Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types

We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genomewide predictions of regulatory activity associated with each functional characterization dataset across many cell types based on available epigenomic data. It then for each cell type produces (1) ChromScoreHMM genome annotations based on the combinatorial and spatial patterns within these predictions and (2) ChromScore tracks of overall predicted regulatory activity. ChromActivity provides a resource for analyzing and interpreting the human regulatory genome across diverse cell types.
Applying Hierarchical Generalized Additive Models to Non-Compartmental Analysis of Pharmacokinetic Data

Non-compartmental analysis (NCA) is a popular strategy for obtaining estimates of pharmacokinetic parameters, while requiring both minimal structural assumptions, and limited input by the analyst. As typically applied, its scope and depth are constrained by its statistical simplicity. Embedding the NCA within a hierarchical generalized additive model (HGAM) may facilitate the simultaneous analysis of data from multiple subjects, estimation of covariate effects in one stage, and implementation of censored responses, similarly to the capabilities of nonlinear multilevel models as widely applied in pharmacometrics. HGAM is an interesting extension to multilevel linear models that allows the effects of predictors to be implemented as smooth functions, which has been widely implemented in various disciplines to nonlinear trends, including for longitudinal data. This approach extends the capability of previous implementations of spline-based methods applied to NCA, within an accessible workflow in open software. Application of HGAM to two example datasets, one describing oral drug administration, and one describing IV and oral drug administration with categorical covariates and censoring, illustrates the overall approach, including parameter estimation, visualization and model checking, and uncertainty quantification. A Bayesian approach to estimation facilitates interpretable expressions of the uncertainty in individual parameters, population parameters, and functions of parameters such as contrasts.
Regression and Alignment for Functional Data and Network Topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales—from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.
Recurrent chromosome destabilization through repeat-mediated rearrangements in a fungal pathogen

Genomic instability caused by chromosomal rearrangements has severe consequences for organismal fitness and progression of cancerous cell lines. The triggers of destabilized chromosomes remain poorly understood but are often assumed to be associated with fragile sites. Here, we retrace a runaway chromosomal degeneration process observed in a fungal pathogen using telomere-to-telomere assemblies across an experimental pedigree. We show that the same fragile sites triggered reproducible, large-scale rearrangements through non-allelic recombination. Across the four-generation pedigree, chromosomal rearrangements were accompanied by non-disjunction events and caused aneuploid progeny to carry up to four chromosomal copies. We identify a specific transposable element as the most likely trigger for the repeated chromosomal degeneration. The element is associated with higher virulence of the pathogen and has undergone a burst increasing copy numbers across the genome. Chromosome sequences are also targeted by a genome defense mechanism active on duplicated sequences, which may contribute to decay. Our work identifies the exact sequence triggers initiating chromosome instability and perpetuating degenerative cycles. Dissecting proximate causes leading to run-away chromosomal degeneration expands our understanding of chromosomal evolution beyond cancer lines.
Spatial IMIX: A Mixture Model Approach to Spatially Correlated Multi-Omics Data Integration

Spatial high-throughput omics data allow scientists to study gene activity in a tissue sample and map where it occurs at the same time. This enables the possibility to investigate important early cancer-initiating events occur in normal-appearing tissue and gene activities that progress and carry through tumor tissue, as defined by “field effect. ” The “field effect” genes are differentially expressed or methylated genes in the spatially resolved high-dimensional datasets with respect to the pathology subtype in each geographical sample across the tissue region. Current statistical methods for spatially resolved genomics data focus on the association of omics data with spatial coordinates without being able to incorporate and test for the association with the sample subtypes. In addition, analytical methods are underdeveloped for spatially resolved multi-omics data integration. We propose a novel statistical frame-work ‘spatial IMIX’ to integratively analyze spatially resolved high-dimensional multi-omics data associated with a specific trait, such as sample subtypes while modeling the spatial correlations between samples and the inter-data-type correlations between omics data simultaneously. Through extensive simulations, spatial IMIX demonstrated well-controlled type I error, great power by relaxing the independence assumptions between data types, model selection features, and the ability to control FDR across data types. Data applications to a geographically annotated tissue area of bladder cancer discovered cancer-initiating gene activities and revealed interesting fundamental biological mechanisms through path-way analysis. We have implemented our method in R package ‘spatialimix’ available at https://github.com/ziqiaow/spatialimix.
Linear modeling of brain activity during selective attention to continuous speech: the critical role of the N1 effect in event-related potentials to acoustic edges

Recent work in the field of neural speech tracking provided evidence for a cortical rep-resentation of speech through superposition of event-related responses to acoustic edges, an idea closely related to the popular linear modeling approach to study cortical syn-chronization to speech via magneto- or electroencephalography (M/EEG). However, it is still unclear to what extent speech-evoked event-related potentials (ERPs) including well-established phenomena, e.g., the N1 selective attention effect, contribute to the regression-based analyses. Here, we addressed this question by analyzing an EEG dataset obtained during a simple multispeaker selective attention task in which participants were cued to attend to only one of two competing speakers. Segmenting the ongoing EEG based on acoustic edges, we were able to replicate previous findings of event-related responses to speech in MEG data with particularly clear P1-N1-P2 complexes. Crucially, speech-evoked ERPs exhibited significant effects of attention in line with the auditory N1 effect. Comparing speech-evoked ERPs to the linear regression results revealed two major find-ings. First, temporal response functions (TRFs) obtained from forward modeling were strongly temporally as well as spatially correlated with corresponding true ERPs. Sec-ond, effects of attention demonstrated by the stimulus reconstruction (SR) accuracies obtained from backward modeling appeared to be driven by a consistent generation of speech-evoked ERPs including the N1 effect. Taken together, our observations reveal a direct link between ERPs to acoustic edges in speech and the linear TRF and SR mod-eling techniques. We emphasize the enhancement in signal-to-noise ratio provided by repeatedly evoked N1 responses to be a critical factor in facilitating the tracking and subsequent higher-order processing of selectively attended speech. In addition to that, the findings imply a cortical speech representation through superimposed speech-evoked ERPs in accordance with recent arguments promoting the neural evoked-response speech tracking model.
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

De novo peptide sequencing is a promising approach for novel peptide discovery. We use a novel concept of complementary spectra to enhance ion information and propose a de novo sequencing model π-HelixNovo based on Transformer architecture. π-HelixNovo outperforms other state-of-the-art models and enhances the taxonomic resolution of gut metaproteome, taking a significant step forward in de novo sequencing.
Inherited retinal degeneration: T-type voltage-gated channels, Na+/Ca2+-exchanger and calpain-2 promote photoreceptor cell death

Inherited retinal degeneration (IRD) refers to a group of untreatable blinding diseases characterized by a progressive loss of photoreceptors. IRD pathology is often linked to an excessive activation of cyclic nucleotide-gated channels (CNGC) leading to Na+– and Ca2+-influx, subsequent activation of voltage-gated Ca2+-channels (VGCC), and further Ca2+ influx. However, whether and how exactly intracellular Ca2+ overload contributes to photoreceptor degeneration is still controversial. Here, we used whole-retina and single-cell RNA-sequencing to compare gene expression between the rd1 mouse model for IRD and wild-type (wt) mice. Differentially expressed genes were linked to several Ca2+–signalling related pathways. To explore this further, organotypic retinal explant cultures derived from rd1 and wt mice were treated with the intracellular Ca2+-chelator BAPTA-AM and with inhibitors for different Ca2+-permeable channels, including CNGC, L-type VGCC, T-type VGCC, Ca2+-release-activated channel (CRAC), and Na+/Ca2+ exchanger (NCX). Moreover, we employed the compound NA-184 to selectively inhibit the Ca2+-dependent protease calpain-2. The overall activity of poly(ADP-ribose) polymerases (PARPs), sirtuin-type histone-deacetylases, calpains, as well as the activation of calpain-1, and –2 were analysed in situ on retinal tissue sections. Cell viability was assessed via the TUNEL assay. While rd1 photoreceptor cell death was reduced by BAPTA-AM, the effects of Ca2+-channel blockers were ambiguous, with T-type VGCC and NCX inhibition showing protection, while blocking CNGC and CRAC was detrimental. Activity of calpains and PARPs generally followed similar trends as cell death. Remarkably, sirtuin activity and calpain-1 activation was associated with photoreceptor protection, while calpain-2 activity was linked to degeneration. Accordingly, the calpain-2 inhibitor NA-184 protected rd1 photoreceptors. Together, these results indicate that Ca2+ overload in rd1 photoreceptors may be triggered by T-type VGCC in conjunction with NCX. High Ca2+-levels likely suppress the protective activity of calpain-1 and promote neurodegeneration via activation of calpain-2. Our study details the complexity of Ca2+-signalling in photoreceptors and emphasizes the importance of identifying and targeting degenerative processes to achieve a therapeutic benefit for IRD.
Model-Agnostic Auditing: A Lost Cause?

Tools for interpretable machine learning (IML) or explainable artificial intelligence (xAI) can be used to audit algorithms for fairness or other desiderata. In a black-box setting without access to the algorithm’s internal structure an auditor may be limited to methods that are model-agnostic. These methods have severe limitations with important consequences for outcomes such as fairness. Among model-agnostic IML methods, visualizations such as the partial dependence plot (PDP) or individual conditional expectation (ICE) plots are popular and useful for displaying qualitative relationships. Although we focus on fairness auditing with PDP/ICE plots, the consequences we highlight generalize to other auditing or IML/xAI applications. This paper questions the validity of auditing in high-stakes settings with contested values or conflicting interests if the audit methods are model-agnostic.
An Empirical Study of Mobile Code Offloading in Unpredictable Environments

Mobile code offloading is a well-known technique for enhancing the capabilities of mobile platforms by transparently leveraging the resources to the cloud. Although this technique has been studied for years, little empirical evidence exists to demonstrate its alleged benefits in terms of performance in real-life situations. All studies conducted on this topic have so far been relegated to controlled environments in laboratory settings. As such, there is no evidence of how and how well this technique performs in real-life scenarios, where network unreliability is the norm. In this work, we present the first empirical study of an Android mobile application integrated with a code offloading framework being tested in the wild. We distributed an application that contains a set of benchmarks in APK format and deployed it on a wide gamut of Android devices to which we had no physical access. We carefully detail the methodology and infrastructure we used to monitor the benchmarks’ performance of 18 devices. Overall, our results show that the accuracy of the decision-making engine is heavily affected by a couple of factors, mainly the network diagnosis and connection type. Therefore, determining whether or not it is more convenient to execute a given task in the cloud is a difficult task. We summarize five lessons we learned by performing our experiment that we believe should be considered for future experiments in this area.
Adoption Management: A Review of the Benefits Management and the Adoption Literature

The adoption of Information Systems (IS) is among the critical topics for IS research and practice. However, despite rich insights across IS literature streams, many IS implementation projects still struggle with user adoption and realising benefits. This paper argues that a knowledge gap exists because research has paid little attention to the definition, manifestations, and effects of adoption management in mandatory environments. The article proposes definitions and systematically reviews findings re-lated to adoption management at the cross-section of the adoption and benefits management literature. The findings show that adoption management manifests across the two literature streams as constructs, models, and methods. While some articles examine these manifestations in the analysis, many discuss adoption management as an afterthought in the implications for practice in discussion sections, and few articles explicitly label it adoption management. We discuss these and other gaps and provide avenues for future research.
Diagnosing Cloud Microphysical Process Information from Remote Sensing Measurements—A Feasibility Study Using Aircraft Data. Part I: Tropical Anvils Measured during TC4

AbstractThe authors investigate whether radar remote sensing of a certain class of ice clouds allows for characterization of the precipitation rates and aggregation processes. The NASA DC-8 collected the measurements in tropical anvils during July and August 2007 as part of the Tropical Composition, Cloud and Climate Coupling (TC4) experiment. Measured hydrometeor size distributions are used to estimate precipitation rates (P) and to solve the hydrodynamical collection equation. These distributions are also used to estimate radar reflectivity factors (Z) and Doppler velocities (Vd) at W, Ka, and Ku bands. Optimal estimation techniques are then used to estimate the uncertainty in retrieving P and aggregation rates (A) from combinations of Z and Vd. It is found that diagnosing information about A requires significant averaging and that a dual-frequency combination of W and Ka bands seems to provide the most information for the ice clouds sampled during TC4. Furthermore, the addition of Vd with expected unce.
Addressing Automation Bias through Verifiability

The phenomenon of human bias finds a new facet in the hybrid human-machine interaction of today’s digitized decision-making systems: automation bias describes the fact that human decision-makers overly trust machine-generated decision proposals, sometimes against their better knowledge. Although human involvement in hybrid human-machine systems is the practical rule compared to fully automated systems in institutionalized decision-making processes, it is not clear how this can be operationalized in a safe, adequate and legally compliant way. In its current legislated form, human interaction does not ensure meaningful human involvement, it also represents a systemic avenue to shirk responsibility by decision support system manufacturers and deployers. In this paper, we analyze the literature on human performance in automated systems and automation bias and identify verification behavior as the key variable ameliorating automation bias. Based on the empirical evidence for automation bias and its cognitive-behavioral correlates, we propose verifiability as a minimum necessary requirement for meaningful human involvement. We argue that verifiability might be subdivided into 1) the intrinsic verification complexity of a system, 2) factors relating to the verification propensity of a user, and 3) the contextual factors influencing verification.
Theory of the Color Change of NaxWO3 as a Function of Na-Charge Doping

We report theoretical investigations of the coloration of WO{sub 3} upon charge insertion using sodium tungsten bronze (Na{sub x}WO{sub 3}) as a model system. Our results explain well the systematic color change of Na{sub x}WO{sub 3} from dark blue to violet, red-orange, and finally to golden yellow as sodium concentration x increases from 0.3 to unity. Proper accounts for both the interband and the intraband contributions to the optical response are found to be very important for a detailed understanding of the coloration mechanism in this system.
RELATIONAL THINKING SUPPORTED BY AN ALGEBRAIC MODELING TOOL ON THE WEB

A new web-based implementation of the FeliX system that combines algebra and geometry in a way that supports relational thinking is introduced. It allows to explore the role of algebra for the modelling of geometrical relations. The rationale behind the system is described, its design principles based on mathematical logic are explained and some use-cases are described. Especially, the didactical implications of a new feature that incorporates minimization is elaborated. Examples range from elementary explorations of geometric and algebraic relations to advanced applications.
Evolution of Wave Packets Constructed Based on the Scattering Wave Functions for One-Dimensional Potential

The evolution problem of wave processes in a field of a one-dimensional potential of an arbitrary is considered. We consider the wave packets constructed on a base of linear combinations of the scattering wave functions which are taken with magnitudes of state indices near to resonance tunneling case. It is shown that the wave processes with different parities of asymptotes describe a same wave process for which the group and phase velocities have opposite direction. The conducted consideration allows to state that the bound states can be interpreted as wave packets of the scattering wave functions to be constructed under a certain way.
Numerical modelling for the recovery of an unknown flux in semilinear parabolic problems with nonstandard boundary conditions.

We consider a 2nd order semilinear parabolic initial boundary value problem (IBVP) in a bounded domain Ω ⊂ R , with nonstandard boundary conditions (BCs). More precisely, at some boundary part, we impose a Neumann BC containing an unknown additive space-constant α(t), accompanied of a nonlocal (integral) Dirichlet side condition. We pass to a suitable variational formulation and prove the uniqueness of the solution to the semilinear IBVP considered. Using Rothe’s method for the time discretization we design the numerical scheme for the approximation of the solution u and also of the unknown function α. ∗This work was supported by the VEO-project no. 011 VO 697.
Consumer Surveillance and Financial Fraud

Companies near constantly surveil their customers to collect, analyze, and profit from their private information. A prevailing concern is that the market for private data and security breaches expose consumers to financial fraud. In this study, we exploit Apple’s App Tracking Transparency (ATT) policy, which greatly limited the tracking and sharing of personal information on the iOS platform, providing a major shock to the data industry. Using a difference-in-differences design and granular variations in iOS user shares across the US, we find that if 10% more people disallow tracking, the number of financial fraud complaints in the average zip code decreases by approximately 3.21%. We then show that the effects are concentrated in complaints related to lax data security and privacy, identified using keyword searches and machine learning on complaint narratives, and in complaints about firms that engage in intensive consumer surveillance and lack data safeguards. Our evidence quantifies one of the main consumer cost of lax data security standards.
Hybrid Algorithm for E-commerce Applications

Encryption is the processes of converting plaintext message (human readable form of message) to cipher text (scrambled form of message) while decryption is the reverse process of encryption. There are specialized algorithms used to handle the encryption/decryption process in e-commerce applications such as CRYPTON, DES, CAST and others. The chief algorithm used in the E-commerce applications for decoding and encoding large volumes of data is the RSA algorithm because of its level of security. Despite its use its major drawback is that of speed. The algorithm is slow in both encryption and decryption process and therefore could not cope up with large volumes of data. With that that problem in mind a hybrid algorithm was designed by harmonizing AES and RSA with the hope that the weaknesses of RSA will be offset by AES and vice versa.