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Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice.

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While experimental methods combined with integrative structural biology has been the most effective way to get high accuracy structures and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneeredab initiohigh accuracy single chain modeling. Since then, different customizations expanded the number of conformational states accessible through AF2. Here, we further extended AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and Kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in automatic fashion.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

Conformational changes are an essential component of functional cycles of many proteins such as transporters or receptors. Despite their importance, the characterization of functionally-relevant structural intermediates is often a hard task for both experimental and computational methods. This limitation can be at least partially relieved by integrating experimental data within computational approaches. Such integrative structural biology strategies can yield an ensemble of models consistent with experimental data. While the macromolecular modeling suite Rosetta has been extensively used to predict protein structures de novo using various types of experimental data, an all-purpose conformational change modeling approach has been absent from this suite. Here, we introduce and benchmark ConfChangeMover (CCM), a new Rosetta Mover tailored to model conformational changes in proteins using sparse experimental data. CCM is able to rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with sparse experimental double electron-electron resonance (DEER) restraints and simulated Cα - Cα distance restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. Thus, our method will contribute to the biophysical characterization of protein dynamics.

Introduction The actual role of landmarks labeling before three-dimensional (3D) facial acquisition is still debated. In this study, several measurements were compared among textured labeled (TL), unlabeled (NL), and untextured (NTL) 3D facial models. Materials and methods The face of 50 subjects was acquired through stereophotogrammetry. Landmark coordinates were extracted from TL, NL, and NTL facial models, and 33 linear and angular measurements were calculated, together with surface area and volume. Accuracy of measurements among TL, NL, and NTL models was assessed through calculation of relative technical error of measurement (rTEM). The intra- and inter-observer errors for each type of facial model were calculated. Results Intra- and inter-observer error of measurements increased passing from textured to NTL and NL 3D models. Average rTEMs between TL models, and NTL and NL models were 4.5 ± 2.6% and 4.7 ± 2.8%, respectively, almost all measurements being classified as "very good" or "good." Only for orbital height and its inclination, mandibular ramus length, nasal convexity, alar slope angle, and facial divergence, rTEM was classified as "moderate" or "poor." Conclusions Accuracy and precision of measurements decrease when landmarks are not previously labeled; attention must be taken when measurements have a low magnitude or involve landmarks requiring palpation.

ZnT8 is a human zinc(II) transporter expressed at the membrane of secretory granules where it contributes to insulin storage importing zinc ions from the cytosol. In the human population, the two most common ZnT8 variants carry an arginine (R325) or a tryptophan (W325) in position 325. The former variant has the most efficient kinetics in zinc transport and has been correlated to a higher risk of developing insulin resistance. On the contrary, the W325 variant is less active and protects against type-2-diabetes. Here, we used molecular dynamics (MD) simulations to investigate the main differences between the R325 and W325 variants in the interaction with zinc(II) ions. Our simulations suggested that the position of the metal ion within the transport site was not the same for the two variants, underlying a different rearrangement of the transmembrane (TM) helices in the channel. The W325 variant featured a peculiar zinc environment not detected in the experimental structures. With respect to conformational dynamics, we observed that the R325 variant was significantly more flexible than W325, with the main role played by the transmembrane domain (TMD) and the C-terminal domain (CTD). This dynamics affected the packing of the TM helices and thus the channel accessibility from the cytosol. The dimer interface that keeps the two TM channels in contact became looser in both variants upon zinc binding to the transport site, suggesting that this may be an important step toward the switch from the inward- to the outward-facing state of the protein.

The favorable outcome of in vivo and ex vivo gene therapy approaches in several Lysosomal Storage Diseases suggests that these treatment strategies might equally benefit GM2 gangliosidosis. Tay-Sachs and Sandhoff disease (the main forms of GM2 gangliosidosis) result from mutations in either the HEXA or HEXB genes encoding, respectively, the α- or β-subunits of the lysosomal β-Hexosaminidase enzyme. In physiological conditions, α- and β-subunits combine to generate β-Hexosaminidase A (HexA, αβ) and β-Hexosaminidase B (HexB, ββ). A major impairment to establishing in vivo or ex vivo gene therapy for GM2 gangliosidosis is the need to synthesize the α- and β-subunits at high levels and with the correct stoichiometric ratio, and to safely deliver the therapeutic products to all affected tissues/organs. Here, we report the generation and in vitro validation of novel bicistronic lentiviral vectors (LVs) encoding for both the murine and human codon optimized Hexa and Hexb genes. We show that these LVs drive the safe and coordinate expression of the α- and β-subunits, leading to supranormal levels of β-Hexosaminidase activity with prevalent formation of a functional HexA in SD murine neurons and glia, murine bone marrow-derived hematopoietic stem/progenitor cells (HSPCs), and human SD fibroblasts. The restoration/overexpression of β-Hexosaminidase leads to the reduction of intracellular GM2 ganglioside storage in transduced and in cross-corrected SD murine neural progeny, indicating that the transgenic enzyme is secreted and functional. Importantly, bicistronic LVs safely and efficiently transduce human neurons/glia and CD34+ HSPCs, which are target and effector cells, respectively, in prospective in vivo and ex vivo GT approaches. We anticipate that these bicistronic LVs may overcome the current requirement of two vectors co-delivering the α- or β-subunits genes. Careful assessment of the safety and therapeutic potential of these bicistronic LVs in the SD murine model will pave the way to the clinical development of LV-based gene therapy for GM2 gangliosidosis.

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

Available estimates indicate that 30–40 % of all proteins need at least one metal ion to perform their biological function. Therefore, they are called metalloproteins. The correct biosynthesis of metalloproteins requires living organisms to be able to cope with issues such as the limited bioavailability or the potential cytotoxicity of several essential metals. Thus, organisms have developed complex machineries that guarantee the proper intracellular concentration and distribution among compartments of each metal, i.e. metal homeostasis. To understand how the different proteins responsible for metal homeostasis carry out their function, it is necessary to investigate their three-dimensional (3D) structure and mobility at the atomic level. Nuclear magnetic resonance spectroscopy is one of the main experimental techniques providing this information. Computer simulations of molecular dynamics (MD) complement experimental information by showing how the 3D structure fluctuates over time and as a function of environmental conditions, with the possibility of exploring a wider range of timescales and conditions than usually amenable to experiment. Here we review numerous applications of MD for the investigation of the structure and dynamics of metalloproteins, and we also mention some technical aspects related to the parametrization of metals in commonly used force fields.

Aim: To evaluate outcomes related to antiplatelet therapy in patients with ST-elevation myocardial infarction (STEMI) admitted to the San Gerardo Hospital in Monza, an extracorporeal membrane oxygenation (ECMO) reference center in the Monza-Brianza area. Methods: This retrospective study enrolled patients with STEMI hospitalized between 2013 and 2017. Results: This study included 653 patients (mean age: 67.5 years, 71% male). Across the study period, ticagrelor use showed consistent increases, from 22% of patients during 2013 to 85% in 2017. Cardiac arrest prehospitalization occurred in 100 patients (15.3%), either at home (n = 85, 13.0%) or during transfer (n = 15, 2.3%); 46 patients underwent ECMO for refractory cardiac arrest. Rates of 90-day survival (hazard ratio [HR]: 2.4, 95% confidence interval [CI]: 1.3-4.4, P = .004) and ST resolution (odds ratio [OR]: 2.5, 95% CI: 1.6-4.1, P = .000) were higher with ticagrelor than with other antiplatelet agents. When analyzed by each agent, patients on ticagrelor had longer survival (HR: 0.4, 95% CI: 0.2-0.8, P = .008) than patients on clopidogrel and more frequent ST resolution than those on clopidogrel or prasugrel (OR: 0.4, 95% CI: 0.2-0.7, P = .002 and OR: 0.4, 95% CI: 0.2-0.7, P = .006). There was no difference in mortality between ticagrelor and prasugrel. Conclusions: Changes in the treatment of high-risk patients with STEMI over time are in line with changes in treatment guidelines. In these patients, ticagrelor is associated with significantly improved 90-day mortality compared with clopidogrel.

The constant increase of computational power has made feasible to investigate the folding mechanism of small proteins using molecular dynamics (MD). Metal-binding proteins (metalloproteins) are usually complicated to model, largely due to the presence of the metal cofactor. Thus, the study of metal-coupled folding is still challenging. In this work, we addressed the folding process of Pyrococcus furiosus rubredoxin (PfRd), a 53-residue protein binding a single iron ion, using different MD methods. Starting from an extended conformation of the polypeptide chain where we preserved the coordination of the metal ion, a classical MD simulation and an extensive accelerated MD run were performed to reconstruct the folding process of the metal-bound protein. For comparison, we simulated also the dynamics of folded PfRd devoid of the metal cofactor (apo-form), starting from the folded structure. For these MD trajectories, we computed various structural and biochemical properties. In addition, we took advantage of available experimental data to quantify the degree to which our simulations sampled conformations close to the native folded state. We observed that the compaction of the hydrophobic core is the main feature driving the folding of the structure. However, we could not reach a fully folded conformation within our trajectories, because of the incomplete removal of the solvent from the core. Altogether, the various MD simulations, including that of the folded apo-form of the protein, suggest that an improvement in the accuracy of the protein force-field is still needed.

We investigated the kinetics of the release of iron(II) ions from the internal cavity of human H-ferritin as a function of pH. Extensive molecular dynamics simulations of the entire 24-mer ferritin provided atomic-level information on the release mechanism. Double protonation of His residues at pH 4 facilitates the removal of the iron ligands within the C3 channel through the formation of salt bridges, resulting in a significantly lower release energy barrier than pH 9.

Background YiiP is a bacterial zinc-for-proton antiporter belonging to the cation diffusion facilitator family. The zinc(II) ions are transported across the cell membrane, from the cytosol to the extracellular space. Methods We performed atomistic molecular dynamics simulations of the YiiP dimer with zinc(II) ions in solution to elucidate how the metal ions interact with the protein while moving from the cytosol to the transport site. Results We observed that of the two cavities of the dimer, only one was accessible from the cytosol during transport. Zinc(II) binding to D49 of the transport site triggered a rearrangement of the transmembrane domain that closed the accessible cavity. Finally, we analyzed the free-energy profiles of metal transit in the channel and observed the existence of a high barrier preventing release from the transport site. Conclusions The observed dynamics is consistent with the dimer-dimer interface forming a stable scaffold against which the rest of the trans-membrane rearranges. General significance Zinc(II) transporters are present in all kingdoms of life. The present study highlights structural features that might be of general relevance.

This paper evaluates the effect of decentralization on innovation at the provincial level in Italy. We exploit quasi-natural experiments associated with three waves of reforms occurred in 1992, 2001 and 2004, to establish 8, 4, and 3 new provinces, respectively. Using a difference-in-difference estimation approach, we find evidence of a significant detrimental effect of (further) decentralization on innovation for Northern and Central Italian provinces. We suggest a potential mechanism that may explain the reduction in innovation associated with the aforementioned reforms. We argue that this finding can be rationalized with the costs imposed by the "mafia transplantation" phenomenon, as we find that the new provinces that were more exposed to "mafiosi in confino" reduced their innovation output more extensively. We perform a number of robustness checks that corroborate our main findings.

Selling internationally requires products that resonate with an international customer base and therefore an approach to markets that is in keeping with diverse cultures (i.e., relational capital). As emphasized by international business studies, this relational capital is in turn related to the successful teaming of a diverse workforce, as this process teaches employees to operate in multicultural environments. This knowledge becomes like an intangible asset to which firms can resort, also when engaging in international transactions. We explore this channel empirically, investigating the impact of workforce diversity on firms' exporting performances and find that ethnic diversity further justifies firms' different presence in international markets. Since hiring is not a random practice, and firms ultimately select into ethnically different labor forces, we exploit the EU enlargement of 2004 to instrument for the diversity of the pool of workers locally recruitable. Because migrants tend to settle where the attitude toward them is most favorable, we use the median voter's political ideology at firm's location to measure the hostility at time of settlement. This gives our instrument spatial variation besides time variation.

Allogeneic fetal-derived human neural stem cells (hfNSCs) that are under clinical evaluation for several neurodegenerative diseases display a favorable safety profile, but require immunosuppression upon transplantation in patients. Neural progenitors derived from patient-specific induced pluripotent stem cells (iPSCs) may be relevant for autologous ex vivo gene-therapy applications to treat genetic diseases with unmet medical need. In this scenario, obtaining iPSC-derived neural stem cells (NSCs) showing a reliable “NSC signature” is mandatory. Here, we generated human iPSC (hiPSC) clones via reprogramming of skin fibroblasts derived from normal donors and patients affected by metachromatic leukodystrophy (MLD), a fatal neurodegenerative lysosomal storage disease caused by genetic defects of the arylsulfatase A (ARSA) enzyme. We differentiated hiPSCs into NSCs (hiPS-NSCs) sharing molecular, phenotypic, and functional identity with hfNSCs, which we used as a “gold standard” in a side-by-side comparison when validating the phenotype of hiPS-NSCs and predicting their performance after intracerebral transplantation. Using lentiviral vectors, we efficiently transduced MLD hiPSCs, achieving supraphysiological ARSA activity that further increased upon neural differentiation. Intracerebral transplantation of hiPS-NSCs into neonatal and adult immunodeficient MLD mice stably restored ARSA activity in the whole central nervous system. Importantly, we observed a significant decrease of sulfatide storage when ARSA-overexpressing cells were used, with a clear advantage in those mice receiving neonatal as compared with adult intervention. Thus, we generated a renewable source of ARSA-overexpressing iPSC-derived bona fide hNSCs with improved features compared with clinically approved hfNSCs. Patient-specific ARSA-overexpressing hiPS-NSCs may be used in autologous ex vivo gene therapy protocols to provide long-lasting enzymatic supply in MLD-affected brains.

Previous studies have shown that reducing intrathoracic pressure during inspiration significantly increases right atrial volume (through an increase in venous return) with a slight reduction of right atrial pressure [(1)][1]. In patients with an atrial septal defect, the same phasic, inspiration

Cystic fibrosis (CF) is mainly caused by the deletion of Phe 508 (DeltaF508) in the cystic fibrosis transmembrane conductance regulator (CFTR) protein that is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. New drugs able to rescue DeltaF508-CFTR trafficking are eagerly awaited. An integrated bioinformatics and surface plasmon resonance (SPR) approach was here applied to investigate the rescue mechanism(s) of a series of CFTR-ligands including VX809, VX770 and some aminoarylthiazole derivatives (AAT). Computational studies tentatively identified a large binding pocket in the DeltaF508-CFTR nucleotide binding domain-1 (NBD1) and predicted all the tested compounds to bind to three sub-regions of this main pocket. Noticeably, the known CFTR chaperone keratin-8 (K8) seems to interact with some residues located in one of these sub-pockets, potentially interfering with the binding of some ligands. SPR results corroborated all these computational findings. Moreover, for all the considered ligands, a statistically significant correlation was determined between their binding capability to DeltaF508-NBD1 measured by SPR and the pockets availability measured by computational studies. Taken together, these results demonstrate a strong agreement between the in silico prediction and the SPR-generated binding data, suggesting a path to speed up the identification of new drugs for the treatment of cystic fibrosis.

Recent studies have concluded that R&D grants can induce firms to export and that exporting and innovating can be complementary activities at the firm level. Yet the trade literature has paid little attention to the scope of innovation policy as a stimulus to both trade and innovation. To investigate this question we rely on a general work-horse model of trade and firm heterogeneity with firm investments in R&D activities. The multiplicity of equilibria together with the interplay of innovation and trade policies uncover novel results. In particular, we show that the effects of either policy depend on the degree of protectionism in a country. Therefore, countries can respond differently to the same policy, and similarly to different policies. In such a context, different governments may face different degrees of freedom regarding how to achieve a given target. This finding leads us to discuss the issue of policy coordination.

Are Born Globals really different from firms with other start-up histories? We address this question based on a unique longitudinal data set that tracks all Danish manufacturing start-ups founded between 1994 and 2008 (23,201 firms). This novel application of register data allows us to provide the first detailed account of Born Globals compared to proper control groups of other start-ups. Chiefly we investigate firm performance, which in turn permits interference on socioeconomic impact. We find that the occurrence of BGs is not specific to certain sectors, nor does their frequency change in light of rapid ICT progress. However, we find that Born Globals have significantly higher turnover and employment levels as well as job growth rates. Moreover, they show a considerably wider market reach, but little to no productivity advantage compared to firms with less or later internationalization. Thus, Born Globals are special in some but not all aspects.

The Calabria's Ionian continental margin is characterized by an extremely narrow continental shelf and by an very steep continental slope (up to 20 degrees). The continental slope is deeply incised by canyons which drain the massive continental sedimentary supply. The canyon heads are quite large and consist of numerous tributaries generally affecting the shelf break. The canyons are deep, with high slope gradient, high sinuosity indexes and their heads have a marked dendritic morphology reaching a few hundred meters from the coastline. These canyons are characterized by V-shape cross-sections in their upper parts and U-shape cross-sections in their terminal parts. These conditions are favorable to sediment buildup on the narrow continental shelf and therefore, under seismic action it may give rise to submarine landslides. In this study, we propose a 3D numerical modeling of tsunami waves generated by an underwater slump with "Geowave" software. The simulated gravity instability zone is near the "Venere 1" head Canyon situated close Cirò Marina coastal area. The availability of a detailed bathymetric map both with a good knowledge of the local geology allowed us to obtain reliable simulation results. Interesting results of tsunami simulation, in the above-mentioned shoreline areas, show that the amplitude of wave run-up ranges from 0.50 m up to 0.90 m. This difference in run-up is ascribed to the directional effects and to the different coastal morphology.

Distributed generation has become a consolidated phenomenon in distribution grids in the last few years. Even though the matter is very articulated and complex, islanding operation of distribution grids is being considered as a possible measure to improve service continuity. In this paper, a novel static converter control strategy to obtain frequency and voltage regulation in an islanded distribution grid is proposed. Two situations are investigated: in the former, one electronic converter and one synchronous generator are present, while in the latter only static generation is available. In the first case, converter control will realize virtual inertia and efficient frequency regulation by mean of a PID regulator; this approach allows to emulate high equivalent inertia, hence limiting the rate of change of frequency and maximum frequency deviation, and, in the meantime, to obtain faster frequency regulation, which could not be possible with traditional regulators. In the second situation, a Master–Slave approach will be adopted to maximize frequency and voltage stability. Even though the presented results are obtained in a grid with only two generators, the proposed approach can be extended to more general configurations with few generation units. Simulation results confirm that the proposed control allows islanded operation with high frequency and voltage stability under heavy load variations.

While determinants of FDI patterns have received widespread attention, the timing of their surge remains largely unexplained. According to the proximity–concentration trade-off argument, a surge in FDI in times of decaying international transportation costs seemingly represents a paradox. Besides transportation costs, other factors have contextually changed: in particular, the uncertainty that firms bear has increased. Enriching the classical choice problem of a multinational firm with insights from the literature on investment under uncertainty, we illustrate how different types of uncertainty determine the timing and optimal entry mode (i.e. FDI or export) of a multinational enterprise into a new market.

The binding of paramagnetic metal ions to proteins produces a number of different effects on the NMR spectra of the system. In particular, when the magnetic susceptibility of the metal ion is anisotropic, pseudocontact shifts (PCSs) arise and can be easily measured. They constitute very useful restraints for the solution structure determination of metal-binding proteins. In this context, there has been great interest in the use of lanthanide(III) ions to induce PCSs in diamagnetic proteins, e.g. through the replacement native calcium(II) ions. By preparing multiple samples in each of which a different ion of the lanthanide series is introduced, it is possible to obtain multiple independent PCS datasets that can be used synergistically to generate protein structure ensembles (typically called bundles). For typical NMR-based determination of protein structure, it is necessary to perform an energetic refinement of such initial bundles to obtain final structures whose geometric quality is suitable for deposition in the PDB. This can be conveniently done by using restrained molecular dynamics simulations (rMD) in explicit solvent. However, there are no available protocols for rMD using multiple PCS datasets as part of the restraints. In this work, we extended the PCS module of the AMBER MD package to handle multiple datasets and tuned a previously developed protocol for NMR structure refinement to achieve consistent convergence with PCS restraints. Test calculations with real experimental data show that this new implementation delivers the expected improvement of protein geometry, resulting in final structures that are of suitable quality for deposition. Furthermore, we observe that also initial structures generated only with traditional restraints can be successfully refined using traditional and PCS restraints simultaneously.

This study analyzes the effect of public R&D subsidies on private R&D expenditure in a sample of French firms during the period 1993-2009. We evaluate whether there is any input additionality of public R&D subsidies by distinguishing between R&D tax credit recipient and non-recipient firms. In addition, combining difference-in-differences with propensity score and exact (both simple and categorical) matching methods, we assess the effect of R&D subsidies between treated (subsidy recipients) and controls (subsidy non-recipients) as well as between differently treated (small, medium and large subsidy recipient) firms. Furthermore, we implement a dose-response matching approach to determine the optimality of public R&D subsidy provisions. We find evidence of either no additionality or substitution effects between public and private R&D expenditure. Crowding-out effects appear to be more pronounced for medium-high levels of public subsidies, and generally under the R&D tax credit regime. A number of robustness checks corroborate our main findings. (C) 2016 Elsevier B.V. All rights reserved.

As the firm gravitates toward being the core in analyses of international trade, the possibilities for learning from cross-disciplines studies increase. Managerial resources underlie export initiation in the theory of the multinational enterprise developed in international business studies, but they are largely ignored in empirical studies of international trade. This is probably not because they are unimportant, but more due to the challenges of identifying and measuring these resources. We exploit Danish employer-employee matched data to overcome this challenge and analyse the impact of managers' international experience, together with other managerial characteristics, on the likelihood that the firm starts exporting. We find that productivity and fixed costs associated with exporting are not the sole determinants of selection of firms into international markets, but that 'managerial inputs' are just as important. Our data allow us to identify manager export experience based on the CEO's historical career as documented in official registry statistics, a feature that makes our analysis different from other work that relies on self-assessments.

While quotas can be expressed in tariff-equivalent terms and have identical economic effects under some conditions, they do not share the same welfare implications with tariffs in the presence of a piecemeal reform (second-best). In this paper we show that this non-welfare equivalence persists in perfect competition when countries undergo regional integration. A Pareto improving customs union is nevertheless viable in both protection regimes, but it requires different trade policy adjustments. When we extend the analysis beyond the competitive framework and consider shocks to the economy or imperfect markets, this general desirability of unions is unfortunately lost. But we show that, interestingly, the equivalence between tariff and quota regimes can still arise under particular circumstances, and only quotas provide countries with full insurance from price fluctuations.

As the firm gravitates to the core analysis of international trade models, the possibilities to learn from the theory of the multinational enterprise developed in international business studies increase. The managerial resources and capabilities that are so emphasized in this theory for export initiation have largely been neglected in the empirical studies of international trade. Probably not because they are unimportant, but rather because of the challenge to identify and measure them. We exploit Danish employer-employee matched data to overcome this barrier and analyze the impact of managers’ international experience together with other managerial characteristics on the likelihood that the firm starts exporting. We find that productivity and fixed costs associated to exporting are not the sole determinants of the selection of firms into international markets, but “managerial inputs” are as important. Our data allows us to identify managers’ export experience based on the CEOs’ historical career as documented in official registry statistics. This puts our study apart from earlier survey based studies which rely on self-assessments.