An innovative strategy to investigate microbial protein modifications in a reliable fast and sensitive way: A therapy oriented proof of concept based on UV-C irradiation of SARS-CoV-2 spike protein.

The characterization of modifications of microbial proteins is of primary importance to dissect pathogen lifecycle mechanisms and could be useful in identifying therapeutic targets. Attempts to solve this issue yielded only partial and non-exhaustive results. We developed a multidisciplinary approach by coupling in vitro infection assay, mass spectrometry (MS), protein 3D modelling, and surface plasma resonance (SPR). As a proof of concept, the effect of low UV-C (273 nm) irradiation on SARS-CoV-2 spike (S) protein was investigated. Following UV-C exposure, MS analysis identified, among other modifications, the disruption of a disulphide bond within the conserved S2 subunit of S protein. Computational analyses revealed that this bond breakage associates with an allosteric effect resulting in the generation of a closed conformation with a reduced ability to bind the ACE2 receptor. The UV-C-induced reduced affinity of S protein for ACE2 was further confirmed by SPR analyses and in vitro infection assays. This comprehensive approach pinpoints the S2 domain of S protein as a potential therapeutic target to prevent SARS-CoV-2 infection. Notably, this workflow could be used to screen a wide variety of microbial protein domains, resulting in a precise molecular fingerprint and providing new insights to adequately address future epidemics.

Update on the Feasibility and Progress on Robotic Breast Surgery.

BACKGROUND: Robotic nipple-sparing mastectomy (RNSM) may allow for more precise anatomic dissection and improved cosmetic outcomes over conventional open nipple-sparing mastectomy; however, data regarding the feasibility and safety of the procedure are limited. OBJECTIVE: The aim of this study was to present and discuss perioperative surgical outcomes and early oncologic follow-up data on consecutive patients undergoing RNSM from June 2014 to January 2019. METHODS: Patients underwent RNSM and immediate robotic breast reconstruction through an axillary incision at a single institution. Perioperative data, complications at 3 months postoperatively, pathological data, and adjuvant therapies were recorded. Local recurrence-free, disease-free, and overall survival were analyzed. RESULTS: Overall, 73 women underwent 94 RNSM procedures. Indications were invasive breast cancer in 39 patients, ductal carcinoma in situ in 17 patients, and BRCA mutation in 17 patients. Mean surgery time was 3 h and 32 min. One-step reconstruction with implant occurred in 89.4% of procedures. The rate of complications requiring reoperation was 4.3%, and the rate of flap or nipple necrosis was 1.1%. Median follow-up was 19 months (range 3.1-44.8). No local recurrences occurred. Overall survival at 12, 24, or 60 months was 98% (95% confidence interval 86-100%). CONCLUSION: We observed a low complication rate in 94 consecutive RNSM procedures, demonstrating the procedure is technically feasible and safe. We found no early local failures at 19 months follow-up. Long-term follow-up is needed to confirm oncologic safety. Future clinical trials to study the advantages and disadvantages of RNSM are warranted.

BITS 2017: the annual meeting of the Italian Society of Bioinformatics.

This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.

An infrastructure for precision medicine through analysis of big data.

BACKGROUND: Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed. RESULTS: In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses. CONCLUSIONS: The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

Total Breast Reconstruction With Fat Grafting After Internal Expansion and Expander Removal.

INTRODUCTION: The development of fat grafting techniques both in cosmetic and reconstructive breast surgery has highlighted the need to recreate a breast mound using similar tissues and without visible scarring. Our technique combines skin expansion by conventional expanders and breast volume replacement by serial fat grafting and final expander removal. MATERIALS AND METHODS: Fat grafting had been performed both by Coleman technique and Bodyjet system. We report a series of 12 cases enrolled between 2008 and 2013. Each case has been presented in this article, focusing on received treatment, observed complications, and results. RESULTS: Satisfactory results were observed 6 months after the last session in most of the concluded reconstructions with a low complication rate.

Removing duplicate reads using graphics processing units.

BACKGROUND: During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis. RESULTS: In this work we propose GPU-DupRemoval, a new implementation of our method able to (i) cluster reads without constraints on the maximum length of the prefixes, (ii) support both single- and paired-end read libraries, and (iii) analyze large clusters of potential duplicates. CONCLUSIONS: Due to the massive parallelization obtained by exploiting graphics cards, GPU-DupRemoval removes duplicate reads faster than other cutting-edge solutions, while outperforming most of them in terms of amount of duplicates reads.

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units.

BACKGROUND: Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome. METHODS: G-SNPM is a tool that maps a short sequence representative of a SNP against a reference DNA sequence in order to find the physical position of the SNP in that sequence. In G-SNPM each SNP is mapped on its related chromosome by means of an automatic three-stage pipeline. In the first stage, G-SNPM uses the GPU-based short-read mapping tool SOAP3-dp to parallel align on a reference chromosome its related sequences representative of a SNP. In the second stage G-SNPM uses another short-read mapping tool to remap the sequences unaligned or ambiguously aligned by SOAP3-dp (in this stage SHRiMP2 is used, which exploits specialized vector computing hardware to speed-up the dynamic programming algorithm of Smith-Waterman). In the last stage, G-SNPM analyzes the alignments obtained by SOAP3-dp and SHRiMP2 to identify the absolute position of each SNP. RESULTS AND CONCLUSIONS: To assess G-SNPM, we used it to remap the SNPs of some commercial chips. Experimental results shown that G-SNPM has been able to remap without ambiguity almost all SNPs. Based on modern GPUs, G-SNPM provides fast mappings without worsening the accuracy of the results. G-SNPM can be used to deal with specialized Genome Wide Association Studies (GWAS), as well as in annotation tasks that require to update the SNP mapping probes.

Do clinicopathological features of the cancer patient relate with nipple areolar complex necrosis in nipple-sparing mastectomy?

BACKGROUND: The selections of nipple-sparing mastectomy (NSM) are principally depending on oncologic indication and oncologic safety. The main complication of NSM is nipple areolar complex (NAC) necrosis, and it is usually related to surgical technique. However, the patients' clinicopathological factors should be also considered. METHOD: We retrospectively reviewed 934 consecutive NSM patients during 2002-2007 at the European Institute of Oncology, Milan, Italy. We identified a group of patient who had NAC excision because of NAC necrosis and compared this group with those who had successful NAC conservation. We analyzed the association between the risk of NAC necrosis and the clinicopathological features of the patients. RESULTS: Among 934 NSM, 772 were invasive cancers and 162 were in situ cancers. Of the 934, 40 NAC (4.2%) were removed during the postoperative period because of necrosis. When we considered age, BMI, menopausal status, smoking status, tumor size, axillary lymph node status, in situ or invasive cancer histology, presence of extensive situ component, grading, estrogen receptor, progesterone receptor, HER2/neu overexpression, Ki-67 proliferative index, and peritumoral vascular invasion, no association was observed between patients' clinicopathological features and NAC necrosis incidence. CONCLUSIONS: In our study, clinicopathological features have no significant impact on necrosis complication in therapeutic NSMs. Positive retroareolar margin is the risk of necrosis. Further studies are required to avoid bias due to the different cancer treatments such as different reconstruction techniques and intraoperative radiation protocols. The correlation between breast morphology and NAC necrosis should also be investigated in the future.

ADVICE: a new approach for near-real-time monitoring of surface displacements in landslide hazard scenarios.

We present a new method for near-real-time monitoring of surface displacements due to landslide phenomena, namely ADVanced dIsplaCement monitoring system for Early warning (ADVICE). The procedure includes: (i) data acquisition and transfer protocols; (ii) data collection, filtering, and validation; (iii) data analysis and restitution through a set of dedicated software; (iv) recognition of displacement/velocity threshold, early warning messages via SMS and/or emails; (v) automatic publication of the results on a dedicated webpage. We show how the system evolved and the results obtained by applying ADVICE over three years into a real early warning scenario relevant to a large earthflow located in southern Italy. ADVICE has speed-up and facilitated the understanding of the landslide phenomenon, the communication of the monitoring results to the partners, and consequently the decision-making process in a critical scenario. Our work might have potential applications not only for landslide monitoring but also in other contexts, as monitoring of other geohazards and of complex infrastructures, as open-pit mines, buildings, dams, etc.

Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks.

This methodological article is mainly aimed at establishing a bridge between classification and regression tasks, in a frame shaped by performance evaluation. More specifically, a general procedure for calculating performance measures is proposed, which can be applied to both classification and regression models. To this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. This policy, called generalized token sharing, allows to a) assess models trained on both classification and regression tasks, b) evaluate the importance of input features, and c) inspect the behavior of multilayer perceptrons by looking at their hidden layers. The occurrence of success and failure patterns at the hidden layers of multilayer perceptrons trained and tested on selected regression problems, together with the effectiveness of layer-wise training, is also discussed.

Framing Apache Spark in life sciences.

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.

Paget's disease as a local recurrence after nipple-sparing mastectomy: clinical presentation, treatment, outcome, and risk factor analysis.

BACKGROUND: Paget's disease is a rare clinical and histological type of local recurrence (LR) after breast cancer treatment both in case of conservative surgery or nipple-sparing mastectomy (NSM) with or without intraoperative radiation. METHODS: We performed an analysis of 861 NSM with electron beam intraoperative radiotherapy (ELIOT) patients treated at the European Institute of Oncology from 2002 to 2008, focused on Paget's disease local recurrence. RESULTS: In 861 patients (713 invasive carcinoma and 148 intraepithelial neoplasia), there were 36 local recurrences (4.18%), and among these were 7 Paget's disease local recurrences (0.8%). Median follow-up was 50 months. Four cases presented with nipple areola complex (NAC) erosions, two crusted lesions, and one ulcerated NAC. The average latency period from the NSM to Paget's disease local recurrence is 32 months (range, 12-49). Complete NAC removal was performed in all seven recurrences. The average follow-up after NAC removal was 47.4 months (range, 20-78). We found neither locoregional relapse nor metastatic event in this group. All patients were alive without disease. CONCLUSIONS: Paget's disease local recurrence can be found in a significant proportion after NSM. Any suspicious lesion on NAC requires prompt pathological confirmation. Primary carcinoma with ductal intraepithelial neoplasia or invasive ductal carcinoma with extensive in situ component, negative hormonal receptor, high pathological grade, overexpression of HER2/neu, and "HER2 positive (nonluminal)" subtype tend to be significantly associated with more Paget's disease local recurrence and should be followed carefully.

Update on breast reconstruction techniques and indications.

Breast reconstruction is considered as part of the breast cancer treatment when a mastectomy is required. Implants or expanders are the most frequent techniques used for the reconstructions. Expander provides usually a better symmetry. A contralateral mastoplasty often is required to improve the symmetry. The nipple areola complex, which can be preserved in certain conditions, is usually removed and can be reconstructed in a second stage under local anesthesia. In case of radical mastectomy and/or radiotherapy, a musculocutaneous flap, such as rectus abdominis or latissimus dorsi autologous flaps, is required. When microsurgical facilities are available, free or perforator flaps respecting the muscle are preferred to decrease the donor site complications. In situ carcinomas or prophylactic mastectomy can be reconstructed immediately as well as invasive carcinoma according to the recent literature. Locally advanced breast cancer can be reconstructed after complete oncologic treatment. Radiotherapy of the thoracic wall is proposed in case of lymph node metastases, raising the discussion about the technique choice and the timing of the reconstruction. Plastic surgery procedures can improve the cosmetic results of the conservative surgery, also extending its indications and reducing both mastectomy and reexcision rates. Oncoplasty techniques are becoming more and more sophisticated, requiring the skill of trained plastic surgeons. Numerous publications confirm the psychosocial benefit resulting from the breast reconstruction.

R2R3-MYBs in Durum Wheat: Genome-Wide Identification, Poaceae-Specific Clusters, Expression, and Regulatory Dynamics Under Abiotic Stresses.

MYB transcription factors (TFs) represent one of the biggest TF families in plants, being involved in various specific plant processes, such as responses to biotic and abiotic stresses. The implication of MYB TFs in the tolerance mechanisms to abiotic stress is particularly interesting for crop breeding, since environmental conditions can negatively affect growth and productivity. Wheat is a worldwide-cultivated cereal, and is a major source of plant-based proteins in human food. In particular, durum wheat plays an important role in global food security improvement, since its adaptation to hot and dry conditions constitutes the base for the success of wheat breeding programs in future. In the present study, a genome-wide identification of R2R3-MYB TFs in durum wheat was performed. MYB profile search and phylogenetic analyses based on homology with Arabidopsis and rice MYB TFs led to the identification of 233 R2R3-TdMYB (Triticum durum MYB). Three Poaceae-specific MYB clusters were detected, one of which had never been described before. The expression of eight selected genes under different abiotic stress conditions, revealed that most of them responded especially to salt and drought stress. Finally, gene regulatory network analyses led to the identification of 41 gene targets for three TdR2R3-MYBs that represent novel candidates for functional analyses. This study provides a detailed description of durum wheat R2R3-MYB genes and contributes to a deeper understanding of the molecular response of durum wheat to unfavorable climate conditions.

A new strategy to map landslides with a generalized convolutional neural network.

Rapid mapping of event landslides is crucial to identify the areas affected by damages as well as for effective disaster response. Traditionally, such maps are generated with visual interpretation of remote sensing imagery (manned/unmanned airborne systems or spaceborne sensors) and/or using pixel-based and object-based methods exploiting data-intensive machine learning algorithms. Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data. These methods follow a standard supervised learning workflow that involves training a model using a landslide inventory covering a relatively small area. The trained model is then used to predict landslides in the surrounding regions. Here, we propose a new strategy, i.e., a progressive CNN training relying on combined inventories to build a generalized model that can be applied directly to a new, unexplored area. We first prove the effectiveness of CNNs by training and validating on event landslides inventories in four regions after earthquakes and/or extreme meteorological events. Next, we use the trained CNNs to map landslides triggered by new events spread across different geographic regions. We found that CNNs trained on a combination of inventories have a better generalization performance, with a bias towards high precision and low recall scores. In our tests, the combined training model achieved the highest (Matthews correlation coefficient) MCC score of 0.69 when mapping landslides in new unseen regions. The mapping was done on images from different optical sensors, resampled to a spatial resolution of 6 m, 10 m, and 30 m. Despite a slightly reduced performance, the main advantage of combined training is to overcome the requirement of a local inventory for training a new deep learning model. This implementation can facilitate automated pipelines providing fast response for the generation of landslide maps in the post-disaster phase. In this study, the study areas were selected from seismically active zones with a high hydrological hazard distribution and vegetation coverage. Hence, future works should also include regions from less vegetated geographic locations.

Transcriptomic analysis of Rhodococcus opacus R7 grown on polyethylene by RNA-seq.

Plastic waste management has become a global issue. Polyethylene (PE) is the most abundant synthetic plastic worldwide, and one of the most resistant to biodegradation. Indeed, few bacteria can degrade polyethylene. In this paper, the transcriptomic analysis unveiled for the first time Rhodococcus opacus R7 complex genetic system based on diverse oxidoreductases for polyethylene biodegradation. The RNA-seq allowed uncovering genes putatively involved in the first step of oxidation. In-depth investigations through preliminary bioinformatic analyses and enzymatic assays on the supernatant of R7 grown in the presence of PE confirmed the activation of genes encoding laccase-like enzymes. Moreover, the transcriptomic data allowed identifying candidate genes for the further steps of short aliphatic chain oxidation including alkB gene encoding an alkane monooxygenase, cyp450 gene encoding cytochrome P450 hydroxylase, and genes encoding membrane transporters. The PE biodegradative system was also validated by FTIR analysis on R7 cells grown on polyethylene.

A subfascial variant of the deep inferior epigastric artery demonstrated by preoperative multidetector computed tomographic angiography: a case report.

Precise preoperative imaging by multidetector computed tomographic (MDCT) angiography for planning of deep inferior epigastric artery perforator (DIEP) flap dissection has been reported for enormous advantages in terms of reduced operative time and minimized flap-related complications. This case report shows a particularly rare anatomical subfascia variant of deep inferior epigastric artery (DIEA) which can be preoperatively demonstrated by MDCT angiogram. Therefore, the intraoperative finding also confirms the radiologic data and results in meticulous flap harvesting during incision on anterior rectus sheath. Additionally, the authors emphasize on performing preoperative high quality imaging for DIEP intervention precisely for specific vulnerable course of subfascial plane DIEP, which is rare but tends to be at risk without foreknowing its exact course.

The Punatsangchhu-I dam landslide illuminated by InSAR multitemporal analyses.

We use multitemporal analyses based on Synthetic Aperture Radar differential interferometry (DInSAR) to study the slope adjacent to the large Punatsangchhu-I hydropower plant, a concrete gravity dam under construction in Bhutan since 2009. Several slope failures affected the site since 2013, probably as a consequence of toe undercutting of a previously unrecognised active landslide. Our results indicate that downslope displacement, likely related to the natural instability, was already visible in 2007 on various sectors of the entire valley flank. Moreover, the area with active displacements impinging on the dam site has continuously increased in size since 2007 and into 2018, even though stabilization measures have been implemented since 2013. Stabilisation measures currently only focus on a small portion of the slope, however, the unstable area is larger than previously evaluated. Highly damaged rock is present across many areas of the entire valley flank, indicating that the volumes involved may be orders of magnitude higher than the area on which stabilisation efforts have been concentrated after the 2013 failure. The results highlight that satellite-based DInSAR could be systematically used to support decision making processes in the different phases of a complex hydropower project, from the feasibility study, to the dam site selection and construction phase.