Miracle-dz/UniFAR-FTUs huggingface dataset:
Why don't mice lacking the mitochondrial Ca2+ uniporter experience an energy crisis?

Current dogma holds that the heart balances energy demand and supply effectively and sustainably by sequestering enough Ca2+ into mitochondria during heartbeats to stimulate metabolic enzymes in the tricarboxylic acid (TCA) cycle and electron transport chain (ETC). This process is called excitation‐contraction‐bioenergetics (ECB) coupling. Recent breakthroughs in identifying the mitochondrial Ca2+ uniporter (MCU) and its associated proteins have opened up new windows for interrogating the molecular mechanisms of mitochondrial Ca2+ homeostasis regulation and its role in ECB coupling. Despite remarkable progress made in the past 7 years, it has been surprising, almost disappointing, that germline MCU deficiency in mice with certain genetic background yields viable pups, and knockout of the MCU in adult heart does not cause lethality. Moreover, MCU deficiency results in few adverse phenotypes, normal performance, and preserved bioenergetics in the heart at baseline. In this review, we briefly assess the existing literature on mitochondrial Ca2+ homeostasis regulation and then we consider possible explanations for why MCU‐deficient mice are spared from energy crises under physiological conditions. We propose that MCU and/or mitochondrial Ca2+ may have limited ability to set ECB coupling, that other mitochondrial Ca2+ handling mechanisms may play a role, and that extra‐mitochondrial Ca2+ may regulate ECB coupling. Since the heart needs to regenerate a significant amount of ATP to assure the perpetuation of heartbeats, multiple mechanisms are likely to work in concert to match energy supply with demand.
Dose-Response Relationship between Exercise Duration and Executive Function in Older Adults

This study aimed to determine the dose-response relationship between exercise duration and task switching in older adults. Acute moderate intensity aerobic exercise for 20 min resulted in shorter response times than control and 10-min sessions in the heterogeneous, non-switch, and switch conditions, but not in the homogeneous condition. Additionally, linear and cubic trends between exercise duration and global switching performance as well as local switching performance were revealed with faster times being predicted by longer duration exercise; however, the cubic relationship resulted in performance following the 45-min session being not significantly different from the other three sessions. Acute aerobic moderate intensity exercise for 20 min is an effective duration to improve task switching. Although a longer duration of exercise is not optimal for benefiting task switching, it does not harm task switching in older adults and hence may be of value for other health-related reasons.
Markov-Chain Monte-Carlo methods and non-identifiabilities

Abstract We consider the problem of sampling from high-dimensional likelihood functions with large amounts of non-identifiabilities via Markov-Chain Monte-Carlo algorithms. Non-identifiabilities are problematic for commonly used proposal densities, leading to a low effective sample size. To address this problem, we introduce a regularization method using an artificial prior, which restricts non-identifiable parts of the likelihood function. This enables us to sample the posterior using common MCMC methods more efficiently. We demonstrate this with three MCMC methods on a likelihood based on a complex, high-dimensional blood coagulation model and a single series of measurements. By using the approximation of the artificial prior for the non-identifiable directions, we obtain a sample quality criterion. Unlike other sample quality criteria, it is valid even for short chain lengths. We use the criterion to compare the following three MCMC variants: The Random Walk Metropolis Hastings, the Adaptive Metropolis Hastings and the Metropolis adjusted Langevin algorithm.
Distributed sparse bundle adjustment algorithm based on three-dimensional point partition and asynchronous communication

Sparse bundle adjustment (SBA) is a key but time- and memory-consuming step in three-dimensional (3D) reconstruction. In this paper, we propose a 3D point-based distributed SBA algorithm (DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment (A-DSBA) to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism (SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm (running on eight nodes with 48 cores) is up to 41 times that of the serial SBA (running on a single node).
SHsearch: a method for fast remote homology detection

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.
Diversity of Adipose Tissue Immune Cells: Are All Eosinophils Created Equal?

In this issue of BioEssays, the article by Knights et al. provides a well-written dissection of the current knowledge gaps in the Immunometabolism field concerning the role of eosinophils in adipose tissue (AT) homeostasis and whole-body metabolism. Though it does not exhaustively describe all published studies on eosinophils in AT, it highlights several key studies to illustrate the field’s progression. The authors acknowledge that AT eosinophil function, not just number, needs to be more thoroughly investigated. With obesity and its comorbidities affecting a third of the US population and quickly rising world-wide, the study of AT has become more pertinent than ever. AT is now recognized as an important endocrine organ that stores and supplies the body with adequate energy, secretes hormones to influence vital processes such as hunger and energy expenditure, and stabilizes body temperature through insulation and thermogenesis. Obesity impairs AT function, due in part to a massive inflammatory overhaul. While the most prominent immunemodulation in AT during obesity is macrophage number and inflammatory state, many other immune cells are known to be altered, such as eosinophils. The study of AT eosinophils is less than a decade old with the first publication noting the presence of eosinophils in AT as recent as 2011. Since then a series of publications have shown positive effects of eosinophils on AT function, including weight loss, reduced AT inflammation, improved AT insulin sensitivity, and increased whole body glucose tolerance. However, not all publications have shown a positive role for eosinophils in AT health, thoroughly discussed in. For example, in the piece that this brief commentary accompanies, Knights et al. contemplate the implications of a recent paper from our lab in which elevating AT eosinophils did not rescue metabolic impairments during obesity. Knights and colleagues discuss the important new concept that eosinophil expression profiles should be examined since increasing eosinophils alone does not always improve AT health. In line with this thinking, our lab has been curious to determine if AT eosinophils exist on an E1/E2 spectrum, akin to M1/M2macrophage polarization and Th1 and Th2 T cells. Some evidence supports our idea of an E1/E2 spectrum: while eosinophils classically polarize M2-like
Synthesis and exploration of clock spines

This study addresses the problem of developing a synthesis algorithm for clock spine networks, which is able to systematically explore the clock resources and clock variation tolerance. The idea is to transform the problem of allocating and placing clock spines on a plane into a slicing floorplan optimisation (SFO) problem, in which every candidate of clock spine network structures is uniquely expressed into a postfix notation to enable a fast cost computation in the SFO. As a result, the authors proposed synthesis algorithm can explore the diverse structures of the clock spine network to find globally optimal ones within acceptable run time. Through experiments with benchmark circuits, it is shown that the proposed algorithm is able to synthesise the clock spine networks with 38% reduced clock skew over the clock tree structures, even 11% reduced clock power. In addition, in comparison with the clock mesh structures, the proposed clock spine networks have comparable tolerance to clock skew variation while using considerably less clock resources, reducing clock power by 36%.
The neuronal migration hypothesis of dyslexia: A critical evaluation 30 years on

The capacity for language is one of the key features underlying the complexity of human cognition and its evolution. However, little is known about the neurobiological mechanisms that mediate normal or impaired linguistic ability. For developmental dyslexia, early postmortem studies conducted in the 1980s linked the disorder to subtle defects in the migration of neurons in the developing neocortex. These early studies were reinforced by human genetic analyses that identified dyslexia susceptibility genes and subsequent evidence of their involvement in neuronal migration. In this review, we examine recent experimental evidence that does not support the link between dyslexia and neuronal migration. We critically evaluate gene function studies conducted in rodent models and draw attention to the lack of robust evidence from histopathological and imaging studies in humans. Our review suggests that the neuronal migration hypothesis of dyslexia should be reconsidered, and the neurobiological basis of dyslexia should be approached with a fresh start.
Investing in Legal Advice

I study the effect of strategic product market considerations on the resource spending decision of plaintiffs in patent lawsuits. I use a unique sample of US patent litigation cases and focus on an important decision of the plaintiffs; their decision to hire external attorneys. I find that plaintiffs hire significantly more attorneys if they are in a horizontal relationship with the defendant rather than in a vertical relationship, i.e., if the litigants compete on the product market. This strategic product market effect is more pronounced the more concentrated the product market is.
Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes Alzheimer’s Disease Neuroimaging Initiative

The demented brain wiring undergoes several changes with dementia progression. However, in early dementia stages, particularly early mild cognitive impairment (eMCI), these remain challenging to spot. Hence, developing accu-rate diagnostic techniques for eMCI identiﬁcation is critical for early intervention to prevent the onset of Alzheimer’s Disease (AD). There is a large body of machine-learning based research developed for classifying different brain states (e.g., AD vs MCI). These works can be fundamentally grouped into two cate-gories. The ﬁrst uses correlational methods, such as canonical correlation analysis (CCA) and its variants, with the aim to identify most correlated features for diagnosis. The second includes discriminative methods, such as feature selection methods and linear discriminative analysis (LDA) and its variants to identify brain features that distinguish between two brain states. However, existing methods examine these correlational and discriminative brain data independently , which overlooks the complementary information provided by both techniques, which could prove to be useful in the classiﬁcation of patients with dementia. On the other hand, how early dementia affects cortical brain connections in morphology remains largely unexplored. To address these limitations, we propose a joint correlational and discriminative ensemble learning framework for eMCI di-agnosis that leverages a novel brain network representation, derived from the cor-tex. Speciﬁcally, we devise ‘the shallow convolutional brain multiplex’ (SCBM), which not only measures the similarity in morphology between pairs of brain regions, but also encodes the relationship between two morphological brain networks. Then, we represent each individual brain using a set of SCBMs, which are used to train joint ensemble CCA-SVM and LDA-based classiﬁer. Our framework outperformed several state-of-the-art methods by 3-7% including independent correlational and discriminative methods.
¡Vamos!: Observations of Pedestrian Interactions with Driverless Cars in Mexico

How will pedestrians from different regions interact with an approaching autonomous vehicle? Understanding differences in pedestrian culture and responses can help inform autonomous cars how to behave appropriately in different regional contexts. We conducted a field study comparing the behavioral response of pedestrians between metropolitan Mexico City (N=113) and Colima, a smaller coastal city (N=81). We hid a driver in a car seat costume as a Wizard-of-Oz prototype to evoke pedestrian interaction behavior at a crosswalk or street. Pedestrian interactions were coded for crossing decision, crossing pathway, pacing, and observational behavior. Most distinctly, pedestrians in Mexico City kept their pace and more often crossed in front of the vehicle, while those in Colima stopped in front of the car more often.
Morbid obesity and hypertension: The role of perirenal fat

Accumulation of fat in renal sinus and hilum is associated with hypertension development. We evaluated the relationship between perirenal fat and hypertension in a population of morbidly obese patients and the potential variations after sleeve‐gastrectomy. Two hundred and eighty‐four morbidly obese patients were included in the study, and 126 underwent sleeve‐gastrectomy. At baseline and 10‐12 months after surgery, we evaluated anthropometric parameters, blood pressure, glycometabolic, and lipidic assessment, and performed an ultrasonographic evaluation of visceral fat area and perirenal fat thickness. The perirenal fat thickness in hypertensive obese was higher than in nonhypertensive (13.6 ± 4.8 vs 11.6 ± 4.1, P = 0.001). It showed a significant direct correlation with age, waist circumference, BMI, systolic blood pressure (SBP), insulinemia, HOMA‐IR, glycated hemoglobin, and creatinine. The independent predictors (R2 = 0.129) of SBP were perirenal fat thickness (β = 0.160, P = 0.022) and age (β = 0.175, P = 0.011). After surgery, perirenal fat thickness significantly decreased (from 13 ± 4 to 9 ± 4 mm, P <0.001). In the 89 hypertensive obese patients who underwent sleeve‐gastrectomy, we observed a significant decrease in antihypertensive medications needed. Sixteen patients suspended therapy. The perirenal fat thickness in obese patients could be a valuable tool to define the risk of developing hypertension, providing the clinician with an additional parameter to define those who need a more aggressive treatment and could benefit most from bariatric surgery.
Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Point clouds with ever-increasing volume are regular data in 3D city modelling, in which building reconstruction is a significant part. The photogrammetric point cloud, generated from UAS (Unmanned Aerial System) imagery, is a novel type of data in building reconstruction. Its positive characteristics, alongside its challenging qualities, provoke discussions on this theme of research. In this paper, patch-wise detection of the points of window frames on facades and roofs are undertaken using this kind of data. A density-based multi-scale filter is devised in the feature space of normal vectors to globally handle the matter of high volume of data and to detect edges. Color information is employed for the downsized data to remove the inner clutter of the building. Perceptual organization directs the approach via grouping and the Gestalt principles, to segment the filtered point cloud and to later detect window patches. The evaluation of the approach displays a completeness of 95% and 92%, respectively, as well as a correctness of 95% and 96%, respectively, for the detection of rectangular and partially curved window frames in two big heterogeneous cluttered datasets. Moreover, most intrusions and protrusions cannot mislead the window detection approach. Several doors with glass parts and a number of parallel parts of the scaffolding are mistaken as windows when using the large-scale object detection approach due to their similar patterns with window frames. Sensitivity analysis of the input parameters demonstrates that the filter functionality depends on the radius of density calculation in the feature space. Furthermore, successfully employing the Gestalt principles in the detection of window frames is influenced by the width determination of window partitioning.
Incentive Mechanism of Data Storage Based on Blockchain for Wireless Sensor Networks

In this paper, the blockchain technology is utilized to build the first incentive mechanism of nodes as per data storage for wireless sensor networks (WSNs). In our system, the nodes storing the data are rewarded with digital money. The more the data stored by the node, the more the reward it achieves. Moreover, two blockchains are constructed. One is utilized to store data of each node and another is to control the access of data. In addition, our proposal adopts the provable data possession to replace the proof of work (PoW) in original bitcoins to carry out the mining and storage of new data blocks, which greatly reduces the computing power comparing to the PoW mechanism. Furthermore, the preserving hash functions are used to compare the stored data and the new data block. The new data can be stored in the node which is closest to the existing data, and only the different subblocks are stored. Thus, it can greatly save the storage space of network nodes.
Who is Generation A?: Investigating the Experience of Automated Driving for Different Age Groups

The prevalence of Automated Driving Systems (ADS) is expected to open up many possibilities for different user groups with individual needs and challenges. Former secondary/tertiary tasks can become primary tasks, and driving with all its interactions and responsibilities steps back or disappears at all. At higher levels of AD it is expected that the elderly could maintain or regain individual mobility, thus, play a major role for future markets. To understand individual mindsets concerning technology acceptance and user needs we conducted an explorative interview study (N=27). In a simulated automated driving environment, driving experience over time was compared across three age groups (elderly people >65, younger adults <30, younger adults <30 with age simulation suite), utilizing the STAM model for content analysis. Results of the age-comparison indicate no major differences in the general technology acceptance, however, fine-grained analysis revealed interesting differences in participants' perceptions concerning UX design requirements.
ChplBlamer: A Data-centric and Code-centric Combined Profiler for Multi-locale Chapel Programs

Parallel programming is hard, and it is even harder to analyze parallel programs and identify specific performance bottlenecks. Chapel is an emerging Partitioned-Global-Address-Space (PGAS) language that provides productive parallel programming. Most established profilers either completely lack the capacity to profile Chapel programs or generate information that cannot provide insightful guidance in a user-level context. To address this issue, we developed ChplBlamer to pinpoint performance losses due to data distribution and remote data accesses. We use a data-centric and code-centric combined approach to help Chapel users quickly identify performance bottlenecks in the source. To demonstrate the utility of ChplBlamer, we studied three multi-locale Chapel benchmarks. For each benchmark, ChplBlamer found the causes of the performance losses. With the optimization guidance provided by ChplBlamer, we significantly improved the performance by up to 4x with little code modification.
Ascorbic Acid in Colon Cancer: From the Basic to the Clinical Applications

Given the safety and potential benefits of intravenous ascorbic acid (AA) administration in cancer patients, there is merit in further exploring this therapeutic concept. In this review, we discuss the potential benefits of intravenous AA administration on colorectal cancer and we specifically focus on its effect on glycolysis in mutant and wild type RAS. We perform a PubMed and Ovid MEDLINE search using ascorbic acid, intravenous vitamin C, KRAS mutation, BRAF mutation and colorectal cancer (CRC) as keywords. At the cellular level, colorectal cancer cells undergo a metabolic shift called the Warburg effect to allow for more glucose absorption and utilization of glycolysis. This shift also allows AA to enter which leads to a disruption in the Warburg effect and a shutdown of the downstream KRAS pathway in mutated KRAS colon cancer cells. At the clinical level, AA is associated with tumour regression in advanced disease and improved tolerability and side effects of standard therapy. Based on these findings, we conclude that further clinical trials are needed on a larger scale to examine the therapeutic benefits of AA in colon cancer.
Evaluating How Interfaces Influence the User Interaction with Fully Autonomous Vehicles

With increasing automation, occupants of fully autonomous vehicles are likely to be completely disengaged from the driving task. However, even with no driving involved, there are still activities that will require interfaces between the vehicle and passengers. This study evaluated different configurations of screens providing operational-related information to occupants for tracking the progress of journeys. Surveys and interviews were used to measure trust, usability, workload and experience after users were driven by an autonomous low speed pod. Results showed that participants want to monitor the state of the vehicle and see details about the ride, including a map of the route and related information. There was a preference for this information to be displayed via an onboard touchscreen device combined with an overhead letterbox display versus a smartphone-based interface. This paper provides recommendations for the design of devices with the potential to improve the user interaction with future autonomous vehicles.