Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters

In invasive electrophysiological recordings, a variety of neuronal oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neuronal oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illustrate the effects of spatial mixing on measuring neuronal oscillations in invasive electrophysiological recordings and demonstrate the benefits of using data-driven referencing schemes in order to improve measurement of neuronal oscillations. We discuss referencing as the application of a spatial filter. Spatio-spectral decomposition is used to estimate data-driven spatial filters, a computationally fast method which specifically enhances signal-to-noise ratio for oscillations in a frequency band of interest. We show that application of these data-driven spatial filters has benefits for data exploration, investigation of temporal dynamics and assessment of peak frequencies of neuronal oscillations. We demonstrate multiple use cases, exploring between-participant variability in presence of oscillations, spatial spread and waveform shape of different rhythms as well as narrowband noise removal with the aid of spatial filters. We find high between-participant variability in the presence of neural oscillations, a large variation in spatial spread of individual rhythms and many non-sinusoidal rhythms across the cortex. Improved measurement of cortical rhythms will yield better conditions for establishing links between cortical activity and behavior, as well as bridging scales between the invasive intracranial measurements and noninvasive macroscale scalp measurements.
Destabilization of β-cell FIT2 by saturated fatty acids contribute to ER stress and diabetes

Western type diets are linked to obesity and diabetes partly because of their high saturated fatty acid (SFA) content. We found that SFAs, but not unsaturated fatty acids (USFAs), reduced the number of lipid droplets (LDs) within pancreatic β-cells. Mechanistically, SFAs but not USFAs disabled LD biogenesis by inducing palmitoylation and subsequent ERAD-C mediated degradation of LD formation protein, Fat storage-Inducing Transmembrane protein 2 (FIT2). Targeted ablation of FIT2 reduced β-cell LD numbers, lowered β-cell ATP levels, reduced Ca2+ signaling, downregulated β-cell transcription factors (RNA sequencing analysis), and exacerbated diet-induced diabetes in mice. Subsequent mass spectrometry studies revealed increased C16:0 ceramide accumulation in islets of mice lacking β-cell FIT2 under lipotoxic conditions. Inhibition of ceramide synthases ameliorated the enhanced ER stress. Overexpression of FIT2 increased number of intracellular LDs and rescued SFA-induced ER-stress and apoptosis thereby highlighting the protective role of FIT2 and LDs against β-cell lipotoxicity and diet-induced diabetes.
The aging DNA methylome reveals environment-by-aging interactions in a model teleost

The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (“epigenetic aging”) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.
Spontaneous neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Spontaneous fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on invasive electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.
Search for evidence of trend slow-down in the long-term TOMS/SBUV total ozone data record: the importance of instrument drift uncertainty

We have developed a merged ozone data set (MOD) for the period October 1978 through June 2006 combining total ozone measurements (Version 8 retrieval) from the TOMS (Nimbus 7, Earth Probe) and SBUV/SBUV2 (Nimbus 7, NOAA 9/11/16) series of satellite instruments. We use the MOD data set to search for evidence of ozone recovery in response to the observed leveling off of chlorine and bromine compounds in the stratosphere. A crucial step in any time series analysis is the evaluation of uncertainties. In addition to the standard statistical time series uncertainties, we evaluate the possible instrument drift uncertainty for the MOD data set. We combine these two sources of uncertainty and apply them to a cumulative sum of residuals (CUSUM) analysis for trend slow-down. For the extra-polar mean between 60° S and 60° N, the apparent slow-down in trend is found to be clearly significant if instrument uncertainties are ignored. When instrument uncertainties are added, the slow-down becomes marginally significant at the 2σ level. For the mid-latitudes of the northern hemisphere (30° to 60° N) the trend slow-down is highly significant at the 2σ level, while in the southern hemisphere the trend slow-down has yet to meet the 2σ significance criterion. The rate of change of chlorine/bromine compounds is similar in both hemispheres, and we expect the ozone response to be similar in both hemispheres as well. The asymmetry in the trend slow-down between hemispheres likely reflects the influence of dynamical variability, and thus a clearly statistically significant response of total ozone to the leveling off of chlorine and bromine in the stratosphere is not yet indicated.
Instructor-learner neural synchronization during elaborated feedback predicts learning transfer

The provision of feedback with complex information beyond the correct answer, i.e., elaborated feedback, can powerfully shape learning outcomes such as transfer, i.e., the ability to extend what has been learned in one context to new contexts. However, an understanding of neurocognitive processes of elaborated feedback during instructor-learner interactions remains elusive. Here, a two-person interactive design is used during simultaneous recording of functional near-infrared spectroscopy (fNIRS) signals from adult instructor-learner dyads. Instructors either provided elaborated feedback (i.e., correct answer and an example) or simple feedback (i.e., correct answer only) to learners during a concept learning task. Our results showed that elaborated feedback produced comparable levels of retention to simple feedback, however, transfer was significantly enhanced by elaboration. We also noted significant instructor-learner neural synchronization in frontoparietal regions during the provision of elaborated feedback, especially when examples were provided. Further, interpersonal neural synchronization in the parietal cortex successfully predicted transfer of knowledge to novel contexts. This prediction was retained for both learner-delayed and learner-preceding neural synchronization. These findings point toward transfer effects of elaborated feedback provided in a social context can be predictable through interpersonal neural synchronization, which may hold important implications for real-world learning and pedagogical efficacy. Educational Impact and Implications Statement Feedback provides learners with crucial information regarding the gap between what has currently been achieved and what remains to be achieved, and thus plays a critical role in any learning process. In real-world settings, feedback is typically provided and received through social interaction, and high-quality “elaborated feedback” contains complex information that goes beyond the correct answer. This study aims to elucidate the neurocognitive processes underpinning elaborated feedback during instructor-learner interactions. We detected significant instructor-learner neural synchronization in mutual frontoparietal brain regions during elaborated feedback, particularly during the provision of specific elaborated information (i.e., concrete examples). Moreover, this synchronization (including learner-delayed and learner-preceded synchronization) in the parietal region predicted whether the learners transferred learning to novel examples of learned psychology concepts. This study advances current understanding on the neural mechanisms for elaborated feedback and the role of social interaction in feedback effects. These results may have important implications for successful real-world learning and communication, and related pedagogical applications in educational settings.
Savings in Human Force Field Learning Supported by Feedback Adaptation

Abstract Savings have been described as the ability of healthy humans to relearn a previously acquired motor skill faster than the first time, which in the context of motor adaptation suggests that the learning rate in the brain could be adjusted when a perturbation is recognized. Alternatively, it has been argued that apparent savings were the consequence of a distinct process that instead of reflecting a change in the learning rate, revealed an explicit re-aiming strategy. Based on recent evidence that feedback adaptation may be central to both planning and control, we hypothesized that this component could genuinely accelerate relearning in human adaptation to force fields (FFs) during reaching. Consistent with our hypothesis, we observed that on re-exposure to a previously learned FF, the very first movement performed by healthy volunteers in the relearning context was better adapted to the external disturbance, and this occurred without any anticipation or cognitive strategy because the relearning session was started unexpectedly. We conclude that feedback adaptation is a medium by which the nervous system can genuinely accelerate learning across movements.
Natural Sulfur Emissions into the Atmosphere

Natural atmospheric sulfur emission rates are reviewed for important components of the sulfur cycle. A summary of emission estimates is provided for vegetation, coastal and wetland ecosystems, inland soils, and oceanic environments. A brief discussion of sea salt sulfate, aeolian sources, volcanic activity, and biomass burning is also included. The emissions from plants and inland soils, may play a significant role in global sulfur cycling and very little work has been reported covering this subject. Large uncertainties continue to exist in the identity and the emission rates of the sulfur compounds and thus it is not reasonable to extrapolate these data in an attempt to balance the global sulfur cycle. Moreover interactions of enhanced anthropogenic activity with various components of the sulfur cycle need to be considered.
Blockchain-Enabled Public Key Encryption with Multi-Keyword Search in Cloud Computing

-e emergence of the cloud storage has brought great convenience to people’s life. Many individuals and enterprises have delivered a large amount of data to the third-party server for storage. -us, the privacy protection of data retrieved by the user needs to be guaranteed. Searchable encryption technology for the cloud environment is adopted to ensure that the user information is secure with retrieving data. However, most schemes only support single-keyword search and do not support file updates, which limit the flexibility of the scheme. To eliminate these problems, we propose a blockchain-enabled public key encryption scheme with multi-keyword search (BPKEMS), and our scheme supports file updates. In addition, smart contract is used to ensure the fairness of transactions between data owner and user without introducing a third party. At the data storage stage, our scheme realizes the verifiability by numbering the files, which ensures that the ciphertext received by the user is complete. In terms of security and performance, our scheme is secure against inside keyword guessing attacks (KGAs) and has better computation overhead than other related schemes.
PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

-e collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies εtotal-PLDP. -en, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme.
Recognition of Disease Genetic Information from Unstructured Text Data Based on BiLSTM-CRF for Molecular Mechanisms

Disease relevant entities are an important task in mining unstructured text data from the biomedical literature for achieving biomedical knowledge. Autism spectrum disorder (ASD) is a disease related to a neurological and developmental disorder characterized by deficits in communication and social interaction and by repetitive behaviour. However, this kind of disease remains unclear to date. In this study, it identifies entities associated with disease using the machine learning of a computational way from text data collection for molecular mechanisms related to ASD. Entities related to disease are extracted from the biomedical literature related to autism by using deep learning with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) model. Compared other previous works, the approach is promising for identifying entities related to disease. e proposed approach including five types of molecular entities is evaluated by GENIA corpus to obtain an F-score of 76.81%. e work has extracted 9146 proteins, 145 RNAs, 7680 DNAs, 1058 cell-types, and 981 cell-lines from the autism biomedical literature after removing repeated molecular entities. Finally, we perform GO and KEGG analyses of the test dataset. is study could serve as a reference for further studies on the etiology of disease on the basis of molecular mechanisms and provide a way to explore disease genetic information.
A Camouflage Text-Based Password Approach for Mobile Devices against Shoulder-Surfing Attack

Authentication in mobile devices is inherently vulnerable to attacks and has the weakness of being susceptible to shoulder-surfing attack. Shoulder-surfing attack is a type of attack that uses direct observation techniques such as looking over someone’s shoulder to get information. )is paper aims to introduce a novel way of concealing the password within a contingent of randomly selected entries. In particular, the traditional password concept where what you input is what you get is redefined by proposing the camouflage characters approach. Based on this approach, three defensive techniques are introduced for mobile devices. By using an Android platform, the introduced techniques are implemented. Experimental studies are conducted in order to evaluate both security and usability perspectives. )e empirical results showed that the proposed approach is reasonably resistant against shoulder-surfing attacks and usable for participants. Moreover, it is possible to choose very short passwords, while insuring that the password remains hidden amongst a large number of key presses. Based on the achieved results, the proposed approach is recommended to be a new avenue in the field of security to produce very simple and yet very complicated passwords, to be observed by the attacker, at the same time.
Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms

Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), andMahalanobis distance-based approach (MDB)—were used for detection. ,e best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. ,e experimental results showed that the designed features are more effective than the currently used ones. ,e results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.
Practical Multiauthority Attribute-Based Access Control for Edge-Cloud-Aided Internet of Things

With the assistance of edge computing which reduces the heavy burden of the cloud center server by using the network edge servers, the Internet of +ings (IoTs) architectures enable low latency for real-time devices and applications. However, there still exist security challenges on data access control for the IoT. Multiauthority attribute-based encryption (MA-ABE) is a promising technique to achieve access control over encrypted data in cross-domain applications. Based on the characteristics and technical requirements of the IoT, we propose an efficient fine-grained revocable large universe multiauthority access control scheme. In the proposed scheme, the most expensive encryption operations have been executed in the user’s initialization phase by adding a reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Massive decryption operations are outsourced to the near-edge servers for reducing the computation overhead of decryption. An efficient revocation mechanism is designed to change users’ access privileges dynamically. Moreover, the scheme supports ciphertext verification. Only valid ciphertext can be stored and transmitted, which saves system resources. With the help of the chameleon hash function, the proposed scheme is proven CCA2-secure under the q-DPBDHE2 assumption. +e performance analysis results indicate that the proposed scheme is efficient and suitable in edge computing for the IoT.
Preparation of a biomimetic superhydrophobic ZnO coating on an X90 pipeline steel surface

Inspired by the self-cleaning property of the lotus leaf surface, a novel biomimetic coating with superhydrophobicity is introduced in this article. Moreover, the morphologies, crystal structures and chemical composition of the specimens were investigated using field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) and Fourier transform infrared spectroscopy (FTIR). After electrodeposition of the Zn coating, a ZnO coating with micro and nano structures (perpendicular to the substrate surface) was obtained on an X90 pipeline steel surface after it was processed with the appropriate hydrothermal treatment time. The water contact angle (CA) of the ZnO coating, which had upright micro and nano structures, was about 137.85° without chemical modification. After further modification with a low-surface-energy material, the ZnO coating presented superhydrophobicity with the water CA of 157.59° and the sliding angle (SA) was less than 10°. Therefore, this study changed the traditional application of the deposited coating that was mostly used as a protective layer and might open up a new application field for deposited metal coatings to fabricate microstructures with functional applications, including superhydrophobic coatings.