Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study

Background: Elderly patients undergoing hip fracture repair surgery are at increased risk of delirium due to aging, comorbidities, and frailty. But current methods for identifying the high risk of delirium among hospitalized patients have moderate accuracy and require extra questionnaires. Artificial intelligence makes it possible to establish machine learning models that predict incident delirium risk based on electronic health data. Methods: We conducted a retrospective case-control study on elderly patients (≥65 years of age) who received orthopedic repair with hip fracture under spinal or general anesthesia between June 1, 2018, and May 31, 2019. Anesthesia records and medical charts were reviewed to collect demographic, surgical, anesthetic features, and frailty index to explore potential risk factors for postoperative delirium. Delirium was assessed by trained nurses using the Confusion Assessment Method (CAM) every 12 h during the hospital stay. Four machine learning risk models were constructed to predict the incidence of postoperative delirium: random forest, eXtreme Gradient Boosting (XGBoosting), support vector machine (SVM), and multilayer perception (MLP). K-fold cross-validation was deployed to accomplish internal validation and performance evaluation. Results: About 245 patients were included and postoperative delirium affected 12.2% (30/245) of the patients. Multiple logistic regression revealed that dementia/history of stroke [OR 3.063, 95% CI (1.231, 7.624)], blood transfusion [OR 2.631, 95% CI (1.055, 6.559)], and preparation time [OR 1.476, 95% CI (1.170, 1.862)] were associated with postoperative delirium, achieving an area under receiver operating curve (AUC) of 0.779, 95% CI (0.703, 0.856). The accuracy of machine learning models for predicting the occurrence of postoperative delirium ranged from 83.67 to 87.75%. Machine learning methods detected 16 risk factors contributing to the development of delirium. Preparation time, frailty index uses of vasopressors during the surgery, dementia/history of stroke, duration of surgery, and anesthesia were the six most important risk factors of delirium. Conclusion: Electronic chart-derived machine learning models could generate hospital-specific delirium prediction models and calculate the contribution of risk factors to the occurrence of delirium. Further research is needed to evaluate the significance and applicability of electronic chart-derived machine learning models for the detection risk of delirium in elderly patients undergoing hip fracture repair surgeries.
Interneuron Heterotopia in the Lis1 Mutant Mouse Cortex Underlies a Structural and Functional Schizophrenia-Like Phenotype

LIS1 is one of the principal genes related to Type I lissencephaly, a severe human brain malformation characterized by an abnormal neuronal migration in the cortex during embryonic development. This is clinically associated with epilepsy and cerebral palsy in severe cases, as well as a predisposition to developing mental disorders, in cases with a mild phenotype. Although genetic variations in the LIS1 gene have been associated with the development of schizophrenia, little is known about the underlying neurobiological mechanisms. We have studied how the Lis1 gene might cause deficits associated with the pathophysiology of schizophrenia using the Lis1/sLis1 murine model, which involves the deletion of the first coding exon of the Lis1 gene. Homozygous mice are not viable, but heterozygous animals present abnormal neuronal morphology, cortical dysplasia, and enhanced cortical excitability. We have observed reduced number of cells expressing GABA-synthesizing enzyme glutamic acid decarboxylase 67 (GAD67) in the hippocampus and the anterior cingulate area, as well as fewer parvalbumin-expressing cells in the anterior cingulate cortex in Lis1/sLis1 mutants compared to control mice. The cFOS protein expression (indicative of neuronal activity) in Lis1/sLis1 mice was higher in the medial prefrontal (mPFC), perirhinal (PERI), entorhinal (ENT), ectorhinal (ECT) cortices, and hippocampus compared to control mice. Our results suggest that deleting the first coding exon of the Lis1 gene might cause cortical anomalies associated with the pathophysiology of schizophrenia.
Mixed Method Research on Football Coaches’ Competitive Behavior

Objective The purpose of this study was to present the reliability of three validated measures, namely the System of Analysis of Instruction in Competition, the Questionnaire on Coach Instructional Behavior Expectations, and the Questionnaire on Coach Instructional Behavior Perception that could be used in a mix-method approach. Methods Three instruments underwent a robust process of construct and reliability analysis. Inter− and intra-observer reliability was tested for the observational instrument using Cohen’s k-agreement measure. Reliability values above 0.85 were considered as a good agreement between and within observers. To verify the internal consistency of the questionnaires, the correlation coefficients were considered. Results The results related to intra-observer and inter-observer reliability showed that intra-observer reliability k-agreement values ranged between 0.912 and 1 for observer 1, and 0.82 and 1 for observer 2. For inter-observer reliability, k-agreement values ranged between 0.885 and 1 between observers. Thus, values for reliability are above acceptable. The correlation coefficient values recorded for the questionnaires on instruction expectations in the competitive moment were above 0.82 and significant (p < 0.05), and for the questionnaire on instruction perception in competition above 0.88 and significant (p < 0.05). The pilot study showed some divergent results across expectations, behavior during competition, and perception about the instruction behavior. Conclusion The observational system and the expectations and perceptions questionnaires, used in a complementary way, can be considered as a mix-method approach for studies aiming to examine coaches’ competitive behavior.
A Computational Study of Astrocytic GABA Release at the Glutamatergic Synapse: EAAT-2 and GAT-3 Coupled Dynamics

Neurotransmitter dynamics within neuronal synapses can be controlled by astrocytes and reflect key contributors to neuronal activity. In particular, Glutamate (Glu) released by activated neurons is predominantly removed from the synaptic space by perisynaptic astrocytic transporters EAAT-2 (GLT-1). In previous work, we showed that the time course of Glu transport is affected by ionic concentration gradients either side of the astrocytic membrane and has the propensity for influencing postsynaptic neuronal excitability. Experimental findings co-localize GABA transporters GAT-3 with EAAT-2 on the perisynaptic astrocytic membrane. While these transporters are unlikely to facilitate the uptake of synaptic GABA, this paper presents simulation results which demonstrate the coupling of EAAT-2 and GAT-3, giving rise to the ionic-dependent reversed transport of GAT-3. The resulting efflux of GABA from the astrocyte to the synaptic space reflects an important astrocytic mechanism for modulation of hyperexcitability. Key results also illustrate an astrocytic-mediated modulation of synaptic neuronal excitation by released GABA at the glutamatergic synapse.
Clinical and Pharmacologic Differences of CDK4/6 Inhibitors in Breast Cancer

Targeted therapies such as Cyclin Dependent Kinase 4 and 6 (CDK 4/6) inhibitors have improved the prognosis of metastatic hormone receptor (HR) positive breast cancer by combating the resistance seen with traditional endocrine therapy. The three approved agents currently in the market are palbociclib, ribociclib and abemaciclib. Besides the overall similarities associated with CDK4/6 inhibition, there are differences between the three approved agents that may explain the differences noted in unique clinical scenarios- monotherapy, patients with brain metastases or use in the adjuvant setting. This review article will explore the preclinical and pharmacological differences between the three agents and help understand the benefits seen with these agents in certain subgroups of patients with metastatic HR positive breast cancer.
Deep Koopman Representation for Control over Images (DKRCI)

The Koopman operator provides a means to represent nonlinear systems as infinite dimensional linear systems in a lifted state space. This enables the application of linear control techniques to nonlinear systems. However, the choice of a finite number of lifting functions, or Koopman observables, is still an unresolved problem. Deep learning techniques have recently been used to jointly learn these lifting function along with the Koopman operator. However, these methods require knowledge of the system’s state space. In this paper, we present a method to learn a Koopman representation directly from images and control inputs. We then demonstrate our deep learning architecture on a cart-pole system with external inputs.
Efficient All-or-Nothing Public Key Encryption With Authenticated Equality Test

Public key encryption with equality test (PKEET for short) is a new cryptographic primitive which allows a proxy to check whether two ciphertexts encrypted under different public keys are of the same plaintext. PKEET has become a prospective candidate for various application scenarios, such as data management on encrypted databases, personal health record systems, spam filtering in encrypted email systems. In this paper, we propose an efficient all-or-nothing public key encryption scheme with authenticated equality test (AoN-PKEAET for short). In comparison with the existing PKEET schemes, the proposed scheme provides a new feature of ciphertext authentication before the plaintext equality test. It ensures that no misjudgement occurs when testing the equality between ciphertexts. Specially, the $\mathsf {Test}$ algorithm in the proposed scheme first authenticates the compared ciphertexts and the equality test is fulfilled only if the ciphertexts are authenticated as valid. With this new feature, the $\mathsf {Test}$ algorithm cannot output 0 (which means that the encrypted messages are different) when the two input ciphertexts are of an identical message, or it cannot output 1 (which means that the encrypted messages are identical) when the two input ciphertexts are of different messages. Under a redefined security model for AoN-PKEAET, we give the detailed security proof of the proposed scheme.
A Novel Approach to Information Hiding Technique using ASCII Mapping Based Image Steganography

Steganography is hiding secret data in a cover without suspicion. This paper proposes a new information hiding technique using image steganography based on ASCII code matching. The cover image file is first read as decimal and the American Standard Code of Information Interchange (ASCII) code characters are searched, then the ASCII codes positions are stored in a mapping table. Similarly, the secret message is converted into decimal and hence matched to know the characters’ positions. Compression and encryption have also been applied to the characters’ positions of the secret message before being sent. The great advantage is that there will be no change in the image size nor pixels’ values, only the positions of the secret message are rather noted. Moreover, several experimental tests have been applied to demonstrate the efficiency and performance of the proposed method to evaluate it according to the widely used metrics: Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and the hiding capacity. From the security aspect, Structural Similarity Index Measure (SSIM) together with the Histogram analysis have been performed. The proposed method is further compared with some related works. The experimental results verified the effectiveness of the proposed method.
Comparative Studies of Oxidative Stability Properties of Biodiesel Produced from Azadirachta indica and Hevea brasiliensis seeds.

The aim of the study was to establish the stability of biodiesel due to oxidation. Two non-edible seed oils were used; Azadirachta indica and Hevea brasiliensis were used to produce biodiesel and first rapid oxidative stability of the biodiesel were assessed then their properties with and without additives compared thereof over a range of one year storage period. The results of stability of the biodiesels for rapid induction and long term period with and without additives revealed that physico-chemical properties of the biodiesel produced from Azadirachta indica and Hevea brasiliensis seeds oil changed with the time. For the rapid test, none of the biodiesel samples could pass the 6 hrs oxidation test with neem biodiesel exhibiting the best oxidation stability and the rubber seed the worst. Both the additives, namely 0.5% tert -butyl hydroquinone (TBHQ) and 0.1% Ecotive increased the oxidation stability of all the three biodiesel samples to more than 6hrs. The one year storage stability study revealed that per-oxide value, density, viscosity and acid number increased for all the methyl esters with time. However, the calorific value was found to be decreasing for all the samples. The rate of change of different physico-chemical properties was found to be less in case of all the biodiesel with an additive. However, Ecotive appeared to be a better additive as compared to TBHQ for increasing the shelf life of biodiesel.
Impact on addiction of online platforms on quality of life: Age and Gender as moderators

The excessive usage of online platforms is inviting several unwanted problems in the society. The excessive use of online platforms is adversely interfering in many social activities. This uncontrolled and excessive use of online platforms is causing addiction to the users. This is unexpectedly impeding the normal social flow of life culminating an adverse effect on the individuals’ quality of life. Studies reveal that age and gender have influence towards addiction. In this background, the purpose of this study is to identify the factors impacting addiction of online platforms. From studies of several addiction theories, some hypotheses have been formulated and a conceptual model has been developed. These have been validated by Partial Least Square – Structural Equation Modeling (PLS-SEM) analysis with the help of survey involving 320 usable respondents. The study highlights that loneliness, perceived enjoyment, depression, perceived ease of use and perceived usefulness act as vital predictors of addiction of online platforms that impacts quality of life. The moderating factors age and gender are found to have effective impacts on the influence of predictors on the addiction of online platforms. The article is ended mentioning the limitations of this study incorporating the scopes for the future researchers to nurture the untouched points.
Image De-Noising With Machine Learning: A Review

Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in the images. Image de-noising has become an integral part of the image processing workflow. It is used to attenuate the noises and accentuate the specific image information stored within. Machine learning is an important tool in the image-de-noising workflow in terms of its robustness, accuracy, and time requirement. This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. The motivation, algorithm and framework of different machine learning de-noisers are analyzed. These de-noisers are compared using PSNR as quality assessment metric on some benchmark datasets. The best de-noising results for different noise types are discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. CNN+LSTM, and MC2RNet are most suitable CNN-based Poisson de-noisers. For impulse noise removal, Blind CNN, and CNN+PSO perform well. For mixed noise removal, WDL, EM-CNN, CNN, SDL, and Mixed CNN are prominent. De-noisers like GRDN and DDFN show accurate results in the domain of real-world de-noising.
GNFCVulFinder: NDEF Vulnerability Discovering for NFC-Enabled Smart Mobile Devices Based on Fuzzing

Near-field communication (NFC) is a set of communication protocols that enable two electronic devices. Its security and reliability are welcomed by mobile terminal manufactures, banks, telecom operators, and third-party payment platforms. Simultaneously, it has also drawn more and more attention from hackers and attackers, and NFC-enabled devices are facing increasing threats. To improve the security of the NFC technology, the paper studied the technology of discovering security vulnerabilities of NFC Data Exchange Format (NDEF), the most important data transmission protocol. In the paper, we proposed an algorithm, GTCT (General Test Case Construction and Test), based on fuzzing to construct test cases and test the NDEF protocol. GTCT adopts four strategies to construct test cases, manual, generation, mutation, and “reverse analysis,” which can detect logic vulnerabilities that fuzzing cannot find and improve the detection rate. Based on GTCT, we designed an NDEF vulnerability discovering framework and developed a tool named “GNFCVulFinder” (General NFC Vulnerability Finder). By testing 33 NFC system services and applications on Android andWindows Phones, we found eight vulnerabilities, including DoS vulnerabilities of NFC service, logic vulnerabilities about opening Bluetooth/Wi-Fi/torch, design flaws about the black screen, and DoS of NFC applications. Finally, we give some security suggestions for the developer to enhance the security of NFC.
Deep Reinforcement Learning for Task Offloading in Edge Computing Assisted Power IoT

Power Internet of Things (PIoT) is a promising solution to meet the increasing electricity demand of modern cities, but real-time processing and analysis of huge data collected by the devices is challengeable due to limited computing capability of devices and long distance from the cloud center. In this paper, we consider the edge computing assisted PIoT where the computing tasks of the devices can be either processed locally by the devices, or offloaded to edge servers. Aiming to maximize the long-term system utility which is defined as a weighted sum of reduction in latency and energy consumption, we propose a novel task offloading algorithm based on deep reinforcement learning, which jointly optimizes task scheduling, transmit power of the PIoT devices, and computing resource allocation of the edge servers. Specifically, the task execution on each edge server is modeled as a queuing system, in which the current queue state may affect the waiting time for the next tasks. The transmit power and computing resource allocation is first optimized, respectively, and then a deep Q-learning algorithm is adopted to make task scheduling decisions. Numerical results show that the proposed method can improve the system utility.
R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation

In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.
Surfactant effect on cloud condensation nuclei for two‐component internally mixed aerosols

This work presents experimental data on the cloud condensation nuclei (CCN) activity of two‐component mixtures containing surfactants. Nine binary systems were tested combining strong ionic (sodium dodecyl sulfate) and nonionic surfactants (Zonyl FS‐300 and Triton X‐100) with nonsurfactant compounds (glucose, ammonium sulfate, or sodium chloride). Control tests were performed for systems combining organic (glucose) and inorganic compounds (ammonium sulfate or sodium chloride). Results show that CCN activity deviates strongly relative to predictions made from measurements of bulk surface tension. Köhler theory accounting for surface tension reduction and surface partitioning underpredicts the CCN activity of particles containing Zonyl FS‐300 and Triton X‐100. Partitioning theory better describes data for Zonyl FS‐300 and Triton X‐100 when limiting surface adsorption to 1.5 monolayers of the growing drop. Deviations from predictions were observed. Likely explanations include solute‐solute interactions and nonspherical particle shape. The findings presented here examine in detail the perturbation of CCN activity by surfactants and may offer insight into both the success and limitations of physical models describing CCN activity of surface active molecules.
Analysis of SWIPT-Enabled Relay Networks with Full-Duplex Destination-Aided Jamming

Simultaneous wireless information and power transfer (SWIPT) is a major breakthrough in the field of low-power wireless information transmissions. In this paper, the secrecy performance of the SWIPT-enabled relay network with full-duplex destination-aided jamming is assessed, where both the power-splitting (PS) and time-switching (TS) schemes at the relay are considered with the linear and nonlinear energy harvesting models. The relay harvests energy from the confidential signal and artificial noise sent by the source and destination, respectively, and forwards the amplified signal to the destination, in the presence of an eavesdropper. The analytical closed-form expressions of the connection outage probability (COP), secrecy outage probability (SOP), and transmission outage probability (TOP) for PS- and TS-based schemes are derived, and the closed-form expression of the lower bound of ergodic secrecy capacity (ESC) is calculated. The asymptotic-form expressions of the COP, SOP, TOP, and ESC are further analyzed to capture the valuable information in the high SNR regime. Numerical results verify the correctness of analytical results, reveal the effects of the PS/TS ratio, and transmit the signal-to-noise ratio on secrecy performance.
Partition Coefficients of Pharmaceuticals as Functions of Temperature and pH

Liquid–liquid extraction is a potential separation process for the purification of active pharmaceutical ingredients (APIs). The design of an extraction step requires knowledge of the API partition coefficient, which strongly depends on the solvent system and process conditions. Usually, cost-intensive experiments have to be performed to select the most suitable solvent system and the best process conditions. The number of experiments can be reduced by predicting the partition coefficient using perturbed chain statistical associating theory (PC-SAFT). In this work, modeling results and experimental data were compared for the partition coefficients of the APIs nicotinamide and salicylamide in different solvent systems at temperatures from 293.15 to 328.15 K and at pH values varying between 5.2 and 10.3. The results show that PC-SAFT is able to predict the API partition coefficients for different solvent systems as functions of temperature and pH.