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Objective: To examine whether putatively atypical neuronal activity during internal attention in ADHD yields insights into processes underlying emotion dysregulation. Methods: We used a word processing paradigm to assess neural activations in adults with ADHD ( N = 46) compared to controls ( N = 43). We measured effects of valence, applied partial-least squares correlation analysis to assess multivariate brainbehavior relationships and ran subgroup analyses to isolate results driven by pure ADHD ( N = 18). Results: During internal attention, ADHD, compared to controls, have (1) increased activation in the right angular gyrus (rAG), which appears driven by pure, not comorbid, ADHD and (2) diminished activation in the insula and fronto-striatal circuitry. Diminished activations were driven by negatively-valenced internal attention and negatively correlated with increased affective lability within the ADHD group. Conclusion: Internal attention in ADHD is associated with increased rAG activation, possibly reflecting difficulty converging external and internal information, and diminished activation within emotion regulation circuitry.

The widespread application of modern machine learning has increased the need for robust statistical algorithms. This work studies one such fundamental statistical measure known as the Tukey depth. We study the problem in the continuum (population) limit. In particular, we derive the associated necessary conditions, which take the form of a first-order partial differential equation. We discuss the classical interpretation of this necessary condition as the viscosity solution of a Hamilton-Jacobi equation, but with a non-classical Hamiltonian with discontinuous dependence on the gradient at zero. We prove that this equation possesses a unique viscosity solution and that this solution always bounds the Tukey depth from below. In certain cases, we prove that the Tukey depth is equal to the viscosity solution, and we give some illustrations of standard numerical methods from the optimal control community which deal directly with the partial differential equation. We conclude by outlining several promising research directions both in terms of new numerical algorithms and theoretical challenges.

In this work we study statistical properties of graph-based clustering algorithms that rely on the optimization of balanced graph cuts, the main example being the optimization of Cheeger cuts. We consider proximity graphs built from data sampled from an underlying distribution supported on a generic smooth compact manifold $${\mathcal {M}}$$ M . In this setting, we obtain high probability convergence rates for both the Cheeger constant and the associated Cheeger cuts towards their continuum counterparts. The key technical tools are careful estimates of interpolation operators which lift empirical Cheeger cuts to the continuum, as well as continuum stability estimates for isoperimetric problems. To the best of our knowledge the quantitative estimates obtained here are the first of their kind.

Based on an affective neuroscience approach to appraisal theories of emotion, the present study tested the hypothesis that the amygdala is involved in the processing of concern-relevance. Thirty-five students with varying intrinsic academic intrinsic motivation performed three target detection tasks (i.e. targets vs. nontargets) using affectively neutral letter stimuli as targets. In one task, participants were given a cover story that their task-performance was indicative of future academic success. This intrinsic motivation task was controlled with two other tasks: an extrinsic motivation task allowing participants to earn money for themselves based on their task-performance, and a baseline task where participants were simply requested to perform the task while we were calibrating the scanner. Results illustrated an interaction effect both at the whole-brain and region-of-interest levels. When accounting for all three conditions, only during the intrinsic motivation condition did bilateral amygdala activation significantly increase with increasing academic intrinsic motivation scores when responding to targets (versus nontargets). Findings suggest inter-individual intrinsic motivations modulate amygdala response to otherwise neutral stimuli, consistently with the proposal that the amygdala is sensitive to the degree to which stimuli are relevant to the individual’s concerns. This stresses the need for more personalized analyses of brain responses to stimuli and tasks that are typically considered as “neutral” and hold important implications for psychiatric populations suffering from deficiencies in affective processing, particularly anxiety disorders.

Habitual behavior has been identified as a key process involved in a variety of mental health disorders. Previous research has shown that habit is not a unitary construct. The present study examined how different components of habitual behavior relate to problematic reward-seeking behaviors and compulsive behaviors. Four hundred and one participants completed a French version of the Creature of Habit Scale, which measures two components of habitual behavior: routine and automaticity. Participants also completed questionnaires assessing compulsivity, impulsivity, stress, and a variety of problematic reward-seeking behaviors, from which five transdiagnostic factors were extracted using an exploratory factor analysis. A dynamic network analysis indicated that the two habitual behavior components were differentially related to these transdiagnostic factors: routine was associated to compulsivity, while automaticity was associated to problematic media consumption. These findings suggest that taking the non-unitary architecture of habit into account may help to better understand the role of habit in mental health.

Little is known about manta ray population size, structure, and connectivity in the Philippines. In collaboration with dive operators, non-governmental organizations, and authorities, sightings of manta rays were collated into a single national database. Using in-water photographs and videos gathered through citizen science and dedicated research efforts, this study compiled sightings between 2004 and 2020, showing 22 separate sites throughout the archipelago with manta rays present. A total of 392 individual reef manta rays (Mobula alfredi) and 107 oceanic manta rays (Mobula birostris) were identified from the collected footage. Four specific sites in the provinces of Masbate and Palawan together hosted 87% of all identified individuals and accounted for 94% of sightings, highlighting these areas are key aggregation sites. This study also reports movements of M. birostris within the Philippines, based on photo-identification of three individuals moving 150 km between Cebu and Masbate. Despite the growing number of recreational divers in Daanbantayan and San Jacinto, an 80% decline in M. birostris sightings was observed at these sites. To ensure effective future conservation, it is recommended that efforts focus on the identification and protection of manta ray hotspots and migratory corridors, the creation of a sustainable tourism framework, and most importantly, on the implementation of mitigation strategies to reduce fisheries interactions.

Recent numerical simulations have shown the existence of multiple self-similar solutions to the Cauchy problem for the 2-dimensional incompressible Euler equation, with initial vorticity in L l o c p ( R 2 ) , 1 ≤ p + ∞ . Toward a rigorous validation of these computations, in this paper we analytically construct self-similar solutions (i) on an outer domain of the form { | x | > R } , and (ii) in a neighborhood of the points where the solution exhibits a spiraling vortex singularity. The outer solution is obtained as the fixed point of a contractive transformation, based on the Biot-Savart formula and integration along characteristics. The inner solution is constructed using a system of adapted coordinates, following the approach of V. Elling (2016) [17] .

The foundational adenine base editors (for example, ABE7.10) enable programmable A•T to G•C point mutations but editing efficiencies can be low at challenging loci in primary human cells. Here we further evolve ABE7.10 using a library of adenosine deaminase variants to create ABE8s. At NGG protospacer adjacent motif (PAM) sites, ABE8s result in ~1.5× higher editing at protospacer positions A5-A7 and ~3.2× higher editing at positions A3-A4 and A8-A10 compared with ABE7.10. Non-NGG PAM variants have a ~4.2-fold overall higher on-target editing efficiency than ABE7.10. In human CD34+ cells, ABE8 can recreate a natural allele at the promoter of the γ-globin genes HBG1 and HBG2 with up to 60% efficiency, causing persistence of fetal hemoglobin. In primary human T cells, ABE8s achieve 98-99% target modification, which is maintained when multiplexed across three loci. Delivered as messenger RNA, ABE8s induce no significant levels of single guide RNA (sgRNA)-independent off-target adenine deamination in genomic DNA and very low levels of adenine deamination in cellular mRNA.

Allogeneic chimeric antigen receptor T-cell (CART) therapies require multiple gene edits to be clinically tractable. Most allogeneic CARTs have been created using gene editing techniques that induce DNA double-stranded breaks (DSBs), resulting in unintended on-target editing outcomes with potentially unforeseen consequences. Cytosine base editors (CBEs) install C•G to T•A point mutations in T cells, with between 90% and 99% efficiency to silence gene expression without creating DSBs, greatly reducing or eliminating undesired editing outcomes following multiplexed editing as compared with clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9). Using CBE, we developed 7CAR8, a CD7-directed allogeneic CART created using 4 simultaneous base edits. We show that CBE, unlike CRISPR-Cas9, does not impact T-cell proliferation, lead to aberrant DNA damage response pathway activation, or result in karyotypic abnormalities following multiplexed editing. We demonstrate 7CAR8 to be highly efficacious against T-cell acute lymphoblastic leukemia (T-ALL) using multiple in vitro and in vivo models. Thus, CBE is a promising technology for applications requiring multiplexed gene editing and can be used to manufacture quadruple-edited 7CAR8 cells, with high potential for clinical translation for relapsed and refractory T-ALL.

Secondary restless legs syndrome is now considered one of the most common disorders you have never heard of. With a prevalence of around 5 to 15%, this disorder affects a significant portion of the population and therefore requires attention. The objective of this review was to look at all of the possible secondary causes of restless legs syndrome and whether there is strong enough evidence to support these associations. It is now widely accepted that the first depiction of possible RLS was outlined in 1672 by the well-known English Physician Sir Thomas Willis. Restless legs syndrome diagnoses has to meet the criteria set out by the international restless legs syndrome study group (IRLSSG). Restless legs syndrome can present in many various forms and can occur at any time throughout life. Patients who suffer from restless legs syndrome will often experience motor and sensory symptoms, or one of the two components. There are also other associated features such as sleep disturbances and fatigue, that can often negatively affect the quality of life of the sufferer. Restless legs syndrome can be treated with various nonpharmacological and pharmacological options. In this review we found a strong association between restless legs syndrome and iron deficiency, renal disease and pregnancy. Parkinson’s disease, essential tremor, some neuropathies and familial ataxias also showed some strong evidence towards an increase in the prevalence of restless legs syndrome in these disorders, however more long-term studies need to be done. We looked at many other possible associations, such as multiple sclerosis, myelinopathy, fibromyalgia and stroke. These, however, lack concrete findings to their associations, with possible alternative factors contributing to these associations with restless legs syndrome. It is important that restless legs syndrome sufferers be identified. Patients with mild symptoms, might simply need reassurance, while more moderate to severe symptoms might need pharmacological treatment. It is imperative that these secondary causes be screened when making the diagnosis of restless legs syndrome, as by treating them the symptoms of restless legs syndrome may be resolved and therefore not require other treatment modalities specific to restless legs syndrome., Pacijenti koji pate od sindroma nemirnih nogu često će imati motoričke i senzorne simptome, ili jednu od dvije komponente. Postoje i druge povezane značajke poput poremećaja spavanja i umora, koje često mogu negativno utjecati na kvalitetu života oboljelog. Sindrom nemirnih nogu može se liječiti raznim nefarmakološkim i farmakološkim mogućnostima. U ovom smo pregledu pronašli snažnu povezanost između sindroma nemirnih nogu i nedostatka željeza, bolesti bubrega i trudnoće. Parkinsonova bolest, esencijalni tremor, neke neuropatije i obiteljske ataksije također su pokazali neke snažne dokaze o Sekundarni sindrom nemirnih nogu sada se smatra jednim od najčešćih poremećaja za koji nikada niste čuli. S prevalencijom od oko 5 do 15%, ovaj poremećaj pogađa značajan dio populacije i zato zahtijeva pažnju. Cilj ovog pregleda bio je sagledati sve moguće sekundarne uzroke sindroma nemirnih nogu i postoje li dovoljno snažni dokazi koji podupiru ove asocijacije. Sada je široko prihvaćeno da je prvi prikaz mogućeg RLS-a izložio 1672. godine poznati engleski liječnik Sir Thomas Willis. Dijagnoze sindroma nemirnih nogu moraju udovoljavati kriterijima koje je postavila međunarodna istraživačka skupina za sindrom nemirnih nogu (IRLSSG). Sindrom nemirnih nogu može se pojaviti na mnogo različitih načina i može se javiti povećanju prevalencije sindroma nemirnih nogu kod ovih poremećaja, međutim potrebno je napraviti dugoročnija ispitivanja. Pregledali smo mnoge druge moguće povezanosti, poput multiple skleroze, mijelinopatije, fibromialgije i moždanog udara. Njima, međutim, nedostaju konkretni nalazi o njihovim udrugama, a mogući alternativni čimbenici doprinose tim udruženjima sa sindromom nemirnih nogu. Važno je identificirati oboljele od sindroma nemirnih nogu. Pacijentima s blagim simptomima možda će jednostavno trebati osiguranje, dok umjerenim do težim simptomima može biti potrebno farmakološko liječenje. Ove sekundarne uzroke morat će se pregledati prilikom postavljanja dijagnoze sindroma nemirnih nogu, jer se njihovim liječenjem simptomi sindroma nemirnih nogu mogu riješiti i stoga ne zahtijevaju druge modalitete liječenja specifične za sindrom nemirnih nogu.

The Executive Order on Advancing American Kidney Health aimed to slow the progression of kidney disease, increase access to kidney transplantation, and expand home dialysis. In order to support the kidney health strategy laid out by the Advancing American Kidney Health, the National Institutes of Health, the National Institute of Diabetes, and Digestive, and Kidney Diseases, as well as other funding agencies must dedicate robust research funding to kidney disease. Currently, federal research investment for kidney health is less than 1% of Medicare fee-for-service expenditures for Americans with kidney disease. To address disparities in federal research funding, nephrology organizations are working together to advocate for increased federal commitment to kidney disease research. Underfunding of kidney disease research impedes scientific opportunities and innovation and prevents the collaboration of young investigators with research faculty that can accelerate the exodus of talent within the nephrology research workforce. This review provides an overview of the current state of federal research funding for kidney disease within the United States. In addition, we discuss ongoing advocacy efforts and programs that aim to increase federal funding for kidney-related research and accelerate the development of new and better therapies.

Despite the high prevalence and economic burden of chronic kidney disease (CKD) in the United States, federal funding for kidney-related research, prevention, and education activities under the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) remains substantially lower compared to other chronic diseases. More federal support is needed to promote critical research that will expand knowledge of kidney health and disease, develop new and effective therapies, and reduce health disparities. In 2021, the National Kidney Foundation (NKF) convened 2 Research Roundtables (preclinical and clinical research), comprising nephrology leaders from prominent US academic institutions and the pharmaceutical industry, key bodies with expertise in research, and including individuals with CKD and their caregivers and kidney donors. The goal of these roundtables was to identify priorities for preclinical and clinical kidney-related research. The research priorities identified by the Research Roundtables and presented in this position statement outline attainable opportunities for groundbreaking and critically needed innovations that will benefit patients with kidney disease in the next 5-10 years. Research priorities fall within 4 preclinical science themes (expand data science capability, define kidney disease mechanisms and utilize genetic tools to identify new therapeutic targets, develop better models of human disease, and test cell-specific drug delivery systems and utilize gene editing) and 3 clinical science themes (expand number and inclusivity of clinical trials, develop and test interventions to reduce health disparities, and support implementation science). These priorities in kidney-related research, if supported by additional funding by federal agencies, will increase our understanding of the development and progression of kidney disease among diverse populations, attract additional industry investment, and lead to new and more personalized treatments.

This chapter summarizes primary care principles for recognizing psychologically traumatized patients and engaging them in treatment. Psychological trauma is a psychiatric injury that occurs in response to severely stressful events and can become chronic if not treated. Trauma-informed clinicians understand how psychological trauma changes how people feel, think, and behave. Recognizing and engaging a psychologically traumatized patient for diagnosis and treatment is one of the most satisfying clinical experiences in primary care, opening doors for both patient and provider. While posttraumatic stress disorder (PTSD) is a common psychiatric disorder, the condition tends to be under-recognized. PTSD recognition is challenging owing to stigma, the variety of presentations, the non-specific nature, and range of severity of PTSD symptoms, and high co-occurrence with other conditions. Patients can be reluctant to seek care, disclose traumatizing events, or remember events. PTSD is more prevalent in people with addictions, chronic physical health conditions, chronic pain, medically unexplained symptoms, other psychiatric disorders, and occupations such as military and public safety personnel. Fourteen diverse case examples demonstrate some of the many faces of PTSD in adults. Barriers and facilitators to recognizing PTSD in primary care and targeted use of screening tools are discussed.

This paper completely resolves the asymptotic development of order 2 2 by Γ \Gamma -convergence of the mass-constrained Cahn–Hilliard functional. Important new results on the slow motion of interfaces for the mass preserving Allen–Cahn equation and the Cahn–Hilliard equations in higher dimension are obtained as an application.

Kidney disease is an important US public health problem because it affects over 37 million Americans, and Medicare expenditures for patients with chronic kidney disease now alone exceed $130 billion annually. Kidney disease is characterized by strong racial, ethnic, and socioeconomic disparities, and reducing kidney disease incidence will positively impact US health disparities. Due to the aging of the US population and an unabated obesity epidemic, the number of patients receiving treatment for kidney failure is anticipated to increase, which will escalate kidney disease health expenditures. The historical and current investment in kidney-related research via the National Institute of Diabetes and Digestive and Kidney Diseases has severely lagged behind ongoing expenditures for kidney disease care. Increasing research investment will identify, develop, and increase implementation of interventions to slow kidney disease progression, reduce incidence of kidney failure, enhance survival, and improve quality of life. This perspective states the urgent reasons why increasing investment in kidney-related research is important for US public health. The National Kidney Foundation and the American Society of Nephrology are working together to advocate for increased funding for the National Institute of Diabetes and Digestive and Kidney Diseases. The long-term goal is to reduce the burden of kidney disease in the US population and improve the quality of life of patients living with kidney disease.

Dropout is a popular tool employed in training neural networks for learning tasks. In shallow networks, a variety of recent works have provided a theoretical understanding of the regularizing effect of dropout. The case of deeper networks is, up to this point, only partially understood from a theoretical standpoint. This work studies the theory of dropout from a nonparametric viewpoint, which is considered a proxy for the deepest layers of a neural network. Utilizing ideas from the calculus of variations, it is shown that in the nonparametric limit, the simplest version of dropout has no regularizing effect. Quantified covering number bounds are given describing the failure of dropout to regularize, and simple numerical illustrations are also given to explore the effect of dropout during a transition towards nonparametric settings.

The paper considers distributed gradient flow (DGF) for multi-agent nonconvex optimization. DGF is a continuous-time approximation of distributed gradient descent that is often easier to study than its discrete-time counterpart. The paper has two main contributions. First, the paper considers optimization of nonsmooth, nonconvex objective functions. It is shown that DGF converges to critical points in this setting. The paper then considers the problem of avoiding saddle points. It is shown that if agents' objective functions are assumed to be smooth and nonconvex, then DGF can only converge to a saddle point from a zero-measure set of initial conditions. To establish this result, the paper proves a stable manifold theorem for DGF, which is a fundamental contribution of independent interest. In a companion paper, analogous results are derived for discrete-time algorithms.

BackgroundT-cell lymphomas and leukemias are a class of diseases lacking durable effective therapies, where median survival for patients suffering from relapsed/refractory disease is often measured in months. Translation of B-cell targeting CAR-T therapeutic success to T-cell malignancies comes with significant challenges. Notably, the shared expression of target antigens on malignant T-cells and in the T-cell product itself results in CAR-T activation and fratricide during manufacturing. To overcome the challenges associated with creating CD5-targeting CAR-Ts, we developed a process to simultaneously base edit five target genes, including CD5 and PD1, to produce potency-enhanced allogeneic anti-CD5 CAR T-cells for use as an off-the-shelf treatment for T-cell malignancies.MethodsAnti-CD5 CAR-Ts were produced in a GMP-compatible process using T-cells isolated from healthy human donors. T-cells were modified using base editing technology to simultaneously knock-out five target genes in a single electroporation step. Edited T-cells were transduced with a lentivirus encoding a second-generation anti-CD5 CAR. Knockout frequencies were evaluated by flow cytometry and next-generation sequencing. Anti-CD5 CAR-Ts were then characterized for their specificity in vitro and potency in in vivo xenograft tumor models.ResultsSimultaneous base editing at five genomic loci resulted in anti-CD5 CAR-Ts edited with 94–98% efficiency at each target gene, greatly diminishing the likelihood of GvHD, CAR-T rejection, fratricide, and checkpoint inhibitor activation. In addition, CD5 has an established role as a negative regulator of TCR signaling, and T cells lacking CD5 have enhanced proliferative capacity.1 Anti-CD5 CAR T-cells with or without CD5 KO demonstrated equally potent cytotoxicity and cytokine production in vitro against CD5 expressing tumor lines. However, CD5 KO greatly improved in vivo efficacy of anti-CD5 CAR-Ts in a murine model of T-ALL against established tumor xenografts. Mice previously cleared of tumor underwent a second tumor challenge to assess the persistence of anti-CD5 CAR-T cells and were cleared of tumor a second time, indicating extended persistence of functional anti-CD5 CAR-T cells in vivo.ConclusionsOur approach addresses current technological limitations in developing and applying CAR-Ts that target T-cell malignancies and demonstrates that simultaneous multiplex base editing of up to five targets can create universally compatible, fratricide-resistant, therapeutically active anti-CD5 CAR-Ts. We further demonstrate that CD5 knockout produces CAR-T cells with enhanced potency capable of clearing multiple tumor challenges in vivo. We are progressing this CD5-targeting CAR-T cell product towards potential clinical development for the treatment of T cell malignancies and other CD5+ hematological tumors.ReferenceGuillaume V, Peredo G, Romain R. CD5, an Undercover Regulator of TCR Signaling. Frontiers in Immunology 2018;9:2900.Ethics ApprovalAll animal studies were performed according to the guidelines and approval of the Institutional Animal Care and Use Committee.

This paper discusses the model problem presented in "A model for system uncertainty in reinforcement learning", Systems and Control Letters, 2018, for certain tasks in reinforcement learning. The model provides a framework to deal with situations in which the system dynamics is not known and encodes the available information about the state dynamics as a measure on the space of functions. Such a measure is updated in time, taking into account all the previous measurements of the state variable and extracting new information from them. Here we will mainly focus on the differences between the present model and central algorithms used in reinforcement learning (i.e. value iteration and Thompson sampling).

The foundational adenine base editors (for example, ABE7.10) enable programmable A•T to G•C point mutations but editing efficiencies can be low at challenging loci in primary human cells. Here we further evolve ABE7.10 using a library of adenosine deaminase variants to create ABE8s. At NGG protospacer adjacent motif (PAM) sites, ABE8s result in ~1.5× higher editing at protospacer positions A5-A7 and ~3.2× higher editing at positions A3-A4 and A8-A10 compared with ABE7.10. Non-NGG PAM variants have a ~4.2-fold overall higher on-target editing efficiency than ABE7.10. In human CD34

The paper studies a distributed gradient descent (DGD) process and considers the problem of showing that in nonconvex optimization problems, DGD typically converges to local minima rather than saddle points. The paper considers unconstrained minimization of a smooth objective function. In centralized settings, the problem of demonstrating nonconvergence to saddle points of gradient descent (and variants) is typically handled by way of the stable-manifold theorem from classical dynamical systems theory. However, the classical stable-manifold theorem is not applicable in distributed settings. The paper develops an appropriate stable-manifold theorem for DGD showing that convergence to saddle points may only occur from a low-dimensional stable manifold. Under appropriate assumptions (e.g., coercivity), this result implies that DGD typically converges to local minima and not to saddle points.

This paper investigates the use of methods from partial differential equations and the Calculus of variations to study learning problems that are regularized using graph Laplacians. Graph Laplacians are a powerful, flexible method for capturing local and global geometry in many classes of learning problems, and the techniques developed in this paper help to broaden the methodology of studying such problems. In particular, we develop the use of maximum principle arguments to establish asymptotic consistency guarantees within the context of noise corrupted, non-parametric regression with samples living on an unknown manifold embedded in $\mathbb{R}^d$. The maximum principle arguments provide a new technical tool which informs parameter selection by giving concrete error estimates in terms of various regularization parameters. A review of learning algorithms which utilize graph Laplacians, as well as previous developments in the use of differential equation and variational techniques to study those algorithms, is given. In addition, new connections are drawn between Laplacian methods and other machine learning techniques, such as kernel regression and k-nearest neighbor methods.

Before the end of World War Two, many nations had implemented entitlements for veterans, such as disability pensions, health care, and vocational rehabilitation. However, the concept of disability has evolved since then, and some nations have modernized disability compensation. We examined how disability is assessed, how compensation is structured, and modern principles in designing and reforming veteran disability compensation for 11 nations through a survey, interviews with subject matter experts, and a literature review. While there are some similarities in these schemes, substantial differences were found in the components of the schemes and the context in which they operate. This makes best practices difficult to identify and limits the application of certain aspects of program design to other nations. As a result, this chapter presents five key interrelated guiding principles in designing effective compensation systems: adequacy of compensation for losses; program affordability; integration of rehabilitation; simplicity; and comprehensiveness as well as a description of modern concepts of disability. This provided a framework for developing five recommendations that can help guide nations without compensation schemes in their development and to guide reforms among nations with existing schemes.