Co(II) Substitution Enhances the Esterase Activity of a de Novo Designed Zn(II) Carbonic Anhydrase.

Carbonic Anhydrases (CAs) have been a target for de novo protein designers due to the simplicity of the active site and rapid rate of the reaction. The first reported mimic contained a Zn(II) bound to three histidine imidazole nitrogens and an exogenous water molecule, hence closely mimicking the native enzymes' first coordination sphere. Co(II) has served as an alternative metal to interrogate CAs due to its d7 electronic configuration for more detailed solution characterization. We present here the Co(II) substituted [Co(II)(H2O/OH-)]N(TRIL2WL23H)3 n+ that behaves similarly to native Co(II) substituted human-CAs. Like the Zn(II) analogue, the cobalt-derivative at slightly basic pH is incapable of hydrolyzing p-nitrophenylacetate (pNPA); however, as the pH is increased a significant activity develops, which at pH values above 10 eventually yields a catalytic efficiency that exceeds that of the [Zn(II)(OH-)]N(TRIL2WL23H)3 + peptide complex. X-ray absorption analysis is consistent with an octahedral species at pH 7.5 that converts to a 5-coordinate species by pH 11. UV-vis spectroscopy can monitor this transition, giving a pKa for the conversion of 10.3. We assign this conversion to the formation of a 5-coordinate Co(II)(Nimid)3(OH)(H2O) species. The pH dependent kinetic analysis indicates the maximal rate (kcat), and thus the catalytic efficiency (kcat/Km), follow the same pH profile as the spectroscopic conversion to the pentacoordinate species. This correlation suggests that the chemically irreversible ester hydrolysis corresponds to the rate determining process.

Ultrafast X-ray Absorption Spectroscopy Reveals Excited-State Dynamics of B12 Coenzymes Controlled by the Axial Base.

Polarized time-resolved X-ray absorption spectroscopy at the Co K-edge is used to probe the excited-state dynamics and photolysis of base-off methylcobalamin and the excited-state structure of base-off adenosylcobalamin. For both molecules, the final excited-state minimum shows evidence for an expansion of the cavity around the Co ion by ca. 0.04 to 0.05 Å. The 5-coordinate base-off cob(II)alamin that is formed following photodissociation has a structure similar to that of the 5-coordinate base-on cob(II)alamin, with a ring expansion of 0.03 to 0.04 Å and a contraction of the lower axial bond length relative to that in the 6-coordinate ground state. These data provide insights into the role of the lower axial ligand in modulating the reactivity of B12 coenzymes.

3D Body shape for regional and appendicular body composition estimation.

Body composition is correlated to bone mineral density, muscle strength, and physical performance. This is important for diagnosing conditions like sarcopenia, which is defined as the age-associated decrease in muscle mass leading to decreased mobile function, increased frailty, and imbalance. Existing methods for body composition measurement either suffer from inaccurate results or require expensive equipment such as Dual-energy x-ray absorptiometry (DXA). Although DXA measures lean mass and not muscle mass, previous studies have considered extremity lean mass as appendicular skeletal muscle mass (ASMM) approximation. In this study, we develop a new shape descriptor to predict regional body composition (in particular, regional lean mass) from 3D body shapes. In addition, we propose a neural network for ASMM assessment which is calculated by lean mass. We evaluate the effectiveness by comparing adjusted R-Squared values and Root Mean Square Error (RMSE). In our experiment, the regression models utilizing level circumference as the training feature outperforms all regional anthropometric measurements and lowers the average RMSE by about 21%. For ASMM, the proposed neural network, which combines shape features and demographic features, surpasses all other traditional regression models and reaches the lowest RMSE at 1.85 kg. Compared to the vanilla linear regression model, our approach improves the RMSE by 17%. The experimental results suggest that the 3D body shape has the potential to be used to predict body composition, and in particular lean mass, for the whole body as well as specific regions of the body.

Watching Excited State Dynamics with Optical and X-ray Probes: The Excited State Dynamics of Aquocobalamin and Hydroxocobalamin.

Femtosecond time-resolved X-ray absorption (XANES) at the Co K-edge, X-ray emission (XES) in the Co Kß and valence-to-core regions, and broadband UV-vis transient absorption are combined to probe the femtosecond to picosecond sequential atomic and electronic dynamics following photoexcitation of two vitamin B12 compounds, hydroxocobalamin and aquocobalamin. Polarized XANES difference spectra allow identification of sequential structural evolution involving first the equatorial and then the axial ligands, with the latter showing rapid coherent bond elongation to the outer turning point of the excited state potential followed by recoil to a relaxed excited state structure. Time-resolved XES, especially in the valence-to-core region, along with polarized optical transient absorption suggests that the recoil results in the formation of a metal-centered excited state with a lifetime of 2-5 ps. This combination of methods provides a uniquely powerful tool to probe the electronic and structural dynamics of photoactive transition-metal complexes and will be applicable to a wide variety of systems.

An Automatic Grading System for Neonatal Endotracheal Intubation with Multi-Task Convolutional Neural Network.

Neonatal endotracheal intubation (ETI) is an intricate medical procedure that poses considerable challenges, demanding comprehensive training to effectively address potential complications in clinical practice. However, due to limited access to clinical opportunities, ETI training relies heavily on physical manikins to develop a certain level of competence before clinical exposure. Nonetheless, traditional training methods prove ineffective due to scarcity of expert instructors and the absence of internal situational awareness within the manikins, preventing thorough performance assessment for both trainees and instructors. To address this gap, there is a need to develop an automatic grading system that can assist trainees in performance assessment. In this paper, we proposed a multi-task Convolutional Neural Network (MTCNN) based model for assessing ETI proficiency, specifically targeting key performance features recommended by expert instructors. The model comprises three modules: an ETI simulation module that captures the ETI procedures performed on a standard neonatal task trainer manikin, an automatic grading module that extracts and grades the identified key performance features, and a data visualization module that presents the assessment results in a user-friendly manner. The experimental results demonstrated that the proposed automatic grading system achieved an average classification accuracy of 93.6%. This study established the successful integration of intuitive observed features with latent features derived from multivariate time series (MTS) data, coupled with multi-task deep learning techniques, for the automatic assessment of ETI performance. Clinical relevance­: The proposed automatic grading system facilitates an enhanced neonatal endotracheal intubation training experience for neonatologists.

Liver Fat Assessment with Body Shape.

Hepatic steatosis has become a serious health concern among the general population, but especially for those who are obese. Liver fat can increase the risk of cirrhosis and even liver cancer. Current standard methods to assess hepatic steatosis, such as liver biopsy and CT/MR imaging techniques, are expensive and/or may have associated risks to health. In this paper, we use body shapes to assess hepatic steatosis using both traditional linear regression models and a deep neural network. We apply our models to a medical dataset and evaluate the approaches for both regression and classification. We compare the performance of several models via popular evaluation metrics. The experimental results indicate that our proposed neural network outperforms the vanilla linear regression model by 22.37% in RMSE and the accuracy by 18%. The R-squared value of the neural model is more than 0.72 and the accuracy reaches 78%. Hence, the body shape features can provide an additional accurate and affordable choice to monitor the degree of the patient's liver fat. Clinical relevance - This paper presents a low cost and convenient approach to predict liver fat percentage using body shapes. This approach will not replace the gold standard for assessing hepatic steatosis. However, with the wide availability for depth cameras (including on smartphones), the approach promises to provide another modality that can be deployed widely in clinical setting as well for home use for telehealth.

Time-resolved spectroscopy: Advances in understanding the electronic structure and dynamics of cobalamins.

Time resolved spectroscopy provides unique insight into the structure and function of cobalamins. In these experiments, the cobalamin is initially excited by a short "pump" pulse in the UV-visible region and then characterized at some later time using a short "probe" pulse. The emphasis in this chapter is on both UV-visible and X-ray probe pulses, with a particular focus on the unique information provided by the latter. The principles of time-resolved spectroscopy are reviewed, with an emphasis on ultrafast measurements (time scales less than ~10ps) to characterize short-lived cobalamin excited states. Several practical considerations are discussed, with a focus on the technical details that are necessary to obtain high quality, interpretable data. These include sample delivery, polarization, and excitation power. Some of the theoretical approaches to interpreting data are discussed.

Unexpected plasticity of the quaternary structure of iron-manganese superoxide dismutases.

Protein 3D structure can be remarkably robust to the accumulation of mutations during evolution. On the other hand, sometimes a single amino acid substitution can be sufficient to generate dramatic and completely unpredictable structural consequences. In an attempt to rationally alter the preferences for the metal ion at the active site of a member of the Iron/Manganese superoxide dismutase family, two examples of the latter phenomenon were identified. Site directed mutants of SOD from Trichoderma reesei were generated and studied crystallographically together with the wild type enzyme. Despite being chosen for their potential impact on the redox potential of the metal, two of the mutations (D150G and G73A) in fact resulted in significant alterations to the protein quaternary structure. The D150G mutant presented alternative inter-subunit contacts leading to a loss of symmetry of the wild type tetramer, whereas the G73A mutation transformed the tetramer into an octamer despite not participating directly in any of the inter-subunit interfaces. We conclude that there is considerable intrinsic plasticity in the Fe/MnSOD fold that can be unpredictably affected by single amino acid substitutions. In much the same way as phenotypic defects at the organism level can reveal much about normal function, so too can such mutations teach us much about the subtleties of protein structure.

Cu(I) Binding to Designed Proteins Reveals a Putative Copper Binding Site of the Human Line1 Retrotransposon Protein ORF1p.

Long interspersed nuclear elements-1 (L1) are autonomous retrotransposons that encode two proteins in different open reading frames (ORF1 and ORF2). The ORF1p, which may be an RNA binding and chaperone protein, contains a three-stranded coiled coil (3SCC) domain that facilitates the formation of the biologically active homotrimer. This 3SCC domain is composed of seven amino acid (heptad) repeats as found in native and designed peptides and a stammer that modifies the helical structure. Cysteine residues occur at three hydrophobic positions (2 a and 1 d sites) within this domain. We recently showed that the cysteine layers in ORF1p and model de novo designed peptides bind the toxic metalloid lead(II) with high affinities, a feature that had not been previously recognized. However, there is little understanding of how essential metal ions might interact with this metal binding domain. We have, therefore, investigated the copper(I) binding properties of analogous de novo designed 3SCCs that contain cysteine layers within the hydrophobic core. The results from UV-visible and X-ray absorption spectroscopy show that these designed peptides bind Cu(I) with high affinity in a pH-dependent manner. At pH 9, monomeric trigonal planar Cu(I)S3 centers are formed with 1 equiv of metal, while dinuclear centers form with a second equivalent of metal. At physiologic pH conditions, the dinuclear center forms cooperatively. These data suggest that ORF1p is capable of binding two copper ions to its tris(cysteine) layers. This has major implications for ORF1p coiled coil domain stability and dynamics, ultimately potentially impacting the resulting biological activity.

An Auxiliary Tasks Based Framework for Automated Medical Skill Assessment with Limited Data.

Automated medical skill assessment facilitates medical education by merging varying clinical experiences across instructors for standardizing medical training. However, medical datasets for training such automated assessment rarely have satisfactory sizes due to the cost of data collection, safety concerns and privacy restrictions. Current medical training relies on evaluation rubrics that usually include multiple auxiliary labels to support the overall evaluation from varying aspects of the procedure. In this paper, we explore machine learning algorithms to design a generalizable auxiliary task-based framework for medical skill assessment to address training automated systems with limited data. Our framework exhaustively mines valid auxiliary information in the evaluation rubric to pre-train the feature extractor before training the skill assessment classifier. Notably, a new regression-based multitask weighting method is the key to pre-train a meaningful feature representation comprehensively, ensuring the evaluation rubric is well imitated in the final model. The overall evaluation task can be fine-tuned based on the pre-trained rubric-based feature representation. Our experimental results on two medical skill datasets show that our work can significantly improve performance, achieving 85.9% and 97.4% accuracy in the intubation dataset and surgical skill dataset, respectively.

S2FLNet: Hepatic steatosis detection network with body shape.

Fat accumulation in the liver cells can increase the risk of cardiac complications and cardiovascular disease mortality. Therefore, a way to quickly and accurately detect hepatic steatosis is critically important. However, current methods, e.g., liver biopsy, magnetic resonance imaging, and computerized tomography scan, are subject to high cost and/or medical complications. In this paper, we propose a deep neural network to estimate the degree of hepatic steatosis (low, mid, high) using only body shapes. The proposed network adopts dilated residual network blocks to extract refined features of input body shape maps by expanding the receptive field. Furthermore, to classify the degree of steatosis more accurately, we create a hybrid of the center loss and cross entropy loss to compact intra-class variations and separate inter-class differences. We performed extensive tests on the public medical dataset with various network parameters. Our experimental results show that the proposed network achieves a total accuracy of over 82% and offers an accurate and accessible assessment for hepatic steatosis.

Nitrite reductase activity within an antiparallel de novo scaffold.

Copper nitrite reductase (CuNiR) is a copper enzyme that converts nitrite to nitric oxide and is an important part of the global nitrogen cycle in bacteria. The relatively simple CuHis3 binding site of the CuNiR active site has made it an enticing target for small molecule modeling and de novo protein design studies. We have previously reported symmetric CuNiR models within parallel three stranded coiled coil systems, with activities that span a range of three orders of magnitude. In this report, we investigate the same CuHis3 binding site within an antiparallel three helical bundle scaffold, which allows the design of asymmetric constructs. We determine that a simple CuHis3 binding site can be designed within this scaffold with enhanced activity relative to the comparable construct in parallel coiled coils. Incorporating more complex designs or repositioning this binding site can decrease this activity as much as 15 times. Comparing these constructs, we reaffirm a previous result in which a blue shift in the 1s to 4p transition energy determined by Cu(I) X-ray absorption spectroscopy is correlated with an enhanced activity within imidazole-based constructs. With this step and recent successful electron transfer site designs within this scaffold, we are one step closer to a fully functional de novo designed nitrite reductase.

Open Reading Frame 1 Protein of the Human Long Interspersed Nuclear Element 1 Retrotransposon Binds Multiple Equivalents of Lead.

The human long interspersed nuclear element 1 (LINE1) has been implicated in numerous diseases and has been suggested to play a significant role in genetic evolution. Open reading frame 1 protein (ORF1p) is one of the two proteins encoded in this self-replicating mobile genetic element, both of which are essential for retrotransposition. The structure of the three-stranded coiled-coil domain of ORF1p was recently solved and showed the presence of tris-cysteine layers in the interior of the coiled-coil that could function as metal binding sites. Here, we demonstrate that ORF1p binds Pb(II). We designed a model peptide, GRCSL16CL23C, to mimic two of the ORF1p Cys3 layers and crystallized the peptide both as the apo-form and in the presence of Pb(II). Structural comparison of the ORF1p with apo-(GRCSL16CL23C)3 shows very similar Cys3 layers, preorganized for Pb(II) binding. We propose that exposure to heavy metals, such as lead, could influence directly the structural parameters of ORF1p and thus impact the overall LINE1 retrotransposition frequency, directly relating heavy metal exposure to genetic modification.

Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and magnetic resonance imaging have the disadvantages of prohibitive cost or ionizing radiation. Recently, body shape based techniques using body scanners and depth cameras, have brought new opportunities for improving body composition estimation by intelligently analyzing body shape descriptors. In this paper, we present a multi-task deep neural network method utilizing a conditional generative adversarial network to predict the pixel level body composition using only 3D body surfaces. The proposed method can predict 2D subcutaneous and visceral fat maps in a single network with a high accuracy. We further introduce an interpreted patch discriminator which optimizes the texture accuracy of the 2D fat maps. The validity and effectiveness of our new method are demonstrated experimentally on TCIA and LiTS datasets. Our proposed approach outperforms competitive methods by at least 41.3% for the whole body fat percentage, 33.1% for the subcutaneous and visceral fat percentage, and 4.1% for the regional fat predictions.

Interpreting the Rhetoric of Visual Advertisements.

Visual media have important persuasive power, but prior computer vision approaches have predominantly ignored the persuasive aspects of images. In this work, we propose a suite of data and techniques that enable progress on understanding the messages that visual advertisements convey. We make available a dataset of 64,832 image ads and 3,477 video ads, annotated with ten types of information: the topic and sentiment of the ad; whether it is funny, exciting, or effective; what action it prompts the viewer to do, and what arguments it provides for why this action should be taken; symbolic associations that the ad relies on; the metaphorical object transformations on which especially creative ads rely; and the climax in video ads. We develop methods that use multimodal cues, i.e., both visuals and slogans, for both the image and video domains. Our methods rely on finding poignant content spatially and temporally. We also examine the creative story construction in ads: for videos, we learn to predict when the climax occurs (if any), and how effective the story is; for images, we analyze how object transformations in ads metaphorically depict product properties.

Region of Interest Selection for Functional Features.

Feature selection is a critical component in supervised learning to improve model performance. Searching for the optimal feature candidates can be NP-hard. With limited data, cross-validation is widely used to alleviate overfitting, which unfortunately suffers from high computational cost. We propose a highly innovative strategy in feature selection to reduce the overfitting risk but without cross-validation. Our method selects the optimal sub-interval, i.e., region of interest (ROI), of a functional feature for functional linear regression where the response is a scalar and the predictor is a function. For each candidate sub-interval, we evaluate the overfitting risk by calculating a necessary sample size to achieve a pre-specified statistical power. Combining with a model accuracy measure, we rank these sub-intervals and select the ROI. The proposed method has been compared with other state-of-the-art feature selection methods on several reference datasets. The results show that our proposed method achieves an excellent performance in prediction accuracy and reduces computational cost substantially.

The Photoactive Excited State of the B12-Based Photoreceptor CarH.

We have used transient absorption spectroscopy in the UV-visible and X-ray regions to characterize the excited state of CarH, a protein photoreceptor that uses a form of B12, adenosylcobalamin (AdoCbl), to sense light. With visible excitation, a nanosecond-lifetime photoactive excited state is formed with unit quantum yield. The time-resolved X-ray absorption near edge structure difference spectrum of this state demonstrates that the excited state of AdoCbl in CarH undergoes only modest structural expansion around the central cobalt, a behavior similar to that observed for methylcobalamin rather than for AdoCbl free in solution. We propose a new mechanism for CarH photoreactivity involving formation of a triplet excited state. This allows the sensor to operate with high quantum efficiency and without formation of potentially dangerous side products. By stabilizing the excited electronic state, CarH controls reactivity of AdoCbl and enables slow reactions that yield nonreactive products and bypass bond homolysis and reactive radical species formation.

Automated Assessment of Neonatal Endotracheal Intubation Measured by a Virtual Reality Simulation System.

Manual assessment from experts in neonatal endotracheal intubation (ETI) training is a time-consuming and tedious process. Such subjective, highly variable, and resource-intensive assessment method may not only introduce inter-rater/intra-rater variability, but also represent a serious limitation in many large-scale training programs. Moreover, poor visualization during the procedure prevents instructors from observing the events occurring within the manikin or the patient, which introduces an additional source of error into the assessment. In this paper, we propose a physics-based virtual reality (VR) ETI simulation system that captures the entire motions of the laryngoscope and the endotracheal tube (ETT) in relation to the internal anatomy of the virtual patient. Our system provides a complete visualization of the procedure, offering instructors with comprehensive information for accurate assessment. More importantly, an interpretable machine learning algorithm was developed to automatically assess the ETI performance by training on the performance parameters extracted from the motions and the scores rated by experts. Our results show that the leave-one-out-cross-validation (LOOCV) classification accuracy of the automated assessment algorithm is 80%, which indicates that our system can reliably conduct a consistent and standardized assessment for ETI training.

Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.

Neonatal endotracheal intubation (ETI) is an important, complex resuscitation skill, which requires a significant amount of practice to master. Current ETI practice is conducted on the physical manikin and relies on the expert instructors' assessment. Since the training opportunities are limited by the availability of expert instructors, an automatic assessment model is highly desirable. However, automating ETI assessment is challenging due to the complexity of identifying crucial features, providing accurate evaluations and offering valuable feedback to trainees. In this paper, we propose a dilated Convolutional Neural Network (CNN) based ETI assessment model, which can automatically provide an overall score and performance feedback to pediatric trainees. The proposed assessment model takes the captured kinematic multivariate time-series (MTS) data from the manikin-based augmented ETI system that we developed, automatically extracts the crucial features of captured data, and eventually provides an overall score as output. Furthermore, the visualization based on the class activation mapping (CAM) can automatically identify the motions that have significant impact on the overall score, thus providing useful feedback to trainees. Our model can achieve 92.2% average classification accuracy using the Leave-One-Out-Cross-Validation (LOOCV).

An Interprotein Co-S Coordination Complex in the B12-Trafficking Pathway.

The CblC and CblD chaperones are involved in early steps in the cobalamin trafficking pathway. Cobalamin derivatives entering the cytoplasm are converted by CblC to a common cob(II)alamin intermediate via glutathione-dependent alkyltransferase or reductive elimination activities. Cob(II)alamin is subsequently converted to one of two biologically active alkylcobalamins by downstream chaperones. The function of CblD has been elusive although it is known to form a complex with CblC under certain conditions. Here, we report that CblD provides a sulfur ligand to cob(II)alamin bound to CblC, forming an interprotein coordination complex that rapidly oxidizes to thiolato-cob(III)alamin. Cysteine scanning mutagenesis and EPR spectroscopy identified Cys-261 on CblD as the sulfur donor. The unusual interprotein Co-S bond was characterized by X-ray absorption spectroscopy and visualized in the crystal structure of the human CblD thiolato-cob(III)alamin complex. Our study provides insights into how cobalamin coordination chemistry could be utilized for cofactor translocation in the trafficking pathway.