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Treatment of the soluble insulin receptor from human placenta with 1.25 mM dithiothreitol and 75 mM Tris at pH 8.5 results in complete reduction of interhalf disulfide bonds (class 1 disulfides) and dissociation of the tetrameric receptor into the dimeric alpha beta form. The alpha beta receptor halves exhibit a reduced affinity for insulin binding (Böni-Schnetzler, M., Rubin, J. B., and Pilch, P. F. (1986) J. Biol. Chem. 261, 15281-15287). Kinetic experiments reveal that reduction of class 1 disulfides is a faster process than the loss of affinity for ligand, indicating that events subsequent to reduction of interhalf disulfides are responsible for the affinity change. We show that a third class of alpha subunit intrachain disulfides is more susceptible to reduction at pH 7.6 than at pH 8.5 and appears to form part of the ligand binding domain. Reduction of the intrachain disulfide bonds in this part of the alpha subunit leads to a loss of insulin binding. Modification of this putative binding domain by dithiothreitol can be minimized if reduction is carried out at pH 8.5. When the insulin receptor in placental membranes is reduced at pH 8.5, the receptor’s affinity for insulin is not changed when binding is measured in the membrane. However, the Kd for insulin binding is reduced 10-fold when alpha beta receptor halves are subsequently solubilized. Scatchard analysis of insulin binding to reduced or intact receptors in the membrane and in soluble form together with sucrose density gradient analysis of soluble receptors suggests that alpha beta receptor halves remain associated in the membrane after reduction, but they are dissociated upon solubilization. We interpret these results to mean that the association of two ligand binding domains, 2 alpha beta receptor halves, is required for the formation of an insulin receptor with high affinity for ligand

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time–concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from −23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from −13.5 ± 8.8% to −0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (−2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Bogotch (2002) proposed that educational leadership cannot be separated from social justice, and this is a sentiment that is taken to heart at an urban Title-1 school district in Houston, Texas. To intentionally address the need for such alignment between mission and practice, the district implemented a recruitment and talent development initiative that actively sought to change the district's talent pool. This program develops effective leaders through a fellowship that focuses on building and leading an effective team, coaching and managing with excellence, and solving high-impact, complex problems. Using a position-subject approach (Conrad, Haworth, & Millar, 2001), this paper evaluates the extent to which the program uplifts diverse voices in school leadership and the extent to which the program successfully prepares participants for leadership roles. This manuscript includes a description of a successful and inclusive school leadership pipeline, an explanation of challenges and resistance faced for both leadership initiatives, and strategies to continue growth in this particular area of educational leadership through data collected via survey, interview, and autoethnographic reflections.

Treatment of the soluble insulin receptor from human placenta with 1.25 mM dithiothreitol and 75 mM Tris at pH 8.5 results in complete reduction of interhalf disulfide bonds (class 1 disulfides) and dissociation of the tetrameric receptor into the dimeric alpha beta form. The alpha beta receptor halves exhibit a reduced affinity for insulin binding (Böni-Schnetzler, M., Rubin, J. B., and Pilch, P. F. (1986) J. Biol. Chem. 261, 15281-15287). Kinetic experiments reveal that reduction of class 1 disulfides is a faster process than the loss of affinity for ligand, indicating that events subsequent to reduction of interhalf disulfides are responsible for the affinity change. We show that a third class of alpha subunit intrachain disulfides is more susceptible to reduction at pH 7.6 than at pH 8.5 and appears to form part of the ligand binding domain. Reduction of the intrachain disulfide bonds in this part of the alpha subunit leads to a loss of insulin binding. Modification of this putative binding domain by dithiothreitol can be minimized if reduction is carried out at pH 8.5. When the insulin receptor in placental membranes is reduced at pH 8.5, the receptor’s affinity for insulin is not changed when binding is measured in the membrane. However, the Kd for insulin binding is reduced 10-fold when alpha beta receptor halves are subsequently solubilized. Scatchard analysis of insulin binding to reduced or intact receptors in the membrane and in soluble form together with sucrose density gradient analysis of soluble receptors suggests that alpha beta receptor halves remain associated in the membrane after reduction, but they are dissociated upon solubilization. We interpret these results to mean that the association of two ligand binding domains, 2 alpha beta receptor halves, is required for the formation of an insulin receptor with high affinity for ligand

Abstract The unfolding COVID-19 pandemic, and the unprecedented social and economic costs it has inflicted, provide an important opportunity to scrutinize the interplay between the resilience of small and medium-sized enterprises (SMEs) and the resilience of the communities they are embedded in. In this article, we articulate the specific ways that SMEs play a crucial, and underappreciated role in building resilience to human and natural hazards, and provide new opportunities to accelerate the adoption of sustainability practices through the configuration of ‘enabling ecosystems’ geared towards promoting sustainability in the private sector. We argue that capacity-building and experimentation are not only required within companies, but also throughout this emerging supportive ecosystem of policies, resources (i.e. finance, materials, skills), governance actors, and intermediaries to adequately focus investment, technical capabilities and innovation. Ultimately, we call for a new transdisciplinary action research agenda that centers on SMEs as pivotal actors and amplifiers of community resilience; while recognizing that these firms are themselves in need of support to secure their own capacity to respond to, and transform in light of, crises. This research program calls for recognizing and applying the lessons that the pandemic presents to the urgent need for accelerated climate action. This will be enabled by developing more targeted approaches to collaborative capacity-building activities in SMEs that feed into experimentation and allow for the accelerated adoption of deliberate and strategic resilient business practices and models.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Technology has certainly altered the landscape in which students learn today. The use of technology in today's classrooms is continually increasing as educators seek ways to engage learners and maximize learning potential. Incorporating Web 2.0 tools into the classroom can not only encourage collaboration among learners, but also provide a way for students to apply their knowledge. For such benefits to be gained, the Web 2.0 tools must be relevant and purposeful. In this study, researchers surveyed pre-service teachers at one institution of higher learning, whom took classes that integrated several Web 2.0 tools into the curriculum, and sought which instructional tools pre-service teachers found most beneficial, and hence, are more likely to use in their future classrooms.

This Article surveys cases from the United States Court of Appeals for the Eleventh Circuit from January 1, 2023, through December 31, 2023, in which immigration law was a central focus of the case.1 During this time, the Eleventh Circuit decided several of these cases. As such, the cases discussed herein are those that explore jurisdictional boundaries of the Eleventh Circuit's appellate review of immigration cases, and those that interpret standards that impact an immigrant's ability to obtain relief. This Article discusses (1) removal cancellation, including the court's jurisdiction to review such decisions, the standard for removal cancellation, and the constitutionality of denying such applications; and (2) other important immigration issues, including asylum petitions and the ability of state governments to impact federal immigration policy. Finally, it concludes with recent developments in immigration law. [ABSTRACT FROM AUTHOR]

Abstract The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

Over 1 billion people are living at the frontlines of climate change in mountain areas, where warming rates outpace the global average and are driving significant changes in environments and ecosystem services. These changes are exacerbating socioeconomic difficulties faced by many mountain communities, and are already intensifying vulnerabilities across mountain areas globally. The situation is indicative of pervasive and consequential deficits in adaptation, and calls attention to the need for a better understanding of existing adaptation efforts, as well as the prospects for increasing the quantity and quality of adaptation action in mountain regions. In response, this MountainAgenda article introduces a conceptual framework for adaptation gaps. It then uses data from 2 major global-scale adaptation reviews to shed light on the nature and true magnitude of the adaptation gap in mountains. It reveals shortcomings in available adaptation options, deficits in the uptake of existing adaptation support, and a general lack of coherence between existing adaptations and keystone global agreements relevant to climate change adaptation. These shortcomings are largely related to soft limits to adaptation that constrain responses across mountain areas. In this article, we provide recommendations for closing the adaptation gap in mountains and suggest that this will require deeply collaborative efforts that are rooted in local needs, aspirations, and ways of knowing, but that are also supported by external capacity building and implementation resources. In many instances, this will resemble a transformative approach to adaptation. The conceptual framework presented here is broadly applicable and can also be utilized to identify and close adaptation gaps in social-ecological contexts beyond mountains.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time–concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and k t r a n s results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from −23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding k t r a n s error was reduced from −13.5 ± 8.8% to −0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding k t r a n s error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and k t r a n s error (−2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset. [ABSTRACT FROM AUTHOR]