Search

The BL17B beamline at the Shanghai Synchrotron Radiation Facility was first designed as a versatile high‐throughput protein crystallography beamline and one of five beamlines affiliated to the National Facility for Protein Science in Shanghai. It was officially opened to users in July 2015. As a bending magnet beamline, BL17B has the advantages of high photon flux, brightness, energy resolution and continuous adjustable energy between 5 and 23 keV. The experimental station excels in crystal screening and structure determination, providing cost‐effective routine experimental services to numerous users. Given the interdisciplinary and green energy research demands, BL17B beamline has undergone optimization, expanded its range of experimental methods and enhanced sample environments for a more user‐friendly testing mode. These methods include single‐crystal X‐ray diffraction, powder crystal X‐ray diffraction, wide‐angle X‐ray scattering, grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), and fully scattered atom pair distribution function analysis, covering structure detection from crystalline to amorphous states. This paper primarily presents the performance of the BL17B beamline and the application of the GIWAXS methodology at the beamline in the field of perovskite materials. [ABSTRACT FROM AUTHOR]

Small cell lung cancer (SCLC) is a deadly neuroendocrine malignancy, notorious for its rapid tumor growth, early metastasis, and relatively "cold" immune environment. Only standard chemotherapies and a few immune checkpoint inhibitors have been approved for SCLC treatment, revealing an urgent need for novel therapeutic approaches. Moreover, SCLC has been recently recognized as a malignancy with high intratumoral and intertumoral heterogeneity, which explains the modest response rate in some patients and the early relapse. Molecular subtypes defined by the expression of lineage-specific transcription factors (ASCL1, NEUROD1, POU2F3, and, in some studies, YAP1) or immune-related genes display different degrees of neuroendocrine differentiation, immune cell infiltration, and response to treatment. Despite the complexity of this malignancy, a few biomarkers and targets have been identified and many promising drugs are currently undergoing clinical trials. In this review, we integrate the current progress on the genomic landscape of this shapeshifting malignancy, the characteristics and treatment vulnerabilities of each subtype, and promising drugs in clinical phases. [ABSTRACT FROM AUTHOR]

BL19U1, an energy‐tunable protein complex crystallography beamline at the Shanghai Synchrotron Radiation Facility, has emerged as one of the most productive MX beamlines since opening to the public in July 2015. As of October 2023, it has contributed to over 2000 protein structures deposited in the Protein Data Bank (PDB), resulting in the publication of more than 1000 scientific papers. In response to increasing interest in structure‐based drug design utilizing X‐ray crystallography for fragment library screening, enhancements have been implemented in both hardware and data collection systems on the beamline to optimize efficiency. Hardware upgrades include the transition from MD2 to MD2S for the diffractometer, alongside the installation of a humidity controller featuring a rapid nozzle exchanger. This allows users to opt for either low‐temperature or room‐temperature data collection modes. The control system has been upgraded from Blu‐Ice to MXCuBE3, which supports website‐mode data collection, providing enhanced compatibility and easy expansion with new features. An automated data processing pipeline has also been developed to offer users real‐time feedback on data quality. [ABSTRACT FROM AUTHOR]

The widespread use of encrypted traffic poses challenges to network management and network security. Traditional machine learning-based methods for encrypted traffic classification no longer meet the demands of management and security. The application of deep learning technology in encrypted traffic classification significantly improves the accuracy of models. This study focuses primarily on encrypted traffic classification in the fields of network analysis and network security. To address the shortcomings of existing deep learning-based encrypted traffic classification methods in terms of computational memory consumption and interpretability, we introduce a Parameter-Efficient Fine-Tuning method for efficiently tuning the parameters of an encrypted traffic classification model. Experimentation is conducted on various classification scenarios, including Tor traffic service classification and malicious traffic classification, using multiple public datasets. Fair comparisons are made with state-of-the-art deep learning model architectures. The results indicate that the proposed method significantly reduces the scale of fine-tuning parameters and computational resource usage while achieving performance comparable to that of the existing best models. Furthermore, we interpret the learning mechanism of encrypted traffic representation in the pre-training model by analyzing the parameters and structure of the model. This comparison validates the hypothesis that the model exhibits hierarchical structure, clear organization, and distinct features. [ABSTRACT FROM AUTHOR]

Duke Kunshan University (DKU), a new liberal arts and science university in China, with Duke University and Wuhan University as academic partners, is an experiment in twenty-first-century characteristics and global challenges. DKU's undergraduate degree program features an innovative, integrated, and interdisciplinary curriculum focused on cross-disciplinary challenges and research experiences. Its interdisciplinary research centers and graduate programs help attract international faculty and students, while its residential setting promotes diverse, rich, and meaningful interactions beyond the classroom. The shared vision for DKU among students, parents, faculty, staff, partner universities, and other stakeholders helps ensure the university's success. And the phenomenal achievements of the first cohort of graduates from the inaugural class of 2022 is evidence that the DKU experiment is working, despite many challenges. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to investigate serum neopterin levels in patients with acute pancreatitis (AP) and its predictive value for mortality in cases of severe acute pancreatitis (SAP).

Real-time computation tasks in vehicular edge computing (VEC) provide convenience for vehicle users. However, the efficiency of task offloading seriously affects the quality of service (QoS). The predictive-mode task offloading is limited by computation resources, storage resources and the timeliness of vehicle trajectory data. Meanwhile, machine learning is difficult to deploy on edge servers. In this paper, we propose a vehicle trajectory prediction method based on the vehicle frequent pattern for task offloading in VEC. First, in the initialization stage, a T-pattern prediction tree (TPPT) is constructed based on the historical vehicle trajectory data. Secondly, when predicting the vehicle trajectory, the vehicle frequent itemset with the largest vehicle trajectory support is found in the vehicle frequent itemset of the TPPT. Finally, in the update stage, the TPPT is updated in real time with the predicted vehicle trajectory results. Meanwhile, based on the proposed prediction method, the strategies of task offloading and optimization algorithm are designed to minimize energy consumption with time constraints. The experiments are carried out on real-vehicle datasets and the Capital Bikeshare datasets. The results show that compared with the baseline T-pattern method, the accuracy of the prediction method is improved by more than 10% and the prediction efficiency is improved by more than 6.5 times. The vehicle trajectory prediction method based on the vehicle frequent pattern has high accuracy and prediction efficiency, which can solve the problem of vehicle trajectory prediction for task offloading. [ABSTRACT FROM AUTHOR]

Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) is essential in vehicle applications such as traffic control and in-vehicle services. In the task offloading process of VEC, predictive-mode transmission based on deep learning is constrained by limited computational resources. Furthermore, the accuracy of deep learning algorithms in VEC is compromised due to the lack of edge computing features in algorithms. To solve these problems, this paper proposes a task offloading optimization approach that enables edge servers to store deep learning models. Moreover, this paper proposes the LTransformer, a transformer-based framework that incorporates edge computing features. The framework consists of pre-training, an input module, an encoding–decoding module, and an output module. Compared with four sequential deep learning methods, namely a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), a Gated Recurrent Unit (GRU), and the Transformer, the LTransformer achieves the highest accuracy, reaching 80.1% on the real dataset. In addition, the LTransformer achieves 0.008 s when predicting a single trajectory, fully satisfying the fundamental requirements of real-time prediction and enabling task offloading optimization. [ABSTRACT FROM AUTHOR]

In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions. [ABSTRACT FROM AUTHOR]

Background: This study aimed to examine the association between cardiopulmonary function, health-related quality of life (HRQOL) and cognitive function among nursing home residents aged 80 years and over. Methods: A nursing home-based, cross-sectional study was implemented among 677 aged over 80 years in Shanghai, China. A total of 197 participants underwent effective cardiopulmonary function examinations. Mini-Mental Status Examination (MMSE) and Short Form-36 scales (SF-36) were used to assess cognitive function and HRQOL, respectively. Results: Decline in left ventricular ejection fractions (LVEF) [adjusted odds ratio (AOR), 1.98; 95% confidential interval (CI), 1.03–3.81)] and vital capacity (VC) (AOR, 2.08; 95%CI, 1.07–4.04) was associated with cognitive impairment. After adjusting confounding factors, relationships between cognitive function and physical functioning (PF) (AOR, 0.98; 95%CI, 0.97–0.99) still existed. Conclusions: Healthcare professionals should pay more attention to cardiopulmonary health and HRQOL in the nursing home residents. Actions of public health strategies focus on the improvement of cardiopulmonary function, and PF among older nursing home residents with cognitive impairment is required. [ABSTRACT FROM AUTHOR]

Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions. [ABSTRACT FROM AUTHOR]

In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection. [ABSTRACT FROM AUTHOR]

Background and Aim: Any single discrete blood pressure (BP) measurement is not enough to estimate adverse cardiovascular events. We aim to comprehensively investigate the association between BP indicators and stroke. Methods: An observational cohort study was conducted among 2888 Shanghai community-aged residents from 2014 to 2018, and a nested case-control study was designed to identify the association between BP indicators and stroke. In total 415 cases of stroke detected during the study period were selected as the case group and 415 non-stroke subjects, matched with factors of age and gender, were randomly selected from the cohort as control group. Results: Multivariate logistic regression analysis revealed that systolic blood pressure (SBP) (adjusted odds ratio [AOR] 1.02, 95% confidence interval [CI] 1.02– 1.03), pulse pressure (PP) (AOR 1.03, 95% CI 1.02– 1.04), mean arterial pressure (MAP) (AOR1.02, 95% CI 1.01– 1.04) and pulse pressure index (PPI) (AOR 25.68, 95% CI 3.19– 206.90) increased the risk of stroke, respectively. After fitting both BP indicators and covariates, isolated abnormal SBP (AOR 2.55, 95% CI 1.74– 3.72) or PP ≥ 50 mmHg (AOR 1.66, 95% CI 1.08– 2.56) independently increased risk of stroke. Conclusion: Besides SBP, PP and multiple factors, assessment should be taken into account comprehensively for stroke prevention and management. [ABSTRACT FROM AUTHOR]

The human monoclonal antibody Fab X5 neutralizes a broad range of HIV-1 primary isolates. It is reported that in the asymmetric unit there are two crystallographically independent Fab fragments.

Gram‐negative bacteria defend against the toxicity of polymyxins by modifying their outer membrane lipopolysaccharide (LPS). This modification mainly occurs through the addition of cationic molecules such as phosphoethanolamine (PEA). EcEptC is a PEA transferase from Escherichia coli (E. coli). However, unlike its homologs CjEptC (Campylobacter jejuni) and MCR‐1, EcEptC is unable to mediate polymyxin resistance when overexpressed in E. coli. Here, we report crystal structures of the C‐terminal putative catalytic domain (EcEptCΔN, 205–577 aa) of EcEptC in apo and Zn2+‐bound states at 2.10 and 2.60 Å, respectively. EcEptCΔN is arranged into an α‐β‐α fold and equipped with the zinc ion in a conserved mode. Coupled with isothermal titration calorimetry (ITC) data, we provide insights into the mechanism by which EcEptC recognizes Zn2+. Furthermore, structure comparison analysis indicated that disulfide bonds, which play a key role in polymyxin resistance, were absent in EcEptCΔN. Supported by structural and biochemical evidence, we reveal mechanistic implications for disulfide bonds in PEA transferase‐mediated polymyxin resistance. Significantly, because the structural effects exhibited by disulfide bonds are absent in EcEptC, it is impossible for this protein to participate in polymyxin resistance in E. coli. Database: Structural data are available in the PDB under the accession numbers 6A82 and 6A83. Enzyme: EC 2.7.8.43 EcEptC is a phosphoethanolamine (PEA) transferase from Escherichia coli that is unable to mediate polymyxin resistance. Here, we report the crystal structures of the C‐terminal catalytic domain of the protein and demonstrate that it lacks disulfide bonds that are essential in polymyxin resistance. This feature distinguishes EcEptC from CjEptC (Campylobacter jejuni) and other PEA transferases and sheds light on the underlying mechanism. [ABSTRACT FROM AUTHOR]

The title compounds were conveniently synthesized by a phase transfer catalyzed approach in two steps and one pot using the rhamnopyranosyl chloride as key intermediate. The two-step one-pot procedure is more convenient and environmentally friendly, compared to the published synthetic routes. Besides, the structure-reactivity relationship and the plausible mechanism for this base-catalyzed glycosylation were discussed by means of the Hammett equation. [ABSTRACT FROM PUBLISHER]

It is particularly necessary for biomedical researchers to obtain applicable biosamples accurately and efficiently, especially from a biobank with multiple-disease catalogs. To optimize the retrieval procedure, especially in the early stages of a non-automatic biobank, we developed a procedure that combined the electronic information system with a graphically designed printed recording system, which assisted in retrieving the samples quickly in a visualized way. In this procedure, we designed tables depending on the structure of equipment and registered the corresponding information in the tables layer by layer. Different samples from different types of diseases were first registered in the electronic system with the specific pre-allocation and barcodes. Then they were stored in the allocated position using their respective barcodes. In this way, the sample number and the location information in the electronic database were completely matched with the printed record. When the samples are needed, it is convenient to check the electronic information with the printed record. This procedure provides a convenient way to record the sample information during its lifecycle, and helps the administrator to double check information about the sample. The current solution offers an easy way for the transformation of a non-automatic biobank from the small-scale early-stage to the large-scale highly-automated level. [ABSTRACT FROM AUTHOR]

Reprogramming of somatic cells in the enucleated egg made Dolly, the sheep, the first successfully cloned mammal in 1996. However, the mechanism of sheep somatic cell reprogramming has not yet been addressed. Moreover, sheep embryonic stem (ES) cells are still not available, which limits the generation of precise gene-modified sheep. In this study, we report that sheep somatic cells can be directly reprogrammed to induced pluripotent stem (iPS) cells using defined factors (Oct4, Sox2, c-Myc, Klf4, Nanog, Lin28, SV40 large T and hTERT). Our observations indicated that somatic cells from sheep are more difficult to reprogram than somatic cells from other species, in which iPS cells have been reported. We demonstrated that sheep iPS cells express ES cell markers, including alkaline phosphatase, Oct4, Nanog, Sox2, Rex1, stage-specific embryonic antigen-1, TRA-1-60, TRA-1-81 and E-cadherin. Sheep iPS cells exhibited normal karyotypes and were able to differentiate into all three germ layers both in vitro and in teratomas. Our study may help to reveal the mechanism of somatic cell reprogramming in sheep and provide a platform to explore the culture conditions for sheep ES cells. Moreover, sheep iPS cells may be directly used to generate precise gene-modified sheep. [ABSTRACT FROM AUTHOR]

Shikimate kinase (SK) and other enzymes in the shikimate pathway are potential targets for developing non-toxic antimicrobial agents, herbicides, and anti-parasite drugs, because the pathway is essential in the above species but is absent from mammals. The crystal structure of Mycobacterium tuberculosis SK (MtSK) in complex with MgADP has been determined at 1.8 A˚ resolution, revealing critical information for the structure-based design of novel anti-M. tuberculosis agents. MtSK, with a five-stranded parallel β-sheet flanked by eight α-helices, has three domains: the CORE domain, the shikimate-binding domain (SB), and the LID domain. The ADP molecule is bound with its adenine moiety sandwiched between the side-chains of Arg110 and Pro155, its β-phosphate group in the P-loop, and the α and β-phosphate groups hydrogen bonded to the guanidinium group of Arg117. Arg117 is located in the LID domain, is strictly conserved in SK sequences, is observed for the first time to interact with any bound nucleotide, and appears to be important in both substrate binding and catalysis. The crystal structure of MtSK (this work) and that of Erwinia chrysanthemi SK suggest a concerted conformational change of the LID and SB domains upon nucleotide binding. [Copyright &y& Elsevier]

Abstract DUX4 plays critical role in the molecular pathogenesis of the neuromuscular disorder facioscapulohumeral muscular dystrophy and acute lymphoblastic leukemia in humans. As a master transcription regulator, DUX4 can also bind the promoters and activate the transcription of hundreds ZGA-associated genes. Here we report on the structural and biochemical studies of DUX4 double homeodomains (DUX4-DH), representing the only structures contain both homeodomain 1 (HD1) and homeodomain 2 (HD2). HD1 and HD2 adopt classical homeobox fold; via the helix inserted into the major groove and the N-terminal extended loop inserted into the minor groove, HD1 and HD2 recognize the box1 (5′-TAA-3′) and box2 (5′-TGA-3′) nucleotides of the consensus sequence, respectively. Among the box1 and box2 linking nucleotides (CCTAA), the two adenine residues are reported to be highly conserved; however, they are not directly recognized by DUX4-DH in the structures. Besides different nucleotides, our ITC analysis indicated that DUX4-DH can also tolerate various changes in the linker length. Our studies not only revealed the basis for target DNA recognition by DUX4, but also advanced our understanding on multiple gene activation by DUX4. Highlights • DUX4 is a master regulator that controls the expression of multiple genes. • DUX4-DH/DNA complex structure was solved at high resolution. • DH1 and DH2 domains of DUX4 play important role in consensus sequence recognition. • DUX4 can tolerate length and/or identity variation in target DNA linker region. [ABSTRACT FROM AUTHOR]

SCARF1 (scavenger receptor class F member 1, SREC-1 or SR-F1) is a type I transmembrane protein that recognizes multiple endogenous and exogenous ligands such as modified low-density lipoproteins (LDLs) and is important for maintaining homeostasis and immunity. But the structural information and the mechanisms of ligand recognition of SCARF1 are largely unavailable. Here, we solve the crystal structures of the N-terminal fragments of human SCARF1, which show that SCARF1 forms homodimers and its epidermal growth factor (EGF)-like domains adopt a long-curved conformation. Then, we examine the interactions of SCARF1 with lipoproteins and are able to identify a region on SCARF1 for recognizing modified LDLs. The mutagenesis data show that the positively charged residues in the region are crucial for the interaction of SCARF1 with modified LDLs, which is confirmed by making chimeric molecules of SCARF1 and SCARF2. In addition, teichoic acids, a cell wall polymer expressed on the surface of gram-positive bacteria, are able to inhibit the interactions of modified LDLs with SCARF1, suggesting the ligand binding sites of SCARF1 might be shared for some of its scavenging targets. Overall, these results provide mechanistic insights into SCARF1 and its interactions with the ligands, which are important for understanding its physiological roles in homeostasis and the related diseases., Competing Interests: YW, FX, GL, CC, BY, ZZ, DK, FC, YL, ZF, LC, YY, YG, YH No competing interests declared, (© 2024, Wang, Xu, Li et al.)

Cancer is a life-threatening destructive disease. In the past several decades, the incidence of cancer has been dramatically increased mostly due to lifestyle changes. Chemotherapy plays an important role in the treatment of cancer, but the development of resistance against chemotherapeutic agents, the side effects, and non-specific toxicity threatens the efficiency of anticancer agents. Accordingly, it is important to develop novel anticancer drugs. Beyond the classical antibacterial activity, macrolides also demonstrated potential effects against both drug-sensitive and drug-resistant cancers through modulating diverse targets and signaling pathways, so rational design of macrolides may generate valuable therapeutic interventions for the treatment of cancers. The purpose of the present review article is to outline the current developments in macrolides with an emphasis on anticancer activity, structure-activity relationships, and mechanisms of action to lay the path for the development of novel macrolide anticancer candidates., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

The UbiA superfamily of intramembrane prenyltransferases catalyzes an isoprenyl transfer reaction in the biosynthesis of lipophilic compounds involved in cellular physiological processes. Digeranylgeranylglyceryl phosphate (DGGGP) synthase (DGGGPase) generates unique membrane core lipids for the formation of the ether bond between the glycerol moiety and the alkyl chains in archaea and has been confirmed to be a member of the UbiA superfamily. Here, the crystal structure is reported to exhibit nine transmembrane helices along with a large lateral opening covered by a cytosolic cap domain and a unique substrate-binding central cavity. Notably, the lipid-bound states of this enzyme demonstrate that the putative substrate-binding pocket is occupied by the lipidic molecules used for crystallization, indicating the binding mode of hydrophobic substrates. Collectively, these structural and functional studies provide not only an understanding of lipid biosynthesis by substrate-specific lipid-modifying enzymes but also insights into the mechanisms of lipid membrane remodeling and adaptation., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Type I polyketide synthase 13 (Pks13) is involved in the final step of the biosynthesis of mycolic acid in Mycobacterium tuberculosis . Recent articles have reported that Pks13 is an essential enzyme in the mycolic acid biosynthesis pathway, and it has been deeply studied as a drug target in Tuberculosis. We report a high-resolution structure of the acyltransferase (AT) domain of Pks13 at 2.59 Å resolution. Structural comparison with the full-length AT domain (PDB code, 3TZW, and 3TZZ) reveals a different orientation of the C-terminal helix and rearrangement of some conserved residues., Competing Interests: The authors declare that they have no competing interests.

To investigate the prevalence of cognitive impairment and its risk factors among Chinese elders aged over 80 years, a community-based, cross-sectional study was conducted from May to June 2016 in Shanghai, China. Cognitive function was measured by using Mini-Mental Status Examination. Multiple logistic regression assessed associations between risk factors and cognitive impairment. Of 480 participants, 30% were diagnosed with cognitive impairment. Women [adjusted odds ratio (AOR): 1.71, 95% confidence interval (CI): 1.03-2.83], solitary life (AOR: 3.15, 1.89-5.26), monthly income less than 2000 Chinese yuan (AOR: 3.47, 1.18-10.23) were significantly associated with increased risk of cognitive impairment, compared with men, non-solitary life, and monthly income greater than 4000 Chinese yuan, respectively. Overweight (AOR: 0.59, 0.36-0.97), being physically active at least 60 minutes per day (AOR: 0.59, 0.35-0.95), antihypertensive drugs users (AOR: 0.45, 0.28-0.72), and lipid lowering drugs users (AOR: 0.21, 0.06-0.76) significantly lowered the risk of cognitive impairment, compared with normal weight, inadequate outdoor activity, and non-medication users, respectively. Accordingly, this study found that women, solitary life, lower income was associated with increased risk of cognitive impairment, while overweight, being physically active, and antihypertensive and lipid lowering drugs usage might lower the risk.

Cryptorchidism is a common congenital birth defect in human beings with the possible complication of infertility. An in vitro model of cryptorchidism might be valuable due to the inaccessibility of human embryos for research purposes. In this study, we reprogrammed urine cells containing genetic variations in insulin-like factor 3, zinc finger (ZNF) 214, and ZNF215 from a cryptorchid patient by introducing human OCT4, SOX2, C-MYC, and KLF4 with lentivirus. The cells were then replated on irradiated mouse embryonic fibroblasts and cultured with the human embryonic stem (ES) cell medium. The compact colonies with well-defined borders were manually picked, and 2 induced pluripotent cell lines were fully characterized. Our results demonstrated that these 2 cell lines were similar to human ES cells in morphological appearance, marker expression, and epigenetic status of the pluripotent cell-specific gene, OCT4. These cells could be differentiated into cells of all 3 germ layers in teratomas and in vitro, including into the VASA-positive germ cell lineage. Both parental urine cells and the reprogrammed cells possessed the normal karyotype and the same short tandem repeat loci, indicating that these 2 cell population share the same genetic identity. This establishment and characterization of human urine-derived cryptorchid-specific induced pluripotent stem cells could present a good human genetic system for future studies investigating the molecular mechanism of cryptorchidism.