Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring associations between the teat apex metagenome and Staphylococcus aureus intramammary infection risk in primiparous cows under organic directives

The primary objective of this study was to identify associations between teat apex microbiome and Staphylococcus aureus intramammary infection (IMI) risk in primiparous cows during the first 5 weeks after calving. We performed a case-control study using shotgun metagenomics of the teat apex and culture-based milk data collected longitudinally from 710 primiparous cows on 5 organic dairy farms. We observed a strong association between S. aureus DNA in the metagenomic teat apex data prior to parturition and the odds of S. aureus IMI after parturition (OR = 38.9, 95% CI: 14.84-102.21). Differential abundance analysis confirmed this association, with cases having a 23.8 higher log fold change (LFC) in abundance of S. aureus in their samples compared to controls. Of the most prevalent microorganisms in controls, those associated with a lower risk of post-calving S. aureus IMI included Microbacterium phage Min 1 (OR = 0.37, 95% CI: 0.25-0.53), Corynebacterium efficiens (OR = 0.53, 95% CI: 0.30-0.94), Kocuria polaris (OR = 0.54, 95% CI: 0.35-0.82), Micrococcus terreus (OR = 0.64, 95% CI: 0.44-0.93) and Dietzia alimentaria (OR = 0.45, 95% CI: 0.26-0.75). Microcin B17 was the most prevalent antibacterial peptide on the teat apex of cases and controls (99.7% in both groups). The predicted abundance of Microcin B17 was also higher in cases compared to controls (LFC 0.26). Cow and farm random effects often explained a large proportion of the observed variability in the teat apex microbiome, suggesting that our results need to be interpreted within the context of the random effects. IMPORTANCE Intramammary infections (IMI) caused by Staphylococcus aureus remain an important problem for the organic dairy industry. The microbiome on the external skin of the teat apex may play a role in mitigating S. aureus IMI risk, in particular the production of antimicrobial peptides (AMPs) by commensal microbes. However, current studies of the teat apex microbiome utilize a 16S approach, which precludes detection of genomics features such as AMPs. Therefore, further research using a shotgun metagenomic approach is needed to understand what role pre-partum teat apex microbiome dynamics play in IMI risk.
Urban communication systems : neighborhoods and the search for community

This book is a call for more research into urban systems in general and communication patterns specifically within geographically defined units of analysis. Community is a key concept in discussions of how technology is affecting people's lives today. As the Internet links people to larger "virtual communities," we need to revisit how neighborhoods help people find community if urban areas are to thrive as social systems. The book treats the urban system as the focus in a unique attempt to integrate the literature from communication with other disciplines focusing on cities. The book employs macro systems theory in conjunction with other theories to explain how neighborhoods fit into people's search for community.
Cerebral Small Vessel Disease: Early-Life Antecedents and Long-Term Implications for the Brain, Aging, Stroke, and Dementia

Cerebral small vessel disease is common in older adults and increases the risk of stroke, cognitive impairment, and dementia. While often attributed to midlife vascular risk factors such as hypertension, factors from earlier in life may contribute to later small vessel disease risk. In this review, we summarize current evidence for early-life effects on small vessel disease, stroke and dementia focusing on prenatal nutrition, and cognitive ability, education, and socioeconomic status in childhood. We discuss possible reasons for these associations, including differences in brain resilience and reserve, access to cognitive, social, and economic resources, and health behaviors, and we consider the extent to which these associations are independent of vascular risk factors. Although early-life factors, particularly education, are major risk factors for Alzheimer disease, they are less established in small vessel disease or vascular cognitive impairment. We discuss current knowledge, gaps in knowledge, targets for future research, clinical practice, and policy change.
Multimodal subspace independent vector analysis captures latent subspace structures in large multimodal neuroimaging studies

We present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources across multiple data modalities by defining linked and modality-specific subspaces. In particular, MSIVA enables the estimation of independent subspaces of various sizes within modalities and their one-to-one linkage to corresponding subspaces across modalities. We compare MSIVA to a fully unimodal initialization baseline and a fully multimodal initialization baseline, and evaluate all three approaches with five distinct subspace structures on synthetic and neuroimaging datasets. We first demonstrate that MSIVA and the unimodal baseline can identify the correct ground-truth subspace structures from the incorrect ones in multiple synthetic datasets, while the multimodal baseline fails at detecting high-dimensional subspace structures. We then show that MSIVA can better capture the latent subspace structure with the minimum loss value in two large multimodal neuroimaging datasets compared to the unimodal baseline. Our results from subsequent per-subspace canonical correlation analysis (CCA) and brain-phenotype modeling demonstrate that the sources from the optimal subspace structure are strongly associated with phenotype measures, including age, sex and schizophrenia-related effects. Our proposed methodology MSIVA can be applied to capture linked and unique biomarkers from multimodal neuroimaging data.
Machine Learning the Operator Content of the Critical Self-Dual Ising-Higgs Gauge Model

We study the critical properties of the Ising-Higgs gauge theory in $(2+1)D$ along the self-dual line which have recently been a subject of debate. For the first time, using machine learning techniques, we determine the low energy operator content of the associated field theory. Our approach enables us to largely refute the existence of an emergent current operator and with it the standing conjecture that this transition is of the $XY^*$ universality class. We contrast these results with the ones obtained for the $(2+1)D$ Ashkin-Teller transverse field Ising model where we find the expected current operator. Our numerical technique extends the recently proposed Real-Space Mutual Information allowing us to extract sub-leading non-linear operators. This allows a controlled and computationally scalable approach to target CFT spectrum and discern universality classes beyond $(1+1)D$ from Monte Carlo data.
Current Directions in Psychological Science

Eyewitnesses are often called upon to report information about what they have seen. A wealth of research from the past century has demonstrated, however, that eyewitness memory is malleable and vulnerable to distorting influences, including the effects of misinformation. In this article, we review recent developments in research related to the misinformation effect, including individual differences in susceptibility, neuroimaging approaches, and protective interview procedures that may better elicit accurate event details. We conclude with a section on related false memory research.
A Novel Network Representation Learning With Three Feature Integrations

Network representation learning is one of the important works of analyzing network information. Its purpose is to learn a vector for each node in the network and map it into the vector space, and the resulting number of node dimensions is much smaller than the number of nodes in the network. Most of the current work only considers local features and ignores other features in the network, such as attribute features. Aiming at such problems, this paper proposes novel mechanisms of combining network topology, which models node text information and node clustering information on the basis of network structure and then constrains the learning process of network representation to obtain the optimal network node vector. The method is experimentally verified on three datasets: Citeseer (M10), DBLP (V4), and SDBLP. Experimental results show that the proposed method is better than the algorithm based on network topology and text feature. Good experimental results are obtained, which verifies the feasibility of the algorithm and achieves the expected experimental results.
Neurofeedback for post-traumatic stress disorder: systematic review and meta-analysis of clinical and neurophysiological outcomes

ABSTRACT Background: Posttraumatic stress disorder (PTSD) is a debilitating condition affecting millions of people worldwide. Existing treatments often fail to address the complexity of its symptoms and functional impairments resulting from severe and prolonged trauma. Electroencephalographic Neurofeedback (NFB) has emerged as a promising treatment that aims to reduce the symptoms of PTSD by modulating brain activity. Objective: We conducted a systematic review and meta-analysis of ten clinical trials to answer the question: how effective is NFB in addressing PTSD and other associated symptoms across different trauma populations, and are these improvements related to neurophysiological changes? Method: The review followed the Preferred Reporting Items for Systematic Reviews and Meta analyses guidelines. We considered all published and unpublished randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) involving adults with PTSD as a primary diagnosis without exclusion by type of trauma, co-morbid diagnosis, locality, or sex. Ten controlled studies were included; seven RCTs and three NRSIs with a total number of participants n = 293 (128 male). Only RCTs were included in the meta-analysis (215 participants; 88 male). Results: All included studies showed an advantage of NFB over control conditions in reducing symptoms of PTSD, with indications of improvement in symptoms of anxiety and depression and related neurophysiological changes. Meta-analysis of the pooled data shows a significant reduction in PTSD symptoms post-treatment SMD of −1.76 (95% CI −2.69, −0.83), and the mean remission rate was higher in the NFB group (79.3%) compared to the control group (24.4%). However, the studies reviewed were mostly small, with heterogeneous populations and varied quality. Conclusions: The effect of NFB on the symptoms of PTSD was moderate and mechanistic evidence suggested that NFB leads to therapeutic changes in brain functioning. Future research should focus on more rigorous methodological designs, expanded sample size and longer follow-up. HIGHLIGHTS Neurofeedback (NFB) was found to have moderate beneficial effects on PTSD symptoms, and positive effects on secondary outcomes such as depression and anxiety, according to a meta-analysis of seven randomised controlled trials (RCTs). The beneficial effects of NFB were observed across diverse populations, including those with different types of trauma (military and civilians) and from different ethnic backgrounds. Results suggest that modulation of alpha rhythm might be a viable NFB protocol in patients with PTSD, as changes in neurophysiological functioning, such as connectivity in the Default Mode Network (DMN) and Salience Network (SN), were observed post-NFB and were correlated with a decrease in PTSD severity.
Agile Requirements Prioritization : What Happens in Practice and What Is Described in Literature ”

Agile software development consists of a multitude of iterations in between which the decision what requirements to implement is re-evaluated by a process of requirements re-prioritization. In 2010, Racheva, Daneva, Herrmann, and Wieringa (2010b) examined 22 di↵erent Requirements Prioritization (RP) methods and extracted an abstracted, conceptual model of the RP process from a generic perspective. Four months later, Racheva, Daneva, and Herrmann (2010a) present an improved version of this model based on a case study among 11 practitioners of companies that follow agile methodologies. The paper discussed in this method description (Bakalova, Daneva, Herrmann, & Wieringa, 2011) presents the same improved version of this model. In fact, a majority of this paper is identical to Racheva et al. (2010a), with the addition of mapping the concepts of the aforementioned 22 RP methods to the aspects of the improved model. The end-goal of the method is to inform decision-makers (clients) of the important concepts that are considered during a RP iteration by practitioners. In the end, this information helps the client in three ways: ”(i) to navigate through the agile process of delivering business value; (ii) to make explicit the tacit assumptions in di↵erent RP methods; (iii) to identify those possible pieces/sources of information important to the outcome of the prioritization and, consequently, to the project. ” (Bakalova et al., 2011, page 183). Agile Requirements Prioritization consists of three subsequent activities: the team starts with Value Estimation of all requirements on the project backlog. Next, the team takes the value of each requirement into account when creating the prioritized project backlog during Requirements Prioritization. Finally, the project manager starts Iteration Planning, he selects a number of requirements to be completed in the next iteration and puts them on the final deliverable: the iteration backlog. During each of these activities, up to eight aspects are taken into account when making decision on requirements prioritization: (1) Project Context consisting of project settings such as ’project size’ or ’size of the client’s organization’, which also has a significant impact on (2) Prioritization Criteria together with estimated Business Value, (3) Estimated Size broken down into functional size of a requirement and a size/e↵ort estimation, (4) Input from the Developer providing advice and alternative solutions, (5) External Change represents events during the project that impact the company, business environment or product, (6) Learning Experiences comprises of new insights acquired by the client and/or the developers, (7) Project Constraints such as release date or budget and (8) Dependencies between requirements both architectural or chronological. Figure 1 connects all aspects to the relevant activities in a RP process.
Trajectory-based Human Action Recognition

Human activity recognition has been a hot topic for some time. It has several challenges, which makes this task hard and exciting for research. The sparse representation became more popular during the past decade or so. Sparse representation methods represent a video by a set of independent features. The features used in the literature are usually lowlevel features. Trajectories, as middle-level features, capture the motion of the scene, which is discriminant in most cases. Trajectories have also been proven useful for aligning small neighborhoods, before calculating the traditional descriptors. In fact, the trajectory aligned descriptors show better discriminant power than the trajectory shape descriptors proposed in the literature. However, trajectories have not been investigated thoroughly, and their full potential has not been put to the test before this work. This thesis examines trajectories, defined better trajectory shape descriptors and finally it augmented trajectories with disparity information. This thesis formally define three different trajectory extraction methods, namely interest point trajectories (IP), Lucas-Kanade based trajectories (LK), and Farnback optical flow based trajectories (FB). Their discriminant power for human activity recognition task is evaluated. Our tests reveal that LK and FB can produce similar reliable results, although the FB perform a little better in particular scenarios. These experiments demonstrate which method is suitable for the future tests. The thesis also proposes a better trajectory shape descriptor, which is a superset of existing descriptors in the literature. The examination reveals the superior discriminant power of this newly introduced descriptor. Finally, the thesis proposes a method to augment the trajectories with disparity information. Disparity information is relatively easy to extract from a stereo image, and they can capture the 3D structure of the scene. This is the first time that the disparity information fused with trajectories for human activity recognition. To test these ideas, a dataset of 27 activities performed by eleven actors is recorded and hand labelled. The tests demonstrate the discriminant power of trajectories. Namely, the
The Littoral Marine Molluscs of Fanning Island

WHEREAS there is a wealth of taxonomic literature on the marine molluscs of the Indo-WestPacific and a surfeit of speculation on the characteristics and relationships of insular faunas in the region, the ecological aspect of the zoogeography has been largely neglected. An expedition to Fanning Island in the line Islands in January 1970 gave opportunity for determining the commonly occurring reef and lagoon molluscs of this Pacific atoll, and also for conducting preliminary surveys on their general ecology, especially of the dominants, and their local distribution. Information so obtained is used to describe the composition of the Fanning Island marine molluscan fauna and its relationships with those of other Pacific islands. A list of species from the line Islands (Table 1) compiled from collections in the Bernice P. Bishop Museum, Honolulu, Hawaii, made during the Whippoorwill Expedition in 1924 is included for comparative purposes. Fanning Island lies midway in the string of shoals and atolls extending from 8° N latitude to 12° S latitude which constitute the line Islands. Above the equator these islands form the most southerly and easterly fringe of the faunal area termed by Ekman (1953) the "Central Pacific. " The major islands in the lines (Washington, Palmyra, Fanning, Christmas) are about 1,000 miles south, southeast, and northeast of Hawaii, Johnston Island, and the Phoenix Islands, respectively; they are 1,200 miles northwest of Tahiti and 1,500 miles northwest of the Tuamotus. An outlier, Jarvis Island, is 400 miles southwest of Fanning; other outliers such as Malden, Starbuck, and Flint are below the equator.
Collision tumor of primary malignant lymphoma and adenocarcinoma in the colon, diagnosed by molecular genetics analyses: case report and review of literature

Purpose We report one collision tumor of the ascending colon adenocarcinoma and primary diffuse large B-cell lymphoma, not otherwise speci�ed (DLBCL, NOS) with molecular genetics analyses. Methods The present case and twenty-two previous cases with collision tumors of primary colorectal lymphoma and adenocarcinoma were reviewed. Clinicopathological characteristics, molecular genetics analyses, possible pathogenesis, management and prognosis of the tumor were analyzed. ResultsA 74-year-old female patient was found to have an invasive adenocarcinoma in the ascending colon, and underwent laparoscopic radical resection of right colon cancer. The mass comprised of two tumors was found incidentally through postoperative pathological sampling. Molecular genetics and immunohistochemical analyses showed collision tumor of DLBCL, NOS with germinal-center B-cell (GCB) subtype and TP53 mutation, and adenocarcinoma arising in a tubulovillous adenoma in the colon, with BRAF mutation and MLH1 promoter methylation. The mean age of the 23 patients was 73 years. The most cases of the available histopathological staging of adenocarcinoma were stage I (7/16), whereas those of lymphoma components were stage (cid:0) (10/20). The most common histological subtypes of the lymphoma were B-cell lymphomas (22/23). There were 15 cases with follow-up data including 11 alive and 4 dead with a 3-year overall survival rate (OS) of 71.5%. Conclusions Our report highlights the need for pathologists, radiologists, surgeons and oncologists to be aware of the rare possibility of collision tumors. The accurate molecular genetics analyses and comprehensive treatment including surgery combined with chemotherapy are required for the rare cases.
Making AI trustworthy in multimodal and healthcare scenarios

The pervasiveness of artificial intelligence in our daily lives has raised the need to understand and trust the outputs of the learning models, especially when involved in decision processes. As a result, eXplainable Artificial Intelligence has captured more and more interest in the scientific community, providing insights into the behaviour of these systems, ensuring algorithms fairness, transparency and trustworthiness. In this contribution we overview our work on the explainability of deep learning models applied to time series, multimodal data and towards extracting meaningful medical concepts.
Exploring the potentials and challenges of Artificial Intelligence in supporting clinical diagnostics and remote assistance for the health and well-being of individuals

Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and wellbeing as well as the challenges to their uptake in real-world settings.
An Improved Algorithm for Computing Logarithms over GF(p) and Its Cryptographic Significance

A cryptographic system is described which is secure if and only if computing log­ arithms over GF ( p ) is infeasible. Previously published algorithms for computing this function require O ( p 1 / 2 ) complexity in both time and space. An improved algo­ rithm is derived which requires O ( log 2 p ) complexity if p − 1 has only small prime factors. Such values of p must be avoided in the cryptosystem. Constructive uses for the new algorithm are also described.