivatives including Fe microboxes and Fe clusters by a simple annealing process. Based on the superiority of the 3D honeycomb architecture and magnetic-dielectric synergistic effects, the Fe/MXene absorbers demonstrate outstanding microwave absorption capabilities with the optimum reflection loss value of -40.3 dB at 2.00 mm in the low-frequency range from 4.2 to 5.6 GHz. The absorber also manifests superior radar wave attenuation by finite element analysis and exhibits great potential to be a flexible and thermal insulation material in a wide range of temperatures. This work proposes a useful reference for the design of 3D MXene-based porous architectures, and the synergistic magnetic-dielectric strategy further expands the potential of MXene-based absorbers, enabling them to be used as flexible and highly efficient microwave absorbers.

Dynamical modelling of viral infection and cooperative immune protection in COVID-19 patients.

Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.

CBX4 promotes antitumor immunity by suppressing Pdcd1 expression in T cells.

E3 SUMO-protein ligase CBX4 (CBX4), a key component of polycomb-repressive complexes 1 (PRC1), has been reported to regulate a variety of genes implicated in tumor growth, metastasis, and angiogenesis. However, its role in T-cell-mediated antitumor immunity remains elusive. To shed light on this issue, we generated mice with T-cell-specific deletion of Cbx4. Tumor growth was increased in the knockout mice. Additionally, their tumor-infiltrating lymphocytes exhibited impaired tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) production, with an elevated programmed cell death protein 1 (PD-1) level. In fact, dysregulated Pdcd1 expression was observed in all major subsets of peripheral T cells from the knockout mice, which was accompanied by a functional defect in response to T-cell receptor (TCR) stimulation. In support of a direct link between CBX4 and PD-1, Cbx4 overexpression resulted in the downregulation of Pdcd1 expression. Epigenetic analyses indicated that Cbx4 deficiency leads to diminished accumulation of inhibitory histone modifications at conserved region (CR)-C and CR-B sites of the Pdcd1 promoter, namely mono-ubiquitinated histone H2A at lysine 119 (H2AK119ub1) and trimethylated histone H3 at lysine 27 (H3K27me3). Moreover, inhibition of either the E3 ligase activity of polycomb-repressive complexes 1 (PRC1) or the methyltransferase activity of polycomb-repressive complexes 2 (PRC2) restores Pdcd1 expression in Cbx4-transfected cells. Cumulatively, this study reveals a novel function of CBX4 in the regulation of T-cell function and expands our understanding of the epigenetic control of Pdcd1 expression.

Construction and validation of a prognostic nomogram for locally recurrent rectal cancer: a population-based study.

Background: Postoperative recurrence was a life-threatening condition for patients with rectal cancer. Due to the heterogeneity of locally recurrent rectal cancer (LRRC) and controversy of the optimal treatment for patients, it was difficult to predict the prognosis of LRRC. This study aimed to develop and validate a nomogram that could accurately predict the survival probability of LRRC. Methods: Patients diagnosed with LRRC between 2004 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database were included in the analysis. Multiple imputations with chained equations were used for missing values. These patients were further randomized into training set and testing set. Cox regression was used for univariate and multivariate analysis. Potential predictors were screened by the least absolute shrinkage and selection operator (LASSO). The Cox hazards regression model was constructed and it was visualized by nomogram. C-index, calibration curve, and decision curve were used to evaluate the model's predictive ability. Then X-tile was used to calculate the optimal cut-off values for all patients and the cohort was divided into three groups. Results: A total of 744 LRRC patients were enrolled and allocated to the training set (n=503) and the testing set (n=241). Cox regression analysis of the training set yielded meaningfully clinicopathological variables. A survival nomogram was created based on the identification of ten clinicopathological features in the LASSO regression analyses of the training set. The C-index of 3-, 5-year survival probabilities were 0.756, 0.747 in training set, and 0.719, 0.726 in testing set, respectively. The calibration curve and decision curve both demonstrated the satisfactory performance of the nomogram for prognosis prediction. Moreover, the prognosis of LRRC could be well distinguished according to the grouping of risk scores (P<0.001 in three groups). Conclusions: This nomogram was the first prediction model to preliminarily evaluate the survival of LRRC patients, which could provide more accurate and efficient treatment in clinical practice.

Dopamine receptor 1 on CaMKII-positive neurons within claustrum mediates adolescent cocaine exposure-induced anxiety-like behaviors and electro-acupuncture therapy.

Adolescent cocaine exposure (ACE) increases risk of developing psychiatric problems such as anxiety, which may drive relapse in later life, however, its underlying molecular mechanism remains poorly understood. Methods: ACE male mice model were established by exposing to cocaine during adolescent period. Elevated plus maze (EPM) were used to assess anxiety-like behaviors in mice. Within claustrum, local injection of SCH-23390, a specific antagonist for dopamine receptor 1 (D1R), or D1R knocking-down virus were used to regulate D1R function or expression on CaMKII-positive neurons (D1RCaMKII) in vivo. Electro-acupuncture (EA) treatment was performed at acupoints of Baihui and Yintang during withdrawal period. Results: We found that ACE mice exhibited anxiety-like behaviors, along with more activated CaMKII-positive neurons and increased D1RCaMKII levels in claustrum during adulthood. Inhibiting D1R function or knocking-down D1RCaMKII levels in claustrum efficiently reduced claustrum activation, and ultimately suppressed anxiety-like behaviors in ACE mice during adulthood. EA treatment alleviated ACE-evoked claustrum activation and anxiety-like behaviors by suppressing claustrum D1RCaMKII. Conclusion: Our findings identified a novel role of claustrum in ACE-induced anxiety-like behaviors, and put new insight into the D1RCaMKII in the claustrum. The claustrum D1RCaMKII might be a promising pharmacological target, such as EA treatment, to treat drug-induced anxiety-like behaviors.

The PRAK-NRF2 axis promotes the differentiation of Th17 cells by mediating the redox homeostasis and glycolysis.

Oxidative stress is a key feature in both chronic inflammation and cancer. P38 regulated/activated protein kinase (PRAK) deficiency can cause functional disorders in neutrophils and macrophages under high oxidative stress, but the precise mechanisms by which PRAK regulates reactive oxygen species (ROS) elimination and its potential impact on CD4+ T helper subset function are unclear. The present study reveals that the PRAK-NF-E2-related factor 2(NRF2) axis is essential for maintaining the intracellular redox homeostasis of T helper 17(Th17) cells, thereby promoting Th17 cell differentiation and antitumor effects. Through mechanistic analysis, we identify NRF2 as a novel protein substrate of PRAK and find that PRAK enhances the stability of the NRF2 protein through phosphorylation NRF2 Serine(S) 558 independent of protein ubiquitination. High accumulation of cellular ROS caused by loss of PRAK disrupts both glycolysis and PKM2-dependent phosphorylation of STAT3, which subsequently impairs the differentiation of Th17 cells. As a result, Prak knockout (KO) mice display significant resistance to experimental autoimmune encephalomyelitis (EAE) but impaired antitumor immunity in a MC38 tumor model. This work reveals that the PRAK-NRF2-mediated antioxidant pathway is a metabolic checkpoint that controls Th17-cell glycolysis and differentiation. Targeting PRAK is a promising strategy for maintaining an active ROS scavenging system and may lead to potent Th17 cell antitumor immunity.

Biochar Derived from Chinese Herb Medicine Residues for Rhodamine B Dye Adsorption.

In this study, one well-known CHM residue (Atropa belladonna L., ABL) was used to prepare biochar capable of adsorbing rhodamine B (RhB) with an ultrahigh surface area for the first time. Three micropore-rich ABL biochars including ABL@ZnCl2 (1866 m2/g), ABL@H3PO4 (1488 m2/g), and ABL@KOH (590 m2/g) were obtained using the one-step carbonization method with activation agents (ZnCl2, H3PO4, and KOH) via chemical activation and carbonization at 500 °C, and their adsorption performance for RhB was systematically studied with adsorption kinetics, isotherms, and thermodynamics. Through pore diffusion, π-π interaction, and hydrogen bonding, ABL biochar had excellent adsorption performance for RhB. Moreover, when C 0 was 200 mg/L, biochar dosage was 1 g/L, and the contact time was 120 min; the maximum RhB adsorption capacity and removal efficiency on ABL@ZnCl2 and ABL@H3PO4 were 190.63 mg/g, 95% and 184.70 mg/g, 92%, respectively, indicating that it was feasible to prepare biochar from the ABL residue for RhB adsorption. The theoretical maximum adsorption capacities of ABL@ZnCl2 and ABL@H3PO4 for RhB were 263.19 mg/g and 309.11 mg/g at 25 °C, respectively. Furthermore, the prepared biochar showed good economic applicability, with pay back of USD 972/t (ABL@ZnCl2) and USD 987/t (ABL@H3PO4), respectively. More importantly, even after five cycles, ABL@H3PO4 biochar still showed great RhB removal efficiency, suggesting that it had a good application prospect and provided a new method for the resource utilization of traditional CHM residues. Additionally, pore diffusion, π-π interactions, and hydrogen bonding all play roles in the physical adsorption of RhB on ABL biochar. π-π interactions dominated in the early stage of RhB adsorption on ABL@H3PO4, while pore diffusion played a crucial role in the whole adsorption process on both adsorbents.

Prostaglandin E2-Induced AKT Activation Regulates the Life Span of Short-Lived Plasma Cells by Attenuating IRE1α Hyperactivation.

The mechanism regulating the life span of short-lived plasma cells (SLPCs) remains poorly understood. Here we demonstrated that the EP4-mediated activation of AKT by PGE2 was required for the proper control of inositol-requiring transmembrane kinase endoribonuclease-1α (IRE1α) hyperactivation and hence the endoplasmic reticulum (ER) homeostasis in IgM-producing SLPCs. Disruption of the PGE2-EP4-AKT signaling pathway resulted in IRE1α-induced activation of JNK, leading to accelerated death of SLPCs. Consequently, Ptger4-deficient mice (C57BL/6) exhibited a markedly impaired IgM response to T-independent Ags and increased susceptibility to Streptococcus pneumoniae infection. This study reveals a highly selective impact of the PGE2-EP4 signal on the humoral immunity and provides a link between ER stress response and the life span of SLPCs.

Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards.

Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same.

PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis.

In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results.

Performance of Different Risk Indicators in a Multi-Period Polynomial Portfolio Selection Problem Based on the Credibility Measure.

In this paper, we study the portfolio selection problem considering transaction costs under multiple periods. For non-professional investors, it is a critical factor to choose an appropriate model among multiple portfolio selection models in investment. Based on the credibility measure, we formulate a multi-period polynomial portfolio selection model to gather the risk indicators involving variance, semi-variance, entropy, and semi-entropy, helping investors bet on assets. According to the polynomial goal programming (PGP) approach, investors can conquer the fields by combining apposite indicators to build appropriate models. Subsequently, an adjusted genetic algorithm on the foundation of the penalty function is designed to obtain the optimal solution of this multi-period model. The results indicate that the PGP method is suitable for investors to choose the model and assigns the proper models to investors with different risk preferences.