Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Secure Attachment as a Protective Factor Against Later Cognitive Decline and Dementia

The etiology of neurodegenerative disorders such as dementia is complex and incompletely understood. Interest in a developmental perspective to these pathologies is gaining momentum. An early supportive social environment seems to have important implications for social, affective and cognitive abilities across the lifespan. Attachment theory may help to explain the link between these early experiences and later outcomes. This theory considers early interactions between an infant and its caregiver to be crucial to shaping social behavior and emotion regulation strategies throughout adult life. Furthermore, research has demonstrated that such early attachment experiences can, potentially through epigenetic mechanisms, have profound neurobiological and cognitive consequences. Here we discuss how early attachment might influence the development of affective, cognitive, and neurobiological resources that could protect against cognitive decline and dementia. We argue that social relations, both early and late in life, are vital to ensuring cognitive and neurobiological health. The concepts of brain and cognitive reserve are crucial to understanding how environmental factors may impact cognitive decline. We examine the role that attachment might play in fostering brain and cognitive reserve in old age. Finally, we put forward the concept of affective reserve, to more directly frame the socio-affective consequences of early attachment as protectors against cognitive decline. We thereby aim to highlight that, in the study of aging, cognitive decline and dementia, it is crucial to consider the role of affective and social factors such as attachment.
Toward a Coordinated Global Observing System for Seagrasses and Marine Macroalgae

In coastal waters around the world, the dominant primary producers are benthic macrophytes, including seagrasses and macroalgae, that provide habitat structure and food for diverse and abundant biological communities and drive ecosystem processes. Seagrass meadows and macroalgal forests play key roles for coastal societies, contributing to fishery yields, storm protection, biogeochemical cycling and storage, and important cultural values. These socio-economically valuable services are threatened worldwide by human activities, with substantial areas of seagrass and macroalgal forests lost over the last half-century. Tracking the status and trends in marine macrophyte cover and quality is an emerging priority for ocean and coastal management, but doing so has been challenged by limited coordination across the numerous efforts to monitor macrophytes, which vary widely in goals, methodologies, scales, capacity, governance approaches, and data availability. Here, we present a consensus assessment and recommendations on the current state of and opportunities for advancing global marine macrophyte observations, integrating contributions from a community of researchers with broad geographic and disciplinary expertise. With the increasing scale of human impacts, the time is ripe to harmonize marine macrophyte observations by building on existing networks and identifying a core set of common metrics and approaches in sampling design, field measurements, governance, capacity building, and data management. We recommend a tiered observation system, with improvement of remote sensing and remote underwater imaging to expand capacity to capture broad-scale extent at intervals of several years, coordinated with stratified in situ sampling annually to characterize the key variables of cover and taxonomic or functional group composition, and to provide ground-truth. A robust networked system of macrophyte observations will be facilitated by establishing best practices, including standard protocols, documentation, and sharing of resources at all stages of workflow, and secure archiving of open-access data. Because such a network is necessarily distributed, sustaining it depends on close engagement of local stakeholders and focusing on building and long-term maintenance of local capacity, particularly in the developing world. Realizing these recommendations will produce more effective, efficient, and responsive observing, a more accurate global picture of change in vegetated coastal systems, and stronger international capacity for sustaining observations.
Candidate Markers for Stratification and Classification in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune, inflammatory disease, characterized by synovitis in small- and medium-sized joints and, if not treated early and efficiently, joint damage, and destruction. RA is a heterogeneous disease with a plethora of treatment options. The pro-inflammatory cytokine tumor necrosis factor (TNF) plays a central role in the pathogenesis of RA, and TNF inhibitors effectively repress inflammatory activity in RA. Currently, treatment decisions are primarily based on empirics and economic considerations. However, the considerable interpatient variability in response to treatment is a challenge. Markers for a more exact patient classification and stratification are lacking. The objective of this study was to identify markers in immune cell populations that distinguish RA patients from healthy donors with an emphasis on TNF signaling. We employed mass cytometry (CyTOF) with a panel of 13 phenotyping and 10 functional markers to explore signaling in unstimulated and TNF-stimulated peripheral blood mononuclear cells from 20 newly diagnosed, untreated RA patients and 20 healthy donors. The resulting high-dimensional data were analyzed in three independent analysis pipelines, characterized by differences in both data clean-up, identification of cell subsets/clustering and statistical approaches. All three analysis pipelines identified p-p38, IkBa, p-cJun, p-NFkB, and CD86 in cells of both the innate arm (myeloid dendritic cells and classical monocytes) and the adaptive arm (memory CD4+ T cells) of the immune system as markers for differentiation between RA patients and healthy donors. Inclusion of the markers p-Akt and CD120b resulted in the correct classification of 18 of 20 RA patients and 17 of 20 healthy donors in regression modeling based on a combined model of basal and TNF-induced signal. Expression patterns in a set of functional markers and specific immune cell subsets were distinct in RA patients compared to healthy individuals. These signatures may support studies of disease pathogenesis, provide candidate markers for response, and non-response to TNF inhibitor treatment, and aid the identification of future therapeutic targets.
Vasoreactive Pulmonary Arterial Hypertension Manifesting With Misleading Epileptic Seizure: Diagnostic and Treatment Pitfalls

A 5-year-old girl presented with acute nocturnal episodes of loss of consciousness following abdominal pain and crying. Epilepsy was primarily diagnosed but the course of the disease was suggestive of pulmonary hypertension. An adapted invasive assessment of pulmonary pressure and pharmacological challenge allowed for diagnosing vasoreactive pulmonary arterial hypertension. Initial treatment with sildenafil was not effective. Thus, calcium channel blockers were introduced when positive vasoreactivity was confirmed and permitted to stop the occurrence of the syncope and dramatically improved clinical status. At 2 years follow-up she is well without any complaint and in functional class I. Echocardiography shows a slightly enlarged but not hypertrophied right ventricle with a nearly normalized estimated right ventricular pressure. The last catheterization shows subnormal values of pulmonary arterial pressure (mean pulmonary artery pressure: 24 mmHg) and pulmonary vascular resistance (5, 4 Wood units*m2), normalizing with inhaled Nitric Oxide (mean pulmonary artery pressure of 14 mmHg and pulmonary vascular resistance of 1.5 Wood units*m2). Vasoreactive pulmonary arterial hypertension is a rare entity in children but it should not be misdiagnosed with seizures due to the presence of syncopal episodes. According to current knowledge, this form seems to have a better prognosis than non-reactive pulmonary arterial hypertension and the treatment of choice remains as calcium channel blockers. The management of this case was characterized by successive mishaps and potentially harmful mistakes and underscores the potential risk with pediatric PH evaluation in non-expert centers.
Effects of Different Body Postures on the Intraocular Pressure in Patients with Primary Angle-Closure Disease.

SIGNIFICANCE Eyes with primary angle-closure disease showed higher intraocular pressure (IOP) in the recumbent postures including supine and lateral decubitus (LD) positions compared with the sitting position. PURPOSE The purpose of this study was to investigate the effects of different body postures on the IOP in eyes with primary angle-closure suspect (PACS), primary angle closure (PAC), or primary angle-closure glaucoma (PACG). METHODS This is a comparative case series of 31 patients with bilateral PAC disease, such as PACS, PAC, and PACG. The IOP was measured using Tonopen AVIA in sitting, supine, and right or left LD positions. Patients were instructed to rest their heads on the pillows in supine and LD positions. The eye on the lower side in the LD position was called dependent eye. The main outcome was the difference in IOP among different body postures. RESULTS Among the 62 eyes (31 patients), PACG, PAC, and PACS were noted in 44, 12, and 6 eyes, respectively. The IOP was not significantly different between the two eyes in sitting and supine positions, whereas the IOP was significantly higher in the dependent eye than in the nondependent eye in LD positions. Compared with that measured in the supine position, IOP differences of the dependent eye were higher than those of the nondependent eye when measured in LD position (right LD vs. supine, P = .02; left LD vs. supine, P = .01). In addition, when the study patients were divided into three groups according to their diagnosis, no significant differences were observed in the IOPs and IOP differences among the three groups. CONCLUSIONS Posture-induced IOP changes were comparable among eyes with PACS, PAC, and PACG.
A memetic algorithm approach to network alignment: mapping the classification of mental disorders of DSM-IV with ICD-10

Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify significant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the effectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classification tool on two mental health disorder diagnostic databases.
Predicting the Confusions that Occur in Letter Learning

Marcus is 27 years old, moved to Israel about a year ago, and is literate in his native language. He learned the Hebrew alphabet when he arrived. If you present an isolated letter to him, he will be able to tell you what it is. However, when he tries to identify those letters in large units, namely, words, he makes a consistent set of mistakes.
Efficient personalized community detection via genetic evolution

Personalized community detection aims to generate communities associated with user need on graphs, which benefits many downstream tasks such as node recommendation and link prediction for users, etc. It is of great importance but lack of enough attention in previous studies which are on topics of user-independent, semi-supervised, or top-K user-centric community detection. Meanwhile, most of their models are time consuming due to the complex graph structure. Different from these topics, personalized community detection requires to provide higher-resolution partition on nodes that are more relevant to user need while coarser manner partition on the remaining less relevant nodes. In this paper, to solve this task in an efficient way, we propose a genetic model including an offline and an online step. In the offline step, the user-independent community structure is encoded as a binary tree. And subsequently an online genetic pruning step is applied to partition the tree into communities. To accelerate the speed, we also deploy a distributed version of our model to run under parallel environment. Extensive experiments on multiple datasets show that our model outperforms the state-of-arts with significantly reduced running time.
Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories

In smart factories, process planning and scheduling need to be performed every time a new manufacturing order is received or a factory state change has been detected. A new plan and schedule need to be determined quickly to increase the responsiveness of the factory and enlarge its profit. Simultaneous optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation of process planning and scheduling is presented. It executes a multi-objective genetic algorithm on multiple subpopulations (islands). The number of islands is automatically decided based on the current optimisation state. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.
Predict or screen your expensive assay: DoE vs. surrogates in experimental combinatorial optimization

Statistics-based Design of Experiments (DoE) methodologies are considered the gold standard in existing laboratory equipment for screening predefined experimental assays. Thus far, very little is formally known about their effectiveness in light of global optimization, particularly when compared to Evolutionary Algorithms (EAs). The current study, which was ignited by evolution-in-the-loop of functional protein expression, aims to conduct such a comparison with a focus on Combinatorial Optimization considering a dozen of decision variables, a search-space cardinality of several million combinations, under a budget of a couple of thousands of function evaluations. Due to the limited budget of evaluations, we argue that surrogate-assisted search methods become relevant for this application domain. To this end, we study a specific so-called Categorical Evolution Strategy (CatES). We systematically compare its performance, with and without being assisted by state-of-the-art surrogates, to DoE-based initialization of the search (exploiting the budget partially or entirely). Our empirical findings on noise-free benchmarks show that the surrogate-based approach is superior, as it significantly outperforms the DoE techniques and the CatES alone on the majority of the problems subject to the budget constraint. We conclude by projecting the strengths and weaknesses of EAs versus DoE, when run either directly or surrogate-aided.
Efficient real-time hypervolume estimation with monotonically reducing error

The hypervolume (or S-metric) is a widely used quality measure employed in the assessment of multi- and many-objective evolutionary algorithms. It is also directly integrated as a component in the selection mechanism of some popular optimisers. Exact hypervolume calculation becomes prohibitively expensive in real-time applications as the number of objectives increases and/or the approximation set grows. Assuch, Monte Carlo (MC) sampling is often used to estimate its value rather than exactly calculating it. This estimation is inevitably subject to error. As standard with Monte Carlo approaches, the standard error decreases with the square root of the number of MC samples. We propose a number of real-time hypervolume estimation methods for unconstrained archives --- principally for use in real-time convergence analysis. Furthermore, we show how the number of domination comparisons can be considerably reduced by exploiting incremental properties of the approximated Pareto front. In these methods the estimation error monotonically decreases over time for (i) a capped budget of samples per algorithm generation and (ii) a fixed budget of dedicated computation time per optimiser generation for new MC samples. Results are provided using an illustrative worst-case scenario with rapid archive growth, demonstrating the orders-of-magnitude of speed-up possible.
Gradient Outlier Removal for Gradient‐Domain Path Tracing

We present a new outlier removal technique for a gradient‐domain path tracing (G‐PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G‐PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.
The Blockchain, Today and Tomorrow

Software engineering principles have enabled us to continually increase the complexity of the problems that we can solve using software by promoting the sensible use of abstraction, separation, composition and generalisation. Writing software for a public blockchain makes the code we write publicly available for inspection, and potential exploits may result in the loss of funds that the program manages. This unprecedented level of exposure and ease of exploitation requires new methods to improve our confidence in the correctness of the code that we write. This paper explores the main current techniques used to achieve this increased level of confidence, especially in the area of programming language design, starting from the first Turing Complete blockchain implementation Ethereum, and ending with some of the more principled approaches, Scilla and Rholang.
Clicking using the eyes, a machine learning approach.

This master thesis report describes the work of evaluating the approach of using an eye-tracker and machine learning to generate an interaction model for clicks. In the study, recordings were done from 10 participants using a quiz application, and machine learning was then applied. Models were created with varying quality from a machine learning view, although most models did not work well for interaction. One model was created that enable correct interaction 80\% of the time, although the specific circumstances for success were not identified. The conclusion of the thesis is that the approach works in some cases, but that more research needs to be done to evaluate general suitability, and approaches to make it work reliably.
Megacities, migration and an evolutionary approach to bipolar disorder: a study of Sardinian immigrants in Latin America

Objective: To determine whether people with a Sardinian genetic background who live in the megacities of South America have a higher frequency of hypomania than residents of Sardinia. Methods: A community survey of Sardinian immigrants was carried out in four Brazilian metropoles (n=218) and Buenos Aires (n=306). The results were compared with those of a study involving a similar methodology (Mood Disorder Questionnaire [MDQ] as a screening tool) conducted in seven Italian regions, including a sub-sample from Sardinia. Results: There was a higher prevalence of lifetime hypomania among Sardinians living in the Brazilian metropoles than among those living in Sardinia. This result was also consistent with Sardinian immigrants in Buenos Aires. After stratification by sex and age, the lifetime prevalence of MDQ scores ≥ 8 among Sardinians in South-American megacities and Sardinia was 8.6% vs. 2.9%, respectively (p < 0.0001). Conclusions: The higher frequency of hypomania in migrant populations appears to favor an evolutionary view in which mood disorders may be a maladaptive aspect of a genetic background with adaptive characteristics.
Wnt/β-Catenin Signaling in Liver Cancers

Liver cancer is among the leading global healthcare issues associated with high morbidity and mortality. Liver cancer consists of hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), hepatoblastoma (HB), and several other rare tumors. Progression has been witnessed in understanding the interactions between etiological as well as environmental factors and the host in the development of liver cancers. However, the pathogenesis remains poorly understood, hampering the design of rational strategies aiding in preventing liver cancers. Accumulating evidence demonstrates that aberrant activation of the Wnt/β-catenin signaling pathway plays an important role in the initiation and progression of HCC, CCA, and HB. Targeting Wnt/β-catenin signaling potentiates a novel avenue for liver cancer treatment, which may benefit from the development of numerous small-molecule inhibitors and biologic agents in this field. In this review, we discuss the interaction between various etiological factors and components of Wnt/β-catenin signaling early in the precancerous lesion and the acquired mechanisms to further enhance Wnt/β-catenin signaling to promote robust cancer formation at later stages. Additionally, we shed light on current relevant inhibitors tested in liver cancers and provide future perspectives for preclinical and clinical liver cancer studies.
Influence of oxygen in the development of retinopathy of prematurity.

OBJECTIVE to describe the influence of oxygen in retinopathy of prematurity (ROP) in premature newborns (PTNB) hospitalized in neonatal units of intensive care and undergoing ophthalmological follow-up procedures after hospital discharge. METHOD retrospective cohort study, from January 2014 to June 2016, whose data collection totaled 181 charts. Descriptive and inferential statistical analysis. RESULTS when using oxygen (O2) in 148 PTNB (81.7%), both mask (n=141; 77.9%; p-value <0.001) and the tracheal tube predominated (n=100; 55.25; p-value <0.001) for 15 days in average. The time of use and O2 concentration of the tracheal tube (p-value <0.001), the time of mask use (p-value <0.001) and the time and concentration of O2 of the continuous positive airway pressure (CPAP) (p-value <0.001) were significant to cause ROP in 50 PTNB (11.31%). CONCLUSION the oxygen therapy has influenced the development and severity of ROP, indicating the need to adopt protocols for its use.