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Abstract Background Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood traceability are needed. Here we show that the use of microbiome profiling (MP) coupled with machine learning (ML) allows precise tracing the origin of Manila clams harvested in areas separated by small geographic distances. The study was designed to represent a real-world scenario. Clams were collected in different seasons across the most important production area in Europe (lagoons along the northern Adriatic coast) to cover the known seasonal variation in microbiome composition for the species. DNA extracted from samples underwent the same depuration process as commercial products (i.e. at least 12 h in open flow systems). Results Machine learning-based analysis of microbiome profiles was carried out using two completely independent sets of data (collected at the same locations but in different years), one for training the algorithm, and the other for testing its accuracy and assessing the temporal stability signal. Briefly, gills (GI) and digestive gland (DG) of clams were collected in summer and winter over two different years (i.e. from 2018 to 2020) in one banned area and four farming sites. 16S DNA metabarcoding was performed on clam tissues and the obtained amplicon sequence variants (ASVs) table was used as input for ML MP. The best-predicting performances were obtained using the combined information of GI and DG (consensus analysis), showing a Cohen K-score > 0.95 when the target was the classification of samples collected from the banned area and those harvested at farming sites. Classification of the four different farming areas showed slightly lower accuracy with a 0.76 score. Conclusions We show here that MP coupled with ML is an effective tool to trace the origin of shellfish products. The tool is extremely robust against seasonal and inter-annual variability, as well as product depuration, and is ready for implementation in routine assessment to prevent the trade of illegally harvested or mislabeled shellfish.

Abstract Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson’s Paradox, and (2) model generalization limitations.

IntroductionProstate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research.MethodsThe TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study’s prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks.ResultsThe cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage.DiscussionThis study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.

Genetic markers (especially short tandem repeats or STRs) located on the X chromosome are a valuable resource to solve complex kinship cases in forensic genetics in addition or alternatively to autosomal STRs. Groups of tightly linked markers are combined into haplotypes, thus increasing the discriminating power of tests. However, this approach requires precise knowledge of the recombination rates between adjacent markers. The International Society of Forensic Genetics recommends that recombination rate estimation on the X chromosome is performed from pedigree genetic data while taking into account the confounding effect of mutations. However, implementations that satisfy these requirements have several drawbacks: they were never publicly released, they are very slow and/or need cluster-level hardware and strong computational expertise to use. In order to address these key concerns we developed Recombulator-X, a new open-source Python tool. The most challenging issue, namely the running time, was addressed with dynamic programming techniques to greatly reduce the computational complexity of the algorithm. Compared to the previous methods, Recombulator-X reduces the estimation times from weeks or months to less than one hour for typical datasets. Moreover, the estimation process, including preprocessing, has been streamlined and packaged into a simple command-line tool that can be run on a normal PC. Where previous approaches were limited to small panels of STR markers (up to 15), our tool can handle greater numbers (up to 100) of mixed STR and non-STR markers. In conclusion, Recombulator-X makes the estimation process much simpler, faster and accessible to researchers without a computational background, hopefully spurring increased adoption of best practices.

Diversity and inclusion are addressed within the economics of innovation in an evolutive perspective, by focusing a specific theme (access to information) along the diagonal line between the Gutenberg printing and the network digital technologies. Technology and the market modify the trade-off between diversity and inclusion and the degree of the system’s variety as well. In the transition from the analogic space (pluralism of standards, intermediaries, markets) to the network digital space (inclusive standardized ecosystems, oligopoly of technological intermediaries) variety of access to information has increased, but it is lower than the potential variety.

Abstract Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient’s treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.

Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Background: The exposome drivers are less studied than its consequences but may be crucial in identifying population subgroups with unfavourable exposures. Objectives: We used three approaches to study the socioeconomic position (SEP) as a driver of the early-life exposome in Turin children of the NINFEA cohort (Italy). Methods: Forty-two environmental exposures, collected at 18 months of age (N = 1989), were classified in 5 groups (lifestyle, diet, meteoclimatic, traffic-related, built environment).We performed cluster analysis to identify subjects sharing similar exposures, and intra-exposome-group Principal Component Analysis (PCA) to reduce the dimensionality. SEP at childbirth was measured through the Equivalised Household Income Indicator.SEP-exposome association was evaluated using: 1) an Exposome Wide Association Study (ExWAS), a one-exposure (SEP) one-outcome (exposome) approach; 2) multinomial regression of cluster membership on SEP; 3) regressions of each intra-exposome-group PC on SEP. Results: In the ExWAS, medium/low SEP children were more exposed to greenness, pet ownership, passive smoking, TV screen and sugar; less exposed to NO2, NOX, PM25abs, humidity, built environment, traffic load, unhealthy food facilities, fruit, vegetables, eggs, grain products, and childcare than high SEP children.Medium/low SEP children were more likely to belong to a cluster with poor diet, less air pollution, and to live in the suburbs than high SEP children.Medium/low SEP children were more exposed to lifestyle PC1 (unhealthy lifestyle) and diet PC2 (unhealthy diet), and less exposed to PC1s of the built environment (urbanization factors), diet (mixed diet), and traffic (air pollution) than high SEP children. Conclusions: The three approaches provided consistent and complementary results, suggesting that children with lower SEP are less exposed to urbanization factors and more exposed to unhealthy lifestyles and diet. The simplest method, the ExWAS, conveys most of the information and is more replicable in other populations. Clustering and PCA may facilitate results interpretation and communication.

Abstract Background Genomic DNA has been shaped by mutational processes through evolution. The cellular machinery for error correction and repair has left its marks in the nucleotide composition along with structural and functional constraints. Therefore, the probability of observing a base in a certain position in the human genome is highly context-dependent. Results Here we develop context-dependent nucleotide models. We first investigate models of nucleotides conditioned on sequence context. We develop a bidirectional Markov model that use an average of the probability from a Markov model applied to both strands of the sequence and thus depends on up to 14 bases to each side of the nucleotide. We show how the genome predictability varies across different types of genomic regions. Surprisingly, this model can predict a base from its context with an average of more than 50% accuracy. For somatic variants we show a tendency towards higher probability for the variant base than for the reference base. Inspired by DNA substitution models, we develop a model of mutability that estimates a mutation matrix (called the alpha matrix) on top of the nucleotide distribution. The alpha matrix can be estimated from a much smaller context than the nucleotide model, but the final model will still depend on the full context of the nucleotide model. With the bidirectional Markov model of order 14 and an alpha matrix dependent on just one base to each side, we obtain a model that compares well with a model of mutability that estimates mutation probabilities directly conditioned on three nucleotides to each side. For somatic variants in particular, our model fits better than the simpler model. Interestingly, the model is not very sensitive to the size of the context for the alpha matrix. Conclusions Our study found strong context dependencies of nucleotides in the human genome. The best model uses a context of 14 nucleotides to each side. Based on these models, a substitution model was constructed that separates into the context model and a matrix dependent on a small context. The model fit somatic variants particularly well.

An open challenge of computational and experimental biology is understanding the impact of non-synonymous DNA variations on protein function and, subsequently, human health. The effects of these variants on protein stability can be measured as the difference in the free energy of unfolding (ΔΔG) between the mutated structure of the protein and its wild-type form. Throughout the years, bioinformaticians have developed a wide variety of tools and approaches to predict the ΔΔG. Although the performance of these tools is highly variable, overall they are less accurate in predicting ΔΔG stabilizing variations rather than the destabilizing ones. Here, we analyze the possible reasons for this difference by focusing on the relationship between experimentally-measured ΔΔG and seven protein properties on three widely-used datasets (S2648, VariBench, Ssym) and a recently introduced one (S669). These properties include protein structural information, different physical properties and statistical potentials. We found that two highly used input features, i.e., hydrophobicity and the Blosum62 substitution matrix, show a performance close to random choice when trying to separate stabilizing variants from either neutral or destabilizing ones. We then speculate that, since destabilizing variations are the most abundant class in the available datasets, the overall performance of the methods is higher when including features that improve the prediction for the destabilizing variants at the expense of the stabilizing ones. These findings highlight the need of designing predictive methods able to exploit also input features highly correlated with the stabilizing variants. New tools should also be tested on a not-artificially balanced dataset, reporting the performance on all the three classes (i.e., stabilizing, neutral and destabilizing variants) and not only the overall results.

The high cosine similarity between some single-base substitution mutational signatures and their characteristic flat profiles could suggest the presence of overfitting and mathematical artefacts. The newest version (v3.3) of the signature database available in the Catalogue Of Somatic Mutations In Cancer (COSMIC) provides a collection of 79 mutational signatures, which has more than doubled with respect to previous version (30 profiles available in COSMIC signatures v2), making more critical the associations between signatures and specific mutagenic processes. This study both provides a systematic assessment of the de novo extraction task through simulation scenarios based on the latest version of the COSMIC signatures and highlights, through a novel approach using archetypal analysis, which COSMIC signatures are redundant and more likely to be considered as mathematical artefacts. 29 archetypes were able to reconstruct the profile of all the COSMIC signatures with cosine similarity >0.8. Interestingly, these archetypes tend to group similar original signatures sharing either the same aetiology or similar biological processes. We believe that these findings will be useful to encourage the development of new de novo extraction methods avoiding the redundancy of information among the signatures while preserving the biological interpretation.

Abstract Aims Risk stratification in patients with advanced chronic heart failure (HF) is an unmet need. Circulating microRNA (miRNA) levels have been proposed as diagnostic and prognostic biomarkers in several diseases including HF. The aims of the present study were to characterize HF‐specific miRNA expression profiles and to identify miRNAs with prognostic value in HF patients. Methods and results We performed a global miRNome analysis using next‐generation sequencing in the plasma of 30 advanced chronic HF patients and of matched healthy controls. A small subset of miRNAs was validated by real‐time PCR (P

Background Phoenician and Punic expansions have been protagonists of intense trade networks and settlements in the Mediterranean Sea. Aims The maternal genetic variability of ancient Punic samples from the Sardinian necropolis of Tharros was analysed, with the aim to explore genetic interactions and signatures of past population events. Subjects and methods The mtDNA HVS-I and coding region SNPs were analysed in 14 Punic samples and 74 modern individuals from Cabras and Belvì (for which the HVS-II region was also analysed). The results were compared with 5,590 modern Euro-Mediterranean sequences and 127 ancient samples. Results While contemporary groups fall within the genetic variability of other modern Sardinians, our Punic samples reveal proximity to present-day North-African and Iberian populations. Furthermore, Cabras and Belvì cluster mainly with pre-Phoenician groups, while samples from Tharros project with other Punic Sardinian individuals. Conclusion This study provides the first preliminary insights into the population dynamics of the Punic site of Tharros. While the number of currently available samples does not allow definitive investigation of the connection with indigenous Sardinian groups, our results seem to confirm internal migratory phenomena in the central-western Mediterranean and female participation in the Punic mobility.

Abstract Background Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. Results In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. Conclusions To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

Abstract This short report includes 5 eyes of 5 patients (mean age 63.2 ± 12 years) who underwent a tectonic keratoplasty [deep anterior lamellar keratoplasty (DALK) or penetrating keratoplasty (PK)] in order to rehabilitate the eye after the extrusion of the non-perforating keratoprosthesis (Kpro) KeraKlear (KeraMed, USA). The non-perforating Kpro was extruded after a mean period of 21.4 ± 21.8 months due to melting. In two cases, the keratoplasty was performed the same day of the non-perforating Kpro removal due to a severe melting, while in the other three cases it was performed one to 3 months later. Two eyes received a DALK, but in 3 eyes a macroscopic Descemet membrane perforation forced the conversion into a PK. The mean follow-up period after the keratoplasty was 16.8 ± 6.6 months. No cases of rejection were recorded. All the 5 eyes achieved “anatomical success” (transparent graft, with no signs of infection or inflammation). Two eyes showed limited “functional success” because the achievement of the best visual potential was prevented by the development of glaucomatous optic atrophy during the follow-up period. In conclusion, this short report presents an unexpected success of a keratoplasty performed with a tectonic purpose after the extrusion of the non-perforating Kpro because the corneal graft remained transparent, without neovascularization or scarring during the follow-up period. This initial evidence shows some encouraging results regarding graft survival rate and the achievement of a useful visual rehabilitation with keratoplasty after a non-perforating Kpro failure instead of repeating the Kpro implantation.

Abstract Background To analyze the clinical results of an artificial neural network (ANN) that has been processed in order to improve the predictability of intracorneal ring segments (ICRS) implantation in keratoconus. Methods This retrospective, comparative, nonrandomized, pilot, clinical study included a cohort of 20 keratoconic eyes implanted with intracorneal ring segments KeraRing (Mediphacos, Belo Horizonte, Brazil) using the ANN (ANN group) and 20 keratoconic eyes implanted with KeraRing using the manufacturer’s nomograms (nomogram group). Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA) (visual acuity is expressed in decimal value and in LogMAR value in brackets), manifest refraction, corneal topography, tomography, aberrometry, pachymetry and volume analysis (Sirius System. CSO, Firenze, Italy) were performed during the preoperative visit; and the two groups, ANN group and nomogram group, did not differ significantly preoperatively in all of the parameters evaluated. These preoperative values were compared with the results obtained at the third-month visit. Mann-Whitney test and Wilcoxon test were used for the statistical analyses. Results The spherical equivalent and the keratometric values decreased significantly in both groups. The CDVA improved from 0.60 ± 0.23 (0.22 LogMAR) pre-operatively to 0.73 ± 0.21 (0.14 LogMAR) post-operatively in the ANN group (p

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

Abstract Background Single session radiosurgery represents a widely accepted treatment for intracranial meningiomas. However, this approach could involve a high risk of treatment-related complications when applied to large volume lesions. In these cases and for those not suitable for surgical resection, radiosurgery in multisession setting could represents a viable option. The literature results are reassuring in terms of correlated adverse events as well as in terms of tumor control. However, no prospective long-term results are available. In this scenario, we design a prospective monocentric phase II study, in order to verify the safety of a multisession radiosurgery schedule delivering 25 Gy in 5 daily fractions. Methods Patients diagnosed with large and/or near to critical structures, intracranial meningiomas have been treated by means of multisession radiosurgery in both exclusive and postoperative settings. The primary study aim is safety that has been being prospectively scored based on international scales, including NCI Common Toxicity criteria, version 4.03, Barrow Neurological Institute pain intensity score, Barrow Neurological Institute facial numbness score and House-Brackmann Facial Nerve Grading System for qualitative analysis. Secondary aim is treatment efficacy in terms of local control that has been being assessed on volumetric analysis. Discussion This is the first prospective phase II trial on multisession radiosurgery for large and/or near to critical structures intracranial meningiomas. If positive results will be found, this study could represent the starting point for a phase III trial exploring the role of multisession radiosurgery in the exclusive and postoperative radiation therapy treatment of intracranial meningiomas. Trial registration Trial registration: clinicaltrials.gov platform (Multisession Radiosurgery in Large Meningiomas –MuRaLM- identifier NCT02974127). Registered: November 28, 2016. Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02974127?term=radiosurgery&cond=Intracranial+Meningioma&draw=2&rank=1

V Pinzi,1 L Fariselli,1 M Marchetti,1 M Scorsetti,2 P Navarria2 1Neurosurgery Department, Radiotherapy Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; 2Radiotherapy and Radiosurgery Department, Humanitas Cancer Center and Research Hospital, Rozzano, Milan, ItalyCorrespondence: V PinziNeurosurgery Department, Radiotherapy Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Via G. Celoria 11, Milan 20131, ItalyTel +390223942312Email valentinapinzi@yahoo.itAbstract: Treatment options for intracranial meningiomas are surgical resection alone, surgery followed by adjuvant radiation therapy (RT), or exclusive RT. Parasagittal and parafalcine meningiomas are a subgroup of meningeal disease located close to the vascular structures. Considering the frequent venous invasion, a complete resection is not possible in the majority of cases, and even if a Simpson Grade I resection can be performed, the risk of recurrence is relevant. To date, few studies are focused on parasagittal and parafalcine meningiomas. Because of their specific related issues, particular considerations on decision-making process, outcome, and toxicity follow-up are mandatory. In fact, parasagittal and parafalcine meningiomas require a clear-cut radiological assessment, as well as a tailored toxicity risk evaluation. Moreover, similarly to other meningioma sites, also for parasagittal and parafalcine ones, a standardization of local control, toxicity, and quality of life evaluation is needed in order to lead to a pooled analysis of the results. In this context, our aim was to review the literature data regarding the role of both single-session and multisession radiosurgery (RS), and stereotactic radiotherapy (SRT) for parasagittal and parafalcine meningioma management, summarizing available data on safety and efficacy. It was also discussed how RS and SRT can be performed in a setting of evolving views concerning the treatment paradigm of the parasagittal and parafalcine meningiomas.Keywords: meningioma, parasagittal meningioma, parafalcine meningioma, stereotactic radiotherapy, radiosurgery, radiotherapy

Abstract Background Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ∆∆G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ∆∆G(A → B) = −∆∆G(B → A), where A and B are amino acids. Results Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ∆∆G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ∆∆G values of ~ 0.5 and ~ 0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ∆∆G of a reciprocal variation as almost equal (depending on the sequence profile) to -∆∆G of the direct variation. This is a valuable property that is missing in the majority of the methods. Conclusions Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ∆∆G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods.

The IBA myQA® SRS high-resolution solid-state detector was evaluated in the context of robotic radiosurgery delivered using CyberKnife®. The performance was investigated for periodic machine delivery quality assurance (DQA) and patient-specific treatment verification. myQA® SRS is a 140 × 120 mm CMOS matrix with 400 µm resolution, allocated in a cylindrical ABS phantom topped by a hemispheric cap. Evaluations included: periodic DQA tests, angular response, dose-rate dependence and Iris variable aperture collimator field size measurements. For patient-specific QA various intracranial targets were studied (Gamma Index, 3%/1 mm agreement criteria), taking into account also the detector’s angular response. Results for periodic DQA were in accordance with the machine commissioning data. Dose-rate dependence was confirmed, and angular response tests resulted in a signal decay >5% when beams were delivered outside a ±50° amplitude cone with respect to the vertical direction. Concerning patient-specific QA, >50° angled beams elimination led to a Gamma Index passing rates improvement ranging between +3% and +115%. IBA myQA® SRS proved to be a suitable device for many CyberKnife® constancy DQA checks, providing high-resolution real-time results. Patient-specific Gamma tests showed high passing rates once angular dependence corrections were performed, even in high complexity treatments such as those for trigeminal neuralgia targets.

During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility.

Chiara Fariselli, Giuseppe Giannaccare, Michela Fresina, Piera Versura Ophthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine, School of Medicine (DIMES), Alma Mater Studiorum University of Bologna and St Orsola-Malpighi Teaching Hospital, Bologna, Italy Aim: To assess the ocular surface parameters, inflammatory marker level in tears, and mucin expression in conjunctival epithelium before and after treatment with trehalose/hyaluronate tear substitute in dry eye (DE) patients.Patients and methods: Fifteen DE patients were evaluated in an open-label, pilot study at enrollment, after 2 days of washout (baseline) and after 1 and 2 months (endpoint) of treatment with a trehalose/hyaluronate tear substitute (one drop/eye/three times daily). Data for symptoms of discomfort (Ocular Surface Disease Index and Visual Analogue Scale pain score), tear film (Schirmer test I, tear film breakup time), ocular surface damage (corneal National Eye Institute) and conjunctival van Bijsterveld scores, impression cytology scored by Nelson’s grade and goblet cells (GCs) number/mm2 analysis, and MUC4 immunostaining, and inflammation (interleukin [IL]-1β, IL-6, and IL-8 levels) were measured.Results: Significant changes at endpoint as compared to baseline were found for Ocular Surface Disease Index score (respectively, mean±SD, 22.2±2.9 vs 38.7±12.7), Visual Analogue Scale score (3.4±1.3 vs 6.6±1.4), tear film breakup time (8.6±1.28 vs 6.17±1.9 seconds), corneal staining (National Eye Institute grade 1.23±0.64 vs 3.37±0.49), conjunctival staining (1.73±1.14 vs 4.17±0.91), impression cytology (Nelson grade 1.10±0.20 vs 1.63±0.54), and GC density (139.9±22.0 vs 107.8±16.2 GC/mm2). IL-1β, IL-6, and IL-8 tear levels showed a significant decrease at endpoint as compared to baseline (respectively, pg/mL tears: 12.3±6.9, 26.6±25.2, 743.5±477.7 vs 33.6±17.3, 112.0±24.3, 1,139.2±671.7).Conclusions: A decrease in ocular discomfort symptoms, surface damage, and tear cytokine levels was shown after 2 months’ treatment with trehalose/hyaluronate tear substitute in DE patients, along with a significant GC density recovery. These results may be associated with the synergic action of both trehalose and hyaluronic acid in targeting different entries of the DE vicious loop. Keywords: dry eye, trehalose, hyaluronic acid, tear substitute, inflammation

Abstract Background Local recurrences after breast conserving treatment are mainly close to the original tumor site, and as such shorter fractionation strategies focused on and nearest mammary gland, i.e. accelerated partial breast irradiation (APBI), have been developed. Stereotactic APBI has been attempted, although there is little experience using CyberKnife (CK) for early breast cancer. Methods This pilot study was designed to assess the feasibility of CK-APBI on 20 evaluable patients of 29 eligible, followed for 2 years. The primary endpoint was acute/sub-acute toxicity; secondary endpoints were late toxicity and the cosmetic result. Results Mean pathological tumor size was 10.5 mm (±4.3, range 3–18), 8 of these patients were classified as LumA-like, 11 as LumB-like, and 1 as LumB-HER2-enriched. Using CK-APBI with Iris, the treatment time was approximately 60 min (range~ 35 to ~ 120). All patients received 30 Gy in five fractions delivered to the PTV. The median number of beams was 180 (IQR 107–213; range:56–325) with a median PTV isodose prescription of 86.0% (IQR 85.0–88.5; range:82–94). The median PTV was 88.1 cm3 (IQR 63.8–108.6; range:32.3–238.8). The median breast V100 and V50 was 0.6 (IQR 0.1–1.5; range:0–13) and 18.6 (IQR 13.1–21.7; range:7.5–37), respectively. The median PTV minimum dose was 26.2 Gy (IQR 24.7–27.6; range 22.3–29.3). Mild side effects were recorded during the period of observation. Cosmetic evaluations were performed by three observers from the start of radiotherapy up to 2 years. Patients’ evaluation progressively increase from 60% to 85% of excellent rating; this trend was similar to that of external observer. Conclusions These preliminary results showed the safe feasibility of CK-APBI in early breast cancer, with mild acute and late toxicity and very good cosmetic results. Trial registration The present study is registered at Clinicaltrial.gov (NCT02896322). Retrospectively egistered August 4, 2016.

This article presents the results of a study on the bryological flora of the historic Park of Monte Sole, in the Bologna Apennines (Emilia-Romagna region). A total of 129 taxa were identified, including 9 new findings for the region. The study makes use of the regional cartographic grid, a methodology already used in the description of the vascular flora of the region, applied here for the first time to the bryological flora. If used in future studies, this method will allow a standardization of our knowledge on the distribution of the bryological flora in the region.

Predictions are fundamental in science as they allow to test and falsify theories. Predictions are ubiquitous in bioinformatics and also help when no first principles are available. Predictions can be distinguished between classifications (when we associate a label to a given input) or regression (when a real value is assigned). Different scores are used to assess the performance of regression predictors; the most widely adopted include the mean square error, the Pearson correlation (ρ), and the coefficient of determination (or R 2 ). The common conception related to the last 2 indices is that the theoretical upper bound is 1; however, their upper bounds depend both on the experimental uncertainty and the distribution of target variables. A narrow distribution of the target variable may induce a low upper bound. The knowledge of the theoretical upper bounds also has 2 practical applications: (1) comparing different predictors tested on different data sets may lead to wrong ranking and (2) performances higher than the theoretical upper bounds indicate overtraining and improper usage of the learning data sets. Here, we derive the upper bound for the coefficient of determination showing that it is lower than that of the square of the Pearson correlation. We provide analytical equations for both indices that can be used to evaluate the upper bound of the predictions when the experimental uncertainty and the target distribution are available. Our considerations are general and applicable to all regression predictors.

Sequence files formats (FASTA and FASTQ) are commonly used in bioinformatics, molecular biology and biochemistry. With the advent of next-generation sequencing (NGS) technologies, the number of FASTQ datasets produced and analyzed has grown exponentially, urging the development of dedicated software to handle, parse, and manipulate such files efficiently. Several bioinformatics packages are available to filter and manipulate FASTA and FASTQ files, yet some essential tasks remain poorly supported, leaving gaps that any workflow analysis of NGS datasets must fill with custom scripts. This can introduce harmful variability and performance bottlenecks in pivotal steps. Here we present a suite of tools, called SeqFu (Sequence Fastx utilities), that provides a broad range of commands to perform both common and specialist operations with ease and is designed to be easily implemented in high-performance analytical pipelines. SeqFu includes high-performance implementation of algorithms to interleave and deinterleave FASTQ files, merge Illumina lanes, and perform various quality controls (identification of degenerate primers, analysis of length statistics, extraction of portions of the datasets). SeqFu dereplicates sequences from multiple files keeping track of their provenance. SeqFu is developed in Nim for high-performance processing, is freely available, and can be installed with the popular package manager Miniconda.