Overview of the ImageCLEF 2015 medical classification task

This articles describes the ImageCLEF 2015 Medical Clas-sification task. The task contains several subtasks that all use a dataset of figures from the biomedical open access literature (PubMed Cen-tral). Particularly compound figures are targeted that are frequent inthe literature. For more detailed information analysis and retrieval it isimportant to extract targeted information from the compound figures.The proposed tasks include compound figure detection (separating com-pound from other figures), multi–label classification (define all sub typespresent), figure separation (find boundaries of the subfigures) and modal-ity classification (detecting the figure type of each subfigure). The tasksare described with the participation of international research groups inthe tasks. The results of the participants are then described and analysedto identify promising techniques

Guest Editorial: Sustainable growth and development in the food and beverage sector

The main reason for writing this Editorial on the Special Issue 'Sustainable growth and development in the food and beverage sector” is to acknowledge the brilliant contribution of the researchers who have enriched the British Food Journal (BFJ) with their contributions. This Special Issue contributes to the literature on the advancement of technologies and their impact on individuals" behaviours, measures to be taken for environmental protection and green consumption, extends research in the field of sustainable supply chain management, consumer perceptions and reactions to food products and provides various tools to manage individuals" behaviours. Previous studies have already identified changes adapted to the environment. Consumers are becoming increasingly demanding and require their needs to be met as quickly as possible (Baker et al., 2020). The literature also demonstrates the growing interest of digitalization in institutions, even affecting customer consumption (Zhuang et al., 2021). Selby et al. (2021) claim that individuals with low levels of physical activity, poor diet and smoking tend to acquire higher risks on their adaptations. Another issue is the recent COVID-19 pandemic. This has resulted in numerous restrictions in daily life, including social isolation and lack of defined protocols (Jaworski, 2021). Individuals" lifestyles have been modified, and they have had to adapt in order not to spread the virus. According to Ammar et al. (2020), food consumption and eating patterns were characterised as more unhealthy during confinement

Enhancement of Hydrolysis through the Formation of Mixed Heterometal Species: Al3+/CH3Sn3+ Mixtures

ABSTRACT: The hydrolysis of mixed-metal cations (Al3+/CH3Sn3+) was studied in aqueous solutions of NaNO3, at I = 1.00 ± 0.05 mol·dm−3 and T = 298.15 K, by potentiometric technique. Several hydrolytic mixed species are formed in this mixed system, namely, Alp(CH3Sn)q(OH)r with (p, q, r) = (1, 1, 4), (1, 1, 5), (1, 1, 6), (2, 1, 4), (1, 2, 5), (1, 4, 11), (1, 3, 8), and (7, 6, 32). The stability of these species, expressed by the equilibrium: pAl3+ + qCH3Sn3+ + rOH− = Alp(CH3Sn)q(OH)r 3(p+q)−r, βpqr OH, can be modeled by the empirical relationship: log βpqr OH = −3.34 + 2.67p + 9.23(q + r). By using the equilibrium constant Xpqr relative to the formation reaction: pAl(p+q)(OH)r + q(CH3Sn)(p+q)(OH)r = (p + q)Alp(CH3Sn)q(OH)r, it was found that the formation of heterometal mixed species is thermodynamically favored, and the extra stability can be expressed as a function of the difference in the stability of parent homometal species. This leads, in turn, to a significant enhancement of hydrolysis and solubility

Overview of the ImageCLEF 2016 Medical Task

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results

Quality and reactivity of dissolved organic matter in a Mediterranean river across hydrological and spatial gradients.

Understanding DOM transport and reactivity in rivers is essential to having a complete picture of the global carbon cycle. In this study, we explore the effects of hydrological variability and downstream transport on dissolved organic matter (DOM) dynamics in a Mediterranean river. We sampled the main stem of the river Tordera from the source to the sea, over a range of fifteen hydrological conditions including extreme events (flood and drought). By exploring spatial and temporal gradients of DOM fluorescence properties, river hydrology was found to be a significant predictor of DOM spatial heterogeneity. An additional space-resolved mass balance analysis performed on four contrasting hydrological conditions revealed that this was due to a shift in the biogeochemical function of the river. Flood conditions caused a conservative transport of DOM, generating a homogeneous, humic-like spatial profile of DOM quality. Lower flows induced a non-conservative, reactive transport of DOM, which enhanced the spatial heterogeneity of DOM properties. Moreover, the downstream evolution of DOM chemostatic behaviour revealed that the role of hydrology in regulating DOM properties increased gradually downstream, indicating an organised inter-dependency between the spatial and the temporal dimensions. Overall, our findings reveal that riverine DOM dynamics is in constant change owing to varying hydrological conditions, and emphasize that in order to fully understand the role of rivers in the global carbon cycle, it is necessary to take into account the full range of hydrological variability, from floods to droughts

A New Mid-Infrared and X-ray Machine Learning Algorithm to Discover Compton-thick AGN

We present a new method to predict the line-of-sight column density (NH) values of active galactic nuclei (AGN) based on mid-infrared (MIR), soft, and hard X-ray data. We developed a multiple linear regression machine learning algorithm trained with WISE colors, Swift-BAT count rates, soft X-ray hardness ratios, and an MIR-soft X-ray flux ratio. Our algorithm was trained off 451 AGN from the Swift-BAT sample with known NH and has the ability to accurately predict NH values for AGN of all levels of obscuration, as evidenced by its Spearman correlation coefficient value of 0.86 and its 75% classification accuracy. This is significant as few other methods can be reliably applied to AGN with Log(NH <) 22.5. It was determined that the two soft X-ray hardness ratios and the MIR-soft X-ray flux ratio were the largest contributors towards accurate NH determination. This algorithm will contribute significantly to finding Compton-thick (CT-) AGN (NH >= 10^24 cm^-2), thus enabling us to determine the true intrinsic fraction of CT-AGN in the local universe and their contribution to the Cosmic X-ray Background

Dove è finito il gruppo?

Gli autori affrontano la questione della “sparizione” nella contemporaneità dei gruppi e della gruppalità in differenti contesti dove tradizionalmente il “fare gruppi” era diventato una consuetudine: ambito clinico, terapeutico, istituzionale. Ci si interroga se la difficoltà riscontrata dai professionisti della salute mentale e del benessere psicosociale, esperti in gruppi, di avviare e mantenere setting collettivi, sia una delle numerose manifestazioni della deriva neoliberista che investe le vite di tutti, le relazioni, la concezione del mondo e della vita.The authors deal with the question of the "disappearance" of groups and of groupality in the contemporary world in different contexts where traditionally "making groups" had become a habit: clinical, therapeutic, institutional sphere. The question arises whether the difficulty encountered by professionals in mental health and psychosocial well-being, experts in groups, to initiate and maintain collective settings, is one of the numerous manifestations of the neoliberal drift that affects everyone's lives, relationships, the conception of the world and life

La trasmissione tra le generazioni

La trasmissione psichica e culturale tra generazioni è un processo essenziale che presenta sfide specifiche nel tempo attuale. Mentre la trasmissione tradizionale avveniva attraverso la parola e la presenza fisica, in spazi e tempi definiti, oggi essa si realizza attraverso sistemi digitali che annullano lo spazio pubblico e offrono un'infinita fonte virtuale di informazioni, ma priva di connessione generazionale. La memoria e il calcolo vengono sostituiti dall'informazione immediatamente disponibile, facendo sì che la formazione si riduca a un accumulo di notizie e tecniche, privo di approfondimento e personalizzazione. Questo processo sembra interrompere la trasmissione autentica, sostituendola con un enorme manuale d'uso virtuale. Le istituzioni accademiche tendono a privilegiare saperi oggettivi e impersonali, escludendo l'aspetto relazionale delle esperienze. La razionalità attuale si concentra sull'efficienza immediata a scapito dell'autonomia e dell'identità dell'operatore. La trasmissione richiede cura e fiducia, e, per essere efficace e autentica, richiede tempo, fiducia e relazioni profonde.The psychic and cultural transmission between generations is an essential process that faces specific challenges in the present time. While traditional transmission occurred through spoken word and physical presence, in defined spaces and times, today it takes place through digital systems that eliminate public space and offer an infinite virtual source of information, but lack generational connection. Memory and calculation are replaced by immediately available information, reducing education to a mere accumulation of facts and techniques, devoid of depth and personalization. This process seems to interrupt authentic transmission, replacing it with a massive virtual user manual. Academic institutions tend to prioritize objective and impersonal knowledge, excluding the relational aspect of experiences. Current rationality focuses on immediate efficiency at the expense of the autonomy and identity of the operator. Transmission requires care and trust, and to be effective and authentic, it necessitates time, trust, and deep relationships

3D Convolutional Neural Networks for Diagnosis of Alzheimer’s Disease via structural MRI

Alzheimer’s Disease (AD) is a widespread neurodegenerative disease caused by structural changes in the brain and leads to deterioration of cognitive functions. Patients usually experience diagnostic symptoms at later stages after irreversible neural damage occurs. Early detection of AD is crucial in maximizing patients' quality of life and to start treatments to decelerate the progress of the disease. Early detection may be possible via computer-assisted systems using neuroimaging data. Among all, deep learning utilizing magnetic resonance imaging (MRI) have become a prominent tool due to its capability to extract high-level features through local connectivity, weight sharing, and spatial invariance. This paper describes our investigation of the classification accuracy based on two publicly available data sets, namely, ADNI and OASIS, by building a 3D VGG variant convolutional network (CNN). We used 3D models to avoid information loss, which occurs during the process of slicing 3D MRI into 2D images and analyzing them by 2D convolutional filters. We also conducted a pre-processing of the data to enhance the effectiveness and classification performance of the model. The proposed model achieved 73.4% classification accuracy on ADNI and 69.9% on OASIS dataset with 5-fold cross-validation (CV). These results are comparable to other studies using various convolutional models. However, our subject-based divided dataset has only one MRI of a single patient to prevent possible data leakage whereas some other studies have different screenings of the same patients "over a time period'" in their datasets