Benchmarking machine learning robustness in Covid-19 genome sequence classification.

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models.

Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity.

Objective: To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA).

Research Design And Methods: Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S.

On the Role of Theory and Modeling in Neuroscience.

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement.

Extracting medication changes in clinical narratives using pre-trained language models.

An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate patient care. Healthcare providers or the patients themselves may initiate changes to patient medication. Medication changes take many forms, including prescribed medication and associated dosage modification.

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World.

Principal component analysis (PCA) is a widely used dimensionality reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA). In this paper, we present ThreSPCA, a provably accurate algorithm based on thresholding the Singular Value Decomposition for the SPCA problem, without imposing any restrictive assumptions on the input covariance matrix.

Energy Cost for Flocking of Active Spins: The Cusped Dissipation Maximum at the Flocking Transition.

We study the energy cost of flocking in the active Ising model (AIM) and show that, besides the energy cost for self-propelled motion, an additional energy dissipation is required to power the alignment of spins. We find that this additional alignment dissipation reaches its maximum at the flocking transition point in the form of a cusp with a discontinuous first derivative with respect to the control parameter. To understand this singular behavior, we analytically solve the two- and three-site AIM models and obtain the exact dependence of the alignment dissipation on the flocking order parameter and control parameter, which explains the cusped dissipation maximum at the flocking transition.

Constant-Cost Implementations of Clifford Operations and Multiply-Controlled Gates Using Global Interactions.

We consider quantum circuits composed of single-qubit operations and global entangling gates generated by Ising-type Hamiltonians. It is shown that such circuits can implement a large class of unitary operators commonly used in quantum algorithms at a very low cost-using a constant or effectively constant number of global entangling gates. Specifically, we report constant-cost implementations of Clifford operations with and without ancillae, constant-cost implementation of the multiply-controlled gates with linearly many ancillae, and an O(log^{*}(n)) cost implementation of the n-controlled single-target gates using logarithmically many ancillae.

Implementation of a Hardware-Assisted Bluetooth-Based COVID-19 Tracking Device in a High School: Mixed Methods Study.

Background: Contact tracing is a vital public health tool used to prevent the spread of infectious diseases. However, traditional interview-format contact tracing (TCT) is labor-intensive and time-consuming and may be unsustainable for large-scale pandemics such as COVID-19.

Objective: In this study, we aimed to address the limitations of TCT.

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry).

Simulation of oscillatory dynamics induced by an approximation of grid cell output.

Grid cells, in entorhinal cortex (EC) and related structures, signal animal location relative to hexagonal tilings of 2D space. A number of modeling papers have addressed the question of how grid firing behaviors emerge using (for example) ideas borrowed from dynamical systems (attractors) or from coupled oscillator theory. Here we use a different approach: instead of asking how grid behavior emerges, we take as a given the experimentally observed intracellular potentials of superficial medial EC neurons during grid firing.

Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia.

Background And Hypothesis: Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text.

Predictive Modeling of Huntington's Disease Unfolds Thalamic and Caudate Atrophy Dissociation.

Background: Atrophy in the striatum is a hallmark of Huntington's disease (HD), including the period before clinical motor diagnosis (before-CMD), but it extends to other subcortical structures. The study of the covariation of these structures could improve the detection of disease-related longitudinal progression before-CMD, provide mechanistic insights of the disease, and potentially be used to obtain accurate prospective estimates of atrophy before-CMD and early after-CMD.

Methods: We analyzed data from 337 before-CMD individuals, 236 healthy control subjects, and 95 early after-CMD individuals from three studies, and we used nine subcortical regions volumes in two analyses.

A multi-state dynamic process confers mechano-adaptation to a biological nanomachine.

Adaptation is a defining feature of living systems. The bacterial flagellar motor adapts to changes in the external mechanical load by adding or removing torque-generating (stator) units. But the molecular mechanism behind this mechano-adaptation remains unclear.

Detecting motor symptom fluctuations in Parkinson's disease with generative adversarial networks.

Parkinson's disease is a neurodegenerative disorder characterized by several motor symptoms that develop gradually: tremor, bradykinesia, limb rigidity, and gait and balance problems. While there is no cure, levodopa therapy has been shown to mitigate symptoms. A patient on levodopa experiences cycles in the severity of their symptoms, characterized by an ON state-when the drug is active-and an OFF state-when symptoms worsen as the drug wears off.

A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.

Importance: An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records.

Objectives: To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA).

Hamiltonian Engineering with Multicolor Drives for Fast Entangling Gates and Quantum Crosstalk Cancellation.

Quantum computers built with superconducting artificial atoms already stretch the limits of their classical counterparts. While the lowest energy states of these artificial atoms serve as the qubit basis, the higher levels are responsible for both a host of attractive gate schemes as well as generating undesired interactions. In particular, when coupling these atoms to generate entanglement, the higher levels cause shifts in the computational levels that lead to unwanted ZZ quantum crosstalk.

Extended far-ultraviolet emission in distant dwarf galaxies.

Blue compact dwarf (BCD) galaxies are low-luminosity (absolute K-band magnitude, M > -21 mag), metal-poor (1/50 ≤ Z/Z ≤ 1/2, where Z is the metallicity in terms of the solar metallicity Z), centrally concentrated galaxies with bright clumps of star formation. Cosmological surface-brightness dimming and the small size of BCDs limit their detection at high redshifts, making their formation process difficult to observe. Observations of BCDs are needed at intermediate redshifts, where they are still young enough to show their formative stages, particularly in the outer regions where cosmic gas accretion should drive evolution.

Mechanisms of Quasi van der Waals Epitaxy of Three-Dimensional Metallic Nanoislands on Suspended Two-Dimensional Materials.

Understanding structure at the interface between two-dimensional (2D) materials and 3D metals is crucial for designing novel 2D/3D heterostructures and improving the performance of many 2D material devices. Here, we quantify and discuss the 2D/3D interface structure and the 3D morphology in several materials systems. We first deposit faceted Au nanoislands on graphene and transition metal dichalcogenides, using measurements of the equilibrium island shape to determine values for the 2D/Au interface energy and examining the role of surface reconstructions, chemical identity, and defects on the grown structures.

Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons.

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology.

Protein concentration fluctuations in the high expression regime: Taylor's law and its mechanistic origin.

Protein concentration in a living cell fluctuates over time due to noise in growth and division processes. In the high expression regime, variance of the protein concentration in a cell was found to scale with the square of the mean, which belongs to a general phenomenon called Taylor's law (TL). To understand the origin for these fluctuations, we measured protein concentration dynamics in single .

How to Simulate Quantum Measurement without Computing Marginals.

We describe and analyze algorithms for classically simulating measurement of an n-qubit quantum state in the standard basis, that is, sampling a bit string from the probability distribution determined by the Born rule. Our algorithms reduce the sampling task to computing poly(n) amplitudes of n-qubit states; unlike previously known techniques they do not require computation of marginal probabilities. Two classes of quantum states are considered: output states of polynomial-size quantum circuits, and ground states of local Hamiltonians with an inverse polynomial spectral gap.

Modeling Bacterial Flagellar Motor With New Structure Information: Rotational Dynamics of Two Interacting Protein Nano-Rings.

In this article, we develop a mathematical model for the rotary bacterial flagellar motor (BFM) based on the recently discovered structure of the stator complex (MotAMotB). The structure suggested that the stator also rotates. The BFM is modeled as two rotating nano-rings that interact with each other.