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Cardiac sarcoidosis is associated with a high mortality rate. Given multiple barriers to obtaining cardiac PET imaging, we suspect individuals with access to this imaging modality are not representative of the Sarcoid patient population, which in the United States are predominantly Black females.To evaluate the demographics of patients with cardiac PET access and the cost-effectiveness of cardiac PET/MR imaging relative to standard of care.This is a retrospective, observational study. The demographic information of patients with suspected cardiac sarcoidosis and cardiac PET/CT imaging within a national registry of sarcoidosis were reviewed (n = 4561). An individual-level, continuous, time-state transition model was used for the evaluation of long-term cost-effectiveness for the combined cardiac PET/MR compared to standard of care cardiac MR followed by cardiac PET/CT.Patients who underwent cardiac PET in the national registry had 88.35% higher odds of being male (p 0.001) and 43.82% higher odds of being White (p = 0.003) than their counterparts who did not have cardiac PET imaging. Combined cardiac PET/MR had overall lower total lifetime costs ($8761 vs $10,777) and overall improved expected quality of life-years compared to the standard of care (0.77 vs 0.69).The findings suggest that patients with access to cardiac PET/CT are not representative of the patient population most likely to have cardiac sarcoidosis in this limited study evaluation. Universal insurance coverage should be considered for Cardiac PET imaging as same day cardiac PET and MR imaging has potential long-term cost and quality of life benefit.

Background: Kidney autotransplantation is a useful technique to be reserved for cases in which kidney function is compromised by a complex anatomical configuration, such as long ureteral strictures and renal vascular anomalies not suitable for in situ reconstruction. Robot-assisted kidney autotransplantation (RAKAT) presents a novel, minimally invasive, and highly accurate approach. Objective: The aim of this study is to present the largest cohort of patients who underwent either extracorporeal (eRAKAT) or intracorporeal (iRAKAT) RAKAT, to confirm safety and feasibility and to compare the two approaches. Design, setting, and participants: We retrospectively analyzed prospectively followed patients undergoing eRAKAT and totally intracorporeal RAKAT in a total of three institutions. Surgical procedure: Extracorporeal RAKAT and iRAKAT. Measurements: Surgical and functional outcomes of patients subjected to eRAKAT and iRAKAT were measured. Results and limitations: Between January 2017 and February 2021, 29 patients underwent RAKAT: 15 eRAKAT and 14 iRAKAT. No statistical difference in the preoperative data was recorded. The analysis of intraoperative variables showed a statistically significant difference between eRAKAT and iRAKAT in cold ischemia time (median [interquartile range {IQR}]: 151 [125-199] vs 27.5 [20-55]; p < 0.001) and total ischemia time (median [IQR]: 196.2 [182-241] vs 81.5 [73-88]; p < 0.001). However, faster renal function recovery in favor of eRAKAT was observed during the first 90 d, with comparable renal function at 1 yr. The 90-d Clavien-Dindo >2 complications were 13.8%. It is important to stress that RAKAT, and above all iRAKAT, should be performed by surgeons with experience in robotic renal, vascular, and transplant surgery. Conclusions: Both eRAKAT and iRAKAT represent promising minimally invasive techniques in selected cases with acceptable ischemia time and comparable longterm operative outcomes. Patient summary: In selected patients, both extra-and intracorporeal robot-assisted kidney autotransplantation represent valid alternatives in case of long ureteral strictures and renal vascular anomalies not suitable for in situ reconstruction. (c) 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Combination treatments of s.c. 4T1 tumors with Clec9A-mAFN do not cause toxicity.

Combination treatments of B16 tumors with Clec9A-mAFN do not cause toxicity, in contrast to combinations with WT mIFN.

Combining checkpoint inhibition with Clec9A-mAFN does not cause toxicity.

Low dose WT mIFN cannot prevent tumor growth, even when targeted to the tumor as an immunocytokine.

Combination treatments of orthotopic 4T1 tumors with Clec9A-mAFN do not cause toxicity.

Clec9A-AcTaferon specifically induces IFN signaling signature in CD11c+ CD8+ DC.

An ideal generic cancer immunotherapy should mobilize the immune system to destroy tumor cells without harming healthy cells and remain active in case of recurrence. Furthermore, it should preferably not rely on tumor-specific surface markers, as these are only available in a limited set of malignancies. Despite approval for treatment of various cancers, clinical application of cytokines is still impeded by their multiple toxic side effects. Type I IFN has a long history in the treatment of cancer, but its multifaceted activity pattern and complex side effects prevent its clinical use. Here we develop AcTakines (Activity-on-Target cytokines), optimized (mutated) immunocytokines that are up to 1,000-fold more potent on target cells, allowing specific signaling in selected cell types only. Type I IFN-derived AcTaferon (AFN)-targeting Clec9A+ dendritic cells (DC) displayed strong antitumor activity in murine melanoma, breast carcinoma, and lymphoma models and against human lymphoma in humanized mice without any detectable toxic side effects. Combined with immune checkpoint blockade, chemotherapy, or low-dose TNF, complete tumor regression and long-lasting tumor immunity were observed, still without adverse effects. Our findings indicate that DC-targeted AFNs provide a novel class of highly efficient, safe, and broad-spectrum off-the-shelf cancer immunotherapeutics with no need for a tumor marker.Significance: Targeted type I interferon elicits powerful antitumor efficacy, similar to wild-type IFN, but without any toxic side effects. Cancer Res; 78(2); 463–74. ©2017 AACR.

Clec9A-targeting does not induce hematological deficits.

CD70 is an attractive target for chimeric antigen receptor (CAR) T cell therapy as treatment for both solid and liquid malignancies. However, functionality of CD70-specific CARs is only modest. Here, we optimized a CD70-specific VHH based CAR (nanoCAR). We evaluated the nanoCARs in clinically relevant modelsin vitro, using co-cultures of CD70-specific nanoCAR T cells with malignant rhabdoid tumor organoids, andin vivoby using a diffuse large B cell lymphoma (DLBCL) patient-derived xenograft (PDX) model. Whereas the nanoCAR T cells were highly efficient in organoid co-cultures, they showed only modest efficacy in the PDX model. Knocking out CD70 expression by the nanoCAR T cells resulted in dramatically enhanced functionality in the PDX model, suggesting that endogenous CD70 interaction with the nanoCAR induces exhaustion. Through single-cell transcriptomics, we obtained evidence that CD70KO CD70-specific nanoCAR T cells are protected from antigen induced exhaustion. Our data shows that CARs targeted to endogenous T cell antigens, negatively affect CAR T cell functionality by inducing an exhausted state which can be overcome by knocking out the specific target, in this case CD70.

On a global scale, pesticide use has almost doubled since 1990, with the world market expected to reach $130 billion by the end of 2023. With a rapidly growing world population, the use of pesticides, also called plant protection products (PPP), has played an important role in increasing crop yields to ensure adequate food availability. In the early 1960s, growing concern and awareness about the potential for PPP to non-specifically affect the surrounding ecosystem led to a growing field of resistance. Governmental and intergovernmental bodies have since placed sustainable agricultural practices at the top of their agendas, leading to the use of PPP becoming an increasingly controversial topic of discussion. This policy analysis broadly describes PPP regulation systems in the US and the EU by providing historic accounts of key policy developments of PPP use and their regulations. A direct comparison between regulatory systems for PPP in the US and in the EU is then explored. Washington State and Belgium were chosen as case studies in order to provide a more detailed look into the complexities of such systems and allow for a comparative approach to examine the opportunities and challenges for policy changes. Additionally, suggestions as to what the EU and the US entities can learn from one another to improve the respective PPP regulation systems are investigated. Finally, the analysis explores the potential of strengthening transatlantic cooperation through the establishment of an intergovernmental framework that deals with collection of scientific evidence on PPP and their use. As a result, this analysis acts as a tool for policymakers to better comprehend the different approaches to PPP regulation in the US and the EU as the need to feed the growing world population becomes more urgent, all while safeguarding human and ecosystem health through well-informed policies on PPP use.

Following usage-based views on language learning, input that learners are exposed to is the foundation of the linguistic features that are acquired. Inspired by the essential role of vocabulary for comprehension, studies have determined the vocabulary demands of various sources of input (e.g., novels, movies), but many questions remain regarding L2 textbooks, a central component of L2 classroom input. Moreover, current research into L2 textbooks has focused exclusively on English as L2. Flanders presents a unique case in that there are two main foreign languages: English and French. Therefore, a cross-lingual corpus consisting of English and French L2 textbook reading materials was compiled (ca. 300,000 words per L2) in order to investigate what the vocabulary demands are (= RQ1), how the demands evolve across secondary education (= RQ2), and how target language influences the (evolution of) demands (= RQ3). A custom Python script was developed to calculate lexical profiles, i.e., categorisations of the textbooks' vocabulary into word frequency lists. Typically, lexical profiling research relies on word families as lexical unit, but we opted for lemmas instead in light of the higher morphological demands of French. Results showed that knowledge of the 11,000 most frequent lemmas was required to reach unassisted comprehension of the first grade English materials, as opposed to 18,000 for French. From there, English shows a systematic and fairly gradual increase in vocabulary demands across grade levels, whereas French demands seem to fluctuate, an evolution which could hinder French L2 development. Pedagogical implications will be addressed.

In data-driven optimization, sample average approximation is known to suffer from the so-called optimizer's curse that causes optimistic bias in evaluating the solution performance. This can be tackled by adding a "margin" to the estimated objective value, or via distributionally robust optimization (DRO), a fast-growing approach based on worst-case analysis, which gives a protective bound on the attained objective value. However, in all these existing approaches, a statistically guaranteed bound on the true solution performance either requires restrictive conditions and knowledge on the objective function complexity, or otherwise exhibits an over-conservative rate that depends on the distribution dimension. We argue that a special type of DRO offers strong theoretical advantages in regard to these challenges: It attains a statistical bound on the true solution performance that is the tightest possible in terms of exponential decay rate, for a wide class of objective functions that notably does not hinge on function complexity. Correspondingly, its calibration also does not require any complexity information. This DRO uses an ambiguity set based on a KL-divergence smoothed by the Wasserstein or Levy-Prokhorov distance via a suitable distance optimization. Computationally, we also show that such a DRO, and its generalized version using smoothed $f$-divergence, is not much harder than standard DRO problems using the $f$-divergence or Wasserstein distance, thus supporting the strengths of such DRO as both statistically optimal and computationally viable.

Introduction Hereditary transthyretin-mediated (hATTR) amyloidosis, a genetic disease caused by mutations in the transthyretin gene, leads to progressive sensory and autonomic neuropathy and/or cardiomyopathy and is associated with renal and ophthalmologic manifestations and a poor prognosis. Methods This is a retrospective study based on data collected from the medical records of patients with hATTR amyloidosis treated with patisiran between 01 July 2018 and 01 February 2021. Six Belgian neuromuscular reference centers participated, covering all patisiran-treated hATTR amyloidosis patients at the study time. This study was conducted to collect data requested in the context of the reimbursement of patisiran in Belgium. Results Thirty-one patients were diagnosed with hATTR amyloidosis with polyneuropathy, Coutinho stage 1 or 2, and eligible for active treatment during the data collection period. Of the hATTR amyloidosis patients treated with patisiran (n = 12), seven and five had polyneuropathy stages 1 and 2, respectively. Six patients had cardiac symptoms (New York Heart Association class 2 or above). Follow-up information was available for nine patients. Following patisiran treatment, eight patients showed stable or improved assessments for most neurological or cardiological parameters. Only one patient presented with worsening statuses at the end of the data collection period. Conclusions The patients with hATTR amyloidosis in Belgium have similar baseline demographics and disease characteristics to those studied in the patisiran APOLLO study and show a similar therapeutic response in the real-world, altering the expected disease progression in most patients.

Recent work have demonstrated that robustness (to "corruption") can be at odds with generalization. Adversarial training, for instance, aims to reduce the problematic susceptibility of modern neural networks to small data perturbations. Surprisingly, overfitting is a major concern in adversarial training despite being mostly absent in standard training. We provide here theoretical evidence for this peculiar "robust overfitting" phenomenon. Subsequently, we advance a novel distributionally robust loss function bridging robustness and generalization. We demonstrate both theoretically as well as empirically the loss to enjoy a certified level of robustness against two common types of corruption--data evasion and poisoning attacks--while ensuring guaranteed generalization. We show through careful numerical experiments that our resulting holistic robust (HR) training procedure yields SOTA performance. Finally, we indicate that HR training can be interpreted as a direct extension of adversarial training and comes with a negligible additional computational burden. A ready-to-use python library implementing our algorithm is available at https://github.com/RyanLucas3/HR_Neural_Networks.

Simulations of high-density deuterium plasmas in a lower single-null magnetic configuration based on a TCV discharge are presented. We evolve the dynamics of three charged species (electrons, D$^{+}$ and D$_{2}^{+}$), interacting with two neutrals species (D and D$_2$) through ionization, charge-exchange, recombination and molecular dissociation processes. The plasma is modelled by using the drift-reduced fluid Braginskii equations, while the neutral dynamics is described by a kinetic model. To control the divertor conditions, a D$_2$ puffing is used and the effect of increasing the puffing strength is investigated. The increase in fuelling leads to an increase of density in the scrape-off layer and a decrease of the plasma temperature. At the same time, the particle and heat fluxes to the divertor target decrease and the detachment of the inner target is observed. The analysis of particle and transport balance in the divertor volume shows that the decrease of the particle flux is caused by a decrease of the local neutral ionization together with a decrease of the parallel velocity, both caused by the lower plasma temperature. The relative importance of the different collision terms is assessed, showing the crucial role of molecular interactions, as they are responsible for increasing the atomic neutral density and temperature, since most of the D neutrals are produced by molecular activated recombination and D$_2$ dissociation. The presence of strong electric fields in high-density plasmas is also shown, revealing the role of the $E \times B$ drift in setting the asymmetry between the divertor targets. Simulation results are in agreement with experimental observations of increased density decay length, attributed to a decrease of parallel transport, together with an increase of plasma blob size and radial velocity.

We propose a data-driven technique to automatically learn the uncertainty sets in robust optimization. Our method reshapes the uncertainty sets by minimizing the expected performance across a family of problems while guaranteeing constraint satisfaction. We learn the uncertainty sets using a novel stochastic augmented Lagrangian method that relies on differentiating the solutions of the robust optimization problems with respect to the parameters of the uncertainty set. We show sublinear convergence to stationary points under mild assumptions, and finite-sample probabilistic guarantees of constraint satisfaction using empirical process theory. Our approach is very flexible and can learn a wide variety of uncertainty sets while preserving tractability. Numerical experiments show that our method outperforms traditional approaches in robust and distributionally robust optimization in terms of out of sample performance and constraint satisfaction guarantees. We implemented our method in the open-source package LROPT.

Background Women in Pakistan suffer from a high rate of depression. The stress of low-income, illiteracy, exposure to violence and living in a patriarchal society are predisposing vulnerabilities for depression, particularly during and following pregnancy. The resilience of an individual plays a significant role in promoting prenatal mental health, but this has yet to be thoroughly researched. In this article, our objective is to identify the core characteristics of resilience among pregnant women, which will then help us in developing an intervention. Methods The exploratory-descriptive study was conducted over 6 months in five different antenatal hospitals in Sindh, Pakistan. A total of 17 semi-structured interviews were conducted with pregnant women, purposefully selected with heterogeneous characteristics to explore diverse perspectives, while symptoms of depression were quantified by the Edinburgh Postnatal Depression Scale before the interview. Verbatim transcriptions were coded openly and merged into categories and themes. Result A total of six themes emerged from in-depth thematic analysis: 1) purpose of life, 2) dealing with emotions, 3) believing in yourself, 4) optimistic approach, 5) strengthening support and relationship and 6) spirituality and humanity. Women agreed that these characteristics could help them improve their mental health. Conclusion In conclusion, these themes were the core components of pregnant women’s resilience which ultimately could help to promote prenatal mental health. These pave a pathway towards developing culturally and contextually resilience interventions aimed at enhancing mental health of pregnant women which then may improve neonatal and family mental wellbeing.

Traumatic triceps tendon ruptures are rare and known to result in substantial disability unless appropriate surgical treatment is performed. A traumatic rupture can occur due to a fall onto the outstretched hand. Tearing of the triceps tendon results in a valgus load onto the elbow, which can lead to injuries to the radial head/neck and/or the ulnar collateral ligament. Hence, attention must be paid to associated pathologies after diagnosis of rupture to the distal triceps tendon. Our surgical procedure in these cases includes diagnostic arthroscopy to detect concomitant injuries. In the following, we present three cases where we performed an open suture bridge repair of the triceps tendon followed by suture repair of the ulnar collateral ligament. All patients recovered well and reported no elbow pain or limited range of motion. The ASES and Mayo Elbow Performance scores were 100 at the 1-year follow-up.Traumatische distale Trizepssehnenverletzungen sind seltene Verletzungen. Nichtsdestotrotz ist die korrekte Therapie entscheidend für die Funktion des Armes. Der Sturz auf den ausgestreckten Arm kann die Ursache sein. Die Ruptur der Sehne im Unfallmoment verändert die Krafteinwirkung, sodass Begleitverletzungen wie die Radiuskopffraktur oder der Riss des medialen, ulnaren Kollateralbandes nicht selten sind. Daher sind mögliche Begleitverletzungen bei einer distalen Trizepssehnenruptur dezidiert zu prüfen. Gemäß unserem Vorgehen kann neben der MRT-Diagnostik eine Arthroskopie hilfreich sein, um mögliche Begleitpathologie adäquat zu erfassen und zu adressieren. Im Folgenden werden drei Fälle vorgestellt, die operativ mittels Trizepssehnen-Naht sowie Refixation des medialen ulnaren Kollateralbandes versorgt wurden. Das 1-Jahres-Followup zeigte keine Bewegungseinschränkungen oder Instabilitäten, der ASES- und Mayo Elbow Score waren 100.

Robust optimization is a tractable and expressive technique for decision-making under uncertainty, but it can lead to overly conservative decisions when pessimistic assumptions are made on the uncertain parameters. Wasserstein distributionally robust optimization can reduce conservatism by being data-driven, but it often leads to very large problems with prohibitive solution times. We introduce mean robust optimization, a general framework that combines the best of both worlds by providing a trade-off between computational effort and conservatism. We propose uncertainty sets constructed based on clustered data rather than on observed data points directly thereby significantly reducing problem size. By varying the number of clusters, our method bridges between robust and Wasserstein distributionally robust optimization. We show finite-sample performance guarantees and explicitly control the potential additional pessimism introduced by any clustering procedure. In addition, we prove conditions for which, when the uncertainty enters linearly in the constraints, clustering does not affect the optimal solution. We illustrate the efficiency and performance preservation of our method on several numerical examples, obtaining multiple orders of magnitude speedups in solution time with little-to-no effect on the solution quality.

We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual information on a potentially large number of covariates to take context specific actions which are superior to any static decision. When working with noisy or corrupt data, however, such nominal prescriptive methods can be prone to adverse overfitting phenomena and fail to generalize on out-of-sample data. In this paper we combine ideas from robust optimization and the statistical bootstrap to propose novel prescriptive methods which safeguard against overfitting. We show indeed that a particular entropic robust counterpart to such nominal formulations guarantees good performance on synthetic bootstrap data. As bootstrap data is often a sensible proxy to actual out-of-sample data, our robust counterpart can be interpreted to directly encourage good out-of-sample performance. The associated robust prescriptive methods furthermore reduce to convenient tractable convex optimization problems in the context of local learning methods such as nearest neighbors and Nadaraya-Watson learning. We illustrate our data-driven decision-making framework and our novel robustness notion on a small newsvendor problem.

Patients with obsessive-compulsive disorder (OCD) are likely to be affected by the COVID-19 crisis since fear of contamination is highly prevalent in this illness and disease reminders are omnipresent during this crisis. The current study aimed to investigate the impact of the pandemic and the lockdown on the mental health, well-being and coping abilities of OCD patients and their families in order to increase our understanding of the underlying mechanisms of the disorder. Twenty-two patients and 13 family members were interviewed one-to-one about their experiences and challenges caused by the pandemic and home-confinement directives. Verbatim transcripts of the interviews were analyzed using inductive thematic analysis. Five overarching themes were identified: (1) changing point of reference: confusion and legitimization of OCD behavior, (2) coping strategies were challenged: too much or too little exposure to obsessional concerns, (3) distress but also relief in some areas, (4) developing a new equilibrium within the family, (5) changes in accessibility and nature of therapy: perils and merits of online treatment. These findings make clear the importance of the accessibility of mental health services during this pandemic through direct patient contacts or in a remote format. In therapy therapists should focus on challenging the changing point of reference, providing practical advice on coping, stimulating to engage in exposure and encouraging patients to seek social support. Furthermore, it is important to involve family members in therapy to support and coach them to be validating, supportive and encouraging, without accommodating to the OCD behaviour.

Perinatal mental health issues in women can lead to a variety of health complications for both mother and child. Building resilience can strengthen coping mechanisms for pregnant women to improve their mental health and protect themselves and their children. The study aims to develop and validate the contextual and cultural appropriateness of the Safe Motherhood-Accessible Resilience Training (SM-ART) intervention for pregnant women in Pakistan. A three-phase approach was used to develop and validate an intervention that promotes resilience in pregnant women. Phase I comprised a needs assessment with stakeholders (pregnant women and key informants) to elicit opinions regarding module content. In Phase II, an intervention to build resilience was developed with the help of a literature review and formative assessment findings, and Phase III involved the validation of the intervention by eight mental health experts. The experts assessed the Content Validity Index (CVI) of the SM-ART intervention on a self-developed checklist. The resultant SM-ART intervention consists of six modules with strong to perfect CVI scores for each of the modules. Qualitative responses endorsed the strengths of the intervention as having innovative and engaging activities, contextual and cultural relevance, and a detailed, comprehensive facilitator guide. SM-ART was successfully developed and validated and is now ready for testing to promote the resilience of pregnant women at risk of perinatal mental illness.

We propose a novel and efficient algorithm for detection of specular reflections and light sources (highlights) in cinematic content. The detection of highlights is important for reconstructing them properly in the conversion of the low dynamic range (LDR) to high dynamic range (HDR) content. Highlights are often difficult to be distinguished from bright diffuse surfaces, due to their brightness being reduced in the conventional LDR content production. Moreover, the cinematic LDR content is subject to the artistic use of effects that change the apparent brightness of certain image regions (e.g. limiting depth of field, grading, complex multi-lighting setup, etc.). To ensure the robustness of highlights detection to these effects, the proposed algorithm goes beyond considering only absolute brightness and considers five different features. These features are: the size of the highlight relative to the size of the surrounding image structures, the relative contrast in the surrounding of the highlight, its absolute brightness expressed through the luminance (luma feature), through the saturation in the color space (maxRGB feature) and through the saturation in white (minRGB feature). We evaluate the algorithm on two different image data-sets. The first one is a publicly available LDR image data-set without cinematic content, which allows comparison to the broader State of the art. Additionally, for the evaluation on cinematic content, we create an image data-set consisted of manually annotated cinematic frames and real-world images. For the purpose of demonstrating the proposed highlights detection algorithm in a complete LDR-to-HDR conversion pipeline, we additionally propose a simple inverse-tone-mapping algorithm. The experimental analysis shows that the proposed approach outperforms conventional highlights detection algorithms on both image data-sets, achieves high quality reconstruction of the HDR content and is suited for use in LDR-to-HDR conversion.

Plasmodium falciparum circumsporozoite protein (PfCSP) and Pfs25 are leading candidates for the development of pre-erythrocytic and transmission-blocking vaccines (TBV), respectively. Although considerable progress has been made in developing PfCSP- and Pfs25-based vaccines, neither have elicited complete protection or transmission blocking in clinical trials. The combination of antigens targeting various life stages is an alternative strategy to develop a more efficacious malaria vaccine. In this study, female and male mice were immunized with DNA plasmids encoding PfCSP and Pfs25, administered alone or in combination via intramuscular in vivo electroporation (EP). Antigen-specific antibodies were analyzed for antibody titers, avidity and isotype by ELISA. Immune protection against sporozoite challenge, using transgenic P. berghei expressing PfCSP and a GFP-luciferase fusion protein (PbPfCSP-GFP/Luc), was assessed by in vivo bioluminescence imaging and blood-stage parasite growth. Transmission reducing activity (TRA) was evaluated in standard membrane feeding assays (SMFA). High levels of PfCSP- and Pfs25-specific antibodies were induced in mice immunized with either DNA vaccine alone or in combination. No difference in antibody titer and avidity was observed for both PfCSP and Pfs25 between the single DNA and combined DNA immunization groups. When challenged by PbPfCSP-GFP/Luc sporozoites, mice immunized with PfCSP alone or combined with Pfs25 revealed significantly reduced liver-stage parasite loads as compared to mice immunized with Pfs25, used as a control. Furthermore, parasite liver loads were negatively correlated with PfCSP-specific antibody levels. When evaluating TRA, we found that immunization with Pfs25 alone or in combination with PfCSP elicited comparable significant transmission reduction. Our studies reveal that the combination of PfCSP and Pfs25 DNAs into a vaccine delivered by in vivo EP in mice does not compromise immunogenicity, infection protection and transmission reduction when compared to each DNA vaccine individually, and provide support for further evaluation of this DNA combination vaccine approach in larger animals and clinical trials.

Transformations and restorations of existing buildings may have to solve major issues regarding the buildings’ structural safety. Timber floors for instance may be damaged due to decay, a faulty de...

We describe an unusual aggregation of Bullsnakes (Pituophis catenifer sayi) near a nest site in Saskatchewan. Bullsnake is a wide-ranging oviparous colubrid that reaches the northern tip of its continental range on the prairies and badlands of Canada. At that location, it is considered a species of Special Concern, but, until recently, has been the subject of relatively few natural history reports. This is significant, because, at the northern edge of their range, Bullsnakes may behave differently than elsewhere due to thermal limitations experienced at high latitudes. On 29 June 2019, we observed a mass of five adult Bullsnakes in a Silver Sagebrush (Artemisia cana) shrub on a slope in southwestern Saskatchewan. Aggregations of Bullsnakes are known to occur at hibernacula, during mating, and inside nest chambers before and after oviposition. However, we are unaware of the occurrence of surface aggregations in any other situation. We suggest that these Bullsnakes may have been exhibiting communal gestation, a behaviour thought to be rare in oviparous snakes whereby gravid females congregate before parturition or oviposition for thermal stability or protection from predators.

We present a novel method for sparse polynomial regression. We are interested in that degree r polynomial which depends on at most k inputs, counting at most $$\ell$$ℓ monomial terms, and minimizes the sum of the squares of its prediction errors. Such highly structured sparse regression was denoted by Bach (Advances in neural information processing systems, pp 105–112, 2009) as sparse hierarchical regression in the context of kernel learning. Hierarchical sparse specification aligns well with modern big data settings where many inputs are not relevant for prediction purposes and the functional complexity of the regressor needs to be controlled as to avoid overfitting. We propose an efficient two-step approach to this hierarchical sparse regression problem. First, we discard irrelevant inputs using an extremely fast input ranking heuristic. Secondly, we take advantage of modern cutting plane methods for integer optimization to solve the remaining reduced hierarchical $$(k, \ell )$$(k,ℓ)-sparse problem exactly. The ability of our method to identify all k relevant inputs and all $$\ell$$ℓ monomial terms is shown empirically to experience a phase transition. Crucially, the same transition also presents itself in our ability to reject all irrelevant features and monomials as well. In the regime where our method is statistically powerful, its computational complexity is interestingly on par with Lasso based heuristics. Hierarchical sparsity can retain the flexibility of general nonparametric methods such as nearest neighbors or regression trees (CART), without sacrificing much statistical power. The presented work hence fills a void in terms of a lack of powerful disciplined nonlinear sparse regression methods in high-dimensional settings. Our method is shown empirically to scale to regression problems with $$n\approx 10{,}000$$n≈10,000 observations for input dimension $$p\approx 1000$$p≈1000.

We present the Branch-and-Bound Performance Estimation Programming (BnB-PEP), a unified methodology for constructing optimal first-order methods for convex and nonconvex optimization. BnB-PEP poses the problem of finding the optimal optimization method as a nonconvex but practically tractable quadratically constrained quadratic optimization problem and solves it to certifiable global optimality using a customized branch-and-bound algorithm. By directly confronting the nonconvexity, BnB-PEP offers significantly more flexibility and removes the many limitations of the prior methodologies. Our customized branch-and-bound algorithm, through exploiting specific problem structures, outperforms the latest off-the-shelf implementations by orders of magnitude, accelerating the solution time from hours to seconds and weeks to minutes. We apply BnB-PEP to several setups for which the prior methodologies do not apply and obtain methods with bounds that improve upon prior state-of-the-art results. Finally, we use the BnB-PEP methodology to find proofs with potential function structures, thereby systematically generating analytical convergence proofs., Published in Mathematical Programming Series A

The effectiveness of containment measures has been shown to depend on both epidemiological and sociological mechanisms, most notably compliance with national lockdown rules. Yet, there has been growing discontent with social distancing rules during national lockdowns across several countries, particularly among certain demographic and socio-economic groups. Using a highly granular dataset on compliance of over 105,000 individuals between March and May 2020 in the United Kingdom (UK), we find that compliance with lockdown policies was initially high in the overall population during the earlier phase of the pandemic, but that compliance fell substantially over time, especially among specific segments of society. Warmer temperatures increased the non-compliance of individuals who are male, divorced, part-time employed, and/or parent of more than two children. Thus, while epidemiologically the virus spread was naturally more limited during the warmer period of 2020, sociologically the higher temperature led to lower individual-level compliance with public health measures. As long as new strains emerge, governments may therefore be required to complement vaccination campaigns with targeted and time limited restrictions. Since non-complying individuals at the beginning of the pandemic share certain characteristics with vaccination sceptics, understanding their compliance behaviour will remain essential for future policymaking.

Textbooks are a vital source of input in the L2 classroom. Studies have determined the lexical complexity of a variety of input types (e.g., novels, audio-visual media) by calculating lexical profiles (e.g., Nation, 2006; Webb, 2010), i.e., estimates of the distribution of words across frequency levels – the assumption being that higher word frequencies equal lower demands. However, lexical profiling research into L2 textbooks is limited. Moreover, the few existing studies tend to focus exclusively on English, ignoring languages to which learners may have considerably lower out-of-school exposure (e.g., French; cf. Peters et al., 2019). To address these gaps, this cross-linguistic corpus-driven study investigates both English and French L2 textbooks and aims to determine (RQ1) what the lexical profiles are of the reading materials found within these textbooks, (RQ2) how these demands evolve across secondary education and (RQ3) how the approach differs based on L2 (English-French). A corpus of approximately 200,000 tokens per L2 was compiled by selecting the reading texts from 36 Flemish secondary school L2 textbooks. To determine the vocabulary demands (cf. RQ1), a custom Python script was developed that creates a lexical profile for each text by categorising the words into existing word frequency lists. A crucial decision in the lexical profiling process is the choice of lexical unit. Typically, lexical profiles are reported in terms of word families (i.e., lexical units encompassing all inflections and derivations of a headword, e.g., 'depend', 'depends' and 'dependable' are part of the same family), but recent research has shown that these may overestimate the vocabulary knowledge of learners who struggle with morphology (e.g., Brown et al., 2020). Moreover, we argue that word families are especially unsuitable for French, considering its additional morphological challenges when compared to English. For instance, a learner may know the meaning of the infinitive 'résoudre', but not of the rather different inflected form 'résolvons'. To give a cross-linguistic insight that is as complete as possible, two other lexical units are explored: the word type (i.e., each token counted separately, e.g., 'depend' and 'depends' are distinct units) and the lemma (i.e., a headword and all its inflections, e.g. 'depend' and 'depends' fall under the same lemma '(to) depend'). Our lexical profiles are based on the subtitle-based frequency lists Subtlex-UK (van Heuven et al., 2014) for English and Lexique (New et al., 2004) for French. The profiles are supplemented with measures of lexical density (i.e., the ratio of content words to the total number of words) and lexical diversity (determined using the Measure of Textual Lexical Diversity). To determine the evolution of these different features across grade levels as well as the ways in which they differ across English and French (cf. RQ2 and RQ3), multilevel regression modelling and pairwise comparisons between grades and L2s will be performed. This study is ongoing and entering the analysis phase. In my presentation, preliminary results will be discussed, with a special focus on the methodological decisions that needed to be made. Pedagogical implications for text selection in L2 teaching will be addressed.

AimExamination of central compensatory mechanisms following peripheral vocal nerve injury and recovery is essential to build knowledge about plasticity of the neural network underlying phonation. The objective of this prospective multiple-cases longitudinal study is to describe brain activity in response to unilateral vocal fold paralysis (UVFP) management and to follow central nervous system adaptation over time in three patients with different nervous and vocal recovery profiles.Materials and methodsParticipants were enrolled within 3 months of the onset of UVFP. Within 1 year of the injury, the first patient did not recover voice or vocal fold mobility despite voice therapy, the second patient recovered voice and mobility in absence of treatment and the third patient recovered voice and vocal fold mobility following an injection augmentation with hyaluronic acid in the paralyzed vocal fold. These different evolutions allowed comparison of individual outcomes according to nervous and vocal recovery. All three patients underwent functional magnetic resonance imaging (fMRI task and resting-state) scans at three (patient 1) or four (patients 2 and 3) time points. The fMRI task included three conditions: a condition of phonation and audition of the sustained [a:] vowel for 3 s, an audition condition of this vowel and a resting condition. Acoustic and aerodynamic measures as well as laryngostroboscopic images and laryngeal electromyographic data were collected.Results and conclusionThis study highlighted for the first time two key findings. First, hyperactivation during the fMRI phonation task was observed at the first time point following the onset of UVFP and this hyperactivation was related to an increase in resting-state connectivity between previoulsy described phonatory regions of interest. Second, for the patient who received an augmentation injection in the paralyzed vocal fold, we subsequently observed a bilateral activation of the voice-related nuclei in the brainstem. This new observation, along with the fact that for this patient the resting-state connectivity between the voice motor/sensory brainstem nuclei and other brain regions of interest correlated with an aerodynamic measure of voice, support the idea that there is a need to investigate whether the neural recovery process can be enhanced by promoting the restoration of proprioceptive feedback.