Miracle-dz/UniFAR-FTUs huggingface dataset:
The Prognostic Value of Lactate in Cardiac Intensive Care Unit Patients With Cardiac Arrest and Shock

Supplemental Digital Content is available in the text ABSTRACT Background: Lactate is a prognostic marker in critically ill patients, although currently available illness severity scores do not include lactate as a predictive parameter. We sought to describe the association between lactate and hospital mortality in patients admitted to the cardiac intensive care unit (CICU) with cardiac arrest (CA) and shock. Methods: Retrospective observational analysis of Mayo Clinic CICU patients admitted from 2007 to 2018 with measured lactate on admission, including patients with and without CA or shock. We examined hospital mortality as a function of admission lactate in patients. Multivariable logistic regression was used to determine predictors of hospital mortality. Results: We included 3,042 patients with a median age of 70 years (IQR 60–80), including 41% females, 26% with CA, and 39% with shock. The median APACHE-IV predicted mortality was 24% (IQR 11–51%), and the median admission lactate was 1.8 mmol/L (IQR 1.1–3.0). Hospital mortality occurred in 23% of patients and rose progressively with higher admission lactate, including in patients with and without CA or shock. After multivariable adjustment for clinical characteristics, therapies, and illness severity, a higher lactate remained associated with increased hospital mortality (adjusted OR 1.13 per mmol/L, 95% CI 1.06–1.20, P < 0.001). Conclusions: Admission lactate levels are strongly associated with increased hospital mortality among CICU patients, including those with and without CA or shock. The prognostic value of lactate levels is independent of established ICU prognostic scores and dependent on admission diagnosis, which may help inform clinicians caring for CICU patients.
Effects of summer weather on reproductive success of the Red-backed Shrike (Lanius collurio)

Capsule Summer weather affects reproductive success in the Red-backed Shrike (Lanius collurio). Aims To investigate the relationship between reproductive success and weather conditions in the European breeding areas. Methods Using a Principal Component Analysis, we investigate how summer (July) weather affects reproductive success in two populations in northwestern Europe. Further, we assess the general relationship between reproductive success and weather in a review of published results. Results Weather is correlated with reproductive success in five out of six European populations, including the two populations at the northwestern range edge as well as three out of the four reviewed populations, all in central Europe. In general, sunny, warm and dry weather increases reproductive success, while rainy and cold weather has the opposite effect. Conclusion Our findings suggest that the warmer summers predicted under a future climate could increase the reproductive success of the Red-backed Shrike. Yet the effect of more extreme weather is yet to be examined. Future investigation of the relationship between weather and the population dynamics across a large spatial scale could help us estimate for which species we should expect general effects of climate change.
Modified Label Propagation on Manifold With Applications to Fault Classification

In process monitoring, fault classification performance heavily relies on the labels of training data. However, the labeled data are inadequate and difficult to obtain because they require experienced human annotators. In this paper, a modified label propagation (MLP) method is proposed to propagate labels from labeled data to unlabeled data. The proposed label propagation algorithm has the following advantages: (1) It constructs a global and local consistency framework with the aid of a data graph, manifold learning, and data labels. This framework follows the assumption that data on the manifold will have similar structures, and nearby data will have similar labels. (2) Considering the inner relationship between the unlabeled data and historical data, a new definition for the initial label matrix is offered, which is significant for label propagation. (3) The new method propagates labels in a low-dimensional manifold space, which is different from most existing label propagation methods that propagate them in the original space. The results reveal that under the global and local consistency framework, soft labels of unlabeled data are given more effective predictions. With additional soft labels of unlabeled data, the MLP-based fault classification method is introduced. The simulation results obtained using a toy example demonstrate the label propagation performance of the MLP, and those obtained for the penicillin fermentation process verify the effectiveness of the MLP-based fault classification method.
Attachment Styles and Communication of Displeasing Truths

This work explores how humans manage the communication of a displeasing and face-threatening truth and how the communicative strategies of the sender and the reaction of the receiver are influenced by their attachment style. Two experimental studies demonstrate that the attachment styles of both senders and receivers can influence the communicative styles of the sender, the emotions that both senders and receivers feel or attribute to their interlocutor, and the reactions of the receivers. In Study 1, couples of participants played a bogus computer game, ostensibly to test their abilities. Subsequently, “the spokesperson” was given the task to communicate to the “the receiver” a bogus low score of the other and a high score of oneself. Finally, all participants completed an adult Attachment Style Questionnaire (ASQ). A content analysis of the verbal messages of the spokespersons showed two main communication styles: frankness and mitigation. The results suggest that the spokespersons’ attachment style influences these communication styles. Using a similar procedure, Study 2 showed that spokespersons with a high avoidant attachment more frequently used frankness when communicating low scores to the receivers. Furthermore, the emotions and impressions reported by anxious and avoidant spokespersons and receivers, respectively, confirm the negative model of the self or the other, typical, respectively, in anxious and avoidant attachment. The detection of communicative strategies stemming from different attachment styles might be of use in user modeling and the planning of personalized systems.
Quality attributes in patterns related to microservice architecture: a Systematic Literature Review

Microservices is an interesting option for those who want to migrate their systems to improve performance, maintainability, scalability, and interoperability. Microservice architecture is a collection of self-sufficient services working together to provide functionalities. Nowadays, there are many options to build microservices, some of them are lead by patterns. However, the mapping between quality attributes and patterns is not clear yet. This systematic literature review presents a microservice pattern collection, it describes their benefits and the association between patterns and quality attributes. Finally, some metrics of quality attributes are identified.
Meta-Analysis of Sex Differences in ADHD Symptoms and Associated Cognitive Deficits

Objective: A meta-analysis was carried out to determine whether there are sex differences among children and adolescents with ADHD on the primary symptoms of ADHD and on executive and attentional functioning. Method: Studies published from 1997 to 2017 comparing boys and girls with a valid ADHD diagnosis were retained. Results: The meta-analysis found boys with ADHD to be more hyperactive than girls with ADHD and boys to have more difficulties in terms of motor response inhibition and cognitive flexibility. Results also confirm that youths with ADHD have more executive deficits than non-ADHD peers have, but there is no sex difference in this regard. Conclusion: Results show that there are sex differences in the behavioral expression of the difficulties related to ADHD. This highlights the importance of pursuing research to refine the profile of girls with ADHD and to develop diagnostic criteria adapted to each sex.
Urdu-Text Detection and Recognition in Natural Scene Images Using Deep Learning

Urdu text is a cursive script and belongs to a non-Latin family of other cursive scripts like Arabic, Chinese, and Hindi. Urdu text poses a challenge for detection/localization from natural scene images, and consequently recognition of individual ligatures in scene images. In this paper, a methodology is proposed that covers detection, orientation prediction, and recognition of Urdu ligatures in outdoor images. As a first step, the custom FasterRCNN algorithm has been used in conjunction with well-known CNNs like Squeezenet, Googlenet, Resnet18, and Resnet50 for detection and localization purposes for images of size $320\times 240$ pixels. For ligature Orientation prediction, a custom Regression Residual Neural Network (RRNN) is trained/tested on datasets containing randomly oriented ligatures. Recognition of ligatures was done using Two Stream Deep Neural Network (TSDNN). In our experiments, five-set of datasets, containing 4.2K and 51K Urdu-text-embedded synthetic images were generated using the CLE annotation text to evaluate different tasks of detection, orientation prediction, and recognition of ligatures. These synthetic images contain 132, and 1600 unique ligatures corresponding to 4.2K and 51K images respectively, with 32 variations of each ligature (4-backgrounds and font 8-color variations). Also, 1094 real-world images containing more than 12k Urdu characters were used for TSDNN’s evaluation. Finally, all four detectors were evaluated and used to compare them for their ability to detect/localize Urdu-text using average-precision (AP). Resnet50 features based FasterRCNN was found to be the winner detector with AP of.98. While Squeeznet, Googlenet, Resnet18 based detectors had testing AP of.65,. 88, and.87 respectively. RRNN achieved and accuracy of 79% and 99% for 4k and 51K images respectively. Similarly, for characters classification in ligatures, TSDNN attained a partial sequence recognition rate of 94.90% and 95.20% for 4k and 51K images respectively. Similarly, a partial sequence recognition rate of 76.60% attained for real world-images.
Behavioral and Neural Arguments of Motivational Influence on Decision Making During Uncertainty

The scientific world is increasingly interested in motivation, primarily due to the suspected impact on decision-making abilities, particularly in uncertain conditions. To explore this plausible relationship, 28 healthy participants were included in the study and performed decision-making and motivational tasks while their neural activity was recorded. All participants performed the Iowa Gambling Task (IGT) and were split into two groups based on their score, one favorable group with 14 participants who performed advantageously and one undecided group with 14 participants who failed to develop the correct strategy on the IGT. In addition, all participants performed the Effort Expenditure for Reward Task (EEfRT), which defines the motivational level of each participant by the effort that participants agree to do in function of reward magnitudes and probabilities to receive these reward (10, 50, and 90%). The completion of both tasks allowed for the exploration of the relationship between the motivational level and decision-making abilities. The EEfRT was adapted to electroencephalography (EEG) recordings to explore how motivation could influence reward experience. Behavioral results showed no difference in EEfRT performances on the whole task between the two groups’ performances on the IGT. However, there was a negative correlation between the difficulty to develop an optimal strategy on the IGT and the percentage of difficult choices at the 90% condition on the EEfRT. Each probability condition has been previously associated to different motivational and emotional states, with the 90% condition associated to the reward sensitivity. This behavioral result leads to the hypothesis that reward sensitivity may induce an inability to develop an optimal strategy on the IGT. Group analysis demonstrated that only the undecided group showed a P300 during the processing of the outcome, whereas the favorable group showed a blunted P300. Similarly, there was a negative correlation between the P300 amplitude and the ability to develop an optimal strategy on the IGT. In conclusion, behavioral and neuronal data provides evidence that the propensity to focus only on the immediate outcomes is related to the development of an inefficient strategy on the IGT, without influence of motivation.
A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms

In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this article, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) The deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) The combined approach can significantly improve the peak detection rate; the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind–Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind–Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.
Improving structure-based virtual screening performance via learning from scoring function components

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.
Diabetic Retinopathy Detection: Methods and Challenges

Diabetic retinopathy (DR) mostly affects the people as a result of diabetes and it leads to blindness. Diabetic retinopathy (DR) affects the eyes as a result of increased blood glucose levels. Among people in the 70-year-old age group, 50% of deaths are associated with diabetes. Detection of diabetes at a early stage and taking a proper treatment can reduce vision loss among the patients. Once DR symptoms are recognized, the severity of the disease must be assessed in order to prescribe the correct medication. There are five stages of severity of diabetic retinopathy, Mild Non Proliferative Diabetic Retinopathy (NPDR),Moderate NPDR, Severe NPDR, Proliferative Diabetic Retinopathy(PDR) and No DR(Diabetic Retinopathy). This paper summarizes DR detection methods and problems.
Reactivity of astrocytes in the periaqueductal gray matter of rats treated with monosodium glutamate.

INTRODUCTION The astrocytic S100β calcium-binding protein performs numerous intra- and extracellular functions, promoting the survival of central nervous system (CNS) structures. Its increased synthesis and release are a manifestation of reactive glial behavior, crucial for the maintenance of proper neuronal function, particularly under the pathological conditions. The periaqueductal gray matter (PAG) is a mindbrain area composed of four parts (dorsomedial (dm), dorsolateral (dl), lateral (l) and ventrolateral (vl)) which are involved in pain sensing and defensive reactions of the body. The aim of this study was to evaluate the S100β protein immunoreactive (S100β-IR) astrocytes in adult rats after administration of monosodium glutamate (MSG). MATERIAL AND METHODS The animals were administered the saline solution (group C), 2 g/kg b.w. MSG (group I) and 4 g/kg b.w. MSG (group II). The study was carried out on the brain sections stained by immunohistochemical peroxidase-antiperoxidase method with a primary mouse antibody against the S100β protein. RESULTS The analyses showed the presence of the S100β-immunoreactive cells in dm, dl, l, vl PAG of all animals. In the C and I group animals, the PAG astrocytes were characterized mainly by the presence of the studied protein in the nucleus and cytoplasm of the cell body. In the group II rats in all parts of PAG, the S100β-IR cells with numerous, thicker and branched processes were observed. A decrease in the number of the S100β-IR cells was found in PAG in the MSG-treated animals, particularly with the larger dose. The number of cells with the S100β expression was comparable in vl PAG in all rats. CONCLUSIONS MSG administered parenterally to the higher dose to adult rats affects the immunoreactivity of S100β protein in PAG. Phenotypic changes of the studied cells may indicate reactivity of glial cells and increased expression of the studied protein whereas a decrease in their number may result from the increased protein secretion into the extracellular space or cytotoxic death of glial cells.
SmartCart : Mall Assistant

The Indian retail industry is a multi-billion dollar industry with the total consumption expenditure amounting to US$ 1,824 billion in 2017 [1]. With such enormous numbers accounting for consumer spending, consumers deserve a more convenient shopping experience because people choose offline shopping for the physical experience it provides them. Due to technological advancements in the domains of pervasive and ubiquitous computing, it is possible to provide customers with a shopping experience where they can use indoor navigation on their smartphones to search for the products that they want to buy, get detailed information of the same, and do not have to stand in queues at the bill payment counter. We aim to provide this improvement by building an assistive system that uses technologies such as augmented reality, machine learning, and ubiquitous computing. The same system is furthermore conceptualized to target the visually impaired consumer segment and provide them with a more accessible and less intimidating shopping experience.
Differential Fault Detection Scheme for Islanded AC Microgrids Using Digital Signal Processing and Machine Learning Techniques

The conventional overcurrent-based fault detection schemes are not applicable to islanded microgrids due to their dependence on significant fault current transients. This paper introduces a differential protection-based fault detection, localization, and classification method by using Digital Signal Processing and machine learning techniques. The proposed method is capable of detecting any type of line faults in the system and provides information on the fault location and type of fault including faulty phase information. In this method, differential current and voltage signals from both the ends of a line are sampled processed using Fast Fourier Transform to extract the features. Two different classification models for fault detection and classification and one regression model for fault localization are developed using Support Vector Machine technique. The proposed method is extensively validated in an IEC-based and IEEE 34-bus based islanded microgrid models. Further, the performance of the proposed method is verified with clean as well as noisy data, where the noise is extracted from real system measurements. The extensive test results indicate that the proposed method is highly reliable in providing an effective protection measure for safe and secure microgrid operation.
Modulation With Metaheuristic Approach for Cascaded-MPUC49 Asymmetrical Inverter With Boosted Output

This work introduces a 49-level Asymmetrical Inverter (AMLI) with boosted output based on the cascaded operation of two 7-Level Modified Packed U-Cell inverters (MPUC-7). The converter is capable of operation with a boosted voltage of up to 1.714 times the maximum DC voltage employed. It requires only 12 active switches and 4 voltage sources. With the sources set in the ratio of 14:7:2:1, the 7-level output of the two converters is so utilized that the 72 = 49-level output voltage is generated across the load. A detailed explanation of level formation is discussed. This converter is operated using an Artificial Neural Network (ANN) which is trained for the harmonic elimination in the output voltage waveform. For the calculation of optimum angles, a meta-heuristic based Genetic Algorithm (GA) technique is employed. The generation of 49-level output requires 24 transitions in one quarter of a cycle. All these angles are generated for various desired output voltages, and the ANN is trained offline for the same. The converter and its control are simulated in MATLAB/Simulink® environment, and the results are verified on the experimental setup. The multilevel output thus obtained is nearly sinusoidal and the Total Harmonic Distortion (THD) thus produced is under the specified limit of IEEE.
A file system for safely interacting with untrusted USB flash drives

This paper introduces RBFuse, a system for interacting with USB flash drives safely in commodity OSes while bypassing the complex and bug-prone USB stack on the host. RBFuse prevents attacks in which malicious USB flash drives exploit low-level USB driver vulnerabilities to compromise the host machine. The simple idea behind RBFuse is to remap the USB stack to a virtual machine and export the flash drive’s file system as a mountable virtual file system. The result of this decomposition is that the host can access all the files in the flash drive without speaking USB. This is beneficial from a security standpoint, since the VFS interface is small, has well-defined semantics, and can be formally verified. RBFuse requires no hardware modifications and introduces low overhead.