A Double Perovskite Single Crystal CsCuAgI3.

Eco-friendly halide double perovskites are attracting significant attention as potential substitutes for traditional lead-based halide perovskites. However, their typically wide or indirect band gap limits further technological advancement. This study presents a new, eco-friendly, all-inorganic millimeter-scale CsCuAgI3 single crystal (SC). The crystal exhibits a direct band gap of 2.02 eV at the G-point, markedly superior to that of traditional double perovskites. The absorption and photoluminescence spectra further corroborate its band gap attributes. Owing to the B-site Cu-Ag disorder, the crystal possesses a higher Urbach energy (119 meV), indicative of structural disorder. CsCuAgI3 exhibits a wide Stokes shift of 230 nm, a wide full width at half-maximum (fwhm) of 152 nm, a long fluorescence lifetime of 7.29 µs, and excellent stability. In addition, a photoelectric detection prototype was prepared using a CsCuAgI3 single crystal. Using a 375 nm laser as the excitation source, the device showed a very sensitive photoelectric response, clocking in at (0.37/0.21) seconds. This work offers new insights into developing novel lead-free double perovskite single crystals and exploring their potential applications.

Acid-Modified Hierarchical Porous Rare-Earth-Containing Y Zeolite as a Highly Active and Stable Catalyst for Olefin Removal.

Rare-earth-containing ultrastable Y zeolite (ReUSY) was modified by oxalic acid solution leaching treatment and applied in industrial units for catalytic olefin removal from aromatic hydrocarbons. The porous structure and amount of acidity of the modified ReUSY (denoted as ReUSY-x, where x indicated the amount of oxalic acid in solution) could be tuned by different concentrations of oxalic acid solution, and the ReUSY-x samples exhibited different catalytic performances. Based on the best catalytic performance of ReUSY-25, an industrial catalyst was prepared and applied in industrial units for catalytic olefin removal. The industrial catalyst exhibited excellent activity and regeneration stability with a long lifetime of about 2 years, which was about 13 times longer than that of activated clay.

CTumorGAN: a unified framework for automatic computed tomography tumor segmentation.

PURPOSE: Unlike the normal organ segmentation task, automatic tumor segmentation is a more challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution, as well as the diversity and individual characteristics of data acquisition procedures and devices. Consequently, most of the recently proposed methods have become increasingly difficult to be applied on a different tumor dataset with good results, and moreover, some tumor segmentors usually fail to generalize beyond those datasets and modalities used in their original evaluation experiments. METHODS: In order to alleviate some of the problems with the recently proposed methods, we propose a novel unified and end-to-end adversarial learning framework for automatic segmentation of any kinds of tumors from CT scans, called CTumorGAN, consisting of a Generator network and a Discriminator network. Specifically, the Generator attempts to generate segmentation results that are close to their corresponding golden standards, while the Discriminator aims to distinguish between generated samples and real tumor ground truths. More importantly, we deliberately design different modules to take into account the well-known obstacles, e.g., severe class imbalance, small tumor localization, and the label noise problem with poor expert annotation quality, and then use these modules to guide the CTumorGAN training process by utilizing multi-level supervision more effectively. RESULTS: We conduct a comprehensive evaluation on diverse loss functions for tumor segmentation and find that mean square error is more suitable for the CT tumor segmentation task. Furthermore, extensive experiments with multiple evaluation criteria on three well-established datasets, including lung tumor, kidney tumor, and liver tumor databases, also demonstrate that our CTumorGAN achieves stable and competitive performance compared with the state-of-the-art approaches for CT tumor segmentation. CONCLUSION: In order to overcome those key challenges arising from CT datasets and solve some of the main problems existing in the current deep learning-based methods, we propose a novel unified CTumorGAN framework, which can be effectively generalized to address any kinds of tumor datasets with superior performance.

Exome sequencing identified six copy number variations as a prediction model for recurrence of primary prostate cancers with distinctive prognosis.

BACKGROUND: Prostate cancer (PCa) is a common type of malignancy, which represents one of the leading causes of death among men worldwide. Copy number variations (CNVs) and gene fusions play important roles in PCa and may serve as markers for the prognosis of this condition. METHODS: We have presently conducted an analysis of CNVs and gene fusions in PCa, using whole exome sequencing (WES) data of primary tumors. For this, a cohort of 74 PCa patients, including 30 recurrent and 44 non-recurrent cases, were assessed during 5 years of follow-up. RESULTS: We have identified 66 CNVs that were specific to the primary tumor tissues from the recurrent PCa group. Most of duplicated genomic regions were located in 8q2, suggesting that this chromosomal region could be important for the prognosis of PCa. Meanwhile, we have developed a random forest model, using six selected CNVs, with an accuracy near 90% for predicting PCa recurrence according to a 10-fold cross validation. In addition, we have detected 16 recurrent oncogenic gene fusions in PCa. Among these, ALK (ALK receptor tyrosine kinase)-involved fusions were the most common type of gene fusion (n=7). Four of these fusions (i.e., EML4-ALK, STRN-ALK, CLTC-ALK, ETV6-ALK) were previously identified in other cancer types, while the remaining three gene fusions (FRYL-ALK, ABL1-ALK, and BCR-ALK) were here identified. CONCLUSIONS: Our findings expand the current understanding in regard to prostate carcinogenesis. Current data might be further used for assay development as well as to predict PCa recurrence, using primary tissues.

Whole Exome Sequencing Identifies Putative Predictors of Recurrent Prostate Cancer with High Accuracy.

Prostate cancer (PCa) is a highly common cancer among men but lacks robust diagnostics that can predict disease recurrence after initial treatment, for example, with radical prostatectomy. Recent advances in genomics and next-generation sequencing heralded the discovery of biomarkers such as the androgen receptor gene (AR) splice events, the TMPRSS2:EGR gene fusion, long noncoding RNA MALAT-1 and SCHLAP1 for PCa prognosis. Still, the question of why some patients experience recurrence, whereas others do not introduce marked uncertainty for both patients and physicians. We report here the whole exome sequencing of 30 recurrent and 44 nonrecurrent PCa patients. We identified 72 and 34 specific somatic single nucleotide variations in the recurrent and the nonrecurrent group, respectively, and developed a classification model to forecast PCa recurrence using a random forest model. The model displayed a sensitivity and specificity of 87.8% and 94.4%, respectively, for identifying the patients with recurrent PCa. These observations warrant further research in independent and larger clinical samples so as to inform future diagnostics innovation for PCa prognosis and recurrence.

CHI3L1 promotes tumor progression by activating TGF-ß signaling pathway in hepatocellular carcinoma.

CHI3L1 (YKL40) is a secreted glycoprotein and elevated serum CHI3L1 level has been proved to be associated with poor prognosis in many human cancers. However, the mechanism of how CHI3L1 causes poor prognosis in cancers is still unknown. Here, considering that CHI3L1 is a liver specific/enriched protein, we use hepatocellular carcinoma as a model to study the function of CHI3L1. We showed that, both in vivo and in vitro, overexpression of CHI3L1 could promote liver cancer cells growth, migration and invasion. We then used RNA-seq to analyze the expression profiles of CHI3L1 overexpressed in two HCC cell lines and found that CHI3L1 overexpression affected genes that were involved in cell-cell adhesion, extracellular exosome and adherens junction. Western blot analysis further revealed that CHI3L1 could activate TGF-ß signal pathways. Our data added new understanding of the mechanism of CHI3L1's action. 1) CHI3L1 promoted cancer cell proliferation by regulating cell cycles; 2) CHI3L1 promoted cancer cell invasion and metastasis; 3) CHI3L1 regulate liver cancer potentially by regulating the TGF-ß signaling pathways; 4) CHI3L1 has direct kinase activities or activate kinase to phosphorylate SMAD2, SMAD3.

Whole genome sequencing of matched tumor, adjacent non-tumor tissues and corresponding normal blood samples of hepatocellular carcinoma patients revealed dynamic changes of the mutations profiles during hepatocarcinogenesis.

Hepatocellular carcinoma (HCC) has become the third most deadly disease worldwide and HBV is the major factor in Asia and Africa. We conducted 9 WGS (whole genome sequencing) analyses for matched samples of tumor, adjacent non-tumor tissues and normal blood samples of HCC patients from three HBV positive patients. We then validated the mutations identified in a larger cohort of 177 HCC patients. We found that the number of the unique somatic mutations (average of 59,136) in tumor samples is significantly less than that in adjacent non-tumor tissues (average 83, 633). We discovered that the TP53 R249S mutation occurred in 7.7% of the HCC patients, and it was significantly associated with poor diagnosis. In addition, we found that the L104P mutation in the VCX gene (Variable charge, X-linked) was absent in white blood cell samples, but present at 11.1% frequency in the adjacent tissues and increased to 14.6% in HCC tissues, suggesting that this mutation might be a tumor driver gene driving HCC carcinogenesis. Finally, we identified a TK1-RNU7 fusion, which would result in a deletion of 103 amino acids from its C-terminal. The frequencies of this fusion event decreased from the adjacent tissues (29.2%) to the tumors (16.7%), suggesting that a truncated thymidine Kinase1 (TK1) caused by the fusion event might be deleterious and be selected against during tumor progression. The three-way comparisons allow the identification of potential driver mutations of carcinogenesis. Furthermore, our dataset provides the research community a valuable dataset for identifying dynamic changes of mutation profiles and driver mutations for HCC.

Context-Aware Reviewer Assignment for Trust Enhanced Peer Review.

Reviewer assignment is critical to peer review systems, such as peer-reviewed research conferences or peer-reviewed funding applications, and its effectiveness is a deep concern of all academics. However, there are some problems in existing peer review systems during reviewer assignment. For example, some of the reviewers are much more stringent than others, leading to an unfair final decision, i.e., some submissions (i.e., papers or applications) with better quality are rejected. In this paper, we propose a context-aware reviewer assignment for trust enhanced peer review. More specifically, in our approach, we first consider the research area specific expertise of reviewers, and the institution relevance and co-authorship between reviewers and authors, so that reviewers with the right expertise are assigned to the corresponding submissions without potential conflict of interest. In addition, we propose a novel cross-assignment paradigm, and reviewers are cross-assigned in order to avoid assigning a group of stringent reviewers or a group of lenient reviewers to the same submission. More importantly, on top of them, we propose an academic CONtext-aware expertise relevanCe oriEnted Reviewer cross-assignmenT approach (CONCERT), which aims to effectively estimate the "true" ratings of submissions based on the ratings from all reviewers, even though no prior knowledge exists about the distribution of stringent reviewers and lenient reviewers. The experiments illustrate that compared with existing approaches, our proposed CONCERT approach can less likely assign more than one stringent reviewers or lenient reviewers to a submission simultaneously and significantly reduce the influence of ratings from stringent reviewers and lenient reviewers, leading to trust enhanced peer review and selection, no matter what kind of distributions of stringent reviewers and lenient reviewers are.

Effects of survivin interference RNA on non-small cell lung carcinoma.

OBJECTIVES: The primary purpose of this study was to investigate the in vitro and in vivo effect of survivin interference RNA (siRNA) on non-small cell lung cancer. METHODS: Lentivirus was used as a vector to transfer siRNA into human lung cancer A549 cells. The proliferation of the cancer cells was assessed by MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay. The lentivirus-mediated siRNA was also injected into the transplanted A549 tumor tissues in mice. Tumour growth was assessed after 11 injections over a period of 21 days. RESULTS: Compared with the placebo and the blank lentiviral vector groups, the siRNA treatment group had reduced cell growth rate following 4 days of the treatment (P < 0.01). The average size of the transplanted A549 tumours in the siRNA treatment group (0.75+/-0.16 cm3, n=8) was smaller than in the placebo (2.09+/-0.22 cm3, n=6) or the blank lentivrial vector groups (1.89+/-0.18 cm3, n=6) (P < 0.01). The tumour growth inhibition rate in the siRNA groups was 46.1%. CONCLUSION: Lentivirus-mediated siRNA therapy inhibits the growth of human lung cancer cells in vitro. The siRNA therapy also suppresses the growth of the transplanted lung cancer in mice.

3-Anilino-N-p-tolyl-benzamide.

The title compound, C(20)H(18)N(2)O, which crystallizes with two independent mol-ecules (A and B) in the asymmetric unit, is composed of three aromatic rings (I, II and III). The conformation of the two independent mol-ecules is slightly different. The dihedral angles between the central aromatic ring II and rings I and III are 47.13 (9) and 89.36 (9)°, respectively, for mol-ecule A, and 29.60 (9) and 70.72 (9)°, respectively, for mol-ecule B. Rings I and III are inclined to one another by 86.57 (9)° in mol-ecule A, and 64.59 (10)° in mol-ecule B. The mol-ecular structures are stabilized by intra-molecular N-H⋯O hydrogen bonds. In the crystal structure, mol-ecules are linked through inter-molecular N-H⋯O hydrogen bonds, forming chains propagating in the [010] direction. In addition, a number of C-H⋯π inter-actions are observed.

N-Phenyl-anthranilic anhydride.

The complete mol-ecule of the title compound, C(26)H(20)N(2)O(3), is generated by crystallographic twofold symmetry, with the central O atom lying on the rotation axis. The conformation is stabilized by an intra-molecular N-H⋯O hydrogen bond. The dihedral angle between the inner and outer aromatic ring planes is 61.12 (5)°.

Efficient algorithms to explore conformation spaces of flexible protein loops.

Several applications in biology - e.g., incorporation of protein flexibility in ligand docking algorithms, interpretation of fuzzy X-ray crystallographic data, and homology modeling - require computing the internal parameters of a flexible fragment (usually, a loop) of a protein in order to connect its termini to the rest of the protein without causing any steric clash. One must often sample many such conformations in order to explore and adequately represent the conformational range of the studied loop. While sampling must be fast, it is made difficult by the fact that two conflicting constraints - kinematic closure and clash avoidance - must be satisfied concurrently. This paper describes two efficient and complementary sampling algorithms to explore the space of closed clash-free conformations of a flexible protein loop. The "seed sampling" algorithm samples broadly from this space, while the "deformation sampling" algorithm uses seed conformations as starting points to explore the conformation space around them at a finer grain. Computational results are presented for various loops ranging from 5 to 25 residues. More specific results also show that the combination of the sampling algorithms with a functional site prediction software (FEATURE) makes it possible to compute and recognize calcium-binding loop conformations. The sampling algorithms are implemented in a toolkit (LoopTK), which is available at https://simtk.org/home/looptk.

On the structure of the inverse kinematics map of a fragment of protein backbone.

Loop closure in proteins requires computing the values of the inverse kinematics (IK) map for a backbone fragment with 2n > or = 6 torsional degrees of freedom (dofs). It occurs in a variety of contexts, e.g., structure determination from electron-density maps, loop insertion in homology-based structure prediction, backbone tweaking for protein energy minimization, and the study of protein mobility in folded states. The first part of this paper analyzes the global structure of the IK map for a fragment of protein backbone with 6 torsional dofs for a slightly idealized kinematic model, called the canonical model. This model, which assumes that every two consecutive torsional bonds C(alpha)--C and N--C(alpha) are exactly parallel, makes it possible to separately compute the inverse orientation map and the inverse position map. The singularities of both maps and their images, the critical sets, respectively, decompose SO(3) x R(3) into open regions where the number of IK solutions is constant. This decomposition leads to a constructive proof of the existence of a region in R(3) x SO(3) where the IK of the 6-dof fragment attains its theoretical maximum of 16 solutions. The second part of this paper extends this analysis to study fragments with more than 6 torsional dofs. It describes an efficient recursive algorithm to sample IK solutions for such fragments, by identifying the feasible range of each successive torsional dof. A numerical homotopy algorithm is then used to deform the IK solutions for a canonical fragment into solutions for a noncanonical fragment. Computational results for fragments ranging from 8 to 30 dofs are presented.