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The occurrence of antibiotic resistance genes (ARGs) in various environments has drawn worldwide attention due to their potential risks. Previous studies have reported that a variety of substances can enhance the occurrence and dissemination of ARGs. However, few studies have compared the response of ARGs under the stress of different organic matters in biological wastewater treatment systems. In this study, seven organic pollutants were added into wastewater treatment bioreactors to investigate their impacts on the ARG occurrence in activated sludge. Based on high-throughput sequencing, it was found that the microbial communities and ARG patterns were significantly changed in the activated sludge exposed to these organic pollutants. Compared with the non-antibiotic refractory organic matters, antibiotics not only increased the abundance of ARGs but also significantly changed the ARG compositions. The increase of Gram-negative bacteria (e.g., Archangium, Prosthecobacter and Dokdonella) carrying ARGs could be the main cause of ARG proliferation. In addition, significant co-occurrence relationships between ARGs and mobile genetic elements were also observed in the sludge samples, which may also affect the ARG diversity and abundance during the organic matter treatment in the bioreactors. Overall, these findings provide new information for better understanding the ARG occurrence and dissemination caused by organic pollutants in wastewater treatment systems.

Since the "seed and soil" hypothesis was proposed, the biological functions of the tumor microenvironment (TME), especially its stromal components, have received increasing attention. Cancer-associated fibroblasts (CAFs) are the major components of the stromal region, providing material support for tumor cell proliferation, migration, and invasion. Furthermore, CAFs are important mediators of suppressing immune responses by attracting the accumulation of immunosuppressive cells through cytokine/chemokine secretion. In this review, we summarized the major cytokines, chemokines and metabolites, including transforming growth factor-β (TGF-β), interleukin-6 (IL-6), C-X-C chemokine ligand (CXCL)12, C-C chemokine ligand (CCL) 2, prostaglandin E2 (PGE2), and other factors, by which CAFs suppress the immune systems in a variety of cancers. More importantly, we highlight potential therapeutic strategies to alleviate the immunosuppression produced by CAFs, thereby inhibiting tumor progression.

A fungus with biocontrol potential was isolated from the roots of hickory trees. The strain named sj18 was classified as a member of the genus Hypoxylon (Hypoxylaceae) after multigene phylogenetic analysis (beta-tubulin gene, internal transcribed spacer, 28S large subunit ribosomal RNA gene, and RNA polymerase II subunit gene). The strain grew well on a PDA with an optimum temperature range between 32 and 34 °C. The fungus had obvious inhibitory effects on Botryosphaeria dothidea, Colletotrichum gloeosporioides, and Gibberella moniliformis in fumigation experiments on solid agar plates. In an inoculation experiment of Chinese cabbage, the fungus was also found to have an obvious repellent effect on cabbage caterpillars. In vitro experiments on Petri dishes showed that the fermentation broth of the sj18 strain could kill 100% of Bursaphelenchus xylophilus within 8 h even if the fermentation broth was diluted 8 times. The inoculation test of Arabidopsis thaliana showed that the fungus could promote the lateral root formation of plants and significantly increase their aboveground biomass. Through the analysis of solid phase microextraction (SPME), it was found that the main volatile components of the fermentation products were azulene 65.39% (61.77% + 3.62%), caryophyllene 7.41%, and eucalyptol 6.83% according to the peak area ratio. Therefore, sj18 can be used as a candidate for the further research and development of biocontrol agents.

Summary: The prevalence of postoperative low anterior resection syndrome (LARS) in patients with colorectal cancer is high, which seriously affects the quality of life after operation. The purpose of this meta-analysis is to systematically evaluate the related factors of LARS in patients with colorectal cancer and provide reference for clinicians when making reasoned decisions. A systematic electronic search of PubMed, Embase, The Cochrane Library, WANFANG and CNKI was performed from 2012 to Dec 2020. We analyzed the risk factors of LARS by extracting baseline data and clinical results. The odds ratio (OR) was used to analyze binary variables. A total of 5102 patients were included in 21 literatures, of which the prevalence of LARS was 49.7% (2538/5102). Meta-analysis showed that there was no significant difference in the influence of age (P = 0.48) and sex (P = 0.68) on LARS, but low tumor height (P

In order to obtain a uniform and effectively toughened poly (lactic acid) film by blending with low content of poly (ethylene octene) (POE) with high elasticity, the tailored interfacial intermolecular interaction and entanglement between the two phases of the PLA/POE blend was innovatively constructed via the facile reactive melt blending process through the reaction of the epoxy/anhydride groups grafted on the POE chains with the end groups of PLA chains (PLA/GPOE-MPOE). It was observed that POE domains were embedded tightly in PLA matrix with a fuzzy interface and abundant interface transition area, and the impact fractured surface of the blend showed an obvious plastic deformation with less occurrence of fibrillation of PLA matrix or interfacial de-bonding. Compared with neat PLA and directly blended PLA/POE blends, the PLA/GPOE-MPOE blend exhibited much higher complex viscosity/storage modulus, much lower tanδ values in the terminal region, and obvious strain-hardening behavior. The deviation in viscoelastic behavior of PLA/GPOE-MPOE from linear PLA indicated the enhanced molecular entanglement between the long-branched chains, resulting in an enhancement of the stretching ability during biaxial drawing of the blend. Uniform PLA/GPOE-MPOE films with draw ratio as high as 7 × 7 were obtained through biaxial stretching, which showed much higher tensile strength and the elongation at break than that of neat PLA and PLA/POE film. This work provides a facile method for fabricating toughening PLA films with application potentials.

Accurate wind power forecasting is a vital factor in day-ahead dispatch and increasing the level of penetration of renewable energy. The feature extraction of meteorological factors related to wind power output is a big challenge to improve forecasting accuracy, and selecting key meteorological factors based on experience will decrease the prediction accuracy. Therefore, a day-ahead wind power combined forecasting approach is innovatively proposed through key meteorological factors selection, data decomposition and reconstruction, combined forecasting model generation, and optimization strategy. Correlated variables namely variational mode decomposition and weighted permutation entropy (VMD-WPE) decomposed historical wind power and key meteorological factors are used as the inputs. A forecasting model based on convolutional neural network (CNN) and long short-term memory network (LSTM) is used to forecast future wind power. Four optimizers with different optimization performances are used to find the best parameters of the forecasting model to obtain accurate prediction results. Multiple comparative experiments from regional wind farms in Ningxia and Jilin of China are utilized as case studies to evaluate the effectiveness of the proposed model. Results show that the proposed approach outperforms other benchmark prediction models, taking into account multiple-error metrics including error metrics, accuracy rate, and improvement percentages.

Background\ud Bone metastasis is the most common metastatic destination in advanced breast cancer, presenting a poor prognosis and clinical challenges in management. To date, the mechanism of bone metastasis in breast cancer remains largely unclear.\ud \ud Methods\ud Differentially expressed genes in primary tumours that developed bone metastases were systematically analysed using both TCGA-BRCA and E-MTAB-4003 databases. Adaptive phenotype in the subsequent bone lesions was analysed in the GSE46161 database. A series of biomarkers including homing, immune escape, angiogenesis, and factors involved in both osteoblastogenesis and osteoclastogenesis were included to dissect the molecular events underlying bone metastasis in breast cancer.\ud \ud Results\ud Upregulated expressions of GDF11 expression is positively correlated with colonization, osteoblastogenesis and osteoclastogenesis, whilst CD151 is positively associated with angiogenesis and immune escape. PAFAH1B2 expression is inversely correlated with the angiogenic process. Reduced YTHDF2 may facilitate cancer cell homing, osteoclastogenesis and immune escape in breast cancer. DPP9, FAS, ZNF519, RPP14 and FAU were evaluated for their potential involvement in for the homing to bone, escaping from immune surveillance, angiogenesis, osteoblastic activity and osteoclastic activity in the multi-step process of bone metastasis.\ud \ud Conclusion\ud GDF11, CD151, PAFAH1B2 and YTHDF2 may play a pivotal role in the predisposition of metastasis to the bone from breast cancer, whilst DPP9, FAS, ZNF519, RPP14 and FAU may be actively involved in the adaptative colonisation of metastatic breast cancer cells in bone.

To achieve a high penetration of renewable energy integration, an effective solution is to explore the interdependence between numerical weather prediction (NWP) data and historical wind power to improve prediction accuracy. This paper proposes a novel combined approach for wind power prediction. The characteristics of NWP and historical wind power data are extracted by using the feature extraction technique, the predictor is designed based on extreme learning machine (ELM) and least squares support vector machine (LSSVM) model, and then key parameters of the prediction models are optimized by improving cuckoo search (ICS) to obtain a reliable value, which is defined as the pre-combined prediction value (PPA). To obtain a reliable result, a variance strategy is developed to allocate the weights of the pre-combined prediction model to obtain the final predicted values. Four seasons dataset collected from regional wind farms in China is utilized as a benchmark experiment to evaluate the effectiveness of the proposed approach. The results of comprehensive numerical cases with different seasons show that the proposed approach, which considers multiple-error metrics, including error metrics, accuracy rate, qualification rate, and improvement percentages, achieves higher accuracy than other benchmark prediction models.

Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive joint inflammation and destruction, deformity, loss of mobility, and permanent disability. Although the cellular and molecular mechanisms involved in RA are understood in detail, no drugs or therapies can completely cure RA. Many long-term efforts have been directed towards a better understanding of RA pathogenesis and the development of new classes of therapeutics. Thus, the ongoing elucidation of pathogenic events underlying RA mostly relies on studies of animalmodels. Herein, we comprehensively review and discuss the characteristics, challenges, and unresolved of issues of various experimental models of RA to provide a basis and reference for the rational selection of experimental RA models for basic investigations into traditional Chinese medicine (TCM).

More and more attentions have been focused on design and synthesis of novel metal-organic framework/graphene oxide (MOF/GO) composites with unique performance. Zirconium-porphyrin MOF (PCN-222) is in-situ synthesis with the existence of GO with −COOH group to artfully fabricate a PCN-222/GO composite. This composite can be employed as functional material to modify the working electrode. Thanks to excellent electrical conductivity of GO, abundant mesoporous channels and numerous Zr(IV) metal sites of PCN-222, this composite can immobilize a large amount of aptamer through strong π-π stacking interaction and high affinity between phosphate group of aptamer and Zr(IV) site of PCN-222 simultaneously. Hence, an ultra-sensitive electrochemical aptasensor based on PCN-222/GO composite can quantificationally detect trace chloramphenicol with limit of detection of 7.04 pg/mL (21.79 pmol/L) from 0.01 ng/mL to 50 ng/mL by electrochemical impedance spectroscopy even in real samples. Meanwhile, this fabricated aptasensor reveals good repeatability, outstanding selectivity and preferable long-term storage. This research provides a useful approach to construct MOF/GO composites for fabricating electrochemical aptasensors in the electrochemical detection field.

Background Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. Methods We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. Results cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. Conclusions Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.