Missing Step Research Articles

Identification of a staphylococcal dipeptidase involved in the production of human body odor

The production of human body odour is the result of the action of commensal skin bacteria, including Staphylococcus hominis, acting to biotransform odourless apocrine gland secretions into volatile chemicals like thioalcohols such as 3-methyl-3-sulphanylhexan-1-ol (3M3SH). As the secreted odour precursor Cys-Gly-3M3SH contains a dipeptide, yet the final enzyme in the biotransformation pathway only functions on Cys-3M3SH, we sought to identify the remaining step in this human-adapted biochemical pathway using a novel coupled enzyme assay. Purification of this activity from S. hominis extracts led to the identification of the M20A-family PepV peptidase (ShPepV) as the primary Cys-Gly-3M3SH dipeptidase. To establish whether this was a primary substrate for PepV, the recombinant protein was purified and demonstrated broad activity against diverse dipeptides. The binding site for Cys-Gly-3M3SH was predicted using modelling, which suggested mutations that might accommodate this ligand more favourably. Indeed, a D437A resulted in an almost 6-fold increase in the kcat/KM, while other introduced mutations reduced or abolished function. Together these data identify an enzyme capable of catalysing the missing step in an ancient human-specific biochemical transformation and suggest that the production of 3M3SH neither uses a dedicated transporter nor peptidase for its breakdown, with only the final cleavage step, catalysed by PatB C-S β-lyase, being a unique enzyme.

Game-based training to promote handwashing, handrub and gloving for hospital pharmacy operators

ObjectivesAdherence to handwashing, handrub and gloving procedures is mandatory for safe, aseptic drug compounding in hospital pharmacies. This study measured participants’ satisfaction and effectiveness of a game-based training tool (Handtastic...

Machine learning for gap‐filling in greenhouse gas emissions databases

AbstractGreenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap‐filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non‐reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritization to accelerate the improvement of datasets. Graph‐based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke‐scot/ML‐for‐GHG‐database‐completion and https://doi.org/10.5281/zenodo.10463104. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Modeling the Emergence of Circuit Organization and Function during Development.

Developing neural circuits show unique patterns of spontaneous activity and structured network connectivity shaped by diverse activity-dependent plasticity mechanisms. Based on extensive experimental work characterizing patterns of spontaneous activity in different brain regions over development, theoretical and computational models have played an important role in delineating the generation and function of individual features of spontaneous activity and their role in the plasticity-driven formation of circuit connectivity. Here, we review recent modeling efforts that explore how the developing cortex and hippocampus generate spontaneous activity, focusing on specific connectivity profiles and the gradual strengthening of inhibition as the key drivers behind the observed developmental changes in spontaneous activity. We then discuss computational models that mechanistically explore how different plasticity mechanisms use this spontaneous activity to instruct the formation and refinement of circuit connectivity, from the formation of single neuron receptive fields to sensory feature maps and recurrent architectures. We end by highlighting several open challenges regarding the functional implications of the discussed circuit changes, wherein models could provide the missing step linking immature developmental and mature adult information processing capabilities.

Vertical and Horizontal Combined Algorithm for Missing Data Imputation in Bridge Health Monitoring System

Structural health monitoring (SHM) provides strong support for bridge safety evaluation by tracking the real-time structural performance. An accurate and efficient imputation method for missing data in the SHM system is of vital importance for bridge management. In this paper, an innovative vertical–horizontal combined (VHC) algorithm is proposed to estimate the missing SHM data by a more comprehensive consideration of different types of information reflected in different time dimensions of the monitoring data. In the vertical time dimension, the variation trends and changing ranges for the missing data are analyzed by the relational analysis from the global view of data. In the horizontal time dimension, the exact value of the missing data is estimated based on the adjacent data inside the missing data set from the local view of data. The proposed VHC algorithm is validated and compared with the current methods in the data imputation for the missing deflection values in the SHM system of an example tie-arch bridge. The results show that the proposed algorithm demonstrates the best imputation performance among all methods for both missing types with the highest imputation accuracy/similarity and satisfying the 95% confidence bound. The application of reward coefficient significantly increases the imputation accuracy for the continuous missing situation and the improvement grows with the missing step number. Loss ratio and sample size also have significant influences on the imputation performance with the proposed method.

The novel amylase function of the carboxyl terminal domain of Amy63

α-amylase plays a crucial role in regulating metabolism and health by hydrolyzing of starch and glycogen. Despite comprehensive studies of this classic enzyme spanning over a century, the function of its carboxyl terminal domain (CTD) with a conserved eight β-strands is still not fully understood. Amy63, identified from a marine bacterium, was reported as a novel multifunctional enzyme with amylase, agarase and carrageenase activities. In this study, the crystal structure of Amy63 was determined at 1.8 Å resolution, revealing high conservation with some other amylases. Interestingly, the independent amylase activity of the carboxyl terminal domain of Amy63 (Amy63_CTD) was newly discovered by the plate-based assay and mass spectrometry. To date, the Amy63_CTD alone could be regarded as the smallest amylase subunit. Moreover, the significant amylase activity of Amy63_CTD was measured over a wide range of temperature and pH, with optimal activity at 60 °C and pH 7.5. The Small-angle X-ray scattering (SAXS) data showed that the high-order oligomeric assembly gradually formed with increasing concentration of Amy63_CTD, implying the novel catalytic mechanism as revealed by the assembly structure. Therefore, the discovery of the novel independent amylase activity of Amy63_CTD suggests a possible missing step or a new perspective in the complex catalytic process of Amy63 and other related α-amylases. This work may shed light on the design of nanozymes to process marine polysaccharides efficiently.

Continuous-mode quantum key distribution with digital signal processing

Continuous-variable quantum key distribution (CVQKD) offers the specific advantage of sharing keys remotely by the use of standard telecom components, thereby promoting cost-effective and high-performance metropolitan applications. Nevertheless, the introduction of high-rate spectrum broadening has pushed CVQKD from a single-mode to a continuous-mode region, resulting in the adoption of modern digital signal processing (DSP) technologies to recover quadrature information from continuous-mode quantum states. However, the security proof of DSP involving multi-point processing is a missing step. Here, we propose a generalized method of analyzing continuous-mode state processing by linear DSP via temporal modes theory. The construction of temporal modes is key in reducing the security proof to single-mode scenarios. The proposed practicality oriented security analysis method paves the way for building classical compatible digital CVQKD.

Toward low-loss mid-infrared Ga2O3–BaO–GeO2 optical fibers

The development of efficient and compact photonic systems in support of mid-infrared integrated optics is currently facing several challenges. To date, most mid-infrared glass-based devices are employing fluoride or chalcogenide glasses (FCGs). Although the commercialization of FCGs-based optical devices has rapidly grown during the last decade, their development is rather cumbersome due to either poor crystallization and hygroscopicity resilience or poor mechanical-thermal properties of the FCGs. To overcome these issues, the parallel development of heavy-metal oxide optical fiber from the barium-germanium-gallium oxide vitreous system (BGG) has revealed a promising alternative. However, over 30 years of fiber fabrication optimization, the final missing step of drawing BGG fibers with acceptable losses for meters-long active and passive optical devices had not yet been reached. In this article, we first identify the three most important factors that prevent the fabrication of low-loss BGG fibers i.e., surface quality, volumic striae and glass thermal-darkening. Each of the three factors is then addressed in setting up a protocol enabling the fabrication of low-loss optical fibers from gallium-rich BGG glass compositions. Accordingly, to the best of our knowledge, we report the lowest losses ever measured in a BGG glass fiber i.e., down to 200 dB km−1 at 1350 nm.

De-adopting low-value care: The missing step in evidence-based practice?

BackgroundLow-value care provides little or no benefit to pediatric patients, has the potential to cause harm, waste healthcare resources, and increase healthcare costs. Nursing has a responsibility to identify and de-adopt low-value practices to help promote quality care. Purpose1) Describe the process of identifying and de-adopting low-value clinical practices guided by a conceptual model using a case study approach.2) Identify facilitators and barriers to de-adoption practices, including levels of stakeholder engagement, organizational structures, and the quality of available scientific and non-scientific evidence. MethodologyAn evidence-based practice (EBP) project investigating the efficacy of antihistamines in decreasing infusion reactions to infliximab identified a low-value practice within a pediatric infusion center. The Synthesis Model for the Process of De-adoption was then applied to guide the de-adoption of this low-value practice. Case study analysis highlighted facilitators and barriers to de-adoption efforts. ConclusionsThe process for de-adopting care is an essential component of EBP and, as such, should be explicated through robust, standardized EBP processes and education. Practice implicationsNurses are best positioned to identify, assess and prioritize low-value practices and facilitate the de-adoption of low-value practice that impact pediatric patients and families.Models to support de-adoption and a focus on site-specific practices including a prepared nursing workforce, continuous evaluation of care processes and the use of resources to assess for contextual determinants facilitates success and sustainability of this essential EBP approach.

Challenges, Barriers, and the Underrepresentation of Black Women in Sustainable Global World Environment

Women make up at least 50.8% of the United States population, and 46.8% are in the professional workforce per Census Quick Facts from 2016. United States Department of Labor, n.d.). Despite making up half of the United States population, women only represent 26% of managing roles in the workforce. In the 2019 study, “Women in the Workplace 2019”, McKinsey & Company found women to still lag in corporate America in areas of salary gaps, promotions due to the broken rung, glass ceilings, lack of training and development, among other gender and racial barriers. Workplace Fairness”, a broken rung is a missing step in the “corporate ladder”, which prevents women in entry-level roles from being promoted into management. The broken rung is the more significant barrier for Black women navigating the workplace. To successfully navigate the workplace and ascend into management roles, Black women saw the need to use perseverance strategies due to underrepresentation and the influence of race and traditional privileged gendered roles. The study’s outcome addresses the challenges, barriers, and perseverance strategies Black women used to ascend into management roles. Mentorship and sponsorship are critical for helping Black women to advance within the workplace. The research study may be significant to Black women managers and future leaders. Without the critical influence of a mentor or sponsor, the Black woman will remain underrepresented in management positions. Further exploration of specific perseverance strategies and how they may have been demonstrated in their collegiate programs to prepare Black women for their professional careers.

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification

Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones. In particular, investigations on the predictive uncertainty of deep-learning models describing non-linear dynamical systems are very limited to date. This paper is aimed at filling this gap and presents preliminary results on uncertainty quantification for system identification with neural state-space models. We frame the learning problem in a Bayesian probabilistic setting and obtain posterior distributions for the neural network's weights and outputs through approximate inference techniques. Based on the posterior, we construct credible intervals on the outputs and define a surprise index which can effectively diagnose usage of the model in a potentially dangerous out-of-distribution regime, where predictions cannot be trusted.

Catalytic Synthesis of Polyribonucleic Acid on Prebiotic Rock Glasses.

Reported here are experiments that show that ribonucleoside triphosphates are converted to polyribonucleic acid when incubated with rock glasses similar to those likely present 4.3–4.4 billion years ago on the Hadean Earth surface, where they were formed by impacts and volcanism. This polyribonucleic acid averages 100–300 nucleotides in length, with a substantial fraction of 3′,-5′-dinucleotide linkages. Chemical analyses, including classical methods that were used to prove the structure of natural RNA, establish a polyribonucleic acid structure for these products. The polyribonucleic acid accumulated and was stable for months, with a synthesis rate of 2 × 10−3 pmoles of triphosphate polymerized each hour per gram of glass (25°C, pH 7.5). These results suggest that polyribonucleotides were available to Hadean environments if triphosphates were. As many proposals are emerging describing how triphosphates might have been made on the Hadean Earth, the process observed here offers an important missing step in models for the prebiotic synthesis of RNA.

The fate of molybdenum in the residues of a Chilean copper ore processing plant

Mining residues have become an important subject for secondary mining and re-processing. They are an inevitable consequence of mining activity with the amount of residues steadily increasing. These residues are heterogeneous bodies, affected by different processes during and after deposition, which might even produce an enrichment of certain valuable elements within parts of the residues. Since geochemistry and mineralogy of the ore can differ heavily due to processing, deposition and weathering within the heaps, the formerly applied processes might be less suitable for re-processing, therefore, these processes should be studied carefully for an effective re-processing. This study examines the fate of molybdenum in the processing of copper ore in a Chilean processing plant. Molybdenum shares the path of Cu from grinding and flotation into the sulfide concentrate. During smelting and converting, the sulfide concentrate is separated gravitationally from the slag. Through the subsequent slag cleaning process, the slag is crushed and submitted to flotation again, but this time the Mo in its oxidized state reports to the tailings and, hence, is deposited on the tailings heap. Drill cores of the heap have been analyzed by X-ray fluorescence spectroscopy revealing a layer of significant Mo enrichment with up to 0.3 wt.-% Mo. From this layer, the minerals within the slag particles were analyzed by more than one thousand electron microprobe analyses. The results show that Molybdenum is enriched mostly in the glassy matrix (median of 0.18 wt.-%), while the spinel phase, which generally incorporates most of the Mo according to literature, contains a smaller fraction of the Mo (median of 0.09 wt.-% Mo). Additionally, within the slag, the glass amount is up to a factor five higher than the magnetite, therefore, the glass poses as the main reservoir for Mo in the heap. Considering the general process, the missing step of Mo-extraction from the sulfide concentrate combined with the slag cleaning process for Cu extraction is responsible for the enrichment of Mo in the slag rich part of the investigated heap.

Reply to “Comment on ‘Proper and improper chiral magnetic interactions’ ”

In our previous Letter [Phys. Rev. B 103, L140408 (2021)], we presented a discussion of the fundamental physical properties of the interactions parameterizing atomistic spin models in connection to first-principles approaches that enable their calculation for a given material. This explained how some of those approaches can apparently lead to magnetic interactions that do not comply with the expected physical properties, such as Dzyaloshinskii-Moriya interactions which are non-chiral and independent of the spin-orbit interaction, and which we consequently termed `improper'. In the preceding Comment [Phys. Rev. B 105, 026401], the authors present arguments based on the distinction between global and local approaches to the mapping of the magnetic energy using first-principles calculations to support their proposed non-chiral Dzyaloshinskii-Moriya interactions and their dismissal of our distinction between `proper' and `improper' magnetic interactions. In this Reply, we identify the missing step in the local approach to the mapping and explain how ignoring this step leads to the identification of magnetic interactions which do not comply with established physical principles and that we have previously termed `improper'.

Peeling in electroadhesion soft grippers

Electroadhesion endows robots with super-human abilities: mechanical geckoes that climb vertical walls and soft grippers that grasp the most delicate objects. Based on electrostatics, the adhesion forces are turned on and off by an electrical signal, promising extremely fast operation, from silent fully solid-state devices. Practical applications of electroadhesion have however been limited to date by two main challenges: (1) the adhesion forces can vary over 1000x by simply changing the angle between the electroadhesive tape and the object, (2) release is often slow due to residual adhesion when voltage is removed.This paper describes a solution to both these issues by understanding and leveraging peeling in electroadhesion. We present simple models for peeling of electroadhesive tapes, predicting a change in peeling force from < 1 mN to over 1 N by changing the angle between the tape and the object from 90° to 0°. The models are in excellent agreement with our peeling experiments with 30 mm long, 20 mm wide, 300μm thick electroadhesion tapes made of silicone rubber with carbon electrodes.We demonstrate an electroadhesion soft gripper that uses motorized fingers to control the peeling angle, as a practical application of our peeling models. By moving the fingers to ensure a low peeling angle (0°) when grasping, the same gripper can successfully pick up from a 10 g cherry tomato (2.5 cm wide) to a 600 g Mango (9 cm wide). By then setting a high peeling angle (> 30°), the gripper reliably and rapidly (< 300 ms) releases those objects, despite residual adhesion.Electroadhesion soft grippers have many advantages, including grasping without squeezing, silent operation, low power consumption (< 1 W) and low weight (1 g per soft finger). Understanding and modelling contact mechanics in electroadhesion devices was an essential missing step for practical applications of electroadhesion in robots and grippers. This paper sheds light on how peeling influences electroadhesion and provides practical tools to design and operate electroadhesion systems.

Micro Activities Recognition in Uncontrolled Environments

Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.

A QM/MM Evaluation of the Missing Step in the Reduction Mechanism of HMG-CoA by Human HMG-CoA Reductase

Statins are important drugs in the regulation of cholesterol levels in the human body that have as a primary target the enzyme β-hydroxy-β-methylglutaryl-CoA reductase (HMGR). This enzyme plays a crucial role in the mevalonate pathway, catalyzing the four-electron reduction of HMG-CoA to mevalonate. A second reduction step of this reaction mechanism has been the subject of much speculation in the literature, with different conflicting theories persisting to the present day. In this study, the different mechanistic hypotheses were evaluated with atomic-level detail through a combination of molecular dynamics simulations (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. The obtained Gibbs free activation and Gibbs free reaction energy (15 kcal mol−1 and −40 kcal mol−1) show that this hydride step takes place with the involvement of a cationic His405 and Lys639, and a neutral Glu98, while Asp715 remains in an anionic state. The results provide an atomic-level portrait of this step, clearly demonstrating the nature and protonation state of the amino acid residues involved, the energetics associated, and the structure and charge of the key participating atoms in the several intermediate states, finally elucidating this missing step.

Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs*

AbstractWhenever treatment effects are heterogeneous, and there is sorting into treatment based on the gain, monotonicity is a condition that both instrumental variable (IV) and fuzzy regression discontinuity (RD) designs must satisfy for their estimate to be interpretable as a local average treatment effect. However, applied economic work often omits a discussion of this important assumption. A possible explanation for this missing step is the lack of a clear framework to think about monotonicity in practice. In this paper, we use an extended Roy model to provide insights into the interpretation of IV and fuzzy RD estimates under various degrees of treatment effect heterogeneity, sorting on gain and violation of monotonicity. We then extend our analysis to two applied settings to illustrate how monotonicity can be investigated using a mix of economic insights, data patterns and formal tests. For both settings, we use a Roy model to interpret the estimate even in the absence of monotonicity. We conclude with a set of recommendations for the applied researcher.

Rethinking the Design of Ionic Conductors Using Meyer–Neldel–Conductivity Plot

AbstractFrom the discovery of the first fast ionic conductor silver iodide in the early 20th century to the recent discovery of lithium fast ionic conductors with ionic conductivities surpassing those of liquid electrolytes, high ionic conductivity σ has often been associated with low activation energy Ea following the Arrhenius equation. However, the Meyer–Neldel rule (MNR) indicates that the Ea and prefactor σ0 are correlated, suggesting the relation between the Ea and σ is, in fact, complex. In this perspective, the use of the Meyer–Neldel–conductivity plot and a critical descriptor, Meyer–Neldel energy Δ0, to guide the search for fast ionic conductors is proposed. Reported lithium, sodium, and magnesium ionic conductors are categorized into three types, depending on the relative magnitude between the Δ0 and thermal energy (kBT) at the application temperature. The process by which σ can be optimized by tuning Ea for these types of ionic conductors is elaborated. This principle can be widely applied to all ionic conductors that obey the MNR at any application temperature. Furthermore, a pressure‐tuning approach to measure the Δ0 rapidly is developed. These findings establish a previously missing step for designing new ionic conductors with improved ionic conductivity.

Kinetic importance of the missing step in dithiobenzoate-mediated RAFT polymerizations of acrylates

The effect of retardation exhibited in dithiobenzoate-mediated polymerizations is perhaps the most controversial kinetic phenomenon discussed in polymer chemistry in the last 20 years. However, the Missing Step reaction (MSR), which involves the termination between a propagating radical and a 3-arm star product, has emerged as a hypothesis to explain the full kinetic picture for acrylate polymerizations. Here, a kinetic model, the most detailed of its kind, is developed to describe and study the MSR. The model is based on the method of moments and includes RAFT pre-equilibrium and equilibrium, backbiting, cross-termination, MSR and chain-length dependent radical termination. After validation through comparison with experimental data available in the literature, the model is used to demonstrate that the kinetic behavior of the propagating and intermediates radicals can be described by the quasi-steady state assumption and that backbiting does not significantly influence predicted conversion profiles and product molecular weights. It is also shown that the induction period observed experimentally can be represented by either slow initiation/reinitiation of primary radicals or by assuming that radical intermediates formed by addition of primary radicals to the RAFT agent have increased stability; however, the associated rate constants for these reactions had to be adjusted from literature values. A comparison between the MSR and Intermediate Radical Termination (IRT) representations RAFT-mediated acrylate polymerization identifies chain end-group functionality (EGF) as a means to further discriminate between these theories.