Miracle-dz/UniFAR-FTUs huggingface dataset:
Optogenetic stimulation of Kiss1ARC terminals in the AVPV induces surge-like luteinizing hormone secretion via glutamate release in mice

Kisspeptin neurons are mainly located in the arcuate (Kiss1ARC, vis-à-vis the GnRH pulse generator) and anteroventral periventricular nucleus (Kiss1AVPV, vis-à-vis the GnRH surge generator). Kiss1ARC send fibre projections that connect with Kiss1AVPV somata. However, studies focused on the role of Kiss1ARC neurons in the LH surge are limited, and the role of Kiss1ARC projections to AVPV (Kiss1ARC→AVPV) in the preovulatory LH surge is still unknown. To investigate its function, this study used optogenetics to selectively stimulate Kiss1ARC→AVPV and measured changes in circulating LH levels. Kiss1ARC in Kiss-Cre-tdTomato mice were virally infected to express channelrhodopsin-2 proteins, and optical stimulation was applied selectively via a fibre optic cannula in the AVPV. Sustained 20 Hz optical stimulation of Kiss1ARC→AVPV from 15:30 to 16:30 h on proestrus effectively induced an immediate increase in LH reaching peak surge-like levels of around 8 ng/ml within 10 min, followed by a gradual decline to baseline over about 40 min. Stimulation at 10 Hz resulted in a non-significant increase in LH levels and 5 Hz stimulation had no effect in proestrous animals. The 20 Hz stimulation induced significantly higher circulating LH levels on proestrus compared with diestrus or estrus, which suggested that the effect of terminal stimulation is modulated by the sex steroid milieu. Additionally, intra-AVPV infusion of glutamate antagonists, AP5+CNQX, completely blocked the increase on LH levels induced by Kiss1ARC→AVPV terminal photostimulation in proestrous animals. These results demonstrate for the first time that optical stimulation of Kiss1ARC→AVPV induces an LH surge-like secretion via glutamatergic mechanisms. In conclusion, Kiss1ARC may participate in LH surge generation by glutamate release from terminal projections in the AVPV.
Construction and Characterisation of Stationary and Mass-Stationary Random Measures on ${\mathbb R}^d$

Mass-stationarity means that the origin is at a typical location in the mass of a random measure. It is an intrinsic characterisation of Palm versions with respect to stationary random measures. Stationarity is the special case when the random measure is Lebesgue measure. The paper presents constructions of stationary and mass-stationary versions through change of measure and change of origin. Further, the paper considers characterisations of mass-stationarity by distributional invariance under preserving shifts agains stationary independent backgrounds.
Plant-based expression platforms to produce high-value metabolites and proteins

Plant-based heterologous expression systems can be leveraged to produce high-value therapeutics, industrially important proteins, metabolites, and bioproducts. The production can be scaled up, free from pathogen contamination, and offer post-translational modifications to synthesize complex proteins. With advancements in molecular techniques, transgenics, CRISPR/Cas9 system, plant cell, tissue, and organ culture, significant progress has been made to increase the expression of recombinant proteins and important metabolites in plants. Methods are also available to stabilize RNA transcripts, optimize protein translation, engineer proteins for their stability, and target proteins to subcellular locations best suited for their accumulation. This mini-review focuses on recent advancements to enhance the production of high-value metabolites and proteins necessary for therapeutic applications using plants as bio-factories.
Kinematical uniqueness of homogeneous isotropic LQC

In a paper by Ashtekar and Campiglia, invariance under volume preserving residual diffeomorphisms has been used to single out the standard representation of the reduced holonomy-flux algebra in homogeneous loop quantum cosmology (LQC). In this paper, we use invariance under all residual diffeomorphisms to single out the standard kinematical Hilbert space of homogeneous isotropic LQC for both the standard configuration space RBohr, as well as for the Fleischhack one R⊔RBohr. We first determine the scale invariant Radon measures on these spaces, and then show that the Haar measure on RBohr is the only such measure for which the momentum operator is hermitian w.r. t. the corresponding inner product. In particular, the measure is forced to be identically zero on R in the Fleischhack case, so that for both approaches, the standard kinematical LQC-Hilbert space is singled out.
Theoretical analysis of the thermoelectric properties of penta-PdX2 (X = Se, Te) monolayer

Based on the successful fabrication of PdSe2 monolayers, the electronic and thermoelectric properties of pentagonal PdX2 (X = Se, Te) monolayers were investigated via first-principles calculations and the Boltzmann transport theory. The results showed that the PdX2 monolayer exhibits an indirect bandgap at the Perdew–Burke–Ernzerhof level, as well as electronic and thermoelectric anisotropy in the transmission directions. In the PdTe2 monolayer, P-doping owing to weak electron–phonon coupling is the main reason for the excellent electronic properties of the material. The low phonon velocity and short phonon lifetime decreased the thermal conductivity (κ l) of penta-PdTe2. In particular, the thermal conductivity of PdTe2 along the x and y transmission directions was 0.41 and 0.83 Wm−1K−1, respectively. Owing to the anisotropy of κ l and electronic structures along the transmission direction of PdX2, an anisotropic thermoelectric quality factor ZT appeared in PdX2. The excellent electronic properties and low lattice thermal conductivity (κ l) achieved a high ZT of the penta-PdTe2 monolayer, whereas the maximum ZT of the p- and n-type PdTe2 reached 6.6 and 4.4, respectively. Thus, the results indicate PdTe2 as a promising thermoelectric candidate.
Natural killer cell-mediated immunopathology in recurrent pregnancy losses Exploration of Immunology

et al. Natural killer cell-mediated immunopathology in recurrent pregnancy losses. Explor Immunol. Abstract Natural killer (NK) cells have a dual role in human reproduction for maternal-fetal tolerance and protection from infection. During the ovarian cycle and pregnancy, peripheral NK (pNK) and uterine NK (uNK) cells dynamically change their proportions and cytotoxicities to prepare and accommodate invading trophoblast and maintain pregnancy. However, dysregulated pNK and uNK cell proportions and cytotoxic activities have been associated with aberrant spiral artery remodeling and trophoblast invasion, leading to implantation failures and recurrent pregnancy losses (RPLs). This review will focus on the role of NK cells in RPLs reviewing the ontogeny of NK cells, changes in pNK and uNK cell levels, and activities during the ovarian cycle, normal pregnancy, and RPL. In addition, the immunopathological role of NK cells in endometrial/decidual vascular development and killer immunoglobin-like receptor (KIR) and human leukocyte antigen (HLA)-C interactions are discussed. shift in the immune system during pNK and therapeutic parameters in patients with RPL.
GALAXY LUMINOSITY FUNCTION AND TULLY–FISHER RELATION: RECONCILED THROUGH ROTATION-CURVE STUDIES

The relation between galaxy luminosity L and halo virial velocity vvir required to fit the galaxy luminosity function differs from the observed Tully–Fisher relation between L and disk speed vrot. Because of this, the problem of reproducing the galaxy luminosity function and the Tully–Fisher relation simultaneously has plagued semianalytic models since their inception. Here we study the relation between vrot and vvir by fitting observational average rotation curves of disk galaxies binned in luminosity. We show that the vrot–vvir relation that we obtain in this way can fully account for this seeming inconsistency. Therefore, the reconciliation of the luminosity function with the Tully–Fisher relation rests on the complex dependence of vrot on vvir, which arises because the ratio of stellar mass to dark matter mass is a strong function of halo mass.
Using semantics to interpret learning needs by organizational climate surveys

Organizational climate survey is a typical tool used to understand and monitor individual, team and organizational features factors that distinguish the company climate. Performing this activity in the final phase of a pandemic means collecting information about changes that remote working brought to the work processes and to the new each organizational dimension, highlighting present or future transformation paths. It is also an implicit survey of potential training needs. The construction of a new dimension of working spaces, which we can describe as an organizational metaverse , happens thanks to the real and deep understanding of changing needs and dynamics in the working processes. Organizational health in terms of identity, relationships, processes, and work results, also represents a starting point for the exploration of new learning needs of the organization. The transformation makes the organization be more and more dynamic, making it to function as a multiform organism, in which the multiple relational dimensions in real and the virtual world means an identity reconstruction and new forms of sociality: it may increase sharing and comparison and rise new needs that the company must know. Combining the climate survey with NLP analysis tools to understand the expressed meanings in written open questions and a collaborative activity, represents a significant experimentation of the multiple dimensions connected between the individual and the organization that becomes not only a physical place, processes, and results, but an increasingly social dimension that must be fully understood. From the analysis of the post-pandemic climate, the organization must be able to understand new useful meanings and need to support its progressive improvement and the well-being and individual satisfaction.
THE WIRED SURVEY. IV. NEW DUST DISKS FROM THE McCOOK & SION WHITE DWARF CATALOG

We have compiled photometric data from the Wide-field Infrared Survey Explorer All Sky Survey and other archival sources for the more than 2200 objects in the original McCook & Sion Catalog of Spectroscopically Identified White Dwarfs. We applied color-selection criteria to identify 28 targets whose infrared spectral energy distributions depart from the expectation for the white dwarf (WD) photosphere alone. Seven of these are previously known WDs with circumstellar dust disks, five are known central stars of planetary nebulae, and six were excluded for being known binaries or having possible contamination of their infrared photometry. We fit WD models to the spectral energy distributions of the remaining ten targets, and find seven new candidates with infrared excess suggesting the presence of a circumstellar dust disk. We compare the model dust disk properties for these new candidates with a comprehensive compilation of previously published parameters for known WDs with dust disks. It is possible that the current census of WDs with dust disks that produce an excess detectable at K-band and shorter wavelengths is close to complete for the entire sample of known WDs to the detection limits of existing near-IR all-sky surveys. The WD dust disk candidates now being found using longer wavelength infrared data are drawn from a previously underrepresented region of parameter space, in which the dust disks are overall cooler, narrower in radial extent, and/or contain fewer emitting grains.
Constructing the classical limit for quantum systems on compact semisimple Lie algebras

We give a general construction for the classical limit of a quantum system defined in terms of generators of an arbitrary compact semisimple Lie algebra, generalizing known results for the and cases. The classical limit depends on the physical problem in question and is determined by the sequence of representations by which it is reached. Only in the simplest cases is it unique. We present explicit formulae useful in determining the classical limit in all important cases.
From networks to pathway analysis

Biological mechanisms stem from complex intracellular interactions spanning across different levels of regulation. Mapping these interactions is fundamental for the understanding of all types of biological conditions, including complex diseases. Each experimental approach carries its own bias and noise. Combining heterogeneous data sources reduces noise and gives a broader sense of the interactions between genes known as functional association, where both direct and indirect interactions are captured. FunCoup is one of the most comprehensive functional association databases, providing networks for 22 organisms in all domains of life. FunCoup uses a naïve Bayesian integration approach to combine 11 different data types and increases the coverage by transferring associations between species via orthologs. Additional insights into the mechanisms of a gene network are provided through tissue specificity filtering and directed regulatory links. Even though FunCoup provides a comprehensive map of the intracellular machinery, gaining insights into conditions such as diseases requires a functional level analysis rather than a gene level analysis. Thus, studying genes that are involved in a condition from a functional perspective requires the usage of pathway enrichment analysis. Several approaches exist, from basic gene overlap to more elaborate analyses that use functional association networks. ANUBIX is a novel network-based analysis (NBA) method that overcomes the high false positive rate issue that previous state-of-the-art NBA approaches have. Additionally, even with accurate methods, a commonly ignored problem is that gene sets derived from experiments are often noisy or contain multiple mechanisms, mixing different pathways which weakens their association to the condition under study. To increase the sensitivity of pathway analysis, we developed a pipeline to cluster gene sets into more homogeneous parts with the aim of unraveling all the mechanisms activated in the studied condition. To facilitate the usage of these tools, we built a web server called PathBIX, a user-friendly platform that allows interactive analysis of all species in FunCoup against multiple pathway databases. Stockholm 2021 http://urn.kb. se/resolve? urn=urn:nbn:se:su:diva-195048 ISBN 978-91-7911-564-7 ISBN 978-91-7911-565-4 Department of Biochemistry and Biophysics Stockholm University, 106 91 Stockholm FROM NETWORKS TO PATHWAY ANALYSIS Miguel Castresana Aguirre
A rapid review on Tangible User Interfaces-augmented storytelling in inclusive education

Tangible User Interfaces (TUIs) application in education showed its benefits and strengths in enhancing learning and effective educational experiences of pupils. One of the potential application fields of TUIs in education is storytelling and narration, where the interaction with tangibles could enhance the involvement of children in listening and creating stories but also the inclusion of children with special needs. However, it is crucial to provide a methodological framework as a guide, not only to design inclusive learning through TUIs-enhanced storytelling, but also to evaluate TUIs application expected to sustain these practices, professionals, and practitioners in early years’ education. The present rapid review focuses on TUIs developed with the aim of fostering educational and inclusive storytelling activities with a view to identify relevant studies that evaluated the effectiveness of TUIs in educational settings to enhance inclusive storytelling practices. studies published until 2022 were identified. The results reveal gaps in the current literature and a paucity of relevant studies on TUIs for inclusive storytelling, moreover none of the included in the review implemented a high-quality evaluation design. Further empirical research is necessary to collect evidence that TUIs are effective for enhancing children’s storytelling experience and allow inclusion of special needs ones.
Stability and Instability of the Einstein–Lichnerowicz Constraint System

We investigate the relevance of the conformal method by investigating stability issues for the Einstein-Lichnerowicz conformal constraint system in a nonlinear scalar-field setting. We prove the stability of the system with respect to arbitrary perturbations of generic focusing physics data on closed locally conformally flat manifolds, in any dimension. We also show that our stability result is sharp by constructing explicit instability examples when its assumptions are not satisfied. Our results apply to a more general class of constraint-like systems.
A scaling-limit approach to the theory of laser transition

The conditions for the appearance of a sharp laser transition are formulated in terms of a scaling limit, involving vanishing cavity loss and light-matter coupling, k → 0, g → 0, such that g2/k stays finite. It is shown analytically that in this asymptotic parameter domain, and for pump rates above the threshold value, the photon output becomes large in a sense that is specified, and the photon statistics becomes strictly Poissonian. Numerical examples for the case of a two-level and a three-level emitter are presented and discussed in relation to the analytic result.
On homotopy Lie bialgebroids

A well-known result of A. Vaintrob characterizes Lie algebroids and their morphisms in terms of homological vector fields on supermanifolds. We give an interpretation of Lie bialgebroids and their morphisms in terms of odd symplectic dg-manifolds, building on the approach of D. Roytenberg. This extends naturally to the homotopy Lie case and leads to the notion of $L_\infty$-bialgebroids and $L_\infty$-morphisms between them.
A solution of the minimum-time velocity planning problem based on lattice theory

For a vehicle on an assigned path, we find the minimum-time speed law that satisfies kinematic and dynamic constraints, related to maximum speed and maximum tangential and transversal acceleration. We present a necessary and sufficient condition for the feasibility of the problem and a simple operator, based on the solution of two ordinary differential equations, which computes the optimal solution. Theoretically, we show that the problem feasible set, if not empty, is a lattice, whose supremum element corresponds to the optimal solution.
Dirac lattice

We study the emergence of a Dirac fermionic field in the low energy description of non-relativistic dynamical models on graphs admitting a continuum limit. The Dirac fermionic field appears as the effective field describing the excitations above a point-like Fermi surface. Together with the Dirac fermionic field an effective space-time metric also emerges. We analyze the conditions for such Fermi points to appear in general, paying special attention to the cases of two and three spatial dimensions.
Responsible investments: An analysis of preference

The objective of this study is to analyze whether environmental, social, and governance (ESG) investments generate different abnormal excess returns depending on preferences towards ESG assets in the United States. We aim to verify whether these results are consistent with the previously exposed theories explaining the relationships between ESG preferences and excess returns. To measure investor preferences for ESG assets, two different proxies are used: whether the headquarter of the company resides in a Democratic or Republican state, and how the state scored in an Environmental survey from Yale University. Our paper presents evidence that when investors have a strong preference for socially and environmentally responsible investments (Democrats), the abnormal excess returns on ESG investments in these states are negative. Since ESG-motivated investors gain additional utility by holding green assets, they are willing to sacrifice a portion of their returns to incorporate the ESG factor into their portfolio. Conversely, when investors do not value ESG factors to the same extent (Republicans), abnormal excess returns are not significantly different from zero. As a corollary, we also divided states according to their opinions on environmental issues using a Yale University survey and performed the same analysis, confirming the previous results. Our methodology gains validity since the U.S. is in a context of home bias (Coval and Moskowitz, 1999). As a result, people will tend to invest more in their home state, thus the returns will reflect their preferences. Furthermore, the results of our research and the theories it supports are in agreement with the theories of Pastor et al. (2020) and Pedersen et al. (2020), which connect preferences for ESG investments and expected excess returns.
Simulation of Perovskite channel Thin Film Transistor

A bottom gate thin film transistor has been simulated and analyzed in which the channel is made up of lead perovskite (CH 3 NH 3 PbI 3 ) using Silvaco Atlas software. The perovskite channel thin film transistor shows high field effect mobility and transconductance which makes the transistor very attractive for display devices applications. The field effect mobility of perovskite channel thin film transistor obtained through simulations matches closely with the experimentally reported field effect mobility. The transistor has been modified by adding a metal layer to the gate which significantly enhances the transconductance of the perovskite channel thin film transistor. The same transistor has been simulated by using tin perovskite (CH 3 NH 3 SnI 3 ) instead of lead perovskite (CH 3 NH 3 PbI 3 ) as channel material to avoid harmful effects associated with the presence of lead in lead perovskite . The tin perovskite channel thin film transistor also exhibits high performance parameters which makes the transistor a potentially suitable environment friendly device for modern electronic applications.
Machine Learning in Automating Carbon Sequestration Site Assessment

Carbon capture and sequestration are viewed as an indispensable component to achieve the Paris Agreement climate goal, i.e, keep the global warming within 2 degrees celsius from pre-industrial levels. Once captured, most CO 2 needs to be stored securely for at least decades, preferably in deep underground geological formations. It is economic to inject and store CO 2 near/around a depleted gas/oil reservoir or well, where a geological trap for CO 2 with good sealing properties and some minimum infrastructure exist. In this proposal, with our preliminary work, it is shown that Machine Learning tools like Optical Character Recognition and Natural Language Processing can aid in screening and selection of injection sites for CO 2 storage, facilitate identiﬁcation of possible CO 2 leakage paths in the subsurface, and assist in locating a depleted gas/oil well suitable for CO 2 injection and long term storage. The automated process based on ML tools can also drastically decrease the decision making cycle time in the site selection and assessment phase by reducing human effort. In the longer term, we expect ML tools like Deep Neural Network can also be utilized in CO 2 storage monitoring, injection optimization etc. By injecting CO 2 into a trapping geological underground formation in a safe and sustainable manner, the oil and gas industry can contribute substantially in reducing global warming and achieving the goals of the Paris Agreement by the end of this century.