Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Cross Validation of Space-Based and Ground-Based Radar Observations

Abstract Simultaneous comparison of data collected from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), and the S-band polarimetric radar, operated by the National Center for Atmospheric Research, is made to cross validate the calibration of the PR instrument and to quantify the effects of precipitation attenuation on PR measurements. Data collected during the Texas and Florida Underflights experiment were used in the cross validation. Quantitatively comparing radar reflectivities from two separate platforms that have widely different view angles, beamwidths, and frequencies is a challenging task. Nevertheless, it is extremely important for cross validation. An analysis procedure to implement such cross validation is presented. Theoretical simulation of S-band and Ku-band reflectivities of the rain medium is also presented to characterize the theoretical difference in reflectivities between S band and Ku band in the absence of attenuation. Analysis indicates that, when the attenua.
Females exhibit smaller and less variable patterns of brain activation across a range of motor

46 Past work has shown that there are both structural and functional differences in the brains of 47 females versus males (Ritchie et al. , 2018). Specifically, males have larger cortical and sub-cortical 48 volume and surface area (both total and subregional), while females have greater cortical thickness in 49 most brain regions. Given these differences it is possible that males may show larger volumes of brain 50 activation and greater variability in patterns of brain activity. Yet to date little work has systematically 51 considered whether patterns of brain activity indexed with functional MRI differ between females and 52 males. The current study sought to remediate this issue by employing brain somatotopic motor 53 mapping from a previously published (Ma et al. , 2022) and openly available dataset to test differences 54 in patterns of functional brain activity associated with 12 voluntary movement patterns in females 55 versus males. We discovered that females exhibited smaller volumes of brain activation across all 12 56 movement tasks, and lower patterns of variability between female participants in 10 of the 12 57 movements. We also observed that in these data females had greater cortical thickness which is in 58 alignment with previous structural sex difference analyses (Ritchie et al. , 2018; Wierenga et al. , 2022). 59 Overall, these findings provide a basis for considering sex differences in future fMRI research and 60 provide a foundation of understanding for which various neurological pathologies may present different 61 between females and males.
Invasive plant species interact with drought to shift key functions and families in the native rhizosphere

Background and aims Interactions between species invasions and climate change have the potential to drive changes in plant communities more than either factor alone. One pathway through which these effects can occur is via changes to the rhizosphere microbial community. Invasive plants can alter these microbial communities affecting natives’ abilities to compete with invaders. At the same time, climate change is leading to more frequent extreme wet and dry events. Understanding the response of plant communities to these combined global change drivers requires a comprehensive approach that assesses the relationship between plant competition and belowground rhizosphere microbial community responses. Methods Here we use a field experiment in a California grassland with a set of six native annual forbs (i.e., wildflowers) and three invasive annual grasses to test how competition with invasive plants alters both identity and function in the native rhizosphere microbiome, and whether competition between these groups interacts with rainfall to amplify or ameliorate microbial shifts. Results Metagenomics of rhizosphere communities revealed that drought combined with competition from invaders altered a higher number of functions and families in the native rhizosphere compared to invasive competition alone or drought alone. Watering combined with invasion led to fewer shifts. Conclusion This suggests invasion-driven shifts in the microbial community may be involved in weakening natives’ ability to cope with climate change, especially drought. Understanding the role of the microbial community under invasion and climate change may be critical to mitigating the negative effects of these interacting global change drivers on native communities. Graphical abstract Understanding plant community response to global change drivers requires a comprehensive approach that assesses the relationship between plant competition and belowground rhizosphere microbial community responses. (a) In this work, we use a field experiment in a California grassland with a set of native forbs (purple) and invasive grasses (teal) to assess the combined effects of competition and water availability (drought, control, watered) on the rhizosphere microbiome. (b) Drought combined with competition from invaders altered the relative abundance of 36 functions (white) and 22 microbial families (blue) in the native rhizosphere compared to the effects of competition (3 functions, 16 families) or drought alone on natives (not shown: 5 functions, 0 families). (c) Additionally, regardless of watering treatment, invasive grasses sourced more of the taxonomic community in native-invasive mixes and this was exacerbated during drought. Overall, these results suggest invasion-driven shifts in the microbiome may be involved in weakening natives’ ability to cope with climate change, especially drought.
Mycobacterium tuberculosis – Macrophage Interactome: Molecular Network Structure and Resilience against Antibiotics

Resistance to several antibiotics against Mycobacterium tuberculosis is a serious problem to be solved worldwide. In the present work, we made the statistical analysis of the gene regulatory network of Mycobacterium tuberculosis and of the Mycobacterium tuberculosis-macrophage interactome to find the probable cause of this resistance. The results from this analysis show that both the gene regulatory network and the interactoma have a hierarchical free scale modular structure that assures a high degree of resilience of these networks against external perturbations. In particular, the interactome is a complex hybrid network that results from the formation of novel links between the Mycobacterium tuberculosis and macrophage proteins and from the modification of the previously existing links between the native macrophage proteins, which give rise to novel negative and positive feedback loops that modify the dynamical behavior of the interactome and protect the mycobacterium against the attack with antibiotics by taking control of the macrophage immune response and apoptosis. The statistical analysis of the interactome shows that the highly connected mycobacterium proteins inhA, ahpC, kasA, katG and rpsL exert this control by creating new links with the host proteins FAS and NF-κB. These new hybrid circuits embedded in the hierarchical scale-free modular molecular structure of the interactome produce its high resistance to external perturbations like antibiotics. As consequence, the present work proposes the hypothesis that Mycobacterium tuberculosis antibiotic resistance in vivo during chronic tuberculosis is only a particular case of a more complex problem that is the interactome resilience against antibiotics. Thus, new strategies of drug design are necessary to shatter the complex structure of the Mycobacterium tuberculosis-macrophage interactome.
Barriers of Fintech Adoption in MSMEs: Moderating Role of Innovation Culture

Mirco Small and Medium Enterprises (MSMEs) are at the core of any economy. The financial inclusion MSMEs is essential for any country that dreams of growth. Fintech-based innovative solutions can come to the aid. Not only do they help MSMEs in acquiring a quick loan through digital and peer-to-peer lending, but they also help them in bookkeeping, wealth management, and risk mitigation. However, even with so many helpful solutions, the adoption of Fintech has been lagging. This study tries to find out what are the apprehensions or barriers among the MSME owner towards Fintech adoption. And how cultural innovation manipulated this relationship. The major contribution of our study will be the identification of the most persistent barriers. The study has high practical value for, Fintech firms, government regulators and policymakers, and MSME owners.
Examining the Interplay between Decision-making and Big Data Analytics in driving Decision Value: A Critical Realist Case

To improve organisational performance, much attention has been paid to the value that BDA can produce beyond data-driven decision-making. However, there has been less emphasis on decision value (DV) arising from BDA. To compound this, definitions of DV remain fragmented across different views from social, to technical and economic. We aim to develop an empirically grounded theoretical model characterising how BDA is used in organisations to realize DV and to provide a mechanism-based causal explanation of how DV arises from the interplay between BDA (technical subsystem) and decision-making (social subsystem) using a real-world environment. As a result, we propose using retroductive reasoning through Critical Realism (CR) as the overarching philosophy, and Grounded Theory Methodology (GTM) techniques to collect and analyse data. Potential contributions from this research will include the development of a mechanism-based theory explaining how DV arises from the interplay between BDA and decision-making.
Hardness and approximation algorithms of some graph problems

In the algorithmic area, in the last decades the graph problems have become of wide interest, mainly derived from two aspects: the wide number of real-life situations that can be treated using graphs and their inherent scientific interest. Actually, graph problems cover a number of technical and scientific areas, such as communication theory and game theory, but they also have a number of applications in the sociological, psychological and pedagogical fields. In this work we study graph problems showing the hardness of some of them, even in particular subclasses of graph instances and some approximation algorithms. Specifically, we first study the minimum k-connected spanning subgraph problem showing an approximation algorithm with approximation factor β/(1−β) for complete undirected weighted graphs with weight function obeying the β-sharpened triangle inequality: for every three vertices u, v, z, it holds w(u, v) ≤ β(w(v, z) + w(z, u)). Then, we will treat a restricted class of graph instances: the power-law graphs. We will show that, even restricting ourself to meaningful instances of power-law graphs, almost all the NP-complete problems remains NP-complete also in this subclass. The proof has been carried on by introducing an interesting result which enable us to ”embed” every simple graph in a polynomial-size power-law graph. Finally, we will show some few results relating the game theory with the graph theory. In particular, we will first use the game theory in a setting of selfish routing networks, showing the existence of Nash equilibria in a model derived from the one introduced by Koutsoupias and Papadimitriou (see [132]). Next we study the existence of payment schemes leading to a truthful mechanism when the selfish agents are (partially) verifiable (see also [13]).
Fusion bayésienne de données en imagerie X et ultrasonore

Résumé – La plupart des méthodes classiques de fusion de données en imagerie tomographique opèrent sur des images déjà reconstruites par chacun des systèmes d’imagerie. L’objectif de ce travail est d’utiliser les deux jeux de données issus directement des mesures pour fournir une image reconstruite en exploitant d’une manière optimale les informations apportées par ces données. Plus précisément, cette communication présente un algorithme de reconstruction d’image en tomographie X qui peut intégrer les informations contenues dans un jeux de données échographiques obtenu sur le même objet.
Elicitable Risk Measures

A statistical functional is elicitable if it can be defined as the minimizer of a suitable expected scoring function (see Gneiting (2011), Ziegel (2013) and the references therein). With financial applications in view, we suggest a slightly more restrictive definition than Gneiting (2011), and we derive several necessary conditions. For monetary risk measures, we show that elicitability leads to a subclass of the shortfall risk measures introduced in Follmer and Schied (2002). In the coherent case, we show that the only elicitable risk measures are the expectiles. Further, we provide an alternative proof of the result in Ziegel (2013) that the only coherent comonotone elicitable risk measure is the expected loss.
From liking to not liking: A proposed experiment design to explore consumer perceptions of health wearable notifications

Despite the increase in the adoption of health-wearables, most studies have focused on intentions to use the wearables, with less focus on perceptions related to their use, particularly how consumers perceive the interruptiveness of notifications the wearables provide to alert consumers or state that requires their attention. Based on the argument that wearable notifications influence consumer perceptions, we propose an experiment to develop and test a hybrid model anchored in mere exposure theory that suggests an inverted-U-shaped distribution for notification liking, where familiarity with the notifications through repeated exposure drives increased liking, while habituation, fatigue, and notification satiation drive a simultaneous decrease in liking. We propose to test this model using a vignette-based factorial survey approach. Highlighting changes in consumers’ perceptions related to the interruptiveness of wearable notifications, we expect to contribute to IS research by adapting mere exposure effect and the literature that are currently focused on adoption decisions.
A superlinear convergence feasible sequential quadratic programming algorithm for bipedal dynamic walking robot via discrete mechanics and optimal control

For periodic gait optimization problem of the bipedal walking robot, a class of global and feasible sequential quadratic programming algorithm (FSQPA) is proposed based on discrete mechanics and optimal control. The optimal controls and trajectories are solved by the modified FSQPA. The algorithm can rapidly converge to a stable gait cycle by selecting an appropriate initial gait; otherwise, the algorithm only needs one step correction that generates a stable gait cycle. Under appropriate conditions, we provide a rigorous proof of global convergence and well‐defined properties for the FSQPA. Numerical results show that the algorithm is feasible and effective. Meanwhile, it reveals the movement mechanism in the process of bipedal dynamic walking, which is the velocity oscillations. Furthermore, we overcome the oscillatory behavior via the FSQPA, which makes the bipedal robot walk efficiently and stably on even terrain. The main result is illustrated on a hybrid model of a compass‐like robot through simulations and is utilized to achieve bipedal locomotion via FSQPA. To demonstrate the effectiveness of the high‐dimensional bipedal robot systems, we will conduct numerical simulations on the model of RABBIT with nonlinear, hybrid, and underactuated dynamics. Numerical simulation results show that the FSQPA is feasible and effective. Copyright © 2015 John Wiley & Sons, Ltd.
Striatal spatial heterogeneity, clustering, and white matter association of GFAP+ astrocytes in a mouse model of Huntington’s disease

Introduction Huntington’s disease (HD) is a neurodegenerative disease that primarily affects the striatum, a brain region that controls movement and some forms of cognition. Neuronal dysfunction and loss in HD is accompanied by increased astrocyte density and astrocyte pathology. Astrocytes are a heterogeneous population classified into multiple subtypes depending on the expression of different gene markers. Studying whether mutant Huntingtin (HTT) alters specific subtypes of astrocytes is necessary to understand their relative contribution to HD. Methods Here, we studied whether astrocytes expressing two different markers; glial fibrillary acidic protein (GFAP), associated with astrocyte activation, and S100 calcium-binding protein B (S100B), a marker of matured astrocytes and inflammation, were differentially altered in HD. Results First, we found three distinct populations in the striatum of WT and symptomatic zQ175 mice: GFAP+, S100B+, and dual GFAP+S100B+. The number of GFAP+ and S100B+ astrocytes throughout the striatum was increased in HD mice compared to WT, coinciding with an increase in HTT aggregation. Overlap between GFAP and S100B staining was expected, but dual GFAP+S100B+ astrocytes only accounted for less than 10% of all tested astrocytes and the number of GFAP+S100B+ astrocytes did not differ between WT and HD, suggesting that GFAP+ astrocytes and S100B+ astrocytes are distinct types of astrocytes. Interestingly, a spatial characterization of these astrocyte subtypes in HD mice showed that while S100B+ were homogeneously distributed throughout the striatum, GFAP+ preferentially accumulated in “patches” in the dorsomedial (dm) striatum, a region associated with goal-directed behaviors. In addition, GFAP+ astrocytes in the dm striatum of zQ175 mice showed increased clustering and association with white matter fascicles and were preferentially located in areas with low HTT aggregate load. Discussion In summary, we showed that GFAP+ and S100B+ astrocyte subtypes are distinctly affected in HD and exist in distinct spatial arrangements that may offer new insights to the function of these specific astrocytes subtypes and their potential implications in HD pathology.
Specific correction of pyruvate kinase deficiency-causing point mutations by CRISPR/Cas9 and single-stranded oligodeoxynucleotides

Pyruvate kinase deficiency (PKD) is an autosomal recessive disorder caused by mutations in the PKLR gene. PKD-erythroid cells suffer from an energy imbalance caused by a reduction of erythroid pyruvate kinase (RPK) enzyme activity. PKD is associated with reticulocytosis, splenomegaly and iron overload, and may be life-threatening in severely affected patients. More than 300 disease-causing mutations have been identified as causing PKD. Most mutations are missense mutations, commonly present as compound heterozygous. Therefore, specific correction of these point mutations might be a promising therapy for the treatment of PKD patients. We have explored the potential of precise gene editing for the correction of different PKD-causing mutations, using a combination of single-stranded oligodeoxynucleotides (ssODN) with the CRISPR/Cas9 system. We have designed guide RNAs (gRNAs) and single-strand donor templates to target four different PKD-causing mutations in immortalized patient-derived lymphoblastic cell lines, and we have detected the precise correction in three of these mutations. The frequency of the precise gene editing is variable, while the presence of additional insertions/deletions (InDels) has also been detected. Significantly, we have identified high mutation-specificity for two of the PKD-causing mutations. Our results demonstrate the feasibility of a highly personalized gene-editing therapy to treat point mutations in cells derived from PKD patients.
Experimental investigation of training schedule on home-based working memory training in healthy older adults

Introduction The efficacy of working memory training (WMT) for cognitive enhancement in healthy older adults has been extensively investigated. Typically, WMT results in improved performance on the training task, but limited or no transfer of improvement to other cognitive tasks. Accordingly, there is a need to identify optimal intervention parameters to maximize training and transfer task effects of WMT. The current study aimed to investigate the effect of training schedule on training and transfer task performance of WMT in healthy older adults. A secondary aim was to examine the feasibility of participants performing the intervention online at home, unsupervised, and using their personal devices. Methods Participants (N = 71; mean age: 66 years) completed sixteen WMT or active-control sessions over eight (distributed) or four (intensive) weeks. Adaptive verbal and spatial n-back tasks were used as the WMT tasks. We tested near transfer effects to a digit-span task and far transfer effects to an abstract relational reasoning task. Results Participants successfully performed the cognitively demanding intervention using their own devices, online at home, and with minimal contact with the researcher. We observed a significant improvement in WMT task performance in the WMT group relative to active-controls, but no evidence of near or far transfer. Similar training effects were observed irrespective of the intensity of the training schedule. Discussion Our results suggest that comparable benefits could be observed when using less intensive schedules that may be more easily accommodated into everyday life.
Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.
How forests survive alongside flammable open ecosystems: conservation implications for Africa

Mosaics of closed, fire-sensitive forests and open flammable ecosystems are common across Africa and other parts of the world. The open ecosystems have long been interpreted as products of deforestation, but diverse lines of evidence point to their origins millions of years before humans. There is widespread concern over the survival of fire-sensitive forest in a flammable matrix, leading to diverse measures, including fire suppression, to protect forests. But if both systems are ancient, how did fire-sensitive forests survive the regular fires in the flammable open ecosystems? Here we discuss recent research on this topic, with a focus on Africa, including the stability of these mosaics through time and the factors accounting for this. These include local topography, variation in grass flammability, the presence of fire-tolerant forest margin tree species, and fauna that create firebreaks along the forest edge. We go on to discuss the conditions under which mosaics are less stable, for example during extreme fires, and consider the uncertain future of mosaicked landscapes under climate change. Finally, we suggest a set of guidelines for consideration by conservation managers concerned about fire damage to forest patches.