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Influenza vaccine effectiveness and immunogenicity can be compromised with repeated vaccination. We assessed immunological markers in a cohort of healthcare workers (HCW) from six public hospitals around Australia during 2020-2021. Sera were collected pre-vaccination and ~14 and ~180 days post-vaccination and assessed in haemagglutination inhibition assay against egg-grown vaccine and equivalent cell-grown viruses. Responses to vaccination were compared by the number of prior vaccinations. Baseline sera were available for 595 HCW in 2020 and 1031 in 2021. 5% had not been vaccinated during five years prior to enrolment and 55% had been vaccinated every year. Post-vaccination titres for all vaccine antigens were lowest among HCW vaccinated in all 5-prior years and highest among HCW with 0 or 1 prior vaccinations, even after adjustment. This was observed for both influenza A subtypes and was dependent on pre-vaccination titre. Expanded cohorts are needed to better understand how this translates to vaccine effectiveness.

BACKGROUND: A bioactive myelin basic protein (MBP) fragment, comprising MBP84-104, is released in sciatic nerve after chronic constriction injury (CCI). Intraneural injection (IN) of MBP84-104 in an intact sciatic nerve is sufficient to induce persistent neuropathic pain-like behavior via robust transcriptional remodeling at the injection site and ipsilateral dorsal root ganglia (DRG) and spinal cord. The sex (female)-specific pronociceptive activity of MBP84-104 associates with sex-specific changes in cholesterol metabolism and activation of estrogen receptor (ESR)1 signaling. METHODS: In male and female normal and post-CCI rat sciatic nerves, we assessed: (i) cholesterol precursor and metabolite levels by lipidomics; (ii) MBP84-104 interactors by mass spectrometry of MBP84-104 pull-down; and (iii) liver X receptor (LXR)α protein expression by immunoblotting. To test the effect of LXRα stimulation on IN MBP84-104-induced mechanical hypersensitivity, the LXRα expression was confirmed along the segmental neuraxis, in DRG and spinal cord, followed by von Frey testing of the effect of intrathecally administered synthetic LXR agonist, GW3965. In cultured male and female rat DRGs exposed to MBP84-104 and/or estrogen treatments, transcriptional effect of LXR stimulation by GW3965 was assessed on downstream cholesterol transporter Abc, interleukin (IL)-6, and pronociceptive Cacna2d1 gene expression. RESULTS: CCI regulated LXRα ligand and receptor levels in nerves of both sexes, with cholesterol precursors, desmosterol and 7-DHC, and oxysterol elevated in females relative to males. MBP84-104 interacted with nuclear receptor coactivator (Ncoa)1, known to activate LXRα, injury-specific in nerves of both sexes. LXR stimulation suppressed ESR1-induced IL-6 and Cacna2d1 expression in cultured DRGs of both sexes and attenuated MBP84-104-induced pain in females. CONCLUSION: The injury-released bioactive MBP fragments induce pronociceptive changes by selective inactivation of nuclear transcription factors, including LXRα. By Ncoa1 sequestration, bioactive MBP fragments render LXRα function to counteract pronociceptive activity of estrogen/ESR1 in sensory neurons. This effect of MBP fragments is prevalent in females due to high circulating estrogen levels in females relative to males. Restoring LXR activity presents a promising therapeutic strategy in management of neuropathic pain induced by bioactive MBP.

Several studies demonstrate gender and partisan differences among Americans in COVID-19 socioeconomic consequences, attitudes, and behaviors. The authors of this study use six waves of panel survey data to explore the intersection of gender and party across COVID-19 mitigation behaviors, concerns, and policy preferences. The authors observe small gender gaps on several measures; however, partisan differences are larger than gender differences when considering the interaction between gender and partisanship. Democratic women are more similar to Democratic men on these measures than to Republican women. On virtually all measures, Republican women report lower levels of mitigation behaviors, worries, and support for expansive government policies compared to Democratic women and men. Analyzing the interaction of gender and partisanship illuminates how individuals navigated the pandemic with respect to identity factors that often pull in different directions. These findings suggest that one's partisan identity is more consequential than gender when it comes to COVID behaviors, concerns, and policy preferences.

Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourettes syndrome, the supplementary motor area in Parkinsons disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

Mechanisms specifying cancer cell states and response to therapy are incompletely understood. Here we show epigenetic reprogramming shapes the cellular landscape of schwannomas, the most common tumors of the peripheral nervous system. We find schwannomas are comprised of 2 molecular groups that are distinguished by activation of neural crest or nerve injury pathways that specify tumor cell states and the architecture of the tumor immune microenvironment. Moreover, we find radiotherapy is sufficient for interconversion of neural crest schwannomas to immune-enriched schwannomas through epigenetic and metabolic reprogramming. To define mechanisms underlying schwannoma groups, we develop a technique for simultaneous interrogation of chromatin accessibility and gene expression coupled with genetic and therapeutic perturbations in single-nuclei. Our results elucidate a framework for understanding epigenetic drivers of tumor evolution and establish a paradigm of epigenetic and metabolic reprograming of cancer cells that shapes the immune microenvironment in response to radiotherapy.

A mechanistic, dynamic model was developed to calculate body composition in growing lambs by calculating heat production (HP) internally from energy transactions within the body. The model has a fat pool (f) and three protein pools: visceral (v), nonvisceral (m), and wool (w). Heat production is calculated as the sum of fasting heat production, heat of product formation (HrE), and heat associated with feeding (HAF). Fasting heat production is represented as a function of visceral and nonvisceral protein mass. Heat associated with feeding (HAF) is calculated as ((1 - km) x MEI), where km is partial efficiency of ME use for maintenance, and MEI = metabolizable energy intake) applies at all levels above and below maintenance. The value of km derived from data where lambs were fed above maintenance was 0.7. Protein change (dp/dt) is the sum of change in the m, v, and w pools, and change in fat is equal to net energy available for gain minus dp/dt. Heat associated with a change in body composition (HrE) is calculated from the change in protein and fat with estimated partial efficiencies of energy use of 0.4 and 0.7 for protein and fat, respectively. The model allows for individuals to gain protein while losing fat or vice versa. When evaluated with independent data, the model performed better than the current Australian feeding standards (Freer et al., 2007) for predicting protein gain in the empty body but did not perform as well as for gain of fat and fleece-free empty body weight. Models performed similarly for predicting clean wool growth. By explicit representation of the major energy using processes in the body, and through simplification of the way body composition is computed in growing animals, the model is more transparent than current feeding systems while achieving similar performance. An advantage of this approach is that the model has the potential for wider applicability across different growth trajectories and can explicitly account for the effects of systematic changes on energy transactions, such as the effects of selective breeding, growth manipulation, or environmental changes.

In an era defined by climate crises and mounting barriers to cross-border movement, this Article examines the intricate relationships between climate change, displacement, and refugee protection in the United States. Through a comprehensive analysis, incorporating insights from interviews with asylum seekers from Mexico and Central America at the U.S.-Mexico border, we present case examples that highlight the convergence of climate change impacts with other drivers of displacement. Our assessment reveals how some individuals affected by climate-related displacement may qualify for refugee protection when climate change impacts intersect with and exacerbate persecution based on protected grounds under U.S. law. Nevertheless, the significant protection gaps for climate-displaced people underscore the urgent need for the development of additional protection pathways as climate change impacts increasingly drive movement across borders.

The biomedical sciences must maintain and enhance a research culture that prioritizes rigour and transparency. The US National Institute of Neurological Disorders and Stroke convened a workshop entitled Catalyzing Communities of Research Rigor Champions that brought together a diverse group of leaders in promoting research rigour and transparency (identified as rigour champions) to discuss strategies, barriers and resources for catalyzing technical, cultural and educational changes in the biomedical sciences. This article summarizes 2 days of panels and discussions and provides an overview of critical barriers to research rigour, perspectives behind reform initiatives and considerations for stakeholders across science. Additionally, we describe applications of network science to foster, maintain and expand cultural changes related to scientific rigour and opportunities to embed rigourous practices into didactic courses, training experiences and degree programme requirements. We hope this piece provides a primer for the wider research community on current discussions and actions and inspires individuals to build, join or expand collaborative networks within their own institutions that prioritize rigourous research practices.

BACKGROUND: Equine herpes virus type 1 (EHV-1) infection in horses is associated with upper respiratory disease, neurological disease, abortions, and neonatal death. REVIEW QUESTION: Does pharmacological therapy decrease either the incidence or severity of disease or infection caused by EHV-1 in domesticated horses? METHODS: A systematic review was preformed searching AGRICOLA, CAB Abstracts, Cochrane, PubMed, Web of Science, and WHO Global Health Index Medicus Regional Databases to identify articles published before February 15, 2021. Selection criteria were original research reports published in peer reviewed journals, and studies investigating in vivo use of therapeutic agents for prevention or treatment of EHV-1 in horses. Outcomes assessed included measures related to clinical outcomes that reflect symptomatic EHV-1 infection or virus infection. We evaluated risk of bias and performed a GRADE evaluation of the quality of evidence for interventions. RESULTS: A total of 7009 unique studies were identified, of which 9 met the inclusion criteria. Two studies evaluated valacyclovir or small interfering RNAs, and single studies evaluated the use of a Parapoxvirus ovis-based immunomodulator, human alpha interferon, an herbal supplement, a cytosine analog, and heparin. The level of evidence ranged between randomized controlled studies and observational trials. The risk of bias was moderate to high and sample sizes were small. Most studies reported either no benefit or minimal efficacy of the intervention tested. CONCLUSIONS AND CLINICAL IMPORTANCE: Our review indicates minimal or limited benefit either as a prophylactic or post-exposure treatment for any of the studied interventions in the mitigation of EHV-1-associated disease outcome.

The function of regulatory elements is highly dependent on the cellular context, and thus for understanding the function of elements associated with psychiatric diseases these would ideally be studied in neurons in a living brain. Massively Parallel Reporter Assays (MPRAs) are molecular genetic tools that enable functional screening of hundreds of predefined sequences in a single experiment. These assays have not yet been adapted to query specific cell types in vivo in a complex tissue like the mouse brain. Here, using a test-case 3UTR MPRA library with genomic elements containing variants from autism patients, we developed a method to achieve reproducible measurements of element effects in vivo in a cell type-specific manner, using excitatory cortical neurons and striatal medium spiny neurons as test cases. This targeted technique should enable robust, functional annotation of genetic elements in the cellular contexts most relevant to psychiatric disease.

PURPOSE: Novel targeted and immunotherapies have improved outcomes in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), but toxicities limit widespread use. The selective Bruton tyrosine kinase (BTK) inhibitor acalabrutinib has activity in patients with R/R DLBCL but durable responses are uncommon. STAT3 inhibition has demonstrated clinical activity in DLBCL. PATIENTS AND METHODS: Final results of the phase I study of acalabrutinib plus STAT3 inhibitor (danvatirsen; AZD9150) in patients with R/R DLBCL are reported. Danvatirsen 200 mg intravenous infusion [Days 1, 3, 5 (Cycle 1); weekly infusions starting Day 8, Cycle 1] was administered in combination with oral acalabrutinib 100 mg twice daily until progressive disease (PD) or unacceptable toxicity. Primary endpoints were safety and tolerability. Secondary endpoints included efficacy, pharmacokinetics, and immunogenicity. RESULTS: Seventeen patients received combination treatment. One dose-limiting toxicity (Grade 3 liver transaminase) occurred in 1 patient. The most common reason for treatment discontinuation was PD (65%). In evaluable patients (n = 17), objective response rate was 24%; median duration of response was 1.9 months. All responders with available DLBCL cell-of-origin data were either activated B-cell or nongerminal center B-cell like subtype. Genetic subtype did not correlate with response. Baseline and longitudinal plasma cell-free DNA (cfDNA) concentrations were mostly higher in nonresponding patients. cfDNA changes were generally concordant with imaging. Pretreatment circulating B-cell levels were higher in responders versus nonresponders. CONCLUSIONS: Targeting both STAT3 and BTK in combination is safe and tolerable but efficacy is limited in R/R DLBCL. Results support evaluation of circulating tumor DNA as a biomarker for clinical response.

Speech neuroprostheses have the potential to restore communication to people living with paralysis, but naturalistic speed and expressivity are elusive1. Here we use high-density surface recordings of the speech cortex in a clinical-trial participant with severe limb and vocal paralysis to achieve high-performance real-time decoding across three complementary speech-related output modalities: text, speech audio and facial-avatar animation. We trained and evaluated deep-learning models using neural data collected as the participant attempted to silently speak sentences. For text, we demonstrate accurate and rapid large-vocabulary decoding with a median rate of 78 words per minute and median word error rate of 25%. For speech audio, we demonstrate intelligible and rapid speech synthesis and personalization to the participants pre-injury voice. For facial-avatar animation, we demonstrate the control of virtual orofacial movements for speech and non-speech communicative gestures. The decoders reached high performance with less than two weeks of training. Our findings introduce a multimodal speech-neuroprosthetic approach that has substantial promise to restore full, embodied communication to people living with severe paralysis.

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a transcription factor-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide optimal experimental design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

Nociceptive afferent signaling evoked by inflammation and nerve injury is mediated by the opening of ligand-gated and voltage-gated receptors or channels localized to cholesterol-rich lipid raft membrane domains. Dorsal root ganglion (DRG) nociceptors express high levels of toll-like receptor 4 (TLR4), which also localize to lipid rafts. Genetic deletion or pharmacologic blocking of TLR4 diminishes pain associated with chemotherapy-induced peripheral neuropathy (CIPN). In DRGs of mice with paclitaxel-induced CIPN, we analyzed DRG neuronal lipid rafts, expression of TLR4, activation of transient receptor potential cation channel subfamily V member 1 (TRPV1), and TLR4-TRPV1 interaction. Using proximity ligation assay, flow cytometry, and whole-mount DRG microscopy, we found that CIPN increased DRG neuronal lipid rafts and TLR4 expression. These effects were reversed by intrathecal injection of apolipoprotein A-I binding protein (AIBP), a protein that binds to TLR4 and specifically targets cholesterol depletion from TLR4-expressing cells. Chemotherapy-induced peripheral neuropathy increased TRPV1 phosphorylation, localization to neuronal lipid rafts, and proximity to TLR4. These effects were also reversed by AIBP treatment. Regulation of TRPV1-TLR4 interactions and their associated lipid rafts by AIBP covaried with the enduring reversal of mechanical allodynia otherwise observed in CIPN. In addition, AIBP reduced intracellular calcium in response to the TRPV1 agonist capsaicin, which was increased in DRG neurons from paclitaxel-treated mice and in the naïve mouse DRG neurons incubated in vitro with paclitaxel. Together, these results suggest that the assembly of nociceptive and inflammatory receptors in the environment of lipid rafts regulates nociceptive signaling in DRG neurons and that AIBP can control lipid raft-associated nociceptive processing.

Study of α6β4 nicotinic acetylcholine receptors (nAChRs) as a pharmacological target has recently gained interest because of their involvement in analgesia, control of catecholamine secretion, dopaminergic pathways, and aversive pathways. However, an extensive characterization of the human α6β4 nAChRs has been vitiated by technical difficulties resulting in poor receptor expression. In 2020, Knowland and collaborators identified BARP (β-anchoring and regulatory protein), a previously known voltage-gated calcium channel suppressor, as a novel human α6β4 chaperone. Here, we establish that co-expression of human BARP with human α6β4 in Xenopus oocytes, resulted in the functional expression of human α6β4 receptors with acetylcholine-elicited currents that allow an in-depth characterization of the receptor using two electrode voltage-clamp electrophysiology together with diverse agonists and receptor mutations. We report: 1) an extended pharmacological characterization of the receptor, and 2) key residues for agonist-activity located in or near the first shell of the binding pocket. SIGNIFICANCE STATEMENT: The human α6β4 nicotinic acetylcholine receptor has attained increased interest because of its involvement in diverse physiological processes and diseases. Although recognized as a pharmacological target, development of specific agonists has been hampered by limited knowledge of its structural characteristics and by challenges in expressing the receptor. By including the chaperone β-anchoring and regulatory protein for enhanced expression and employing different ligands, we have studied the pharmacology of α6β4, providing insight into receptor residues and structural requirements for ligands important to consider for agonist-induced activation.

ObjectiveDespite advances in the treatment of psychiatric diseases, currently available therapies do not provide sufficient and durable relief for as many as 30-40% of patients. Neuromodulation, including deep brain stimulation (DBS), has emerged as a potential therapy for persistent disabling disease, however it has not yet gained widespread adoption. In 2016, the American Society for Stereotactic and Functional Neurosurgery (ASSFN) convened a meeting with leaders in the field to discuss a roadmap for the path forward. A follow-up meeting in 2022 aimed to review the current state of the field and to identify critical barriers and milestones for progress.DesignThe ASSFN convened a meeting on June 3, 2022 in Atlanta, Georgia and included leaders from the fields of neurology, neurosurgery, and psychiatry along with colleagues from industry, government, ethics, and law. The goal was to review the current state of the field, assess for advances or setbacks in the interim six years, and suggest a future path forward. The participants focused on five areas of interest: interdisciplinary engagement, regulatory pathways and trial design, disease biomarkers, ethics of psychiatric surgery, and resource allocation/prioritization. The proceedings are summarized here.ConclusionThe field of surgical psychiatry has made significant progress since our last expert meeting. Although weakness and threats to the development of novel surgical therapies exist, the identified strengths and opportunities promise to move the field through methodically rigorous and biologically-based approaches. The experts agree that ethics, law, patient engagement, and multidisciplinary teams will be critical to any potential growth in this area.

Mesolimbic nicotinic acetylcholine receptor (nAChRs) activation is necessary for nicotine reinforcement behavior, but it is unknown whether selective activation of nAChRs in the dopamine (DA) reward pathway is sufficient to support nicotine reinforcement. In this study, we tested the hypothesis that activation of β2-containing (β2*) nAChRs on VTA neurons is sufficient for intravenous nicotine self-administration (SA). We expressed β2 nAChR subunits with enhanced sensitivity to nicotine (referred to as β2Leu9'Ser) in the VTA of male Sprague Dawley (SD) rats, enabling very low concentrations of nicotine to selectively activate β2* nAChRs on transduced neurons. Rats expressing β2Leu9'Ser subunits acquired nicotine SA at 1.5 μg/kg/infusion, a dose too low to support acquisition in control rats. Saline substitution extinguished responding for 1.5 μg/kg/inf, verifying that this dose was reinforcing. β2Leu9'Ser nAChRs also supported acquisition at the typical training dose in rats (30 μg/kg/inf) and reducing the dose to 1.5 μg/kg/inf caused a significant increase in the rate of nicotine SA. Viral expression of β2Leu9'Ser subunits only in VTA DA neurons (via TH-Cre rats) also enabled acquisition of nicotine SA at 1.5 μg/kg/inf, and saline substitution significantly attenuated responding. Next, we examined electrically-evoked DA release in slices from β2Leu9'Ser rats with a history of nicotine SA. Single-pulse evoked DA release and DA uptake rate were reduced in β2Leu9'Ser NAc slices, but relative increases in DA following a train of stimuli were preserved. These results are the first to report that β2* nAChR activation on VTA neurons is sufficient for nicotine reinforcement in rats.

Selective serotonin reuptake inhibitors (SSRIs) are the most prescribed treatment for individuals experiencing major depressive disorder. The therapeutic mechanisms that take place before, during, or after SSRIs bind the serotonin transporter (SERT) are poorly understood, partially because no studies exist on the cellular and subcellular pharmacokinetic properties of SSRIs in living cells. We studied escitalopram and fluoxetine using new intensity-based, drug-sensing fluorescent reporters targeted to the plasma membrane, cytoplasm, or endoplasmic reticulum (ER) of cultured neurons and mammalian cell lines. We also used chemical detection of drug within cells and phospholipid membranes. The drugs attain equilibrium in neuronal cytoplasm and ER at approximately the same concentration as the externally applied solution, with time constants of a few s (escitalopram) or 200-300 s (fluoxetine). Simultaneously, the drugs accumulate within lipid membranes by ≥18-fold (escitalopram) or 180-fold (fluoxetine), and possibly by much larger factors. Both drugs leave cytoplasm, lumen, and membranes just as quickly during washout. We synthesized membrane-impermeant quaternary amine derivatives of the two SSRIs. The quaternary derivatives are substantially excluded from membrane, cytoplasm, and ER for >2.4 h. They inhibit SERT transport-associated currents sixfold or 11-fold less potently than the SSRIs (escitalopram or fluoxetine derivative, respectively), providing useful probes for distinguishing compartmentalized SSRI effects. Although our measurements are orders of magnitude faster than the therapeutic lag of SSRIs, these data suggest that SSRI-SERT interactions within organelles or membranes may play roles during either the therapeutic effects or the antidepressant discontinuation syndrome.SIGNIFICANCE STATEMENT Selective serotonin reuptake inhibitors stabilize mood in several disorders. In general, these drugs bind to SERT, which clears serotonin from CNS and peripheral tissues. SERT ligands are effective and relatively safe; primary care practitioners often prescribe them. However, they have several side effects and require 2-6 weeks of continuous administration until they act effectively. How they work remains perplexing, contrasting with earlier assumptions that the therapeutic mechanism involves SERT inhibition followed by increased extracellular serotonin levels. This study establishes that two SERT ligands, fluoxetine and escitalopram, enter neurons within minutes, while simultaneously accumulating in many membranes. Such knowledge will motivate future research, hopefully revealing where and how SERT ligands engage their therapeutic target(s).

Mitigating human-caused mortality for large carnivores is a pressing global challenge for wildlife conservation. However, mortality is almost exclusively studied at local (within-population) scales creating a mismatch between our understanding of risk and the spatial extent most relevant to conservation and management of wide-ranging species. Here, we quantified mortality for 590 radio-collared mountain lions statewide across their distribution in California to identify drivers of human-caused mortality and investigate whether human-caused mortality is additive or compensatory. Human-caused mortality, primarily from conflict management and vehicles, exceeded natural mortality despite mountain lions being protected from hunting. Our data indicate that human-caused mortality is additive to natural mortality as population-level survival decreased as a function of increasing human-caused mortality and natural mortality did not decrease with increased human-caused mortality. Mortality risk increased for mountain lions closer to rural development and decreased in areas with higher proportions of citizens voting to support environmental initiatives. Thus, the presence of human infrastructure and variation in the mindset of humans sharing landscapes with mountain lions appear to be primary drivers of risk. We show that human-caused mortality can reduce population-level survival of large carnivores across large spatial scales, even when they are protected from hunting.

OBJECTIVE: Patient-clinician goal concordance is associated with improved outcomes in certain chronic diseases but not explored in rheumatoid arthritis (RA). We examined goal concordance, correlates of concordance, and the association of concordance with health outcomes. METHODS: Adult patients with RA seen at least 1 time in the prior 12 months at 1 of 2 rheumatology clinics participated. Patients and their clinicians independently ranked top 3 goals for RA treatment from 8 options before a routine visit. Patients completed postvisit surveys on health, demographic information, health literacy, and adherence. Goal concordance was defined as the patients number 1 goal being among the clinicians top 3 goals for that patient. Bivariable and multivariable logistic regression models were used to examine correlates of concordance. RESULTS: Patients were 58% female and 16% Spanish-speaking, and 29% had limited health literacy. Among 204 patient-clinician dyads, 20% were goal-discordant. Have less pain was selected by both patient and clinician in 81% of dyads, followed by have fewer problems doing daily activities by 63%. Otherwise, clinicians prioritized avoiding side effects, whereas patients ranked improved sleep, fatigue, and mood. Longer disease duration was associated with discordance (median 13.3 years, interquartile range [IQR] 5.2-20 among discordant vs. 7 years, IQR 4-14; P = 0.039); higher depressive symptoms were associated with concordance (8.1% vs. 24%; P = 0.04). Goal concordance was associated with higher medication adherence (adjusted odds ratio 2.76 [95% confidence interval 1.01, 7.56]). CONCLUSION: One in 5 patient-clinician dyads had discordant treatment goals. Goal concordance was associated with higher medication adherence. Studies to improve goal elicitation and communication of patients with RAs priorities are needed.

OBJECTIVE: No widely accepted clinical guidelines, and scant directly applicable pragmatic research, are available to guide the prescription of psychiatric medications in low-threshold outpatient settings, such as street outreach, urgent care, and crisis care, as well as walk-in, shelter, and bridge and transition clinics. Providers frequently prescribe medications in these settings without patients having firm psychiatric diagnoses and without medical records to guide clinical decision making. Persons who receive medications in these settings often seek help voluntarily and intermittently for mental illness symptoms. However, because of structural and individual factors, such patients may not engage in longitudinal outpatient psychiatric care. The authors reviewed the literature on psychiatric medication prescribing in low-threshold settings and offer clinical considerations for such prescribing. METHODS: The authors conducted a rapid literature review (N=2,215 abstracts), which was augmented with up-to-date clinical prescribing literature, the authors collective clinical experience, and DSM-5 section II diagnostic criteria to provide considerations for prescribing medications in low-threshold settings. RESULTS: For individuals for whom diagnostic uncertainty is prominent, a symptom-based diagnostic and treatment approach may be best suited to weigh the risks and benefits of medication use in low-threshold settings. Practical considerations for treating patients with clinical presentations of psychosis and trauma, as well as mood, anxiety, and substance use disorders, in low-threshold settings are discussed. CONCLUSIONS: An urgent need exists to invest in pragmatic research and guideline development to delineate best-practice prescribing in low-threshold settings.

BackgroundTrichotillomania (TTM) and skin picking disorder (SPD) are common and often debilitating mental health conditions, grouped under the umbrella term of body-focused repetitive behaviors (BFRBs). Recent clinical subtyping found that there were three distinct subtypes of TTM and two of SPD. Whether these clinical subtypes map on to any unique neurobiological underpinnings, however, remains unknown.MethodsTwo hundred and fifty one adults [193 with a BFRB (85.5% [n = 165] female) and 58 healthy controls (77.6% [n = 45] female)] were recruited from the community for a multicenter between-group comparison using structural neuroimaging. Differences in whole brain structure were compared across the subtypes of BFRBs, controlling for age, sex, scanning site, and intracranial volume.ResultsWhen the subtypes of TTM were compared, low awareness hair pullers demonstrated increased cortical volume in the lateral occipital lobe relative to controls and sensory sensitive pullers. In addition, impulsive/perfectionist hair pullers showed relative decreased volume near the lingual gyrus of the inferior occipital-parietal lobe compared with controls.ConclusionsThese data indicate that the anatomical substrates of particular forms of BFRBs are dissociable, which may have implications for understanding clinical presentations and treatment response.

How do Great Gray Owls (Strix nebulosa) capture voles (Cricetidae) through a layer of snow? As snow is a visual barrier, the owls locate voles by ear alone. To test how snow absorbs and refracts vole sound, we inserted a loudspeaker under the snowpack and analysed sound from the loudspeaker, first buried, then unburied. Snow attenuation coefficients rose with frequency (0.3 dB cm-1 at 500 Hz, 0.6 dB cm-1 at 3 kHz) such that low-frequency sound transmitted best. The Great Gray Owl has the largest facial disc of any owl, suggesting they are adapted to use this low-frequency sound. We used an acoustic camera to spatially localize sound source location, and show that snow also refracts prey sounds (refractive index: 1.16). To an owl not directly above the prey, this refraction creates an 'acoustic mirage': prey acoustic position is offset from its actual location. Their hunting strategy defeats this mirage because they hover directly over prey, which is the listening position with least refraction and least attenuation. Among all birds, the Great Gray Owl has the most extreme wing morphologies associated with quiet flight. These extreme wing traits may function to reduce the sounds of hovering, with implications for bioinspiration.

PURPOSE: Primary open-angle glaucoma (POAG) is a degenerative eye disease for which early treatment is critical to mitigate visual impairment and irreversible blindness. POAG-associated loci individually confer incremental risk. Genetic risk score(s) (GRS) could enable POAG risk stratification. Despite significantly higher POAG burden among individuals of African ancestry (AFR), GRS are limited in this population. A recent large-scale, multi-ancestry meta-analysis identified 127 POAG-associated loci and calculated cross-ancestry and ancestry-specific effect estimates, including in European ancestry (EUR) and AFR individuals. We assessed the utility of the 127-variant GRS for POAG risk stratification in EUR and AFR Veterans in the Million Veteran Program (MVP). We also explored the association between GRS and documented invasive glaucoma surgery (IGS). DESIGN: Cross-sectional study. PARTICIPANTS: MVP Veterans with imputed genetic data, including 5830 POAG cases (445 with IGS documented in the electronic health record) and 64 476 controls. METHODS: We tested unweighted and weighted GRS of 127 published risk variants in EUR (3382 cases and 58 811 controls) and AFR (2448 cases and 5665 controls) Veterans in the MVP. Weighted GRS were calculated using effect estimates from the most recently published report of cross-ancestry and ancestry-specific meta-analyses. We also evaluated GRS in POAG cases with documented IGS. MAIN OUTCOME MEASURES: Performance of 127-variant GRS in EUR and AFR Veterans for POAG risk stratification and association with documented IGS. RESULTS: GRS were significantly associated with POAG (P < 5 × 10-5) in both groups; a higher proportion of EUR compared with AFR were consistently categorized in the top GRS decile (21.9%-23.6% and 12.9%-14.5%, respectively). Only GRS weighted by ancestry-specific effect estimates were associated with IGS documentation in AFR cases; all GRS types were associated with IGS in EUR cases. CONCLUSIONS: Varied performance of the GRS for POAG risk stratification and documented IGS association in EUR and AFR Veterans highlights (1) the complex risk architecture of POAG, (2) the importance of diverse representation in genomics studies that inform GRS construction and evaluation, and (3) the necessity of expanding diverse POAG-related genomic data so that GRS can equitably aid in screening individuals at high risk of POAG and who may require more aggressive treatment.

Male sex is a strong risk factor for autism spectrum disorder (ASD). The leading theory for a "female protective effect" (FPE) envisions males and females have "differing thresholds" under a "liability threshold model" (DT-LTM). Specifically, this model posits that females require either a greater number or larger magnitude of risk factors (i.e., greater liability) to manifest ASD, which is supported by the finding that a greater proportion of females with ASD have highly penetrant genetic mutations. Herein, we derive testable hypotheses from the DT-LTM for ASD, investigating heritability, familial recurrence, correlation between ASD penetrance and sex ratio, population traits, clinical features, the stability of the sex ratio across diagnostic changes, and highlight other key prerequisites. Our findings reveal that several key predictions of the DT-LTM are not supported by current data, requiring us to establish a different conceptual framework for evaluating alternate models that explain sex differences in ASD.

Nicotinic acetylcholine receptors (nAChRs) play an important role in neurotransmission and are also involved in addiction and several disease states. There is significant interest in therapeutic targeting of nAChRs; however, achieving selectivity for one subtype over others has been a longstanding challenge, given the close structural similarities across the family. Here, we characterize binding interactions in the α3β4 nAChR subtype via structure-function studies involving noncanonical amino acid mutagenesis and two-electrode voltage clamp electrophysiology. We establish comprehensive binding models for both the endogenous neurotransmitter ACh and the smoking cessation drug cytisine. We also use a panel of C(10)-substituted cytisine derivatives to probe the effects of subtle changes in the ligand structure on binding. By comparing our results to those obtained for the well-studied α4β2 subtype, we identify several features of both the receptor and agonist structure that can be utilized to enhance selectivity for either α3β4 or α4β2. Finally, we characterize binding interactions of the α3β4-selective partial agonist AT-1001 to determine factors that contribute to its selectivity. These results shed new light on the design of selective nAChR-targeted ligands and can be used to inform the design of improved therapies with minimized off-target effects.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Variation in nutrition is a key determinant of growth, body composition, and the ability of animals to perform to their genetic potential. Depending on the quality of feed available, animals may be able to overcome negative effects of prior nutritional restriction, increasing intake and rates of tissue gain, but full compensation may not occur. A 2 × 3 × 4 factorial serial slaughter study was conducted to examine the effects of prior nutritional restriction, dietary energy density, and supplemental rumen undegradable protein (RUP) on intake, growth, and body composition of lambs. After an initial slaughter (n = 8), 124 4-mo-old Merino cross wethers (28.4 ± 1.8 kg) were assigned to either restricted (LO, 500 g/d) or unrestricted (HI, 1500 g/d) intake of lucerne and oat pellets. After 8 wk, eight lambs/group were slaughtered and tissue weights and chemical composition were measured. Remaining lambs were randomly assigned to a factorial combination of dietary energy density (7.8, 9.2, and 10.7 MJ/kg DM) and supplemental RUP (0, 30, 60, and 90 g/d) and fed ad libitum for a 12- to 13-wk experimental period before slaughter and analysis. By week 3 of the experimental period, lambs fed the same level of energy had similar DMI (g/d) and MEI (MJ/d) (P > 0.05), regardless of prior level of nutrition. Restricted-refed (LO) lambs had higher rates of fat and protein gain than HI lambs (P < 0.05) but had similar visceral masses (P > 0.05). However, LO lambs were lighter and leaner at slaughter, with proportionally larger rumens and livers (P < 0.05). Tissue masses increased with increasing dietary energy density, as did DMI, energy and nitrogen (N) retention (% intake), and rates of protein and fat gain (P < 0.05). The liver increased proportionally with increasing dietary energy density and RUP (P < 0.05), but rumen size decreased relative to the empty body as dietary energy density increased (P < 0.05) and did not respond to RUP (P > 0.05). Fat deposition was greatest in lambs fed 60 g/d supplemental RUP (P < 0.05). However, lambs fed 90 g/d were as lean as lambs that did not receive supplement (P0, P > 0.05), with poorer nitrogen retention and proportionally heavier livers than P0 lambs (P < 0.05). In general, visceral protein was the first tissue to respond to increased intake during refeeding, followed by non-visceral protein and fat, highlighting the influence of differences in tissue response over time on animal performance and body composition.

We report a reagentless, intensity-based S-methadone fluorescent sensor, iS-methadoneSnFR, consisting of a circularly permuted GFP inserted within the sequence of a mutated bacterial periplasmic binding protein (PBP). We evolved a previously reported nicotine-binding PBP to become a selective S-methadone-binding sensor, via three mutations in the PBP's second shell and hinge regions. iS-methadoneSnFR displays the necessary sensitivity, kinetics, and selectivity─notably enantioselectivity against R-methadone─for biological applications. Robust iS-methadoneSnFR responses in human sweat and saliva and mouse serum enable diagnostic uses. Expression and imaging in mammalian cells demonstrate that S-methadone enters at least two organelles and undergoes acid trapping in the Golgi apparatus, where opioid receptors can signal. This work shows a straightforward strategy in adapting existing PBPs to serve real-time applications ranging from subcellular to personal pharmacokinetics.

Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized μECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, μECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to less than or equal to ±200 μm, that is, a single cortical column. Biophysical simulations reproduce experimental findings and indicate that neurons in cortical layers V and VI contribute ∼85% of evoked high-gamma signal recorded at the surface. Cell number and synchrony were the primary biophysical properties determining laminar contributions to evoked μECoG signals, whereas distance was only a minimal factor. Thus, evoked μECoG signals primarily originate from neurons in the infragranular layers of a single cortical column.SIGNIFICANCE STATEMENT ECoG methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. We investigated the localization and origins of sensory-evoked ECoG responses. We experimentally found that ECoG responses were anisotropically localized to a cortical column. Biophysically detailed simulations revealed that neurons in layers V and VI were the primary sources of evoked ECoG responses. These results indicate that evoked ECoG high-gamma responses are primarily generated by the population spike rate of pyramidal neurons in layers V and VI of single cortical columns and highlight the possibility of understanding how microscopic sources produce mesoscale signals.

Disorders such as Trichotillomania (TTM) and skin-picking disorder (SPD) are associated with reduced flexibility and increased internally focused attention. While the basal ganglia have been hypothesized to play a key role, the mechanisms underlying learning and flexible accommodation of new information is unclear. Using a Bayesian Learning Model, we evaluated the neural basis of learning and accommodation in individuals with TTM and/or SPD. Participants were 127 individuals with TTM and/or SPD (TTM/SPD) recruited from three sites (age 18-57, 84% female) and 26 healthy controls (HC). During fMRI, participants completed a shape-button associative learning and reversal fMRI task. Above-threshold clusters were identified where the Initial Learning-Reversals BOLD activation contrast differed significantly (p

Trichotillomania (hair pulling disorder) and skin picking disorder are common and often debilitating mental health conditions, grouped under the umbrella term of body focused repetitive behaviors (BFRBs). Although the pathophysiology of BFRBs is incompletely understood, reward processing dysfunction has been implicated in the etiology and sustention of these disorders. The purpose of this study was to probe reward processing in BFRBs. 159 adults (125 with a BFRB [83.2% (n = 104) female] and 34 healthy controls [73.5% (n = 25) female]) were recruited from the community for a multi-center between-group comparison using a functional imaging (fMRI) monetary reward task. Differences in brain activation during reward anticipation and punishment anticipation were compared between BFRB patients and controls, with stringent correction for multiple comparisons. All group level analyses controlled for age, sex and scanning site. Compared to controls, BFRB participants showed marked hyperactivation of the bilateral inferior frontal gyrus (pars opercularis and pars triangularis) compared to controls. In addition, BFRB participants exhibited increased activation in multiple areas during the anticipation of loss (right fusiform gyrus, parahippocampal gyrus, cerebellum, right inferior parietal lobule; left inferior frontal gyrus). There were no significant differences in the win-lose contrast between the two groups. These data indicate the existence of dysregulated reward circuitry in BFRBs. The identified pathophysiology of reward dysfunction may be useful to tailor future treatments.

The purpose of this study was to investigate whether long-term television viewing patterns, a common sedentary behavior, in early to mid-adulthood is associated with gray matter brain volume in midlife and if this is independent of physical activity. We evaluated 599 participants (51% female, 44% black, mean age 30.3 ± 3.5 at baseline and 50.2 ± 3.5 years at follow-up and MRI) from the prospective Coronary Artery Risk Development in Young Adults (CARDIA) study. We assessed television patterns with repeated interviewer-administered questionnaire spanning 20 years. Structural MRI (3T) measures of frontal cortex, entorhinal cortex, hippocampal, and total gray matter volumes were assessed at midlife. Over the 20 years, participants reported viewing an average of 2.5 ± 1.7 h of television per day (range: 0-10 h). After multivariable adjustment, greater television viewing was negatively associated with gray matter volume in the frontal (β = - 0.77; p = 0.01) and entorhinal cortex (β = - 23.83; p = 0.05) as well as total gray matter (β = - 2.09; p = 0.003) but not hippocampus. These results remained unchanged after additional adjustment for physical activity. For each one standard deviation increase in television viewing, the difference in gray matter volume z-score was approximately 0.06 less for each of the three regions (p

This protocol explains the pipeline for condition-dependent metabolite yield prediction using Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER targets metabolic engineering applications via a hybrid model integrating transcription factor (TF)-gene regulatory network (TRN) with a Bayesian network (BN) inferred from transcriptomic expression data to effectively regulate metabolic reactions. For E. coli and yeast, TRIMER achieves reliable knockout phenotype and flux predictions from the deletion of one or more TFs at the genome scale. For complete details on the use and execution of this protocol, please refer to Niu et al. (2021).

Neuroprostheses have the potential to restore communication to people who cannot speak or type due to paralysis. However, it is unclear if silent attempts to speak can be used to control a communication neuroprosthesis. Here, we translated direct cortical signals in a clinical-trial participant (ClinicalTrials.gov; NCT03698149) with severe limb and vocal-tract paralysis into single letters to spell out full sentences in real time. We used deep-learning and language-modeling techniques to decode letter sequences as the participant attempted to silently spell using code words that represented the 26 English letters (e.g. "alpha" for "a"). We leveraged broad electrode coverage beyond speech-motor cortex to include supplemental control signals from hand cortex and complementary information from low- and high-frequency signal components to improve decoding accuracy. We decoded sentences using words from a 1,152-word vocabulary at a median character error rate of 6.13% and speed of 29.4 characters per minute. In offline simulations, we showed that our approach generalized to large vocabularies containing over 9,000 words (median character error rate of 8.23%). These results illustrate the clinical viability of a silently controlled speech neuroprosthesis to generate sentences from a large vocabulary through a spelling-based approach, complementing previous demonstrations of direct full-word decoding.

We evaluated neuropathological consequences of fetal ZIKV exposure in rhesus monkeys, a translatable animal model for human neural development, by carrying out quantitative neuroanatomical analyses of the nearly full-term brains of fetuses infected with ZIKV and procedure-matched controls. For each animal, a complete cerebral hemisphere was evaluated using immunohistochemical (IHC) and neuroanatomical techniques to detect virus, identify affected cell types, and evaluate gross neuroanatomical abnormalities. IHC staining revealed the presence of ZIKV in the frontal lobe, which contained activated microglia and showed increased apoptosis of immature neurons. ZIKV-infected animals exhibited macrostructural changes within the visual pathway. Regional differences tracked with the developmental timing of the brain, suggesting inflammatory processes related to viral infiltration swept through the cortex, followed by a wave of cell death resulting in morphological changes. These findings may help explain why some infants born with normal sized heads during the ZIKV epidemic manifest developmental challenges as they age.

The tree complex is a simplicial complex defined in recent work of Belk, Lanier, Margalit, and Winarski with applications to mapping class groups and complex dynamics. This article introduces a connection between this setting and the convex polytopes known as associahedra and cyclohedra. Specifically, we describe a characterization of these polytopes using planar embeddings of trees and show that the tree complex is the barycentric subdivision of a polyhedral cell complex for which the cells are products of associahedra and cyclohedra.Mathematics Subject Classifications: 05C05, 05C10, 20F65, 52B11Keywords: Associahedra, cyclohedra, planar trees, mapping class groups

Nicotinic partial agonists provide an accepted aid for smoking cessation and thus contribute to decreasing tobacco-related disease. Improved drugs constitute a continued area of study. However, there remains no reductionist method to examine the cellular and subcellular pharmacokinetic properties of these compounds in living cells. Here, we developed new intensity-based drug-sensing fluorescent reporters (iDrugSnFRs) for the nicotinic partial agonists dianicline, cytisine, and two cytisine derivatives - 10-fluorocytisine and 9-bromo-10-ethylcytisine. We report the first atomic-scale structures of liganded periplasmic binding protein-based biosensors, accelerating development of iDrugSnFRs and also explaining the activation mechanism. The nicotinic iDrugSnFRs detect their drug partners in solution, as well as at the plasma membrane (PM) and in the endoplasmic reticulum (ER) of cell lines and mouse hippocampal neurons. At the PM, the speed of solution changes limits the growth and decay rates of the fluorescence response in almost all cases. In contrast, we found that rates of membrane crossing differ among these nicotinic drugs by >30-fold. The new nicotinic iDrugSnFRs provide insight into the real-time pharmacokinetic properties of nicotinic agonists and provide a methodology whereby iDrugSnFRs can inform both pharmaceutical neuroscience and addiction neuroscience.

Although recall proceeds most naturally in the order of encoding, people can also recall items in reverse order. Prior studies raise the question of whether recall performance differs as a function of direction. To shed new light on this question, we examined the temporal dynamics of forward and backward serial recall. Consistent with prior work, we find similar levels of recall accuracy in forward and backward recall. However, detailed analyses of recall dynamics demonstrated higher accuracy for recall initiation and lower accuracy for recall transitions in backward compared to forward recall. Pre-cuing subjects to the direction of recall eliminated the difference in recall initiation and reduced, but did not erase, effects seen in recall transitions. We show that backward recall benefits and suffers from the recency effect: By promoting access to end of list items, recency facilitates initiation, but it hinders correct transitions by promoting fill-in-errors following omissions.

There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER's applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

The Forward Physics Facility (FPF) is a proposal to create a cavern with thespace and infrastructure to support a suite of far-forward experiments at theLarge Hadron Collider during the High Luminosity era. Located along the beamcollision axis and shielded from the interaction point by at least 100 m ofconcrete and rock, the FPF will house experiments that will detect particlesoutside the acceptance of the existing large LHC experiments and will observerare and exotic processes in an extremely low-background environment. In thiswork, we summarize the current status of plans for the FPF, including recentprogress in civil engineering in identifying promising sites for the FPF andthe experiments currently envisioned to realize the FPF's physics potential. Wethen review the many Standard Model and new physics topics that will beadvanced by the FPF, including searches for long-lived particles, probes ofdark matter and dark sectors, high-statistics studies of TeV neutrinos of allthree flavors, aspects of perturbative and non-perturbative QCD, andhigh-energy astroparticle physics.

Social support (SS) predicts health outcomes among patients living with HIV. We administered a brief, validated measure of SS, the Multifactoral Assessment of Perceived Social Support, within a patient-reported outcomes assessment of health domains in HIV care at 4 U.S. clinics in English and Spanish (n = 708). In univariate analysis, low SS was associated with poorer engagement in care, antiretroviral adherence, and health-related quality of life; current methamphetamine/crystal use, depression, anxiety, and HIV stigma (all p

BackgroundTechnology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication.MethodsWe implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them.ResultsWe decoded sentences from the participant's cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period.ConclusionsIn a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149.).

BackgroundA central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations, and their modulation by external factors, using high-dimensional and stochastic neural recordings. Parametric statistical models (e.g., coupling, encoding, and decoding models), play an instrumental role in accomplishing this goal. However, extracting conclusions from a parametric model requires that it is fit using an inference algorithm capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. The recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation.New methodWe used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation.ComparisonWe fit functional coupling, encoding, and decoding models across a battery of neural datasets using both UoI and baseline inference procedures (e.g., ℓ1-penalized GLMs), and compared the structure of their fitted parameters.ResultsAcross recording modality, brain region, and task, we found that UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance. We obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that relied on fewer single-units.ConclusionsTogether, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.

BackgroundA central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations, and their modulation by external factors, using high-dimensional and stochastic neural recordings. Parametric statistical models (e.g., coupling, encoding, and decoding models), play an instrumental role in accomplishing this goal. However, extracting conclusions from a parametric model requires that it is fit using an inference algorithm capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. The recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation.New methodWe used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation.ComparisonWe fit functional coupling, encoding, and decoding models across a battery of neural datasets using both UoI and baseline inference procedures (e.g., ℓ1-penalized GLMs), and compared the structure of their fitted parameters.ResultsAcross recording modality, brain region, and task, we found that UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance. We obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that relied on fewer single-units.ConclusionsTogether, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.