Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Angiotensin-Converting Enzyme Inhibitor or Angiotensin Receptor Blocker Use With Outcomes After Acute Kidney Injury

Importance Patients with acute kidney injury (AKI) are at an increased long-term risk of death. Effective strategies that improve long-term outcomes in patients with AKI are unknown. Objective To evaluate whether the use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) after hospital discharge is associated with better outcomes in patients with AKI. Design, Setting, and Participants This retrospective cohort study used data from the Alberta Kidney Disease Network population database to evaluate 46 253 adults 18 years or older with an episode of AKI during a hospitalization between July 1, 2008, and March 31, 2015, in Alberta, Canada. All patients who survived to hospital discharge were followed up for a minimum of 2 years. Exposures Use of an ACEI or ARB within 6 months after hospital discharge. Main Outcomes and Measures The primary outcome was mortality; secondary outcomes included hospitalization for a renal cause, end-stage renal disease (ESRD), and a composite outcome of ESRD or sustained doubling of serum creatinine concentration. An AKI was defined as a 50% increase between prehospital and peak in-hospital serum creatinine concentrations. Propensity scores were used to construct a matched-pairs cohort of patients who did and did not have a prescription for an ACEI or ARB within 6 months after hospital discharge. Results The study evaluated 46 253 adults (mean [SD] age, 68.6 [16.4] years; 24 436 [52.8%] male). Within 6 months of discharge, 22 193 (48.0%) of the participants were prescribed an ACEI or ARB. After adjustment for comorbidities, ACEI or ARB use before admission, demographics, baseline kidney function, other factors related to index hospitalization, and prior health care services, ACEI or ARB use was associated with lower mortality in patients with AKI after 2 years (adjusted hazard ratio, 0.85; 95% CI, 0.81-0.89). However, patients who received an ACEI or ARB had a higher risk of hospitalization for a renal cause (adjusted hazard ratio, 1.28; 95% CI, 1.12-1.46). No association was found between ACEI or ARB use and progression to ESRD. Conclusions and Relevance Among patients with AKI, ACEI or ARB therapy appeared to be associated with lower mortality but a higher risk of hospitalization for a renal cause. These results suggest a potential benefit of ACEI or ARB use after AKI, but cautious monitoring for renal-specific complications may be warranted.
Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

Introduction Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and a photoplethysmograph (PPG) that make them unsuitable for true wearable applications. Therefore, developing a single PPG-based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods The University of Queensland vital sign dataset (online database) was accessed to extract raw PPG signals and its corresponding reference BPs (systolic BP and diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5 s for each) were extracted, preprocessed, and normalised in both width and amplitude. Three most significant pulse features (pulse area, pulse rising time, and width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (regression tree, multiple linear regression (MLR), and support vector machine (SVM)). A 10-fold cross-validation was applied to obtain overall BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (normotensive, hypertensive, and hypotensive). Finally, they were compared with the ISO standard for noninvasive BP device validation (average difference no greater than 5 mmHg and SD no greater than 8 mmHg). Results In terms of overall estimation accuracy, the regression tree achieved the best overall accuracy for SBP (mean and SD of difference: −0.1 ± 6.5 mmHg) and DBP (mean and SD of difference: −0.6 ± 5.2 mmHg). MLR and SVM achieved the overall mean difference less than 5 mmHg for both SBP and DBP, but their SD of difference was >8 mmHg. Regarding the estimation accuracy in each BP categories, only the regression tree achieved acceptable ISO standard for SBP (−1.1 ± 5.7 mmHg) and DBP (−0.03 ± 5.6 mmHg) in the normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion This study developed and compared three machine learning algorithms to estimate BPs using PPG only and revealed that the regression tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the regression tree algorithm achieved acceptable measurement accuracy only in the normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories.
Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10[Formula: see text]Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10[Formula: see text]Gbps worked quite well.
Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks

Automated methods for Alzheimer’s disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer’s dementia classification using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.
Nonlinear Modeling and Qualitative Analysis of Coupled Vibrations in a Drill String

A coupled model for axial/torsional/lateral vibrations of the drill string is presented, in which the nonlinear dynamics and qualitative analysis method are employed to find out the key factors and sensitive zone for coupled vibration. The drill string is simplified as an equivalent shell under axial rotation. After dimensionless processing, the mathematical model for coupled axial/torsional/lateral vibrations of the drill string is obtained. The Runge–Kutta–Fehlberg method is employed for the numerical simulation, and the rules that govern the changing of the torsional and axial excitation are revealed. And the stability domains of the explicit Runge–Kutta method are analyzed. Furthermore, the suggestions for field applications are also presented. It is demonstrated by simulation results that the lateral/axial/torsional vibrations exist simultaneously and couple with each other. The system will obtain a stable period motion with an axial excitation zone before the coupled vibration in the three directions, and continue to increase the axial excitation to cause the coupled vibration easily. The torsional excitation of the drill string mainly contributes to the coupled vibration in the three directions when in a specific rotation speed zone. The system is more likely to obtain a periodic motion through adjusting the torsional excitation out of this zone.
Prognostic value of NEK2 overexpression in digestive system cancers: a meta-analysis and systematic review

Background and objective Many studies have reported that NEK2 is overexpressed in digestive system cancers (DSCs) and is also correlated with patient survival. We performed a meta-analysis to comprehensively evaluate the prognostic role of NEK2 expression in DSCs. Materials and methods A comprehensive literature search was performed using PubMed, EMBASE, and Web of Science. Synthesized hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the influence of NEK2 overexpression on the prognosis and clinicopathological features of patients with DSCs. Results A total of 13 studies involving 1,917 patients was included. Overall, patients with high NEK2 expression had poorer overall survival (HR =1.45; 95% CI: 1.15–1.83; P=0.002) and disease-free survival/recurrence-free survival (HR =2.28; 95% CI: 1.54–3.37; P<0.0001). Furthermore, subgroup analysis also suggested that elevated NEK2 expression was associated with poorer overall survival in patients with hepatocellular carcinoma (HR =1.45; 95% CI: 1.05–2.00; P=0.02) and colorectal cancer (HR =2.03; 95% CI: 1.16–3.54; P=0.01). Additionally, NEK2 overexpression was also associated with pretreatment serum AFP level (OR =1.79; 95% CI: 1.23–2.61; P<0.01) and portal vein thrombosis (OR =2.74; 95% CI: 1.22–6.17; P=0.01) in hepatocellular carcinoma. Conclusion NEK2 might act as a useful prognostic predictor and a potential therapeutic target in DSCs. However, multicenter homogeneous studies with larger sample sizes are needed to further confirm our findings owing to some limitations in our meta-analysis.
Artificial Intelligence User Support System for SAP Enterprise Resource Planning

An intelligent system for SAP ERP user support is proposed in this paper. It enables automatic replies on users’ requests for support, saving time for problem analysis and resolution and improving responsiveness for end users. The system is based on an ensemble of machine learning algorithms of multiclass text classification, providing efficient question understanding, and a special framework for evidence retrieval, providing the best answer derivation.
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds

We study the complexity of training neural network models with one hidden nonlinear activation layer and an output weighted sum layer. We analyze Gradient Descent applied to learning a bounded target function on $n$ real-valued inputs. We give an agnostic learning guarantee for GD: starting from a randomly initialized network, it converges in mean squared loss to the minimum error (in $2$-norm) of the best approximation of the target function using a polynomial of degree at most $k$. Moreover, for any $k$, the size of the network and number of iterations needed are both bounded by $n^{O(k)}\log(1/\epsilon)$. In particular, this applies to training networks of unbiased sigmoids and ReLUs. We also rigorously explain the empirical finding that gradient descent discovers lower frequency Fourier components before higher frequency components. We complement this result with nearly matching lower bounds in the Statistical Query model. GD fits well in the SQ framework since each training step is determined by an expectation over the input distribution. We show that any SQ algorithm that achieves significant improvement over a constant function with queries of tolerance some inverse polynomial in the input dimensionality $n$ must use $n^{\Omega(k)}$ queries even when the target functions are restricted to a set of $n^{O(k)}$ degree-$k$ polynomials, and the input distribution is uniform over the unit sphere; for this class the information-theoretic lower bound is only $\Theta(k \log n)$. Our approach for both parts is based on spherical harmonics. We view gradient descent as an operator on the space of functions, and study its dynamics. An essential tool is the Funk-Hecke theorem, which explains the eigenfunctions of this operator in the case of the mean squared loss.
Cyp2b-Knockdown Mice Poorly Metabolize Corn Oil and Are Age-Dependent Obese.

We previously made a RNAi-based cytochrome P450 2b (Cyp2b)-knockdown (Cyp2b-KD) mouse to determine the in vivo role of the Cyp2b subfamily in xenobiotic detoxification. Further studies reported here indicate a role for Cyp2b in unsaturated fatty-acid (UFA) metabolism and in turn obesity. Mice were treated intraperitoneally (i.p.) with 100 μL corn oil as a carrier or the potent Cyp2b-inducer 3,3',5,5'-Tetrachloro-1,4-bis(pyridyloxy)benzene (TCPOBOP (TC)) dissolved in corn oil. Surprisingly, female Cyp2b-KD mice but not male mice showed increased liver lipid accumulation. Male Cyp2b-KD mice had higher serum triacylglycerols, cholesterol, very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) than wildtype (WT) mice; females had higher cholesterol, LDL, and HDL. Thus, Cyp2b-KD mice are unable to clear a high bolus dose of corn oil, potentially because the Cyp2b-KD mice were unable to metabolize the UFA in the corn oil. Therefore, WT and Cyp2b-KD mice were housed for 35 weeks and necropsies performed to test whether Cyp2b-KD mice develop age onset obesity. Cyp2b-KD mice exhibited a significant increase in body weight caused by an increase in white adipose tissue deposition relative to WT mice. Serum cholesterol, triacylglycerol, LDL, and VLDL were significantly greater in 35-week-old Cyp2b-KD males compared to WT males; only serum triacylglycerol and LDL were higher in females. In conclusion, changes in Cyp2b expression led to perturbation in lipid metabolism and depuration in Cyp2b-KD mice. This suggests that Cyp2b is more than a detoxification enzyme, but also involved in the metabolism of UFA, as Cyp2b-KD mice have increased the body weight, fat deposition, and serum lipids.
Mining High Utility Sequential Patterns Using Multiple Minimum Utility

High utility sequential patterns (HUSP) mining has recently received a lot of attention from researchers. Many algorithms have been proposed to mine HUSP and most of them only use a single minimum utility, which implicitly assumes that all items in the database are of the same importance (such as profit), or other information based on users’ concern in the database. This is often not the case in real-life applications. Although a few methods have been proposed to mine high utility itemsets (HUI) with multiple minimum utility (MMU), they are not suitable for mining HUSP with MMU because an item may occur more than one time in a sequence and may have multiple utility values. In this paper, we propose a novel method, called HUSpan-MMU, to efficiently mine HUSP with MMU from sequential utility-based databases. A lexicographic quantitative sequence tree (LQS-tree) is used to extract the complete set of HUSP. Meanwhile, two pruning methods are used to reduce the search space in the LQS-tree. Experimental results on both synthetic and real datasets show that HUSpan-MMU can efficiently mine HUSP with MMU from utility-based databases.
Transkernel: An Executor for Commodity Kernels on Peripheral Cores

Modern mobile devices have numerous ephemeral tasks. These tasks are driven by background activities, such as push notifications and sensor readings. In order to execute these tasks, the whole platform has to periodically wake up beforehand, and go to sleep afterwards. During this process, the OS kernel operates on power state of various IO devices, which has been identified as the bottleneck for energy efficiency. To this end, we want to offload this kernel phase to a more energy efficient, microcontroller level core, named peripheral core. To execute commodity OS on a peripheral core, existing approaches either require much engineering effort or incur high execution cost. Therefore, we proposed a new OS model called transkernel. By utilizing cross-ISA dynamic binary translation (DBT) technique, transkernel creates a virtualized environment on the peripheral core. It relies on a small set of stable interfaces. It is specialized for frequently executed kernel path. It exploits ISA similarities to reduce DBT overhead. We implement a transkernel model on ARM platform. With novel design and optimization, we demonstrate that a transkernel can gain energy efficiency. Moreover, it provides a new OS design to harness heterogeneous SoCs.
Unsupervised Learning via Maximizing Mutual Information in Neural Population Coding

Shannon’s information theory is a widely used tool for many applications including statistical inference, natural language processing, thermal physics, pattern recognition and neuro-science etc. However, it is a big challenge to effectively calculate the mutual information (MI) for these applications. In this paper, we propose an effective asymptotic bounds and approximation for evaluating MI in the context of neural population coding, especially for the case of high-dimensional inputs. Based on that, an unsupervised framework is presented to learn representations, e.g, complete, overcomplete or un-dercomplete bases from the input datasets. Our experiments on MNIST digits image and natural image patches data sets showed robustness and efﬁciency for extracting salient features compared to existing methods.
Descriptor : Fluctuations in anthropogenic nighttime lights from satellite imagery for fi ve cities in Niger and Nigeria

Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 2000–2005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.
Evidence of response to pembrolizumab in a patient with Lynch syndrome-related metastatic colon cancer

Patients with Lynch Syndrome (LS) are at high risk of developing colorectal cancer at an early age. Germline mutations in DNA mismatch repair genes and microsatellite instability are clear signatures of this autosomal dominant disorder. Here, we report the clinical history of a 38-year-old patient with LS-related metastatic colon cancer treated in Chile with immunotherapy (pembrolizumab). The patient exhibited a pathogenic deletion in Epithelial cell Adhesion Molecule (EPCAM) and mutS homolog 2 (MSH2) genes, and after diagnosis received 12 cycles of FOLFOX. The tumor mass, however, continued to grow, and a new metastatic mucinous adenocarcinoma of 13 mm appeared at the level of the 11th right dorsal vertebra. To treat these lesions, the patient received immunotherapy scheme with pembrolizumab (200 mg every 21 days). After only four cycles, the patient’s symptoms improved and the lesions showed less metabolic activity. After 12 cycles with pembrolizumab, the patient started palliative radiation and systemic second-line treatment with FOLFIRI and Avastin. The immunotherapy scheme with pembrolizumab was capable of delaying the second-line treatment for at least 8 months, becoming a useful therapeutic option for this patient. Thus, our study highlights the importance of implementing immunotherapy treatment programs for LS-colorectal cancer patients in South American countries.
Pattern Matching Based Sensor Identification Layer for an Android Platform

As sensor-related technologies have been developed, smartphones obtain more information from internal and external sensors. This interaction accelerates the development of applications in the Internet of Things environment. Due to many attributes that may vary the quality of the IoT system, sensor manufacturers provide their own data format and application even if there is a well-defined standard, such as ISO/IEEE 11073 for personal health devices. In this paper, we propose a client-server-based sensor adaptation layer for an Android platform to improve interoperability among nonstandard sensors. Interoperability is an important quality aspect for the IoT that may have a strong impact on the system especially when the sensors are coming from different sources. Here, the server compares profiles that have clues to identify the sensor device with a data packet stream based on a modified Boyer-Moore-Horspool algorithm. Our matching model considers features of the sensor data packet. To verify the operability, we have implemented a prototype of this proposed system. The evaluation results show that the start and end pattern of the data packet are more efficient when the length of the data packet is longer.
Intelligent Drone Swarm for Search and Rescue Operations at Sea

In recent years, a rising numbers of people arrived in the European Union, traveling across the Mediterranean Sea or overland through Southeast Europe in what has been later named as the European migrant crisis. In the last 5 years, more than 16 thousands people have lost their lives in the Mediterranean sea during the crossing. The United Nations Secretary General Strategy on New Technologies is supporting the use of Artificial Intelligence (AI) and Robotics to accelerate the achievement of the 2030 Sustainable Development Agenda, which includes safe and regular migration processes among the others. In the same spirit, the central idea of this project aims at using AI technology for Search And Rescue (SAR) operations at sea. In particular, we propose an autonomous fleet of self-organizing intelligent drones that would enable the coverage of a broader area, speeding-up the search processes and finally increasing the efficiency and effectiveness of migrants rescue operations.
Reduced cerebrovascular reactivity among adolescents with bipolar disorder

Cardiovascular disease (CVD) is excessive and premature among individuals with bipolar disorder (BD). Cerebrovascular reactivity (CVR), reflecting vasodilatory capacity of cerebral blood vessels in response to vasoactive substances, is a marker of cerebrovascular health. Despite informative findings in other diseases, CVR has not previously been examined in BD.