The rockefeller university

Coherent anti-Stokes Raman scattering (CARS) is a powerful nonlinear spectroscopic technique widely used in biological imaging, chemical analysis, and combustion and flame diagnostics. The adoption of pulse shapers in CARS has emerged as a useful approach, offering precise control of optical waveforms. By tailoring the phase, amplitude, and polarization of laser pulses, the pulse shaping approach enables selective excitation, spectral resolution improvement, and non-resonant background suppression in CARS. This paper presents a comprehensive review of applying pulse shaping techniques in CARS spectroscopy for biophotonics. There are two different pulse shaping strategies: passive pulse shaping and active pulse shaping. Two passive pulse shaping techniques, hybrid CARS and spectral focusing CARS, are reviewed. Active pulse shaping using a programmable pulse shaper such as spatial light modulator (SLM) is discussed for CARS spectroscopy. Combining active pulse shaping and passive shaping, optimizing CARS with acousto-optic programmable dispersive filters (AOPDFs) is discussed and illustrated with experimental examples conducted in the authors' laboratory. These results underscore pulse shapers in advancing CARS technology, enabling improved sensitivity, specificity, and broader applications across diverse scientific fields.

In Cancer Cell, two studies unveil mechanisms by which co-option of the protein synthesis machinery promotes cancer progression and potential therapeutic interventions. Kuzuoglu-Ozturk et al. show that eIF4A-mediated enhancement of oncogenic transcript translation initiation drives cancer progression, while Weller et al. demonstrate how aberrant transfer RNA (tRNA) modification disrupts translational fidelity to produce neoantigens.

Background: This paper describes an innovative cluster randomized controlled trial design to evaluate the comparative effectiveness of two approaches to preventing significant destabilization, leading to unplanned hospitalization and increased disability for patients with high comorbidity, that is, multiple chronic diseases defined by an enhanced Charlson Comorbidity Index >= 4. Methods: A total of 1974 patients were randomized in four waves at each of the sixteen Federally Qualified Health Centers (FQHCs) in four health systems -two in New York and two in Chicago. The two interventions compared 1) Patient-Centered Medical Home (PCMH) as implemented by the FQHCs (usual care control); or 2) PCMH plus a coaching intervention delivered by Health Coaches (experimental) helping patients identify life goals to encourage self-management enhanced by a positive affect/self-affirmation strategy. The two primary patient-centered clinical outcomes are 1) Unplanned hospitalizations; and 2) Within-patient changes in quality of life and disability, as measured by the World Health Organization Disability Assessment Scale 2 (WHODAS 2.0). The hypotheses are: 1) intervention patients will have a 5 % relative reduction in unplanned hospitalizations as compared to control patients; and 2) reduced disability measured by WHODAS2.0; 3) destabilization or 'tipping points' leading to hospitalization will be more often triggered by psychosocial issues than by medical Issues. Conclusion: This cluster RCT has the potential to transform the care for patients with high comorbidity by helping motivate patients to engage in self-management and to successfully navigate the barriers, challenges, and stresses leading to destabilization, hospitalization, and increased disability. ClinicalTrials.gov registration number: NCT04176510

Holistic review has been widely adopted in medical education as a means of promoting equity in the application process and diversity in the medical workforce. Artificial intelligence (AI) is a rapidly emerging technology already having an impact on the medical school and residency application process as students and faculty alike increasingly turn to AI tools to automate some steps in the preparation and evaluation of application materials. While AI may have the potential to improve the holistic admissions process by increasing efficiency and adding some measure of standardization among reviewers, the authors caution that this promise does not come without certain pitfalls. AI models may introduce new sources of bias and amplify existing ones, which, when combined with a lack of transparency regarding their use in the admissions process, may perpetuate the very inequities that holistic review seeks to minimize. The authors call for the medical education community to establish clear regulations to govern the acceptable use of AI in the admissions process and for a principled adoption of AI tools in a way that is sustainable for applicants and reviewers in the future.

The gamma-tubulin ring complex (gamma-TuRC) is an essential multiprotein assembly that provides a template for microtubule nucleation. The gamma-TuRC is recruited to microtubule-organizing centers (MTOCs) by the evolutionarily conserved attachment factor NEDD1. However, the structural basis of the NEDD1-gamma-TuRC interaction is not known. Here, we report cryo-EM structures of NEDD1 bound to the human gamma-TuRC in the absence or presence of the activating factor CDK5RAP2. We found that the C-terminus of NEDD1 forms a tetrameric alpha-helical assembly that contacts the lumen of the gamma-TuRC cone and orients its microtubule-binding domain away from the complex. The structure of the gamma-TuRC simultaneously bound to NEDD1 and CDK5RAP2 reveals that both factors can associate with the "open" conformation of the complex. Our results show that NEDD1 does not induce substantial conformational changes in the gamma-TuRC but suggest that anchoring of gamma-TuRC-capped microtubules by NEDD1 would be structurally compatible with the significant conformational changes experienced by the gamma-TuRC during microtubule nucleation.

In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamental bottleneck: the number of correlations one would like to include in a model grows larger than the available data. In this undersampled regime, one must focus on a sparse subset of correlations; the optimal choice contains the maximum information about patterns of activity or, equivalently, minimizes the entropy of the inferred maximum entropy model. Applying this "minimax entropy" principle is generally intractable, but here we present an exact and scalable solution for pairwise correlations that combine to form a tree (a network without loops). Applying our method to over 1000 neurons in the mouse hippocampus, we find that the optimal tree of correlations reduces our uncertainty about the population activity by 14% (over 50 times more than a random tree). Despite containing only 0.1% of all pairwise correlations, this minimax entropy model accurately predicts the observed large-scale synchrony in neural activity and becomes even more accurate as the population grows. The inferred Ising model is almost entirely ferromagnetic (with positive interactions) and exhibits signatures of thermodynamic criticality. Together, these results suggest that a large amount of information may be compressed into a small number of correlations between neurons, and provide the tools for identifying the most important correlations in other complex living systems.

INTRODUCTION: Alzheimer's disease (AD) is characterized by amyloid-beta (A beta), hyperphosphorylated tau, chronic neuroinflammation, blood-brain barrier (BBB) damage, and synaptic dysfunction, leading to neuronal loss and cognitive deficits. Vascular proteins, including fibrinogen, extravasate into the brain, further contributing to damage and inflammation. Fibrinogen's interaction with A beta is well-established, but how this interaction contributes to synaptic dysfunction in AD is unknown. METHODS: Organotypic hippocampal cultures (OHC) were exposed to A beta 42 oligomers, fibrinogen, or A beta 42/fibrinogen complexes. Synaptotoxicity was analyzed by Western blot. A beta 42 oligomers, fibrinogen, or their complexes were intracerebroventricularly injected into mice. Histopathological AD markers, synaptotoxicity, neuroinflammation, and vascular markers were observed by Western blot and immunofluorescence. RESULTS: A beta 42/fibrinogen complexes led to synaptic loss, tau181 phosphorylation, neuroinflammation, and BBB disruption, independent of Mac1/CD11b receptor signaling. Blocking A beta 42/fibrinogen complex formation prevented synaptotoxicity. DISCUSSION: These findings indicate that the A beta 42/fibrinogen complex has a synergistic impact on hippocampal synaptotoxicity and neuroinflammation. Highlights Fibrinogen binds to the central region of A beta, forming a plasmin-resistant complex. The A beta/fibrinogen complex induces synaptotoxicity, inflammation, and BBB disruption. Synaptotoxicity induced by the complex is independent of Mac1 receptor signaling.

Failure to anticipate new forms of antibiotic resistance has led to resistance developing rapidly to virtually all antibiotics that have entered clinical use. Many of the most problematic types of resistance originated in the environment, where ancient arms races between antibiotic-producing microbes and their competitors have created vast arsenals of antibiotics and resistance. Seizing on the knowledge that resistance in nature is frequently a harbinger of future clinical resistance, we propose introducing an additional step into the antibiotic development process that exploits the susceptibility of development candidates to environmental resistance as a metric for prioritizing lead compounds and as a roadmap for their structural optimization. Using the antibiotic albicidin as a model, we show how the environmental resistome can guide the development of more resistance-tolerant leads. We used metagenomic surveys to identify resistance vulnerabilities for albicidin and guide the synthesis of analogs that evade the resistance threats. We found that natural albicidin analogs (congeners) were especially enriched in structural features that escape resistance, which inspired our syntheses and provided compelling evidence for the evolution of families of antibiotics in response to resistance in nature. The coupling of metagenomics-based resistance surveillance with structural optimizations of new antibiotics is a broadly applicable approach that is easily integrated into antibiotic development programs to generate compounds that are more resilient in the face of resistance.

How does growth encode form in developing organisms? Many different spatiotemporal growth profiles may sculpt tissues into the same target 3D shapes, but only specific growth patterns are observed in animal and plant development. In particular, growth profiles may differ in their degree of spatial variation and growth anisotropy; however, the criteria that distinguish observed patterns of growth from other possible alternatives are not understood. Here we exploit the mathematical formalism of quasiconformal transformations to formulate the problem of "growth pattern selection" quantitatively in the context of 3D shape formation by growing 2D epithelial sheets. We propose that nature settles on growth patterns that are the "simplest" in a certain way. Specifically, we demonstrate that growth pattern selection can be formulated as an optimization problem and solved for the trajectories that minimize spatiotemporal variation in areal growth rates and deformation anisotropy. The result is a complete prediction for the growth of the surface, including not only a set of intermediate shapes, but also a prediction for cell displacement along those surfaces in the process of growth. Optimization of growth trajectories for both idealized surfaces and those observed in nature show that relative growth rates can be uniformized at the cost of introducing anisotropy. Minimizing the variation of programmed growth rates can therefore be viewed as a generic mechanism for growth pattern selection and may help us to understand the prevalence of anisotropy in developmental programs.