Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Proteomic Analysis Reveals the Key Molecular Events Driving Phaeocystis globosa Bloom and Dissipation

Phaeocystis globosa is a marine-bloom-forming haptophyte with a polymorphic life cycle alternating between free-living cells and a colonial morphotype, that produces high biomass and impacts ecological structure and function. The mechanisms of P. globosa bloom formation have been extensively studied, and various environmental factors are believed to trigger these events. However, little is known about the intrinsic biological processes that drive the bloom process, and the mechanisms underlying P. globosa bloom formation remain enigmatic. Here, we investigated a P. globosa bloom occurring along the Chinese coast and compared the proteomes of in situ P. globosa colonies from bloom and dissipation phases using a tandem mass tag (TMT)-based quantitative proteomic approach. Among the 5540 proteins identified, 191 and 109 proteins displayed higher abundances in the bloom and dissipation phases, respectively. The levels of proteins involved in photosynthesis, pigment metabolism, nitrogen metabolism, and matrix substrate biosynthesis were distinctly different between these two phases. Ambient nitrate is a key trigger of P. globosa bloom formation, while the enhanced light harvest and multiple inorganic carbon-concentrating mechanisms support the prosperousness of colonies in the bloom phase. Additionally, colonies in the bloom phase have greater carbon fixation potential, with more carbon and energy being fixed and flowing toward the colonial matrix biosynthesis. Our study revealed the key biological processes underlying P. globosa blooms and provides new insights into the mechanisms behind bloom formation.
BOVEDA, the Bolivian Vegetation Ecology Database: first stage, the Chacoan forests

Bolivia is a country exceptionally rich in biodiversity and home to about 20,000 vascular plant species and 15 plant formations. Therefore, it is particularly important to document the biodiversity of this territory. The aim of the Bolivian Vegetation Ecology Database (BOVEDA; GIVD ID SA-BO-005) is to record floristic and ecological data of Bolivian vegetation. In the first stage, the database hosts 320 relevés from one of the most unique biogeographical units in the country, the Chaco. In total, 633 species belonging to 114 families have been recorded. Data on vegetation structure, soil, flooding regime and geomorphology have also been stored. The following nine vegetation structural types were identified: (1) deciduous forests of alluvial plains on well to moderately well drained soils; (2) deciduous to semideciduous Chaco forests transitional to the Andes; (3) deciduous and sclerophyllous Cerrado thorn-woodlands and shrublands transitional to the Chaco (Abayoy); (4) xeromorphic thorn shrubland and thickets on vertic, poorly drained soils; (5) woodlands and savannas on sand dunes and aeolian surfaces; (6) freshwater swamp forests; (7) saltwater swamp forests; (8) phreatophytic forests; (9) deciduous to semideciduous Chaco forests transitional to the Chiquitania. Further steps will be to incorporate new types of vegetation already recorded in the field such as Altiplano shrublands, Andean wetlands, Andean Polylepis forests, and vegetation of the dry inter-Andean valleys. Taxonomic reference: Jørgensen et al. (2015). Abbreviations: BOVEDA = Bolivian Vegetation Ecology Database; GIVD = Global Index of Vegetation-Plot Databases.
Delocalization in harmonic chains with long-range correlated random masses

We study the nature of collective excitations in harmonic chains with masses exhibiting long-range correlated disorder with a power spectrum proportional to ${1/k}^{\ensuremath{\alpha}},$ where k is the wave vector of the modulations on the random masses landscape. Using a transfer-matrix method and exact diagonalization, we compute the localization length and participation ratio of eigenmodes within the band of allowed energies. We find extended vibrational modes in the low-energy region for $\ensuremath{\alpha}g1. $ In order to study the time evolution of an initially localized energy input, we calculate the second moment ${M}_{2}(t)$ of the energy spatial distribution. We show that ${M}_{2}(t),$ besides being dependent of the specific initial excitation and exhibiting an anomalous diffusion for weakly correlated disorder, assumes a ballistic spread in the regime $\ensuremath{\alpha}g1$ due to the presence of extended vibrational modes.
Spin channel induced directional dependent spin exchange interactions between divacantly substituted Fe atoms in graphene

In this study, we investigate the isolated magnetic interactions between two identical Fe atoms divacantly-substituted into graphene. Using density functional theory, we simulated the electronic and magnetic properties for a supercell of graphene with spatial variation of the Fe atoms along either the armchair or zig-zag directions. Overall, we find that the exchange interaction between the two Fe atoms fluctuates from ferromagnetic to antiferromagnetic as a function of the spatial distance in the armchair direction. Given the induced magnetic moment and increased density of states at the Fermi level by the surrounding carbon atoms, we conclude that an RKKY-like interaction may characterize the exchange interactions between the Fe atoms. Furthermore, we examined the same interactions for Fe atoms along the zig-zag direction in graphene and found no evidence for an RKKY interaction as this system shows standard superexchange between the transition-metal impurities. Therefore, we determine that Fe-substituted graphene produces a directional-dependent spin interaction, which may provide stability to spintronic and multifunctional devices and applications for graphene.
An Experimental Study on an Underexpanded Annular Impinging Jet

An underexpanded annular impinging jet is studied experimentally. The aerodynamic characteristics of the impinging jet depend significantly on the distance H between the nozzle exit plane and the impinged flat plate. The annular impinging jet diverges in the downstream direction provided H is smaller than a critical value H c . The feature of the jet changes significantly at H=H c
How Has COVID-19 Impacted Our Language Use?

The COVID-19 pandemic has led to severe consequences for people’s mental health. The pandemic has also influenced our language use, shaping our word formation habits. The overuse of new metaphorical meanings has received particular attention from the media. Here, we wanted to investigate whether these metaphors have led to the formation of new semantic associations in memory. A sample of 120 university students was asked to decide whether a target word was or was not related to a prime stimulus. Responses for pandemic pairs in which the target referred to the newly acquired metaphorical meaning of the prime (i.e., “trench”—“hospital”) were compared to pre-existing semantically related pairs (i.e., “trench”—“soldier”) and neutral pairs (i.e., “trench”—“response”). Results revealed greater accuracy and faster response times for pandemic pairs than for semantic pairs and for semantic pairs compared to neutral ones. These findings suggest that the newly learned pandemic associations have created stronger semantic links in our memory compared to the pre-existing ones. Thus, this work confirms the adaptive nature of human language, and it underlines how the overuse of metaphors evoking dramatic images has been, in part, responsible for many psychological disorders still reported among people nowadays.
UCLCHEM: A Gas-grain Chemical Code for Clouds, Cores, and C-Shocks

We present a publicly available, open source version of the time-dependent, gas-grain chemical code UCLCHEM. UCLCHEM propagates the abundances of chemical species through a large network of chemical reactions in a variety of physical conditions. The model is described in detail, along with its applications. As an example of possible uses, UCLCHEM is used to explore the effect of protostellar collapse on commonly observed molecules, and study the behavior of molecules in C-type shocks. We find the collapse of a simple Bonnor–Ebert sphere successfully reproduces most of the behavior of CO, CS, and NH3 from cores observed by Tafalla et al. (), but cannot predict the behavior of N2H+. In the C-shock application, we find that molecules can be categorized such that they become useful observational tracers of shocks and their physical properties. Although many molecules are enhanced in shocked gas, we identify two groups of molecules in particular. A small number of molecules are enhanced by the sputtering of the ices as the shock propagates, and then remain high in abundance throughout the shock. A second, larger set is also enhanced by sputtering, but then destroyed as the gas temperature rises. Through these applications, the general applicability of UCLCHEM is demonstrated.
A ZIP model accounting for response bias in randomized response

Randomized response (RR) is an interview technique that ensures confidentiality when sensitive questions are asked. In RR the answer to a sensitive question is partly determined by a randomizing device. Respondent may nevertheless feel uncomfortable with the RR design and give the least sensitive response, irrespective of the outcome of the randomizing device. This kind of self-protective response behavior yields biased prevalence estimates. In this paper we present the zero-inflated Poisson model to account for self-protective response bias in the presence of an RR variable with multiple, ordered response categories. An example from a Dutch social security survey is provided.
Hot and dense homogeneous nucleonic matter constrained by observations, experiment, and theory

We construct a new class of phenomenological equations of state for homogeneous matter for use in simulations of hot and dense matter in local thermodynamic equilibrium. We construct a functional form which respects experimental, observational and theoretical constraints on the nature of matter in various density and temperature regimes. Our equation of state matches (i) the virial coefficients expected from nucleon-nucleon scattering phase shifts, (ii) experimental measurements of nuclear masses and charge radii, (iii) observations of neutron star radii, (iv) theory results on the equation of state of neutron matter near the saturation density, and (v) theory results on the evolution of the EOS at finite temperatures near the saturation density. Our analytical model allows one to compute the variation in the thermodynamic quantities based on the uncertainties in the nature of the nucleon-nucleon interaction. Finally, we perform a correction to ensure the equation of state is causal at all densities, temperatures, and electron fractions.
Label-Free and Bioluminescence-Based Nano-Biosensor for ATP Detection

A bioluminescence-based assay for ATP can measure cell viability. Higher ATP concentration indicates a higher number of living cells. Thus, it is necessary to design an ATP sensor that is low-cost and easy to use. Gold nanoparticles provide excellent biocompatibility for enzyme immobilization. We investigated the effect of luciferase proximity with citrate-coated gold, silver, and gold–silver core–shell nanoparticles, gold nanorods, and BSA–Au nanoclusters. The effect of metal nanoparticles on the activity of luciferases was recorded by the luminescence assay, which was 3–5 times higher than free enzyme. The results showed that the signal stability in presence of nanoparticles improved and was reliable up to 6 h for analytes measurements. It has been suggested that energy is mutually transferred from luciferase bioluminescence spectra to metal nanoparticle surface plasmons. In addition, we herein report the 27-base DNA aptamer for adenosine-5′-triphosphate (ATP) as a suitable probe for the ATP biosensor based on firefly luciferase activity and AuNPs. Due to ATP application in the firefly luciferase reaction, the increase in luciferase activity and improved detection limits may indicate more stability or accessibility of ATP in the presence of nanoparticles. The bioluminescence intensity increased with the ATP concentration up to 600 µM with a detection limit of 5 µM for ATP.
Dynamic Guidance Virtual Fixtures for Guiding Robotic Interventions: Intraoperative MRI-guided Transapical Cardiac Intervention Paradigm

The advent of intraoperative real-time image guidance has led to the emergence of new surgical interventional paradigms including image-guided robot assistance. Most often the use of an intraoperative imaging modality is limited to visual perception of the area of procedure. In this work, we propose a system for performing interventions with real-time Magnetic Resonance Imaging (rtMRI). The described computational core, processes on-the-fly rtMRI and generates dynamic guidance virtual fixture that in turn is used to update visualization and a force-feedback interface. The system was experimentally tested by applying it to a simulated Transapical Aortic Valve Implantation with a virtual robotic manipulator. The study results demonstrate significant improvement in the surgical task by decreasing the duration of the procedure and increasing safety in the presence of cardiac and breathing motion.
Transfer learning in BCI-EEG

– Transfer learning deals with the problem of variability between subjects. In brain-computer interface (BCI), it aims at using the knowledge of previous subjects having performed the same kind of experiments. Transfer Component Analysis (TCA) is a transfer learning method that searches for a low-dimensional feature space which reduces the difference between domain distributions. In this paper, we propose a novel manner to apply the TCA method in the Riemannian geometry. First, the method, originally proposed to be applied in the Euclidean space, is tested to align the variance of the spatially ﬁltered EEG epochs of different subjects. Then, the method is adapted to align covariance matrix based feature. Our approach is applied on a publicly available MI BCI-database. Results indicate that our method gives better results than the traditional TCA that is based on feature vectors.
Can Fulfillment of Social Responsibility Enable Enterprises to Innovate? The Role of Corporate Financialization and Agency Costs

This study constructs a panel model to conduct an empirical analysis on the influence of fulfillment of social responsibility on corporate innovation and its mechanism based on the annual data of A-share listed companies in China from 2010 to 2020. Research results show that (1) fulfillment of social responsibility has a positive effect on corporate innovation. Compared with enterprises with high economic policy uncertainty and low equity balance and non-state-owned enterprises, the implementation of social responsibility has a larger impact on the innovation of enterprises with low economic policy uncertainty and high equity balance and state-owned enterprises. (2) Corporate financialization and agency costs play a mediating role in the relationship between fulfillment of social responsibility and corporate innovation. Fulfillment of social responsibility can promote enterprise innovation by alleviating the capital-crowding effect caused by enterprise financialization and reducing agency costs. (3) Commercial credit has a positive moderating effect on the relationship between fulfillment of social responsibility and enterprise innovation. (4) Institutional investors have a negative moderating effect on the relationship between the fulfillment of social responsibility and enterprise innovation. The results can provide important theoretical guidance and serve as a decision-making reference for standardizing corporate social responsibility behavior and realizing the high-quality development of the Chinese economy.
T Mobile Health Interventions Examining medication adherence outcomes among patients with cancer

BACKGROUND: Treatment for cancer is trending toward oral therapies, which patients can self-manage from home. Proper adherence to oral therapy is vital to safe and optimal care in this setting. Mobile health interventions (i.e., text message reminders, mobile applications, and automated calls) are an evolving strategy aimed at improving medication adherence for patients on long-term oral therapies. OBJECTIVES: This review aims to provide an over-view of research outcomes for the use of mobile health interventions among patients with cancer. METHODS: A comprehensive review of CINAHL ® , MEDLINE ® , and PubMed ® was completed. Eleven articles were eligible for inclusion in this review. FINDINGS: Mobile health interventions are an acceptable approach among patients with cancer and may improve adherence outcomes for those at highest risk for suboptimal adherence.
An assessment of Multi Object Tracking on low framerate conditions

Résumé – La performance des méthodes de l’état de l’art en suivi multi-objets est généralement montrée sur des vidéos à fréquence image élevée, où les objets bougent très peu d’une image à l’autre. Il y a cependant des intérêts à travailler dans le cas plus difficile d’une fréquence faible ou alternativement de mouvements forts. Cet article étudie à quel point la performance de suivi est affectée par une diminution de fréquence image, et identifie les méthodes les plus adaptées à cet usage alternatif. Abstract – The performance of state-of-the-art multiple object tracking methods is usually shown on high-framerate videos, for which objects move very little between two consecutive frames. Nonetheless, there is an interest in working in the harder scenario of low framerate or similarly, strong motion. This article studies how much tracking performance is affected by a decrease in image frequency, and identifies the methods that are better suited for this alternative use-case.
Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions

Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict, the ( conflict avoidance effect ), and (2) road user response should a conflict be entered, the ( collision avoidance effect ). This study examined the collision avoidance effect of an automated driving system (ADS) using a human behavior reference model. The potential benefits of ADS technology have been widely anticipated, and reliable performance benchmarking methodologies for assessing ADS performance is an essential component of determining system readiness. The reference model used in this study reflects the response time and evasive action (referred to as collision avoidance) of a human driver that is n on- i mpaired, with e yes on the conflict (NIEON) - a consistently performing, always-attentive driver that does not exist in the human population. Notably, the NIEON model is a tool for evaluating collision avoidance only, as it inherits the pre-conflict behaviors (includes conflict avoidance) of the ADS that is being evaluated. In this way, the testing regime isolates the analysis to focus only on the collision avoidance effect . Counterfactual simulations of the Waymo Driver and the NIEON model were run on potentially avoidable (excludes rear-end struck) responder collision scenarios (ADS is responding to some potential collision partners sudden, unexpected actions). All of these simulated events were reconstructed fatal crashes that occurred during a 10-year period in the Operational Design Domain of the Waymo ADS in Chandler, Arizona (Scanlon et al., 2021). In this area, users of the commercial Waymo One service can hail an ADS without a human in the driver’s seat. A conflict must be entered to examine the collision avoidance effect using the NIEON model (or else there is no potential collision to examine performance). 16 total conflicts were identified for this study from a prior analysis by Scanlon et al. (2021). Regarding the collision avoidance effect , of 16 conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated
Gradient Descent for Symmetric Tensor Decomposition

Symmetric tensor decomposition is of great importance in applications. Several studies have employed a greedy approach, where the main idea is to (cid:12)rst (cid:12)nd a best rank-one approximation of a given tensor, and then repeat the process to the residual tensor by subtracting the rank-one component. In this paper, we focus on (cid:12)nding a best rank-one approximation of a given or-thogonally order-3 symmetric tensor. We give a geometric landscape analysis of a nonconvex optimization for the best rank-one approximation of orthogonally symmetric tensors. We show that any local minimizer must be a factor in this orthogonally symmetric tensor decomposition, and any other critical points are linear combinations of the factors. Then, we propose a gradient descent algorithm with a carefully designed initialization to solve this nonconvex optimization problem, and we prove that the algorithm converges to the global minimum with high probability for orthogonal decomposable tensors. This result, combined with the landscape analysis, reveals that the greedy algorithm will get the tensor CP low-rank decomposition. Numerical results are provided to verify our theoretical results.
FreqyWM: Frequency Watermarking for the New Data Economy

We present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against various attacks and derive analytical bounds for the false positive probability of erroneously detecting a watermark on a dataset that does not carry it. Our technique is applicable to both single dimensional and multidimensional datasets, is independent of token type, allows for a fine control of the introduced distortion, and can be used in a variety of use cases that involve buying and selling data in contemporary data marketplaces.
Discretization-Agnostic Deep Self-Supervised 3D Surface Parameterization

We present a novel self-supervised framework for learning the discretization-agnostic surface parameterization of arbitrary 3D objects with both bounded and unbounded surfaces. Our framework leverages diffusion-enabled global-to-local shape context for each vertex first to partition the unbounded surface into multiple patches using the proposed self-supervised PatchNet and subsequently perform independent UV parameterization of these patches by learning forward and backward UV mapping for individual patches. Thus, our framework enables learning a discretization-agnostic parameterization at a lower resolution and then directly inferring the parameterization for a higher-resolution mesh without retraining. We evaluate our framework on multiple 3D objects from the publicly available SHREC [Lian et al. 2011] dataset and report superior/faster UV parameterization over conventional methods.