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The government in Beijing has established a platform that covered the monthly electricity-use data of 11,370 public buildings, carried out a series of measures to control the quantity of buildings' electricity-consumption and its growth since 2013. Presently, it is trying to find out which buildings that will more probably have a rising trend or higher energy-saving potential than the others, so as to promote the energy-saving momentum and make its management more refine and targeted. Common methods used by the government for these tasks are traditional statistical charts and plots, the analysis results of which always highly rely on the experts' subjective judgment. Moreover, identifying the right objects from such a large database by manpower is also a big challenge. Therefore, this study proposed a series of data-mining methods, including k-means clustering, C4.5 decision tree, 2-dimension scatter diagram and outlier detection, to investigate and model the change patterns as well as compare the levels of the buildings' electricity-consumption. Results indicated that the information wanted by the government was successfully explored from the massive data. Finally, the paper gave some policy proposals on energy-use supervision and data management to help achieve the goal of further energy conservation in public buildings.

Pyruvate carboxylase (PC) catalyzes the two-step carboxylation of pyruvate to produce oxaloacetate, playing a key role in the maintenance of metabolic homeostasis in cells. Given its involvement in multiple diseases, PC has been regarded as a potential therapeutic target for obesity, diabetes, and cancer. Albeit acetyl-CoA has been recognized as the allosteric regulator of PC for over 60 years, the underlying mechanism of how acetyl-CoA induces PC activation remains enigmatic. Herein, by using time-resolved cryo-electron microscopy, we have captured the snapshots of PC transitional states during its catalytic cycle. These structures and the biochemical studies reveal that acetyl-CoA stabilizes PC in a catalytically competent conformation, which triggers a cascade of events, including ATP hydrolysis and the long-distance communication between the two reactive centers. These findings provide an integrated picture for PC catalysis and unveil the unique allosteric mechanism of acetyl-CoA in an essential biochemical reaction in all kingdoms of life.

The Crimean-Congo hemorrhagic fever virus (CCHFV), a member of the genus Orthonairovirus and family Nairoviridae, is transmitted by ticks and causes severe hemorrhagic disease in humans. To study the epidemiology of CCHFV in different ecosystems in Xinjiang, China, a total of 58,932 ticks were collected from Tarim Basin, Junggar Basin, Tianshan Mountain, and Altai Mountain from 2014 to 2017. Hyalomma asiaticum asiaticum was the dominant tick species in Tarim and Junggar basins, whereas Dermacentor nuttalli and Hyalomma detritum were found in Tianshan Mountain and Altai Mountain, respectively. Reverse transcription-polymerase chain reaction of the CCHFV small (S) genome segment was used for the molecular detection. The CCHFV-positive percentage was 5.26%, 6.85%, 1.94%, and 5.56% in Tarim Basin, Junggar Basin, Tianshan Mountain, and Altai Mountain, respectively. Sequences of the S segment were used for phylogenetic analysis and the results showed that the newly identified CCHFV strains belonged to two clades. Our study confirms that H. asiaticum asiaticum is the major vector of CCHFV in desert habitats which is consistent with previous studies, and also suggests that H. detritum and D. nuttalli are emerging vectors for CCHFV in Xinjiang. Moreover, this study reports the presence of CCHFV in the mountain habitat of Xinjiang for the first time, suggesting that future surveillance of CCHFV should also include mountainous areas. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12250-018-0016-3) contains supplementary material, which is available to authorized users.

Actual information of occupant presence is an essential input for the simulation of building energy use and home automation system. Previous studies installed motion sensors, cameras, environment sensor networks or smart meters to detect the occupant presence status directly or indirectly in offices. These approaches would raise the household cost and privacy concerns when were applied to residential buildings. Recently, home indoor environment monitors are becoming a part of daily life with the population of smart home products, and massive indoor environment data in the residential buildings could be collected remotely. Therefore, we conducted an experiment in three bedrooms that were already installed with home indoor environment monitors, based on the hypothesis that information of occupant presence could be extracted from indoor environment data by appropriate data mining approach. Two algorithms were designed in the experiment for the data mining, where one recognized the presence status directly from the environment data, and the other indirectly from the usage information of light and AC that were recognized from illuminance, air temperature and relative humidity. Results showed that it was difficult to develop an algorithm that performed well both on the training and testing datasets when we expected to directly mine the occupant presence from indoor environment parameters, but the performance could be greatly improved when we added the bridge information of light and AC operations into the algorithm, and then the recognition accuracy could be improved higher than 92% in all the three rooms.

Tick-borne viral diseases have attracted much attention in recent years because of their increasing incidence and threat to human health. Severe fever with thrombocytopenia syndrome phlebovirus (SFTSV) and Heartland virus (HRTV) were recently identified as tick-borne phleboviruses (TBPVs) in Asia and the United States, respectively, and are associated with severe human diseases with similar clinical manifestations. In this study, we report the first identification and isolation of a novel TBPV named Guertu virus (GTV) from Dermacentor nuttalli ticks in Xinjiang Province, China, where TBPVs had not been previously discovered. Genome sequence and phylogenetic analyses showed that GTV is closely related to SFTSV and HRTV and was classified as a member of the genus Phlebovirus, family Phenuiviridae, order Bunyavirales. In vitro and in vivo investigations of the properties of GTV demonstrated that it was able to infect animal and human cell lines and can suppress type I interferon signaling, similar to SFTSV, that GTV nucleoprotein (NP) can rescue SFTSV replication by replacing SFTSV NP, and that GTV infection can cause pathological lesions in mice. Moreover, a serological survey identified antibodies against GTV from serum samples of individuals living in Guertu County, three of which contained neutralizing antibodies, suggesting that GTV can infect humans. Our findings suggested that this virus is a potential pathogen that poses a threat to animals and humans. Further studies and surveillance of GTV are recommended to be carried out in Xinjiang Province as well as in other locations.

Dear Editor, Severe fever with thrombocytopenia syndrome virus (SFTSV) is a newly identified viral pathogen of the genus Phlebovirus in the family Bunyaviridae (Sun et al., 2012). SFTSV was first identified from patient serum samples in China (Li et al., 2013; Ning et al., 2015).

Accurate energy saving effect evaluation analysis of building energy efficiency retrofit is of benefit to obtain technology optimization and fast return of investment. According to the implement sequence, evaluation methods can be divided into post evaluation and prediction evaluation. The energy saving effect of an air-conditioning system retrofit project was analyzed by these two models respectively. The post evaluation model was built based on the spot test data and a parameter called as Refrigeration Operation Energy saving Effect Ratio (ROEER). The prediction evaluation model was built based on Back-Propagation Artificial Neural Network by the use of MATLAB Neural Network Toolbox. The comparison result between these two kinds of evaluation models match well with each other. These two models can be used to predict and evaluate energy saving effect of air-conditioning system retrofit to further improve the real energy saving effect of building energy efficiency retrofit.