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To effectively diagnose and monitor the vertical collusion in construction project bidding, this paper developed a comprehensive evaluation model with deep neural network and transfer learning. By this model, the collusion characteristics of bidders, tenderers, and bid evaluation experts were mined from limited data set hidden and collusion tendency was evaluated. Firstly, 18 evaluation indicators were established from literature review, court file summarization, typical case analysis, and expert consultation. Then, a comprehensive evaluation model was developed with the deep neural network and transfer learning. Finally, the model was trained and tested with the collected data set. The test results showed that the developed model achieved 87.3% identification accuracy in collusion tendency evaluation of different subjects.

RNA-seq results from HBEC-shp53 and HBEC-shp53-PCDH7 cells

Supplementary Methods, References, and Figure Legends, Supplementary Fig. S1-High magnification images of PCDH7 IHC staining in normal human lung and lung adenocarcinomas. Supplementary Fig. S2-NSCLC tissue microarray analysis of PCDH7 immunoexpression. Supplementary Fig. S3-PCDH7 induces cellular transformation of HBEC cells and induces a gene expression program that promotes cell proliferation. Supplementary Fig. S4-All PCDH7 isoforms induce MAPK signaling in HBEC-shp53-KRASG12V cells. Supplementary Fig. S5-PCDH7 synergizes with mutant EGFR to promote tumor formation. Supplementary Fig. S6-CRISPR/Cas9-mediated inhibition of PCDH7 sensitizes NSCLC cells to MEK and ERK inhibitors. Supplementary Fig. S7-PCDH7 isoforms A, C, and D bind SET in EGFR-mutant cells. Supplementary Fig. S8-SET localization studies in HBECs and NSCLC cells and requirement of SET for PCDH7-induced MAPK pathway activation. Supplementary Fig. S9-Treatment of NSCLC cells with a SET inhibitor FTY720 induces cell death. Supplementary Table S1. Expression and survival analysis of PCDH family members in lung adenocarcinoma. Supplementary Table S2. Affymetrix probe IDs analyzed for each PCDH family member. Supplementary Table S3. Summary of clinical and pathologic information of the cohort of NSCLC patient tissue blocks used for TMA generation. Supplementary Table S4. Analysis of tissue microarray correlations. Supplementary Table S5. Correlation between PCDH7 upregulation and alterations in KRAS or EGFR. Supplementary Table S7. List of antibodies utilized in this study. Supplementary Table S8. List of primer sequences utilized in this study.

Non–small cell lung cancer (NSCLC) is the leading cause of cancer-associated deaths worldwide. Given the efficacy of membrane proteins as therapeutic targets in human malignancies, we examined cell-surface receptors that may act as drivers of lung tumorigenesis. Here, we report that the PROTOCADHERIN PCDH7 is overexpressed frequently in NSCLC tumors where this event is associated with poor clinical outcome. PCDH7 overexpression synergized with EGFR and KRAS to induce MAPK signaling and tumorigenesis. Conversely, PCDH7 depletion suppressed ERK activation, sensitized cells to MEK inhibitors, and reduced tumor growth. PCDH7 potentiated ERK signaling by facilitating interaction of protein phosphatase PP2A with its potent inhibitor, the SET oncoprotein. By establishing an oncogenic role for PCDH7 in lung tumorigenesis, our results provide a rationale to develop novel PCDH7 targeting therapies that act at the cell surface of NSCLC cells to compromise their growth. Cancer Res; 77(1); 187–97. ©2016 AACR.

SB copy number analysis and validation using lentivirus/retrovirus.

Supplementary Figure Legend from Therapeutic Expression of an Anti-Death Receptor 5 Single-Chain Fixed-Variable Region Prevents Tumor Growth in Mice

CIS gene orthologues that are altered in AML.

The clinical use of the single-chain fixed-variable (scFv) fragments of recombinant monoclonal antibodies as credible alternatives for classic therapeutic antibodies has two limitations: rapid blood clearance and inefficient local expression of functional molecules. In attempt to address these issues, we have developed a novel gene therapy protocol in which the anti-death receptor 5 (DR5) scFv fragments were either in vitro expressed in several tumor cell lines, or in vivo expressed in mice, using recombinant adeno-associated virus (rAAV)–mediated gene transfer. Viral transduction using the rAAV-S3C construct, which encodes a scFv molecule (S3C scFv) specific to DR5, led to stable expression in tumor cell lines and showed apoptosis-inducing activity in vitro, which could be inhibited by recombinant DR5 but not by DR4. A single i.m. injection of rAAV-S3C virus in nude mice resulted in stable expression of DR5-binding S3C scFv proteins in mouse sera for at least 240 days. Moreover, the expression of S3C scFv was associated with significant suppression of tumor growth and the increase of tumor cell apoptosis in previously established s.c. human lung LTEP-sml and liver Hep3B tumor xenografts. (Cancer Res 2006; 66(24): 11946-53)

All CIS genes identified in the comprehensive Ba/F3 mutagenesis screen.

Description of additional methods and procedures used in the study. Also includes Supplementary References.

Aberrant signaling through cytokine receptors and their downstream signaling pathways is a major oncogenic mechanism underlying hematopoietic malignancies. To better understand how these pathways become pathologically activated and to potentially identify new drivers of hematopoietic cancers, we developed a high-throughput functional screening approach using ex vivo mutagenesis with the Sleeping Beauty transposon. We analyzed over 1,100 transposon-mutagenized pools of Ba/F3 cells, an IL3-dependent pro-B-cell line, which acquired cytokine independence and tumor-forming ability. Recurrent transposon insertions could be mapped to genes in the JAK/STAT and MAPK pathways, confirming the ability of this strategy to identify known oncogenic components of cytokine signaling pathways. In addition, recurrent insertions were identified in a large set of genes that have been found to be mutated in leukemia or associated with survival, but were not previously linked to the JAK/STAT or MAPK pathways nor shown to functionally contribute to leukemogenesis. Forced expression of these novel genes resulted in IL3-independent growth in vitro and tumorigenesis in vivo, validating this mutagenesis-based approach for identifying new genes that promote cytokine signaling and leukemogenesis. Therefore, our findings provide a broadly applicable approach for classifying functionally relevant genes in diverse malignancies and offer new insights into the impact of cytokine signaling on leukemia development. Cancer Res; 76(4); 773–86. ©2015 AACR.

Legends for Supplementary Tables S1-S4 and Supplementary Figures S1-S13.

Air-conditioning system operation pattern recognition plays an important role in the fault diagnosis and energy saving of the building. Most machine learning methods need labeled data to train the model. However, the difficulty of obtaining labeled data is much greater than that of unlabeled data. Therefore, unsupervised clustering models are proposed to study the operation pattern recognition of the refrigeration, heating and hot water combined air-conditioning (RHHAC) system. Clustering methods selected in this study include K-means, Gaussian mixture model clustering (GMMC) and spectral clustering. Further, correlation analysis is used to eliminate the redundant characteristic variables of the clustering model. The operating data of the RHHAC system are used to evaluate the performance of proposed clustering models. The results show that clustering models, after removing redundant variables by correlation analysis, can also identify the defrosting operation mode. Moreover, for the GMMC model, the running time is reduced from 27.80 s to 10.04 s when the clustering number is 5. The clustering performance of the original feature set model is the best when the number of clusters of the spectral clustering model is two and three. The clustering hit rate is 98.99%, the clustering error rate is 0.58% and the accuracy is 99.42%.

To the best of our knowledge, this is the first scoping review of Sleep improvement strategies for the elderly with psychogenic sleep disorders; scoping review is an effective way to answer a wide range of research questions and will therefore be able to outline psychogenic sleep intervention programs with a focus on negative emotions such as depression and anxiety and improvement of sleep Four team members work together to develop a rigorous search strategy, including three researchers with systematic and evidence-based training and a sleep expert, which makes our search strategy more professional The protocol is guided by Arksey and O’Malley’s framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension to improve methodological rigor, identify limitations of existing interventions for psychogenic sleep disorders in the elderly, and describes a transparent approach to comprehensively identify literature on interventions for psychogenic sleep disorders in the elderly population, which increases reproducibility.

Background Long noncoding RNAs (lncRNAs) play important roles in many physiological and pathological processes, this indicates that lncRNAs can serve as potential targets for gene therapy. Stable expression is a fundamental technology in the study of lncRNAs. The lentivirus is one of the most widely used delivery systems for stable expression. However, it was initially designed for mRNAs, and the applicability of lentiviral vectors for lncRNAs is largely unknown. Results We found that the lentiviral vector produces lncRNAs with improper termination, appending an extra fragment of ~ 2 kb to the 3ʹ-end. Consequently, the secondary structures were changed, the RNA–protein interactions were blocked, and the functions were impaired in certain lncRNAs, which indicated that lentiviral vectors are not ideal delivery systems of lncRNAs. Here, we developed a novel lncRNA delivery method called the Expression of LncRNAs with Endogenous Characteristics using the Transposon System (ELECTS). By inserting a termination signal after the lncRNA sequence, ELECTS produces transcripts without 3ʹ-flanking sequences and retains the native features and function of lncRNAs, which cannot be achieved by lentiviral vectors. Moreover, ELECTS presents no potential risk of infection for the operators and it takes much less time. ELECTS provides a reliable, convenient, safe, and efficient delivery method for stable expression of lncRNAs. Conclusions Our study demonstrated that improper transcriptional termination from lentiviral vectors have fundamental effects on molecular action and cellular function of lncRNAs. The ELECTS system developed in this study will provide a convenient and reliable method for the lncRNA study. Graphic Abstract

Background: The elderly grow as age, sleep quality becomes worse, and psychogenic insomnia occupies a large proportion, which can lead to cognitive impairment and an increased risk of cardiovascular disease, even leading to suicide. It also places a psychological and financial burden on families. It is because of the characteristics of psychogenic insomnia: the two-way effect of negative emotions and insomnia, coupled with the aging of the elderly themselves, that psychogenic insomnia in the elderly is more difficult to treat. Considering the side effects of medication in the elderly, therefore, identifying non-pharmacological interventions for the elderly with psychogenic insomnia is a matter of urgent research for a solution that will help health professionals and stakeholders (nursing staff, patients, and community). Methods and analysis: We will undertake the review using the Joanna Briggs Institute (JBI) methodology. We develop search strategies and will search 6 databases without a date limit, and select articles published in Chinese and English. Retrieve and store all the records in Endnote 20 and NoteExpress, followed by the title and abstract screening as well as a full-text review. Results will be reported using PRISMA flow charts. Use standardized data extraction tables to extract data. All screening and extraction will be performed independently by two authors. We will present the final results in the form of descriptive summaries and charts. Discussion We anticipate that the scoping review will highlight the available resources and evidence on interventions that improve the sleep quality of the elderly. This may contribute to informed empirical evidence for professionals to enhance the functional recovery of the elderly. It may also reveal what interventions nurses could do. Scoping review registration The protocol has been registered prospectively on the Open Science Framework: http：//osf.io/urdhy/

Retroviruses and long terminal repeat retrotransposons rely on integrase (IN) to insert their complementary DNA (cDNA) into the genome of host cells. Nevertheless, in the absence of IN, retroelements can retain notable levels of insertion activity. We have characterized the IN-independent pathway of Tf1 and found that insertion sites had homology to the primers of reverse transcription, which form single-stranded DNAs at the termini of the cDNA. In the absence of IN activity, a similar bias was observed with HIV-1. Our studies showed that the Tf1 insertions result from single-strand annealing, a noncanonical form of homologous recombination mediated by Rad52. By expanding our analysis of insertions to include repeat sequences, we found most formed tandem elements by inserting at preexisting copies of a related transposable element. Unexpectedly, we found that wild-type Tf1 uses the IN-independent pathway as an alternative mode of insertion.

In the in vitro fertilization and embryo transfer (IVF-ET) treatments, blastocyst culture is the method of choice for the generation of the embryos. Blastocysts can present different growth, quality, availability, and morphological characteristics that can be used to evaluate them. Although extreme blastocyst formation failures have been associated with the alteration of a single gene, the molecular factors responsible for arrested embryos remain unknown. RNA-sequencing (RNA-seq) is a promising tool for facilitating transcriptomic studies in early human embryos, thus allowing the investigation of gene expression discrepancies associated with different morphological criteria. Herein, we performed transcriptome analyses of the different stages of arrested human embryos. We identified candidate genes and related cell signaling pathways potentially associated with either arrested or developed embryos. Specifically, the three genes (MOV10L1, DDX4, and FKBP6) related to both DNA methylation and piRNA metabolic pathway might be involved in embryo development. Additionally, the transcriptome of arrested early blastocysts was significantly different from developed late blastocysts. Although the gene expression profiles identified were not significantly different between low- and high-quality late blastocysts, a significant difference in the profiles of day 5 and day 6 available late blastocysts was observed, which may be related to the clinical pregnancy rate associated with IVF-ET. Furthermore, we show that some chimeric RNAs may be functional in blastocyst development. Our findings uncovered new molecular markers that can be used for embryonic development detection, which might act as a tool for blastocyst selection for subsequent transfer.

The development of stable and low-cost catalysts with high reactivity to replace Pt-based ones is the central focus but challenging for hydrogen evolution reaction (HER). The incorporation of single atoms into two-dimensional (2D) supports has been demonstrated as an effective strategy because of the highly active single atomic sites and extremely large surface area of two-dimensional materials. However, the doping of single atoms is normally performed on the surface suffering from low stability, especially in acidic media. Moreover, it is experimentally challenging to produce monolayered 2D materials with atomic doping. Here, we propose a strategy to incorporate single foreign Fe atoms to substitute W atoms in sandwiched two-dimensional WS2. Because of the charge transfer between the doped Fe atom and its neighboring S atoms on the surface, the proximate S atoms become active for HER. Our theoretical prediction is later verified experimentally, showing an enhanced catalytic reactivity of Fe-doped WS2 in HER with the Volmer-Heyrovsky mechanism involved. We refer to this strategy as proximity catalysis, which is expected to be extendable to more sandwiched two-dimensional materials as substrates and transition metals as dopants.

In China, esophageal squamous cell carcinoma (ESCC) accounts for more than 90% of all esophageal cancer cases. Interleukin 13 (IL-13) was widely reported to play a key role in tumor progression. Our previous study reported that IL-13 was a favorable predictive marker for the overall survival of esophageal squamous cell carcinoma (ESCC) patients, but how IL-13 contributes to ESCC progression remains unknown. This study aims to explore the role of IL-13 and its underlying downstream molecular mechanisms in ESCC progression.Tissue microarrays including 262 primary ESCC tumor tissues were collected and analyzed. The expression of IL-13 in ESCC tumor tissue was detected with immunohistochemistry staining (IHC). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to qualify the expressions ofIL-13 was expressed in the esophageal epithelium cells and ESCC tumor cells. High IL-13 expression in ESCC tumor cells predicted a good prognosis for patients. Recombinant human IL-13 raisedIn summary, our study suggests that IL-13 might improve the prognosis of ESCC by promoting the terminal differentiation of ESCC cells. This may offer potential new therapeutic target for early treatment of ESCC.

The timely fault diagnosis of HVAC systems is important for building energy saving, equipment maintenance and indoor comfort. The Gaussian mixture model method has rarely been studied in the fault diagnosis application of HVAC systems. Therefore, a novel fault diagnosis strategy is proposed based on the Gaussian mixture model (GMM) method for the variable refrigerant flow air-conditioning system. To reduce excessive input variables resulting in large model complexity and long running time, the principal component analysis approach (PCA) is used to perform data dimensionality reduction. Therefore, the fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system. These faults include refrigerant undercharge, refrigerant overcharge, outdoor unit fouling and four-way reversing valve faults. Experiments are carried out under three heating conditions. Results show that the PCA-GMM approach can effectively reduce the running time. Especially for the VVV type model, the running time is reduced from 176.78 s to 15.18 s. Meanwhile, established PCA-GMMs still have good fault diagnosis correct rates when the input data dimension is reduced. Especially, some PCA-GMMs have fault diagnosis correct rates of over 99% when the number of principal components exceeds 7.

A molecule-confined two-dimensional (2D) hybrid superlattice is emerging for uncovering the chemical properties as well as distinctive physical phenomenon arising from the interface electronic stat...

Chromosome rearrangement plays important roles in development, carcinogenesis and evolution. However, its mechanism and subsequent effects are not fully understood. Large-scale chromosome rearrangement has been performed in the simple eukaryote, wine yeast, but the relative research in mammalian cells remains at the level of individual chromosome rearrangement due to technical limitations. In this study, we used CRISPR-Cas9 to target the highly repetitive human endogenous retrotransposons, LINE-1 and Alu, resulting in a large number of DNA double-strand breaks in the chromosomes. While this operation killed the majority of the cells, we eventually obtained live cell groups. Karyotype analysis and genome re-sequencing proved that we have achieved global chromosome rearrangement (GCR) in human cells. The copy number variations of the GCR genomes showed typical patterns observed in tumor genomes. The ATAC-seq and RNA-seq further revealed that the epigenetic and transcriptomic landscapes were deeply reshaped by GCR. Gene expressions related to p53 pathway, DNA repair, cell cycle and apoptosis were greatly altered to facilitate the cell survival. Our study provided a new application of CRISPR-Cas9 and a practical approach for GCR in complex mammalian genomes.

China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system.

Ultrathin transition metal dichalcogenides (TMDs) are of particular interest as low-cost alternatives to highly active electrocatalysts because of their high surface activation energy. However, the...

Top7 is a de novo designed protein with atomic level accuracy and shows a folded structure not found in nature. Previous studies showed that the folding of Top7 is not cooperative and involves various folding intermediate states. In addition, various fragments of Top7 were found to fold on their own in isolation. These features displayed by Top7 are distinct from those of naturally occurring proteins of a similar size and suggest a rough folding energy landscape. However, it remains unknown if and how the intra-polypeptide chain interactions among the neighboring sequences of Top7 affect the folding of these Top7 fragments. Here we used single-molecule optical tweezers to investigate the folding–unfolding pathways of full length Top7 as well as its C-terminal fragment (CFr) in different sequence environments. Our results showed that the mechanical folding of Top7 involves an intermediate state that likely involves non-native interactions/structure. More importantly, we found that the folding of CFr is entirely dependent upon its sequence context in which it is located. When in isolation, CFr indeed folds into a cooperative structure showing near-equilibrium unfolding–folding transitions at ∼6.5 pN in OT experiments. However, CFr loses its autonomous cooperative folding ability and displays a folding pathway that is dependent on its interactions with its neighboring sequence/structure. This context-dependent folding dynamics and pathway of CFr are distinct from those of naturally occurring proteins and highlight the critical importance of intra-chain interactions in shaping the overall energy landscape and the folding pathway of Top7. These new insights may have important implications on the de novo design of proteins., Optical tweezers experiments reveal that the folding of the C-terminal fragment of Top7 (cFr) is context-dependent. Depending on its neighboring sequence, cFr shows very different folding pathways and folding kinetics.

Short-term energy consumption prediction has fundamental importance in many HVAC system management tasks, such as demand-side management, short-term maintenance, etc. Currently, the prevailing data-driven techniques, especially supervised machine learning methods, are widely applied for short-term energy consumption prediction. Deep reinforcement learning (DRL), as the state-of-the-art machine learning techniques, have been applied for HVAC system control, but rarely for energy consumption prediction. In this paper, a DRL algorithm, namely Deep Deterministic Policy Gradient (DDPG), is firstly introduced for short-term HVAC system energy consumption prediction. Moreover, Autoencoder (AE), which is powerful in processing data in their raw form, is incorporated into DDPG method to extract the high-level features of state space and optimize the prediction model. The operation data of the ground source heat pump (GSHP) system of an office building in Henan province, China is used to train and assess the proposed models. The results demonstrate that the proposed DDPG based models can achieve better prediction performance than common supervised models like BP Neural Network and Support Vector Machine. This study is an enlightening work which may inspire other researchers to tap the potential of DRL algorithms in this field.

Identifying abnormal energy consumption in the variable refrigerant flow (VRF) system is important for its energy efficiency enhancement and energy saving. The energy performances of the VRF system are extremely distinct under various operation conditions. In order to evaluate the VRF’s energy performance, this study proposes a data-mining-based method to identify the abnormal energy consumption under different operation conditions. The local outlier factor (LOF) algorithm is used to distinguish transient data and three operation conditions are partitioned through clustering analysis. Besides, the correlation analysis is employed for key variables extraction. Then, the energy consumption is forecasted by support vector regression (SVR). The residuals of the measured energy values and the predicted values are applied in support vector data description (SVDD) algorithm to develop the responding D statistic as abnormal energy identification threshold. Finally, experiment data of various refrigerant charge level (RCL) are used to validate the proposed method. The methodology is sensitive to identifying abnormal energy consumption in VRFs at various RCLs. Nearly 100% of the abnormal energy consumption data can be accurately identified at some refrigerant charge levels. Operation condition partitioning can definitely enhance the identification efficiency.

This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.

Energy demand prediction is able to improve the energy efficiency and energy savings of the residential buildings. In this article, the prediction models are developed based on ensemble learning methods which combined extreme gradient boosting, extreme learning machine, multiple linear regression with support vector regression. According to the importance analysis of the random forest method, the optimal set of feature variables is selected. Besides, a historical energy comprehensive variable named EWMA was added in the prediction models to improve the prediction accuracy. A ground source heat pump for residential buildings located in Henan, China, is selected as a model for examining 2-hour ahead heating load forecasting. Results showed that the proposed prediction model based on ensemble learning could reduce the MAE of the testing set prediction result, which ranged from 29.1% to 70%.

This study presents a case study of public buildings using a novel deep learning method to forecast indoor air temperature. The aim is to explore the potential of long short-term memory (LSTM) model in forecasting indoor temperature, and a novel LSTM model modified by error correction model is established. The performance of the two models is compared with popular prediction methods in the building field. Results show that the proposed novel LSTM model has slight advantages in level indoor temperature prediction performance comparing with other common machine learning methods. However, it outperforms other models including original LSTM in terms of directional prediction accuracy, and accurately predicts the indoor temperature variation trend. This work is enlightening and may have a further reference to the feasibility study of indoor air temperature prediction model.

A refrigerant charge fault detection strategy was proposed using regression method. First, characteristic variable parameters that are sensitive to the refrigerant charge level of variable refrigerant flow (VRF) system were select. Then, regression models of refrigerant charge amount fault detection were established by using multiple linear regression and support vector regression respectively. The training process of the regression model only needs the normal refrigerant charge operation data, which does not need the complete refrigerant charge level experimental data. Refrigerant charge fault experimental data which contains refrigerant undercharge, overcharge and leakage were used to test the developed fault detection models. Results show that the detection performance of the support vector regression model is better than the multiple linear regression model. In addition, for the refrigerant leakage fault, the support vector regression model can detect the leakage fault after 3 minutes of refrigerant leakage.

Transposons are mobile DNAs that can move to different locations in host genomes. The integration site selection of transposons is critical for both themselves and host cells. Studies on the integration of retrotransposons and retroviruses have focused more on the global preference than on the local preference. The local preferences of retrotransposons are usually weak and of large diversity. Here, we analyzed hundreds of thousands of independent integration events of the Tf1 retrotransposon in Schizosaccharomyces pombe. The consensus sequence at the Tf1 integration sites shows a palindromic pattern, which can be divided into four sections, each of them contains one or more CGnTA units with a period of 10 base pairs, indicating interaction with subunits of the integrase oligomer in the pre-integration complex. Moreover, the analysis on the nucleosome occupancy flanking Tf1 target sites shows that Tf1 integration favors regions with one entire nucleosome depletion.

The development of stable and low-cost catalysts with high reactivity to replace Pt-based ones is the central focus but challenging for hydrogen evolution reaction (HER). The incorporation of single atoms into two-dimensional (2D) supports has been demonstrated as an effective strategy because of the highly active single atomic sites and extremely large surface area of two-dimensional materials. However, the doping of single atoms is normally performed on the surface suffering from low stability, especially in acidic media. Moreover, it is experimentally challenging to produce monolayered 2D materials with atomic doping. Here, we propose a strategy to incorporate single foreign Fe atoms to substitute W atoms in sandwiched two-dimensional WS