Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilience of a tropical montane pine forest to fire and severe droughts

Higher temperatures, declining precipitation, changing cloud cover and increased wildfires threaten tropical montane pine forests by overriding the environmental heterogeneity that typically buffers these systems from catastrophic fires. Severe fires threaten to overwhelm forest resilience and tip this biome into alternate vegetation states. This study focused on long‐term dynamics of montane Pinus occidentalis forests in the Cordillera Central, Dominican Republic after a ~1000 km2 fire in 2005, the largest since 1965. We used long‐term records to investigate climate before and after the fire, and 19‐year dataset of pre‐ and post‐fire vegetation change from a network of 55 permanent plots (20 small 0.05 ha plots and 35 large 0.1 ha plots) established in 1999 to model overstorey and understorey vegetation dynamics. The 2005 fire was synchronized with the most extreme drought in the region in over 60 years. The fire burned from <1600 to >3000 m a.s. l. in elevation across windward and leeward slopes, creating a mosaic of low‐, moderate‐ and high‐severity patches. Lower elevations, leeward slopes and stands with a higher proportion of smaller pine trees all burned at higher severities. Growth rates of trees that survived the fire remained lower than pre‐fire rates 13 years after the fire. The highest post‐fire mortality rates were soon after the fire and in the census immediately following subsequent post‐fire droughts. Post‐fire pine seedling abundance was significantly greater in stands with higher basal area of live canopy trees and significantly reduced by increased shrub abundance in the understorey. Understorey composition recovered rapidly to pre‐fire states in sites affected by low‐ and moderate‐severity fires, but sites affected by high‐severity fires remained dissimilar to pre‐fire composition 13 years after the fire. Even though high‐severity patches had persistently low pine regeneration, 100% of small plots and 96% of large plots had at least one pine sapling or canopy tree recruit by 2018. Shrub taxa survived the fire in higher numbers and recovered to pre‐fire densities much faster than the pine, especially in high‐severity burns. Synthesis. Climate change has increased the likelihood of wildfires in tropical montane pine forests, with long‐lasting effects on vegetation dynamics. However, this biome may prove resilient to increasingly severe fires in the near future, given the ongoing recovery of Pinus occidentalis forests in Hispaniola despite repeated severe droughts. Nevertheless, highly drought‐ and fire‐resistant taxa (e.g. shrubs) may form alternate stable states in drier portions of tropical montane landscapes in the future as droughts and high‐severity fires become more common.
In Vitro Anti-Proliferative and Apoptotic Effects of Hydroxytyrosyl Oleate on SH-SY5Y Human Neuroblastoma Cells

The antitumor activity of polyphenols derived from extra virgin olive oil and, in particular the biological activity of HTyr, has been studied extensively. However, the use of HTyr as a therapeutic agent for clinical applications is limited by its low bioavailability and rapid excretion in humans. To overcome these limitations, several synthetic strategies have been optimized to prepare lipophenols and new compounds derived from HTyr to increase lipophilicity and bioavailability. One very promising ester is hydroxytyrosyl oleate (HTyr-OL) because the chemical structure of HTyr, which is responsible for several biological activities, is linked to the monounsaturated chain of oleic acid (OA), giving the compound high lipophilicity and thus bioavailability in the cellular environment. In this study, the in vitro cytotoxic, anti-proliferative, and apoptotic induction activities of HTyr-OL were evaluated against SH-SY5Y human neuroblastoma cells, and the effects were compared with those of HTyr and OA. The results showed that the biological activity of HTyr was maintained in HTyr-OL treatments at lower dosages. In addition, the shotgun proteomic approach was used to study HTyr-OL-treated and untreated neuroblastoma cells, revealing that the antioxidant, anti-proliferative and anti-inflammatory activities of HTyr-OL were observed in the unique proteins of the two groups of samples.
Bisphenol Exposure Disrupts Cytoskeletal Organization and Development of Pre-Implantation Embryos

The endocrine disrupting activity of bisphenol compounds is well documented, but less is known regarding their impact on cell division and early embryo formation. Here, we tested the effects of acute in vitro exposure to bisphenol A (BPA) and its common substitute, bisphenol F (BPF), during critical stages of mouse pre-implantation embryo development, including the first mitotic division, cell polarization, as well as morula and blastocyst formation. Timing of initial cleavage was determined by live-cell imaging, while subsequent divisions, cytoskeletal organization and lineage marker labeling were assessed by high-resolution fluorescence microscopy. Our analysis reveals that brief culture with BPA or BPF impeded cell division and disrupted embryo development at all stages tested. Surprisingly, BPF was more detrimental to the early embryo than BPA. Notably, poor embryo development was associated with cytoskeletal disruptions of the actomyosin network, apical domain formation during cell polarization, actin ring zippering for embryo sealing and altered cell lineage marker profiles. These results underscore that bisphenols can disrupt cytoskeletal integrity and remodeling that is vital for early embryo development and raise concerns regarding the use of BPF as a ‘safe’ BPA substitute.
Papaya Leaf Extracts as Potential Dengue Treatment: An In-Silico Study

Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) cause serious public health problems, with nearly 390 million people affected and 20,000 deaths per year in tropical and subtropical countries. Despite numerous attempts, no antiviral drug or vaccine is currently available to combat the manifestation. The challenge of discovering an efficient vaccine is enhanced by the surplus presence of efficient vectors and drug resistance from the virus. For centuries, papaya (Carica papaya) extracts have been traditionally used to treat DF, DHF, and DSS. In the present study, we systematically investigated seven compounds isolated from papaya leaf extract with regard to their potential as inhibitors for non-structural (NS) proteins, NS3 and NS5, which play a crucial role in viral RNA replication. The computational tools applied stretched across classical molecular docking, molecular dynamics (MD) simulations and SwissADME used to calculate binding affinities; binding free energies; Absorption, Distribution, Metabolism, and Excretion (ADME); and drug-likeness properties, thus, identifying Kaempferol, Chlorogenic acid, and Quercetin as potential candidates, with Kaempferol and Quercetin scoring best. Therefore, for the Kaempferol and Quercetin complexes, hybrid quantum mechanical/molecular mechanical (QM/MM) geometry and frequency calculations were performed, followed by the local mode analysis developed in our group to quantify Kaempferol-NS and Quercetin-NS hydrogen bonding. Given the non-toxic nature and the wide availability of the Kaempferol and Quercetin papaya extract in almost all of the susceptible regions, and our results showing high NS3 and NS5 binding affinities and energies, strong hydrogen bonding with both NS3 and NS5, and excellent ADME properties, we suggest Kaempferol and Quercetin as a strong NS3 and NS5 inhibitor to be further investigated in vitro.
CLDN1 Sensitizes Triple-Negative Breast Cancer Cells to Chemotherapy

Simple Summary Triple-negative breast cancer (TNBC) treatment represents a major challenge in oncology. TNBC evolves into chemotherapy resistance for 60 to 70% of the patients. About 77% of the TNBC displays a lack of claudin-1 (CLDN1), a major tight junction component. We demonstrated that CLDN1 increased the sensitivity of TNBC cell lines to the main chemotherapeutic agents commonly used for breast cancer treatment. Our data support the idea that CLDN1 may be a good predictive chemotherapy response marker to help therapeutic management of TNBC patients. In longer terms, this study could allow new treatment protocols creation aimed to induce CLDN1 expression in TNBCs to increase their sensitivity to chemotherapy. Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype that constitutes 15–20% of breast cancer cases worldwide. Current therapies often evolve into chemoresistance and lead to treatment failure. About 77% of the TNBC lacks claudin-1 (CLDN1) expression, a major tight junction component, and this absence is correlated with poorer prognostic. Little is known about CLDN1 role on the chemosensitivity of breast cancer. Our clinical data analysis reveals that CLDN1 low expression is correlated to a poor prognostic in TNBC patients. Next, the sensitivity of various TNBC “claudin-1-high” or “claudin-1-low” cells to three compounds belonging to the main class of chemotherapeutic agents commonly used for the treatment of TNBC patients: 5-fluorouracil (5-FU), paclitaxel (PTX) and doxorubicin (DOX). Using RNA interference and stable overexpressing models, we demonstrated that CLDN1 expression increased the sensitivity of TNBC cell lines to these chemotherapeutic agents. Taken together, our data established the important role of CLDN1 in TNBC cells chemosensitivity and supported the hypothesis that CLDN1 could be a chemotherapy response predictive marker for TNBC patients. This study could allow new treatment protocols creation aimed to induce CLDN1 expression in TNBCs to increase their sensitivity to chemotherapy.
International Journal of Public Health Science (IJPHS)

Received Jan 22, 2022 Revised Aug 7, 2022 Accepted Aug 29, 2022 Functional health literacy (FHL) involves skills in writing, reading, oral expression, comprehension, and numerical calculations to successfully function and complete health-related tasks. This study aimed to develop and validate an instrument for measuring FHL in the Philippines using a mixedmethod design. FHL-5TEST is an instrument consisting of five questions developed in consultation with local experts, translated to major Philippine languages, and underwent translation analysis, pretesting, cultural adaptation, and validation. A cross-sectional study was conducted in a random sample of 15-70-year-old Filipino residents to validate the instrument. The FHL-5TEST demonstrated high internal consistency, with a Cronbach’s alpha of 0.7690. Across different cut-off values, the sensitivity and specificity of the instrument in detecting limited FHL ranged from 60.9% to 86.0% and from 61.6% to 97.2%, respectively. The study demonstrated that the FHL-5TEST is a valid and reliable instrument to measure functional health literacy in the Philippines.
AI/ML for Network Security: The Emperor has no Clothes

Several recent research efforts have proposed Machine Learning (ML)-based solutions that can detect complex patterns in network traffic for a wide range of network security problems. However, without understanding how these black-box models are making their decisions, network operators are reluctant to trust and deploy them in their production settings. One key reason for this reluctance is that these models are prone to the problem of underspecification, defined here as the failure to specify a model in adequate detail. Not unique to the network security domain, this problem manifests itself in ML models that exhibit unexpectedly poor behavior when deployed in real-world settings and has prompted growing interest in developing interpretable ML solutions (e.g., decision trees) for "explaining'' to humans how a given black-box model makes its decisions. However, synthesizing such explainable models that capture a given black-box model's decisions with high fidelity while also being practical (i.e., small enough in size for humans to comprehend) is challenging. In this paper, we focus on synthesizing high-fidelity and low-complexity decision trees to help network operators determine if their ML models suffer from the problem of underspecification. To this end, we present Trustee, a framework that takes an existing ML model and training dataset as input and generates a high-fidelity, easy-to-interpret decision tree and associated trust report as output. Using published ML models that are fully reproducible, we show how practitioners can use Trustee to identify three common instances of model underspecification; i.e., evidence of shortcut learning, presence of spurious correlations, and vulnerability to out-of-distribution samples.
What factors affect targeting and bids in online advertising?: a field measurement study

Targeted online advertising is a well-known but extremely opaque phenomenon. Though the targeting capabilities of the ad tech ecosystem are public knowledge, from an outside perspective, it is difficult to measure and quantify ad targeting at scale. To shed light on the extent of targeted advertising on the web today, we conducted a controlled field measurement study of the ads shown to a representative sample of 286 participants in the U.S. Using a browser extension, we collected data on ads seen by users on 10 popular websites, including the topic of the ad, the value of the bid placed by the advertiser (via header bidding), and participants' perceptions of targeting. We analyzed how ads were targeted across individuals, websites, and demographic groups, how those factors affected the amount advertisers bid, and how those results correlated with participants' perceptions of targeting. Among our findings, we observed that the primary factors that affected targeting and bid values were the website the ad appeared on and individual user profiles. Surprisingly, we found few differences in how advertisers target and bid across demographic groups. We also found that high outliers in bid values (10x higher than baseline) may be indicative of retargeting. Our measurements provide a rare in situ view of targeting and bidding across a diversity of users.
Hyberbolic [i.e. Hyperbolic] conservation laws in continuum physics

Balance Laws. - Introductioin to Continuum Physics. - Hyperbolic Systems of Balance Laws. - The Cauchy Problem. - Entropy and the Stability of Classical Solutions. - The L1 Theory for Scalar Conservation Laws. - Hyperbolic Systems of Balance Laws in One-Space Dimension. - Admissible Shocks. - Admissible Wave Fans and the Riemann Problem. - Generalized Characteristics. - Genuinely Nonlinear Scalar Conservation Law. - Genuinely Nonlinear Systems of Two Conservation Laws. - The Random Choice Method. - The Front Tracking Method and Standard Riemann Semigroups. - Construction of BV Volutions by the Vanishing Viscosity Method. - Compensated Compactness. - Bibliography. - Author Index. - Subject Index.
Value Added Statement: how the wealth created by branches of economic activity was distributed from 1999 to 2018?

Purpose: This study aimed to verify the extent to which the distribution of the wealth generated by the largest publicly-held and private companies from the main branches of economic activity operating in Brazil differs in terms of the tax burden, capital remuneration (own and third parties) and personnel remuneration from 1999 to 2018. Methodology: Value Added Statements of the largest publicly held and private companies included in the Accounting Actuarial and Financial Research Institute Foundation database between 1999 and 2018 were analyzed using multiple linear regressions. The Mann-Whitney test was performed to identify significant differences in the wealth distributed by the main sectors. Results and contributions: The results indicate significant differences in the distribution of the wealth generated by the main branches of the economy (manufacturing, trade, services, financial institutions, and insurance companies) to employees, shareholders, and creditors. Differences were also found in the proportion of wealth allocated to the government through taxes. This study’s main contributions include presenting a significant imbalance in the distribution of the wealth generated by the different branches of economic activity; the manufacturing, trade, and services branches bore a much higher tax burden than banks and insurance companies over the 20-year-period, negatively impacting the amount these three branches distributed to employees and shareholders.
Probabilistic Hash-and-Sign with Retry in the Quantum Random Oracle Model

A hash-and-sign signature based on preimage-sampleable function (PSF) (Gentry et al. [STOC 2008]) is secure in the Quantum Random Oracle Model (QROM) if the PSF is collision-resistant (Boneh et al. [ASI-ACRYPT 2011]) or one-way (Zhandry [CRYPTO 2012]). However, trapdoor functions (TDFs) in code-based and multivariate-quadratic-based (MQ-based) signatures are not PSFs; for example, underlying TDFs of the Courtois-Finiasz-Sendrier (CFS), Unbalanced Oil and Vinegar (UOV), and Hidden Field Equations (HFE) signatures are not surjection. Thus, such signature schemes adopt probabilistic hash-and-sign with retry . This paradigm is secure in the (classical) Random Oracle Model (ROM), assuming that the underlying TDF is non-invertible; that is, it is hard to find a preimage of a given random value in the range (e.g., Sakumoto et al. [PQCRYPTO 2011] for the modified UOV/HFE signatures). Unfortunately, there is no known security proof for the probabilistic hash-and-sign with retry in the QROM . We give the first security proof for the probabilistic hash-and-sign with retry in the QROM, assuming that the underlying non-PSF TDF is non-invertible. Our reduction from the non-invertibility is tighter than the existing ones that apply to only signature schemes based on PSFs. We apply the security proof to code-based and MQ-based signatures. Moreover, we extend the proof into the multi-key setting by using prefix hashing (Du-man et al. [ACM CCS 2021]). preimage sampleable function. Probabilistic Hash-and-Sign with Retry in the QROM: We affirmatively answer Q1 by giving the first reduction from the INV of the underlying TDF to the EUF-CMA security of the probabilistic hash-and-sign with retry in the QROM ( main theorem ). Also, we show that a signature scheme is sEUF-CMA-secure if the underlying TDF is an injection.
Gamma-Ray Burst Afterglows with Energy Injection: Homogeneous Versus Wind External Media

Assuming an adiabatic evolution of a gamma-ray burst (GRB) fireball interacting with an external medium, we calculate the hydrodynamics of the fireball with an energy injection from a strongly magnetic millisecond pulsar through magnetic dipole radiation, and obtain the light curve of the optical afterglow from the fireball by synchrotron radiation. The results are given both for an homogeneous external medium and for a wind ejected by GRB progenitor. Our calculations are also available in both ultra-relativistic and non-relativistic phases. Furthermore, the observed R-band light curve of GRB000301C can be well fitted in our model, which might provide a probe of the properties of GRB progenitors.
Relationship-speciﬁc investment and hold-up problems in supply chains: theory and experiments

Supply chains today routinely use third parties for many strategic activities, such as manufacturing, R&D, or software development. These activities often include relationship-speciﬁc investment on the part of the vendor, while ﬁnal outcomes can be uncertain. Therefore, writing complete contracts for such arrangements is often not feasible, but incomplete contracts, especially when rela-tionship-speciﬁc investment is required, may leave the supplier vulnerable to a version of the ‘‘hold-up problem,’’ which is known to result in sub-optimal levels of investment. We model the phenomenon as a sequential move game with asymmetric information. Absent behavioral considerations, the unique Perfect Bayesian Equilibrium implies zero investment. However, with social preferences, the hold-up problem may be mitigated. We propose a model that incorporates social preferences and random errors, and solve for the equilibrium. In addition, we look at reputation and ﬁnd it to be effective for increasing investment. We conduct laboratory experiments with human subjects and ﬁnd that a model with social preferences and random errors organizes our data well.
Improved Sparse Optimization for Machine Learning

We consider the fundamental task of recovering sparse minimizers of a (convex) real function. Instances of this problem are often encountered in machine learning, where the goal is to find small models that can fit the training data. This is especially relevant today, when the mere size of models employed becomes so prohibitive that even transfering them across devices becomes an important challenge [HABN+21]. Previously, sparse minimization had found a lot of traction in the field of compressed sensing [MZ93, CT05, C+06, CRT06, BD09, JTK14]. A series of exciting works have shown that it is possible to recover sparse signals from a small number of measurements. This was surprising as it turned out that recovering an s-sparse signal x1 can be done efficiently using e O (s)2 linear measurements of x. While the underlying problem of finding a sparse solution to an underdetermined linear system is NP-hard in general, it turns out that structure can be leveraged to obtain beyond the worst case guarantees. In this case, the structure of the measurements was crucial to show that recovery can be performed in polynomial time. Furthermore, sparse optimization has been succesfully applied in multiple areas of machine learning and signal processing, including recommender systems, matrix factorization, or robust principal components analysis. While this line of work has been extremely successful, the specific limits of these methods are far from being understood. For example, the current state of the art shows that one can obtain a minimizer as good as the best s-sparse one, while sacrificing a factor of O () in sparsity, where  is a certain condition number of the underlying function [AS20, AS22]. Classical works in compressed sensing considered only the case of constant , but in many situations this can be quite large, thus giving a prohibitive blow up in sparsity. It is therefore important to ask
Embracing Hellman: A Simple Proof-of-Space Search consensus algorithm with stable block times using Logarithmic Embargo

Cryptocurrencies have become tremendously popular since the creation of Bitcoin. However, its central Proof-of-Work consensus mechanism is very power hungry. As an alternative, Proof-of-Space (PoS) was introduced that uses storage instead of computations to create a consensus. However, current PoS implementations are complex and sensitive to the Nothing-at-Stake problem, and use mitigations that aﬀect their permissionless and decentralised nature. We introduce Proof-of-Space Search (PoSS) which embraces Hellman’s time-memory trade-oﬀ to create a much simpler algorithm that avoids the Nothing-at-Stake problem. Additionally, we greatly stabilise block-times using a novel dynamic Logarithmic Embargo (LE) rule. Combined, we show that PoSSLE is a simple and stable alternative to PoW with many of its properties, while being an estimated 10 times more energy eﬃcient and sustaining consistent block times.
Journal Pre-proof Dissociation of ERMES clusters plays a key role in attenuating the endoplasmic reticulum stress

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.