Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular characteristics of lung adenocarcinoma with respect to patient age at diagnosis

Lung cancer is primarily a disease of the elderly, with a median age at diagnosis around 70 years. In our study we sought to address the question of whether and how clinical characteristics, molecular alterations and molecular phenotypes differ between patient populations with early‐stage lung adenocarcinoma (AC) with respect to age at diagnosis. Patients were stratified based on age at diagnosis into five systematic age bins (<50, 50‐60, 60‐70, 70‐80 and ≥80 years). To assess clinicopathological variables on a population‐based level, we accessed data from the national quality registry for lung cancer in Sweden. In parallel, we used compiled datasets from public cohorts to investigate focal and genome‐wide DNA alterations, epigenetic alterations, immune composition and transcriptional patterns in relation to age at diagnosis. Gender, stage, WHO performance and likelihood of receiving chemotherapy as adjuvant treatment were linked to age at diagnosis. Associations between younger patient age and likelihood of harboring certain driver mutations (eg, in EGFR and ALK) were confirmed. We also found an association between age at diagnosis and certain mutational signatures. However, age did not seem to drive transcriptional, copy number, or epigenetic variation in the tumors. Based on our findings, age at diagnosis alone does not appear to provide an additional layer of biological complexity above that of proposed genetic and transcriptional phenotypes of AC.
Scalable analysis of nonlinear systems using convex optimization

In this thesis, we investigate how convex optimization can be used to analyze different classes of nonlinear systems at various scales algorithmically. The methodology is based on the construction of appropriate Lyapunov-type certificates using sum of squares techniques. After a brief introduction on the mathematical tools that we will be using, we turn our attention to robust stability and performance analysis of systems described by Ordinary Differential Equations. A general framework for constrained systems analysis is developed, under which stability of systems with polynomial, non polynomial vector fields and switching systems, as well as estimating the region of attraction and the L2 gain can be treated in a unified manner. Examples from biology and aerospace illustrate our methodology. We then consider systems described by Functional Differential Equations (FDEs), i.e., time-delay systems. Their main characteristic is that they are infinite dimensional, which complicates their analysis. We first show how the complete Lyapunov-Krasovskii functional can be constructed algorithmically for linear time delay systems. Then, we concentrate on delay-independent and delay-dependent stability analysis of nonlinear FDEs using sum of squares techniques. An example from ecology is given. The scalable stability analysis of congestion control algorithms for the Internet is investigated next. The models we use result in an arbitrary interconnection of FDE subsystems, for which we require that stability holds for arbitrary delays, network topologies and link capacities. Through a constructive proof, we develop a Lyapunov functional for FAST - a recently developed network congestion control scheme - so that the Lyapunov stability properties scale with the system size. We also show how other network congestion control schemes can be analyzed in the same way. Finally, we concentrate on systems described by Partial Differential Equations. We show that axially constant perturbations of the Navier-Stokes equations for Hagen-Poiseuille flow are globally stable, even though the background noise is amplified as R3 where R is the Reynolds number, giving a 'robust yet fragile' interpretation. We also propose a sum of squares methodology for the analysis of systems described by parabolic PDEs. We conclude this work with an account for future research.
What You Show is What You Get! Gestures for Microtask Crowdsourcing

Crowdsourcing is a valuable tool to gather human input which enables the development of reliable artificial intelligence systems. Microtask platforms like Prolific and Amazon’s Mechanical Turk have flourished by creating environments where crowd workers can provide such human input in a diverse and representative manner. Such marketplaces have evolved to support several hundreds of workers in earning their primary livelihood through crowd work. Crowd workers, however, often perform these tasks in sub-optimal work environments with poor ergonomics. Additionally, many of the various microtasks require input via the standard method of a mouse and keyboard and are repetitive in nature. As such, crowd workers who primarily earn their livelihoods in microtask marketplaces are at risk of injuries such as carpal tunnel syndrome. By changing the input modality from a mouse and keyboard to gesture-driven input, crowd workers can complete their work while simultaneously improving or safeguarding their physical health. Through three distinct microtasks, we constructed a dataset that enables the exploration of the physical and mental health of crowd workers while using gestures. In this work, we present the process of constructing this dataset, how we applied it, and the future applications we foresee.
Effects of Uncertainty and Knowledge Graph on Perception of Fairness

AI-informed decision making consequential to individuals raises critical concerns on fairness. Fairness introduced by AI models and/or data is perceived by humans (also refers to perception of fairness) implicitly or explicitly. It is a central component of maintaining satisfactory relationships with humans in AI-informed decision making. Furthermore, model uncertainty and knowledge on training data play a crucial role in AI-informed decision making. This paper introduces model uncertainty and knowledge on training data represented by knowledge graphs into AI-informed decision making. We aim to investigate what uncertainty level and knowledge graph, and how they affect user perception of fairness in AI-informed decision making. A user study on judging the recidivism rate of prisoners found that uncertainty of model prediction of recidivism rate can benefit user perception of fairness, but only under low and medium uncertainty conditions. However, we did not find significant effects of knowledge represented by knowledge graph on user perception of fairness. These findings have wide implications in the user interface design of AI-informed decision making applications.
Quantifying the impacts of hydraulic infrastructure on tropical streamflows

Hydraulic infrastructures, such as reservoirs and water diversion channels, cause altered streamflow worldwide. Therefore, this study aimed to assess the coupled impacts of reservoir operations and water transfer on downstream streamflow over 42 years (1979–2020) for a tropical river in Vietnam, the Vu Gia Thu Bon (VGTB). We also quantified variations in the multi‐sub‐basin contributions to the water budget associated with hydraulic structure development. Therefore, a semi‐distributed hydrological model, SWAT (Soil and Water Assessment Tool), was developed for the entire VGTB basin considering two plausible scenarios: with a dam and without a dam. In this study, reservoirs substantially affected the streamflow during the 2011–2020 period when 12 cascading hydropower dams were constructed in the Vu Gia sub‐basins. The cascading reservoirs across the Vu Gia River reduced the annual average streamflow by 28.1% during this period, whereas their influence was augmented by 13.9% at reaches further downstream. In contrast, the local reservoir and flow diversions created on the Thu Bon River resulted in a 6.5% increase in streamflow. The upstream reservoir operation significantly increased streamflow at the midstream stations by 27.8% compared to the no‐dam period. The streamflow decreased in the dry season by 5.6% in the Vu Gia sub‐basins and increased by 61.7% in the Thu Bon sub‐basins. However, the impacts decreased in the wet season by 41.3% due to the operation of reservoirs, in which Dak Mi 4 had the most significant influence. It was found that the water diverted to the Thu Bon River was governed and reduced by the cascading hydropower dams. Therefore, the operation of 11 reservoirs has partially compensated for the lost water in the Vu Gia sub‐basins, to which the Dak Mi 4 plant has transferred 19.7 m3/s (14%). Our findings classify the impact of cascading dams and diversion structures and their interaction with climate change.
Rheology of sheared flexible fiber suspensions via fiber-level simulations

We employ a particle-level simulation technique to investigate the rheology of non-Brownian, flexible fiber suspensions in simple shear flow. The model incorporates a variety of realistic features including fiber flexibility, fiber deformation, and frictional contacts. The viscosity of fiber suspensions is strongly influenced by the fiber equilibrium shape, interfiber friction, and fiber stiffness. The viscosity of the suspension increases as the fiber curvature, the coefficient of friction, or the fiber stiffness is increased. The yield stress of fiber suspensions scales with the volume fraction in a manner similar to that observed experimentally. Fiber suspensions that flocculate exhibit a shear thinning regime that extends to shear rates lower than those observed for homogeneous suspensions.
A Course on Large Deviations With an Introduction to Gibbs Measures

Large deviations: General theory and i.i. d. processes Introductory discussion The large deviation principle Large deviations and asymptotics of integrals Convex analysis in large deviation theory Relative entropy and large deviations for empirical measures Process level large deviations for i.i. d. fields Statistical mechanics Formalism for classical lattice systems Large deviations and equilibrium statistical mechanics Phase transition in the Ising model Percolation approach to phase transition Additional large deviation topics Further asymptotics for i.i. d. random variables Large deviations through the limiting generating function Large deviations for Markov chains Convexity criterion for large deviations Nonstationary independent variables Random walk in a dynamical random environment Appendixes: Analysis Probability Inequalities from statistical mechanics Nonnegative matrices Bibliography Notation index Author index General index
Arteries are finely tuned thermosensors regulating myogenic tone and blood flow

In response to changing blood pressure, arteries adjust their caliber to control perfusion. This vital autoregulatory property, termed vascular myogenic tone, stabilizes downstream capillary pressure. We discovered that tissue temperature critically determines myogenic tone. Heating steeply activates tone in skeletal muscle, gut, brain and skin arteries with temperature coefficients (Q10) of ∼11-20. Further, arterial thermosensitivity is tuned to resting tissue temperatures, making myogenic tone sensitive to small thermal fluctuations. Interestingly, temperature and intraluminal pressure are sensed largely independently and integrated to trigger myogenic tone. We show that TRPV1 and TRPM4 mediate heat-induced tone in skeletal muscle arteries. Variations in tissue temperature are known to alter vascular conductance; remarkably, thermosensitive tone counterbalances this effect, thus protecting capillary integrity and fluid balance. In conclusion, thermosensitive myogenic tone is a fundamental homeostatic mechanism regulating tissue perfusion. One-Sentence Summary Arterial blood pressure and temperature are integrated via thermosensitve ion channels to produce myogenic tone.
Scalable parallel and distributed simulation of an epidemic on a graph

We propose an algorithm to simulate Markovian SIS epidemics with homogeneous rates and pairwise interactions on a fixed undirected graph, assuming a distributed memory model of parallel programming and limited bandwidth. We offer an implementation of the algorithm in the form of pseudocode in the Appendix. Also, we analyze its algorithmic complexity and its induced dynamical system. Finally, we design experiments to show its scalability and faithfulness. We believe this algorithm offers a way of scaling out, allowing researchers to run simulation tasks at a scale that was not accessible before. Furthermore, we believe this algorithm lays a solid foundation for extensions to simulating more advanced epidemic processes and graph dynamics in other fields. Author summary Modeling and simulation are two essential components in many decision-making processes. Many real-world phenomena can be modeled by a spreading process on a graph, such as the gossip protocol in distributed computing, the word-of-mouth effect in marketing, and a contagious disease that spreads among a population. It is not always possible to study these problems analytically, making computer-based simulations the only tool to make predictions about the system under study. Depending on the scale of the system, such simulations can be computationally expensive, especially when a large range of parameters are to be tested. We propose in this article an algorithm to leverage parallel or distributed computing hardware for discrete event simulations and use a simple susceptible-infected-susceptible epidemic to illustrate the key idea of the algorithm. This algorithm allows one to make trade-offs between scalability and accuracy of the simulation. We believe that this algorithm will find wide applications in simulating graph dynamics.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary Structure Prediction

Pretrained large-scale protein language models, such as ESM-1b and ProtTrans, are becoming the fundamental infrastructure for various protein-related biological modeling tasks. Existing works use mainly pretrained protein language models in feature extraction. However, the knowledge contained in the embedding features directly extracted from a pretrained model is task-agnostic. To obtain task-specific feature representations, a reasonable approach is to fine-tune a pretrained model based on labeled datasets from downstream tasks. To this end, we investigate the fine-tuning of a given pretrained protein language model for protein secondary structure prediction tasks. Specifically, we propose a novel end-to-end protein secondary structure prediction framework involving the lightweight fine-tuning of a pretrained model. The framework first introduces a few new parameters for each transformer block in the pretrained model, then updates only the newly introduced parameters, and then keeps the original pretrained parameters fixed during training. Extensive experiments on seven test sets, namely, CASP12, CASP13, CASP14, CB433, CB634, TEST2016, and TEST2018, show that the proposed framework outperforms existing predictors and achieves new state-of-the-art prediction performance. Furthermore, we also experimentally demonstrate that lightweight fine-tuning significantly outperforms full model fine-tuning and feature extraction in enabling models to predict secondary structures. Further analysis indicates that only a few top transformer blocks need to introduce new parameters, while skipping many lower transformer blocks has little impact on the prediction accuracy of secondary structures.
PGM3 inhibition shows cooperative effects with erastin inducing pancreatic cancer cell death via activation of the unfolded protein response

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a poor patient prognosis. Remarkably, PDAC is one of the most aggressive and deadly tumor types and is notorious for its resistance to all types of treatment. PDAC resistance is frequently associated with a wide metabolic rewiring and in particular of the glycolytic branch named Hexosamine Biosynthetic Pathway (HBP). Here we show the effect of the combined treatment between an HBP’s Phosphoglucomutase 3 (PGM3) enzyme inhibitor, named FR054, and erastin (ERA), a recognized ferroptosis inducer, on PDAC cell growth and survival. Noteworthy, the combined treatment applied to PDAC cell lines induces a significant decrease in cell proliferation and a concurrent enhancement of cell death. Furthermore, we show that this combined treatment induces Unfolded Protein Response (UPR), NFE2 Like BZIP Transcription Factor 2 (NRF2) activation, a change in cellular redox state, a greater sensitivity to oxidative stress, a major dependence on glutamine metabolism, and finally ferroptosis cell death. Our study discloses that HBP inhibition enhances, through UPR activation, the ERA effect and therefore might be a novel anticancer mechanism to be exploited as PDAC therapy.
Role of membrane estrogen receptor alpha on the positive feedback of estrogens on LH secretion

Estrogens act through nuclear and membrane-initiated signaling. Estrogen receptor alpha (ERα) is critical for reproduction, but the relative contribution of its nuclear and membrane signaling is unclear. To address this question, we used two complementary approaches: estetrol (E4) a natural estrogen described to act as an agonist of nuclear ERα and a mERα antagonist and the C451A-ERα mouse lacking mERα. E4 dose-dependently blocks ovulation in female rats, but the mechanism underlying this effect is unknown. To determine whether E4 acts centrally to control ovulation, we tested its effect on the positive feedback of estradiol (E2) on LH secretion. In ovariectomized females chronically exposed to a low dose of E2, estradiol benzoate (EB) alone or combined with progesterone (P) induced a LH surge and the associated increase in the number of activated kisspeptin (Kp) and gonadotropin-releasing hormone (GnRH) neurons. However, E4 blocked these effects of EB when provided alone, but not when combined to P. These results indicate that E4 blocked the induction of the positive feedback and the associated neuronal activation in the absence of P, suggesting an antagonistic effect of E4 on mERα as shown in peripheral tissues. In parallel, C451A-ERα females do not show a pre-ovulatory LH surge and the associated activation of Kp and GnRH neurons in response to EB unless they are treated with P. The similarity of the responses of C451A-ERα mice and wild-type females treated with E4 supports a role for membrane-initiated estrogen signaling in the EB-induced LH surge.
Transcriptomic contributions to a modern cytoarchitectonic parcellation of the human cerebral cortex

Transcriptomic contributions to the anatomical, functional, and network layout of the human cerebral cortex (HCC) has become a major interest in cognitive and systems neuroscience. Here, we tested if transcriptomic differences support a modern, algorithmic cytoarchitectonic parcellation of HCC. Using a data-driven approach, we identified a sparse subset of genes that differentially contributed to the cytoarchitectonic parcellation of HCC. A novel metric (cortical thickness/myelination ratio; CT/M ratio), as well as cell density, correlated with gene expression. Enrichment analyses showed that genes specific to the cytoarchitectonic parcellation of the HCC were related to molecular functions such as transmembrane transport and ion channel activity. Together, the novel relationship between transcriptomics and the CT/M ratio bridges the gap among i) gradients at the macroscale, ii) areas at the meso-scale, and iii) cell density at the microscale, as well as supports the recently proposed cortical spectrum theory.
Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs

Abstract. This paper deals with the CCA subspace algorithm proposed in Larimore [Proceeding of 1983 American Control Conference (1983) pp. 445–451], which constitutes an alternative to the classical criteria optimization based approach to the identification of linear dynamic models for a stationary process. Subspace algorithms for the estimation of linear models have been advocated mainly due to their numerical properties. A large variety of different subspace algorithms is known to provide strongly consistent and asymptotically normal estimates of the system under mild assumptions on the noise and the underlying true system. This paper shows that for certain versions of CCA described in the paper the estimates are asymptotically equivalent to pseudo maximum‐likelihood estimates in the sense that the difference in the estimators multiplied by the square root of the sample size converges to zero (in probability). Therefore these versions of CCA are asymptotically efficient for Gaussian innovations.
Vibration Modeling of a Rigid Rotor Supported on the Lubricated Angular Contact Ball Bearings Considering Six Degrees of Freedom and Waviness on Balls and Races

Nonlinear vibration analysis of angular contact ball bearings supporting a rigid rotor ispresented herein considering the frictional moments (load dependent and load independ-ent components of frictional moments) in the bearings. Six degrees of freedom (DOF) ofrigid rotor is considered in the dynamic modeling of the rotor-bearings system. More-over, waviness on surfaces of inner race, outer race, and ball are considered in the modelby representing it as sinusoidal functions with waviness orders of 6, 15, and 25. Twoamplitudes of waviness, 0.05 and 0.2lm, are considered in the investigation looking forthe practical aspects. The proposed model is validated with the experimental results byperforming the experiments. Moreover, the present model has also been validated withpublished results of researchers by incorporating needful changes in the DOF in the pro-posed model. Based on the computed results, it is observed that load dependent frictionalmoment (LDFM) signiﬁcantly enhances the amplitudes of vibrations in comparison toload independent frictional moment (LIFM) irrespective to values of waviness amplitudeand waviness order. The inﬂuence of inner race waviness is relatively more on the vibra-tions in comparison to waviness of outer race and ball. Moreover, vibrations of systemenhance considerably at high amplitude of waviness, increase in the order of waviness,and at elevated operating parameters. [DOI: 10.1115/1.4005140]
Visualization of Counter-rotating Streamwise Vortices in a Rectangular Channel with One-sided Wavy Surface

Counter-rotating streamwise vortices in a rectangular cross-section channel with a wavy surface on one side were visualized by smoke-wire flow visualization technique. The use of six equilateral triangle patterns cut at the leading edge of the wavy surface results in relatively uniform spanwise wavelengths of these vortices. Visualization results show that these vortex structures remained visible until the second peak of surface waviness in the channel for Re = 3350, while for Re = 5300 more engulfment occurs in the spanwise structures of the vortices at the first peak of the surface waviness. The more engulfment could increase the mixing which results in early breakdown of the streamwise vortices prior to turbulence. It is also revealed that the evolution of these counter-rotating streamwise vortices is confined within the boundary layer. It causes the spanwise variation of the boundary layer thickness indicated by the spanwise wavy pattern at the edge of the boundary layer.