Temporal association between antibiotic use and resistance in Gram-negative bacteria

The β-lactam/lactamase inhibitors (BLBLIs) combination drugs are considered an effective alternative to carbapenems. However, there is a growing concern that the increased use of BLBLIs may lead to increased resistance. This study determined the temporal association between the consumption of BLBLI and the antimicrobial resistance in Gram-negative bacteria. In this retrospective study, electronic data on the Gram-negative bacterial isolates, including A. baumannii, P. aeruginosa, E. coli, and K. pneumoniae from in-patients and susceptibility testing results were retrieved from the medical records of the clinical laboratory. A linear regression and cross-correlation analysis were performed on the acquired data. Increasing trends (p<0.05) in the consumption of BIBLI and carbapenem with a median use of 27.68 and 34.46 DDD/1000 PD per quarter were observed, respectively. A decreased trend (p=0.023) in the consumption of fluoroquinolones with a median use of 29.13 DDD/1000 PD per quarter was observed. The resistance rate of K. pneumoniae was synchronized with the BIBLI and carbapenem consumptions with a correlation coefficient of 0.893 (p=0.012) and 0.951 (p=0.016), respectively. The cross-correlation analysis against the consumption of BIBLI and meropenem resistant K. pneumoniae was peaked at 0-quarter lag (r=951, p=0.016). There was an increasing trend in the consumption of BLBLI and carbapenems. The increasing trend in the rates of resistance to piperacillin/tazobactam, in line with the increasing consumption of BLBLI, suggests that BLBLI has to be used with caution and cannot be directly considered as a long-term alternative to carbapenems.

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

Incidence of common infectious diseases in Japan during the COVID-19 pandemic

Recent reports indicate that respiratory infectious diseases were suppressed during the novel coronavirus disease-2019 (COVID-19) pandemic. COVID-19 led to behavioral changes aimed to control droplet transmission or contact transmission. In this study, we examined the incidence of common infectious diseases in Japan during the COVID-19 pandemic. COVID-19 data were extracted from the national data based on the National Epidemiological Surveillance of Infectious Diseases (NESID). Common infectious diseases were selected from notifiable infectious diseases under the NESID. The epidemic activity of the diseases during 2015–2020 was evaluated based on the Infectious Disease Weekly Reports published by the National Institute of Infectious Diseases. Each disease was then categorized according to the route of transmission. Many Japanese people had adopted hygienic activities, such as wearing masks and hand washing, even before the COVID-19 pandemic. We examined the correlation between the time-series of disease counts of common infectious diseases and COVID-19 over time using cross-correlation analysis. The weekly number of cases of measles, rotavirus, and several infections transmitted by droplet spread, was negatively correlated with the weekly number of cases of COVID-19 for up to 20 weeks in the past. According to the difference-in-differences analysis, the activity of influenza and rubella was significantly lower starting from the second week in 2020 than that in 2015–2019. Only legionellosis was more frequent throughout the year than in 2015–2019. Lower activity was also observed in some contact transmitted, airborne-transmitted, and fecal-oral transmitted diseases. However, carbapenem-resistant Enterobacteriaceae, exanthema subitum, showed the same trend as that over the previous 5 years. In conclusion, our study shows that public health interventions for the COVID-19 pandemic may have effectively prevented the transmission of most droplet-transmitted diseases and those transmitted through other routes.

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Evaluating the impacts of non-pharmaceutical interventions on the transmission dynamics of COVID-19 in Canada based on mobile network

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada’s provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

Analysis of the dynamic response of long span bridges with GNSS under typhoon loading

Nowadays, real-time bridge deformation monitoring has attracted more attention due to the development of bridge management system and land transportation safety. Huge civil engineering structures, such as long-span bridge, is susceptible to dynamic deflection caused by various loadings. Hence, precise dynamic response measurement becomes necessary to make structure more reliable and integrated. Currently, Global Navigation Satellite System (GNSS) positioning technology has been commonly used in this field to detect the dynamic displacement of long-span bridges. According to this, real-time data were collected from the Forth Road Bridge to observe the dynamic response of lang span bridges under extreme wind load conditions and this report has also verified the data processing technique of the real-time bridge deformation monitoring system. Compared with other monitoring methods, the method used in this report which combines GNSS and anemometer together has features of high frequency with low lag. Moreover, it also shows the superiority of post-processing and synchronization, background noises could be reduced by embedded software. Finally, according to the cross-correlation analysis, it was found that wind speed and bridge displacement in y-axis have the highest correlation. Also, the reliability of combining method to monitor the dynamic response of long-span bridge and noise reducing method proposed in this report has also been verified.

Price efficiency and safe-haven property of Bitcoin in relation to stocks in the pandemic era

Purpose When investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market. Design/methodology/approach Based on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA). Findings This study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales. Research limitations/implications The behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods. Social implications The paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors. Originality/value The authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.

Swell hindcast statistics for the Baltic Sea

The classic characterisation of swell as regular, almost monochromatic, wave trains does not necessarily accurately describe swell in water bodies shielded from the oceanic wave climate. In such enclosed areas the locally generated swell waves still contribute to processes at the air and seabed interfaces, and their presence can be quantified by partitioning wave components based on their speed relative to the wind. We present swell statistics for the semi-enclosed Baltic Sea using 20 years of swell-partitioned model data. The swell significant wave height was mostly under 2 m, and in the winter (DJF) the mean significant swell height was typically less than 0.4 m; higher swell was found in limited nearshore areas. Swell waves were typically short (under 5 s), with mean periods over 8 s being rare. In open-sea areas the average ratio of swell energy (to total energy) was mostly below 0.4 – significantly less than in the World Ocean. Certain coastal areas were swell dominated over half the time, mostly because of weak winds (U<5 m s−1) rather than high swell heights. Swell-dominated events with a swell height over 1 m typically lasted under 10 h. A cross-correlation analysis indicates that swell in the open sea is mostly generated from local wind sea when wind decays (dominant time lag roughly 15 h). Near the coast, however, the results suggest that the swell is partially detached from the local wind waves, although not necessarily from the weather system that generates them because the highest swell typically arrives with a roughly 10 h delay after the low-pressure system has already passed.

Spectral characteristics of the quasi-biennial oscillation of total Ozone

The 12-month running means of the 50 hPa low latitude zonal wind and total ozone values for the latitude zones NP (North Polar), NT (North Temperate), TRO (Tropical), ST (South Temperate), SP (South Polar) were subjected to special analysis, separately over the two successive 18 year intervals, 1958-1975 and 1976-1993. In the interval 1958-1975, the wind had a prominent peak at 2.45 years and two smaller but significant peaks at 1.98 and 3.05 years. For ozone only NP. NT and ST had roughly similar peaks (2.37, 2.41, 2.48 years), while TRO and SP had different peaks (2.27 and 2.12 years). All ozone series had significant peaks at 20-21 months, barely significant in the wind series. Ozone peaks were noticed in the 3-5 years band also. In the interval 1976-1993, the patterns were different. The wind had only one prominent peak at 2.51 years. For ozone, NP, NT, ST had roughly similar peaks (2.41, 2.45, 2.45 years) while TRO. Speed different peaks (2.32 and 2.29 years). All ozone series had significant peaks at 20-22 months and in the 3-5 year band; but these were absent in the wind series. The 3-5 year band probably indicates ENSO effects. A cross-correlation analysis between wind and ozone showed that TRO maxima coincided with westerly wind maxima. while NT, ST and NT, NP were phase shifted by 4 and 6 seasons.