Synthesis of Functionalized Hexahydrocarbazoles by Beckmann Elimination and Nucleophile-Intercepted Beckmann Fragmentation.

The Beckmann elimination and nucleophile-intercepted Beckmann fragmentation (NuBFr) of oximes starting from regioisomeric indolinyl bicyclic ketones lead to products that are subjected to further synthetic manipulations and ultimately result in the stereospecific formation of densely functionalized hexahydrocarbazoles. The Pd-catalyzed Suzuki-Miyaura cross-coupling reaction of a key alkenyl bromide intermediate with various boronic acids gives arylated products.

Regiodivergent (3 + 2) annulation reactions of oxyallyl cations.

We report a new method for the regiodivergent dearomative (3 + 2) reaction between 3-substituted indoles and oxyallyl cations. Access to both regioisomeric products is possible and is contingent on the presence or absence of a bromine atom on the substituted oxyallyl cation. In this way, we are able to prepare molecules that contain highly-hindered, stereodefined, vicinal, quaternary centers. Detailed computational studies employing energy decomposition analysis (EDA) at the DFT level establishes that regiochemical control arises from either reactant distortion energy or orbital mixing and dispersive forces, depending on the oxyallyl cation. Examination of the Natural Orbitals for Chemical Valence (NOCV) confirms that indole acts as the nucleophilic partner in the annulation reaction.

Understanding the dynamic properties of trees using the motions constructed from multi-beam flash light detection and ranging measurements.

Measuring the three-dimensional motion of trees at every position remains challenging as it requires dynamic measurement technology with sufficient spatial and temporal resolution. Consequently, this study explores the use of a novel multi-beam flash light detection and ranging (LiDAR) sensor to tackle such a sensing barrier. A framework is proposed to record tree vibrations, to construct the motions of tree skeletons from the point-cloud frames recorded by the LiDAR sensor and to derive the dynamic properties of trees. The feasibility of the framework is justified through measurement on a Ficus microcarpa under pull-and-release tests. The relative differences for the first two modal frequencies between the LiDAR and linear variable differential transformer measurements in the displacement Fourier spectra are 0.1% and 2.5%, respectively. The framework is further adopted to study the dynamic response of different trees subjected to typhoons, including a Liquidambar formosana, three Araucaria heterophylla trees, a Sterculia lanceolata, a Celtis sinensis, a Tabebuia chrysantha and a Cinnamomum camphora. Results suggest that broadleaved trees might exhibit vibration in a wide frequency band, whereas the coniferous trees could follow a distinct dominant frequency.

Staying Cool in Space: A Review of Therapeutic Hypothermia and Potential Application for Space Medicine.

Despite rigorous health screenings, medical incidents during spaceflight missions cannot be avoided. With long-duration exploration flights on the rise, the likelihood of critical medical conditions with no suitable treatment on board will increase. Therapeutic hypothermia (TH) could serve as a bridge treatment in space prolonging survival and reducing neurological damage in ischemic conditions such as stroke and cardiac arrest. We conducted a review of published studies to determine the potential and challenges of TH in space based on its physiological effects, the cooling methods available, and clinical evidence on Earth. Currently, investigators have found that application of low normothermia leads to better outcomes than mild hypothermia. Data on the impact of hypothermia on a favorable neurological outcome are inconclusive due to lack of standardized protocols across hospitals and the heterogeneity of medical conditions. Adverse effects with systemic cooling are widely reported, and could be reduced through selective brain cooling and pharmacological cooling, promising techniques that currently lack clinical evidence. We hypothesize that TH has the potential for application as supportive treatment for multiple medical conditions in space and recommend further investigation of the concept in feasibility studies.

Top-k dominating queries on incomplete large dataset.

Top-k dominating (TKD) query is one of the methods to find the interesting objects by returning the k objects that dominate other objects in a given dataset. Incomplete datasets have missing values in uncertain dimensions, so it is difficult to obtain useful information with traditional data mining methods on complete data. BitMap Index Guided Algorithm (BIG) is a good choice for solving this problem. However, it is even harder to find top-k dominance objects on incomplete big data. When the dataset is too large, the requirements for the feasibility and performance of the algorithm will become very high. In this paper, we proposed an algorithm to apply MapReduce on the whole process with a pruning strategy, called Efficient Hadoop BitMap Index Guided Algorithm (EHBIG). This algorithm can realize TKD query on incomplete datasets through BitMap Index and use MapReduce architecture to make TKD query possible on large datasets. By using the pruning strategy, the runtime and memory usage are greatly reduced. What's more, we also proposed an improved version of EHBIG (denoted as IEHBIG) which optimizes the whole algorithm flow. Our in-depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well on TKD query in an incomplete large dataset and shows great performance in a Hadoop computing cluster.

The history of peritoneal dialysis in China: past, present and future trends.

Peritoneal dialysis (PD) was introduced in China more than 60 years ago and has grown continuously since then. Now China leads the first of the world in number of patients on PD. In this manuscript a brief review of the history of peritoneal dialysis in China is presented; this includes a description of pioneers and their important contributions, discussion of peritoneal dialysate, the technique of the use of Tenckhoff catheter, the use of continuous ambulatory peritoneal dialysis (CAPD) and dialysis registration. Current ongoing PD research activities among Chinese PD academicians are also discussed. Finally, we present four areas of future focus: 1) the promotion of PD in rural areas where PD use is still very limited due to the lack of PD awareness and education; 2) PD quality management and continuous quality improvement (CQI) program particularly focusing on PD adequacy and patient rehabilitation; 3) development and enforcement of national standards on PD management; 4) multi-center studies to compare the benefits of PD and hemodialysis (HD) that should include survival, rehabilitation and cost-effectiveness.

A Long Short-Term Memory Network Stock Price Prediction with Leading Indicators.

The accuracy of the prediction of stock price fluctuations is crucial for investors, and it helps investors manage funds better when formulating trading strategies. Using forecasting tools to get a predicted value that is closer to the actual value from a given financial data set has always been a major goal of financial researchers and a problem. In recent years, people have paid particular attention to stocks, and gradually used various tools to predict stock prices. There is more than one factor that affects financial trends, and people need to consider it from all aspects, so research on stock price fluctuations has also become extremely difficult. This paper mainly studies the impact of leading indicators on the stock market. The framework used in this article is proposed based on long short-term memory (LSTM). In this study, leading indicators that affect stock market volatility are added, and the proposed framework is thus named as a stock tending prediction framework based on LSTM with leading indicators (LSTMLI). This study uses stock markets in the United States and Taiwan, respectively, with historical data, futures, and options as data sets to predict stock prices in these two markets. We measure the predictive performance of LSTMLI relative to other neural network models, and the impact of leading indicators on stock prices is studied. Besides, when using LSTMLI to predict the rise and fall of stock prices in the article, the conventional regression method is not used, but the classification method is used, which can give a qualitative output based on the data set. The experimental results show that the LSTMLI model using the classification method can effectively reduce the prediction error. Also, the data set with leading indicators is better than the prediction results of the single historical data using the LSTMLI model.

Domain randomization-enhanced deep learning models for bird detection.

Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

Metabolic Signatures of Life Span Regulated by Mating, Sex Peptide, and Mifepristone/RU486 in Female Drosophila melanogaster.

Mating and transfer of male sex peptide (SP), or transgenic expression of SP, causes inflammation and decreased life span in female Drosophila. Mifepristone rescues these effects, yielding dramatic increases in life span. Here targeted metabolomics data were integrated with further analysis of extant transcriptomic data. Each of 7 genes positively correlated with life span were expressed in the brain or eye and involved regulation of gene expression and signaling. Genes negatively correlated with life span were preferentially expressed in midgut and involved protein degradation, amino acid metabolism, and immune response. Across all conditions, life span was positively correlated with muscle breakdown product 1/3-methylhistidine and purine breakdown product urate, and negatively correlated with tryptophan breakdown product kynurenic acid, suggesting a SP-induced shift from somatic maintenance/turnover pathways to the costly production of energy and lipids from dietary amino acids. Some limited overlap was observed between genes regulated by mifepristone and genes known to be regulated by ecdysone; however, mifepristone was unable to compete with ecdysone for activation of an ecdysone-responsive transgenic reporter. In contrast, genes regulated by mifepristone were highly enriched for genes regulated by juvenile hormone (JH), and mifepristone rescued the negative effect of JH analog methoprene on life span in adult virgin females. The data indicate that mifepristone increases life span and decreases inflammation in mated females by antagonizing JH signaling downstream of male SP. Finally, mifepristone increased life span of mated, but not unmated, Caenorhabditis elegans, in 2 of 3 trials, suggesting possible evolutionary conservation of mifepristone mechanisms.

Diversification of Nucleophile-Intercepted Beckmann Fragmentation Products and Related Density Functional Theory Studies.

The nucleophile-intercepted Beckmann fragmentation (NuBFr) has the potential to be broadly applicable to the synthesis of indoline alkaloid-based natural products. However, the reaction has not been widely adopted, in part, because of limitations associated with the availability of appropriate promoter-Nu reagents. We have devised a stereospecific Ag(I)-promoted reaction for functionalizing NuBFr products to give novel compositions of matter that may be useful in synthesis and medicinal chemistry. With unhindered amine nucleophiles, structurally unique [2.2.2]-bicycloamidines are generated. We also disclose for the first time detailed density functional theory studies, which shed light on the mechanism of the NuBFr and Ag-promoted substitution reaction that supports an unusual aziridinium ion as a key intermediate.

Novel structurally-stable Na-rich Na4V2O7 cathode material with high reversible capacity by utilization of anion redox activity.

A sodium-rich vanadium compound, Na4V2O7, is investigated as a cathode material for sodium-ion batteries, which delivers a high reversible capacity of 194 mA h g-1 after activating to 4.7 V. By limiting the cut-off voltage to 4.4 V, a good capacity retention of 93% after 50 cycles is achieved. The material exhibits a negligible volume variation of 1.04% during Na+ (de-)intercalation, demonstrating that Na4V2O7 is structurally-stable.

Nucleophile-intercepted Beckmann fragmentation reactions.

We describe the first examples of nucleophile-intercepted Beckmann fragmentations of indoline oximes. This reaction uses MsCl as a promoter to give cyano chlorides and is believed to proceed through an aziridinium intermediate via a double stereoinvertive process. Mechanistic insights have led to the further discovery that oxygen, nitrogen, and bromide nucleophiles can be employed for this fragmentation by the use of other promoters. We envision that these products may be useful in the syntheses of members of the akuammiline and koumine families of indoline alkaloids.

The density-based clustering method for privacy-preserving data mining.

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.

Connectivity problems on heterogeneous graphs.

BACKGROUND: Network connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner Network problems: given a reference "database" graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu. RESULTS AND DISCUSSION: We introduce the condition Steiner Network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of C - ϵ , for every C ≥ 2 and ϵ > 0 , and that this bound is tight. Moving beyond the worst case, we explore a special set of instances where the reference graph grows monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network. CONCLUSION: Our results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting.

Nuphar alkaloids induce very rapid apoptosis through a novel caspase-dependent but BAX/BAK-independent pathway.

Nuphar alkaloids, originally isolated from water lilies, induce apoptosis in mammalian cells in less than 1 h, making them possibly the fastest known inducers. However, the mechanism by which this rapid apoptosis occurs remains unknown. We have investigated canonical aspects of apoptosis to determine how the nuphar alkaloid, (+)-6-hydroxythiobinupharidine (6HTBN), induces apoptosis. 6HTBN induced rapid apoptosis in various leukemia, lymphoma, and carcinoma cell lines, suggesting that its mechanism is cell-type independent. It also circumvented resistance of patient-derived chronic lymphocytic leukemia cells generated by co-culture on survival-promoting stroma. Intriguingly, 6HTBN failed to induce apoptosis in platelets. The mechanism of apoptosis involves activation of caspase 9 and caspase 3, but not caspase 8 as previously reported. The release of cytochrome c from mitochondria occurred even in the absence of BAX/BAK and in cells that retained mitochondrial membrane potential. These results suggest a novel mechanism of apoptosis that has previously not been reported. The molecular target of the nuphar alkaloids remains to be determined.

Mining Association rules for Low-Frequency itemsets.

High utility itemset mining has become an important and critical operation in the Data Mining field. High utility itemset mining generates more profitable itemsets and the association among these itemsets, to make business decisions and strategies. Although, high utility is important, it is not the sole measure to decide efficient business strategies such as discount offers. It is very important to consider the pattern of itemsets based on the frequency as well as utility to predict more profitable itemsets. For example, in a supermarket or restaurant, beverages like champagne or wine might generate high utility (profit), but also sell less frequently compared to other beverages like soda or beer. In previous studies, it is observed that people who buy milk, bread, or diapers from a supermarket, also tend to buy beer or soda. But the items like milk, diapers, beer, or soda generate less utility (profit value) compared to beverages like champagne or wine. If we combine items like champagne or wine having high utility but less frequency, with the frequently sold items like milk, diaper, or beer, we can increase the utility of the transaction by providing some discount offers on champagne or wine. In this paper, we are integrating low-frequency itemsets with high-frequency itemsets, both having low or high utility, and provide different association rules for this combination of itemsets. In this way, we can generate a more accurate measure of pattern mining for various business strategies.

Catalytic vinylogous cross-coupling reactions of rhenium vinylcarbenoids.

We report the first example of the rhenium-catalyzed allylation reaction of indolyl compounds by means of cross-coupling with propargyl ethers as non-obvious allylating reagents. Data from isotope-labeling and kinetic isotopic studies are consistent with a mechanism that proceeds by vinylidene formation as the rate determining step, followed by 1,5-hydride shift to generate a key rhenium vinylcarbenoid complex. Bond formation occurs at the vinylogous site and the reaction is conveniently carried out in air.

Synthesis of 2-Aminoimidazolones and Imidazolones by (3 + 2) Annulation of Azaoxyallyl Cations.

The first examples of (3 + 2) annulations between azaoxyallyl cations and cyanamides and nitriles to give the corresponding 2-aminoimidazolones and imidazolones are reported. On the basis of the isolation of unexpected imidate products with certain substrates, it is proposed that the reaction proceeds via fast kinetic O-alkylation followed by rearrangement to the thermodynamically favored 2-aminoimidazolones and imidazolones. The method was applied to the formal synthesis of the antihypertensive drug irbesartan.

A quinolinol-based small molecule with anti-MRSA activity that targets bacterial membrane and promotes fermentative metabolism.

In a loss-of-viability screen of small molecules against methicillin-resistant Staphylococcus aureus (MRSA) USA300, we found a small molecule, designated DNAC-2, which has an MIC of 8 µg ml-1. DNAC-2 is a quinolinol derivative that is bactericidal at 2X MIC. Macromolecular synthesis assays at 2 × MIC of DNAC-2 revealed inhibition of DNA, cell wall, RNA and protein synthesis within fifteen to thirty minutes of treatment when compared to the untreated control. Transmission electron microscopy of DNAC-2-treated cells revealed a significantly thicker cell wall and impaired daughter cell separation. Exposure of USA300 cells to 1 × MIC of DNAC-2 resulted in mislocalization of PBP2 away from the septum in an FtsZ-independent manner. In addition, membrane localization with FM4-64, as well as depolarization study with DiOC2 and lipophilic cation TPP+ displayed membrane irregularities and rapid membrane depolarization, respectively, in DNAC-2-treated cells vs -untreated control. However, DNAC-2 exhibited almost no toxicity toward eukaryotic membranes. Notably, DNAC-2 drives energy generation toward substrate level phosphorylation and the bacteria become more sensitive to DNAC-2 under anaerobic conditions. We propose that DNAC-2 affects USA300 by targeting the membrane, leading to partial membrane depolarization and subsequently affecting aerobic respiration and energy-dependent functional organization of macromolecular biosynthetic pathways. The multiple effects may have the desirable consequence of limiting the emergence of resistance to DNAC-2.