Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Understanding very early onset inflammatory bowel disease (VEOIBD) in relation to inborn errors of immunity.

Inflammatory bowel diseases (IBD) are multifactorial diseases which are caused by the combination of genetic predisposition, exposure factors (environmental and dietary), immune status, and dysbiosis. IBD is a disease which presents at any age, ranging from newborns to the elderly. The youngest of the pediatric IBD population have a more unique presentation and clinical course and may have a different etiology. Very early onset IBD (VEOIBD) patients, designated as those diagnosed prior the age of 6, have distinct features which are more frequent in this patient population including increased incidence of monogenetic causes for IBD (0%-33% depending on the study). This proportion is increased in the youngest subsets, which is diagnosed prior to the age of 2. To date, there are approximately 80 monogenic causes of VEOIBD that have been identified and published. Many of these monogenic causes are inborn errors of immunity yet the majority of VEOIBD patients do not have an identifiable genetic cause for their disease. In this review, we will focus on the clinical presentation, evaluation, and monogenic categories which have been associated with VEOIBD including (1) Epithelial cell defects (2) Adaptive immune defects, (3) Innate Immune/Bacterial Clearance and Recognition defects, and (4) Hyperinflammatory and autoinflammatory disorders. We will highlight differential diagnosis of VEOIBD presentations, as well as evaluation and treatment, which will be helpful for those who study and care for VEOIBD patients outside of the pediatric gastroenterology field. This is a fast-moving field of research which has grown significantly based on knowledge that we gain from our patients. These scientific findings have identified novel mucosal biology pathways and will continue to inform our understanding of gastrointestinal biology.

A radical polymer membrane for simultaneous degradation of organic pollutants and water filtration.

Integrating reactive radicals into membranes that resemble biological membranes has always been a pursuit for simultaneous organics degradation and water filtration. In this research, we discovered that a radical polymer (RP) that can directly trigger the oxidative degradation of sulfamethozaxole (SMX). Mechanistic studies by experiment and density functional theory simulations revealed that peroxyl radicals are the reactive species, and the radicals could be regenerated in the presence of O2. Furthermore, an interpenetrating RP network membrane consisting of polyvinyl alcohol and the RP was fabricated to demonstrate the simultaneous filtration of large molecules in the model wastewater stream and the degradation of ~ 85% of SMX with a steady permeation flux. This study offers valuable insights into the mechanism of RP-triggered advanced oxidation processes and provides an energy-efficient solution for the degradation of organic compounds and water filtration in wastewater treatment.

A call for open science in forensics.

The modern canon of open science consists of five "schools of thought" that justify unfettered access to the fruits of scientific research: i) public engagement, ii) democratic right of access, iii) efficiency of knowledge gain, iv) shared technology, and v) better assessment of impact. Here, we introduce a sixth school: due process. Due process under the law includes a right to "discovery" by a defendant of potentially exculpatory evidence held by the prosecution. When such evidence is scientific, due process becomes a Constitutional mandate for open science. To illustrate the significance of this new school, we present a case study from forensics, which centers on a federally funded investigation that reports summary statistics indicating that identification decisions made by forensic firearms examiners are highly accurate. Because of growing concern about validity of forensic methods, the larger scientific community called for public release of the complete analyzable dataset for independent audit and verification. Those in possession of the data opposed release for three years while summary statistics were used by prosecutors to gain admissibility of evidence in criminal trials. Those statistics paint an incomplete picture and hint at flaws in experimental design and analysis. Under the circumstances, withholding the underlying data in a criminal proceeding violates due process. Following the successful open-science model of drug validity testing through "clinical trials," which place strict requirements on experimental design and timing of data release, we argue for registered and open "forensic trials" to ensure transparency and accountability.

Unique stick-slip crack dynamics of double-network hydrogels under pure-shear loading.

In this work, we have found that a prenotched double-network (DN) hydrogel, when subjected to tensile loading in a pure-shear geometry, exhibits intriguing stick-slip crack dynamics. These dynamics synchronize with the oscillation of the damage (yielding) zone at the crack tip. Through manipulation of the loading rate and the predamage level of the brittle network in DN gels, we have clarified that this phenomenon stems from the significant amount of energy dissipation required to form the damage zone at the crack tip, as well as a kinetic contrast between the rapid crack extension through the yielding zone (slip process) and the slow formation of a new yielding zone controlled by the external loading rate (stick process).

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots.

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Synthesis of bioengineered heparin chemically and biologically similar to porcine-derived products and convertible to low MW heparin.

Heparins have been invaluable therapeutic anticoagulant polysaccharides for over a century, whether used as unfractionated heparin or as low molecular weight heparin (LMWH) derivatives. However, heparin production by extraction from animal tissues presents multiple challenges, including the risk of adulteration, contamination, prion and viral impurities, limited supply, insecure supply chain, and significant batch-to-batch variability. The use of animal-derived heparin also raises ethical and religious concerns, as well as carries the risk of transmitting zoonotic diseases. Chemoenzymatic synthesis of animal-free heparin products would offer several advantages, including reliable and scalable production processes, improved purity and consistency, and the ability to produce heparin polysaccharides with molecular weight, structural, and functional properties equivalent to those of the United States Pharmacopeia (USP) heparin, currently only sourced from porcine intestinal mucosa. We report a scalable process for the production of bioengineered heparin that is biologically and compositionally similar to USP heparin. This process relies on enzymes from the heparin biosynthetic pathway, immobilized on an inert support and requires a tailored N-sulfoheparosan with N-sulfo levels similar to those of porcine heparins. We also report the conversion of our bioengineered heparin into a LMWH that is biologically and compositionally similar to USP enoxaparin. Ultimately, we demonstrate major advances to a process to provide a potential clinical and sustainable alternative to porcine-derived heparin products.

Superoxide radicals mediated by high-spin Fe catalysis for organic wastewater treatment.

Water resources are indispensable basic resources and important environmental carriers; the presence of organic contaminants in wastewater poses considerable risks to the health of both humans and ecosystems. Although the Fenton-like reactions using H2O2 as the oxidant to destroy organic pollutants are attractive, there are still challenges in improving reaction activity under neutral or even alkaline conditions. Herein, we designed a H2O2 activation pathway with O2•- as the main active species and elucidated that the spin interaction between Fe sites and coordinated O atoms effectively promotes the generation of the key intermediate Fe-*OOH. Furthermore, we successfully captured and analyzed the Fe-*OOH intermediate by in situ Raman spectroscopy. When applying FBOB to a continuous-flow reactor, CIP removal efficiency remained at around 90% within 600 min of continuous operation, achieving excellent efficiency, stability, and pH tolerance in removing pollutants.

Estimates of differential toxin expression governing heterogeneous intracellular lifespans of Streptococcus pneumoniae.

Following invasion of the host cell, pore-forming toxins secreted by pathogens compromise vacuole integrity and expose the microbe to diverse intracellular defence mechanisms. However, the quantitative correlation between toxin expression levels and consequent pore dynamics, fostering the intracellular life of pathogens, remains largely unexplored. In this study, using Streptococcus pneumoniae and its secreted pore-forming toxin pneumolysin (Ply) as a model system, we explored various facets of host-pathogen interactions in the host cytosol. Using time-lapse fluorescence imaging, we monitored pore formation dynamics and lifespans of different pneumococcal subpopulations inside host cells. Based on experimental histograms of various event timescales such as pore formation time, vacuolar death or cytosolic escape time and total degradation time, we developed a mathematical model based on first-passage processes that could correlate the event timescales to intravacuolar toxin accumulation. This allowed us to estimate Ply production rate, burst size and threshold Ply quantities that trigger these outcomes. Collectively, we present a general method that illustrates a correlation between toxin expression levels and pore dynamics, dictating intracellular lifespans of pathogens.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Epigenome-augmented eQTL-hotspots reveal genome-wide transcriptional programs in 36 human tissues.

Expression quantitative trait loci (eQTLs) are used to inform the mechanisms of transcriptional regulation in eukaryotic cells. However, the specificity of genome-wide eQTL identification is limited by stringent control for false discoveries. Here, we described a method based on the non-homogeneous Poisson process to identify 125 489 regions with highly frequent, multiple eQTL associations, or 'eQTL-hotspots', from the public database of 59 human tissues or cell types. We stratified the eQTL-hotspots into two classes with their distinct sequence and epigenomic characteristics. Based on these classifications, we developed a machine-learning model, E-SpotFinder, for augmented discovery of tissue- or cell-type-specific eQTL-hotspots. We applied this model to 36 tissues or cell types. Using augmented eQTL-hotspots, we recovered 655 402 eSNPs and reconstructed a comprehensive regulatory network of 2 725 380 cis-interactions among eQTL-hotspots. We further identified 52 012 modules representing transcriptional programs with unique functional backgrounds. In summary, our study provided a framework of epigenome-augmented eQTL analysis and thereby constructed comprehensive genome-wide networks of cis-regulations across diverse human tissues or cell types.

Emergence of disordered branching patterns in confined chiral nematic liquid crystals.

Spatial branching processes are ubiquitous in nature, yet the mechanisms that drive their growth may vary significantly from one system to another. In soft matter physics, chiral nematic liquid crystals provide a controlled setting to study the emergence and growth dynamic of disordered branching patterns. Via an appropriate forcing, a cholesteric phase may nucleate in a chiral nematic liquid crystal, which self-organizes into an extended branching pattern. It is known that branching events take place when the rounded tips of cholesteric fingers swell, become unstable, and split into two new cholesteric tips. The origin of this interfacial instability and the mechanisms that drive the large-scale spatial organization of these cholesteric patterns remain unclear. In this work, we investigate experimentally the spatial and temporal organization of thermally driven branching patterns in chiral nematic liquid crystal cells. We describe the observations through a mean-field model and find that chirality is responsible for the creation of fingers, regulates their interactions, and controls the tip-splitting process. Furthermore, we show that the complex dynamics of the cholesteric pattern behaves as a probabilistic process of branching and inhibition of chiral tips that drives the large-scale topological organization. Our theoretical findings are in good agreement with the experimental observations.

Estimating human mobility in Holocene Western Eurasia with large-scale ancient genomic data.

The recent increase in openly available ancient human DNA samples allows for large-scale meta-analysis applications. Trans-generational past human mobility is one of the key aspects that ancient genomics can contribute to since changes in genetic ancestry-unlike cultural changes seen in the archaeological record-necessarily reflect movements of people. Here, we present an algorithm for spatiotemporal mapping of genetic profiles, which allow for direct estimates of past human mobility from large ancient genomic datasets. The key idea of the method is to derive a spatial probability surface of genetic similarity for each individual in its respective past. This is achieved by first creating an interpolated ancestry field through space and time based on multivariate statistics and Gaussian process regression and then using this field to map the ancient individuals into space according to their genetic profile. We apply this algorithm to a dataset of 3138 aDNA samples with genome-wide data from Western Eurasia in the last 10,000 y. Finally, we condense this sample-wise record with a simple summary statistic into a diachronic measure of mobility for subregions in Western, Central, and Southern Europe. For regions and periods with sufficient data coverage, our similarity surfaces and mobility estimates show general concordance with previous results and provide a meta-perspective of genetic changes and human mobility.

Operando probing and adjusting of the complicated electrode process of multivalent metals at extreme temperature.

Nearly half of the elements in the periodic table are extracted, refined, or plated using electrodeposition in high-temperature melts. However, operando observations and tuning of the electrodeposition process during realistic electrolysis operations are extremely difficult due to severe reaction conditions and complicated electrolytic cell, which makes the improvement of the process very blind and inefficient. Here, we developed a multipurpose operando high-temperature electrochemical instrument that combines operando Raman microspectroscopy analysis, optical microscopy imaging, and a tunable magnetic field. Subsequently, the electrodeposition of Ti-which is a typical polyvalent metal and generally shows a very complex electrode process-was used to verify the stability of the instrument. The complex multistep cathodic process of Ti in the molten salt at 823 K was systematically analyzed by a multidimensional operando analysis strategy involving multiple experimental studies, theoretical calculations, etc. The regulatory effect and its corresponding scale-span mechanism of the magnetic field on the electrodeposition process of Ti were also elucidated, which would be inaccessible with existing experimental techniques and is significant for the real-time and rational optimization of the process. Overall, this work established a powerful and universal methodology for in-depth analysis of high-temperature electrochemistry.

Synthesis and characterization of Craig-type antiaromatic species with [4n + 2] π electrons.

Antiaromaticity is extended from aromaticity as a complement to describe the unsaturated cyclic molecules with antiaromatic destabilization. To prepare antiaromatic species is a particularly challenging goal in synthetic chemistry because of the thermodynamic instability of such molecules. Among that, both Hückel and Möbius antiaromatic species have been reported, whereas the Craig one has not been realized to date. Here, we report the first example of planar Craig antiaromatic species. Eight Craig antiaromatic compounds were synthesized by deprotonation-induced reduction process and were fully characterized as follows. Single-crystal X-ray crystallography showed that these complexes have planar structures composed of fused five-membered rings with clearly alternating carbon-carbon bond lengths. In addition, proton NMR (1H NMR) spectroscopy in these structures showed distinctive upfield shifts of the proton peaks to the range of antiaromatic peripheral hydrogens. Experimental spectroscopy observations, along with density-functional theory (DFT) calculations, provided evidence for the Craig antiaromaticity of these complexes. Further study experimentally and theoretically revealed that the strong exothermicity of the acid-base neutralization process was the driving force for this challenging transformation forming Craig antiaromatic species. Our findings complete a full cycle of aromatic chemistry, opening an avenue for the development of new class of antiaromatic systems.

A variant-dependent molecular clock with anomalous diffusion models SARS-CoV-2 evolution in humans.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans has been monitored at an unprecedented level due to the public health crisis, yet the stochastic dynamics underlying such a process is dubious. Here, considering the number of acquired mutations as the displacement of the viral particle from the origin, we performed biostatistical analyses from numerous whole genome sequences on the basis of a time-dependent probabilistic mathematical model. We showed that a model with a constant variant-dependent evolution rate and nonlinear mutational variance with time (i.e., anomalous diffusion) explained the SARS-CoV-2 evolutionary motion in humans during the first 120 wk of the pandemic in the United Kingdom. In particular, we found subdiffusion patterns for the Primal, Alpha, and Omicron variants but a weak superdiffusion pattern for the Delta variant. Our findings indicate that non-Brownian evolutionary motions occur in nature, thereby providing insight for viral phylodynamics.

Slow-release synthesis of Cu single-atom catalysts with the optimized geometric structure and density of state distribution for Fenton-like catalysis.

Single-atom Fenton-like catalysis has attracted significant attention, yet the quest for controllable synthesis of single-atom catalysts (SACs) with modulation of electron configuration is driven by the current disadvantages of poor activity, low selectivity, narrow pH range, and ambiguous structure-performance relationship. Herein, we devised an innovative strategy, the slow-release synthesis, to fabricate superior Cu SACs by facilitating the dynamic equilibrium between metal precursor supply and anchoring site formation. In this strategy, the dynamics of anchoring site formation, metal precursor release, and their binding reaction kinetics were regulated. Bolstered by harmoniously aligned dynamics, the selective and specific monatomic binding reactions were ensured to refine controllable SACs synthesis with well-defined structure-reactivity relationship. A copious quantity of monatomic dispersed metal became deposited on the C3N4/montmorillonite (MMT) interface and surface with accessible exposure due to the convenient mass transfer within ordered MMT. The slow-release effect facilitated the generation of targeted high-quality sites by equilibrating the supply and demand of the metal precursor and anchoring site and improved the utilization ratio of metal precursors. An excellent Fenton-like reactivity for contaminant degradation was achieved by the Cu1/C3N4/MMT with diminished toxic Cu liberation. Also, the selective ·OH-mediated reaction mechanism was elucidated. Our findings provide a strategy for regulating the intractable anchoring events and optimizing the microenvironment of the monatomic metal center to synthesize superior SACs.

A green edge-hosted zinc single-site heterogeneous catalyst for superior Fenton-like activity.

Developing green heterogeneous catalysts with excellent Fenton-like activity is critical for water remediation technologies. However, current catalysts often rely on toxic transitional metals, and their catalytic performance is far from satisfactory as alternatives of homogeneous Fenton-like catalysts. In this study, a green catalyst based on Zn single-atom was prepared in an ammonium atmosphere using ZIF-8 as a precursor. Multiple characterization analyses provided evidence that abundant intrinsic defects due to the edge sites were created, leading to the formation of a thermally stable edge-hosted Zn-N4 single-atom catalyst (ZnN4-Edge). Density functional theory calculations revealed that the edge sites equipped the single-atom Zn with a super catalytic performance, which not only promoted decomposition of peroxide molecule (HSO5-) but also greatly lowered the activation barrier for •OH generation. Consequently, the as-prepared ZnN4-Edge exhibited extremely high Fenton-like performance in oxidation and mineralization of phenol as a representative organic contaminant in a wide range of pH, realizing its quick detoxification. The atom-utilization efficiency of the ZnN4-Edge was ~104 higher than an equivalent amount of the control sample without edge sites (ZnN4), and the turnover frequency was ~103 times of the typical benchmark of homogeneous catalyst (Co2+). This study opens up a revolutionary way to rationally design and optimize heterogeneous catalysts to homogeneous catalytic performance for Fenton-like application.

Computational Mechanisms Underlying Motivation to Earn Symbolic Reinforcers.

Reinforcement learning is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g., money, points) that are later exchanged for primary reinforcers (e.g., food, drink). Although symbolic reinforcers are ubiquitous in our daily lives, widely used in laboratory tasks because they can be motivating, mechanisms by which they become motivating are less understood. In the present study, we examined how monkeys learn to make choices that maximize fluid rewards through reinforcement with tokens. The question addressed here is how the value of a state, which is a function of multiple task features (e.g., the current number of accumulated tokens, choice options, task epoch, trials since the last delivery of primary reinforcer, etc.), drives value and affects motivation. We constructed a Markov decision process model that computes the value of task states given task features to then correlate with the motivational state of the animal. Fixation times, choice reaction times, and abort frequency were all significantly related to values of task states during the tokens task (n = 5 monkeys, three males and two females). Furthermore, the model makes predictions for how neural responses could change on a moment-by-moment basis relative to changes in the state value. Together, this task and model allow us to capture learning and behavior related to symbolic reinforcement.