Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

Clinical trial of laronidase in Hurler syndrome after hematopoietic cell transplantation.

BackgroundMucopolysaccharidosis I (MPS IH) is a lysosomal storage disease treated with hematopoietic cell transplantation (HCT) because it stabilizes cognitive deterioration, but is insufficient to alleviate all somatic manifestations. Intravenous laronidase improves somatic burden in attenuated MPS I. It is unknown whether laronidase can improve somatic disease following HCT in MPS IH. The objective of this study was to evaluate the effects of laronidase on somatic outcomes of patients with MPS IH previously treated with HCT.MethodsThis 2-year open-label pilot study of laronidase included ten patients (age 5-13 years) who were at least 2 years post-HCT and donor engrafted. Outcomes were assessed semi-annually and compared to historic controls.ResultsThe two youngest participants had a statistically significant improvement in growth compared to controls. Development of persistent high-titer anti-drug antibodies (ADA) was associated with poorer 6-min walk test (6MWT) performance; when patients with high ADA titers were excluded, there was a significant improvement in the 6MWT in the remaining seven patients.ConclusionsLaronidase seemed to improve growth in participants <8 years old, and 6MWT performance in participants without ADA. Given the small number of patients treated in this pilot study, additional study is needed before definitive conclusions can be made

Transmembrane but not soluble helices fold inside the ribosome tunnel

Integral membrane proteins are assembled into the ER membrane via a continuous ribosome-translocon channel. The hydrophobicity and thickness of the core of the membrane bilayer leads to the expectation that transmembrane (TM) segments minimize the cost of harbouring polar polypeptide backbones by adopting a regular pattern of hydrogen bonds to form α-helices before integration. Co-translational folding of nascent chains into an α-helical conformation in the ribosomal tunnel has been demonstrated previously, but the features governing this folding are not well understood. In particular, little is known about what features influence the propensity to acquire α-helical structure in the ribosome. Using in vitro translation of truncated nascent chains trapped within the ribosome tunnel and molecular dynamics simulations, we show that folding in the ribosome is attained for TM helices but not for soluble helices, presumably facilitating SRP (signal recognition particle) recognition and/or a favourable conformation for membrane integration upon translocon entry

Renal clearable catalytic gold nanoclusters for in vivo disease monitoring

Ultra-small gold nanoclusters (AuNCs) have emerged as agile probes for in vivo imaging, as they exhibit exceptional tumour accumulation and efficient renal clearance properties. However, their intrinsic catalytic activity, which can enable increased detection sensitivity, has yet to be explored for in vivo sensing. By exploiting the peroxidase-mimicking activity of AuNCs and the precise nanometer size filtration of the kidney, we designed multifunctional protease nanosensors that respond to disease microenvironments to produce a direct colorimetric urinary readout of disease state in less than 1 h. We monitored the catalytic activity of AuNCs in collected urine of a mouse model of colorectal cancer where tumour-bearing mice showed a 13-fold increase in colorimetric signal compared to healthy mice. Nanosensors were eliminated completely through hepatic and renal excretion within 4 weeks after injection with no evidence of toxicity. We envision that this modular approach will enable rapid detection of a diverse range of diseases by exploiting their specific enzymatic signatures

Photovoltaic restoration of sight with high visual acuity

Patients with retinal degeneration lose sight due to the gradual demise of photoreceptors. Electrical stimulation of surviving retinal neurons provides an alternative route for the delivery of visual information. We demonstrate that subretinal implants with 70-μm-wide photovoltaic pixels provide highly localized stimulation of retinal neurons in rats. The electrical receptive fields recorded in retinal ganglion cells were similar in size to the natural visual receptive fields. Similarly to normal vision, the retinal response to prosthetic stimulation exhibited flicker fusion at high frequencies, adaptation to static images and nonlinear spatial summation. In rats with retinal degeneration, these photovoltaic arrays elicited retinal responses with a spatial resolution of 64 ± 11 μm, corresponding to half of the normal visual acuity in healthy rats. The ease of implantation of these wireless and modular arrays, combined with their high resolution, opens the door to the functional restoration of sight in patients blinded by retinal degeneration

Structure and Dynamics of the G121V Dihydrofolate Reductase Mutant: Lessons from a Transition-State Inhibitor Complex

It is well known that enzyme flexibility is critical for function. This is due to the observation that the rates of intramolecular enzyme motions are often matched to the rates of intermolecular events such as substrate binding and product release. Beyond this role in progression through the reaction cycle, it has been suggested that enzyme dynamics may also promote the chemical step itself. Dihydrofolate reductase (DHFR) is a model enzyme for which dynamics have been proposed to aid in both substrate flux and catalysis. The G121V mutant of DHFR is a well studied form that exhibits a severe reduction in the rate of hydride transfer yet there remains dispute as to whether this defect is caused by altered structure, dynamics, or both. Here we address this by presenting an NMR study of the G121V mutant bound to reduced cofactor and the transition state inhibitor, methotrexate. NMR chemical shift markers demonstrate that this form predominantly adopts the closed conformation thereby allowing us to provide the first glimpse into the dynamics of a catalytically relevant complex. Based on 15N and 2H NMR spin relaxation, we find that the mutant complex has modest changes in ps-ns flexibility with most affected residues residing in the distal adenosine binding domain rather than the active site. Thus, aberrant ps-ns dynamics are likely not the main contributor to the decreased catalytic rate. The most dramatic effect of the mutation involves changes in µs-ms dynamics of the F-G and Met20 loops. Whereas loop motion is quenched in the wild type transition state inhibitor complex, the F-G and Met20 loops undergo excursions from the closed conformation in the mutant complex. These excursions serve to decrease the population of conformers having the correct active site configuration, thus providing an explanation for the G121V catalytic defect

Efficacy of the mRNA-1273 SARS-CoV-2 vaccine at completion of blinded phase

BACKGROUND At interim analysis in a phase 3, observer-blinded, placebo-controlled clinical trial, the mRNA-1273 vaccine showed 94.1% efficacy in preventing coronavirus disease 2019 (Covid-19). After emergency use of the vaccine was authorized, the protocol was amended to include an open-label phase. Final analyses of efficacy and safety data from the blinded phase of the trial are reported. METHODS We enrolled volunteers who were at high risk for Covid-19 or its complications; participants were randomly assigned in a 1:1 ratio to receive two intramuscular injections of mRNA-1273 (100 μg) or placebo, 28 days apart, at 99 centers across the United States. The primary end point was prevention of Covid-19 illness with onset at least 14 days after the second injection in participants who had not previously been infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The data cutoff date was March 26, 2021. RESULTS The trial enrolled 30,415 participants; 15,209 were assigned to receive the mRNA-1273 vaccine, and 15,206 to receive placebo. More than 96% of participants received both injections, 2.3% had evidence of SARS-CoV-2 infection at baseline, and the median follow-up was 5.3 months in the blinded phase. Vaccine efficacy in preventing Covid-19 illness was 93.2% (95% confidence interval [CI], 91.0 to 94.8), with 55 confirmed cases in the mRNA-1273 group (9.6 per 1000 person-years; 95% CI, 7.2 to 12.5) and 744 in the placebo group (136.6 per 1000 person-years; 95% CI, 127.0 to 146.8). The efficacy in preventing severe disease was 98.2% (95% CI, 92.8 to 99.6), with 2 cases in the mRNA-1273 group and 106 in the placebo group, and the efficacy in preventing asymptomatic infection starting 14 days after the second injection was 63.0% (95% CI, 56.6 to 68.5), with 214 cases in the mRNA-1273 group and 498 in the placebo group. Vaccine efficacy was consistent across ethnic and racial groups, age groups, and participants with coexisting conditions. No safety concerns were identified. CONCLUSIONS The mRNA-1273 vaccine continued to be efficacious in preventing Covid-19 illness and severe disease at more than 5 months, with an acceptable safety profile, and protection against asymptomatic infection was observed

From social ties to embedded competencies: The case of business groups

Our current views of economic competition are still rooted in the imagery of the isolated firm that transacts with its buyers, suppliers, and competitors via largely anonymous factor and product markets. Yet this view is fundamentally at odds with the growing importance of business groups in the global economy. We thus need a reconceptualized version of our idea of economic competition, which is capable of explaining competitive advantage at the group-versus-group rather than firm-versus-firm level of analysis. In the present paper we build on insights derived from organizational sociology and organizational economics to develop a business group-level theory of competition and competitive advantage based on embedded competencies

Understanding the Interplay Among Regulatory Self-Efficacy, Moral Disengagement, and Academic Cheating Behaviour During Vocational Education: A Three-Wave Study

The literature has suggested that to understand the diffusion of unethical conduct in the workplace, it is important to investigate the underlying processes sustaining engagement in misbehaviour and to study what occurs during vocational education. Drawing on social-cognitive theory, in this study, we longitudinally examined the role of two opposite dimensions of the self-regulatory moral system, regulatory self-efficacy and moral disengagement, in influencing academic cheating behaviour. In addition, in line with the theories highlighting the bidirectional relationship between cognitive processes and behaviour, we aimed to also examine the reciprocal influence of behaviour on these dimensions over time. Overall, no previous studies have examined the longitudinal interplay between these variables. The sample included 866 (62.8% female) nursing students who were assessed three times annually from the beginning of their vocational education. The findings from a cross-lagged model confirmed that regulatory self-efficacy and moral disengagement have opposite influences on cheating behaviour, that regulatory self-efficacy negatively influences not only the engagement in misconduct but also the justification mechanisms that allow the divorce between moral standards and action, and that moral disengagement and cheating behaviour reciprocally support each other over time. Specifically, not only did moral disengagement influence cheating behaviour even when controlling for its prior levels, but also cheating behaviour affected moral disengagement one year later, controlling for its prior levels. These findings suggest that recourse to wrongdoing could gradually lead to further normalising this kind of behaviour and morally desensitising individuals to misconduct

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues