Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-b Estimation and Inference in Heterogenous Dynamic Cointegrated Panels

We study semi-parametric estimation and inference in cointegrated panels with endogenous feedback, allowing for general time-series and cross-section dependence and heterogeneity.Central to this literature are the fully-modified OLS of Phillips and Hansen (1990) that use a spectral [non-parametric] estimation of the residual asymptotic covariance matrix to eliminate the bias. Despite their parsimony, these estimators are not T-consistent under fixed-b asymptotics, when the size of the bandwidth is a non-zero fraction of the sample size. Is the consequence benign? Test statistics are non-pivotal, and in panels always reject in the N-limit.Feasible FM-OLS are based on estimated OLS residuals, not on true unobserved residuals; this leads to complex asymptotics — even seemingly inextricable, see for instance Vogelsang and Wagner (2014). We introduce new, compact notations for the fixed-b limits of spectral covariance estimators. This permits us to construct T-consistent semi-parametric estimators: a simple estimator that estimates and subtracts the OLS bias, and a pseudo-exogenised estimator.These fixed-b robust estimators considerably improve the size statistics, reduce bias, and RMSE, compared to other semi-parametric estimators.
Evolutionary rescue in a fluctuating environment: periodic versus quasi-periodic environmental changes

No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless, for example, a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. We here investigate evolutionary rescue in an environment that fluctuates between a favourable state, in which the population grows, and a harsh state, in which the population declines. We develop a stochastic model that includes both population dynamics and genetics. We derive analytical predictions for the mean extinction time of a non-adapted population given that it is not rescued, the probability of rescue by a mutation, and the mean appearance time of a rescue mutant, which we validate using numerical simulations. We find that stochastic environmental fluctuations, resulting in quasi-periodic environmental changes, accelerate extinction and hinder evolutionary rescue compared with deterministic environmental fluctuations, resulting in periodic environmental changes. We demonstrate that high equilibrium population sizes and per capita growth rates maximize the chances of evolutionary rescue. We show that an imperfectly harsh environment, which does not fully prevent births but makes the death rate to birth rate ratio much greater than unity, has almost the same rescue probability as a perfectly harsh environment, which fully prevents births. Finally, we put our results in the context of antimicrobial resistance and conservation biology.
Convergent molecular evolution of thermogenesis and circadian rhythm in Arctic ruminants

The muskox and reindeer are the only ruminants that have evolved to survive in harsh Arctic environments. However, the genetic basis of this Arctic adaptation remains largely unclear. Here, we compared a de novo assembled muskox genome with reindeer and other ruminant genomes to identify convergent amino acid substitutions, rapidly evolving genes and positively selected genes among the two Arctic ruminants. We found these candidate genes were mainly involved in brown adipose tissue (BAT) thermogenesis and circadian rhythm. Furthermore, by integrating transcriptomic data from goat adipose tissues (white and brown), we demonstrated that muskox and reindeer may have evolved modulating mitochondrion, lipid metabolism and angiogenesis pathways to enhance BAT thermogenesis. In addition, results from co-immunoprecipitation experiments prove that convergent amino acid substitution of the angiogenesis-related gene hypoxia-inducible factor 2alpha (HIF2A), resulting in weakening of its interaction with prolyl hydroxylase domain-containing protein 2 (PHD2), may increase angiogenesis of BAT. Altogether, our work provides new insights into the molecular mechanisms involved in Arctic adaptation.
Coinfection and the Evolution of Resistance: A Mathematical Analysis

This thesis investigates the effect of coinfection on the emergence of resistant pathogens. Firstly, a multiple infection model with treatment is derived and the conditions for invasion are established. The invasion condition is then related to an equivalent and easier to obtain condition, R0, by applying the Next-Generation Theorem. Due to its biological interpretation, a heuristic derivation of R0 as the invasion condition is also given. Then assuming that resistance comes at a cost to the pathogen, and using a very simple within-host model, we establish under which specific set of biological assumptions we should expect coinfection to increase or decrease R0. Specifically, we obtain that in the no cost of resistance case, reduced transmission case, and increased mortality case, that coinfection will increase the R0 value and that in the reduced growth and poor competitor case that the effect is indeterminate. We also introduced a method for approximating the intrinsic growth rate when the coinfection efficiency is assumed to be small. Using this method, we show that we obtain the same trend for the cost of resistance cases when comparing our estimate for the intrinsic growth rate for the coinfection case versus the intrinsic growth rate for the single infection case. We also use this approximation to estimate the percentage of resistance as a function of time. Finally, we analyze how both the intrinsic growth rate and R0 respond to a changing treatment rate, compared to the intrinsic growth rate and R0 value in the single infection case. We found that the change in R0 and the intrinsic growth rate can be greater or smaller than the change
A Linear Complementarity Approach to the Frictionless Gripper Problem

This article deals with static unilateral phenomena arising in grasping mechanisms and caused by the possible loss of contact between elastic grippers and rigid objects. The problem is formulated as a linear complementarity problem (LCP) that is derived from the equilibrium equations of forces and the compatibility equations for the displacements, as well as from the complementarity inequality restriction describing the contact. A variant of the infeasibility-reducing, complementary pivot al gorithm of Lemke is used for the numerical solution of the arising LCP. Form and force closure conditions are fulfilled in an auto matic way from the solution of the aforementioned LCP. The used algorithm guarantees rapid convergence to the solution of the problem. The theory is illustrated by numerical examples.
Remarks on wave propagation in an acoustic metamaterial modeled as a relaxed micromorphic continuum

In order to describe elastic waves propagation in metamaterials, i.e. solids with heterogeneities or microstructure, it is necessary to consider non‐local or higher‐order models. The relaxed micromorphic model (RMM) proposed here can describe these effects as a continuous material with enriched kinematics. We present a new unit cell giving rise to a metamaterial for acoustic application. The microstructure is engineered to show a band‐gap in the low acoustic regime (600‐2000 Hz) for which waves cannot propagate through the material. We concentrate on the size effects to make full advantage of the particularly beneficial structure that the model provides. The RMM material parameters are fitted using a new algorithm relying on cutoffs and asymptotes (obtained via a Bloch‐Floquet analysis). In particular, by enhancing the kinetic energy of the model with a new inertia term, we enable decreasing curves (modes with negative group velocity).
Relaxed Incremental Formulations for Damage at Finite Strains Including Strain Softening

Relaxation is a promosing technique to overcome mesh‐dependency in computational damage mechanics originating from the non‐convexity of an underlying incremental variational formulation. This technique does not require an internal length scale parameter. However, in case of damage formulations, for many years the decrease of stresses with an increase of strains, referred to as strain‐softening, could not be modeled in the relaxed regime. This contribution discusses several possibilities of relaxation that lead to suitable models for stress‐ and strain‐softening.
Sparse polynomial chaos expansion for nonlinear finite element simulations with random material properties

This contribution deals with the uncertainty quantification for applied nonlinear structural engineering problems, including high stochastic dimensions. A finite element problem with different material models is investigated. The efficiency, accuracy and convergence of sparse PCE are studied numerically and compared with Monte‐Carlo Simulation (MCS) for non‐linear structural analysis including elasto‐plastic and damage models. In both models, the Young's modulus is considered as random fields discretised by Karhunen Loeve Expansion (KLE). In the provided studies, sparse PCE converges fast and is highly efficient for linear elastic and elasto‐plastic material models. However, sparse PCE loses its effectiveness and exhibits lower accuracy for the damage material model.
Usefulness of the lupus low disease activity state as a treatment target in childhood-onset SLE

Objective Treat-to-target (T2T) strategies are advocated to improve prognosis in childhood-onset SLE (cSLE). Proposed T2T states include SLEDAI score of <4 (SLEDAI-LD), limited corticosteroid use (low-CS), and lupus low disease activity state (LLDAS). We sought to compare T2T states for their association with cSLE prognosis under consideration of relevant disease characteristics such as pre-existing damage, race and lupus nephritis (LN). Methods Longitudinal data from 165 patients enrolled in the Cincinnati Lupus Registry were included. LN presence was based on renal biopsy, and patients were followed up until 18 years of age. Results The 165 patients (LN: 45, white: 95) entered the registry within a median of 0 (IQR: 0–1) year post diagnosis and were followed up for a median of 4 (IQR: 2–5) years during which 80%, 92% and 94% achieved LLDAS, low-CS and SLEDAI-LD. Patients with LN were significantly less likely to achieve any T2T state (all p<0.03) and required a significantly longer time to reach them (all p<0.0001). Over the study period, patients maintained low-CS, SLEDAI-LD or LLDAS for a median of 76% (IQR: 48%–100%), 86% (IQR: 55%–100%) or 39% (IQR: 13%–64%) of their follow-up. Significant predictors of failure to maintain LLDAS included LN (p≤0.0062), pre-existing damage (p≤0.0271) and non-white race (p≤0.0013). There were 22%, 20% and 13% of patients who reached SLEDAI-LD, CS-low and LLDAS and nonetheless acquired new damage. Patients with LN had a higher risk of new damage than patients without LN even if achieving low-CS (p=0.009) or LLDAS (p=0.04). Conclusions Patients with LN and pre-existing damage are at higher risk of increased future damage acquisition, even if achieving a T2T state such as LLDAS. Among proposed common T2T states, the LLDAS is the hardest to achieve and maintain. The LLDAS may be considered the preferred T2T measure as it conveys the highest protection from acquiring additional disease damage.
Ore Grade Estimation in Mining Industry from petro-physical data using neural networks

The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data. The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
Cache Programming for Scientific Loops Using Leases

Cache management is important in exploiting locality and reducing data movement. This article studies a new type of programmable cache called the lease cache. By assigning leases, software exerts the primary control on when and how long data stays in the cache. Previous work has shown an optimal solution for an ideal lease cache. This article develops and evaluates a set of practical solutions for a physical lease cache emulated in FPGA with the full suite of PolyBench benchmarks. Compared to automatic caching, lease programming can further reduce data movement by 10% to over 60% when the data size is 16 times to 3,000 times the cache size, and the techniques in this article realize over 80% of this potential. Moreover, lease programming can reduce data movement by another 0.8% to 20% after polyhedral locality optimization.
HornFuzz: Fuzzing CHC solvers

Many advanced program analysis and verification methods are based on solving systems of Constrained Horn Clauses (CHC). Testing CHC solvers is very important, as correctness of their work determines whether bugs in the analyzed programs are detected or missed. One of the well-established and efficient methods of automated software testing is fuzzing: analyzing the reactions of programs to random input data. Currently, there are no fuzzers for CHC solvers, and fuzzers for SMT solvers are not efficient in CHC solver testing, since they do not consider CHC specifics. In this paper, we present HornFuzz, a mutation-based gray-box fuzzing technique for detecting bugs in CHC solvers based on the idea of metamorphic testing. We evaluated our fuzzer on one of the highest performing CHC solvers, Spacer, and found a handful of bugs in Spacer. In particular, some discovered problems are so serious that they require fixes with significant changes to the solver.
The Logic of Typed Feature Structures: Types and Inheritance

From the outside, our feature structures look much like the ψ-terms of Ait-Kaci (1984, 1986) or the feature structures of Pollard and Sag (1987), Moshier (1988) or Pollard and Moshier (1990). In particular, a feature structure is modeled by a possibly cyclic directed graph with labels on all of the nodes and arcs. Each node is labeled with a symbol representing its type, and the arcs are labeled with symbols representing features. We think of our types as organizing feature structures into natural classes. In this role, our types are doing the same duty as concepts in a terminological knowledge representation system (Brachman and Schmolze 1985, Brachman, Fikes, and Levesque 1983, Mac Gregor 1988) or abstract data types in object-oriented programming languages (Cardelli and Wegner 1985). Thus it is natural to think of the types as being organized in an inheritance hierarchy based on their generality. Feature structure unification is then modified so that two feature structures can only be unified if their types are compatible according to the primitive hierarchy of types. In this chapter, we discuss how type inheritance hierarchies can be specified and the restrictions that we impose on them that allow us to define an adequate notion of type inference, which is necessary during unification. These restrictions were first noted by Ait-Kaci (1984) in his unification-based reasoning system. The polymorphism allowed in our type system is based on inheritance in which a subtype inherits information from all of its supertypes. The possibility of more than one supertype for a given type allows for multiple inheritance.
Classification of Corona virus between Humans and Bats using Support Vector Machine

As civilization of humans has grown, the pandemic has also increased. With the increase in population, the transmission of diseases has also increased. One such pandemic that concerns the world is Coronavirus, which is the base of the concerned study. Coronavirus is a large family of viruses that inherits a range of illnesses from the common cold to more severe diseases. The strains of Coronavirus are taken from humans and bats using UniProt Proteome Database. The proteins of these strains reveal the composition of amino acids and their revolutionary rates. These proteins are classified using the radial kernel in the Support Vector Machine classifier, thus training and testing the identified composition of amino acids in proteins of a particular strain. The study then calculates the accuracy of the prediction of distinct proteins in both humans and bats. These predictions further hold the potential in identifying the future identification of distinct proteins in both humans and bats.
Impact of resilience building interventions in semiarid rural agro-communities: lessons from Mberengwa And Zvishavane Districts in South Eastern Zimbabwe

ABSTRACT The research sought to examine the impact of resilience building interventions toward rural livelihoods in Mberengwa and Zvishavane Districts. In this research, a mixed method research design was adopted to enhance the quality, complementarity, and expansion of results. Data were gathered using questionnaires, interviews and field observations. Questionnaires were distributed to 200 household heads in Mberengwa and 120 households selected in Zvishavane Districts using convenience sampling technique. Key informants interviewed included Lutheran Development Services representative, Rural District Council representative, District Crop and Livestock Officer (DCLO). The acquired qualitative data were subjected to content analysis while quantitative data was analyzed using SPSS version 23.0. The study findings show that Villages Savings and Lending, water harvesting, community horticultural gardens, apiculture, climate smart agriculture were some of the initiatives contributing to resilience building. These resilience building interventions contributed toward the attainment of Sustainable Development Goals 1; ending hunger, 2; zero hunger; and 13 climate action. The study recommends that the government, through the relevant ministry should ensure resilience building initiatives are included in their five-year plan, so that they have adequate and realistic implementation time frames.
Ubiquitous Access to Documents: Using Storytelling to Alleviate Cognitive Problems

With the dissemination of computers and the advent of ubiquitous computing, elder citizens and other people with cognitive problems face increasingly large barriers to the interaction with their electronic documents. The average user now handles increasingly large numbers of documents of ever increasing variety both in type and in content. Moreover, those documents can now be accessed, at times and places where such interactions were thus far impossible. Current approaches require documents to be classified prior to storage, which is a significant source of cognitive overload, retrieving documents stored days, months or years ago is even more difficult, especially for collections of large document numbers spread across many different places. Hence, new, innovative ways for storing and retrieving documents are sorely needed. We argue that classification in itself is not necessary. By taking into account the context in which documents are manipulated, narrative descriptions can be interactively constructed and used to later retrieve them. Ubiquitous computing will allow the capture of all relevant contextual information and allow a multimodal narrative access to supplement users’ cognitive shortcomings.
The impact of unplanned 30-day readmission as a quality indicator in pediatric surgery

Introduction Hospital readmission is one of the indicators used to assess quality of service provided in healthcare. Based on accumulated knowledge, risk management teams assess data related to readmissions to find curative solutions for underlying factors. The current article's aim is investigating readmission routes within the workplace in paediatric surgery service during the first 30 days post discharge from Mater Dei Hospital (MDH). Materials and method A retrospective study of children's hospital readmissions between October 2017 and November 2019 was performed, strictly before COVID-19 pandemic. Demographics and clinical records including age, gender, pre-existing comorbidities, diagnosis during primary admission and readmission, procedure carried out, ASA grade, length of stay, and outcomes were collected. All children re-admitted under a single paediatric surgical department within 30 days from initial admission to tertiary referral hospital were included. Patients undergoing emergency visitation without subsequent admissions were excluded. Readmissions were classified into cohorts: elective and emergency, depending on the nature of primary admission. Contributing factors and outcomes were compared. Results 935 surgical admissions (221 elective and 714 emergencies) were registered at MDH over the given period, with an average hospital stay of 3.62 days. Total readmission rate was 1.7% (n = 16). 25% (n = 4) of readmissions were post elective, 75% (n = 12) post emergency admission, with an average stay of 4.37 days and no mortalities. 43.7% (n = 7) were re-admissions post-surgical intervention. Further surgical interventions were necessary in 25% (n = 4) of readmitted patients, the remainder (n = 12) treated conservatively. Conclusion Published reports concerning paediatric surgical readmission rates are limited, challenging healthcare systems. Most readmissions area voidable; therefore, healthcare workers must provide adequate strategies tailored to their resources, efficient multidisciplinary approaches with improved communication to decrease morbidity and prevent readmissions.
Editorial: Predictive short/long-term efficacy biomarkers and resistance mechanisms of CAR-T immunotherapy treatment

COPYRIGHT © 2023 Cheng, Jin and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 31 May 2023 DOI 10.3389/fimmu.2023. 1206441