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Abstract In this article, we present a modification of the NS/SRM technique in which the mastopexy design for skin reduction is undertaken with a wide-base bipedicled (WIBB) flap. The WIBB flap can be applied in both autologous and implant-based breast reconstruction. Our reconstructive algorithm is also presented. The clinical data of patients operated on from June 2017 to November 2022 were collected: 51 patients for a total of 71 breasts. Personal data, BMI, type and volume of implants used, and major and minor complications were analyzed by descriptive statistics. The mean age was 48.3 years. BMI ranged between 21.5 and 30.9 kg/m2. Thirty-one patients underwent unilateral mastectomy, while twenty patients underwent bilateral surgery. In 25 breasts, immediate reconstruction was performed with implants and ADM. In 40 breasts, reconstruction was performed with a subpectoral tissue expander, and in 6 breasts, reconstruction was performed with a DIEP flap. We observed only one case (1.4%) of periprosthetic infection requiring implant removal under general anesthesia. Minor complications occurred in 14.1% of patients. The use of both the WIBB flap and our algorithm maintained a low complication rate in our series, ensuring oncological radicality and a good aesthetic result at the same time.

AbstractRadiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Background:. Patients with previous breast augmentation may need implant removal for mechanical complications or other causes. After prosthesis removal, the residual parenchyma can be reshaped through a mastopexy with rearrangement of breast tissue. Several techniques have been described in the literature, but none of them can be considered the gold standard. In this study, we present our preliminary experience in breast tissue rearranging after implant removal through a novel technique: the “octopus head” dermoglandular flap. Methods:. From January 2019 to October 2022, nine patients (18 breasts) underwent implant removal and simultaneous breast remodeling with the tissue obtained from the dermoglandular excess of the breast and shaped like an octopus head. Patient’s demographic and clinical characteristics, postoperative complications, and patient-reported satisfaction were recorded. Results:. Mean age was 46.7 years. Body mass index ranged between 22.5 and 27.6 kg per m2. The majority of patients had moderate ptosis (67%). Breast implants were removed due to bilateral capsular contracture (n = 3), unilateral implant rupture with contralateral capsular contracture (n = 2), bilateral implant rupture (n = 3), and unilateral periprosthetic seroma (n = 1). We observed two minor complications: one postoperative hemorrhage with subsequent hematoma that was managed conservatively, and one nipple–areola complex malposition that underwent revision surgery. All patients were satisfied with the aesthetic and functional result. Conclusions:. The octopus head dermoglandular flap has proved to be a safe and reliable option for breast tissue rearranging after implant removal, providing a good and stable cosmetic result, a low complication rate, and high patient-reported satisfaction.

Chimeric antigen receptor (CAR) T-cell therapy has proven a breakthrough in cancer treatment in the last decade, giving unprecedented results against hematological malignancies. All approved CAR T-cell products, as well as many being assessed in clinical trials, are generated using viral vectors to deploy the exogenous genetic material into T-cells. Viral vectors have a long-standing clinical history in gene delivery, and thus underwent iterations of optimization to improve their efficiency and safety. Nonetheless, their capacity to integrate semi-randomly into the host genome makes them potentially oncogenic via insertional mutagenesis and dysregulation of key cellular genes. Secondary cancers following CAR T-cell administration appear to be a rare adverse event. However several cases documented in the last few years put the spotlight on this issue, which might have been underestimated so far, given the relatively recent deployment of CAR T-cell therapies. Furthermore, the initial successes obtained in hematological malignancies have not yet been replicated in solid tumors. It is now clear that further enhancements are needed to allow CAR T-cells to increase long-term persistence, overcome exhaustion and cope with the immunosuppressive tumor microenvironment. To this aim, a variety of genomic engineering strategies are under evaluation, most relying on CRISPR/Cas9 or other gene editing technologies. These approaches are liable to introduce unintended, irreversible genomic alterations in the product cells. In the first part of this review, we will discuss the viral and non-viral approaches used for the generation of CAR T-cells, whereas in the second part we will focus on gene editing and non-gene editing T-cell engineering, with particular regard to advantages, limitations, and safety. Finally, we will critically analyze the different gene deployment and genomic engineering combinations, delineating strategies with a superior safety profile for the production of next-generation CAR T-cell.

Large-scale graph processing and Stream processing are two distinct computational paradigms for big data processing. Graph processing deals with computation on graphs of billions of vertices and edges. However, large-scale graph processing frameworks mostly work on graphs that do not change over time, while on the other end of the spectrum, stream processing operates on a continuous stream of data in real-time. Modern-day graphs change very rapidly over time, and finding patterns in temporally evolving graphs could reveal a lot of insights that can not be unveiled using traditional graph computations. We have proposed a novel framework called FlowGraph which could find patterns in dynamic and temporally evolving graphs. Computations on large-scale graphs are iterative and take multiple steps before final results can be calculated, which is very different from stream processing which is one-shot computation. Therefore, the most critical bottleneck of such a system is the time required to process the query. In this work, we have proposed a query optimization technique that could reduce the time required to process the pattern. The proposed system has an optimization technique that could reduce the time required to process the pattern, especially those related to the temporal evolution of the graph. Our method shows for eight clauses the execution time is reduced by 75%, we also proved that this improvement is not affected by the scaling of the graph or the change of elements in given clauses.

Abstract Shared travel offers an important way to increase the accessibility of rail services. However, providing an integrated shared travel capability for rail travel is both a conceptual and technical challenge. This paper presents an overview of Ride2Rail, enabling ‘Easy use for all’ of rail through ridesharing as part of a multimodal journey. Ride2Rail has the overall objective of developing intelligent multimodal mobility, by facilitating the efficient combination of flexible and crowdsourced transport services, such as ridesharing, with scheduled transport. A requirements activity has set out the travel behaviour and system requirements for Ride2Rail. Development activities have covered the technical implementation of Ride2Rail, involving both development of the Ride2Rail functionalities and the Ride2Rail Driver Companion application, integrated within the wider Shift2Rail ecosystem. Demonstration activities have involved the preparation, implementation, execution and monitoring of Ride2Rail at four demonstration sites. This paper outlines the overall approach and findings of the Ride2Rail. This demonstrates the technical feasibility of integrating shared travel, including the architecture for a shared ride capability that can be readily integrated into pre-existing Mobility as a Service (MaaS) platform. Additionally, the paper reports positive user attitudes to this kind of shared travel, within the context of multimodal trips.

Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (“Patient selection” and “Scrolling through radiograph”) with respect to the second attempt, but without statistically significant difference (respectively $p$ = 0.14 and $p$ = 0.13, $p < 0.05$ ). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.

Genome engineering technologies are powerful tools in cell-based immunotherapy to optimize or fine-tune cell functionalities. However, their use for multiple gene edits poses relevant biological and technical challenges. Short hairpin RNA (shRNA)-based cell engineering bypasses these criticalities and represents a valid alternative to CRISPR-based gene editing. Here, we describe a microRNA (miRNA)-based multiplex shRNA platform obtained by combining highly efficient miRNA scaffolds into a chimeric cluster, to deliver up to four shRNA-like sequences. Thanks to its limited size, our cassette could be deployed in a one-step process along with all the CAR components, streamlining the generation of engineered CAR T cells. The plug-and-play design of the shRNA platform allowed us to swap each shRNA-derived guide sequence without affecting the system performance. Appropriately choosing the target sequences, we were able to either achieve a functional KO, or fine-tune the expression levels of the target genes, all without the need for gene editing. Through our strategy we achieved easy, safe, efficient, and tunable modulation of multiple target genes simultaneously. This approach allows for the effective introduction of multiple functionally relevant tweaks in the transcriptome of the engineered cells, which may lead to increased performance in challenging environments, e.g., solid tumors.

Neural cryptanalysis is the study of cryptographic primitives through machine learning techniques. Following Gohr’s seminal paper at CRYPTO 2019, a focus has been placed on improving the accuracy of such distinguishers against specific primitives, using dedicated training schemes, in order to obtain better key recovery attacks based on machine learning. These distinguishers are highly specialized and not trivially applicable to other primitives. In this paper, we focus on the opposite problem: building a generic pipeline for neural cryptanalysis. Our tool is composed of two parts. The first part is an evolutionary algorithm for the search of good input differences for neural distinguishers. The second part is DBitNet, a neural distinguisher architecture agnostic to the structure of the cipher. We show that this fully automated pipeline is competitive with a highly specialized approach, in particular for SPECK32, and SIMON32. We provide new neural distinguishers for several primitives (XTEA, LEA, HIGHT, SIMON128, SPECK128) and improve over the state-of-the-art for PRESENT, KATAN, TEA and GIMLI.

The essay reconstructs the conflict over emancipation that unfolded in the United States in the decades before the Civil War, focusing on the discourse of Northern political economy and particularly on the writings of Henry Charles Carey (1793-1879). The essay’s goal is to show that, between the 1830s and the 1850s, Northern economists, against radical abolitionism, elaborated a gradual and limited vision of emancipation, in the attempt to guarantee, even after the end of slavery, the command over black labor in the South and the persistence of American capitalism’s racial and class hierarchies. Moreover, the essay highlights how this vision of emancipation, despite being influential on the Republican Party and the Lincoln administration, was defeated by the slaves’ «general strike» during the Civil War.

The ability to perform automated conversions between different data formats is key to achieving interoperability between heterogeneous systems. Conversions require the definition of mappings between concepts of separate data specifications, which is typically a difficult and time-consuming task. In this article, we present a technique that exploits, in part, semantic web technologies to automatically suggest mappings to users based on both linguistic and structural similarities between terms of different data specifications. In addition, we show how a machine-learned linguistic model created by gathering data from domain-specific sources can help increase the accuracy of the suggested mappings. The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests using specifications from the transportation domain.

Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare. Accordingly, this study introduces an automated AI-based pipeline to replace the current operator-based tibia and femur 3D reconstruction procedure enhancing TKA preoperative planning. Leveraging an 822 CT image dataset, a novel patch-based method and an improved segmentation label generation algorithm were coupled to a Combined Edge Loss UNet (CEL-UNet), a novel CNN architecture featuring an additional decoding branch to boost the bone boundary segmentation. Root Mean Squared Errors and Hausdorff distances compared the predicted surfaces to the reference bones showing median and interquartile values of 0.26 (0.19–0.36) mm and 0.24 (0.18–0.32) mm, and of 1.06 (0.73–2.15) mm and 1.43 (0.82–2.86) mm for the tibia and femur, respectively, outperforming previous results of our group, state-of-the-art, and UNet models. A feasibility analysis for a PSI-based surgical plan revealed sub-millimetric distance errors and sub-angular alignment uncertainties in the PSI contact areas and the two cutting planes. Finally, operational environment testing underscored the pipeline’s efficiency. More than half of the processed cases complied with the PSI prototyping requirements, reducing the overall time from 35 min to 13.1 s, while the remaining ones underwent a manual refinement step to achieve such PSI requirements, performing the procedure four to eleven times faster than the manufacturer standards. To conclude, this research advocates the need for real-world applicability and optimization of AI solutions in orthopedic surgical practice.

Metabolic reprogramming is associated with cancer development and therapy resistance. Here, the authors show that downregulation of the serine biosynthesis enzyme PHGDH in a fraction of patients is associated with relapse in platinum-treated ovarian cancers and to NAD+ and PARP activity upregulation.

Abstract Background KRAS is the predominant oncogene mutated in pancreatic ductal adenocarcinoma (PDAC), the fourth cause of cancer-related deaths worldwide. Mutant KRAS-driven tumors are metabolically programmed to support their growth and survival, which can be used to identify metabolic vulnerabilities. In the present study, we aimed to understand the role of extracellularly derived fatty acids in KRAS-driven pancreatic cancer. Methods To assess the dependence of PDAC cells on extracellular fatty acids we employed delipidated serum or RNAi-mediated suppression of ACSL3 (to inhibit the activation and cellular retention of extracellular fatty acids) followed by cell proliferation assays, qPCR, apoptosis assays, immunoblots and fluorescence microscopy experiments. To assess autophagy in vivo, we employed the Kras G12D/+ ;p53 flox/flox ;Pdx1-CreERT2 (KPC) mice crossed with Acsl3 knockout mice, and to assess the efficacy of the combination therapy of ACSL3 and autophagy inhibition we used xenografted human cancer cell-derived tumors in immunocompromised mice. Results Here we show that depletion of extracellularly derived lipids either by serum lipid restriction or suppression of ACSL3, triggers autophagy, a process that protects PDAC cells from the reduction of bioenergetic intermediates. Combined extracellular lipid deprivation and autophagy inhibition exhibits anti-proliferative and pro-apoptotic effects against PDAC cell lines in vitro and promotes suppression of xenografted human pancreatic cancer cell-derived tumors in mice. Therefore, we propose lipid deprivation and autophagy blockade as a potential co-targeting strategy for PDAC treatment. Conclusions Our work unravels a central role of extracellular lipid supply in ensuring fatty acid provision in cancer cells, unmasking a previously unappreciated metabolic vulnerability of PDAC cells.

Frequency-domain electromagnetic induction (EMI) methods are commonly used to map vast areas quickly and with minimum logistical efforts. Unfortunately, they are often characterized by a very limited number of frequencies and severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) approaches are usually considered more reliable; for example, they do not require specific calibration procedures and can be easily inverted in 2D/3D. However, ERT surveys are, by far, more demanding and time consuming, allowing for the deployment of a few acquisition lines per day. Ideally, the optimal would be to have the advantages of both approaches: ease of acquisition while keeping robustness and reliability. The present work raises from the necessity to cope with this issue and from the importance of enforcing realistic constraints to the data inversion without being limited to (over)simplistic spatial constraints (for example, characterizing the smooth and/or sharp regularization). Accordingly, the present research demonstrates, by means of synthetic and field data, how the EMI inversion—based on realistic prior models—can be further enhanced by incorporating additional pre-existing pieces of information. While the proposed scheme is quite general, in the specific examples discussed here, these additional pieces of information are, respectively, a reference model along a line across the survey area, and an ERT section. The field EMI results were verified against extensive ground penetrating radar (GPR) measurements and boreholes.

Successful cancer immune therapy correlates with a T cell-inflamed tumour microenvironment. Authors show here that co-administration of a self-adjuvanting protein vaccine and an antigen-expressing oncolytic virus in an optimised regimen strongly enhances T cell immunogenicity and may turn non-inflamed tumours proinflammatory and less resistant to checkpoint blockade therapy.

Inducible nitric oxide synthase (iNOS) is essential to the production of nitric oxide (NO), an efficient effector molecule against intracellular human pathogens such as Leishmania protozoan parasites. Some strains of Leishmania are known to bear a viral endosymbiont termed Leishmania RNA virus 1 (LRV1). Recognition of LRV1 by the innate immune sensor Toll-like receptor-3 (TLR3) leads to conditions worsening the disease severity in mice. This process is governed by type I interferon (type I IFNs) arising downstream of TLR3 stimulation and favoring the formation of secondary metastatic lesions. The formation of these lesions is mediated by the inflammatory cytokine IL-17A and occurs in the absence, or low level of, protective cytokine IFN-γ. Here, we described that the presence of LRV1 led to the initial expression of iNOS and low production of NO that failed to control infection. We subsequently showed that LRV1-triggered type I IFN was essential but insufficient to induce robust iNOS induction, which requires strong activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). Leishmania guyanensis carrying LRV1 (LgyLRV1+) parasites mitigated strong iNOS production by limiting NF-kB activation via the induction of tumor necrosis factor-alpha-induced protein 3 (TNFAIP3), also known as A20. Moreover, our data suggested that production of LRV1-induced iNOS could be correlated with parasite dissemination and metastasis via elevated secretion of IL-17A in the draining lymph nodes. Our findings support an additional strategy by which LRV1-bearing Leishmania guyanensis evaded killing by nitric oxide and suggest that low levels of LRV1-induced NO might contribute to parasite metastasis.

Here, Pelliciari et al. present resonant inelastic X-ray scattering on monolayer samples of unconventional superconductor FeSe, finding evidence for gapped and dispersionless spin excitations. These experiments are very difficult due to the extremely small scattering volume of the FeSe monolayer.

In the future, assistive robots will spread to everyday settings and regularly interact with humans. This paper introduces a deployment approach for assistive robotic applications where human-robot interaction is the main element. The deployment infrastructure hinges on a model-to-code transformation technique and a ROS-based middleware layer and enables deployment in real life or simulation in a virtual environment. The approach fits into a model-driven framework for the formal verification of interactive scenarios. At design-time, the application analyst estimates the most likely outcome of the robotic mission through Statistical Model Checking of a Stochastic Hybrid Automata network modeling the scenario. We introduce an innovative approach to convert a specific subset of Stochastic Hybrid Automata into executable code to control the robot and respond to human actions. Deploying or simulating the application allows analysts to validate the results obtained at design time or to refine the formal model based on runs in the real or the virtual scene. The methodology’s effectiveness is tested via simulation of use cases from the healthcare setting, which can significantly benefit from this kind of approach thanks to its innovative features related to human physiology and autonomous behavior.

Dipartimento di Culture, Politica e Società, Università di Torino, 9 ottobre 2020

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Unet architectures are being investigated for automatic image segmentation of bones in CT scans because of their ability to address size-varying anatomies and pathological deformations. Nonetheless, changes in mineral density, narrowing of joint spaces and formation of largely irregular osteophytes may easily disrupt automatism requiring extensive manual refinement. A novel Unet variant, called CEL-Unet, is presented to boost the segmentation quality of the femur and tibia in the osteoarthritic knee joint. The neural network embeds region-aware and two contour-aware branches in the decoding path. The paper features three main technical novelties: 1) directed connections between contour and region branches progressively at different decoding scales; 2) pyramidal edge extraction in the contour branch to perform multi-resolution edge processing; 3) distance-weighted cross-entropy loss function to increase delineation quality at the sharp edges of the shapes. A set of 700 knee CT scans was used to train the model and test segmentation performance. Qualitatively CEL-Unet correctly segmented cases where the state-of-the-art architectures failed. Quantitatively, the Jaccard indexes of femur and tibia segmentation were 0.98 and 0.97, with median 3D reconstruction errors less than 0.80 and 0.60 mm, overcoming competitive Unet models. The results were evaluated against knee arthroplasty planning based on personalized surgical instruments (PSI). Excellent agreement with reference data was found for femoral (0.11°) and tibial (0.05°) alignments of the distal and proximal cuts computed on the reconstructed surfaces. The bone segmentation was effective for large pathological deformations and osteophytes, making the techniques potentially usable in PSI-based surgical planning, where the reconstruction accuracy of the bony shapes is one of the main critical factors for the success of the operation.

The essay offers a critical overview of recent historiographies on the nineteenth-century United States that allows on the one hand to grasp the imperial and colonial character of U.S. nineteenth-century territorial expansion, on the other hand, it accounts for nineteenth-century U.S. industrial growth and market building both as a quest for independence on the world market and as an expansive imperial process. In this respect, an understanding of colonization and industry as the two driving forces of nineteenth-century U.S. history can prove crucial for a comprehensive interpretation of Friedrich List's and Henry Charles Carey's national project for capitalist development.

The genetic mechanisms underlying mate choice decisions can inform our understanding of speciation. A study on Heliconius butterflies identifies 5 candidate genes that would allow sympatric species to evolve distinct preferences without altering their visual perception of the wider environment.

Abstract Infections caused by protozoan parasites burden the world with huge costs in terms of human and animal health. Most parasitic diseases caused by protozoans are neglected, particularly those associated with poverty and tropical countries, but the paucity of drug treatments and vaccines combined with increasing problems of drug resistance are becoming major concerns for their control and eradication. In this climate, the discovery/repurposing of new drugs and increasing effort in vaccine development should be supplemented with an exploration of new alternative/synergic treatment strategies. Viruses, either native or engineered, have been employed successfully as highly effective and selective therapeutic approaches to treat cancer (oncolytic viruses) and antibiotic-resistant bacterial diseases (phage therapy). Increasing evidence is accumulating that many protozoan, but also helminth, parasites harbour a range of different classes of viruses that are mostly absent from humans. Although some of these viruses appear to have no effect on their parasite hosts, others either have a clear direct negative impact on the parasite or may, in fact, contribute to the virulence of parasites for humans. This review will focus mainly on the viruses identified in protozoan parasites that are of medical importance. Inspired and informed by the experience gained from the application of oncolytic virus- and phage-therapy, rationally-driven strategies to employ these viruses successfully against parasitic diseases will be presented and discussed in the light of the current knowledge of the virus biology and the complex interplay between the viruses, the parasite hosts and the human host. We also highlight knowledge gaps that should be addressed to advance the potential of virotherapy against parasitic diseases.

Hepatocytes grown in a dish are immature and do not metabolize compounds as a real liver would. Here, the authors supply stem cell-derived hepatocytes with amino acids at a higher concentration than nutritionally necessary, changing the metabolism of these cells, making them more mature and useful for drug screening and toxicity studies.

Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.

Será que o papel japonês é o material mais adequado para o facing de pintura sobre tela? Seria benéfica a utilização de papéis específicos adequados a cada caso? Este estudo procurou dar resposta a estas questões e dar a conhecer, dos papéis testados, qual o mais adequado ao facing em pintura a óleo sobre tela. Para o efeito, foram executadas maquetes, de três cores diferentes. Em cada cor foram realizadas duas texturas, uma lisa e outra empastada. Foram também definidas, em cada cor e textura, três áreas: duas para aplicação de dois vernizes distintos e outra para permanecer sem verniz. As maquetes foram realizadas para que se obtivessem todas as combinações dos materiais anteriormente descritos e cada papel foi testado em cada uma destas combinações. Realizados os ensaios e analisados os resultados, concluiu-se que, dos materiais testados, o papel japonês nem sempre foi o mais eficaz, tendo sido superado pelo Wet-strength Tissue, um papel celulósico, por demonstrar facilidade de aplicação, pela resistência do papel e pela perda de fibras.

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected.

Infantile onset hereditary spastic paralysis (IAHSP) is a rare neurological disease diagnosed in less than 50 children worldwide. It is transmitted with a recessive pattern and originates from mutations of the ALS2 gene, encoding for the protein alsin and involved in differentiation and maintenance of the upper motoneuron. The exact pathogenic mechanisms of IAHSP and other neurodevelopmental diseases are still largely unknown. However, previous studies revealed that, in the cytosolic compartment, alsin is present as an active tetramer, first assembled from dimer pairs. The C-terminal VPS9 domain is a key interaction site for alsin dimerization. Here, we present an innovative drug discovery strategy, which identified a drug candidate to potentially treat a patient harboring two ALS2 mutations: one truncation at lysine 1457 (not considered) and the substitution of arginine 1611 with a tryptophan (R1611W) in the C-terminus VPS9. With a protein modeling approach, we obtained a R1611W mutant model and characterized the impact of the mutation on the stability and flexibility of VPS9. Furthermore, we showed how arginine 1611 is essential for alsin’s homo-dimerization and how, when mutated to tryptophan, it leads to an abnormal dimerization pattern, disrupting the formation of active tetramers. Finally, we performed a virtual screening, individuating an already therapy-approved compound (MK4) able to mask the mutant residue and re-establishing the alsin tetramers in HeLa cells. MK4 has now been approved for compassionate use.

PROTACs® are expected to strongly impact the future of drug discovery. Therefore, in this work we firstly performed a statistical study to highlight the distribution of E3 ligases and POIs collected in PROTAC-DB, the main online database focused on degraders. Moreover, since the emerging technology of protein degradation deals with large and complex chemical structures, the second part of the paper focuses on how to set up a property-based design strategy to obtain oral degraders. For this purpose, we calculated a pool of seven previously ad hoc selected 2D descriptors for the 2258 publicly available degraders in PROTAC-DB (average values: MW= 972.9 Da, nC= 49.5, NAR= 4.5, PHI= 17.3, nHDon= 4.5, nHAcc= 17.7 and TPSA= 240 Å2) and compared them to a dataset of 50 bRo5 orally approved drugs. Then, a chemical space based on nC, PHI and TPSA was built and subregions with optimal permeability and bioavailability were identified. Bioavailable degraders (ARV-110 and ARV-471) tend to be closer to the Ro5 region, using mainly semi-rigid linkers. Permeable degraders, on the other hand, are placed in an average central region of the chemical space but chameleonicity could allow them to be located closer to the two Arvinas compounds. ©2021 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). ©2021 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

Combining different immunotherapy approaches is currently building the future of immunotherapy, with the view to maximize anti-tumoral efficacy for larger patient population. The KISIMA™ platform allows the development of protein-based cancer vaccines able to induce tumor-specific T cell response resulting in anti-tumoral efficacy in various mouse models. Intra-tumoral administration of stimulator of interferon gene agonists (STINGa) was shown to induce a potent inflammatory response leading to the development of tumor-specific immunity. Here, we explored the efficacy and mechanisms of action of subcutaneous STINGa treatment combined with therapeutic vaccination in various mouse tumor models. This combinatory treatment highly enhanced frequency and effector function of both peripheral and intra-tumoral antigen-specific CD8 T cells, promoting potent IFNγ and TNFα production along with increased cytotoxicity. Moreover, combination therapy favorably modulated the tumor microenvironment by dampening immune-suppressive cells and increasing CD4 T cell infiltration together with their polarization toward Th1 phenotype. Combination with STINGa treatment improved the effect of therapeutic vaccination, resulting in a prolonged control and slower growth of B16-OVA and TC-1 tumors. Altogether, the results presented here highlight the potential of combining STINGa with a therapeutic protein vaccine for cancer treatment.

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.

In recent years, financial markets changed for globalization. Today, a wide range of investment opportunities is available to investors. In this new scenario, markets are related, but each of them has specific characteristics with particular opportunities for investors. An investment choice can be influenced by numerous qualitative and quantitative factors that often conflict with one other. Thus, portfolio management choice is a multi-criteria decision problem today, so it requires flexible and analytic decision tools for investors. For this task, the Analytic Hierarchy Process (AHP) is suitable. We propose a theoretical model to analyse an investment choice problem taking into account different financial markets. Return of stock market, performance of the government bond and calendar effects in the financial markets considered are the evaluation criteria used in our model. The proposed model has strengths and weaknesses. First, through the AHP methodology the problem can be decomposed into a dominance hierarchy. Then, the subjective judgements expressed by means of pairwise comparisons are cheked in order to verify their consistency. Moreover, it is flexible and allows us to check if the ranking changes based on varying criteria weights. However, in that model we assume that criteria and alternatives are independent. Furthermore, our research lacks of a numerical application to test the model. Keywords: AHP, calendar anomalies, government bonds, stock market, investment choice. JEL Classifications: C13, C44, G11, G14, G40 DOI: https://doi.org/10.32479/ijefi.8995

Il 2018 sta tramontando e con esso gli ultimi fuochi accesi per il quarantennale della legge Basaglia. [...]

Protozoan parasites contain negatively charged polymers of a few up to several hundreds of phosphate residues. In other organisms, these polyphosphate (polyP) chains serve as an energy source and phosphate reservoir, and have been implicated in adaptation to stress and virulence of pathogenic organisms. In this study, we confirmed first that the polyP polymerase vacuolar transporter chaperone 4 (VTC4) is responsible for polyP synthesis in Leishmania parasites. During Leishmania in vitro culture, polyP is accumulated in logarithmic growth phase and subsequently consumed once stationary phase is reached. However, polyP is not essential since VTC4-deficient (vtc4–) Leishmania proliferated normally in culture and differentiated into infective metacyclic parasites and into intracellular and axenic amastigotes. In in vivo mouse infections, L. major VTC4 knockout showed a delay in lesion formation but ultimately gave rise to strong pathology, although we were unable to restore virulence by complementation to confirm this phenotype. Knockdown of VTC4 did not alter the course of L. guyanensis infections in mice, suggesting that polyP was not required for infection, or that very low levels of it suffice for lesion development. At higher temperatures, Leishmania promastigotes highly consumed polyP, and both knockdown or deletion of VTC4 diminished parasite survival. Thus, although polyP was not essential in the life cycle of the parasite, our data suggests a role for polyP in increasing parasite survival at higher temperatures, a situation faced by the parasite when transmitted to humans.

The various symptomatic outcomes of cutaneous leishmaniasis relates to the type and potency of its underlying inflammatory responses. Presence of the cytoplasmic Leishmania RNA virus-1 (LRV1) within Leishmania guyanensis, worsens lesional inflammation and parasite burden, as the viral dsRNA genome acts as a potent innate immunogen stimulating Toll-Like-Receptor-3 (TLR3). Here we investigated other innate pattern recognition receptors capable of reacting to dsRNA and potentially contributing to LRV1-mediated inflammatory pathology. We included the cytoplasmic dsRNA sensors, namely, the RIG-like receptors (RLRs) and the inflammasome-dependent and -independent Nod-like-receptors (NLRs). Our study found no role for RLRs or inflammasome-dependent NLRs in the pathology of L. guyanensis infection irrespective of its LRV1-status. Further, neither LRV1-bearing L. guyanensis (LgyLRV1+) nor LRV1-negative L. guyanensis (LgyLRV1-) activated the inflammasome in vitro. Interestingly, similarly to L. donovani, L. guyanensis infection induced the up-regulation of the A20 protein, known to be involved in the evasion of inflammasome activation. Moreover, we observed that LgyLRV1+ promoted the transcription of inflammasome-independent NLRC2 (also called NOD2) and NLRC5. However, only NLRC2 showed some contribution to LRV1-dependent pathology. These data confirmed that the endosomal TLR3 pathway is the dominant route of LRV1-dependent signalling, thus excluding the cytosolic and inflammasome pathways. We postulate that avoidance of the inflammasome pathways is likely an important mechanism of virulence in Leishmania infection irrespective of the LRV1-status.

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

Eye tracking techniques based on deep learning are rapidly spreading in a wide variety of application fields. With this study, we want to exploit the potentiality of eye tracking techniques in ocular proton therapy (OPT) applications. We implemented a fully automatic approach based on two-stage convolutional neural networks (CNNs): the first stage roughly identifies the eye position and the second one performs a fine iris and pupil detection. We selected 707 video frames recorded during clinical operations during OPT treatments performed at our institute. 650 frames were used for training and 57 for a blind test. The estimations of iris and pupil were evaluated against the manual labelled contours delineated by a clinical operator. For iris and pupil predictions, Dice coefficient (median = 0.94 and 0.97), Szymkiewicz–Simpson coefficient (median = 0.97 and 0.98), Intersection over Union coefficient (median = 0.88 and 0.94) and Hausdorff distance (median = 11.6 and 5.0 (pixels)) were quantified. Iris and pupil regions were found to be comparable to the manually labelled ground truths. Our proposed framework could provide an automatic approach to quantitatively evaluating pupil and iris misalignments, and it could be used as an additional support tool for clinical activity, without impacting in any way with the consolidated routine.

Abstract Background Roughly one-quarter of upper limb prosthesis users reject their prosthesis. Reasons for rejection range from comfort, to cost, aesthetics, function, and more. This paper follows a single user from training with and testing of a novel upper-limb myoelectric prosthesis (the SoftHand Pro) for participation in the CYBATHLON rehearsal to training for and competing in the CYBATHLON 2016 with a figure-of-nine harness controlled powered prosthesis (SoftHand Pro-H) to explore the feasibility and usability of a flexible anthropomorphic prosthetic hand. Methods The CYBATHLON pilot took part in multiple in-lab training sessions with the SoftHand Pro and SoftHand Pro-H; these sessions focused on basic control and use of the prosthetic devices and direct training of the tasks in the CYBATHLON. He used these devices in competition in the Powered Arm Prosthesis Race in the CYBATHLON rehearsal and 2016 events. Results In training for the CYBATHLON rehearsal, the subject was able to quickly improve performance with the myoelectric SHP despite typically using a body-powered prosthetic hook. The subject improved further with additional training using the figure-of-nine harness-controlled SHPH in preparation for the CYBATHLON. The Pilot placed 3rd (out of 4) in the rehearsal. In the CYBATHLON, he placed 5th (out of 12) and was one of only two pilots who successfully completed all tasks in the competition, having the second-highest score overall. Conclusions Results with the SoftHand Pro and Pro-H suggest it to be a viable alternative to existing anthropomorphic hands and show that the unique flexibility of the hand is easily learned and exploited.

Proportional and simultaneous control algorithms are considered as one of the most effective ways of mapping electromyographic signals to an artificial device. However, the applicability of these methods is limited by the high number of electromyographic features that they require to operate—typically twice as many the actuators to be controlled. Indeed, extracting many independent electromyographic signals is challenging for a number of reasons—ranging from technological to anatomical. On the contrary, the number of actively moving parts in classic prostheses or extra-limbs is often high. This paper faces this issue, by proposing and experimentally assessing a set of algorithms which are capable of proportionally and simultaneously control as many actuators as there are independent electromyographic signals available. Two sets of solutions are considered. The first uses as input electromyographic signals only, while the second adds postural measurements to the sources of information. At first, all the proposed algorithms are experimentally tested in terms of precision, efficiency, and usability on twelve able-bodied subjects, in a virtual environment. A state-of-the-art controller using twice the amount of electromyographic signals as input is adopted as benchmark. We then performed qualitative tests, where the maps are used to control a prototype of upper limb prosthesis. The device is composed of a robotic hand and a wrist implementing active prono-supination movement. Eight able-bodied subjects participated to this second round of testings. Finally, the proposed strategies were tested in exploratory experiments involving two subjects with limb loss. Results coming from the evaluations in virtual and realistic settings show encouraging results and suggest the effectiveness of the proposed approach.

Il terzo numero 2018 di RP avrà un focus dedicato al tema della ricerca psichiatrica [...]

The coupling between lattice vibration quanta and valence electrons can induce charge-density modulations and decisively influence the transport properties of materials, e.g., leading to conventional superconductivity. In high-critical-temperature superconductors, where electronic correlation is the main actor, the actual role of electron-phonon coupling (EPC) is being intensely debated theoretically and investigated experimentally. We present an in-depth study of how the EPC strength can be obtained directly from resonant inelastic x-ray scattering (RIXS) data through the theoretical approach derived by Ament et al. [Europhys. Lett. 95, 27008 (2011)EULEEJ0295-507510.1209/0295-5075/95/27008]. The role of the model parameters (e.g., phonon energy ω_{0}, intermediate state lifetime 1/Γ, EPC matrix element M, and detuning energy Ω) is thoroughly analyzed, providing general relations among them that can be used to make quantitative estimates of the dimensionless EPC g=(M/ω_{0})^{2} without detailed microscopic modeling. We then apply these methods to very high-resolution Cu L_{3}-edge RIXS spectra of three Nd_{1+x}Ba_{2−x}Cu_{3}O_{7−δ} films. For the insulating antiferromagnetic parent compound, the value of M as a function of the in-plane momentum transfer is obtained for Cu-O bond-stretching (breathing) and bond-bending (buckling) phonon branches. For the underdoped and the nearly optimally doped samples, the effects of Coulomb screening and of charge-density-wave correlations on M are assessed. In light of the anticipated further improvements of the RIXS experimental resolution, this work provides a solid framework for an exhaustive investigation of the EPC in cuprates and other quantum materials.

The aim of this study is to analyse the impact of gender diversity on the financial performance of Italian listed companies. A cross-sectional regression used about the data of the year 2016 of Italian listed firms. The study is a mix of descriptive and explanatory research. The model is a scoring model, composed by two variables: Board of Directors (BoD), and Top Management (TM). We investigate the impact of these variables on the Return on Equity, the Price book value, and the EBITDA. The study shows a positive relationship between financial performance and the composition of the BoD and the operational performance and the composition of the Top management. The cross sectional regression highlights also the non-relation on the Return on Equity. Anyway these contrasting results confirm the properness of the model for this kind of research. Keywords: Gender Diversity, Financial Perfomance, Borad of Directors, Italian Listed Companies JEL Classifications: G32, M14, G30