Tharros - Capo San Marco in the Phoenician and Punic Age. Geophysical investigations and virtual rebuilding

The project described in this paper was started in 2012 and concerns the study of the relationship between the urban and suburban districts of the Phoenician and Punic city of Tharros (Cabras, Oristano). The structures of Phoenician and Punic Tharros have been largely cancelled by the Roman occupation. For this reason it is very difficult to determine the original function of many of the neighbourhoods during the Carthaginian period. The archaeological excavation primarily involved the southern necropolis of Capo San Marco. The cemetery must still be fully explored and understood under several aspects, mainly because of the devastation of the site caused by the repeated plundering of the ancient tombs which occurred during the 19th century. In addition to the new dig activities, a 3D topographical survey aimed at the complete documentation of the site and at the virtual rebuilding of the Phoenician and Punic funerary landscape was completed. Another goal of the project is the insertion of this sector of the promontory into the usual tourist route, in order to foster the public fruition of Capo San Marco, while continuing to adopt proper scientific methods and modern techniques. In this direction, geophysical prospecting surveys were carried out in the southern sector of the Capo San Marco, near the so-called ‘Rustic Temple’, in order to assess human presence in the farthest point of the Sinis peninsula (characterised by the presence of the Late Punic ruins of a probable light-house with sacred functions), and across the whole isthmus Sa Codriola towards the hill of San Giovanni, with the aim of analysing the northern boundary of the cemetery and its relationship to the city. The Punic-Roman settlement is now enclosed in the archaeological park, which is a fraction of what was supposed to be the administrative capital of Carthage in Sardinia. 3D modelling and virtual reconstructions were focused also on the residential Punic and Roman area inside the park. The integrated application of the most advanced topographical and geophysical techniques to the site greatly contributed to the recording and understanding of the ancient landscape

Leveraging Automated Mixed-Low-Precision Quantization for Tiny Edge Microcontrollers

The severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if leveraging an effective 8-bit quantization scheme. To tackle this issue, in this paper we present an automated mixed-precision quantization flow based on the HAQ framework but tailored for the memory and computational characteristics of MCU devices. Specifically, a Reinforcement Learning agent searches for the best uniform quantization levels, among 2, 4, 8 bits, of individual weight and activation tensors, under the tight constraints on RAM and FLASH embedded memory sizes. We conduct an experimental analysis on MobileNetV1, MobileNetV2 and MNasNet models for Imagenet classification. Concerning the quantization policy search, the RL agent selects quantization policies that maximize the memory utilization. Given an MCU-class memory bound of 2 MB for weight-only quantization, the compressed models produced by the mixed-precision engine result as accurate as the state-of-the-art solutions quantized with a non-uniform function, which is not tailored for CPUs featuring integer-only arithmetic. This denotes the viability of uniform quantization, required for MCU deployments, for deep weights compression. When also limiting the activation memory budget to 512 kB, the best MobileNetV1 model scores up to 68.4% on Imagenet thanks to the found quantization policy, resulting to be 4% more accurate than the other 8-bit networks fitting the same memory constraints

Reconstruction of protein structures from a vectorial representation

We show that the contact map of the native structure of globular proteins can be reconstructed starting from the sole knowledge of the contact map's principal eigenvector, and present an exact algorithm for this purpose. Our algorithm yields a unique contact map for all 221 globular structures of PDBselect25 of length $N \le 120$. We also show that the reconstructed contact maps allow in turn for the accurate reconstruction of the three-dimensional structure. These results indicate that the reduced vectorial representation provided by the principal eigenvector of the contact map is equivalent to the protein structure itself. This representation is expected to provide a useful tool in bioinformatics algorithms for protein structure comparison and alignment, as well as a promising intermediate step towards protein structure prediction.Comment: 4 pages, 1 figur

High Throughput Protein Similarity Searches in the LIBI Grid Problem Solving Environment

Bioinformatics applications are naturally distributed, due to distribution of involved data sets, experimental data and biological databases. They require high computing power, owing to the large size of data sets and the complexity of basic computations, may access heterogeneous data, where heterogeneity is in data format, access policy, distribution, etc., and require a secure infrastructure, because they could access private data owned by different organizations. The Problem Solving Environment (PSE) is an approach and a technology that can fulfil such bioinformatics requirements. The PSE can be used for the definition and composition of complex applications, hiding programming and configuration details to the user that can concentrate only on the specific problem. Moreover, Grids can be used for building geographically distributed collaborative problem solving environments and Grid aware PSEs can search and use dispersed high performance computing, networking, and data resources. In this work, the PSE solution has been chosen as the integration platform of bioinformatics tools and data sources. In particular an experiment of multiple sequence alignment on large scale, supported by the LIBIPSE, is presented

Statistical properties of contact vectors

We study the statistical properties of contact vectors, a construct to characterize a protein's structure. The contact vector of an N-residue protein is a list of N integers n_i, representing the number of residues in contact with residue i. We study analytically (at mean-field level) and numerically the amount of structural information contained in a contact vector. Analytical calculations reveal that a large variance in the contact numbers reduces the degeneracy of the mapping between contact vectors and structures. Exact enumeration for lengths up to N=16 on the three dimensional cubic lattice indicates that the growth rate of number of contact vectors as a function of N is only 3% less than that for contact maps. In particular, for compact structures we present numerical evidence that, practically, each contact vector corresponds to only a handful of structures. We discuss how this information can be used for better structure prediction.Comment: 20 pages, 6 figure

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

BACKGROUND: Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. RESULTS: Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. CONCLUSION: Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known

Stereotactic Radiosurgery for Postoperative Spine Malignancy: A Systematic Review and International Stereotactic Radiosurgery Society Practice Guidelines.

To determine safety and efficacy of postoperative spine stereotactic body radiation therapy (SBRT) in the published literature, and to present practice recommendations on behalf of the International Stereotactic Radiosurgery Society. A systematic review of the literature was performed, specific to postoperative spine SBRT, using PubMed and Embase databases. A meta-analysis for 1-year local control (LC), overall survival (OS), and vertebral compression fracture probability was conducted. The literature search revealed 251 potentially relevant articles after duplicates were removed. Of these 56 were reviewed in-depth for eligibility and 12 met all the inclusion criteria for analysis. 7 studies were retrospective, 2 prospective observational and 3 were prospective phase 1 and 2 clinical trials. Outcomes for a total of 461 patients and 499 spinal segments were reported. Ten studies used a magnetic resonance imaging (MRI) scan fused to computed tomography (CT) simulation for treatment planning, and 2 investigations reported on all patients receiving a CT-myelogram at the time of planning. Meta-analysis for 1 year LC and OS was 88.9% and 57%, respectively. The crude reported vertebral compression fracture rate was 5.6%. One case of myelopathy was described in a patient with a previously irradiated spinal segment. One patient developed an esophageal fistula requiring surgical repair. Postoperative spine SBRT delivers a high 1-year LC with acceptably low toxicity. Patients who may benefit from this include those with oligometastatic disease, radioresistant histology, paraspinal masses, or those with a history of prior irradiation to the affected spinal segment. The International Stereotactic Radiosurgery Society recommends a minimum interval of 8 to 14 days after invasive surgery before simulation for SBRT, with initiation of radiation therapy within 4 weeks of surgery. An MRI fused to the planning CT, or the use of a CT-myelogram, are necessary for target and organ-at-risk delineation. A planning organ-at-risk volume (PRV) of 1.5 to 2 mm for the spinal cord is advised

New decoding algorithms for Hidden Markov Models using distance measures on labellings

<p>Abstract</p> <p>Background</p> <p>Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries.</p> <p>Results</p> <p>We give a set of algorithms to compute the conditional probability of all labellings "near" a reference labelling <it>λ </it>for a sequence <it>y </it>for a variety of definitions of "near". In addition, we give optimization algorithms to find the best labelling for a sequence in the robust sense of having all of its feature boundaries nearly correct. Natural problems in this domain are <it>NP</it>-hard to optimize. For membrane proteins, our algorithms find the approximate topology of such proteins with comparable success to existing programs, while being substantially more accurate in estimating the positions of transmembrane helix boundaries.</p> <p>Conclusion</p> <p>More robust HMM decoding may allow for better analysis of sequence features, in reasonable runtimes.</p

Stereotactic radiosurgery for secretory pituitary adenomas: systematic review and International Stereotactic Radiosurgery Society practice recommendations.

A systematic review was performed to provide objective evidence on the use of stereotactic radiosurgery (SRS) in the management of secretory pituitary adenomas and develop consensus recommendations. The authors performed a systematic review of the English-language literature up until June 2018 using the PRISMA guidelines. The PubMed (Medline), Embase, and Cochrane databases were searched. A total of 45 articles reporting single-institution outcomes of SRS for acromegaly, Cushing's disease, and prolactinomas were selected and included in the analysis. For acromegaly, random effects meta-analysis estimates for crude tumor control rate, crude endocrine remission rate, and any new hypopituitarism rates were 97.0% (95% CI 96.0%-98.0%), 44.0% (95% CI 35.0%-53.0%), and 17.0% (95% CI 13.0%-23.0%), respectively. For Cushing's disease, random effects estimates for crude tumor control rate, crude endocrine remission rate, and any new hypopituitarism rate were 92.0% (95% CI 87.0%-95.0%), 48.0% (95% CI 35.0%-61.0%), and 21.0% (95% CI 13.0%-31.0%), respectively. For prolactinomas, random effects estimates for crude tumor control rate, crude endocrine remission rate, and any new hypopituitarism rate were 93.0% (95% CI 90.0%-95.0%), 28.0% (95% CI 19.0%-39.0%), and 12.0% (95% CI 6.0%-24.0%), respectively. Meta-regression analysis did not show a statistically significant association between mean margin dose with crude endocrine remission rate or mean margin dose with development of any new hypopituitarism rate for any of the secretory subtypes. SRS offers effective tumor control of hormone-producing pituitary adenomas in the majority of patients but a lower rate of endocrine improvement or remission