Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Radio-based Device-free Activity Recognition with Radio Frequency Interference

Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present

Learn to Recognise::Exploring Priors of Sparse Face Recognition on Smartphones

Face recognition is one of the important components of many smart devices apps, e.g. face unlocking, people tagging and games on smart phones, tablets or smart glasses. Sparse Representation Classification (SRC) is a state-of-theart face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV, e.g., Eigenface algorithm. The success of SRC is due to its use of `1 optimisation, which makes SRC robust to noise and occlusions. Since `1 optimisation is computationally intensive, SRC uses random projection matrices to reduce the dimension of the `1 problem. However, random projection matrices do not give consistent classification accuracy as they ignored the prior knowledge of the training set. In this paper, we propose to exploit the prior knowlege of the training set to improve the recognition accuracy. It first learns the optimised projection matrix from the training set to produce consistent recognition performance then applies `1-based classification based on the group sparsity structure of SRC to further improve the recognition accuracy. Our evaluations, based on publicly available databases and real experiment, show that face recognition using optimised projection matrix is 8-17% more accurate than its random counterpart and Eigenface algorithm, and the recognition accuracy can be further improved by up to 5% by exploiting group sparsity structure. Furthermore, the optimised projection matrix does not have to be re-calculated even if new faces are added to the training set. We implement the SRC with optimised projection matrix on Android smartphones and find that the computation of residuals in SRC is a severe bottleneck, taking up 85-90% of the computation time. To address this problem, we propose a method to compute the residuals approximately, which is 50 times faster with little sacrificing recognition accuracy. Lastly, we demonstrate the feasibility the our new algorithm by the implementation and evaluation of a new face unlocking app and show its robustness to variation of poses, facial expressions, lighting changes and occlusions

Face recognition on smartphones via optimised Sparse Representation Classification

Face recognition is an element of many smartphone apps, e.g. face unlocking, people tagging and games. Sparse Representation Classification (SRC) is a state-of-the-art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV. The success of SRC is due to its use of l1, which makes SRC robust to noise and occlusions. Since l1 optmisation is computationally intensive, SRC uses random projection matrices to reduce the dimension of the l1 problem. However, random projection matrices do not give consistent classification accuracy. In this paper, we propose a method to optimise the projection matrix for l1-based classification1. Our evaluations, based on publicly available databases and real experiment, show that face recognition based on the optimised projection matrix can be 5-17% more accurate than its random counterpart and OpenCV algorithms. Furthermore, the optimised projection matrix does not have to be re-calculated even if new faces are added to the training set. We implement the SRC with optimised projection matrix on Android smartphones and find that the computation of residuals in SRC is a severe bottleneck, taking up 85-90% of the computation time. To address this problem, we propose a method to compute the residuals approximately, which is 50 times faster but without sacrificing recognition accuracy. Lastly, we demonstrate the feasibility of our new algorithm by the implementation and evaluation of a new face unlocking app and show its robustness to variation to poses, facial expressions, lighting changes and occlusions

dRTI: Directional Radio Tomographic Imaging

Radio tomographic imaging (RTI) enables device free localisation of people and objects in many challenging environments and situations. Its basic principle is to detect the changes in the statistics of some radio quality measurements in order to infer the presence of people and objects in the radio path. However, the localisation accuracy of RTI suffers from complicated radio propagation behaviours such as multipath fading and shadowing. In order to improve RTI localisation accuracy, we propose to use inexpensive and energy efficient electronically switched directional (ESD) antennas to improve the quality of radio link behaviour observations, and therefore, the localisation accuracy of RTI. We implement a directional RTI (dRTI) system to understand how directional antennas can be used to improve RTI localisation accuracy. We also study the impact of the choice of antenna directions on the localisation accuracy of dRTI and propose methods to effectively choose informative antenna directions to improve localisation accuracy while reducing overhead. We evaluate the performance of dRTI in diverse indoor environments and show that dRTI significantly outperforms the existing RTI localisation methods based on omni-directional antennas

Real-Time and Robust Compressive Background Subtraction for Embedded Camera Networks

Real-time target tracking is an important service provided by embedded camera networks. The first step in target tracking is to extract the moving targets from the video frames, which can be realised by using background subtraction. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computationally efficient. We propose a baseline version which uses luminance only and then extend it to use colour information. The key idea is to use random projection matrics to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, to show the computational efficiency of our methods is not platform specific, we implement it on various platforms. The real implementation shows that our proposed method is consistently better and is up to six times faster, and consume significantly less resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application

GRPEL2 Knockdown Exerts Redox Regulation in Glioblastoma

Malignant brain tumors are responsible for catastrophic morbidity and mortality globally. Among them, glioblastoma multiforme (GBM) bears the worst prognosis. The GrpE-like 2 homolog (GRPEL2) plays a crucial role in regulating mitochondrial protein import and redox homeostasis. However, the role of GRPEL2 in human glioblastoma has yet to be clarified. In this study, we investigated the function of GRPEL2 in glioma. Based on bioinformatics analyses from the Cancer Gene Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we inferred that GRPEL2 expression positively correlates with WHO tumor grade (p p p p GRPEL2 knockdown efficiently inhibited cellular proliferation. Moreover, GRPEL2 suppression induced cell cycle arrest at the sub-G1 phase. Furthermore, GRPEL2 silencing decreased intracellular reactive oxygen species (ROS) without impending mitochondria membrane potential. The cellular oxidative respiration measured with a Seahorse XFp analyzer exhibited a reduction of the oxygen consumption rate (OCR) in GBM cells by siGRPEL2, which subsequently enhanced autophagy and senescence in glioblastoma cells. Taken together, GRPEL2 is a novel redox regulator of mitochondria bioenergetics and a potential target for treating GBM in the future