Miracle-dz/UniFAR-FTUs huggingface dataset:
Nano-titanium dioxide exposure and autophagy: a systematic review and meta-analysis

Abstract Nano-titanium dioxide (nano-TiO2) is widely used in many aspects of human life. However, there are still inconsistent conclusions about the effects of nano-TiO2 exposure on autophagy. Therefore, a systematic review and meta-analysis were used to reveal whether nano-TiO2 induces dysregulation of autophagy. The results showed that LC3 II, Beclin-1 protein, and LC3 II/LC3 I protein ratio were significantly increased, but p62 protein was insignificantly changed. Subgroup analyses showed that p62 and Beclin-1 protein were increased significantly when the treatment time of nano-TiO2 was >24 h. Nano-TiO2 can induce dysregulation of autophagy and cause stagnation of autophagy after prolonged exposure.
Mitogenomic phylogeny of Typhlocybinae (Hemiptera: Cicadellidae) reveals homoplasy in tribal diagnostic morphological traits

Abstract The subfamily Typhlocybinae is a ubiquitous, highly diverse group of mostly tiny, delicate leafhoppers. The tribal classification has long been controversial and phylogenetic methods have only recently begun to test the phylogenetic status and relationships of tribes. To shed light on the evolution of Typhlocybinae, we performed phylogenetic analyses based on 28 newly sequenced and 19 previously sequenced mitochondrial genomes representing all currently recognized tribes. The results support the monophyly of the subfamily and its sister‐group relationship to Mileewinae. The tribe Zyginellini is polyphyletic with some included genera derived independently within Typhlocybini. Ancestral character state reconstruction suggests that some morphological characters traditionally considered important for diagnosing tribes (presence/absence of ocelli, development of hind wing submarginal vein) are homoplastic. Divergence time estimates indicate that the subfamily arose during the Middle Cretaceous and that the extant tribes arose during the Late Cretaceous. Phylogenetic results support establishment of a new genus, Subtilissimia Yan & Yang gen. nov., with two new species, Subtilissimia fulva Yan & Yang sp. nov. and Subtilissimia pellicula Yan & Yang sp. nov.; but indicate that two previously recognized species of Farynala distinguished only by the direction of curvature of the processes of the aedeagus are synonyms, that is, Farynala dextra Yan & Yang, 2017 equals Farynala sinistra Yan & Yang, 2017 syn. nov. A key to tribes of Typhlocybinae is provided.
Some Clues about Enzymes from Psychrophilic Microorganisms

Enzymes purified from psychrophilic microorganisms prove to be efficient catalysts at low temperatures and possess a great potential for biotechnological applications. The low-temperature catalytic activity has to come from specific structural fluctuations involving the active site region, however, the relationship between protein conformational stability and enzymatic activity is subtle. We provide a survey of the thermodynamic stability of globular proteins and their rationalization grounded in a theoretical approach devised by one of us. Furthermore, we provide a link between marginal conformational stability and protein flexibility grounded in the harmonic approximation of the vibrational degrees of freedom, emphasizing the occurrence of long-wavelength and excited vibrations in all globular proteins. Finally, we offer a close view of three enzymes: chloride-dependent α-amylase, citrate synthase, and β-galactosidase.
Abbreviated MDS-UPDRS for Remote Monitoring in PD Identified Using Exhaustive Computational Search

Background The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) comprises 50 items, consisting of historical questions and motor ratings, typically taking around 30 minutes to complete. We sought to identify an abbreviated version that could facilitate use in clinical practice or used remotely via telemedicine. Methods To create an 8-item version we conducted an “exhaustive search” of all possible subsets. We measured explained variance in comparison to the 50-item version using linear regression, with the “optimal” subset maximising this while also meeting remote assessment practicality constraints. The subset was identified using a dataset collected by the Parkinson's Progression Markers Initiative and validated using an MDS Non-Motor Symptoms Scale validation study dataset. Results The optimal remote version comprised items from all parts of the MDS-UPDRS and was found to act as an unbiased estimator of the total 50-item score. This version had an explained variance score of 0.844 and was highly correlated with the total MDS-UPDRS score (Pearson's r = 0.919, p-value <0.0001). Another subset that maximised explained variance score without adhering to remote assessment practicality constraints provided similar results. Conclusion This result demonstrates that the total scores of an abbreviated form identified by computational statistics had high agreement with the MDS-UPDRS total score. Whilst it cannot capture the richness of information of the full MDS-UPDRS, it can be used to create a total score where practicality limits the application of the full MDS-UPDRS, such as remote monitoring. Further validation will be required, including in specific subgroups and advanced disease stages, and full validation of clinimetric properties.
Jerry Kiernan Ramakrishnan Srikant Yirong Xu

The Hippocratic Oath has guided the conduct of physicians for centuries. Inspired by its tenet of preserving privacy, we argue that future database systems must include responsibility for the privacy of data they manage as a founding tenet. We enunciate the key privacy principles for such Hippocratic database systems. We propose a strawman design for Hippocratic databases, identify the technical challenges and problems in designing such databases, and suggest some approaches that may lead to solutions. Our hope is that this paper will serve to catalyze a fruitful and exciting direction for future database research.
Perceptions of Barriers to Motherhood: Female STEM PhD Students’ Changing Family Plans

Despite recent pronatalist policies in Hungary, the country has not boosted birth rates at the expected rate. Higher educated women still delay the transition to first birth, a smaller proportion of planned children are born than in Western European countries, and the level of childlessness has also been increasing. As a post‐socialist legacy, prevailing traditional family and gender norms strongly constrain the reconciliation of work and family roles, which can prevent women from realizing their childbearing intentions or drive them to live a childfree life. Qualitative studies about how the fertility decisions of women are formed are scarce, particularly in relation to male‐dominated high‐skilled professions, where the realization of family plans can be especially challenging. The present article explores the barriers to motherhood among female engineers. Results of 27 semi‐structured interviews with mainly childless female PhD students in 2014–2015 show that the women were subject to strong social expectations that negatively influenced their fertility plans. On the family side, these involve becoming a mother and being responsible for child care and household chores; on the work side, challenges include the knowledge‐intensiveness of jobs and a male career model that hardly tolerates the role of motherhood. As a result, the respondents had further delayed childbearing, forecast reconsidering family plans after first childbirth, and in one case, opted for voluntary childlessness. Women also reflected on how their fertility is at stake due to their postponed motherhood and the cumulative effects of hazardous laboratory work. Several intervention points are suggested to stakeholders.
Cuticular hydrocarbons as caste-linked cues in Neotropical swarm-founding wasps

Wasps (Vespidae) are important organisms to understand the evolution of social behaviour. Wasps show different levels of sociality, which includes solitary to highly eusocial organisms. In social insect species, queens and workers differ in physiology and morphology. The Neotropical swarm-founding wasps (Epiponini) show a variety of caste syndromes. In this clade, the caste-flexibility is a unique characteristic, in which workers can become queens and swarm to start a new nest. The investigation of the caste system comparing several Epiponini species show a clear-cut morphological distinction between queens and workers, with a morphological continuum between queens and workers. However, whether cuticular hydrocarbons (CHCs) are used as cues for caste recognition in swarm-founding wasps is still unknown. We studied whether CHCs may display caste-linked differences in eleven species of Epiponini wasps and if CHCs differences would follow morphological patterns. Our results suggest that queens and workers of Epiponini wasps are chemically different from each other at two levels, qualitatively and quantitatively, or merely quantitatively. This variation seems to exist regardless of their morphological traits and may be useful to help us understanding how chemical communication evolved differently in these species.
Advanced vibration control of large structures with distributed multi-actuator system and partial state information

In this paper, the design and performance of partial-state controllers with incomplete multi-actuation systems for the seismic protection of tall buildings is investigated. The proposed approach considers a partially instrumented multi-story building with an incomplete system of interstory force-actuation devices implemented at selected levels of the building, and an associated set of collocated sensors that measure the interstory drifts and velocities corresponding to the instrumented stories. The main elements of the proposed controller design methodology are presented by means of a twenty-story building with two different actuation schemes. For these control configurations, partial-state controllers are designed following a static output-feedback H8 controller design approach, and the corresponding frequency and time responses are investigated. The obtained results clearly indicate that the proposed partial-state controllers are effective in mitigating the building seismic response. They also show up that a suitable distribution of the instrumented stories is a relevant factor in the control system design.
AI Privacy Opinions between US and Chinese People

ABSTRACT This paper comparatively studies people’s opinions on AI privacy between the US and China. Based on data collected from Twitter and Weibo, we perform text clustering and content analysis to classify opinion types and analyze the symptoms of opinion polarization and drivers by regression analysis. Results show that US people express more concerns about AI privacy, focusing more on privacy disclosure by AI applications. In contrast, Chinese people are more optimistic about AI’s role in promoting privacy protection. Security, economics, and application are driving factors leading to the polarization of the US people, while technologies and algorithms influence the polarization of Chinese people. This study offers methodological guidance for examining the relationship between AI and user privacy. It also guides the government agencies and other practitioners in developing the policies on AI regulations for privacy protection.
Power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal

Abstract With the urbanization in recent years, the power line interference noise in electromagnetic signal is increasing day by day, and has gradually become an unavoidable component of noises in magnetotelluric signal detection. Therefore, a kind of power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal is put forward in this paper. The method first uses wavelet decomposition to change single-channel signal into multi-channel signal, and then takes advantage of blind source separation principle of independent component analysis to eliminate power line interference noise. There is no need to choose the layer number of wavelet decomposition and the wavelet base of wavelet decomposition according to the observed signal. On the treatment effect, it is better than the previous power line interference removal method based on independent component analysis. Through the de-noising processing to actual magnetotelluric measuring data, it is shown that this method makes both the apparent resistivity curve near 50 Hz and the phase curve near 50 Hz become smoother and steadier than before processing, i.e., it effectively eliminates the power line interference noise.
A Compact Noise Covariance Matrix Model for MVDR Beamforming

Acoustic beamforming is routinely used to improve the SNR of the received signal in applications such as hearing aids, robot audition, augmented reality, teleconferencing, source localisation and source tracking. The beamformer can be made adaptive by using an estimate of the time-varying noise covariance matrix in the spectral domain to determine an optimised beam pattern in each frequency bin that is specific to the acoustic environment and that can respond to temporal changes in it. However, robust estimation of the noise covariance matrix remains a challenging task especially in non-stationary acoustic environments. This paper presents a compact model of the signal covariance matrix that is defined by a small number of parameters whose values can be reliably estimated. The model leads to a robust estimate of the noise covariance matrix which can, in turn, be used to construct a beamformer. The performance of beamformers designed using this approach is evaluated for a spherical microphone array under a range of conditions using both simulated and measured room impulse responses. The proposed approach demonstrates consistent gains in intelligibility and perceptual quality metrics compared to the static and adaptive beamformers used as baselines.
Fast and Low-Drift Visual Odometry With Improved RANSAC-Based Outlier Removal Scheme for Intelligent Vehicles

Visual odometry estimates the ego-motion of a vehicle using only the input of a single or multiple cameras mounted on the vehicle. This paper focuses on the research of the stereo visual odometry system of intelligent vehicles, and discusses how to improve the robustness, accuracy and efficiency. A new robust estimation algorithm, Locally Optimized Progressive Sample Consensus algorithm, is proposed. Compared with the RANSAC algorithm, it can not only improve the accuracy of model estimation, but also can find more inliers to terminate the iteration process in advance, thereby speeding up the algorithm. A decoupling-based motion estimation algorithm is proposed. Monocular method is used to estimate the rotation parameters, which eliminates the influence of mismatching between left and right frames on rotation estimation. Moreover, when estimating the translational motion, the decoupling method makes the normalized re-projection error criterion better distinguish between inliers and outliers. The performance of the method is evaluated on the KITTI benchmark dataset by comparing it with the existing visual odometry systems. The experimental results show that the proposed technique has a high accuracy and efficiency.
Study on the effect of acetyl L-carnitine on cognitive and cellular disorders resulting from lipopolysaccharide-induced inflammation in male rats: emphasis on AKT / mTOR cell pathways and inflammation in the hippocampus

Background: The brain in exposure to irritating factors may lead to production of some chemical factors and proinflammatory cytokines and neuroinflammation. Over time, the chronic neuroinflammation will cause some cognitive disorders and neurodegenerative diseases. The present study studied the effect of acetyl L carnitine on cognitive and cellular disorders derived from lipopolysaccharide (LPS)induced inflammation, with emphasis on cellular pathways in the hippocampus of male rat. Materials and methods: In this study, we used LPS with dose of 500 μg/kg of animal’s weight regarding to the previous studies. Acetyl L carnitine was prescribed with three doses of 30, 60 and 100 mg/kg among rats received LPS. Passive avoidance behavior was used to assess learning and memory, and biochemical and molecular methods were used for mTOR, p-AKT, and inflammatory factors. Results: Acetyl L carnitine improved passive avoidance memory as well as working memory in a dosedependent manner. It also prevented the death of hippocampal neurons by inhibiting neuroinflammation by reducing the expression of IL-6 and IL-1B and increasing the levels of mTOR and AKT-p. Conclusion: The present study showed that acetyl L-carnitine can reduce the destructive effects of lipopolysaccharide, which appears to be through increased levels of mTOR and AKT.
Racial and ethnic differences in pediatric pulmonary hypertension: An analysis of the Pediatric Pulmonary Hypertension Network Registry (PPHNet)

Objective— To investigate racial and ethnic differences in pulmonary hypertension (PH) subtypes and survival differences in the pediatric population. Study design— A retrospective analysis of a cohort of PH patients (aged ≤ 18 years) enrolled in the Pediatric Pulmonary Hypertension Network (PPHNet) registry between years 2014 and 2018, comprising subjects at eight Pediatric PH Centers throughout North America (n=1,417). Results— Among children diagnosed after the neonatal period, pulmonary arterial hypertension (PAH) was more prevalent among Asians (OR 1.83; 95% CI 1.21 – 2.79; p=0.0045), lung disease-associated PH among Blacks (OR 2.09; 95% CI
Differentiation in Differentiation Method and Neural Network to Investigate Different Criteria of Economic Productivity

Economic productivity is one of the most important concepts in economics. Increasing economic productivity will boost a society. This study intends to compare the impact of different criteria on the economic productivity of Iranian factories with differential methods and neural networks. The model estimation results using the difference-in-difference method show that exports have a significant positive effect on productivity growth. The model estimation results using the neural network method show that export has a significant positive effect on productivity growth. The presentation of export development variables in two methods of difference in difference and neural networks showed that the level of productivity could be increased well according to the predictability of the neural network.
EEG Activation Does Not Differ in Simple and Complex Episodes of Disorders of Arousal: A Spectral Analysis Study

Purpose Disorders of arousal (DoA) are characterized by incomplete awakening from NREM sleep, with the admixture of both deep sleep and wake EEG activity. Previous observations suggested that changes in EEG activity could be detected in the seconds preceding DoA episodes. The aims of this work were to characterize the topography of EEG spectral changes prior to DoA episodes and to investigate whether or not behavioral complexity could be predicted by changes in EEG immediately preceding behavioral onsets. Patients and Methods We collected 103 consecutive video-polysomnographic recordings of 53 DoA adult patients and classified all episodes as simple, rising and complex arousal movements. For each episode, a 5-second window preceding its motor onset (“pre-event”) and a 60-second window from 2 to 3 minutes before the episodes (“baseline”) were compared. Subsequently, a between-group comparison was performed for the pre-event of simpler versus the more complex episodes. Results Spectral analysis over 325 DoA episodes showed an absolute significant increase prior to DoA episodes in all frequency bands excluding sigma, which displayed the opposite effect. In normalized maps, the increase was relatively higher over the central/anterior areas for both slow and fast frequency bands. No significant differences emerged from the comparison between simpler and more complex episodes. Conclusion Taken together, these results show that deep sleep and wake-like EEG rhythms coexist over overlapping areas before DoA episodes, suggesting an alteration of local sleep mechanisms. Episodes of different complexity are preceded by a similar EEG activation, implying that they possibly share a similar pathophysiology.
Is it in Colloquial Singapore English

Colloquial Singapore English (CSE, commonly known as Singlish) is a linguistic variety used in Singapore, a Southeast Asian nation home to three major ethnic groups: the Chinese (74.35% of the citizen and permanent resident population), the Malays (13.43%), and the Indians (9%) (Singapore Department of Statistics, 2019). It is one of the best known post-colonial varieties of English and has been documented since the emergence of the field of world Englishes (e.g., Greenbaum, 1988; Richards & Tay, 1977). Linguistically, the grammar and lexicon of CSE are systematically imported from other non-English languages used in the island nation (Leimgruber, 2011). From a creolist perspective, it can be viewed as an English-lexifier creole that contains influences from Sinitic languages such as Hokkien, Cantonese and Mandarin, as well as Malay, Tamil and other varieties in the Singapore language ecology (McWhorter, 2007; Platt, 1975). Several distinct features across various levels of language have been investigated in CSE, including phonetics (Starr & Balasubramaniam, 2019), morphosyntax (Bao, 2010; Bao & Wee, 1999), semantics (Hiramoto & Sato, 2012), and pragmatics (Hiramoto, 2012; Leimgruber, 2016; Lim, 2007).
Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning

The financial risk of investing in Bitcoin is increasing, and everyone partic-ipating in the transaction is aware of it. The rise and fall of bitcoin’s value is difficult to predict, and the system is fraught with uncertainty. As a result, this study proposed to use the «Deep learning» technique for predicting fi-nancial risk associated with bitcoin investment, that is linked to its «weighted price» on the bitcoin market’s volatility. The dataset used included Bitcoin historical data, which was acquired «at one-minute intervals» from selected exchanges of January 2012 through December 2020. The deep learning lin-ear-SVM-based technique was used to obtain an advantage in handling the high-dimensional challenges related with bitcoin-based transaction transac-tions large data volume. Four variables («High», «Low», «Close», and «Volume (BTC)». ) are conceptualized to predict weighted price, in order to indi-cate if there is a propensity of financial risk over the effect of their interaction. The results of the experimental investigation show that the fi-nancial risk associated with bitcoin investing is accurately predicted. This has helped to discover engagements and disengagements with doubts linked with bitcoin investment transactions, resulting in increased confidence and trust in the system as well as the elimination of financial risk. Our model had a significantly greater prediction accuracy, demonstrating the utility of deep learning systems in detecting financial problems related to digital currency.
Epilepsy Detection Based on Riemann Potato in Noisy Environment

Epilepsy detection based on electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. The existing feature extraction method not only consumes a lot of time but also leads to epilepsy information loss because of nonideal denoising. Therefore, the paper proposes to use noisy EEG signals to detect epilepsy. The original EEG signal is divided into normal signal and abnormal signal by Riemann potato, and the epilepsy detection model is established based on the normal signal and abnormal signal, respectively. Finally, the 2 detection results are combined as a final result. The detection performance of 94.84%, 83.03% sensitivity, and 97.67% specificity is achieved. The experimental results show that the original noisy signal which is separated by the Riemann potato can have high epilepsy detection performance.