Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Science of Animal Resources

The supplementation with natural ingredients that are rich in polyphenols could improve the quality and functionality of yogurt. Lotus leaf (LL) are abundant in phenolic compounds. We aimed to investigate the effects of LL powder on the quality properties, total phenolic content (TPC), and antioxidant activity of yogurt. Yogurt was supplemented with four different concentrations (0%, 0.2%, 0.5%, and 1%) of LL powder and evaluated for 14 d refrigerated storage. During storage, the titratable acidity (TA) of all LL yogurt groups was higher than that of the control (p<0.05). The L* and a* values decreased by LL addition to the yogurts, while the b* values increased. All LL yogurt groups indicated better viscosity than that of control, and among them, 0.2% LL yogurts had the highest viscosity without significant decrease until the end of the storage duration. The addition of LL into yogurt enhanced the water-holding capacity (WHC) by at least 1.5-fold than that of control for the entire storage duration. The TPC of yogurts gradually increased with the addition of LL (p<0.05) and continued to increase during storage; the 1% LL yogurt at 14 d showed the highest value of 61.94±1.68 μg GAE/g. The DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity and reducing power of the yogurts were also significantly enhanced with increasing LL concentrations (p<0.05). These results suggest that lotus leaf may be useful as a natural ingredient for improving the quality and antioxidant activity of yogurt.
Proteomic Analysis of Porcine Pre-ovulatory Follicle Differentiation Into Corpus Luteum

The luteinization of the follicular cells, following a LH surge, causes extensive molecular and structural changes in preovulatory follicles (POF) that lead to ovulation and ultimate formation of the corpus luteum (CL). The objective of this study was to identify proteins expressed in porcine POF before the LH surge and a new CL formed, 2–3 days after ovulation, and evaluate proteome changes associated with formation of the CL from a follicle. We used 2D-gel electrophoresis-based proteomics and tandem mass spectrometry followed by a functional analysis using Ingenuity Pathway analysis (IPA) to evaluate functional pathways associated with the luteinization process. Protein lysates were prepared from isolated POFs and from the newly formed CL. A total of 422 protein spots were identified in both structures. A total of 15 and 48 proteins or their proteoforms were detected only in the POFs and CL, respectively. An IPA analysis of a POF proteome showed that most of the follicular proteins were involved in cellular infiltration, endoplasmic stress responses, and the protein ubiquitination pathway. Most of the early luteal proteins were associated with steroid metabolism, cell death and survival, free radical scavenging, and the protein ubiquitination pathway. A comparison of a follicular proteome with that of an early luteal proteome revealed that 167 identified proteins or their proteoforms were differentially regulated between POFs and the newly formed CL (p < 0.05 and a fold change of >1.8). Proteins that were significantly more abundant in follicles included cAMP-dependent protein kinase, histone binding protein RBBP4, reticulocalbin, vimentin, and calumenin; more abundant luteal proteins included albumin, farnesyl diphosphate synthase, serine protease inhibitors, elongation factor-1, glutaredoxin, and selenium-binding protein. Proteins that were significantly altered with luteal formation were found to be associated with cholesterol biosynthesis, cell death and survival, and acute phase response. Moreover, upstream regulators of differentially abundant proteins in CL were identified that included insulin growth factor-1, sterol regulatory element-binding transcription factor-1, and nuclear factor erythroid-derived 2. We have identified novel proteins that advance our understanding of (1) processes associated with differentiation of POFs into the CL, (2) possible mechanisms of luteal cell survival, and (3) pathways regulating steroidogenesis in the newly formed CL.
Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.
Twenty Novel Disease Group-Specific and 12 New Shared Macrophage Pathways in Eight Groups of 34 Diseases Including 24 Inflammatory Organ Diseases and 10 Types of Tumors

The mechanisms underlying pathophysiological regulation of tissue macrophage (Mφ) subsets remain poorly understood. From the expression of 207 Mφ genes comprising 31 markers for 10 subsets, 45 transcription factors (TFs), 56 immunometabolism enzymes, 23 trained immunity (innate immune memory) enzymes, and 52 other genes in microarray data, we made the following findings. (1) When 34 inflammation diseases and tumor types were grouped into eight categories, there was differential expression of the 31 Mφ markers and 45 Mφ TFs, highlighted by 12 shared and 20 group-specific disease pathways. (2) Mφ in lung, liver, spleen, and intestine (LLSI-Mφ) express higher M1 Mφ markers than lean adipose tissue Mφ (ATMφ) physiologically. (3) Pro-adipogenic TFs C/EBPα and PPARγ and proinflammatory adipokine leptin upregulate the expression of M1 Mφ markers. (4) Among 10 immune checkpoint receptors (ICRs), LLSI-Mφ and bone marrow (BM) Mφ express higher levels of CD274 (PDL-1) than ATMφ, presumably to counteract the M1 dominant status via its reverse signaling behavior. (5) Among 24 intercellular communication exosome mediators, LLSI- and BM- Mφ prefer to use RAB27A and STX3 than RAB31 and YKT6, suggesting new inflammatory exosome mediators for propagating inflammation. (6) Mφ in peritoneal tissue and LLSI-Mφ upregulate higher levels of immunometabolism enzymes than does ATMφ. (7) Mφ from peritoneum and LLSI-Mφ upregulate more trained immunity enzyme genes than does ATMφ. Our results suggest that multiple new mechanisms including the cell surface, intracellular immunometabolism, trained immunity, and TFs may be responsible for disease group-specific and shared pathways. Our findings have provided novel insights on the pathophysiological regulation of tissue Mφ, the disease group-specific and shared pathways of Mφ, and novel therapeutic targets for cancers and inflammations.
Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas

Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.
Testing the Weiss-Harter-Model: Physical Activity, Self-Esteem, Enjoyment, and Social Support in Children and Adolescents

Background Several theories have been established to explain physical activity (PA) participation. However, many of these theories might not be applicable to adolescent PA behavior as they require a high level of cognitive reflection. Weiss suggests a model for youth which is based on the theoretical concept of Harter, focusing on self-esteem within social, emotional, and developmental aspects to explain behavior. The aim of this study was to test the original and a social support focused alternative version of the Weiss-Harter-model, and to cross-validate the findings in two separate studies. Methods Data from two cross-sectional studies was retrieved and the models tested using structural equation modeling. Participants aged 11–17 years were recruited from a school (Study 1: N = 182) and from the German MoMo study (Study 2: N = 2,274). They filled in questionnaires about perceived competence, social support, self-esteem, PA enjoyment, and minutes of moderate-vigorous PA (MVPA). Results None of the studies showed a good model fit for the original model [Study 1: CFI = 0.870, RMSEA 0.118 (90% CI 0.081–0.158), χ2 = 38.7, p < 0.01; Study 2: CFI = 0.871, RMSEA 0.148 (90% CI 0.140–0.155), χ2 = 1112.6, p < 0.01], explaining only 12% and 17% of MVPA variance, respectively. The alternative model which added the direct paths of social support to MVPA and PA enjoyment had a very good model fit for both Study 1 [CFI = 1.000, RMSEA 0.000 (90% CI 0.000–0.031), χ2 = 4.8, p > 0.05] and Study 2 [CFI = 0.990, RMSEA 0.043 (90% CI 0.035–0.051), χ2 = 103.7, p < 0.01]. The addition of these paths led to changes in effect size and directions of other path coefficients, with self-esteem having a small to meaningless impact on MVPA. The revised models accounted for 38% and 42% explained variance in MVPA, respectively. Discussion The prominent role of self-esteem in the original model could not be confirmed. Instead, the results emphasize the role of social support for PA and PA enjoyment, which is in line with current research. Interventions to increase adolescent PA levels should thus focus more on components of social support instead of self-esteem. Future studies are needed to examine the interplay between social support, PA enjoyment and perceived competence as PA determinants.
How Can Digital Health Technologies Contribute to Sustainable Attainment of Universal Health Coverage in Africa? A Perspective

Background: Innovative strategies such as digital health are needed to ensure attainment of the ambitious universal health coverage in Africa. However, their successful deployment on a wider scale faces several challenges on the continent. This article reviews the key benefits and challenges associated with the application of digital health for universal health coverage and propose a conceptual framework for its wide scale deployment in Africa. Discussion: Digital health has several benefits. These include; improving access to health care services especially for those in hard-to-reach areas, improvements in safety and quality of healthcare services and products, improved knowledge and access of health workers and communities to health information; cost savings and efficiencies in health services delivery; and improvements in access to the social, economic and environmental determinants of health, all of which could contribute to the attainment of universal health coverage. However, digital health deployment in Africa is constrained by challenges such as poor coordination of mushrooming pilot projects, weak health systems, lack of awareness and knowledge about digital health, poor infrastructure such as unstable power supply, poor internet connectivity and lack of interoperability of the numerous digital health systems. Contribution of digital health to attainment of universal health coverage requires the presence of elements such as resilient health system, communities and access to the social and economic determinants of health. Conclusion: Further evidence and a conceptual framework are needed for successful and sustainable deployment of digital health for universal health coverage in Africa.
Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management

Social or humanoid robots do hardly show up in “the wild,” aiming at pervasive and enduring human benefits such as child health. This paper presents a socio-cognitive engineering (SCE) methodology that guides the ongoing research & development for an evolving, longer-lasting human-robot partnership in practice. The SCE methodology has been applied in a large European project to develop a robotic partner that supports the daily diabetes management processes of children, aged between 7 and 14 years (i.e., Personal Assistant for a healthy Lifestyle, PAL). Four partnership functions were identified and worked out (joint objectives, agreements, experience sharing, and feedback & explanation) together with a common knowledge-base and interaction design for child's prolonged disease self-management. In an iterative refinement process of three cycles, these functions, knowledge base and interactions were built, integrated, tested, refined, and extended so that the PAL robot could more and more act as an effective partner for diabetes management. The SCE methodology helped to integrate into the human-agent/robot system: (a) theories, models, and methods from different scientific disciplines, (b) technologies from different fields, (c) varying diabetes management practices, and (d) last but not least, the diverse individual and context-dependent needs of the patients and caregivers. The resulting robotic partner proved to support the children on the three basic needs of the Self-Determination Theory: autonomy, competence, and relatedness. This paper presents the R&D methodology and the human-robot partnership framework for prolonged “blended” care of children with a chronic disease (children could use it up to 6 months; the robot in the hospitals and diabetes camps, and its avatar at home). It represents a new type of human-agent/robot systems with an evolving collective intelligence. The underlying ontology and design rationale can be used as foundation for further developments of long-duration human-robot partnerships “in the wild. ”
Cluster Analyses Reveals Subgroups of Children With Suspected Auditory Processing Disorders

Background Some children appear to not hear well in class despite normal hearing sensitivity. These children may be referred for auditory processing disorder (APD) assessment but can also have attention, language, and/or reading disorders. Despite presenting with similar concerns regarding hearing difficulties in difficult listening conditions, the overall profile of deficits can vary in children with suspected or confirmed APD. The current study used cluster analysis to determine whether subprofiles of difficulties could be identified within a cohort of children presenting for auditory processing assessment. Methods Ninety school-aged children (7–13 years old) with suspected APDs were included in a cluster analysis. All children had their reading, language, cognition and auditory processing assessed. Parents also completed the Children’s Auditory Performance Scale (CHAPS). Cluster analysis was based on tasks where age-norms were available, including word reading (Castles and Coltheart irregular and non-words test), phonological awareness (Queensland University Inventory of Literacy), language [Comprehensive Language of Assessment-4, Comprehensive Assessment of Spoken Language (CASL)], sustained attention (Continuous Performance Test), working memory (digits forward and backward), and auditory processing [Frequency Pattern Test (FPT), Dichotic Digits Test (DDT)]. Hierarchical cluster analysis was undertaken to determine the optimal number of clusters for the data, followed by a k-means cluster analysis. Results Hierarchical cluster analysis suggested a four-group solution. The four subgroups can be summarized as follows: children with (1) global deficits, n = 35; (2) poor auditory processing with good word reading and phonological awareness skills, n = 22; (3) poor auditory processing with poor attention and memory but good language skills, n = 15; and (4) poor auditory processing and attention with good memory skills, n = 18. Conclusion The cluster analysis identified distinct subgroups of children. These subgroups display the variation in areas of difficulty observed across different studies in the literature (e.g., not every child with APD has an attention deficit), highlighting the heterogeneous nature of APD and the need to assess a range of skills in children with suspected APD. It would be valuable for future studies to independently verify these subgroups and to determine whether interventions can be optimized based on these subgroups.
Help! I Need a Remote Guide in My Mixed Reality Collaborative Environment

The help of a remote expert in performing a maintenance task can be useful in many situations, and can save time as well as money. In this context, augmented reality (AR) technologies can improve remote guidance thanks to the direct overlay of 3D information onto the real world. Furthermore, virtual reality (VR) enables a remote expert to virtually share the place in which the physical maintenance is being carried out. In a traditional local collaboration, collaborators are face-to-face and are observing the same artifact, while being able to communicate verbally and use body language, such as gaze direction or facial expression. These interpersonal communication cues are usually limited in remote collaborative maintenance scenarios, in which the agent uses an AR setup while the remote expert uses VR. Providing users with adapted interaction and awareness features to compensate for the lack of essential communication signals is therefore a real challenge for remote MR collaboration. However, this context offers new opportunities for augmenting collaborative abilities, such as sharing an identical point of view, which is not possible in real life. Based on the current task of the maintenance procedure, such as navigation to the correct location or physical manipulation, the remote expert may choose to freely control his/her own viewpoint of the distant workspace, or instead may need to share the viewpoint of the agent in order to better understand the current situation. In this work, we first focus on the navigation task, which is essential to complete the diagnostic phase and to begin the maintenance task in the correct location. We then present a novel interaction paradigm, implemented in an early prototype, in which the guide can show the operator the manipulation gestures required to achieve a physical task that is necessary to perform the maintenance procedure. These concepts are evaluated, allowing us to provide guidelines for future systems targeting efficient remote collaboration in MR environments.
A Combined Mass Spectrometry and Data Integration Approach to Predict the Mitochondrial Poly(A) RNA Interacting Proteome

In order to synthesize the 13 oxidative phosphorylation proteins encoded by mammalian mtDNA, a large assortment of nuclear encoded proteins is required. These include mitoribosomal proteins and various RNA processing, modification and degradation enzymes. RNA crosslinking has been successfully applied to identify whole-cell poly(A) RNA-binding proteomes, but this method has not been adapted to identify mitochondrial poly(A) RNA-binding proteomes. Here we developed and compared two related methods that specifically enrich for mitochondrial poly(A) RNA-binding proteins and analyzed bound proteins using mass spectrometry. To obtain a catalog of the mitochondrial poly(A) RNA interacting proteome, we used Bayesian data integration to combine these two mitochondrial-enriched datasets as well as published whole-cell datasets of RNA-binding proteins with various online resources, such as mitochondrial localization from MitoCarta 2.0 and co-expression analyses. Our integrated analyses ranked the complete human proteome for the likelihood of mtRNA interaction. We show that at a specific, inclusive cut-off of the corrected false discovery rate (cFDR) of 69%, we improve the number of predicted proteins from 185 to 211 with our mass spectrometry data as input for the prediction instead of the published whole-cell datasets. The chosen cut-off determines the cFDR: the less proteins included, the lower the cFDR will be. For the top 100 proteins, inclusion of our data instead of the published whole-cell datasets improve the cFDR from 54% to 31%. We show that the mass spectrometry method most specific for mitochondrial RNA-binding proteins involves ex vivo 4-thiouridine labeling followed by mitochondrial isolation with subsequent in organello UV-crosslinking.
Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry

Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces—interfaces—of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.