Miracle-dz/UniFAR-FTUs huggingface dataset:
HIF-1α Causes LCMT1/PP2A Deficiency and Mediates Tau Hyperphosphorylation and Cognitive Dysfunction during Chronic Hypoxia

Chronic hypoxia is a risk factor for Alzheimer’s disease (AD), and the neurofibrillary tangle (NFT) formed by hyperphosphorylated tau is one of the two major pathological changes in AD. However, the effect of chronic hypoxia on tau phosphorylation and its mechanism remains unclear. In this study, we investigated the role of HIF-1α (the functional subunit of hypoxia-inducible factor 1) in tau pathology. It was found that in Sprague-Dawley (SD) rats, global hypoxia (10% O2, 6 h per day) for one month induced cognitive impairments. Meanwhile it induced HIF-1α increase, tau hyperphosphorylation, and protein phosphatase 2A (PP2A) deficiency with leucine carboxyl methyltransferase 1(LCMT1, increasing PP2A activity) decrease in the rats’ hippocampus. The results were replicated by hypoxic treatment in primary hippocampal neurons and C6/tau cells (rat C6 glioma cells stably expressing human full-length tau441). Conversely, HIF-1α silencing impeded the changes induced by hypoxia, both in primary neurons and SD rats. The result of dual luciferase assay proved that HIF-1α acted as a transcription factor of LCMT1. Unexpectedly, HIF-1α decreased the protein level of LCMT1. Further study uncovered that both overexpression of HIF-1α and hypoxia treatment resulted in a sizable degradation of LCMT1 via the autophagy–-lysosomal pathway. Together, our data strongly indicated that chronic hypoxia upregulates HIF-1α, which obviously accelerated LCMT1 degradation, thus counteracting its transcriptional expression. The increase in HIF-1α decreases PP2A activity, finally resulting in tau hyperphosphorylation and cognitive dysfunction. Lowering HIF-1α in chronic hypoxia conditions may be useful in AD prevention.
Hyperdimensional Hybrid Learning on End-Edge-Cloud Networks

In this paper, we present Hyperdimensional Hybrid Learning (HDHL), which combines model-free and model-based Reinforcement Learning, to effectively reduce the computational cost and environment interaction for optimizing an intelligent cloud service. We first show that Hyperdimensional Q-Learning (QHD), the state-of-the-art Hyperdimensional Computing value-based Reinforcement Learning algorithm, is computationally faster than the Deep Q-Network (DQN) for this task. In addition, we demonstrate how HDHL reduces the number of environment interactions by 4.8× to learn the near optimal configuration. Our evaluation shows that HDHL is computationally more efficient than both Q-Learning algorithms, with the total time being reduced by 21.0× compared to DQN and 16.5× compared to QHD.
Zinc and Zinc Transporters in Dermatology

Zinc is an important trace mineral in the human body and a daily intake of zinc is required to maintain a healthy status. Over the past decades, zinc has been used in formulating topical and systemic therapies for various skin disorders owing to its wound healing and antimicrobial properties. Zinc transporters play a major role in maintaining the integrity of the integumentary system by controlling zinc homeostasis within dermal layers. Mutations and abnormal function of zinc-transporting proteins can lead to disease development, such as spondylocheirodysplastic Ehlers–Danlos syndrome (SCD-EDS) and acrodermatitis enteropathica (AE) which can be fatal if left untreated. This review discusses the layers of the skin, the importance of zinc and zinc transporters in each layer, and the various skin disorders caused by zinc deficiency, in addition to zinc-containing compounds used for treating different skin disorders and skin protection.
Convergent Genomic Signatures of High-Altitude Adaptation among Six Independently Evolved Mammals

Simple Summary In this study, we conducted a comparative genomics approach to explore the convergent evolution of high-altitude adaptation mechanisms in six independently evolved mammals belonging to four orders. The results showed that the convergent evolution of the six species was mainly reflected at the level of rapidly evolving genes, and the functions of these rapidly evolving genes were mainly related to hypoxia response and DNA damage repair. In addition, we found that the convergence of the gene family contraction of high-altitude species is much greater than that of expansion, revealing a possible pattern of species in adapting to high-altitude. Furthermore, we detected a positive selection signature in four genes related to hypoxia response and ultraviolet radiation damage in these six species (FYCO1, ERBIN, SCAMP1 and CXCL10). In general, our study reveals that hypoxia response and UV-radiation might play an important role in the adaptation of independently evolved species to a high-altitude environment, providing a basic perspective for further exploring the high-altitude adaptation mechanism of different related species in the future. Abstract The species living in the Qinghai-Tibet Plateau provide an excellent model system for studying the relationship between molecular convergent evolution and adaptation. Distant species experiencing the same selection pressure (i.e., hypoxia, low temperature and strong ultraviolet radiation) are likely to evolve similar genetic adaptations independently. Here, we performed comparative genomics studies on six independently evolved high-altitude species. The results also showed that the convergent evolution of the six species was mainly reflected at the level of rapidly evolving genes, and the functions of these rapidly evolving genes were mainly related to hypoxia response and DNA damage repair. In addition, we found that high-altitude species had more gene family changes than their low-altitude relatives, except for the order Lagomorpha. The results also show that the convergence of the gene family contraction of high-altitude species is much greater than that of expansion, revealing a possible pattern of species in adapting to high-altitude. Furthermore, we detected a positive selection signature in four genes related to hypoxia response and ultraviolet radiation damage in these six species (FYCO1, ERBIN, SCAMP1 and CXCL10). Our study reveals that hypoxia response might play an important role in the adaptation of independently evolved species to a high-altitude environment, providing a basic perspective for further exploring the high-altitude adaptation mechanism of different related species in the future.
A Study of STT-RAM-based In-Memory Computing Across the Memory Hierarchy

In-memory computing (or processing in memory) is a promising approach to reducing the data transfer bottleneck in computer systems by bringing computation closer to the memory. Prior work proposed using Spin-Transfer Torque RAM (STT-RAM) for in-memory computing to leverage STT-RAM’s numerous advantages, including non-volatility, near-zero leakage power, high area density, better endurance than other non-volatile memory technologies and demonstrated commercial viability. This paper explores, for the first time, the tradeoffs of STT-RAM in-memory computing across the memory hierarchy, including the main memory and cache hierarchy. We explore a system model in which processing in memory (PiM) occurs in non-volatile STT-RAM, whereas processing in cache (PiC) occurs in relaxed retention (volatile) STT-RAM. In relaxed retention STT-RAM caches, the retention time—the duration for which the STT-RAM cell retains data—is significantly reduced to mitigate STT-RAM’s intrinsic write latency and write energy overheads. Importantly, we also analyze the tradeoffs and overheads of data movement for PiC vs. write overheads for PiM for STT-RAMs. The analysis is performed in the context of different kinds of workloads to explore the impacts of various workload characteristics (e.g., temporal locality, computational intensity, CPU-dependent workloads with limited instruction-level parallelism) on PiC/PiM tradeoffs. Using these workloads, we also evaluate computing in STT-RAM vs. SRAM at different levels of the cache hierarchy. Our analysis reveals that STT-RAM-based PiC has promising advantages over PiM in certain workload contexts and offers solutions to some of the challenges that arise in implementing PiC-enabled systems.
Fluorescence lifetime imaging of skin cancer

Fluorescence intensity imaging and fluorescence lifetime imaging microscopy (FLIM) using two photon microscopy (TPM) have been used to study tissue autofluorescence in ex vivo skin cancer samples. A commercially available system (DermaInspect®) was modified to collect fluorescence intensity and lifetimes in two spectral channels using time correlated single photon counting and depth-resolved steady state measurements of the fluorescence emission spectrum. Uniquely, image segmentation has been used to allow fluorescence lifetimes to be calculated for each cell. An analysis of lifetime values obtained from a range of pigmented and non-pigmented lesions will be presented.
Bifurcations caused by grazing incidence in many degrees of freedom impact oscillators

The transition from stable periodic non-impacting motion to impacting motion is analysed for a mechanical oscillator. By using local methods, it is shown that a grazing impact leads to an almost one-dimensional stretching in state space. A condition can then be formulated, such that a grazing trajectory will be stable if the condition is fulfilled. If this is the case, the bifurcation will be continuous and the motion after the bifurcation can be understood by a one-dimensional mapping. This mapping is known to exhibit chaotic solutions as well as arbitrary long stable cycles, depending on parameters.
Optimal management of two parallel stacks in two-level memory

We consider the problem to manage two stacks in two-level memory. It is assumed that the tops of the stacks grow towards one another in the fast memory to which several processors are allowed to have simultaneous access, and the size of the stacks exceeds that of the fast memory. The fast memory stores the tops of the stacks only, while the remaining parts are stored in the second level memory. If the top of one of the stacks becomes empty or the stacks fill all the fast memory, that is, the stack overflow occurs, then a swapping to the second level memory is performed in such a way that each time a certain state of the memory is set and the next step starts. We study how to choose such a state of the memory in order to maximise the average time before the next swapping to the second level memory.
Can Variability of Brain Activity serve as a Metric for Assessing Human Performance during UAS Dual-Task Training

Functional near infrared spectroscopy (fNIRS) is an emerging mobile neuroimaging technology that monitors brain function through measures of cerebral hemodynamics, i.e., concentration of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). fNIRS measures are strongly associated with changes in cognitive effort and offer the ability to assess learning of complex tasks in realistic settings. However, much of this research has focused on investigating the effect of task load changes and training on average brain activity. Increasing empirical evidence has demonstrated that variability of brain activity changes can provide further understanding of cognitive effort and workload. However, there is limited research on evaluation of variability as a feature in fNIRS-related human factors studies, especially within the context of dual-task training. Therefore, this study aimed on evaluating variability of fNIRS measures during simulation-based training of a dual Unmanned Aerial System (UAS) operator task. fNIRS data was collected from 13 young adults who participated in five sessions of testing consisting of skill acquisition (three easy) and transfer (two hard) phases. Standard deviation (SD) and root mean squared successive differences (rMSSD) were used to represent variability. Firstly, variability increased within easy and hard sessions and decreased while transitioning from easy to hard sessions. Secondly, these changes were not only larger in magnitude, but were exactly opposite of those reported from average measures. Thirdly, variability was largest in left dorsolateral PFC (LDLPFC) and right anterior medial PFC (RAMPFC), which are the regions that are expected to have UAS task-evoked activity. Lastly, variability features did not differ between LDLPFC and RAMPFC, even though average did. In conclusion, these findings suggest that variability of fNIRS measures are not only sensitive to skill acquisition, practice, task load, transfer, and subject-specific differences in learning during UAS task training but offer complimentary information to that of typically reported average fNIRS measures.
Unconscious Other’s Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others’ Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI

Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) caused by viewing AI emotion estimation information. Therefore, this study verifies RQ “Do people have a cognitive bias in which impressions of others (i.e., how to see and feel about others) are changed by viewing biased AI’s emotion estimation information? If it exists, can impression manipulation methods that intentionally use this cognitive bias be realized? ” The proposed method for verification makes the emotion estimation system biased so as to estimate emotion more positively/negatively than AI without bias. A prototype system was implemented. Evaluation using video showed that the presentation of biased emotion estimation information causes a phenomenon that quickly and unconsciously changes the way people see and feel others’ impressions, which supported the RQ. Specifically, viewing information that estimated others’ emotions more positively/negatively caused the phenomenon in which the user’s self-judgment was overridden and others’ impressions of emotions, words, and actions were perceived more positively/negatively. The existence of this phenomenon and method indicates that biased emotion estimation AI has the potential to both cause adverse effects on people and support people for good purposes through the manipulation of their impressions. This study provides helpful insights for the design and use of emotion estimation AI considering cognitive biases.
Characterization and heritability of fruit from olive cultivars in the south of Brazil

: The olive tree ( Olea europaea L.) is a species of great importance in history and economic, with large phenotypic variability, represented through a wide range of cultivars spread throughout the world. There are several studies on the characterization of a large number of cultivars of the species. However, there is a need to uncover how these phenotypic traits are heritable, and how they can change when environmental conditions vary. For this reason, the objective here was to estimate the heritability coefficients for fruit characters in six commercial cultivars, analyzed under conditions in the state of Rio Grande do Sul, Brazil. The study was carried out in a single year, based on the analysis of fruit and oil traits on the cultivars Arbequina, Arbosana, Frantoio, Koroneiki, Manzanilla de Sevilla, and Picual, under environmental conditions in the state of Rio Grande do Sul. Phenotypic plasticity and broad-sense heritability were estimated in both inter and intra-cultivar comparisons. From the analyses of variance and heritability, it was observed that some cultivars such as Koroneiki or Picual had their characters mainly under genetic control, while Arbequina was more influenced by genotype-environment interaction. The information derived from this work can help guide the selection of cultivars that are best adapted under the local environments of the southern region of Brazil.
50‐kilobar gasketed diamond anvil cell for single‐crystal x‐ray diffractometer use with the crystal structure of Sb up to 26 kilobars as a test problem

A single‐crystal gasketed diamond anvil cell of a new design for use on a diffractometer is described. It has achieved hydrostatic pressures of 50 kilobars as determined from the ruby fluorescence shift. The design combines several advantages. Nearly half the area of the Ewald sphere is available without having to remount the crystal. For reflecting planes nearly parallel to the diamond anvil faces, all reflections in the angular range ∼4°⩽ϑ⩽85° can be observed. Alignment of the diamonds, as well as loading and alignment of the sample crystals, is very simple and quick. The diffractometer geometry allows accurate measurements of weak reflections as well as accurate determinations of the x‐ray absorption corrections for the cell. The materials of construction are nontoxic except for one diamond mount, a small beryllium cylinder situated so that it is never touched, scraped, or rubbed. As a test problem, the z parameter of antimony has been found to increase linearly from z=0.23357 at 1 bar to z =0.2380±0.0.
Wilcoxon adaptive algorithms for robust identification

By minimising the weighted Wilcoxon norm instead of the weighted Euclidean norm as the cost function, two new Wilcoxon adaptive algorithms are presented. The simulation results in a system identification scenario clearly indicate that the proposed algorithms have superior performance compared with the conventional algorithms when the system output data are contaminated by outliers and the input data are highly correlated.
Measurement of the Longitudinal Proton Structure Function in Diffraction at the H1 Experiment and Prospects for Diffraction at LHC

A measurement of the longitudinal diffractive structure function FDL using the H1 detector at HERA is presented. The structure function is extracted from first measurements of the diffractive cross section ep ! eXY at centre of mass energies ps of 225 and 252GeV at high values of inelasticity y, together with a new measurement at ps of 319GeV, using data taken in 2006 and 2007. Previous H1 data at ps of 301GeV complete the kinematic coverage needed to extract FD L in the range of photon virtualities 2.5 7GeV2 and compared to the analogous quantity for inclusive DIS.
Cholesterols, Apolipoproteins, and Their Associations with the Presence and Severity of Diabetic Retinopathy: A Systematic Review

Serum apolipoproteins have been reported as a more significant marker for diabetic retinopathy (DR) compared with serum cholesterols. This article aims to review the associations between serum cholesterols and apolipoproteins, and the presence and severity of DR. The protocol of this systematic review was registered at the PROSPERO registry (CRD42022303331). We conducted a systematic search of literature published between 2011 to 2022 using the search terms “serum cholesterol” AND/OR “lipoprotein” AND/OR “apolipoprotein” AND/OR “diabetic retinopathy”. Fifteen studies were included in this review. Six studies assessed the association between serum cholesterols, apolipoproteins, and the presence of DR. Three studies reported lower levels of apolipoprotein A1, and one study reported higher levels of apolipoprotein B in patients with DR. The remaining nine studies compared serum cholesterol and apolipoprotein levels according to DR severity. Patients with more severe grades of DR presented with lower apolipoprotein A1 in six (66.7%) studies, higher apolipoprotein B levels in seven (77.8%) studies, and a higher apolipoprotein B/apolipoprotein A1 ratio in six out of seven (85%) studies. In conclusion, serum apolipoproteins, in particular the apolipoprotein B/apolipoprotein A1 ratio, were a more consistent marker for DR severity compared with serum cholesterols.
A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring

The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanical impedance (EMI) techniques have sparked attention among researchers to develop novel monitoring techniques for structural monitoring and evaluation. This study aims to determine the performance of EMI techniques using a piezo sensor to monitor the development of bond strength in reinforced concrete through a pull-out test. The concrete cylindrical samples with embedded steel bars were prepared, cured for 28 days, and a pull-out test was performed to measure the interfacial bond between them. The piezo coupled signatures were obtained for the PZT patch bonded to the steel bar. The damage qualification is performed through the statistical indices, i.e., root-mean-square deviation (RMSD) and correlation coefficient deviation metric (CCDM), were obtained for different displacements recorded for axial pull. Furthermore, this study utilizes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based hybrid model, an effective regression model to predict the EMI signatures. These results emphasize the efficiency and potential application of the deep learning-based hybrid model in predicting EMI-based structural signatures. The findings of this study have several implications for structural health diagnosis using a deep learning-based model for monitoring and conservation of building heritage.
The fully frustrated Ising model in infinite dimensions

The authors solve, subject to the validity of some reasonable assumptions, the 'fully frustrated' Ising model in the limit of infinite dimensions using an extension of the TAP theory for spin glasses. In contrast to the TAP theory of the infinite-range spin glass, an infinite summation of diagrams is required to recover the Gibbs free energy for this model. The model undergoes a first-order transition. The method used to solve the model should have many applications to other physical problems.
Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey

Social isolation (SI) and loneliness are ‘invisible enemies’. They affect older people’s health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care. Autonomous sensor-based approaches can be used to overcome these challenges by enabling unobtrusive and privacy-preserving assessments of SI and loneliness. This paper presents a comprehensive overview of sensor-based tools to assess social isolation and loneliness through a structured critical review of the relevant literature. The aim of this survey is to identify, categorise, and synthesise studies in which sensing technologies have been used to measure activity and behavioural markers of SI and loneliness in older adults. This survey identified a number of feasibility studies using ambient sensors for measuring SI and loneliness activity markers. Time spent out of home and time spent in different parts of the home were found to show strong associations with SI and loneliness scores derived from standard instruments. This survey found a lack of long-term, in-depth studies in this area with older populations. Specifically, research gaps on the use of wearable and smart phone sensors in this population were identified, including the need for co-design that is important for effective adoption and practical implementation of sensor-based SI and loneliness assessment in older adults.