Miracle-dz/UniFAR-FTUs huggingface dataset:
Stuck on a small tropical island: wide in-situ diversification of an urban-dwelling bat

Bats are often the only mammals naturally colonizing isolated islands and are thus an excellent model to study evolutionary processes of insular ecosystems. Here, we studied the Reunion free-tailed bat (Mormopterus francoismoutoui), an endemic species to Reunion Island that has adapted to urban settings. At regional scale, we investigated the evolutionary history of Mormopterus species, as well as on Reunion Island sex-specific and seasonal patterns of genetic structure. We used an extensive spatio-temporal sampling including 1,136 individuals from 18 roosts and three biological seasons (non-reproductive/winter, pregnancy/summer, and mating), with additional samples from Mormopterus species from neighbouring islands (M. jugularis of Madagascar and M. acetabulosus of Mauritius). Complementary information gathered from both microsatellite and mitochondrial markers revealed a high genetic diversity but no signal of spatial genetic structure and weak evidence of female philopatry. Regional analysis suggests a single colonization event for M. francoismoutoui, dated around 175,000 years ago, and followed by in-situ diversification and the evolution of divergent ancestral lineages, which today form a large metapopulation. Population expansion was relatively ancient (55,000 years ago) and thus not linked to human colonization of the island and the availability of new anthropic day-roost sites. Discordant structure between mitochondrial and microsatellite markers suggests the presence of yet-unknown mating sites, or the recent evolution of putative ecological adaptations. Our study illustrates how understanding mechanisms involved in speciation can be challenging and the importance of both mitochondrial and nuclear DNA in resolving the wide in-situ diversification of an urban-dwelling bat, endemic to a small island.
Structural and functional analysis of vaccinia viral fusion complex component protein A28 through NMR and molecular dynamic simulations

Host cell entry of vaccinia virus (a poxvirus) proceeds through multiple steps that involve many viral proteins to mediate cell infection. Upon binding to cells, vaccinia virus membrane fuses with host membranes via a viral entry fusion protein complex comprising 11 proteins: A16, A21, A28, F9, G3, G9, H2, J5, L1, L5 and O3. Despite vaccinia virus having two infectious forms, mature and enveloped, that have different membrane layers, both forms require an identical viral entry fusion complex for membrane fusion. Components of the poxvirus entry fusion complex that have been structurally assessed to date share no known homology with all other type I, II and III viral fusion proteins, and the large number of fusion protein components renders it a unique system to investigate poxvirus-mediated membrane fusion. Here, we determined the NMR structure of a truncated version of vaccinia A28 protein. We also expressed a soluble H2 protein and showed that A28 interacts with H2 protein at a 1:1 ratio in vitro. Furthermore, we performed extensive in vitro alanine mutagenesis to identify A28 protein residues that are critical for H2 binding, entry fusion complex formation, and virus-mediated membrane fusion. Finally, we used molecular dynamic simulations to model full-length A28-H2 subcomplex in membranes. In summary, we characterized vaccinia virus A28 protein and determines residues important in its interaction with H2 protein and membrane components. We also provide a structural model of the A28-H2 protein interaction to illustrate how it forms a 1:1 subcomplex on a modeled membrane.
Quercetin Suppresses TNBC Cell by Targeting ORM2

Background Triple-negative breast cancer (TNBC) is known for its aggressive nature, and Quercetin (QUE) has shown potential anti-cancer effects. Methods We determined the IC50 of QUE for inhibiting cell viability in multiple TNBC, non-TNBC, and normal breast cell lines. We compared the expression of ORM2 in TNBC clinical samples and normal tissues. Additionally, we measured ORM2 expression in TNBC and normal breast cell lines. We determined the IC50 of QUE for inhibiting cell viability after ORM2 knockdown. An orthotopic implantation mice model was used to evaluate the treatment effect of QUE. We also conducted molecular docking and amino acid exchange validation to model the binding of QUE to ORM2. Furthermore, we performed a protein-protein interaction network analysis and GO enrichment analysis of differentially expressed genes associated with ORM2 in TNBC. Results QUE inhibited the viability of both TNBC and non-TNBC cell lines, but it was specifically associated with worse survival in TNBC patients. We observed higher expression of ORM2 in breast cancer cells compared to normal breast cells. Knockdown of ORM2 reduced the viability of TNBC cells. Treatment with QUE inhibited ORM2 expression and decreased viability in TNBC cells. In the animal model, QUE improved survival and downregulated ORM2 expression in tumors. Enrichment analysis provided insights into the potential functions of ORM2. Conclusion Our findings indicate that QUE directly inhibits TNBC cell viability through its interaction with ORM2. These results contribute to our understanding of the anti-cancer mechanisms of QUE in TNBC and highlight ORM2 as a potential therapeutic target.
How well do rudimentary plasticity rules predict adult visual object learning?

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer. Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear – in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings. Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n=2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n=371,000 trials), which we then used to develop (and publicly release) empirical benchmarks for comparing learning models to humans. We evaluated each learning model on these benchmarks, and found that learning models that using specific, high-level contemporary representations are surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem. Author Summary A basic conceptual hypothesis for how an adult brain learns to visually identify a new object is: 1) it re-represents images as points in a fixed, multidimensional space, then 2) it learns linear decision boundaries that separate images of a new object from others, using a single layer of plasticity. This hypothesis is considered biologically plausible, but gauging its power to explain human learning behavior has not been straightforward. In part, this is because it is difficult to model how brains re-represent images during object learning. However, ongoing efforts in neuroscience have led to the identification of specific, image-computable models that are at least partially accurate descriptions of the neural representations involved in primate vision. Here, we asked whether any of those representations, when combined them with simple plasticity rules, could make accurate predictions over a large body of human object learning behavioral measurements. We found that specific models could indeed explain a majority of our behavioral measurements, suggesting the rudimentary, biologically-plausible mechanisms considered here may be sufficient to explain a core aspect of human object learning.
Inference for Diffusion Processes and Stochastic Volatility Models

We discuss parameter estimation for discretely observed, ergodic diffusion processes where the diffusion coefficient does not depend on the parameter. We propose using an approximation of the continuous-time score function as an estimating function. The estimating function can be expressed in simple terms through the drift and the diffusion coefficient and is thus easy to calculate. Simulation studies show that the method performs well.
Stackelberg differential game for insurance under model ambiguity: general divergence

We solve a Stackelberg differential game between a buyer and a seller of insurance policies, in which both parties are ambiguous about the insurable loss. Both the buyer and seller maximize their expected wealth, plus a penalty term that reflects ambiguity, over an exogenous random horizon. Under a mean-variance premium principle and a general divergence that measures the players' ambiguity, we obtain the Stackelberg equilibrium semi-explicitly. Our main results are that the optimal variance loading equals zero and that the seller's robust optimal premium rule equals the net premium under the buyer's optimally distorted probability. Both of these important results generalize those we obtained in [Cao, J., Li, D. , Young, V. R. & Zou, B. (2022). Stackelberg differential game for insurance under model ambiguity. Insurance: Mathematics and Economics, 106, 128–145. ] under squared-error divergence.
Toward a game literacy program for adults in their 50s and older: changing perceptions through game experiences

ABSTRACT There is criticism that discussions surrounding games for older populations are mainly focused on practical approaches to cognitive and physical training. In addition, negative attitudes toward digital games and limited experience contribute to older populations’ isolation from mainstream game culture. This study aims to examine perception changes toward digital games in adults in their 50s and older as they gain actual game experience and an appropriate game literacy program for older populations. We organized eight sessions of gameplay workshops for participants aged 50 years and over (N = 40, age range = 50–85 years) at two senior learning centers located in Seoul and Gyeonggi-do in South Korea. The workshops consisted of lectures for game literacy, co-playing of mobile games, and discussions about game experience. A thematic analysis of participants’ statements and game diaries revealed their perception changes toward games. Participants engaged in a variety of mobile games and felt conflicted between their positive and negative experiences. They reported that their game experience had contributed to identifying the positive values of games, understanding diversity, and improving their understanding of the younger generation. In addition, they indicated that future directions for fostering a game culture in older age groups should include playing for connectivity, health, relaxation, and game literacy education. This study highlights games as a cultural medium that entertains and connects people. By linking theories with practice, we propose a framework for a game literacy program to support older populations’ active participation in game culture.
Positive Implicative BCI-algebras

This note is a continuation of [1] and [2]. We introduce the concept ofpositive implicative BCI--algebras: a BCI-algebra X is said to be positive implicative if itsatisfies (x * (x * y)) * (y * x) =x * (x * (y * (y * x))). The following results are proved:The class of positive implicative BCK-algebras and the class of p--semisimple algebras areproper subclasses of the class of positive implicative BCI-algebras; Let X be a BCI-alge-bra. If its BCK--part B(X) is a positive implicativc BCK--algebra and if its pure BCI--partA(X) is a p--semisimple BCI--algebra, then X is a positive implicative BCI--algebra; ABCI--algebra is implicative iff it is both commutative and positive implicative.
Approximation and Optimization of Global Environmental Simulations with Neural Networks

Solving a system of hundreds of chemical differential equations in environmental simulations has a major computational complexity, and thereby requires high performance computing resources, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of simulations with some factor of speed-up while using less compute resources. In this work, we introduce a neural network based approach (ICONET) to forecast trace gas concentrations without executing the traditional compute-intensive atmospheric simulations. ICONET is equipped with a multifeature Long Short Term Memory (LSTM) model to forecast atmospheric chemicals iteratively in time. We generated the training and test dataset, our target dataset for ICONET, by execution of an atmospheric chemistry simulation in ICON-ART. Applying the ICONET trained model to forecast a test dataset results in a good fit of the forecast values to our target dataset. We discussed appropriate metrics to evaluate the quality of models and presented the quality of the ICONET forecasts with RMSE and KGE metrics. The variety in the nature of trace gases limits the model's learning and forecast skills according to the respective trace gas. In addition to the quality of the ICONET forecasts, we described the computational efficiency of ICONET as its run time speed-up in comparison to the run time of the ICON-ART simulation. The ICONET forecast showed a speed-up factor of 3.1 over the run time of the atmospheric chemistry simulation of ICON-ART, which is a significant achievement, especially when considering the importance of ensemble simulation.
Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: data preservation problem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.
Ultracold bosonic atoms in optical lattices

Title of dissertation: ULTRACOLD BOSONIC ATOMS IN OPTICAL LATTICES Ana Maria Rey, Doctor of Philosophy, 2004 Dissertation directed by: Prof. Theodore R. Kirkpatrick Department of Physics Dr. Charles W. Clark National Institute of Standards and Technology This thesis covers most of my work in the field of ultracold atoms loaded in optical lattices. It makes a route through the physics of cold atoms in periodic potentials starting from the simple noninteracting system and going into the many-body physics that describes the strongly correlated Mott insulator regime. Even though this thesis is a theoretical work all the chapters are linked either with experiments already done or with ongoing experimental efforts. This thesis can be divided into four different parts. The first part comprises chapters 1 to 3. In these chapters, after a brief introduction to the field of optical lattices I review the fundamental aspects pertaining to the physics of systems in periodic potentials. The second part deals with the superfluid weakly interacting regime where standard mean field techniques can be applied. This is covered in chapters 4 and 5. Specifically, chapter 4 introduces the discrete nonlinear Schr ödinger equation (DNLSE) and uses it to model some experiments. In chapter 5 I go one step further and include the small quantum fluctuations neglected in the DNLSE by studying quadratic approximations of the Bose-Hubbard Hamiltonian. Chapters 6 to 8 can be grouped as the third part of the thesis. In them I adopt an effective action formalism, the so called two particle irreducible effective action (2PI) together with the closed time path (CTP) formalism to study far-from-equilibrium dynamics. The many-body techniques discussed in these chapters systematically include higher order quantum corrections, not included in the quadratic approximations of the Hamiltonian, which we show are crucial for a correct description of the quantum dynamics outside the very weakly interacting regime. Finally, chapter 9 to 11 are devoted to study the Mott insulator phase. In these chapters using perturbation theory I study the Mott insulator ground state and its excitation spectrum, the response of the system to Bragg spectroscopy, and propose a mechanism to correct for the residual quantum coherences inherent to the Mott insulator ground state. Even though small these are not ideal for the use of neutral atoms in optical lattice as a tool for quantum computation. Ultracold bosonic atoms in optical lattices
Effectiveness of Hybrid Learning as Technology Based Learning Model in the New Normal Era

Hybrid learning (HL) has become one of the learning models used when entering the new normal. Although various studies have measured HL, the effectiveness of HL model usage remains a concern for universities, especially Bina Nusantara University (BINUS) Jakarta. The main problem is that the use of hybrid learning conducted by BINUS is new. The purpose of this study was to determine the effectiveness of hybrid learning as technology-based learning by students in BINUS Jakarta. The population in this study was active students of BINUS Jakarta, totaling 127 students. The technique of collecting the sample was using random sampling. The statistical method uses Pearson correlation analysis and description. Based on the descriptive analysis the results found that hybrid learning was reflected in the role of technology (helps students in finding the information needed for learning by using gadgets and computers), campus commitment (providing internet facilities, Wi-Fi, computers, audio connectors, television, cameras, internet, and InFocus in each class), learning activities (presentations and discussions, independent assignments, group assignments in the form of projects (problem based), and final tests), and student readiness (previously using a blended learning process). There are 81.1% of students in BINUS Jakarta who feel that the hybrid learning system is effective in the learning process carried out during the Covid-19 condition, at the same time preparing for the new normal era. Hybrid learning, which is most effective for students in BINUS Jakarta, can be felt when there is the role of technology, campus commitment, learning activities, and students' readiness to participate in learning.
Passwords and Cryptwords: The Final Limits on Lengths

Computers get faster every year; brains don’t. Passwords and other memorized credentials have unique usability advantages over tokens and biometrics, so we desire to design secure systems that maintain lengths that users can memorize. Some passwords are subject primarily to online attacks, and are simple to defend with rate limits and lockouts. Others, used to generate encryption keys, must be secure against offline attacks. We coin the term “cryptword” to distinguish these from passwords subject primarily to online attacks. Authentication passwords do not need to get longer as computers get faster, if protected by rate limits and lockouts. Using password key derivation functions (pwKDFs) — a class of preexisting cryptographic algorithms — we show that cryptwords can also remain the same length and maintain their security strength despite advances in computation. We achieve this by regularly updating the pwKDF parameters and regenerating the derived key from the cryptword. In cases where it is not possible to meaningfully regenerate the derived key, such as archival data or public verifiers, cryptword lengths should be chosen to last the lifetime of the data. We provide simple equations that end users and system administrators can use to determine minimal assigned password and cryptword lengths based on personal threat models. We also show how to use the capabilities of cloud computing providers to estimate attacker costs. These same equations give a timeframe for cryptword and secret rotation once the encrypted data leaks. Because these equations do not rely on the current date or current hardware capabilities, they show that if regularly used, password and cryptword lengths can remain constant despite improvements in hardware.