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Abstract Background Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson’s disease (PD). These mutations elevate the LRRK2 kinase activity, making LRRK2 kinase inhibitors an attractive therapeutic. LRRK2 kinase activity has been consistently linked to specific cell signaling pathways, mostly related to organelle trafficking and homeostasis, but its relationship to PD pathogenesis has been more difficult to define. LRRK2-PD patients consistently present with loss of dopaminergic neurons in the substantia nigra but show variable development of Lewy body or tau tangle pathology. Animal models carrying LRRK2 mutations do not develop robust PD-related phenotypes spontaneously, hampering the assessment of the efficacy of LRRK2 inhibitors against disease processes. We hypothesized that mutations in LRRK2 may not be directly related to a single disease pathway, but instead may elevate the susceptibility to multiple disease processes, depending on the disease trigger. To test this hypothesis, we have previously evaluated progression of α-synuclein and tau pathologies following injection of proteopathic seeds. We demonstrated that transgenic mice overexpressing mutant LRRK2 show alterations in the brain-wide progression of pathology, especially at older ages. Methods Here, we assess tau pathology progression in relation to long-term LRRK2 kinase inhibition. Wild-type or LRRK2G2019S knock-in mice were injected with tau fibrils and treated with control diet or diet containing LRRK2 kinase inhibitor MLi-2 targeting the IC50 or IC90 of LRRK2 for 3–6 months. Mice were evaluated for tau pathology by brain-wide quantitative pathology in 844 brain regions and subsequent linear diffusion modeling of progression. Results Consistent with our previous work, we found systemic alterations in the progression of tau pathology in LRRK2G2019S mice, which were most pronounced at 6 months. Importantly, LRRK2 kinase inhibition reversed these effects in LRRK2G2019S mice, but had minimal effect in wild-type mice, suggesting that LRRK2 kinase inhibition is likely to reverse specific disease processes in G2019S mutation carriers. Additional work may be necessary to determine the potential effect in non-carriers. Conclusions This work supports a protective role of LRRK2 kinase inhibition in G2019S carriers and provides a rational workflow for systematic evaluation of brain-wide phenotypes in therapeutic development.

Abstract Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.

Abstract Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.

Background: Loss of control (LOC) eating, the subjective sense that one cannot control what or how much one eats, characterizes binge-eating behaviors pervasive in obesity and related eating disorders. Closed-loop deep-brain stimulation (DBS) for binge eating should predict LOC and trigger an appropriately timed intervention. Objective/hypothesis: This study aimed to identify a sensitive and specific biomarker to detect LOC onset for DBS. We hypothesized that changes in phase-locking value (PLV) predict the onset of LOC-associated cravings and distinguish them from potential confounding states. Methods: Using DBS data recorded from the nucleus accumbens (NAc) of two patients with binge eating disorder (BED) and severe obesity, we compared PLV between inter- and intra-hemispheric NAc subregions for three behavioral conditions: craving (associated with LOC eating), hunger (not associated with LOC), and sleep. Results: In both patients, PLV in the high gamma frequency band was significantly higher for craving compared to sleep and significantly higher for hunger compared to craving. Maximum likelihood classifiers achieved accuracies above 88% when differentiating between the three conditions. Conclusions: High-frequency inter- and intra-hemispheric PLV in the NAc is a promising biomarker for closed-loop DBS that differentiates LOC-associated cravings from physiologic states such as hunger and sleep. Future trials should assess PLV as a LOC biomarker across a larger cohort and a wider patient population transdiagnostically.

Abstract Modifying behaviors, such as alcohol consumption, is difficult. Creating psychological distance between unhealthy triggers and one’s present experience can encourage change. Using two multisite, randomized experiments, we examine whether theory-driven strategies to create psychological distance—mindfulness and perspective-taking—can change drinking behaviors among young adults without alcohol dependence via a 28-day smartphone intervention (Study 1, N = 108 participants, 5492 observations; Study 2, N = 218 participants, 9994 observations). Study 2 presents a close replication with a fully remote delivery during the COVID-19 pandemic. During weeks when they received twice-a-day intervention reminders, individuals in the distancing interventions reported drinking less frequently than on control weeks—directionally in Study 1, and significantly in Study 2. Intervention reminders reduced drinking frequency but did not impact amount. We find that smartphone-based mindfulness and perspective-taking interventions, aimed to create psychological distance, can change behavior. This approach requires repeated reminders, which can be delivered via smartphones.

Abstract Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.

[This corrects the article DOI: 10.1371/journal.pone.0294805.].

Abstract The terahertz frequency range is gaining popularity in security, stealth technology, and the future 6G network communication. For the control of severe terahertz electromagnetic interference (EMI) pollution, frequency‐selective stealth‐capable shielding materials are being explored to mask terahertz signals. For the realization of masking terahertz signals, the robustness, lightweight, and shape‐conformable materials with excellent terahertz EMI shielding/absorption are crucial. Here, the study reports the fabrication of 3D symmetric pyramidal architectural MXene composite films with frequency‐selective stealth performance characteristics via the facile drop casting method. With the high absorption capability of 2D MXene layers, the MXene composite films exhibit substantial terahertz stealth performance. 3D pyramidal microstructure design leads to frequency selective surface‐assisted reflection resonance in the frequency range of 0.6–1.1 THz. The MXene composite film demonstrates an outstanding maximum terahertz shielding effectiveness (SE) of up to 70.4 dB and a specific SE of 0.55 dB µm−1. These terahertz SE values exceed all of those for MX‐based shielding material designs reported in the literature. The investigation will open a new direction toward developing terahertz EMI shielding thin films with easy integration into any surface for stealth capabilities.

Thermoelectric‐based waste heat recovery requires efficient materials to replace conventional non‐eco‐friendly Te‐ and Pb‐based commercial devices. Ternary copper chalcogenide‐based famatinite (Cu3SbS4) compound is one of the practical substitutes for traditional thermoelectric materials. However, the pristine Cu3SbS4 inherits poor structural complexion, large thermal conductivity, and low power conversion efficiency. To develop high‐efficiency Cu3SbS4, InSb nanoinclusions are incorporated via high‐energy ball milling followed by the hot‐press densification method. Incorporating InSb nanoinclusions to lower thermal conductivity via phonon scattering while increasing the thermopower via a carrier energy filtering process. The thermoelectric performance (ZT) of ≈0.4 at 623 K is obtained in Cu3SbS4‐3 mol% InSb nanocomposite, which is ≈140% higher than pure Cu3SbS4. Both mechanical and thermal stability are improved by grain boundary hardening and dispersion strengthening. Thus, a facile nanostructured Cu3SbS4 with added InSb nanoinclusions is delivered as a highly efficient, eco‐friendly, structurally‐, thermally‐, and mechanically‐stable material for next‐generation thermoelectric generators.

Abstract Background Recurrent gene dosage disorders impart substantial risk for psychopathology. Yet, understanding that risk is hampered by complex presentations that challenge classical diagnostic systems. Here, we present a suite of generalizable analytic approaches for parsing this clinical complexity, which we illustrate through application to XYY syndrome. Method We gathered high-dimensional measures of psychopathology in 64 XYY individuals and 60 XY controls, plus additional interviewer-based diagnostic data in the XYY group. We provide the first comprehensive diagnostic description of psychiatric morbidity in XYY syndrome and show how diagnostic morbidity relates to functioning, subthreshold symptoms, and ascertainment bias. We then map behavioral vulnerabilities and resilience across 67 behavioral dimensions before borrowing techniques from network science to resolve the mesoscale architecture of these dimensions and links to observable functional outcomes. Results Carriage of an extra Y-chromosome increases risk for diverse psychiatric diagnoses, with clinically impactful subthreshold symptomatology. Highest rates are seen for neurodevelopmental and affective disorders. A lower bound of

While gender disparities in the science, technology, engineering, and mathematics (STEM) disciplines are widely noted, the citation gap is still understudied and awareness remains low. Here, we address citation inequity in physics and describe individual and collective mitigation initiatives, including the citation diversity statement.

Abstract Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids.

Abstract In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the characteristics of synaptic functions in the human brain. In this aspect, this study designs and develops CsFAPbI3‐based memristive neuromorphic devices that can switch at low power and show larger endurance by adopting the powder engineering methodology. The neuromorphic characteristics of the CsFAPbI3‐based devices exhibit an ultra‐high paired‐pulse facilitation index for an applied electric stimuli pulse. Moreover, the transition from short‐term to long‐term memory requires ultra‐low energy with long relaxation times. The learning and training cycles illustrate that the CsFAPbI3‐based devices exhibit faster learning and memorization process owing to their larger carrier lifetime compared to other perovskites. The results provide a pathway to attain low‐power neuromorphic devices that are synchronic to the human brain's performance.

Myelination is a key developmental process that promotes rapid and efficient information transfer. Myelin also stabilizes existing brain networks and thus may constrain neuroplasticity, defined here as the brain's potential to change in response to experiences rather than the canonical definition as the process of change. Characterizing individual differences in neuroplasticity may shed light on mechanisms by which early experiences shape learning, brain and body development, and response to interventions. The T1-weighted/T2-weighted (T1w/T2w) MRI signal ratio is a proxy measure of cortical microstructure and thus neuroplasticity. Here, in pre-registered analyses, we investigated individual differences in T1w/T2w ratios in children (ages 4–10, n = 157). T1w/T2w ratios were positively associated with age within early-developing sensorimotor and attention regions. We also tested whether socioeconomic status, cognition (crystallized knowledge or fluid reasoning), and biological age (as measured with molar eruption) were related to T1w/T2w signal but found no significant effects. Associations among T1w/T2w ratios, early experiences, and cognition may emerge later in adolescence and may not be strong enough to detect in moderate sample sizes.

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. How can we quantify the information contained in a piece of music? Further, what is the information inferred by a human, and how does that relate to (and differ from) the true structure of a piece? To tackle these questions quantitatively, we present a framework to study the information conveyed in a musical piece by constructing and analyzing networks formed by notes (nodes) and their transitions (edges). Using this framework, we analyze music composed by J. S. Bach through the lens of network science, information theory, and statistical physics. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content and network structure. Moreover, using a model for how humans infer networks of information, we find that the music networks communicate large amounts of information while maintaining small deviations of the inferred network from the true network, suggesting that they are structured for efficient communication of information. We probe the network structures that enable this rapid and efficient communication of information—namely, high heterogeneity and strong clustering. Taken together, our findings shed light on the information and network properties of Bach's compositions. More generally, our simple framework serves as a stepping stone for exploring further musical complexities, creativity, and questions therein.

In interacting dynamical systems, specific local interaction rules for system components give rise to diverse and complex global dynamics. Long dynamical cycles are a key feature of many natural interacting systems, especially in biology. Examples of dynamical cycles range from circadian rhythms regulating sleep to cell cycles regulating reproductive behavior. Despite the crucial role of cycles in nature, the properties of network structure that give rise to cycles still need to be better understood. Here, we use a Boolean interaction network model to study the relationships between network structure and cyclic dynamics. We identify particular structural motifs that support cycles, and other motifs that suppress them. More generally, we show that the presence of dynamical reflection symmetry in the interaction network enhances cyclic behavior. In simulating an artificial evolutionary process, we find that motifs that break reflection symmetry are discarded. We further show that dynamical reflection symmetries are over-represented in Boolean models of natural biological systems. Altogether, our results demonstrate a link between symmetry and functionality for interacting dynamical systems, and they provide evidence for symmetry’s causal role in evolving dynamical functionality.

Abstract Neuroplasticity, defined as the brain’s potential to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here, we investigated the plasticity of the frontoparietal system by asking whether VTA resting-state functional connectivity and myelin map values (T1w/T2w ratios) predicted learning after short-term training on the adaptive n-back (n = 46, ages 18–25). We found that stronger baseline connectivity between VTA and lateral prefrontal cortex predicted greater improvements in accuracy. Lower myelin map values predicted improvements in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.

Children with drug-resistant epilepsy exhibit distinct structural connectomes and changes in network controllability from healthy controls, providing further insight into the pathophysiology of this disease.

Habituation is a process in which animals stop responding to repetitive stimuli, and habituation is altered in autism and other conditions. Here, the authors describe visual habituation networks across the zebrafish brain, and find that fmr1 mutants show slower brain-wide and behavioural habituation.

The fairness of decisions made at various stages of the publication process is an important topic in meta-research. Here, based on an analysis of data on the gender of authors, editors and reviewers for 23,876 initial submissions and 7,192 full submissions to the journal eLife, we report on five stages of the publication process. We find that the board of reviewing editors (BRE) is men-dominant (69%) and that authors disproportionately suggest male editors when making an initial submission. We do not find evidence for gender bias when Senior Editors consult Reviewing Editors about initial submissions, but women Reviewing Editors are less engaged in discussions about these submissions than expected by their proportion. We find evidence of gender homophily when Senior Editors assign full submissions to Reviewing Editors (i.e., men are more likely to assign full submissions to other men (77% compared to the base assignment rate to men RE of 70%), and likewise for women (41% compared to women RE base assignment rate of 30%))). This tendency was stronger in more gender-balanced scientific disciplines. However, we do not find evidence for gender bias when authors appeal decisions made by editors to reject submissions. Together, our findings confirm that gender disparities exist along the editorial process and suggest that merely increasing the proportion of women might not be sufficient to eliminate this bias. Measures accounting for women's circumstances and needs (e.g., delaying discussions until all RE are engaged) and raising editorial awareness to women's needs may be essential to increasing gender equity and enhancing academic publication.

A common problem in reconstructing weighted networks to represent real-world systems is that low-weight edges might appear due to noise, affecting the topology of the inferred network. Here, the authors propose a method based on persistent homology that allows one to investigate the higher-order network organization that can be created by low-weight, noisy edges.

The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives—including machine learning and systems engineering—that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

Across all scales of the physical world, dynamical systems can be usefully represented as abstract networks that encode the systems' units and interunit interactions. Understanding how physical rules shape the topological structure of those networks can clarify a system's function and enhance our ability to design, guide, or control its behavior. In the emerging area of quantum network science, a key challenge lies in distinguishing between the topological properties that reflect a system's underlying physics and those that reflect the assumptions of the employed conceptual model. To elucidate and address this challenge, we study networks that represent nonequilibrium quantum-electronic transport through quantum antidot devices—an example of an open, mesoscopic quantum system. The network representations correspond to two different models of internal antidot states: a single-particle, noninteracting model and an effective model for collective excitations including Coulomb interactions. In these networks, nodes represent accessible energy states and edges represent allowed transitions. We find that both models reflect spin conservation rules in the network topology through bipartiteness and the presence of only even-length cycles. The models diverge, however, in the minimum length of cycle basis elements, in a manner that depends on whether electrons are considered to be distinguishable. Furthermore, the two models reflect spin-conserving relaxation effects differently, as evident in both the degree distribution and the cycle-basis length distribution. Collectively, these observations serve to elucidate the relationship between network structure and physical constraints in quantum-mechanical models. More generally, our approach underscores the utility of network science in understanding the dynamics of quantum systems.

The Association of Pathology Chairs, an organization of American and Canadian academic pathology departments, has a record percent of women department chairs in its ranks (31%), although still not representative of the percent of women pathology faculty (43%). These women chairs were surveyed to determine what had impeded and what had facilitated their academic advancement before becoming chairs. The 2 most frequently identified impediments to their career advancement were heavy clinical loads and the lack of time, training, and/or funding to pursue research. Related to the second impediment, only one respondent became chair of a department which was in a top 25 National Institutes of Health–sponsored research medical school. Eighty-nine percent of respondents said that they had experienced gender bias during their careers in pathology, and 31% identified gender bias as an important impediment to advancement. The top facilitator of career advancement before becoming chairs was a supportive family. Strikingly, 98% of respondents have a spouse or partner, 75% have children, and 38% had children younger than 18 when becoming chairs. Additional top facilitators were opportunities to attend national meetings and opportunities to participate in leadership. Previous leadership experiences included directing a clinical service, a residency training program, and/or a medical student education program. These results suggest important ways to increase the success of women in academic pathology and increasing the percent of women department chairs, including supporting a family life and providing time, encouragement and resources for research, attending national meetings, and taking on departmental leadership positions.

From enzyme binding to robot grasping, the function of many mechanical systems depends upon large, coordinated motions of their components. Such motions arise from a network of physical interactions in the form of links (edges) that transmit forces between constituent elements (nodes) and have been fruitfully modeled in known networks. However, the principled design of precise motions in novel networks is made difficult by the number and nonlinearity of interactions. Here, we formulate a simple but powerful framework for designing fully nonlinear motions using concepts from dynamical systems theory. We demonstrate that a small network unit acts as a one-dimensional map between the distances across pairs of nodes, and we represent the act of combining units as an iteration of this map. By tying the map’s attractors and their stability to the shape and folding sequence in a network of combined units, we program the precise coordinated motion between arbitrarily complex macroscopic shapes, the exact folding sequence between the shapes, and exotic network behaviors such as a mechanical and gate and a period-doubling route to chaos. Further, we construct a unit with a 3-cycle that combines to form a lattice with any positive integer period as a result of Sharkovskii’s theorem. Finally, we construct physical networks and analyze the effect of bond elasticity to demonstrate the framework’s potential and versatility. The precise design of shape change and folding sequence makes this framework ideal as a starting minimal model for many applications, such as robotics, providing a promising direction for future work in metamaterials.

In this paper, we establish the property of conditional full support for two processes: the Ornstein Uhlenbeck and the stochastic integral in which the Brownian Bridge is the integrator and we build the absence of arbitrage opportunities without calculating the risk-neutral probability.

Leadership development and succession planning are critical to ensure continued strength of academic pathology. The Association of Pathology Chairs developed the Pathology Leadership Academy to prepare future academic leaders. The purpose of this report is to describe: (1) Pathology Leadership Academy’s development and curriculum, (2) how Pathology Leadership Academy has met leadership development needs for individuals and academic departments in its first 2 years, (3) Pathology Leadership Academy’s future directions based on program feedback. Results were analyzed from pre- and postprogram needs assessment surveys of pathology chairs and from evaluations from Pathology Leadership Academy participants in the first 2 years. Pathology Leadership Academy curriculum was developed from topics identified as priorities in the chairs’ survey. Twenty-eight (90%) of 31 responding participants were very satisfied/satisfied with Pathology Leadership Academy. Of the 18 responding chairs who sent a participant to Pathology Leadership Academy, 11 (61%) reported that Pathology Leadership Academy met their faculty development goal. Of all responding chairs, 13 (32%) of 41 reported uncertainty as to whether Pathology Leadership Academy is meeting chairs’ goals. Chairs reported that Pathology Leadership Academy provided value to their faculty through preparation for a future leadership role, enhancing skills for a current role, and enhancing understanding of opportunities and challenges in academic medicine. Most chairs (27/43, 66%) said Pathology Leadership Academy should be offered again; 13 (32%) of 43 were uncertain, and 1 (2%) of 43 said no. Initial experience of Pathology Leadership Academy is positive and promising and provides opportunity for leadership succession planning in academic pathology. Pathology Leadership Academy will use participant and chair feedback for ongoing curricular development to ensure topics continue to address major needs of academic pathology.

There has been a recent recognition of the need to prepare PhD-trained scientists for increasingly diverse careers in academia, industry, and health care. The PhD Data Task Force was formed to better understand the current state of PhD scientists in the clinical laboratory workforce and collect up-to-date information on the training and certification of these laboratorians. In this report, we summarize the findings of the PhD Data Task Force and discuss the relevance of the data collected to the future supply of and demand for PhD clinical laboratory scientists. It is clear that there are multiple career opportunities for PhD scientists in academic medical centers, commercial clinical laboratories, biotechnology and pharmaceutical companies, and the federal government. Certified PhD scientists have and will continue to form an important resource for our technologically advancing field, bringing training in scientific methods, and technologies needed for modern laboratory medicine. The data gathered by the PhD Data Task Force will be of great interest to current and future PhD candidates and graduate PhD scientists as they make decisions regarding future career directions.