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ABSTRACT During the second and third trimesters of human gestation, the brain undergoes rapid neurodevelopment thanks to critical processes such as neuronal migration, radial glial scaffolding, and synaptic sprouting. Unfortunately, gathering high‐quality MRI data on the healthy fetal brain is complex, making it challenging to understand this development. To address this issue, we conducted a study using motion‐corrected diffusion tensor imaging (DTI) to analyze changes in the cortical gray matter (CP) and sub‐cortical white matter (scWM) microstructure in 44 healthy fetuses between 23 and 36 weeks of gestational age. We automatically segmented these two tissues and parcellated them into eight regions based on anatomy, including the frontal, parietal, occipital, and temporal lobes, cingulate, sensory and motor cortices, and the insula. We were able to observe distinct patterns of diffusion MRI signals across these regions. Specifically, we found that in the CP, fractional anisotropy (FA) consistently decreased with age, while mean diffusivity (MD) followed a downward‐open parabolic trend. Conversely, in the scWM, FA exhibited an upward‐open parabolic trajectory, while MD followed a downward‐open parabolic trend. Our study underscores the potential for diffusion as a biomarker for normal and abnormal neurodevelopment before birth, especially since most neurodiagnostic tools are not yet available at this stage. This study utilizes advanced diffusion tensor imaging and motion correction techniques to uncover insights into the development of the fetal brain. Dynamic changes in water diffusivity and anisotropy within the fetal telencephalon offer a deeper understanding of neurodevelopmental processes. These findings underscore the regional specialization of the fetal brain.

There is a lack of consensus about the measurement of the muscle viscoelastic features in stroke patients. Additionally, the psychometric properties of the most-commonly used clinical tools remain controversial. Our objective is to investigate the validity and reliability of myotonometry to assess viscoelastic muscle features in stroke survivors. Pubmed, PEDro, Scopus and Cinahl were systematically searched to include studies reporting the psychometric properties of myotonometric devices used in people after stroke. The QUADAS-2 and the COSMIN checklists were used to assess the methodological quality of the studies and the psychometric properties of myotonometry. Nine studies were included in the qualitative synthesis and data from five of these were pooled in a meta-analysis. Overall, low to moderate risk of bias and applicability concerns were observed. Pooled data from intra-rater reliability for muscle tone showed a mean coefficient of correlation of 0.915 (95% CI: 0.880–0.940, I 2 = 69.2%) for upper limbs, and a mean coefficient of 0.785 (95%CI: 0.708–0.844, I 2 = 4.02%) for lower limbs. Myotonometry seems to be a valid and reliable complementary tool to assess muscle viscoelastic properties in stroke survivors, although definite conclusions about concurrent validity need further research.

A descriptive analysis of 416 documents was performed using bibliometric techniques, in order to gather existing knowledge in circular economy focusing on waste management (2007–2020). The results of this study indicate that annual scientific production increased 94% in the last 5 years, highlighting the countries of Italy, Spain, the UK, China, Brazil, and India. Between the most cited documents stand out those related to calorific value of municipal solid waste and waste to energy technologies for achieving circular economy systems. The conceptual analysis indicates strong linkage between circular economy and sustainable production, waste management, and recycling. Emerging research trends evolved from processes and industry-oriented approach (2017) toward waste management, recycling, and circular economy (2019) and sustainable development and urban solid waste (2020). The analysis reveals five dominant circular economy and waste research themes: (1) greenhouse gases; (2) circular economy, waste management, and recycling; (3) life cycle; (4) waste treatment; and (5) anaerobic digestion and recovery; trends research are related to policy interventions, and enforcement of authorities’ regulations to foster circular economy transition, increase the use of practices of recycling and reusing, as well as discourage a growing consumption culture. Results found denote the challenge represented by the implementation of comprehensive policies in circular economy. The above being a key alternative for green recovery in response to the current COVID-19 pandemic.

Interferon lambda 4 (IFNλ4) has shown antiviral activity against RNA viruses, including some coronaviruses. Besides, genetic variants of IFNL4 can be predictive of the clearance of RNA viruses. However, little is known about the effect of these genetic variants on SARS-CoV-2 infection. In this study, we investigated whether there was a relationship of the rs12979860 polymorphism of IFNL4 with COVID-19. We found that the T allele of rs12979860 was overexpressed in COVID-19 patients with regard to the general population without this disease (36.16% vs. 26.40%, p = 6.4 × 10 –4 ; OR 0.633 C vs T; 95% CI 0.487, 0.824), suggesting that this allele could be a risk factor for COVID-19. Accordingly, the CC genotype was significantly lower in COVID-19 patients compared to controls (37.85% vs. 55.51%, p = 8 × 10 –5 ; OR 0.488; 95% CI 0.342, 0.698). These results were not affected by sex, age, and disease severity in patients with COVID-19. Our findings suggest that, like other infectious diseases caused by RNA viruses, genetic variants of IFNL4 can predispose to COVID-19. Confirmation of our results may contribute to better understanding the mechanisms of this disease.

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from \"à la carte\" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

Genomic analysis of tumours has led to the identification of hundreds of cancer genes on the basis of the presence of mutations in protein-coding regions. By contrast, much less is known about cancer-causing mutations in non-coding regions. Here we perform deep sequencing in 360 primary breast cancers and develop computational methods to identify significantly mutated promoters. Clear signals are found in the promoters of three genes. FOXA1 , a known driver of hormone-receptor positive breast cancer, harbours a mutational hotspot in its promoter leading to overexpression through increased E2F binding. RMRP and NEAT1 , two non-coding RNA genes, carry mutations that affect protein binding to their promoters and alter expression levels. Our study shows that promoter regions harbour recurrent mutations in cancer with functional consequences and that the mutations occur at similar frequencies as in coding regions. Power analyses indicate that more such regions remain to be discovered through deep sequencing of adequately sized cohorts of patients. High-depth sequencing of targeted regions in primary breast cancer identifies mutated promoter elements with recurrent mutations at specific and/or nearby bases, suggesting selection of certain non-coding events. Non-coding driver mutations for breast cancer Many cancer genomic studies have characterized the landscape of driver mutations in protein-coding regions, but there has been limited exploration of non-coding regions. Gad Getz and colleagues searched for significantly mutated regulatory regions with high-depth sequencing of targeted regions in 360 primary breast cancers. They identified significantly mutated promoter elements associated with nine genes, and found recurrent mutations at specific and/or nearby bases, suggesting targeting of a specific element. For three of these genes, FOXA1 , RMRP and NEAT1 , they show that these recurrent promoter-proximal mutations influence gene expression and protein binding affinity. The authors suggest that further deep sequencing studies in larger cohorts could identify more as yet undiscovered promoter regions.

This work presents detailed anatomic labels for a spatiotemporal atlas of fetal brain Diffusion Tensor Imaging (DTI) between 23 and 30 weeks of post‐conceptional age. Additionally, we examined developmental trajectories in fractional anisotropy (FA) and mean diffusivity (MD) across gestational ages (GA). We performed manual segmentations on a fetal brain DTI atlas. We labeled 14 regions of interest (ROIs): cortical plate (CP), subplate (SP), Intermediate zone‐subventricular zone‐ventricular zone (IZ/SVZ/VZ), Ganglionic Eminence (GE), anterior and posterior limbs of the internal capsule (ALIC, PLIC), genu (GCC), body (BCC), and splenium (SCC) of the corpus callosum (CC), hippocampus, lentiform Nucleus, thalamus, brainstem, and cerebellum. A series of linear regressions were used to assess GA as a predictor of FA and MD for each ROI. The combination of MD and FA allowed the identification of all ROIs. Increasing GA was significantly associated with decreasing FA in the CP, SP, IZ/SVZ/IZ, GE, ALIC, hippocampus, and BCC (p < .03, for all), and with increasing FA in the PLIC and SCC (p < .002, for both). Increasing GA was significantly associated with increasing MD in the CP, SP, IZ/SVZ/IZ, GE, ALIC, and CC (p < .03, for all). We developed a set of expert‐annotated labels for a DTI spatiotemporal atlas of the fetal brain and presented a pilot analysis of developmental changes in cerebral microstructure between 23 and 30 weeks of GA. We developed a set of expert‐annotated labels for a DTI spatiotemporal atlas of the fetal brain and presented a pilot analysis of developmental changes in cerebral microstructure between 23 and 30 weeks of GA.

This review aims to point out that chronic stress is able to accelerate the appearance of Alzheimer’s disease (AD), proposing the former as a risk factor for the latter. Firstly, in the introduction we describe some human epidemiological studies pointing out the possibility that chronic stress could increase the incidence, or the rate of appearance of AD. Afterwards, we try to justify these epidemiological results with some experimental data. We have reviewed the experiments studying the effect of various stressors on different features in AD animal models. Moreover, we also point out the data obtained on the effect of chronic stress on some processes that are known to be involved in AD, such as inflammation and glucose metabolism. Later, we relate some of the processes known to be involved in aging and AD, such as accumulation of β-amyloid, TAU hyperphosphorylation, oxidative stress and impairement of mitochondrial function, emphasizing how they are affected by chronic stress/glucocorticoids and comparing with the description made for these processes in AD. All these data support the idea that chronic stress could be considered a risk factor for AD.

Many governments and firms do believe that technology can supplant governance and human responsibility. This belief poses the question of who will really benefit from smart cities. This article explores this fundamental question through the study of digital media platforms. The ultimate goal is to understand the link between e-governance and smart city initiatives in our cases of study by testing whether these projects are explicitly for citizens. This article shows how e-platforms represent the use of information and communication technologies with the aim of encouraging citizen participation in decision-making processes, improving information and service delivery, reinforcing transparency, accountability, as well as credibility. Thirteen digital media platforms are surveyed, mostly in cities across countries. These e-platforms raise implementation challenges for both firms and policy makers, and new research opportunities for scientist to build up new research and to experiment with the aim to make the benefits for citizens wider and the participatory dimension stronger.

Coastal risks in the Mediterranean are a result of the complex interplay between hydrometeorological and marine hazards. The region encompasses areas with varying degrees of vulnerability to these hazards, as well as spatial variations in exposure values, making it essential to adopt a comprehensive and nuanced approach to risk assessment and management. It is worth noting that hydrometeorological hazards, such as flash floods, can often have a greater impact than strictly coastal hazards, highlighting the need to consider the full range of potential risks. Therefore, coastal managers must adopt a multi-hazard approach to make sound risk management decisions. This study addresses this need using an index-based framework that assesses the integrated risk in time and space (hereafter referred to as cumulative compound risk) in coastal zones by aggregating the main hydrometeorological and marine hazards, the vulnerability of the territory to both types of hazards, and values at exposure. The framework is designed for use at large spatial scales (applied to a 1100 km coastline in this study), with the basic spatial unit being relevant for management (here set as the municipality in this study). Its application enables the assessment of spatial variations in integrated risk as well as individual hydrometeorological and marine contributions. The combined use of the indices and cluster analysis helps identify similarities and differences in the risk profile of spatial units, and thus, define homogeneous areas from a risk management perspective. In this study, the framework was applied to the Spanish Mediterranean coastline, an area representative of the climatic, geomorphological, and socioeconomic conditions of the Mediterranean coast.