SOLUTION-PROCESSED LOW-VOLTAGE ORGANIC FIELD-EFFECT TRANSISTORS BASED ON ANTHRADITHIOPHENE MOLECULAR SOLIDS

Solution-processed low-voltage organic field-effect transistors (OFETs) have attracted much attention due to their possible application in the fabrication of devices with large area, light weight, low cost and flexibility. This project includes the design and optimization of solution-processed low-voltage organic phototransistors (OPTs) and biosensors which respond to bovine serum albumin (BSA). The OPT device was based on triethylgermylethynyl-substituted anthradithiophene (diF-TEG ADT). Two kinds of dielectric materials were used: 80-nm-thick potassium alumina (PA) and 300-nm-thick thermally grown SiO2. To investigate application in a moist environment, the performance at different relative humidities (R.H.’s) was characterized. Results showed that the device was very stable in high humidity, and exhibited good performance even up to 85% R.H. A major change in drain current (I_DS) was observed when connecting or disconnecting the gate electrode to the device in the dark once the photocurrent was generated. This feature may motivate the application of diF-TEG ADT-based phototransistors as multistage photo-controlled memory devices. For the biosensor device, a sensitive (10 ng/mL) sensor platform for bovine serum albumin (BSA) detection using small molecule-polymer blend transistor was developed. Triethylsilylethynyl-substituted anthradithiophene (diF-TES ADT) was used as the small molecule semiconductor. Blending poly(methyl methacrylate) (PMMA) with diF-TES ADT improved the environmental and electrical stability since they are reported to form a vertically phase-separated structure. The high stability in 0.05 PBS solution and small leakage current also contributed to the application of this device as a biosensor. Moreover, the solution rheology of polymers makes it easier to print them on large flexible substrates

Quantum Algorithm for Unsupervised Anomaly Detection

Anomaly detection, an important branch of machine learning, plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. As one of the most commonly used unsupervised anomaly detection algorithms, the Local Outlier Factor algorithm (LOF algorithm) has been extensively studied. This algorithm contains three steps, i.e., determining the k-distance neighborhood for each data point x, computing the local reachability density of x, and calculating the local outlier factor of x to judge whether x is abnormal. The LOF algorithm is computationally expensive when processing big data sets. Here we present a quantum LOF algorithm consisting of three parts corresponding to the classical algorithm. Specifically, the k-distance neighborhood of x is determined by amplitude estimation and minimum search; the local reachability density of each data point is calculated in parallel based on the quantum multiply-adder; the local outlier factor of each data point is obtained in parallel using amplitude estimation. It is shown that our quantum algorithm achieves exponential speedup on the dimension of the data points and polynomial speedup on the number of data points compared to its classical counterpart. This work demonstrates the advantage of quantum computing in unsupervised anomaly detection

New treatment methods for myocardial infarction

For a long time, cardiovascular clinicians have focused their research on coronary atherosclerotic cardiovascular disease and acute myocardial infarction due to their high morbidity, high mortality, high disability rate, and limited treatment options. Despite the continuous optimization of the therapeutic methods and pharmacological therapies for myocardial ischemia–reperfusion, the incidence rate of heart failure continues to increase year by year. This situation is speculated to be caused by the current therapies, such as reperfusion therapy after ischemic injury, drugs, rehabilitation, and other traditional treatments, that do not directly target the infarcted myocardium. Consequently, these therapies cannot fundamentally solve the problems of myocardial pathological remodeling and the reduction of cardiac function after myocardial infarction, allowing for the progression of heart failure after myocardial infarction. Coupled with the decline in mortality caused by acute myocardial infarction in recent years, this combination leads to an increase in the incidence of heart failure. As a new promising therapy rising at the beginning of the twenty-first century, cardiac regenerative medicine provides a new choice and hope for the recovery of cardiac function and the prevention and treatment of heart failure after myocardial infarction. In the past two decades, regeneration engineering researchers have explored and summarized the elements, such as cells, scaffolds, and cytokines, required for myocardial regeneration from all aspects and various levels day and night, paving the way for our later scholars to carry out relevant research and also putting forward the current problems and directions for us. Here, we describe the advantages and challenges of cardiac tissue engineering, a contemporary innovative therapy after myocardial infarction, to provide a reference for clinical treatment

Person re-identification using local relation-aware graph convolutional network

Local feature extractions have been verified to be effective for person re-identification (re-ID) in recent literature. However, existing methods usually rely on extracting local features from single part of a pedestrian while neglecting the relationship of local features among different pedestrian images. As a result, local features contain limited information from one pedestrian image, and cannot benefit from other pedestrian images. In this paper, we propose a novel approach named Local Relation-Aware Graph Convolutional Network (LRGCN) to learn the relationship of local features among different pedestrian images. In order to completely describe the relationship of local features among different pedestrian images, we propose overlap graph and similarity graph. The overlap graph formulates the edge weight as the overlap node number in the node’s neighborhoods so as to learn robust local features, and the similarity graph defines the edge weight as the similarity between the nodes to learn discriminative local features. To propagate the information for different kinds of nodes effectively, we propose the Structural Graph Convolution (SGConv) operation. Different from traditional graph convolution operations where all nodes share the same parameter matrix, SGConv learns different parameter matrices for the node itself and its neighbor nodes to improve the expressive power. We conduct comprehensive experiments to verify our method on four large-scale person re-ID databases, and the overall results show LRGCN exceeds the state-of-the-art methods

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat