Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of phase plate cryo-EM reveals conformation diversity of therapeutic IgG with 50 kDa Fab fragment resolved below 6 Å

While cryogenic electron microscopy (cryo-EM) is fruitfully used for harvesting high-resolution structures of sizable macromolecules, its application to small or flexible proteins composed of domains like immunoglobulin (IgG) remain challenging. Here, we applied single particle cryo-EM to Rituximab, a therapeutic IgG mediating cancer cell toxicity, to explore its solution conformations. We found Rituximab molecules exhibited aggregates in cryo-EM specimens contrary to its solution behavior, and utilized a non-ionic detergent to successfully disperse them as isolated particles amenable to single particle analysis. As the detergent adversely reduced the protein-to-solvent contrast, we employed phase plate contrast to mitigate the impaired protein visibility. Assisted by phase plate imaging, we obtained a canonical three-arm IgG structure with other structures displaying variable arm densities co-existing in solution, affirming high flexibility of arm-connecting linkers. Furthermore, we showed phase plate imaging enables reliable structure determination of Fab to sub-nanometer resolution from ab initio, yielding a characteristic two-lobe structure that could be unambiguously docked with crystal structure. Our findings revealed conformation diversity of IgG and demonstrated phase plate was viable for cryo-EM analysis of small proteins without symmetry. This work helps extend cryo-EM boundaries, providing a valuable imaging and structural analysis framework for macromolecules with similar challenging features.
A comprehensive map of the ageing blood methylome

During ageing, the human methylome exhibits both differential (i.e. change in mean) and variable (i.e. change in variance) shifts, along with a general rise in entropy. However, it remains unclear whether DNA methylation sites that increasingly diverge between people (i.e. variably methylated positions (VMPs)) are distinct from those undergoing changes in mean methylation levels (i.e. differentially methylated positions (DMPs)), which changes drive entropy, how they contribute to epigenetic age measured by epigenetic clocks, and whether cell type heterogeneity plays a role in these alterations. To address these questions, we conducted a comprehensive analysis using > 32,000 human blood methylomes from 56 datasets (age range = 6-101 years). Our findings revealed an unprecedented proportion of the blood methylome that is differentially methylated with age (48% DMPs; FDR< 0.005) and variably methylated with age (37% VMPs; FDR< 0.005), with many sites overlapping between the two groups (59% of DMPs are VMPs). We observed that bivalent and Polycomb regions become increasingly methylated and divergent between individuals, while quiescent regions lose methylation in a more homogeneous manner between individuals. Unexpectedly, both chronological and biological clocks, but not pace-of-aging clocks, show a strong enrichment for those CpGs that accrue both mean and variance changes during aging. Furthermore, we uncovered that it is the accumulation of DMPs shifting towards a methylation fraction of 50% that drive the increase in entropy, resulting in an overall smoothening of the epigenetic landscape. However, approximately a quarter of DMPs oppose this direction of change, exhibiting anti-entropic effects. While DMPs were mostly unaffected by changes in cell type composition, VMPs and entropy measurements showed moderate sensitivity to such alterations. This investigation represents the largest to date of genome-wide DNA methylation changes and ageing in a single tissue, offering valuable insights into primary molecular changes that hold meaning for chronological and biological ageing.
Nucleosome wrapping state encodes principles of 3D genome organization

Nucleosome is the basic structural unit of the genome. During processes like DNA replication and gene transcription, the conformation of nucleosomes undergoes dynamic changes, including DNA unwrapping and rewrapping, as well as histone disassembly and assembly. However, the wrapping characteristics of nucleosomes across the entire genome, including region-specificity and their correlation with higher-order chromatin organization, remain to be studied. In this study, we investigated the wrapping length of DNA on nucleosomes across the whole genome using wrapping-seq. We discovered that the chromatin of mouse ES cells forms Nucleosome Wrapping Domains (NRDs), which is conserved in yeast and fly. We found that the degree of nucleosome wrapping decreases after DNA replication and is promoted by transcription. Furthermore, we observed that nucleosome wrapping states delineate 3D genome organization and DNA replication timing. In conclusion, we have revealed a novel principle of higher-order chromatin organization.
Daily dynamics of resting-state EEG theta and gamma fluctuations are associated with cognitive performance in healthy aging

Healthy age-related cognitive changes are highly heterogeneous across individuals. This variability is increasingly explained through the lens of spontaneous fluctuations of brain activity, now considered as powerful index of age-related changes. However, brain activity is a biological process modulated by circadian rhythms, and how these fluctuations evolve throughout the day is under investigated. Assessing the daily dynamics of brain fluctuations involves the use of techniques measuring the temporal dynamics of synchronized communications between brain regions, such as electroencephalography. We found that theta and gamma daily fluctuations in the salience-control executive inter-network (SN-CEN) are associated with distinct mechanisms underlying cognitive heterogeneity in aging. Higher levels of SN-CEN theta daily fluctuations appear to be deleterious for memory performance and were associated with higher tau/neuroinflammation rates. In contrast, higher levels of gamma daily fluctuations are positively associated with executive performance, and were associated with lower rate of β-amyloid deposition. Thus, accounting for daily EEG fluctuations of brain activity contributes to better understand subtle brain changes underlying individuals’ cognitive performance in healthy aging.
Toward more realistic drug–target interaction predictions

A number of supervised machine learning models have recently been introduced for the prediction of drug–target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug–target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug–target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug–target interaction prediction results. We also suggest guidelines on how to make the supervised drug–target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug–target interactions for kinase inhibitors.
Invariant point message passing for protein side chain packing

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ-angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1,400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.
Speciﬁcs of Medical Data Mining for Diagnosis Aid: A Survey

Data mining continues to play an important role in medicine; speciﬁcally, for the development of diagnosis aid models used in expert and intelligent systems. Although we can ﬁnd abundant research on this topic, clinicians remain reluctant to use decision support tools. Social pressure explains partly this lukewarm position, but concerns about reliability and credibility are also put forward. To address this reticence, we emphasize the importance of the collaboration between both data miners and clinicians. This survey lays the foundation for such an interaction, by focusing on the speciﬁcs of diagnosis aid, and the related data modeling goals. On this regard, we propose an overview on the requirements expected by the clinicians, who are both the experts and the ﬁnal users. Indeed, we believe that the interaction with clinicians should take place from the very ﬁrst steps of the process and throughout the development of the predictive models, thus not only at the ﬁnal validation stage. Actually, against a current research approach quite blindly driven by data, we advocate the need for a new expert-aware approach. This survey paper provides guidelines to contribute to the design of daily helpful diagnosis aid systems.
TouchBoard: Collaborative Work on Multi-touch Surfaces

Nowadays it is increasingly common to use digital media in face-to-face meetings despite that these meetings normally lack information systems support. Indeed, meeting participants almost always need to connect laptops to screen projectors and take turns while using collaborative software applications. We propose to explore multi-touch technology for brainstorming support, via a large screen: the TouchBoard. This setup provides a workable compromise between the flexibility of a conventional whiteboard and the reusable content afforded by desktop software applications. Through paired user tests, we compared the brainstorm support efficiency of TouchBoard to a typical whiteboard and a mind-mapping application for the PC, CmapTools. The obtained results show that the TouchBoard provides a good tradeoff between the Whiteboard flexibility and the overly structured interface of conventional desktop interfaces such as CmapTools. Indeed, our results show there are major advantages in supporting Brainstorming sessions via the TouchBoard as compared to the other alternatives. These could become more evident in the future though additional improvements to the current prototype to afford greater flexibility in structuring concepts.
Variable-free Discourse Representation Structures *

If we want to develop open-domain, robust semantic parsers for Discourse Representation Theory, a formalism that received considerable attention in formal semantics, then we need to produce large annotated corpora that pair sentences and texts with Discourse Representation Structures. To reduce the manual and mental effort required to construct such a corpus, we propose a simpliﬁed notation for meaning representations that gives precedence to simplicity, without explicit discourse referents and variables, while maintaining the same expressive power.
Highly ef ﬁ cient desalination performance of carbon honeycomb based reverse osmosis membranes unveiled by molecular dynamics simulations

Seawater desalination is vital to our modern civilization. Here, we report that the carbon honeycomb ( CHC ) has an outstanding water permeability and salt rejection in the seawater desalination, as revealed by molecular dynamics simulations. More than 92% of ions are rejected by CHC at applied pressures ranging from 50 to 250 MPa. CHC has a perfect salt rejection at pressures below 150 Mpa. On increasing the applied pressure up to 150 MPa, the salt rejection reduces only to 92%. Pressure, temperature and temperature gradient are noted to play a signi ﬁ cant role in modulating the water ﬂ ux. The water ﬂ ux increases with pressure and temperature. With the introduction of a temperature gradient of 3.5 K nm − 1 , the seawater permeability increases by 33% as compared to room temperature. The water permeability of the CHC is greater than other carbon materials and osmosis membranes including graphene ( 8.7 times ) and graphyne ( 2.1 times ) . It indicates the signi ﬁ cant potential of the CHC for commercial application in water puri ﬁ cation. Supplementary material for this article is available online
Towards Next-Generation Question Answering Over Knowledge Graphs Systems via Accurate Benchmarking and Large-Scale Training

Knowledge graphs (KGs) serve as pivotal information reservoirs, but their intricate structures and the need for expertise in structured query languages limit their accessibility mainly to experts. While the vast potential of KGs in Question Answering (QA) systems is acknowledged, their complexity often requires users to possess specific and advanced querying skills. Since 2010, the advent of QA systems has ushered in a new era where users can employ natural language for queries, simplifying the process. The creation of numerous benchmarking datasets aids in training these modern QA systems, yet choosing the ideal dataset remains challenging. Current datasets, like QALD , come with their own set of limitations, emphasizing the urgency to refine them as KGs gain prominence in sectors such as health-care and finance. Notably, these benchmarks often lack quantitative comparisons and employ diverse creation methods. Additionally, while knowledge graphs continually evolve, benchmarks remain static, failing to capture the latest updates and nuances. In light of these challenges, our research offers a comprehensive solution for both benchmarking and training QA systems that are specifically optimized for KGs. Central to our approach is the introduction of CBench [4, 1]. CBench is an innovative, extensible benchmarking suite devised to provide in-depth analyses of existing QA benchmarks. It delves into the intricate linguistic, syntactic, and structural attributes of the questions and queries contained within these benchmarks. Our assessments revealed notable variations across benchmarks in relation to these properties, making the selection of a mere subset of them an unreliable practice for QA system evaluation. CBench stands out by providing not just conventional metrics but also a granular analysis of the linguistic and structural attributes of both answered and unanswered questions. This detailed insight empowers QA system developers by highlighting areas where their systems excel and pinpointing where improvements are needed. Furthermore, our research introduces Maestro [
Dynamic Na+/H+ exchanger 1 (NHE1) – calmodulin complexes of varying stoichiometry and structure regulate Ca2+-dependent NHE1 activation

Calmodulin (CaM) engages in Ca2+-dependent interactions with numerous proteins, including a still incompletely understood physical and functional interaction with the human Na+/H+-exchanger NHE1. Using nuclear magnetic resonance (NMR) spectroscopy, isothermal titration calorimetry, and fibroblasts stably expressing wildtype and mutant NHE1, we discovered multiple accessible states of this functionally important complex existing in different NHE1:CaM stoichiometries and structures. We determined the NMR solution structure of a ternary complex in which CaM links two NHE1 cytosolic tails. In vitro, stoichiometries and affinities could be tuned by variations in NHE1:CaM ratio and calcium ([Ca2+]) and by phosphorylation of S648 in the first CaM-binding α-helix. In cells, Ca2+-CaM-induced NHE1 activity was reduced by mimicking S648 phosphorylation and by mutation of the first CaM-binding α-helix, whereas it was unaffected by inhibition of Akt, one of several kinases phosphorylating S648. Our results demonstrate a diversity of NHE1:CaM interaction modes and suggest that CaM may contribute to NHE1 dimerization and thereby augment NHE1 regulation. We propose that a similar structural diversity is of relevance to many other CaM complexes.
Workforce Rostering for Decentrally Controlled Production Systems: A Simulation-based Optimization Framework using a Genetic Algorithm

Decentral production control plays a crucial role within the paradigm of Industry 4.0. Due to the fast and ﬂexible decisions on allocation and sequencing required by this type of control, there is no baseline production schedule in advance. This creates a dilemma for efﬁcient staff deployment – typically worker deployment times must be planned at least a few days ahead. To solve this dilemma, we present a simulation-based genetic algorithm, which creates a roster with ﬂexible deployment intervals without a rigid shift pattern based on the production system and job load. In accordance with the zeitgeist and Industry 5.0, we include ﬂexible working time and desired working hours of production workers. For evaluation of the method, we consider worker attendance costs, job delay costs and a cost penalty of work scheduled outside of desired working hours. We forecast the decisions of the decentralized production system by solving a job shop scheduling problem (JSP) extended by manual operations. Our algorithm iteratively uses reasonable solutions of the JSP as basis for roster optimization. With this integrated approach, it is possible to balance job delay costs against worker attendance costs as well as cost for deviation from desired working hours. To ensure compliance with working time legislation, we include appropriate repair operators in the genetic algorithm. We demonstrate the efﬁciency of our heuristic approach by comparison to rigid shift systems and the best of a large number of randomly created rosters.
King’s Research

Objective To assess the impact of hypothermic neural rescue for perinatal asphyxia at birth on healthcare costs of survivors aged 6–7 years, and to quantify the relationship between costs and overall disability levels. Design 6–7 years follow-up of surviving children from the Total Body Hypothermia for Neonatal Encephalopathy (TOBY) trial. setting Community study including a single parental questionnaire to collect information on children’s healthcare resource use.
Eﬃcient Behavior Learning in Human-Robot Collaboration

We present a novel method for a robot to interactively learn, while executing, a joint human-robot. We consider collaborative tasks realized by a team of a human operator and a robot helper that adapts to the human’s task execution preferences. Diﬀerent human operators can have diﬀerent abilities, experiences, and personal preferences so that a particular allocation of activities in the team is preferred over another. Our main goal is to have the robot learn the task and the preferences of the user to provide a more eﬃcient and acceptable joint task execution. We cast concurrent multi-agent cooperation as a semi-Markov decision process and show how to model the team behavior and learn the expected robot behavior. We further propose an interactive learning framework and we evaluate it both in simulation and on a real robotic setup to show the system can eﬀectively learn and adapt to human expectations.
Output-Sensitive Rendering of Detailed Animated Characters for Crowd Simulation

Rendering detailed animated characters is a major limiting factor in crowd simulation. In this paper we present a new representation for 3D animated characters which supports output-sensitive rendering. Each character is encoded through a small collection of textured boxes storing color and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone. A fragment shader is used to recover the original geometry using an adapted version of relief mapping. Unlike competing outputsensitive approaches, our compact representation is able to recover high-frequency surface details and reproduces view-motion parallax. Furthermore, our approach does not require us to predefine the animation sequences nor to select a subset of discrete views. Our user study demonstrates that our approach allows for much more simulated agents with negligible visual artifacts.
Josephson parametric ampliﬁer with Chebyshev gain proﬁle and high saturation

We demonstrate a Josephson parametric ampliﬁer design with a band-pass impedance-matching network based on a third-order Chebyshev prototype. We measured eight ampliﬁers operating at 4.6 GHz that exhibit gains of 20 dB with less than 1-dB gain ripple and bandwidth up to 500 MHz. The ampliﬁers further achieve high-output-saturation powers around − 73 dBm based on the use of rf superconducting quantum interference device arrays as their nonlinear element. We characterize the system readout eﬃ-ciency and its signal-to-noise ratio near saturation using a Sycamore processor, ﬁnding the data consistent with near quantum limited noise performance of the ampliﬁers. In addition, we measure the ampliﬁers’ intermodulation distortion in two-tone experiments as a function of input power and intertone detuning, and observe excess distortion at small detuning with a pronounced dip as a function of signal power, which we interpret in terms of power-dependent dielectric losses.
MINOS: Distributed Consistency and Persistency Protocol Implementation & Offloading to SmartNICs

—To enable high-performance, programmable, and resilient distributed systems, Distributed Data Persistency (DDP) models provide specific data consistency and persistency guarantees. Since these models target leaderless systems (i.e., systems where any node can initiate requests), they deliver high performance and are scalable. However, they are also more complex. In this paper, we develop detailed distributed algorithms for DDP models. They support Linearizable consistency with five different types of persistency. We call these algorithms MINOS-Baseline (MINOS-B) and evaluate them on a 5-node distributed machine. Additionally, to improve performance, we also redesign the algorithms to offload them to a new SmartNIC architecture. The resulting system is called MINOS-Offload (MINOS-O). The MINOS-O SmartNIC introduces optimizations such as selective data coherence in hardware between host and SmartNIC, message batching, and message broadcasting. Our evaluation shows that offloading is very beneficial. It substantially reduces request latency and increases request throughput for various workloads and number of nodes. For example, compared to MINOS-B, MINOS-O reduces the average end-to-end latency of two microservice functions by 35%.