A. Della Corte, M. Farotti and S. Rodríguez Martín
Proc. Amer. Math. Soc. 152 (), 5163-5173.
Abstract, references and article information

Kailun Fang
Nov 30, 2020; 0:RA120.002081v1-mcp.RA120.002081
Research

Oktawia Nilsson
Nov 17, 2020; 0:jlr.RA120000920v1-jlr.RA120000920
Research Articles

Carlota Oleaga
Nov 17, 2020; 0:jlr.RA120000964v1-jlr.RA120000964
Research Articles

Apolipoprotein A-I (ApoA-I) of high-density lipoprotein (HDL) is essential for the transportation of cholesterol between peripheral tissues and the liver. However, specific mutations in Apolipoprotein A-I (ApoA-I) of high-density lipoprotein (HDL) are responsible for a late-onset systemic amyloidosis, the pathological accumulation of protein fibrils in tissues and organs. Carriers of these mutations do not exhibit increased cardiovascular disease risk despite displaying reduced levels of ApoA-I/ HDL-cholesterol. To explain this paradox, we show that the HDL particle profile of patients carrying either L75P or L174S ApoA-I amyloidogenic variants a higher relative abundance of the 8.4 nm vs 9.6 nm particles, and that serum from patients, as well as reconstituted 8.4 and 9.6 nm HDL particles (rHDL), possess increased capacity to catalyze cholesterol efflux from macrophages. Synchrotron radiation circular dichroism and hydrogen-deuterium exchange revealed that the variants in 8.4 nm rHDL have altered secondary structure composition and display a more flexible binding to lipids compared to their native counterpart. The reduced HDL-cholesterol levels of patients carrying ApoA-I amyloidogenic variants are thus balanced by higher proportion of small, dense HDL particles and better cholesterol efflux due to altered, region-specific protein structure dynamics.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates cholesterol metabolism by inducing the degradation of hepatic low-density lipoprotein receptor (LDLR). Plasma PCSK9 has two main molecular forms: a 62-kDa mature form (PCSK9_62) and a 55-kDa, furin-cleaved form (PCSK9_55). PCSK9_55 is considered less active than PCSK9_62 in degrading LDLR. We aimed to identify the site of PCSK9_55 formation (intra- vs. extracellular) and to further characterize the LDLR-degradative function of PCSK9_55 relative to PCSK9_62. Co-expressing PCSK9_62 with furin in cell culture induced formation of PCSK9_55, most of which was found in the extracellular space. Under the same conditions we found that: i) adding a cell-permeable furin inhibitor preferentially decreased the formation of PCSK9_55 extracellularly; ii) using pulse-chase, we observed the formation of PCSK9_55 exclusively extracellularly in a time-dependent manner. A recombinant form of PCSK9_55 was efficiently produced but displayed impaired secretion that resulted in its intracellular trapping. However, the non-secreted PCSK9_55 was able to induce degradation of LDLR, though with 50% lower efficiency compared with PCSK9_62. Collectively, our data show that PCSK9_55 is generated in the extracellular space, and that intracellular PCSK9_55 is not secreted but retains the ability to degrade the LDLR through an intracellular pathway.

In-depth coverage of proteomic analysis could enhance our understanding to the mechanism of the protein functions. Unfortunately, many highly hydrophobic proteins and low-abundance proteins, which play critical roles in signaling networks, are easily lost during sample preparation, mainly attributed to the fact that very few extractants can simultaneously satisfy the requirements on strong solubilizing ability to membrane proteins and good enzyme compatibility. Thus, it is urgent to screen out ideal extractant from the huge compound libraries in a fast and effective way. Herein, by investigating the interior mechanism of extractants on the membrane proteins solubilization and trypsin compatibility, a molecular dynamics simulation system was established as complement to the experimental procedure to narrow down the scope of candidates for proteomics analysis. The simulation data shows that the van der Waals interaction between cation group of ionic liquid and membrane protein is the dominant factor in determining protein solubilization. In combination with the experimental data, 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) is on the shortlist for the suitable candidates from comprehensive aspects. Inspired by the advantages of C12Im-Cl, an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, over 3,300 proteins were confidently identified from 10³ HeLa cells (~100 ng proteins) in a single run, an improvement of 53% over the conventional FASP method. Then the i-FASP method was further successfully applied to the label-free relative quantitation of human liver cancer and para-carcinoma tissues with obviously improved accuracy, reproducibility and coverage than the commonly used urea-based FASP method. The above results demonstrated that the i-FASP method could be performed as a versatile tool for the in-depth coverage proteomic analysis of biological samples.

The complexity and dynamics of the immensely heterogeneous glycoproteome of the prostate cancer (PCa) tumour micro-environment remain incompletely mapped, a knowledge gap that impedes our molecular-level understanding of the disease. To this end, we have used sensitive glycomics and glycoproteomics to map the protein-, cell- and tumour grade-specific N- and O-glycosylation in surgically-removed PCa tissues spanning five histological grades (n = 10/grade) and tissues from patients with benign prostatic hyperplasia (n = 5). Quantitative glycomics revealed PCa grade-specific alterations of the oligomannosidic-, paucimannosidic- and branched sialylated complex-type N-glycans, and dynamic remodelling of the sialylated core 1- and core 2-type O-glycome. Deep quantitative glycoproteomics identified ~7,400 unique N-glycopeptides from 500 N-glycoproteins and ~500 unique O-glycopeptides from nearly 200 O-glycoproteins. With reference to a recent Tissue and Blood Atlas, our data indicate that paucimannosidic glycans of the PCa tissues arise mainly from immune cell-derived glycoproteins. Further, the grade-specific PCa glycosylation arises primarily from dynamics in the cellular makeup of the PCa tumour microenvironment across grades involving increased oligomannosylation of prostate-derived glycoproteins and decreased bisecting GlcNAcylation of N-glycans carried by the extracellular matrix proteins. Further, elevated expression of several oligosaccharyltransferase subunits and enhanced N-glycoprotein site occupancy were observed associated with PCa progression. Finally, correlations between the protein-specific glycosylation and PCa progression were observed including increased site-specific core 2-type O-glycosylation of collagen VI. In conclusion, integrated glycomics and glycoproteomics have enabled new insight into the complexity and dynamics of the tissue glycoproteome associated with PCa progression generating an important resource to explore the underpinning disease mechanisms.

The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution mass spectrometry. By combining meiotic labels annotated from the MGI mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder, was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of ten genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore,  mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.

Diagnosing social behavioural dynamics of corruption Other resource dora.popova 8 December 2021

This interactive toolkit identifies the types of social expectations which sustain selected corrupt practices and provides behaviourally-informed recommendations for changing them.

When tackling a problem as complex as corruption, it is important to understand why and how people behave in different situations where corruption occurs. In contexts where it is easier to engage in corruption than avoid it, identifying the social expectations and informal rules which sustain corrupt practices can advance corruption prevention and deepen collective action.  

Behavioural approaches to corruption offer a better understanding of diverse social settings, group dynamics, power distribution, social motivations, and expectations that contribute to a more tolerant environment for certain forms of the phenomenon. They are also highly complementary to traditional corruption measures, which tend to focus on the enforcement of legal sanctions and deterrents. Behavioural approaches, especially those inspired by social norms theory, highlight complex social characteristics and informal rules of specific corrupt practices, and effectively support implementation of more dynamic context-specific anti-corruption interventions.

Since 2016, the Chatham House Africa programme’s Social Norms and Accountable Governance (SNAG) project has adopted a behavioural approach based on social norms methodology to investigate the social beliefs which motivate different forms of corruption. Drawing on the project’s extensive evidence-gathering and analysis, this toolkit offers users navigable behavioural mapping of contextual factors, beliefs, and expectations surrounding common corrupt practices. 

It aims to support anti-corruption actors in diagnosing informal rules and social expectations which sustain corruption in some societies. It also proposes behavioural-informed guidance for developing or adapting anti-corruption interventions and activities, so they account for informal rules of behaviour such as social norms. 
 
The toolkit supports users to:

• Identify whether and how widespread corrupt practices are motivated by social beliefs and expectations.

• Understand how society influences the types of corrupt activity individuals engage in, or avoid, and the factors informing these choices

• Integrate empirical evidence and behavioural insights into anti-corruption strategies from diagnostics to design, and eventual implementation and evaluation

The toolkit presents evidence from SNAG’s research into three key corrupt practices – bribery, embezzlement, and electoral fraud. Each was examined in the context of typical situations in which they occur, such as law enforcement, healthcare, the power sector, voting, and education while critical factors such as religion, gender, and ethnicity were considered. 

The toolkit presents an overview of specific contexts and behavioural features of the practices and provides behavioural-informed recommendations. It also contains pop-up features with definitions and explanations of key concepts. The toolkit is designed to be expandable, allowing further content and behavioural dynamics to be added.   

DNA polymerase from bacteriophage T7 undergoes large, substrate-induced conformational changes that are thought to account for high replication fidelity, but prior studies were adversely affected by mutations required to construct a Cys-lite variant needed for site-specific fluorescence labeling. Here we have optimized the direct incorporation of a fluorescent un-natural amino acid, (7-hydroxy-4-coumarin-yl)-ethylglycine, using orthogonal amber suppression machinery in Escherichia coli. MS methods verify that the unnatural amino acid is only incorporated at one position with minimal background. We show that the single fluorophore provides a signal to detect nucleotide-induced conformational changes through equilibrium and stopped-flow kinetic measurements of correct nucleotide binding and incorporation. Pre-steady-state chemical quench methods show that the kinetics and fidelity of DNA replication catalyzed by the labeled enzyme are largely unaffected by the unnatural amino acid. These advances enable rigorous analysis to establish the kinetic and mechanistic basis for high-fidelity DNA replication.

WALLISELLEN, Switzerland, Nov. 15, 2023 — Hewlett Packard Enterprise (HPE) today announced that Sauber Motorsport AG is partnering with HPE to advance aerodynamics of its Formula One race cars. The […]

The post HPE Unveils Advanced Computational Fluid Dynamics Solutions for Sauber Motorsport appeared first on HPCwire.

The brain's extracellular matrix (ECM) regulates neuronal plasticity and animal behavior. ECM staining shows a net-like structure around a subset of neurons, a ring-like structure at the nodes of Ranvier, and diffuse staining in the interstitial matrix. However, understanding the structural features of ECM deposition across various neuronal types and subcellular compartments remains limited. To visualize the organization pattern and assembly process of the hyaluronan-scaffolded ECM in the brain, we fused a HaloTag to hyaluronan proteoglycan link protein 1, which links hyaluronan and proteoglycans. Expression or application of the probe in primary rat neuronal cultures enables us to identify spatial and temporal regulation of ECM deposition and heterogeneity in ECM aggregation among neuronal populations. Dual-color birthdating shows the ECM assembly process in culture and in vivo. Sparse expression in mouse brains of either sex reveals detailed ECM architectures around excitatory neurons and developmentally regulated dendritic ECM. Our study uncovers extensive structural features of the brain's ECM, suggesting diverse roles in regulating neuronal plasticity.

Read the series on how FAO [...]

Penn State Mont Alto recently hosted its 19th annual MedCamp, a weeklong day camp providing rising ninth and 10th graders with opportunities to learn about careers in health care and medical fields. Twenty area highschoolers from across Franklin, Adams, Cumberland and York counties were accepted into MedCamp 2024.

Romit Maulik, an assistant professor in the Penn State College of Information Sciences and Technology, was granted a three-year, $360,000 Early Career Program Award from the Army Research Office. 

STATEWIDE, Del. (September 29, 2023) – The 2023 Delaware Arts Summit proudly presents an exceptional lineup of speakers who will inspire and enlighten 200 attendees. This year’s summit, taking place on October 9, promises to be a gathering of visionaries and enthusiasts in the arts and culture sector. With a focus on innovation, collaboration, and […]

Read the in depth Review of Beyerdynamic Lagoon ANC Audio Video. Know detailed info about Beyerdynamic Lagoon ANC configuration, design and performance quality along with pros & cons, Digit rating, verdict based on user opinions/feedback.

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I am attempting to use dbGetNeighbor() function inside the dynamic display HUD so that the distance to the next metal on that layer could be viewed. Think of another line in this dynamic table here... 

My SKILL code is essentially the following:

procedure(getNearestNeighborOnMetal(cv)
let((direction tmpBoundingBox)
direction = internal_function()
tmpBoundingBox = dbCreateRect(geGetEditCellView() "tmp" list(hiGetCommandPoint() hiGetCommandPoint()))
car(dbGetNeighbor(geGetEditCellView() tmpBoundingBox direction))
)
)

this returns the distance to the closest metal based on some tests.

Next, I try to register this function to work in the Dynamic Display / Info Balloon world by executing odcRegisterCustomFunc() for each and every object type (I know, absurd, but trying to debug)

In the dynamic display menu, I toggle the "Custom SKILL Function" check in layoutXL, then hit apply, then OK.

After this I find I am unable to view the changes reflected in any info balloons or in the drawing HUD (above) for this wire. I have tried replacing my function with the sample "customFunc" from the odcRegisterCustomFunc() documentation and was still unable to produce any new output.

Any help diagnosing the use of this feature would be very much appreciated

Learn how Xcelium PowerPlayback App enables the massively parallel Xcelium replay of waveforms for glitch-accurate power estimation of multi-billion gate SoC designs.(read more)

When the Arduino Mega2560 is added to the first serial port, the dynamic memory is 2000 bytes, and when the second serial serial is added, the dynamic memory is 4000 bytes. Now I need to add the third Serial serial port. The dynamic memory is 6000 bytes. Due to the many variables in the program itself, the dynamic memory is not enough. Please help me how to save the dynamic memory?

BAKU, Azerbaijan — The head of the United Nations' nuclear watchdog said Tuesday he's hopeful that meetings this week with Iranian officials, including the country's new president, can lead to a breakthrough in monitoring the country's nuclear program, a longstanding issue that has gained new urgency as Israel has twice struck Iran amid rising tensions in the Middle East. Rafael Mariano Grossi, director general of the International Atomic Energy Agency, will travel to Iran on Wednesday to meet for the first time with President Masoud Pezeshkian, who was elected in July. Grossi said he hopes to build on positive discussions he had with Iranian Foreign Minister Abbas Araghchi during the U.N. General Assembly in September. "We have a problem that we need to solve," Grossi said in an interview at the U.N. climate conference in Azerbaijan. "That is this gap, this lack of confidence, which we should not allow to grow into a self-fulfilling prophecy of using nuclear facilities as targets." He added: "There has been a bit of a dire straits dynamic with Iran that we want to go beyond." Iran is rapidly advancing its atomic program while increasing stockpiles of uranium enriched to near weapons-grade levels, all in defiance of international demands, according to the IAEA. Iran says its program is for energy purposes, not to build weapons. Grossi's visit comes as Israel and Iran have traded missile attacks in recent months after more than a year of war in Gaza, which is governed by Hamas, a group supported by Iran. Grossi noted that international law prohibits the attack of nuclear facilities and "it's obvious that is something that can have radiological consequences." The Biden administration said last month that it had won assurances from Israel that it would not attack nuclear or oil sites. A 2015 nuclear agreement between Iran and world powers put limits on Iran's nuclear program, which the West fears could be used to make nuclear weapons. The deal included the lifting of economic sanctions on Iran. But that deal collapsed after Donald Trump's administration in 2018 pulled the United States from it. That led Iran to abandon all limits put on its program and enrich uranium to up to 60% purity. When asked if the IAEA feared Iran may be developing a bomb, Grossi said he didn't "have any information that would sustain that." He added that inspectors' job was not to "judge intentions," but rather verify that what Iran says about its nuclear program was true. Trump's reelection last week raises questions about whether and how the incoming administration and Iran may engage. Grossi said he had worked with the first Trump administration, which he said engaged in "seamless, professional work," and looked forward to looking with Trump's second administration. "Circumstances have changed in that the problem has grown bigger than it was," said Grossi. "The problem of not finding a solution."

This report emphasizes that open and fair trade is essential for Asia and the Pacific’s transition to low-carbon economies and outlines the important role of Aid for Trade in this transition.

Working with a tiny cantilever, physicists managed to violate the second law of thermodynamics, using less energy than expected to change the cantilever’s motion

Retrotransposable elements (RTEs) are common mobile genetic elements comprising ~42% of the human genome. RTEs play critical roles in gene regulation and function, but how they are specifically involved in complex diseases is largely unknown. Here, we investigate the cellular heterogeneity of RTEs using 12 single-cell transcriptome profiles covering three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis. We identify cell type marker RTEs in neurons, astrocytes, oligodendrocytes, and oligodendrocyte precursor cells that are related to these diseases. The differential expression analysis reveals the landscape of dysregulated RTE expression, especially L1s, in excitatory neurons of multiple neurodegenerative diseases. Machine learning algorithms for predicting cell disease stage using a combination of RTE and gene expression features suggests dynamic regulation of RTEs in AD. Furthermore, we construct a single-cell atlas of retrotransposable elements in neurodegenerative disease (scARE) using these data sets and features. scARE has six feature analysis modules to explore RTE dynamics in a user-defined condition. To our knowledge, scARE represents the first systematic investigation of RTE dynamics at the single-cell level within the context of neurodegenerative diseases.

The poly(A) signal, together with auxiliary elements, directs cleavage of a pre-mRNA and thus determines the 3' end of the mature transcript. In many species, including humans, the poly(A) signal is an AAUAAA hexamer, but we recently found that the deeply branching eukaryote Giardia lamblia uses a distinct hexamer (AGURAA) and lacks any known auxiliary elements. Our discovery prompted us to explore the evolutionary dynamics of poly(A) signals and auxiliary elements in the eukaryotic kingdom. We use direct RNA sequencing to determine poly(A) signals for four protists within the Metamonada clade (which also contains G. lamblia) and two outgroup protists. These experiments reveal that the AAUAAA hexamer serves as the poly(A) signal in at least four different eukaryotic clades, indicating that it is likely the ancestral signal, whereas the unusual Giardia version is derived. We find that the use and relative strengths of auxiliary elements are also plastic; in fact, within Metamonada, species like G. lamblia make use of a previously unrecognized auxiliary element where nucleotides flanking the poly(A) signal itself specify genuine cleavage sites. Thus, despite the fundamental nature of pre-mRNA cleavage for the expression of all protein-coding genes, the motifs controlling this process are dynamic on evolutionary timescales, providing motivation for future biochemical and structural studies as well as new therapeutic angles to target eukaryotic pathogens.

BACKGROUND:PEEP is a cornerstone treatment for children with pediatric ARDS. Unfortunately, its titration is often performed solely by evaluating oxygen saturation, which can lead to inadequate PEEP level settings and consequent adverse effects. This study aimed to assess the impact of increasing PEEP on hemodynamics, respiratory system mechanics, and oxygenation in children with ARDS.METHODS:Children receiving mechanical ventilation and on pressure-controlled volume-guaranteed mode were prospectively assessed for inclusion. PEEP was sequentially changed to 5, 12, 10, 8 cm H2O, and again to 5 cm H2O. After 10 min at each PEEP level, hemodynamic, ventilatory, and oxygenation variables were collected.RESULTS:A total of 31 subjects were included, with median age and weight of 6 months and 6.3 kg, respectively. The main reasons for pediatric ICU admission were respiratory failure caused by acute viral bronchiolitis (45%) and community-acquired pneumonia (32%). Most subjects had mild or moderate ARDS (45% and 42%, respectively), with a median (interquartile range) oxygenation index of 8.4 (5.8–12.7). Oxygen saturation improved significantly when PEEP was increased. However, although no significant changes in blood pressure were observed, the median cardiac index at PEEP of 12 cm H2O was significantly lower than that observed at any other PEEP level (P = .001). Fourteen participants (45%) experienced a reduction in cardiac index of > 10% when PEEP was increased to 12 cm H2O. Also, the estimated oxygen delivery was significantly lower, at 12 cm H2O PEEP. Finally, respiratory system compliance significantly reduced when PEEP was increased. At a PEEP of 12 cm H2O, static compliance had a median reduction of 25% in relation to the initial assessment (PEEP of 5 cm H2O).CONCLUSIONS:Although it may improve arterial oxygen saturation, inappropriately high PEEP levels may reduce cardiac output, oxygen delivery, and respiratory system compliance in pediatric subjects with ARDS with low potential for lung recruitability.

Caenorhabditis elegans is an important model organism for human health and disease, with foundational contributions to the understanding of gene expression and tissue patterning in animals. An invaluable tool in modern gene expression research is the presence of a high-resolution ribosome structure, though no such structure exists for C. elegans. Here, we present a high-resolution single-particle cryogenic electron microscopy (cryo-EM) reconstruction and molecular model of a C. elegans ribosome, revealing a significantly streamlined animal ribosome. Many facets of ribosome structure are conserved in C. elegans, including overall ribosomal architecture and the mechanism of cycloheximide, whereas other facets, such as expansion segments and eL28, are rapidly evolving. We identify uL5 and uL23 as two instances of tissue-specific ribosomal protein paralog expression conserved in Caenorhabditis, suggesting that C. elegans ribosomes vary across tissues. The C. elegans ribosome structure will provide a basis for future structural, biochemical, and genetic studies of translation in this important animal system.

Hydrolases represent an essential class of enzymes indispensable for the metabolism of various clinically essential medications. Individuals exhibit marked differences in the expression and activation of hydrolases, resulting in significant variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs metabolized by these enzymes. The regulation of hydrolase expression and activity involves both genetic polymorphisms and nongenetic factors. This review examines the current understanding of genetic and nongenetic regulators of six clinically significant hydrolases, including carboxylesterase (CES)-1 CES2, arylacetamide deacetylase (AADAC), paraoxonase (PON)-1 PON3, and cathepsin A (CTSA). We explore genetic variants linked to the expression and activity of the hydrolases and their effects on the PK and PD of their substrate drugs. Regarding nongenetic regulators, we focus on the inhibitors and inducers of these enzymes. Additionally, we examine the developmental expression patterns and gender differences in the hydrolases when pertinent information was available. Many genetic and nongenetic regulators were found to be associated with the expression and activity of the hydrolases and PK and PD. However, hydrolases remain generally understudied compared with other drug-metabolizing enzymes, such as cytochrome P450s. The clinical significance of genetic and nongenetic regulators has not yet been firmly established for the majority of hydrolases. Comprehending the mechanisms that underpin the regulation of these enzymes holds the potential to refine therapeutic regimens, thereby enhancing the efficacy and safety of drugs metabolized by the hydrolases.

The antitumor effect of cardiotonic steroids (CTS) has stimulated the search for new methods to evaluate both kinetic and thermodynamic aspects of their binding to Na⁺/K⁺-ATPase (IUBMB Enzyme Nomenclature). We propose a real-time assay based on a chromogenic substrate for phosphatase activity (pNPPase activity), using only two concentrations with an inhibitory progression curve, to obtain the association rate (k_on), dissociation rate (k_off), and equilibrium (K_i) constants of CTS for the structure-kinetics relationship in drug screening. We show that changing conditions (from ATPase to pNPPase activity) resulted in an increase of K_i of the cardenolides digitoxigenin, essentially due to a reduction of k_on. In contrast, the K_i of the structurally related bufadienolide bufalin increased much less due to the reduction of its k_off partially compensating the decrease of its k_on. When evaluating the kinetics of 15 natural and semisynthetic CTS, we observed that both k_on and k_off correlated with K_i (Spearman test), suggesting that differences in potency depend on variations of both k_on and k_off. A rhamnose in C3 of the steroidal nucleus enhanced the inhibitory potency by a reduction of k_off rather than an increase of k_on. Raising the temperature did not alter the k_off of digitoxin, generating a H (k_off) of –10.4 ± 4.3 kJ/mol, suggesting a complex dissociation mechanism. Based on a simple and inexpensive methodology, we determined the values of k_on, k_off, and K_i of the CTS and provided original kinetics and thermodynamics differences between CTS that could help the design of new compounds.

BACKGROUND AND PURPOSE:

Prediction of aneurysm instability is crucial to guide treatment decisions and to select appropriate patients with unruptured intracranial aneurysms (IAs) for preventive treatment. High-resolution 4D MR flow imaging and 3D quantification of aneurysm morphology could offer insights and new imaging markers for aneurysm instability. In this cross-sectional study, we aim to identify 4D MR flow imaging markers for aneurysm instability by relating hemodynamics in the aneurysm sac to 3D morphologic proxy parameters for aneurysm instability.

Today, Boston Dynamics and the Toyota Research Institute (TRI) announced a new partnership “to accelerate the development of general-purpose humanoid robots utilizing TRI’s Large Behavior Models and Boston Dynamics’ Atlas robot.” Committing to working towards a general purpose robot may make this partnership sound like a every other commercial humanoid company right now, but that’s not at all that’s going on here: BD and TRI are talking about fundamental robotics research, focusing on hard problems, and (most importantly) sharing the results.

The broader context here is that Boston Dynamics has an exceptionally capable humanoid platform capable of advanced and occasionally painful-looking whole-body motion behaviors along with some relatively basic and brute force-y manipulation. Meanwhile, TRI has been working for quite a while on developing AI-based learning techniques to tackle a variety of complicated manipulation challenges. TRI is working toward what they’re calling large behavior models (LBMs), which you can think of as analogous to large language models (LLMs), except for robots doing useful stuff in the physical world. The appeal of this partnership is pretty clear: Boston Dynamics gets new useful capabilities for Atlas, while TRI gets Atlas to explore new useful capabilities on.

Here’s a bit more from the press release:

The project is designed to leverage the strengths and expertise of each partner equally. The physical capabilities of the new electric Atlas robot, coupled with the ability to programmatically command and teleoperate a broad range of whole-body bimanual manipulation behaviors, will allow research teams to deploy the robot across a range of tasks and collect data on its performance. This data will, in turn, be used to support the training of advanced LBMs, utilizing rigorous hardware and simulation evaluation to demonstrate that large, pre-trained models can enable the rapid acquisition of new robust, dexterous, whole-body skills.

The joint team will also conduct research to answer fundamental training questions for humanoid robots, the ability of research models to leverage whole-body sensing, and understanding human-robot interaction and safety/assurance cases to support these new capabilities.

For more details, we spoke with Scott Kuindersma (Senior Director of Robotics Research at Boston Dynamics) and Russ Tedrake (VP of Robotics Research at TRI).

How did this partnership happen?

Russ Tedrake: We have a ton of respect for the Boston Dynamics team and what they’ve done, not only in terms of the hardware, but also the controller on Atlas. They’ve been growing their machine learning effort as we’ve been working more and more on the machine learning side. On TRI’s side, we’re seeing the limits of what you can do in tabletop manipulation, and we want to explore beyond that.

Scott Kuindersma: The combination skills and tools that TRI brings the table with the existing platform capabilities we have at Boston Dynamics, in addition to the machine learning teams we’ve been building up for the last couple years, put us in a really great position to hit the ground running together and do some pretty amazing stuff with Atlas.

What will your approach be to communicating your work, especially in the context of all the craziness around humanoids right now?

Tedrake: There’s a ton of pressure right now to do something new and incredible every six months or so. In some ways, it’s healthy for the field to have that much energy and enthusiasm and ambition. But I also think that there are people in the field that are coming around to appreciate the slightly longer and deeper view of understanding what works and what doesn’t, so we do have to balance that.

The other thing that I’d say is that there’s so much hype out there. I am incredibly excited about the promise of all this new capability; I just want to make sure that as we’re pushing the science forward, we’re being also honest and transparent about how well it’s working.

Kuindersma: It’s not lost on either of our organizations that this is maybe one of the most exciting points in the history of robotics, but there’s still a tremendous amount of work to do.

What are some of the challenges that your partnership will be uniquely capable of solving?

Kuindersma: One of the things that we’re both really excited about is the scope of behaviors that are possible with humanoids—a humanoid robot is much more than a pair of grippers on a mobile base. I think the opportunity to explore the full behavioral capability space of humanoids is probably something that we’re uniquely positioned to do right now because of the historical work that we’ve done at Boston Dynamics. Atlas is a very physically capable robot—the most capable humanoid we’ve ever built. And the platform software that we have allows for things like data collection for whole body manipulation to be about as easy as it is anywhere in the world.

Tedrake: In my mind, we really have opened up a brand new science—there’s a new set of basic questions that need answering. Robotics has come into this era of big science where it takes a big team and a big budget and strong collaborators to basically build the massive data sets and train the models to be in a position to ask these fundamental questions.

Fundamental questions like what?

Tedrake: Nobody has the beginnings of an idea of what the right training mixture is for humanoids. Like, we want to do pre-training with language, that’s way better, but how early do we introduce vision? How early do we introduce actions? Nobody knows. What’s the right curriculum of tasks? Do we want some easy tasks where we get greater than zero performance right out of the box? Probably. Do we also want some really complicated tasks? Probably. We want to be just in the home? Just in the factory? What’s the right mixture? Do we want backflips? I don’t know. We have to figure it out.

There are more questions too, like whether we have enough data on the Internet to train robots, and how we could mix and transfer capabilities from Internet data sets into robotics. Is robot data fundamentally different than other data? Should we expect the same scaling laws? Should we expect the same long-term capabilities?

The other big one that you’ll hear the experts talk about is evaluation, which is a major bottleneck. If you look at some of these papers that show incredible results, the statistical strength of their results section is very weak and consequently we’re making a lot of claims about things that we don’t really have a lot of basis for. It will take a lot of engineering work to carefully build up empirical strength in our results. I think evaluation doesn’t get enough attention.

What has changed in robotics research in the last year or so that you think has enabled the kind of progress that you’re hoping to achieve?

Kuindersma: From my perspective, there are two high-level things that have changed how I’ve thought about work in this space. One is the convergence of the field around repeatable processes for training manipulation skills through demonstrations. The pioneering work of diffusion policy (which TRI was a big part of) is a really powerful thing—it takes the process of generating manipulation skills that previously were basically unfathomable, and turned it into something where you just collect a bunch of data, you train it on an architecture that’s more or less stable at this point, and you get a result.

The second thing is everything that’s happened in robotics-adjacent areas of AI showing that data scale and diversity are really the keys to generalizable behavior. We expect that to also be true for robotics. And so taking these two things together, it makes the path really clear, but I still think there are a ton of open research challenges and questions that we need to answer.

Do you think that simulation is an effective way of scaling data for robotics?

Tedrake: I think generally people underestimate simulation. The work we’ve been doing has made me very optimistic about the capabilities of simulation as long as you use it wisely. Focusing on a specific robot doing a specific task is asking the wrong question; you need to get the distribution of tasks and performance in simulation to be predictive of the distribution of tasks and performance in the real world. There are some things that are still hard to simulate well, but even when it comes to frictional contact and stuff like that, I think we’re getting pretty good at this point.

Is there a commercial future for this partnership that you’re able to talk about?

Kuindersma: For Boston Dynamics, clearly we think there’s long-term commercial value in this work, and that’s one of the main reasons why we want to invest in it. But the purpose of this collaboration is really about fundamental research—making sure that we do the work, advance the science, and do it in a rigorous enough way so that we actually understand and trust the results and we can communicate that out to the world. So yes, we see tremendous value in this commercially. Yes, we are commercializing Atlas, but this project is really about fundamental research.

What happens next?

Tedrake: There are questions at the intersection of things that BD has done and things that TRI has done that we need to do together to start, and that’ll get things going. And then we have big ambitions—getting a generalist capability that we’re calling LBM (large behavior models) running on Atlas is the goal. In the first year we’re trying to focus on these fundamental questions, push boundaries, and write and publish papers.

I want people to be excited about watching for our results, and I want people to trust our results when they see them. For me, that’s the most important message for the robotics community: Through this partnership we’re trying to take a longer view that balances our extreme optimism with being critical in our approach.

Boston Dynamics is the master of dropping amazing robot videos with no warning, and last week, we got a surprise look at the new electric Atlas going “hands on” with a practical factory task.

This video is notable because it’s the first real look we’ve had at the new Atlas doing something useful—or doing anything at all, really, as the introductory video from back in April (the first time we saw the robot) was less than a minute long. And the amount of progress that Boston Dynamics has made is immediately obvious, with the video showing a blend of autonomous perception, full body motion, and manipulation in a practical task.

We sent over some quick questions as soon as we saw the video, and we’ve got some extra detail from Scott Kuindersma, senior director of Robotics Research at Boston Dynamics.

If you haven’t seen this video yet, what kind of robotics person are you, and also here you go:

Atlas is autonomously moving engine covers between supplier containers and a mobile sequencing dolly. The robot receives as input a list of bin locations to move parts between.

Atlas uses a machine learning (ML) vision model to detect and localize the environment fixtures and individual bins [0:36]. The robot uses a specialized grasping policy and continuously estimates the state of manipulated objects to achieve the task.

There are no prescribed or teleoperated movements; all motions are generated autonomously online. The robot is able to detect and react to changes in the environment (e.g., moving fixtures) and action failures (e.g., failure to insert the cover, tripping, environment collisions [1:24]) using a combination of vision, force, and proprioceptive sensors.

Eagle-eyed viewers will have noticed that this task is very similar to what we saw hydraulic Atlas (Atlas classic?) working on just before it retired. We probably don’t need to read too much into the differences between how each robot performs that task, but it’s an interesting comparison to make.

For more details, here’s our Q&A with Kuindersma:

How many takes did this take?

Kuindersma: We ran this sequence a couple times that day, but typically we’re always filming as we continue developing and testing Atlas. Today we’re able to run that engine cover demo with high reliability, and we’re working to expand the scope and duration of tasks like these.

Is this a task that humans currently do?

Kuindersma: Yes.

What kind of world knowledge does Atlas have while doing this task?

Kuindersma: The robot has access to a CAD model of the engine cover that is used for object pose prediction from RGB images. Fixtures are represented more abstractly using a learned keypoint prediction model. The robot builds a map of the workcell at startup which is updated on the fly when changes are detected (e.g., moving fixture).

Does Atlas’s torso have a front or back in a meaningful way when it comes to how it operates?

Kuindersma: Its head/torso/pelvis/legs do have “forward” and “backward” directions, but the robot is able to rotate all of these relative to one another. The robot always knows which way is which, but sometimes the humans watching lose track.

Are the head and torso capable of unlimited rotation?

Kuindersma: Yes, many of Atlas’s joints are continuous.

How long did it take you folks to get used to the way Atlas moves?

Kuindersma: Atlas’s motions still surprise and delight the team.

OSHA recommends against squatting because it can lead to workplace injuries. How does Atlas feel about that?

Kuindersma: As might be evident by some of Atlas’s other motions, the kinds of behaviors that might be injurious for humans might be perfectly fine for robots.

Can you describe exactly what process Atlas goes through at 1:22?

Kuindersma: The engine cover gets caught on the fabric bins and triggers a learned failure detector on the robot. Right now this transitions into a general-purpose recovery controller, which results in a somewhat jarring motion (we will improve this). After recovery, the robot retries the insertion using visual feedback to estimate the state of both the part and fixture.

Were there other costume options you considered before going with the hot dog?

Kuindersma: Yes, but marketing wants to save them for next year.

How many important sensors does the hot dog costume occlude?

Kuindersma: None. The robot is using cameras in the head, proprioceptive sensors, IMU, and force sensors in the wrists and feet. We did have to cut the costume at the top so the head could still spin around.

Why are pickles always causing problems?

Kuindersma: Because pickles are pesky, polarizing pests.

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