A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code

Recent experimental evidence suggests that oscillatory activity plays a pivotal role in the maintenance of information in working memory, both in rodents and humans. In particular, cross-frequency coupling between theta and gamma oscillations has been suggested as a core mechanism for multi-item memory. The aim of this work is to present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. We show that this model, with different synapse values, can be used to address different problems, such as the reconstruction of an item from partial information, the maintenance of multiple items simultaneously in memory, without any sequential order, and the reconstruction of an ordered sequence starting from an initial cue. The model consists of four interconnected layers; synapses are trained using Hebbian and anti-Hebbian mechanisms, in order to synchronize features in the same items, and desynchronize features in different items. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment ("imagination phase") and stimulated with high uniform noise, can randomly recover sequences previously learned, and link them together by exploiting the similarity among items

Design of a multi-purpose building "to zero energy consumption" according to european directive 2010/31/ce: Architectural and plant solutions

Considering the significant impact that the residential sector has on energy consumption, it is particularly important to implement policies aimed at improving energy efficiency in buildings for saving primary energy, and also to spread the concept of sustainable development through the use of appropriate technology and proper project criteria for new constructions. For these reasons the Municipality of Città della Pieve promoted the creation of a "Renewable Energy Park" in a deprived area of its territory, so that there were the main technologies for the production of green energy. In this context, it could not be lacking an educational/demonstrative "zero energy consumption" building for multifunctional activities realized with the most innovative techniques to save energy. The building will exemplify the optimization of the benefits derived from improved energy efficiency in synergy with systems of energy production from renewable sources, such as to make possible the transition from "passive" building to get to "active" building. In this paper we describe the technical solutions adopted both in the building envelope and the system concept for the project of that "zero energy consumption" building according to Directive 2010/31/CE. In order to validate the proposed solutions, it has also been carried out a simulation of the behaviour of the building in summer and winter so that it is possible to assess the actual benefits obtained both in terms of energy and in economic terms following the adoption of the proposed solutions

Giant Sigmoid Diverticulum: A Rare Presentation of a Common Pathology

Although colonic diverticulum is a common disease, affecting about 35% of patients above the age of 60, giant sigmoid diverticulum is an uncommon variant of which only relatively few cases have been described in the literature. We report on our experience with a patient affected by giant sigmoid diverticulum who was treated with diverticulectomy. Resection of the diverticulum is a safe surgical procedure, provided that the colon section close to the lesion presents no sign of flogosis or diverticula; in addition, recurrences are not reported after 6-year follow-up

Computer-assisted liver graft steatosis assessment via learning-based texture analysis

Purpose: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Methods: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was 100 × 100. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern (HLBPriu2), and gray-level co-occurrence matrix (FGLCM) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. Results: With the best-performing feature set (HLBPriu2+INT+Blo) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. Conclusions: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR

Use of Artificial Intelligence as an Innovative Method for Liver Graft Macrosteatosis Assessment

The worldwide implementation of a liver graft pool using marginal livers (ie, grafts with a high risk of technical complications and impaired function or with a risk of transmitting infection or malignancy to the recipient) has led to a growing interest in developing methods for accurate evaluation of graft quality. Liver steatosis is associated with a higher risk of primary nonfunction, early graft dysfunction, and poor graft survival rate. The present study aimed to analyze the value of artificial intelligence (AI) in the assessment of liver steatosis during procurement compared with liver biopsy evaluation. A total of 117 consecutive liver grafts from brain-dead donors were included and classified into 2 cohorts: ≥30 versus <30% hepatic steatosis. AI analysis required the presence of an intraoperative smartphone liver picture as well as a graft biopsy and donor data. First, a new algorithm arising from current visual recognition methods was developed, trained, and validated to obtain automatic liver graft segmentation from smartphone images. Second, a fully automated texture analysis and classification of the liver graft was performed by machine-learning algorithms. Automatic liver graft segmentation from smartphone images achieved an accuracy (Acc) of 98%, whereas the analysis of the liver graft features (cropped picture and donor data) showed an Acc of 89% in graft classification (≥30 versus <30%). This study demonstrates that AI has the potential to assess steatosis in a handy and noninvasive way to reliably identify potential nontransplantable liver grafts and to avoid improper graft utilization

Hydrocarbon-bearing sulphate-polymetallic deposits at the Colipilli area, Neuquén Basin, Argentina: Implications in the petroleum system modeling

This work deals with the hydrocarbon-bearing barite-polymetallic mineralizations of the Colipilli area, located in the western sector of the Agrio Fold and Thrust Belt (Neuqu´en Basin, Argentina). The mineralizations consist of bed- and vein-type deposits mainly composed of barite (barite96.99%–celestine2.93%) with minor amounts of Feoxyhydroxides and sulfides. The bed-type deposits have zebra texture and are emplaced along the contact between Late Cretaceous–Paleocene igneous rocks (Naunauco Group) and their Early Cretaceous sedimentary host rocks (e.g., Huitrín Formation). In contrast, the vein-type deposits have breccia texture and are crosscutting the Mulichinco, Agrio and Huitrín formations or the andesitic/dioritic stocks and sills of the Naunauco Group. Different types and families of primary fluid inclusions (FI) were identified in the barite crystals. Fluorescence techniques with UV incident light and Raman spectroscopy allowed FI from completely aqueous to completely organic, including all the intermediate terms, to be identified. The organic FI have blue fluorescence and contain liquid hydrocarbons. The blue fluorescence is correlated with medium to high API gravity values (ca. 40◦), indicating the presence of light hydrocarbons of advanced maturity related with the window for the generation of liquid/gaseous hydrocarbons. Microthermometry studies carried out on aqueous FI revealed that vein-type deposits formed at higher temperatures and salinities (249.7 ◦C and 0.5–9.3 wt % NaCl equivalent) than bed-type deposits (162.2 ◦C and 0.2–7.2 wt % NaCl equivalent). The heat influx provided by the Late Cretaceous– Paleocene magmatism promoted the circulation of inorganic and organic fluids of connate origin and the leaching of metallic and non-metallic elements from the sedimentary pile. During its crystallization, barite trapped fluids with variable hydrocarbon contents. The thermal anomaly associated with the magmatic activity could also have contributed with the maturation of the nearby source rocks and to the development of an atypical petroleum system.Instituto de Recursos Minerale

The Italian real-life post-stroke spasticity survey: Unmet needs in the management of spasticity with botulinum toxin type A

The present national survey seeking to identify unmet needs in the management of spasticity with botulinum toxin type A focused on the use of OnabotulinumoxinA, since this is the brand with the widest range of licensed indications in Italy. Physicians from twenty-four Italian neurorehabilitation units compiled a questionnaire about \u201creal-life\u201d post-stroke spasticity management. OnabotulinumtoxinA was reported to be used in the following average doses: upper limb 316.7 \ub1 79.1 units; lower limb 327.8 \ub1 152.3; upper and lower limb 543.7 \ub1 123.7 units. Of the physicians surveyed, 37.5% felt that increasing the frequency of OnabotulinumtoxinA injection would improve its efficacy; 70.8% use electrical stimulation/electromyography guidance (one fourth of injections with no instrumental guidance). Instrumental evaluation was used by 41.7% of the physicians. The participants expressed the view that early identification of post-stroke spasticity would be facilitated by the availability of a post-stroke checklist, and that this should be used by physiotherapists (91.7%), physiatrists (58.3%), family doctors (50%), stroke unit physicians (25%), patients and caregivers (79.2%). According to our findings, the management of poststroke spasticity has several unmet needs that, were they addressed, might improve these patients\u2019 clinical outcomes and quality of life. These needs concern patient follow-up, where a clearly defined pathway is lacking; furthermore, there is a need to use maximum doses per treatment and to ensure early intervention on post-stroke spasticity

Computer-assisted liver-graft steatosis assessment via learning-based texture analysis

Purpose: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Methods: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was (Formula presented.). This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ((Formula presented.)), and gray-level co-occurrence matrix ((Formula presented.)) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. Results: With the best-performing feature set ((Formula presented.)) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. Conclusions: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR

The uronic acid content of coccolith-associated polysaccharides provides insight into coccolithogenesis and past climate

Unicellular phytoplanktonic algae (coccolithophores) are among the most prolific producers of calcium carbonate on the planet, with a production of ∼1026 coccoliths per year. During their lith formation, coccolithophores mainly employ coccolith-associated polysaccharides (CAPs) for the regulation of crystal nucleation and growth. These macromolecules interact with the intracellular calcifying compartment (coccolith vesicle) through the charged carboxyl groups of their uronic acid residues. Here we report the isolation of CAPs from modern day coccolithophores and their prehistoric predecessors and we demonstrate that their uronic acid content (UAC) offers a species-specific signature. We also show that there is a correlation between the UAC of CAPs and the internal saturation state of the coccolith vesicle that, for most geologically abundant species, is inextricably linked to carbon availability. These findings suggest that the UAC of CAPs reports on the adaptation of coccolithogenesis to environmental changes and can be used for the estimation of past CO2 concentrations