Advances in Kriging-Based Autonomous X-Ray Scattering Experiments.

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection

Do Elliptical Trainers Accurately Estimate Energy Expenditure?

Life Fitness and Precor elliptical exercise trainers are very popular in fitness centers because of the reduced impact on joints as compared to running on a treadmill. Modern elliptical trainers measure heart rate, distance traveled, and provide an estimated amount of calories expended during the exercise bout. Each piece of equipment uses an internal algorithm to estimate energy expenditure based upon resistance and pedal rate. The purpose of this study was to determine if the PRECOR model EFX 556i and Life Fitness model 95X elliptical trainers accurately estimated energy expenditure. Three men and three women performed exercise sessions on each elliptical trainer on 3 separate occasions (2 bouts per day). The order of the machines used for each exercise session was randomized. Each exercise bout lasted 12 minutes (2-min warmup at a resistance of 3, rpm of 70; 10-min measure period at a resistance of 10, rpm of 70). During the test V02 was measured using a Medical Graphics Ultima metabolic cart; heart rate was measured using a Polar heart rate monitor and energy expenditure calculated by the metabolic cart. HR, VO2, and energy expenditure data were recorded each minute of the 10 minute exercise session. The results are shown in the table below. Brand Elliptical Mean (Kcal) MC Mean (Kcal) % Error P Value LF 91 (13.98) 93.54 (14.13) -2.72 0.024 PC 122.93 (12.74) 98.58 (12.68) 24.70 \u3c0.0001 LF = Life Fitness, PC = Precor, MC = Medical Graphics Ultima metabolic cart We conclude that the Life Fitness EFX 556i slightly underestimates, while the Precor model EFX 556i overestimates exercise energy expenditure

Potentials and Challenges of Additive Manufacturing Technologies for Heat Exchanger

The rapid development of additive manufacturing (AM) technologies enables a radical paradigm shift in the construction of heat exchangers. In place of a layout limited to the use of planar or tubular starting materials, heat exchangers can now be optimized, reflecting their function and application in a particular environment. The complexity of form is no longer a restriction but a quality. Instead of brazing elements, resulting in rather inflexible standard components prone to leakages, with AM, we finally can create seamless integrated and custom solutions from monolithic material. To address AM for heat exchangers we both focus on the processes, materials, and connections as well as on the construction abilities within certain modeling and simulation tools. AM is not the total loss of restrictions. Depending on the processes used, delicate constraints have to be considered. But on the other hand, we can access materials, which can operate in a much wider heat range. It is evident that conventional modeling techniques cannot match the requirements of a flexible and adaptive form finding. Instead, we exploit biomimetic and mathematical approaches with parametric modeling. This results in unseen configurations and pushes the limits of how we should think about heat exchangers today

Chapter Potentials and Challenges of Additive Manufacturing Technologies for Heat Exchanger

The rapid development of additive manufacturing (AM) technologies enables a radical paradigm shift in the construction of heat exchangers. In place of a layout limited to the use of planar or tubular starting materials, heat exchangers can now be optimized, reflecting their function and application in a particular environment. The complexity of form is no longer a restriction but a quality. Instead of brazing elements, resulting in rather inflexible standard components prone to leakages, with AM, we finally can create seamless integrated and custom solutions from monolithic material. To address AM for heat exchangers we both focus on the processes, materials, and connections as well as on the construction abilities within certain modeling and simulation tools. AM is not the total loss of restrictions. Depending on the processes used, delicate constraints have to be considered. But on the other hand, we can access materials, which can operate in a much wider heat range. It is evident that conventional modeling techniques cannot match the requirements of a flexible and adaptive form finding. Instead, we exploit biomimetic and mathematical approaches with parametric modeling. This results in unseen configurations and pushes the limits of how we should think about heat exchangers today

Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels

A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously steering experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process

Low-Dose Vertical Inhibition of the RAF-MEK-ERK Cascade Causes Apoptotic Death of KRAS Mutant Cancers

We address whether combinations with a pan-RAF inhibitor (RAFi) would be effective in KRAS mutant pancreatic ductal adenocarcinoma (PDAC). Chemical library and CRISPR genetic screens identify combinations causing apoptotic anti-tumor activity. The most potent combination, concurrent inhibition of RAF (RAFi) and ERK (ERKi), is highly synergistic at low doses in cell line, organoid, and rat models of PDAC, whereas each inhibitor alone is only cytostatic. Comprehensive mechanistic signaling studies using reverse phase protein array (RPPA) pathway mapping and RNA sequencing (RNA-seq) show that RAFi/ERKi induced insensitivity to loss of negative feedback and system failures including loss of ERK signaling, FOSL1, and MYC; shutdown of the MYC transcriptome; and induction of mesenchymal-to-epithelial transition. We conclude that low-dose vertical inhibition of the RAF-MEK-ERK cascade is an effective therapeutic strategy for KRAS mutant PDAC.Peer reviewe

Imprinted antibody responses against SARS-CoV-2 Omicron sublineages

SARS-CoV-2 Omicron sublineages carry distinct spike mutations and represent an antigenic shift resulting in escape from antibodies induced by previous infection or vaccination. We show that hybrid immunity or vaccine boosters result in potent plasma neutralizing activity against Omicron BA.1 and BA.2 and that breakthrough infections, but not vaccination-only, induce neutralizing activity in the nasal mucosa. Consistent with immunological imprinting, most antibodies derived from memory B cells or plasma cells of Omicron breakthrough cases cross-react with the Wuhan-Hu-1, BA.1 and BA.2 receptor-binding domains whereas Omicron primary infections elicit B cells of narrow specificity. While most clinical antibodies have reduced neutralization of Omicron, we identified an ultrapotent pan-variant antibody, that is unaffected by any Omicron lineage spike mutations and is a strong candidate for clinical development

Imprinted antibody responses against SARS-CoV-2 Omicron sublineages

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineages carry distinct spike mutations resulting in escape from antibodies induced by previous infection or vaccination. We show that hybrid immunity or vaccine boosters elicit plasma-neutralizing antibodies against Omicron BA.1, BA.2, BA.2.12.1, and BA.4/5, and that breakthrough infections, but not vaccination alone, induce neutralizing antibodies in the nasal mucosa. Consistent with immunological imprinting, most antibodies derived from memory B cells or plasma cells of Omicron breakthrough cases cross-react with the Wuhan-Hu-1, BA.1, BA.2, and BA.4/5 receptor-binding domains, whereas Omicron primary infections elicit B cells of narrow specificity up to 6 months after infection. Although most clinical antibodies have reduced neutralization of Omicron, we identified an ultrapotent pan-variant–neutralizing antibody that is a strong candidate for clinical development

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19