Miracle-dz/UniFAR-FTUs huggingface dataset:
An enhancer trap system to track developmental dynamics in Marchantia polymorpha

A combination of streamlined genetics, experimental tractability and relative morphological simplicity compared to vascular plants makes the liverwort Marchantia polymorpha an ideal model system for studying many aspects of plant biology. Here we describe a transformation vector combining a constitutive fluorescent membrane marker with a nuclear marker that is regulated by nearby enhancer elements, and use this to produce a library of enhancer trap lines for Marchantia. Screening gemmae from these lines allowed for identification and characterization of novel marker lines, including markers for rhizoids and oil cells. The library allowed the identification of a margin tissue running around the thallus edge, highlighted during thallus development. Expression of this marker is correlated with auxin levels. We generated multiple markers for the meristematic apical notch region, which have different spatial expression patterns, reappear at different times during meristem regeneration following apical notch excision and have varying responses to auxin supplementation or inhibition. This reveals that there are proximodistal substructures within the apical notch that could not be observed otherwise. We employed our markers to study Marchantia sporeling development, observing meristem emergence as defining the protonema-to-prothallus stage transition, and subsequent production of margin tissue during the prothallus stage. Exogenous auxin treatment stalls meristem emergence at the protonema stage but does not inhibit cell division, resulting in callus-like sporelings with many rhizoids, whereas pharmacologically inhibiting auxin synthesis and transport does not prevent meristem emergence. This enhancer trap system presents a useful resource for the community and will contribute to future Marchantia research. Significance Statement The liverwort Marchantia polymorpha is an emerging model species with huge potential for synthetic biology. We generated an enhancer trap library as a resource for Marchantia research and characterized marker lines for several important cell types (rhizoids, thallus margin, apical notch and oil cells). These marker lines can be used to track cellular dynamics in growing plants, exemplified by studying auxin responses and sporeling growth, revealing new insights into thallus development and cell fate specification.
Resveratrol and its analogs suppress HIV replication, oxidative stress, and inflammation in macrophages

Abstract Objectives HIV suppression in brain viral reservoirs, especially macrophages, and microglia is critical to suppress HIV neuropathogenesis and subsequently HIV-associated neurocognitive disorders (HAND). Since most antiretroviral therapy (ART) drugs do not achieve optimal therapeutic concentrations in the brain and can cause neurotoxicity, an alternative/adjuvant therapy is needed to suppress HIV neuropathogenesis. In this study, our objectives were to examine the anti-HIV, antioxidant, and anti-inflammatory potential of resveratrol (RES) and its synthetic analogs 4-(E)-{(p-tolylimino)-methylbenzene-1,2-diol} (TIMBD) and 4-(E)-{(4-hydroxyphenylimino)-methylbenzene,1,2-diol} (HPIMBD) in HIV-infected macrophages. Methods We used HIV replication (viral load), oxidative stress (reactive oxygen species and antioxidant enzymes), and inflammatory response (pro- and anti-inflammatory cytokines/chemokines) assays to achieve the objectives of the study. Results Our results showed that RES and its analogs HPIMBD and TIMBD at 25 µM concentration significantly decrease HIV replication in both primary monocyte-derived macrophages and U1-differentiated macrophages. Moreover, RES and its analogs do not induce any cytotoxicity for up to 3 days in these cells. Further, treatment with RES and TIMBD (25 µM) also reduced the levels of reactive oxygen species without affecting the expression of antioxidant enzymes, SOD1, and catalase in U1 macrophages. Besides, RES and HPIMBD treatment inhibited the proinflammatory cytokines and chemokines in U1 macrophages, which was associated with decreased levels of anti-inflammatory cytokines. Importantly, our western blot experiments show that RES also decreases cellular proinflammatory cytokine IL-1β, which is usually elevated in both myeloid and neuronal cells upon HIV infection. Conclusions Taken together, our results suggest that RES and/or its analogs are important adjuvants that may be used not only to suppress HIV but also oxidative stress and inflammation in brain viral reservoirs.
An Efficient Vectorized Hash Table for Batch Computations

In recent years, the increasing demand for high-performance analytics on big data has led the research on batch hash tables. It is shown that this type of hash table can benefit from the cache locality and multi-threading more than ordinary hash tables. Moreover, the batch design for hash tables is amenable to using advanced features of modern processors such as prefetching and SIMD vectorization. While state-of-the-art research and open-source projects on batch hash tables made efforts to propose improved designs by better usage of mentioned hardware features, their approaches still do not fully exploit the existing opportunities for performance improvements. Furthermore, there is a gap for a high-level batch API of such hash tables for wider adoption of these high-performance data structures. In this paper, we present Vec-HT, a parallel, SIMD-vectorized, and prefetching-enabled hash table for fast batch processing. To allow developers to fully take advantage of its performance, we recommend a high-level batch API design. Our experimental results show the superiority and competitiveness of this approach in comparison with the alternative implementations and state-of-the-art for the data-intensive workloads of relational join processing, set operations, and sparse vector processing.
Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Neural activity is presumed to be correlated with motor behavior almost everywhere in the brain, implying that many different brain processes are involved in generating a behavioral output. Recent studies in multiple non-human species have observed pervasive brain-wide neural activity directly related to motor output. However, similar brain-wide investigations in humans with high-temporal resolution are lacking to date. Here, we recorded invasive data from brain-wide distributed electrodes in humans and reveal global neural dynamics that are predictive of movement across tasks and across participants. The dynamics are remarkably stable between participants with substantially varying electrode configurations and to loss of information. We demonstrate that these global neural dynamics are near brain-wide and present in all participants. Uncovering these global neural dynamics may allow for a more holistic and network-based perspective on motor-related neural activity.
Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

Background Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder (MDD), but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with deeper clinical phenotyping. The goals of this work were to develop and evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, deeply phenotyped, single-site sample. Methods We used regularized canonical correlation analysis (RCCA) to identify data-driven brain-behavior dimensions explaining individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting state fMRI data for N=328 patients with MDD and N=461 healthy controls. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes Results The optimal RCCA model yielded three robust and generalizable brain-behavior dimensions explaining individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified four depression subtypes, each with distinct clinical symptom profiles, abnormal RSFC patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. Conclusions Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in MDD that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that RCCA and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
Box-Behnken Experimental Design for Electrochemical Aptasensor Optimization on Screen Printed Carbon Electrode/Silica-Ceria

This study aims to optimize the epithelial sodium channel (ENaC) electrochemical aptasensor with the Box-Behnken experimental design. ENaC is a protein that plays a role in sodium ion transport in several epithelial tissues and is associated with hypertension. The ENaC protein aptamer is held in place in the electrochemical aptasensor by a modified screen-printed carbon electrode (SPCE) of silica-ceria composite (SiO2-CeO2). The unique structure of a silica matrix with high biocompatibility can form composites through a hydrothermal process. The Box-Behnken (BBD) experimental design is an efficient optimization method of factors that affect the experiment at three levels. The FTIR results of the silica-ceria composites were 549.35 cm-1 (Ce-O), 1095.3 cm-1 (Si-O-Si), and 491.28 cm-1 (Si-O). Meanwhile, SPCE/silica-ceria characterized by differential pulse voltammetry (DPV) showed an increase in peak current [Fe(CN)6]3-/4- from 3.190 μA to 9.073 μA. Three experimental factors, aptamer concentration, streptavidin incubation time, and aptamer incubation time, were optimized with BBD and obtained at 0.5 μg. mL-1, 30 minutes, and 1 hour. The optimum conditions observed resulted in a selective current response for ENaC protein detection. The optimization results can be applied to aptamer-based ENaC protein detection in samples.
Molecular recognition of an aversive odorant by the murine trace amine-associated receptor TAAR7f

There are two main families of G protein-coupled receptors that detect odours in humans, the odorant receptors (ORs) and the trace amine-associated receptors (TAARs). Their amino acid sequences are distinct, with the TAARs being most similar to the aminergic receptors such as those activated by adrenaline, serotonin and histamine. To elucidate the structural determinants of ligand recognition by TAARs, we have determined the cryo-EM structure of a murine receptor, mTAAR7f, coupled to the heterotrimeric G protein Gs and bound to the odorant N,N-dimethylcyclohexylamine (DMCH) to an overall resolution of 2.9 Å. DMCH is bound in a hydrophobic orthosteric binding site primarily through van der Waals interactions and a strong charge-charge interaction between the tertiary amine of the ligand and an aspartic acid residue. This site is distinct and non-overlapping with the binding site for the odorant propionate in the odorant receptor OR51E2. The structure, in combination with mutagenesis data and molecular dynamics simulations suggests that the activation of the receptor follows a similar pathway to that of the β-adrenoceptors, with the significant difference that DMCH interacts directly with one of the main activation microswitch residues.
Research into GNSS levelling using network RTK in Taiwan

Modern height surveying obtains orthometric heights through geoid undulations and ellipsoidal heights derived from global navigation satellite system (GNSS), a process referred to as ‘GNSS levelling’. In this study, an efficient survey technology, the e-global navigation satellite system (e-GNSS), was used instead of static satellite surveying to execute GNSS levelling. The e-GNSS in Taiwan is based on Virtual Reference Station-Real-Time Kinematic technology and networks. The orthometric heights were calculated by subtracting the geoid undulations, derived from three geoid models (gravimetric, hybrid, and geometric models), from the ellipsoidal heights, derived from the e-GNSS. Additionally, two corrector methods, namely the difference and the corrector surface method, were adopted to improve the accuracies and the systematic errors. The experiment showed that the two corrector methods improved the accuracies of the orthometric heights in the gravimetric model by approximately 66.3 and 80.0% and mitigated the systematic errors in the gravity model by approximately 96.2 and 99.9%.
Factors influencing mobile banking adoption in India: The role of government support as a mediator

This study aims to objectively examine the factors influencing mobile banking adoption in India. There has been a model put forth that is founded on the unified theory of acceptance and use of technology and technology readiness. Data was collected from 536 mobile banking users. The primary determinants influencing mobile banking adoption were assessed using structural equation modeling. The study's conclusions indicate that the most significant predictors of behavioral intention to use mobile banking are factors such as performance expectancy, effort expectancy, social influence, optimism, and innovativeness. The relationship between users' behavioral intentions and adoption of mobile banking is partially mediated by government support. With the help of this study, mobile banking service providers in urban India will be better able to develop new services or modify current ones to boost the adoption of mobile banking.
Trait-dependent associations between early- and late-life reproduction in a wild mammal

Early- versus late-life trade-offs are a central prediction of life-history theory that are expected to shape the evolution of ageing. While ageing is widely observed in wild vertebrates, evidence that early–late trade-offs influence ageing rates remains limited. Vertebrate reproduction is a complex, multi-stage process, yet few studies have examined how different aspects of early-life reproductive allocation shape late-life performance and ageing. Here, we use longitudinal data from a 36-year study of wild Soay sheep to show that early-life reproduction predicts late-life reproductive performance in a trait-dependent manner. Females that started breeding earlier showed more rapid declines in annual breeding probability with age, consistent with a trade-off. However, age-related declines in offspring first-year survival and birth weight were not associated with early-life reproduction. Selective disappearance was evident in all three late-life reproductive measures, with longer-lived females having higher average performance. Our results provide mixed support for early–late reproductive trade-offs and show that the way early-life reproduction shapes late-life performance and ageing can differ among reproductive traits.
Distillation of non-locality in porous solids

Non-local interactions are intrinsic to multiscale heterogeneous solids. The strength of the interactions exhibits a position-dependent character whose spatial distribution strongly correlates with the underlying microstructure. In this work, a variable-order fractional calculus-based framework to distill the position-dependent non-local effects is developed. By considering the example of porous plates, theoretical and numerical analyses will illustrate the ability of variable-order mechanics to enable parsimonious and causal models via a synthetic variable-order map that naturally measures the heterogeneous non-locality induced by the microstructure; in other terms, the non-classical role of the microstructure in determining the macroscopic response. The characteristic measure for non-locality, different from the local entanglement resulting from the porosity map, allows distillation of the macroscopic non-locality, analogous to quantum non-locality distillation. The macroscopic distillation also enables the additivity of the order map, that is the ability of assembled order maps to capture the response of combined porous plates.
SMaLL: Software for rapidly instantiating Machine Learning Libraries

Interest in deploying deep neural network (DNN) inference on edge devices has resulted in an explosion of the number and types of hardware platforms that machine learning (ML) libraries must support. High-level programming interfaces, such as TensorFlow, can be readily ported across different devices; however, maintaining performance when porting the low-level implementation is more nuanced. High-performance inference implementations require an effective mapping of the high-level interface to the target hardware platform. Commonly, this mapping may use optimizing compilers to generate code at compile time or high-performance vendor libraries that have been specialized to the target platform. Both approaches rely on expert knowledge across levels to produce an efficient mapping. This makes supporting new architectures difficult and time-consuming. In this work, we present a DNN library framework, SMaLL, that is easily extensible to new architectures. The framework uses a unified loop structure and shared, cache-friendly data format across all intermediate layers, eliminating the time and memory overheads incurred by data transformation between layers. Each layer is implemented by specifying its dimensions and a kernel, the key computing operation of that layer. The unified loop structure and kernel abstraction allows the reuse of code across layers and computing platforms. New architectures only require a few hundred of lines in the kernel to be redesigned. To show the benefits of our approach, we have developed software that supports a range of layer types and computing platforms; this software is easily extensible for rapidly instantiating high-performance DNN libraries. An evaluation of the portability of our framework is shown by instantiating end-to-end networks from the MLPerf:tiny benchmark suite on five ARM platforms and one x86 platform (an AMD Zen 2). We also show that the end-to-end performance is comparable to or better than ML frameworks such as TensorFlow, TVM, and LibTorch.
Anti-Windup Design for a Reaction-Diffusion Equation

This letter focuses on the anti-windup design for saturated 1-D linear reaction-diffusion equation which admits a finite number of unstable poles. We consider a scenario in which the system is controlled via a dynamic, finite-dimensional output feedback controller ensuring closed-loop exponential stability. A method is proposed to design a dynamic anti-windup compensator to maximize the region of attraction and minimize the effect of external perturbations. More precisely, the sufficient conditions for the local exponential stability of the closed-loop system are derived and expressed in terms of a set of matrix inequalities. Using generalized sector conditions and proper change of variables, the conditions are then recast as an optimization problem solving linear matrix inequalities. A numerical example is provided to showcase the proposed method and highlight its effectiveness.
Compounded generalized Weibull distributions - A unified approach

Abstract In this paper we analyze a unified approach to study a family of lifetime distributions of a system consisting of random number of components in series and in parallel proposed by Chowdhury (2014). While the lifetimes of the components are assumed to follow generalized (exponentiated) Weibull distribution, a zero-truncated Poisson is assigned to model the random number of components in the system. The resulting family of compounded distributions describes several well-known distributions as well as some new models. Bivariate extension of the proposed family of distribution is also introduced. Some important statistical and reliability properties of the family of distributions are investigated. The distribution is found to exhibit both monotone and non-monotone failure rates. Parameters of the proposed distributions are estimated by the expectation maximization (EM) algorithm. Some numerical results are obtained through Monte-Carlo simulation. The asymptotic variance-covariance matrices of the estimators are also derived. Potential of the distribution is explored through two real data sets and compared with similar compounded distribution and the results demonstrate that the family of distributions can be considered as a suitable model under several real situations.
Breaking the Negative Cycle: Exploring the Design Space of Stratification for First-Class Datalog Constraints

The λ Dat calculus brings together the power of functional and declarative logic programming in one language. In λ Dat , Datalog constraints are first-class values that can be constructed, passed around as arguments, returned, composed with other constraints, and solved. A significant part of the expressive power of Datalog comes from the use of negation. Stratified negation is a particularly simple and practical form of negation accessible to ordinary programmers. Stratification requires that Datalog programs must not use recursion through negation. For a Datalog program, this requirement is straightforward to check, but for a λ Dat program, it is not so simple: A λ Dat program constructs, composes, and solves Datalog programs at runtime. Hence stratification cannot readily be determined at compile-time. In this paper, we explore the design space of stratification for λ Dat . We investigate strategies to ensure, at compile-time, that programs constructed at runtime are guaranteed to be stratified, and we argue that previous design choices in the Flix programming language have been suboptimal.
Colloids in two-dimensional active nematics: conformal cogs and controllable spontaneous rotation

A major challenge in the study of active systems is to harness their non-equilibrium dynamics into useful work. We address this by showing how to design colloids with controllable spontaneous propulsion or rotation when immersed in active nematics. This is illustrated for discs with tilted anchoring and chiral cogs, for which we determine the nematic director through conformal mappings. Our analysis identifies two regimes of behaviour for chiral cogs: orientation-dependent handedness and persistent active rotation. Finally, we provide design principles for active nematic colloids to achieve desired rotational dynamics.
Construction of a Relevance Knowledge Graph with Application to the LOCAL News Angle

News angles are approaches to journalism content often used to provide a way to present a new report from an event. One particular type of news angle is the LOCAL news angle where a local news outlet focuses on an event by emphasising a local connection. Knowledge graphs are most often used to represent knowledge about a particular entity in the form of relationships to other entities. In this paper we see how we can extract a knowledge sub graph containing entities and relevant relationships that are connected to the locality of a news outlet. The purpose of this graph is to use it for automated journalism or as an aid for the journalist to find local connections to an event, as well as how the local connection relate to the event. We call such a graph a relevance knowledge graph. An algorithm for extracting such a graph from a linked data source like DBpedia is presented and examples of the use of a relevance graph in a LOCAL news angle context are provided.