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Sleep is integral to cardiovascular health1,2. Yet, the circuits that connect cardiovascular pathology and sleep are incompletely understood. It remains unclear whether cardiac injury influences sleep and whether sleep-mediated neural outputs contribute to heart healing and inflammation. Here we report that in humans and mice, monocytes are actively recruited to the brain after myocardial infarction (MI) to augment sleep, which suppresses sympathetic outflow to the heart, limiting inflammation and promoting healing. After MI, microglia rapidly recruit circulating monocytes to the brain’s thalamic lateral posterior nucleus (LPN) via the choroid plexus, where they are reprogrammed to generate tumour necrosis factor (TNF). In the thalamic LPN, monocytic TNF engages Tnfrsf1a-expressing glutamatergic neurons to increase slow wave sleep pressure and abundance. Disrupting sleep after MI worsens cardiac function, decreases heart rate variability and causes spontaneous ventricular tachycardia. After MI, disrupting or curtailing sleep by manipulating glutamatergic TNF signalling in the thalamic LPN increases cardiac sympathetic input which signals through the β2-adrenergic receptor of macrophages to promote a chemotactic signature that increases monocyte influx. Poor sleep in the weeks following acute coronary syndrome increases susceptibility to secondary cardiovascular events and reduces the heart’s functional recovery. In parallel, insufficient sleep in humans reprogrammes β2-adrenergic receptor-expressing monocytes towards a chemotactic phenotype, enhancing their migratory capacity. Collectively, our data uncover cardiogenic regulation of sleep after heart injury, which restricts cardiac sympathetic input, limiting inflammation and damage.

Simultaneous stochastic optimisation frameworks provide a method for optimising long-term production schedules in mining complexes that aim to maximise net present value and manage risk related to supply uncertainty. The uncertainty and local variability related to the quality and quantity of material in the mineral deposits are modelled with a set of stochastic orebody simulations, an input into the simultaneous stochastic optimisation framework. Infill drilling provides opportunities to collect additional information associated with the mineral deposits, which can inform future production scheduling decisions. A framework is developed for optimising infill drilling locations with a criterion that seeks areas that directly affect long-term planning decisions and requires the use of geostatistical simulations. Actor-critic reinforcement learning is applied to identify infill drilling locations in a copper mining complex using this criterion. The case study demonstrates that adapting production scheduling decisions given additional information has the potential to improve the associated production and financial forecasts and identifies a stable area for infill drilling.

Stochastic long-term mine planning has evolved to account for different sources of uncertainty. Typically, the uncertainty and local variability of boundaries in geological domains have been overlooked by experts through their deterministic interpretation of available data. Categorical attributes are used to model geological domains, and their stochastic simulation accounts for the mentioned issues. The ability of two-points simulation methods to reproduce complex patterns or the requirement of a training image in multiple-points simulation methods has limited their implementation in mining environments. The high-order simulation of categorical attributes presents a mathematically consistent framework that overcomes these limitations by using high-order spatial statistics from sample data. The case study at a gold mining complex shows two stochastic mine plans based on two sets of geological realisations: geological domains in the first set are modelled using conventional wireframes, while, in the second, they are simulated through the high-order method. The resulting mine plans are substantially different; while both plans present a similar quantity of metal recovered and lifespan, risk profiles are up to 40% wider, and the expected NPV is 20% higher for the case of simulated geological domains, given the decrease of waste handling costs and the corresponding reduction in environmental footprint.

The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence(SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Among the goals of patient-centric care are the advancement of effective personalized treatment, while minimizing toxicity. The phase 2 I-SPY2.2 trial uses a neoadjuvant sequential therapy approach in breast cancer to further these goals, testing promising new agents while optimizing individual outcomes. Here we tested datopotamab–deruxtecan (Dato-DXd) in the I-SPY2.2 trial for patients with high-risk stage 2/3 breast cancer. I-SPY2.2 uses a sequential multiple assignment randomization trial design that includes three sequential blocks of biologically targeted neoadjuvant treatment: the experimental agent(s) (block A), a taxane-based regimen tailored to the tumor subtype (block B) and doxorubicin–cyclophosphamide (block C). Patients are randomized into arms consisting of different investigational block A treatments. Algorithms based on magnetic resonance imaging and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathological complete response, the primary endpoint. There are two primary efficacy analyses: after block A and across all blocks for the six prespecified breast cancer subtypes (defined by clinical hormone receptor/human epidermal growth factor receptor 2 (HER2) status and/or the response-predictive subtypes). We report results of 103 patients treated with Dato-DXd. While Dato-DXd did not meet the prespecified threshold for success (graduation) after block A in any subtype, the treatment strategy across all blocks graduated in the hormone receptor-negative HER2−Immune−DNA repair deficiency−subtype with an estimated pathological complete response rate of 41%. No new toxicities were observed, with stomatitis and ocular events occurring at low grades. Dato-DXd was particularly active in the hormone receptor-negative/HER2−Immune−DNA repair deficiency−signature, warranting further investigation, and was safe in other subtypes in patients who followed the treatment strategy. ClinicalTrials.gov registration: NCT01042379.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody–drug conjugate, datopotamab–deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin–cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin–cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.

This paper presents the application of adaptive simultaneous stochastic optimization with a representative branching framework to generate a strategic mining plan for the Escondida mining complex, the world’s largest copper-production operation. This adaptive stochastic optimization considers geological uncertainty while integrating investment and operational alternatives in the production schedule of a mining complex. Mining complexes are comprised of interconnected components affected by multiple sources of uncertainty. Thus, they must be optimized simultaneously in order to maximize their value, manage environmental impacts, and minimize risk. Additionally, due to the extensive lives of assets and the dynamic and uncertain environment in which mining complexes operate, it is not reasonable to assume that the current strategic plan will remain optimal. Thus, an operationally feasible method to embed alternatives in the mine plan is used. The method utilized provides a strategic plan with representative branches for future possible investment decisions. Adaptive decisions are made sequentially over time, activating costs and effects over the model. The optimization process chooses the optimal strategic production plan accordingly, as well as the investments made and their timing. The Escondida mining complex is a multi-element, multi-pit operation with nine different processing destinations. Investment options considered herein are truck and shovel fleet sizing, adding a secondary crusher in one of the plants, and investing in a main crusher assigned to one of the pits. Additionally, operational alternatives at the mine and plant levels are included. The adaptive solution shows a substantial probability that the mine plan might change its design substantially due to geological uncertainty, presenting an increased expected net present value when compared to the previously developed stochastic mathematical programming formulation that does not consider adaptive decisions, thus generating a single static strategic production plan for the related mining complex. Further studies at the Escondida mining complex can consider adoptive solutions integrating capital investments pertinent to climate change issues.

In recent years, the control of plant pests and diseases has faced new eco-friendly protocols and the use of biocontrol is an attractive alternative as a green and safer management strategy. The use of biological control agents (BCAs) is a major emerging tool in the field of crop disease or pest management that provides an opportunity to replace chemical pesticides that promote sustainable agriculture. Trichodermaspecies are one of the most efficient BCAs, and the search for new, more bioactive, virulent, and efficient species is a permanent task. In the present study, the potential for biocontrol of four strains of the Trichodermagenus isolated from Coahuila semi-desert was evaluated. The evaluated criteria were: radial growth, growth velocity, and growth rate, as well as the PDA sporulation level. The T1DIA-RRG strain achieves maximum growth at 60 h in radial growth, and a growth rate of 0.60 mm/h in antagonist assays, while T4DIA-ARG strain generates the major spore production (1 × 108spores/g). In addition, a solid-state fermentation process with sugar cane bagasse is proposed to develop biotechnological strategies for biological control agents, which show satisfactory results for fungal strain Trichoderma asperellumT4DIA-ARD with the required spore production level to be considered and used as a greenhouse biocontrol agent.

Semi-mobile in-pit crushing and conveying (IPCC) systems can help reduce truck haulage in open-pit mines by bringing the crusher closer to the excavation areas. Optimizing a production schedule with semi-mobile IPCC requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout, and truck fleet investment decisions. A mining complex with multiple mines and IPCC systems should be optimized simultaneously to find an optimal schedule for the entire value chain. An integrated stochastic optimization framework is proposed to produce long-term production schedules for mining complexes using multiple semi-mobile IPCC systems. The optimization model has flexibility to select the crusher locations and conveyor routes from anywhere inside the pits. The framework uses simulated orebody realizations to consider multi-element grade uncertainty and manage associated risk. A hybrid metaheuristic solution approach based on simulated annealing and evolutionary algorithms is implemented. The method is demonstrated using an iron ore mining complex.

To date, values are not widely acknowledged or discussed within physical climate science. Yet, effective management of values in physical climate science is required for the benefit of both science and society.

This work presents the detailed characterization and analysis of recently reported magnetoelastic high-overtone bulk acoustic resonators (ME-HBARs), which are multimode RF-acoustic (phononic) resonators operating in the ${S}$ -band. These unique devices are fabricated by microtransfer printing (MTP) piezoelectric GaN transducers onto a ferrimagnetic yttrium iron garnet (YIG) substrate. The YIG substrate also supports spin waves (magnons) when biased with an external magnetic field. The resulting phonon–magnon hybridization can be used to suppress or tune the acoustic modes of the ME-HBAR. The experiment spans 66 distinct acoustic resonance modes from 2.4 to 3 GHz, each of which can be suppressed or tuned as much as ±6 MHz, with a bias magnetic field $\le 0.21$ T. The experimental ME-HBAR data show good agreement with analytical modeling of the magnetoelastic hybridization in YIG. Such ME-HBARs can be used as dynamically tunable or switchable resonators, oscillators, comb filters, or frequency selective limiters in RF signal processing subcomponents. By integrating incompatible materials (YIG, epitaxial GaN) and disparate functionalities (spin waves, acoustic waves) into one hybrid multidomain system, this work also demonstrates the power and broad scope of the MTP technique.

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.

While genome wide association studies (GWASs) of Alzheimer’s Disease (AD) in European (EUR) ancestry cohorts have identified approximately 83 potentially independent AD risk loci, progress in non-European populations has lagged. In this study, data from the Million Veteran Program (MVP), a biobank which includes genetic data from more than 650,000 US Veteran participants, was used to examine dementia genetics in an African descent (AFR) cohort. A GWAS of Alzheimer’s disease and related dementias (ADRD), an expanded AD phenotype including dementias such as vascular and non-specific dementia that included 4012 cases and 18,435 controls age 60+ in AFR MVP participants was performed. A proxy dementia GWAS based on survey-reported parental AD or dementia (n= 4385 maternal cases, 2256 paternal cases, and 45,970 controls) was also performed. These two GWASs were meta-analyzed, and then subsequently compared and meta-analyzed with the results from a previous AFR AD GWAS from the Alzheimer’s Disease Genetics Consortium (ADGC). A meta-analysis of common variants across the MVP ADRD and proxy GWASs yielded GWAS significant associations in the region of APOE(p= 2.48 × 10−101), in ROBO1(rs11919682, p= 1.63 × 10−8), and RNA RP11-340A13.2 (rs148433063, p= 8.56 × 10−9). The MVP/ADGC meta-analysis yielded additional significant SNPs near known AD risk genes TREM2(rs73427293, p= 2.95 × 10−9), CD2AP(rs7738720, p= 1.14 × 10−9), and ABCA7(rs73505251, p= 3.26 × 10−10), although the peak variants observed in these genes differed from those previously reported in EUR and AFR cohorts. Of the genes in or near suggestive or genome-wide significant associated variants, nine (CDA, SH2D5, DCBLD1, EML6, GOPC, ABCA7, ROS1, TMCO4, and TREM2) were differentially expressed in the brains of AD cases and controls. This represents the largest AFR GWAS of AD and dementia, finding non-APOEGWAS-significant common SNPs associated with dementia. Increasing representation of AFR participants is an important priority in genetic studies and may lead to increased insight into AD pathophysiology and reduce health disparities.

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study—and most other large-scale human genetics studies—was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P= 5.4 × 10−6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

Exploring the relationship between various neurotransmitters and breast cancer cell growth has revealed their likely centrality to improving breast cancer treatment. Neurotransmitters play a key role in breast cancer biology through their effects on the cell cycle, epithelial mesenchymal transition, angiogenesis, inflammation, the tumor microenvironment and other pathways. Neurotransmitters and their receptors are vital to the initiation, progression and drug resistance of cancer and progress in our biological understanding may point the way to lower-cost and lower-risk antitumor therapeutic strategies. This review discusses multiple neurotransmitters in the context of breast cancer. It also discusses risk factors, repurposing of pharmaceuticals impacting neurotransmitter pathways, and the opportunity for better integrated models that encompass exercise, the intestinal microbiome, and other non-pharmacologic considerations. Neurotransmitters’ role in breast cancer should no longer be ignored; it may appear to complicate the molecular picture but the ubiquity of neurotransmitters and their wide-ranging impacts provide an organizing framework upon which further understanding and progress against breast cancer can be based.

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes. The datasets for the throughput prediction model include penetration rates from blast hole drilling (measurement while drilling), geological domains, material types, rock density, and throughput rates of the operating mill, offering an accessible and cost-effective method compared to other geometallurgical programs. First, the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates. A regression model was constructed to predict throughput rates as a function of blended rock properties, which are informed by a material tracking approach in the mining complex. Finally, the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling. This way, common shortfalls of existing geometallurgical throughput prediction models, that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling, are overcome. A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8. By integrating the prediction model and new stochastic components into optimization, the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill. Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7% per period can be mitigated this way.

Enhancers play an essential role in the etiology of schizophrenia; however, the dysregulation of enhancer activity and its impact on the regulome in schizophrenia remains understudied. To address this gap in our knowledge, we assessed enhancer and gene expression in 1,382 brain samples comprising cases with schizophrenia and unaffected controls. Dysregulation of enhancer expression was concordant with changes in gene expression, and was more closely associated with schizophrenia polygenic risk, suggesting that enhancer dysregulation is proximal to the genetic etiology of the disease. Modeling the shared variance of cis-coordinated genes and enhancers revealed a gene regulatory program that was highly associated with genetic vulnerability to schizophrenia. By integrating coordinated factors with evolutionary constraints, we found that enhancers acquired during human evolution are more likely to regulate genes that are implicated in neuropsychiatric disorders and, thus, hold potential as therapeutic targets. Our analysis provides a systematic view of regulome dysregulation in schizophrenia and highlights its convergence with schizophrenia polygenic risk and human-gained enhancers.

Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a major genetic component. Here, we present a genome-wide association study meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide significant loci, highlighting 76 potential risk genes enriched among genes expressed particularly in early brain development. Overall, ADHD genetic risk was associated with several brain-specific neuronal subtypes and midbrain dopaminergic neurons. In exome-sequencing data from 17,896 individuals, we identified an increased load of rare protein-truncating variants in ADHD for a set of risk genes enriched with probable causal common variants, potentially implicating SORCS3in ADHD by both common and rare variants. Bivariate Gaussian mixture modeling estimated that 84–98% of ADHD-influencing variants are shared with other psychiatric disorders. In addition, common-variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions, including attention.

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n= 77,574) and European (n= 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2and LRP11genes and suggest APOEas a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

The brain–gut axis, a bidirectional network between the central and enteric nervous system, plays a critical role in modulating the gastrointestinal tract function and homeostasis. Recently, increasing evidence suggests that neuronal signaling molecules can promote gastrointestinal cancers, however, the mechanisms remain unclear. Aberrant expression of neurotransmitter signaling genes in colorectal cancer supports the role of neurotransmitters to stimulate tumor growth and metastatic spread by promoting cell proliferation, migration, invasion, and angiogenesis. In addition, neurotransmitters can interact with immune and endothelial cells in the tumor microenvironment to promote inflammation and tumor progression. As such, pharmacological targeting of neurotransmitter signaling represent a promising novel anticancer approach. Here, we present an overview of the current evidence supporting the role of neurotransmitters in colorectal cancer biology and treatment.

To characterize the dysregulation of chromatin accessibility in Alzheimer’s disease (AD), we generated 636 ATAC-seq libraries from neuronal and nonneuronal nuclei isolated from the superior temporal gyrus and entorhinal cortex of 153 AD cases and 56 controls. By analyzing a total of ~20 billion read pairs, we expanded the repertoire of known open chromatin regions (OCRs) in the human brain and identified cell-type-specific enhancer–promoter interactions. We show that interindividual variability in OCRs can be leveraged to identify cis-regulatory domains (CRDs) that capture the three-dimensional structure of the genome (3D genome). We identified AD-associated effects on chromatin accessibility, the 3D genome and transcription factor (TF) regulatory networks. For one of the most AD-perturbed TFs, USF2, we validated its regulatory effect on lysosomal genes. Overall, we applied a systematic approach to understanding the role of the 3D genome in AD. We provide all data as an online resource for widespread community-based analysis.

Microglia are brain myeloid cells that play a critical role in neuroimmunity and the etiology of Alzheimer’s disease (AD), yet our understanding of how the genetic regulatory landscape controls microglial function and contributes to AD is limited. Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically driven variation and cell-specific enhancer–promoter (E-P) interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 new candidate risk genes (KCNN4, FIBPand LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1as a key putative regulator of microglia expression and AD risk. This comprehensive resource capturing variation in the human microglia regulome provides insights into the etiology of neurodegenerative disease.

Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2Aand transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

The brain consists of thousands of neuronal types that are generated by stem cells producing different neuronal types as they age. In Drosophila, this temporal patterning is driven by the successive expression of temporal transcription factors (tTFs)1–6. Here we used single-cell mRNA sequencing to identify the complete series of tTFs that specify most Drosophilaoptic lobe neurons. We verify that tTFs regulate the progression of the series by activating the next tTF(s) and repressing the previous one(s), and also identify more complex mechanisms of regulation. Moreover, we establish the temporal window of origin and birth order of each neuronal type in the medulla and provide evidence that these tTFs are sufficient to explain the generation of all of the neuronal diversity in this brain region. Finally, we describe the first steps of neuronal differentiation and show that these steps are conserved in humans. We find that terminal differentiation genes, such as neurotransmitter-related genes, are present as transcripts, but not as proteins, in immature larval neurons. This comprehensive analysis of a temporal series of tTFs in the optic lobe offers mechanistic insights into how tTF series are regulated, and how they can lead to the generation of a complete set of neurons.

Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n= 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104–106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.

The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity. We identified 45 new genetic regions associated with brain functional signatures (P< 2.8 × 10−11), including associations to the central executive, default mode, and salience networks involved in the triple-network model of psychopathology. A number of brain activity-associated loci colocalized with brain disorders (e.g., the APOEε4 locus with Alzheimer’s disease). Variation in brain function was genetically correlated with brain disorders, such as major depressive disorder and schizophrenia. Together, our study provides a step forward in understanding the genetic architecture of brain functional networks and their genetic links to brain-related complex traits and disorders.

While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant–trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.

This article presents a new geometallurgical framework that builds empirical prediction models of key performance indicators of the processing plant and integrates them into a simultaneous stochastic optimization model for mine production scheduling. The prediction models are created by tracking blended rock properties and matching them with observed responses from the operating processing plant. Several applied case studies in a gold mining complex are presented building prediction models of ball mill throughput, steel ball consumptions, and reagent consumptions from collected data in a gold leaching operation. Finally, the integration of these geometallurgical prediction models into stochastic mine production scheduling is demonstrated to monetize the gained insights. Compared with conventional planning, the resulting mine plans increase yearly profit and are more likely to align with production targets.

The simultaneous stochastic optimization of mining complexes (SSOMC) is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple mines and their processing using interconnected facilities to generate a set of finished products while taking into account geological uncertainty to manage the associated risk. To address the need for self-managed solution approaches that are able to tackle large-scale instances without resorting to aggregation, this work proposes a data-driven framework for heuristic scheduling in a fully self-managed hyper-heuristic to solve the SSOMC. The proposed hyper-heuristic selects the heuristic (perturbation) to be applied in a self-adaptive manner using state-of-the-art reinforcement learning methods to efficiently explore which local search is best suited for a particular search point. Experiments on real-world mining complexes show a significant reduction in the computational time by 30–45%.

Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are debilitating, clinically heterogeneous and of unknown molecular etiology. A transcriptome-wide investigation was performed in 165 acutely infected hospitalized individuals who were followed clinically into the post-acute period. Distinct gene expression signatures of post-acute sequelae were already present in whole blood during acute infection, with innate and adaptive immune cells implicated in different symptoms. Two clusters of sequelae exhibited divergent plasma-cell-associated gene expression patterns. In one cluster, sequelae associated with higher expression of immunoglobulin-related genes in an anti-spike antibody titer-dependent manner. In the other, sequelae associated independently of these titers with lower expression of immunoglobulin-related genes, indicating lower non-specific antibody production in individuals with these sequelae. This relationship between lower total immunoglobulins and sequelae was validated in an external cohort. Altogether, multiple etiologies of post-acute sequelae were already detectable during SARS-CoV-2 infection, directly linking these sequelae with the acute host response to the virus and providing early insights into their development.

Despite experiencing a significant trauma, only a subset of World Trade Center (WTC) rescue and recovery workers developed posttraumatic stress disorder (PTSD). Identification of biomarkers is critical to the development of targeted interventions for treating disaster responders and potentially preventing the development of PTSD in this population. Analysis of gene expression from these individuals can help in identifying biomarkers of PTSD. We established a well-phenotyped sample of 371 WTC responders, recruited from a longitudinal WTC responder cohort using stratified random sampling, by obtaining blood, self-reported and clinical interview data. Using bulk RNA-sequencing from whole blood, we examined the association between gene expression and WTC-related PTSD symptom severity on (i) highest lifetime Clinician-Administered PTSD Scale (CAPS) score, (ii) past-month CAPS score, and (iii) PTSD symptom dimensions using a 5-factor model of re-experiencing, avoidance, emotional numbing, dysphoric arousal and anxious arousal symptoms. We corrected for sex, age, genotype-derived principal components and surrogate variables. Finally, we performed a meta-analysis with existing PTSD studies (total N= 1016), using case/control status as the predictor and correcting for these variables. We identified 66 genes significantly associated with total highest lifetime CAPS score (FDR-corrected p< 0.05), and 31 genes associated with total past-month CAPS score. Our more granular analyses of PTSD symptom dimensions identified additional genes that did not reach statistical significance in our analyses with total CAPS scores. In particular, we identified 82 genes significantly associated with lifetime anxious arousal symptoms. Several genes significantly associated with multiple PTSD symptom dimensions and total lifetime CAPS score (SERPINA1, RPS6KA1, and STAT3) have been previously associated with PTSD. Geneset enrichment of these findings has identified pathways significant in metabolism, immune signaling, other psychiatric disorders, neurological signaling, and cellular structure. Our meta-analysis revealed 10 genes that reached genome-wide significance, all of which were downregulated in cases compared to controls (CIRBP, TMSB10, FCGRT, CLIC1, RPS6KB2, HNRNPUL1, ALDOA, NACA, ZNF429 and COPE). Additionally, cellular deconvolution highlighted an enrichment in CD4 T cells and eosinophils in responders with PTSD compared to controls. The distinction in significant genes between total lifetime CAPS score and the anxious arousal symptom dimension of PTSD highlights a potential biological difference in the mechanism underlying the heterogeneity of the PTSD phenotype. Future studies should be clear about methods used to analyze PTSD status, as phenotypes based on PTSD symptom dimensions may yield different gene sets than combined CAPS score analysis. Potential biomarkers implicated from our meta-analysis may help improve therapeutic target development for PTSD.

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.

Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding population-level variation in enhancer function in the human brain. Besides regulating tissue-specific and cell-type-specific transcription of target genes, enhancers themselves can be transcribed. By jointly analyzing large-scale cell-type-specific transcriptome and regulome data, we cataloged 30,795 neuronal and 23,265 non-neuronal candidate transcribed enhancers. Examination of the transcriptome in 1,382 brain samples identified robust expression of transcribed enhancers. We explored gene–enhancer coordination and found that enhancer-linked genes are strongly implicated in neuropsychiatric disease. We identified expression quantitative trait loci (eQTLs) for both genes and enhancers and found that enhancer eQTLs mediate a substantial fraction of neuropsychiatric trait heritability. Inclusion of enhancer eQTLs in transcriptome-wide association studies enhanced functional interpretation of disease loci. Overall, our study characterizes the gene–enhancer regulome and genetic mechanisms in the human cortex in both healthy and diseased states.

Remarkable advances have been made in schizophrenia (SCZ) GWAS, but gleaning biological insight from these loci is challenging. Genetic influences on gene expression (e.g., eQTLs) are cell type-specific, but most studies that attempt to clarify GWAS loci’s influence on gene expression have employed tissues with mixed cell compositions that can obscure cell-specific effects. Furthermore, enriched SCZ heritability in the fetal brain underscores the need to study the impact of SCZ risk loci in specific developing neurons. MGE-derived cortical interneurons (cINs) are consistently affected in SCZ brains and show enriched SCZ heritability in human fetal brains. We identified SCZ GWAS risk genes that are dysregulated in iPSC-derived homogeneous populations of developing SCZ cINs. These SCZ GWAS loci differential expression (DE) genes converge on the PKC pathway. Their disruption results in PKC hyperactivity in developing cINs, leading to arborization deficits. We show that the fine-mapped GWAS locus in the ATP2A2 gene of the PKC pathway harbors enhancer marks by ATACseq and ChIPseq, and regulates ATP2A2 expression. We also generated developing glutamatergic neurons (GNs), another population with enriched SCZ heritability, and confirmed their functionality after transplantation into the mouse brain. Then, we identified SCZ GWAS risk genes that are dysregulated in developing SCZ GNs. GN-specific SCZ GWAS loci DE genes converge on the ion transporter pathway, distinct from those for cINs. Disruption of the pathway gene CACNA1D resulted in deficits of Ca2+currents in developing GNs, suggesting compromised neuronal function by GWAS loci pathway deficits during development. This study allows us to identify cell type-specific and developmental stage-specific mechanisms of SCZ risk gene function, and may aid in identifying mechanism-based novel therapeutic targets.

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify “druggable” targets. Using our meta-analytic data set (N= 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Previous studies indicate that, in the Jovian magnetosphere, the long‐term trend of the radial profile of relativistic electron intensities is primarily shaped by slow radial diffusion. However, measurements by the Galileo spacecraft reveal the existence of transient increases in MeV electron intensities well above the ambient distribution. It is unclear how common such transient enhancements are, and to which dynamic processes in Jupiter's magnetosphere their occurrence is linked. We investigate the radial distributions of >11 MeV and >1 MeV electron intensities from 9RJto 40RJ(RJ=71492kmdenotes the Jovian radius), measured by the Galileo spacecraft from 1996 to 2002. We find transient enhancements of MeV electrons during seven Galileo crossings, mostly occurring around ∼20RJ. An apparent dawn‐dusk asymmetry of their occurrence is resolved, with a majority of events discovered at dawn. This dawn‐dusk asymmetry, as well as the average recurrence time scale of a few days, implies a potential relationship between the MeV electron transients and the storm‐like dynamics in the middle and outer magnetosphere detected using a variety of Galileo, Juno and remote sensing aurora observations. We suggest that the observations of some of these transients in the inner magnetosphere may result from a synergy between the convective transport by a large‐scale dawn‐dusk electric field and the sources provided by injections in the middle magnetosphere. Seven transient enhancement events of relativistic electrons are identified from Galileo's observations, and an average recurrence time scale of a few days is resolved.Transient events mainly occur at 20 ± 5RJwith a remarkable feature of dawn‐dusk asymmetry.Transient events may result from a synergy between the convective transport due to a dawn‐dusk electric field and injections in the middle magnetosphere. Seven transient enhancement events of relativistic electrons are identified from Galileo's observations, and an average recurrence time scale of a few days is resolved. Transient events mainly occur at 20 ± 5RJwith a remarkable feature of dawn‐dusk asymmetry. Transient events may result from a synergy between the convective transport due to a dawn‐dusk electric field and injections in the middle magnetosphere.