Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiac Autonomic and Cardiac Vagal Control During and After Depressive and Happiness Autobiographical Memories in Patients With Major Depressive Disorder

Background Heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) are indices of cardiac autonomic and cardiac vagal control (CVC), both of which are markers of emotional regulation and physical health. This study examined (1) the differences in cardiac autonomic regulation and CVC during baseline, depressive, and happiness autobiographical memory tasks between participants with major depressive disorder (MDD group) and healthy controls (HC group); (2) the associations between depressive symptoms and cardiac autonomic and CVC; and (3) the reactivity and recovery of cardiac autonomic and CVC between the MDD and HC groups. Methods A total of 168 and 178 participants were included in the MDD and HC groups, respectively. Demographic data and the Beck Depression Inventory-II were collected before the experimental procedure. Lead II electrocardiograph (ECG) was measured during baseline, depressive, and happiness autobiographical memory tasks, and then interbeat intervals from ECG were converted to the time and frequency domains of HRV and RSA. Results The participants in the MDD group showed lower HRV (including standard deviation of normal to normal intervals, low frequency, the natural logarithm of low frequency, and the natural logarithm of high frequency) and CVC (RSA and lnRSA) than those in the HC group. Depressive symptoms were positively correlated with heart rate and negatively correlated with the indices of cardiac autonomic and CVC. There was significantly increased reactivity and recovery of cardiac autonomic and CVC during and after depressive and happiness autobiographical memory tasks in the HC group, but not in the MDD group. Discussion Participants with MDD had cardiac autonomic and CVC dysregulation, decreased reactivity, and did not recover to baseline after emotional provocations. These results can be the theoretical basis for clinical intervention by using HRV biofeedback to restore cardiac autonomic regulation and CVC during and after emotional events in the future.
‘It was kinda like D.I.Y closure’. Using Photovoice to capture the experiences of final year social work students graduating amidst the pandemic

This article examines a recent research project that explored the lived experiences of 5 final year social work students in Scotland who graduated during the coronavirus pandemic. The project used Photovoice as the primary data collection method, followed by a 3 hour long online focus group where the participants and the researcher worked collaboratively to identify themes for further analysis. The findings demonstrated that while the data collected by participants through the photographs and captions were highly personal to each participant, there were recurring themes that connected all of them which were identified broadly as; (dis)connection, closure and identity – all of which were discussed in great detail in a virtual focus group after the data was collected. This article focusses predominantly on the Photovoice method adopted for the study and how this was an effective method for participatory research. This article also focusses on how the pandemic affected the transitionary period between the participants’ identity shift from students to professionals. This study followed the principles of Participatory Action Research which meant that participants and the researcher worked together to cultivate and analyse the data collected and the findings that are discussed here reflect this collaborative process. As this report is being written, numerous new studies, reports and predictions as to the pandemics impact on our collective mental health emerge daily and so it is hoped that this project will serve as a small time stamp as to how the pandemic impacted this small group of students in Scotland and will honour their stories, creating a lasting space for them to be heard among the ever-increasing bombardment of news.
Microbial Interrelationships across Sites of Breastfeeding Mothers and Infants at 6 Weeks Postpartum

Infancy is a critical life stage for the establishment of the gut microbiome. Human milk contains a unique microbial ecosystem that serves as a continuous source of commensal bacteria for the infant. However, the origin of the human milk microbiota, how it is influenced by breastfeeding exclusivity, and its role in infant gut microbiota assembly are not clear. To interrogate these questions, we examined the relationships among fecal, oral, breast skin, and human milk microbiota of 33 exclusively breastfeeding (EBF) and mixed-feeding (MF; human milk + infant formula) mother–infant pairs at 6 weeks postpartum. Here, we show that MF infants have a significantly more diverse oral microbiome comprised of lower relative abundances of Streptococcus and Gemella and higher abundances of Veillonella. Using both SourceTracker2 and FEAST, we demonstrate breast skin and infant saliva as the principal contributing sources to the human milk microbiota. Of the sampled sites, human milk and maternal stool were predicted to contribute the largest fraction to the infant fecal microbiome, but the majority of the community was estimated to arise from unknown sources. Lastly, we identified twenty-one significant co-occurrence relationships between bacteria in human milk and on other maternal and infant body sites. These results demonstrate several unique microbial interrelationships between breastfeeding dyads, providing insight into potential mechanisms of microbial assembly in early life.
Platform re-use : lessons from the automotive industry

The platform strategy adopted by firms in a multi-project context reduces lead-time and development cost, enhances reliability, allows mass customization and increases manufacturing flexibility. While the major challenges of this strategy have been highlighted, the evolution of the platform and its management during its lifecycle is under studied. The paper address this missing point by considering the sustainability of the platform during its life cycle. Design/methodology/approach - For that purpose, the paper has carried out a field methodology research at a car manufacturer six years after the successful setting of the platform strategy. It analyzes at a fine-grained level the development of a second generation product on this existing platform. Findings - Using a model that traces the design decisions taken during this development, it has identified that, in order to reuse the platform over two generations, the engineers implicitly apply, besides the design rules that correspond to the very definition of platform strategy as presented in the literature such as the carry-over and the lean design, a learning routine that challenges these rules. It designates this routine by "smart reuse" because it enables the reuse of the platform from one generation to another. It highlights the interplay between the products and the platform that co-evolve by pointing out the reciprocal prescription relationships. This co-evolution operates through two levels: between the product planning and the platform on one hand and the product development and the platform on the other. Practical implications - The paper has several implications, such as the central role of the platform director in the platform reuse and the platform architecture, mainly its modularity, and its impact on the platform progressive renewal. This research reveals ideas that need to be validated and tested through other methods and in other industrial contexts. Originality/value - The paper offers insights into platform-re-use, focusing on the automotive industry.
Channel investigation of outdoor millimeter-wave access links

This thesis presents measurements, characterizations and modeling approaches of the outdoor millimeter-wave access channel. More specifically, in this thesis I address an overlay concept for millimeter-wave 5G networks and corresponding channel measurement campaigns. These campaigns were planned and executed with an approach that focuses on fine spatial and temporal sampling of the channel. This differs from other reported measurements, which mostly rely on mechanically steerable high gain antennas. The approach chosen in this work greatly reduces the measurement time needed to obtain a channel snapshot from minutes to the order of microseconds, thereby enabling the acquisition of a large number of spatial samples. These samples are an important basis to derive statistically valid information, e.g. on the path loss and dynamic shadow fading caused by the environment. This comes at the price of no inherently available angular information. In this work, a general concept for millimeter-wave based 5G mobile radio access networks is introduced. The channel measurements and modeling approaches presented here focus on urban outdoor access scenarios, which are seen as the most challenging outdoor environments due to the dense nature of the surroundings and high number of users. Two measurements in urban street canyons and open square scenarios are reported, one at 60 GHz and the other at 10 and 60 GHz carrier frequency simultaneously. Another measurement campaigns focused on ground reflection properties for distances up to 1 km at 60 GHz. A fourth campaign was performed using an electronically steerable 64-element antenna array, focusing on human body shadowing mitigation. The last measurement campaign in this work investigates the impact of dynamic shadow fading, caused by cars, buses and pedestrians in a 28 GHz access scenario. Two types of channel models are derived based on the measurements. A path loss model and parameters are introduced that generate instantaneous path loss values. A more sophisticated model, the quasi-deterministic model, is also introduced. This model uses a description of the environment to generate
Social comparisons: A potential mechanism linking problematic social media use with depression

Abstract Background and aims The majority of Australians are regular users of social media, especially young adults. Of concern, is that a minority of people appear to use social media in an addictive or problematic way which is associated with negative psychological outcomes such as depression. Social comparisons, where users compare themselves to others on social media, have also been linked with depression. Therefore, the key aim of the study was to determine whether social comparisons mediate the relationship between Problematic Social Media Use (PSMU) and depression. Method In a two-part study 144 participants (65 females) answered a series of self-report questions assessing factors relating to PSMU and then came into the lab to view a series of social media images, (pre-tested to be upward or downward comparisons). Results Females used social media more problematically, liked more upward than downward comparison images and compared themselves more negatively to others on social media than did males. Higher PSMU scores were associated with depression and low self-esteem and comparing oneself more negatively to others on social media. Finally, focusing on upward comparisons and a tendency to make negative comparisons to others on social media partially mediated the association between PSMU and depression. Discussion and conclusions Social comparisons may function as a mechanism linking PSMU with negative psychological outcomes. Clinical interventions for individuals with PSMU which reduce the focus on upward social comparisons may also reduce negative psychological outcomes such as depression.
Establishment of criminal/profile DNA database and use of forensic intelligence to combat nationwide insecurity issues in Nigeria

Criminal activity is on the rise in Nigeria. The nefarious activity of bandits is on the increase with kidnapping of students and attack on farmers. This creates fear in the terrorized communities. Armed robbery as well as rape of citizens going about their normal duties is also on the increase. The road is becoming increasingly unsafe for the citizenry as well as government officials. The government has mandated individuals to register in the National Identification Number NIN which is linked to the Bank Verification Number (BVN) (a database established to curb crime in the banking sector) and the Subscriber Identity module (SIM). The Establishment of such databases is pertinent however the NIN, BVN, SIM-NIN linkage and fingerprints may not be sufficient in solving criminal cases without DNA profiling of convicted individuals, establishment of forensic laboratories and the creation of criminal DNA profile database CRODER – a Criminal offender DNA Exert Repertoire. The role of DNA profiling as an additional measure is to keep a record of all convicted individuals and the CRODER will be an additional useful measure in profiling criminals and individuals can be adopted as a forensic tool in addition to the captured databases of NIN, BVN and NIN-SIM and will be on the watch list of security operatives. The CRODER forensic tool can be done stepwise by availability of an emergency number to dial for emergency, a dispatch personnel to respond to the call, and vigilante groups or other adhoc security officer in the vicinity of the emergency call to get to the vicinity and preserve the crime scene environment for investigators and detectives to work on. CRODER Forensic tool can then be adopted to solve the crime. Offenders trying to escape enroute the airport, can be checked via CRODER and fingerprint compared and matched with the collection in the database. The CRODER may be a necessary stride to bring kidnappers, bandits, and armed robbers to book in record time and curb the rise in criminal activity, insurgency, banditry, and insecurity in general. If properly done, Nigeria will be a haven for citizens and would-be investors.
Exposure to Airborne Pesticides and Its Residue in Blood Serum of Paddy Farmers in Malaysia

Background: Pesticides manage pests and diseases in agriculture, but they harm the health of agricultural workers. Concentrations of thirteen pesticides were determined in personal air and blood serum of 85 paddy farmers and 85 non-farmers, thereafter associated with health symptoms. Method: Samples were analyzed using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Results: The median concentration of pesticides in personal air samples ranged from 10.69 to 188.49 ng/m3 for farmers and from 5.79 to 73.66 ng/m3 for non-farmers. The median concentration of pesticides in blood serum was from 58.27 to 210.12 ng/mL for farmers and 47.83 to 62.74 ng/mL for non-farmers. Concentration of eleven pesticides in personal air and twelve pesticides in blood serum were significantly higher in farmers than non-farmers (p < 0.05). All pesticides detected in personal air correlated significantly with concentration in the blood serum of farmers (p < 0.05). Health symptoms reported by farmers were dizziness (49.4%), nausea (47.1%), cough (35.3%), chest pain (30.6%), breathing difficulty (23.5%), sore throat (22.4%), vomiting (18.8%), phlegm (16.5%), and wheezing (15.3%). Concentration of pesticides in personal air, blood serum, and health symptoms were not significantly associated. Conclusion: Occupational exposure to pesticides significantly contaminates blood serum of farmers compared to non-farmers.
Covariate-Assisted Community Detection in Multi-Layer Networks

ABSTRACT Communities in multi-layer networks consist of nodes with similar connectivity patterns across all layers. This article proposes a tensor-based community detection method in multi-layer networks, which leverages available node-wise covariates to improve community detection accuracy. This is motivated by the network homophily principle, which suggests that nodes with similar covariates tend to reside in the same community. To take advantage of the node-wise covariates, the proposed method augments the multi-layer network with an additional layer constructed from the node similarity matrix with proper scaling, and conducts a Tucker decomposition of the augmented multi-layer network, yielding the spectral embedding vector of each node for community detection. Asymptotic consistencies of the proposed method in terms of community detection are established, which are also supported by numerical experiments on various synthetic networks and two real-life multi-layer networks.
Numerical Analysis of Degradation and Capacity Loss in Graphite Active Particles of Li-Ion Battery Anodes

It is well known that the performance and durability of lithium-ion batteries (LIBs) can be severely impaired by fracture events that originate in stresses due to Li ion diffusion in fast charge–discharge cycles. Existing models of battery damage overlook either the role of particle shape in stress concentration, the effect of material disorder and preexisting defects in crack initiation and propagation, or both. In this work we present a novel, three-dimensional, and coupled diffusive-mechanical numerical model that simultaneously accounts for all these phenomena by means of (i) a random particle generator and (ii) a stochastic description of material properties implemented within the lattice method framework. Our model displays the same complex fracture patterns that are found experimentally, including crack nucleation, growth, and branching. Interestingly, we show that irregularly shaped active particles can suffer mechanical damage up to 60% higher than that of otherwise equivalent spherical particles, while material defects can lead to damage increments of up to 110%. An evaluation of fracture effects in local Li-ion diffusivity shows that effective diffusion can be reduced up to 25% at the particle core due to lithiation, while it remains at ca. 5% below the undamaged value at the particle surface during delithiation. Using a simple estimate of capacity loss, we also show that the C-rate has a nonlinear effect on battery degradation, and the estimated capacity loss can surpass 10% at a 2C charging rate.
Case report: NUT carcinoma in an elderly woman with unique morphology and immunophenotype highlights a diagnostic pitfall

Background NUT carcinoma (NC) is a rapidly progressing and rare neoplasm that primarily affects younger patients and has a survival time of about 1 year. Most of these neoplasms express epithelial markers with no neuroendocrine markers observed. Retrospective studies have shown that pathologists and clinicians do not have a sufficient understanding of the disease due to the lack of common clinical manifestations, imaging, and morphological features. Case Description A 60-year-old female presented at Beijing Friendship Hospital, showing repetitive epistaxis, nasal pain, and nasal congestion with obvious masses in the right nasal sinus and frontal sinus. Microscope analysis revealed two unique morphological changes which have not been previously reported in the existing literature: (I) small spindle cells with sparse cytoplasm and densely stained nuclei and (II) large tumor cells with abundant cytoplasm, some cells resembling plasma cells. The sudden appearance of keratinization was also a prominent feature. Immunohistochemical staining showed differences between the two cell morphologies. Small spindle cells simultaneously expressed CK5/6 and P40, and the Ki67 proliferation index was 40%. The large round cells did not express CK5/6 and P40 but were focal positive for synaptophysin and the Ki67 index was 10%. NUT and P63 were strongly expressed in both cell types and fluorescence in situ hybridization (FISH) revealed BRD4-NUTM1 translocation. Following 20 rounds of postoperative radiation treatment, the patient was alive and no recurrence or metastasis was observed during a 5-month follow-up. Conclusions We present novel information from the oldest known and surviving patient of NC originating in the nasal cavity with unique morphological features and different immunohistochemical results. NUT antibody testing should be performed in undifferentiated or poorly differentiated malignancies, particularly those with either or both cytoplasmic vacuolation of medium-sized cells and abrupt keratinization, irrespective of patient age.
Hypersexual Behavior and Depression Symptoms among Dating App Users

The use of Dating Applications (DAs) is widespread, and in some cases could be associated with psychosexological issues. Hence, we decided to investigate hypersexual behavior and depression symptoms among DA users and non-users. We recruited a snowball convenience sample of 1000 subjects through an online platform in 2020 for a cross-sectional study. One hundred and nineteen (11.9%) were classified as DA users and 881 as non-users. All subjects completed a sociodemographic questionnaire, the Hypersexual Behavior Inventory (HBI), to assess hypersexuality, and the Patient Health Questionnaire (PHQ-9) to evaluate depression symptoms. We primarily found higher levels of hypersexual behavior and depression symptoms in DA users compared to non-users. In particular, HBI Total (users = 42.27 ± 16.37 vs. non-users = 31.85 ± 12.06; p < 0.0001), HBI Coping (users = 17.92 ± 8.01 vs. non-users = 13.52 ± 6.03; p < 0.0001), HBI Control (users = 14.73 ± 6.68 vs. non-users = 10.71 ± 4.95; p < 0.0001), HBI Consequences (users = 9.62 ± 4.33 vs. non-users = 7.60 ± 3.37; p < 0.0001), PHQ-9 Depression (users = 12.82 ± 6.64 vs. nonusers = 10.05 ± 5.84; p < 0.0001). On the whole, we found that hypersexual behavior and depression symptoms strongly characterize DAs users; this evidence could represent an important associated factor in DA use for sexual purposes.
End-to-End Calcification Distribution Pattern Recognition for Mammograms: An Interpretable Approach with GNN

Abstract Purpose: We aimed to develop a novel interpretable artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 292 patients, which showed calcifications according to the mammographic reports and diagnosed breast cancers, were collected. The calcification distributions were classified as diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer with multiple lexicons such as mass, asymmetry, or architectural distortion without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph-convolutional-network-based model was developed. A total of 581 mammographic images from 292 cases of breast cancer were divided based on the calcification distribution pattern: diffuse (n = 67), regional (n = 115), group (n = 337), linear (n = 8), or segmental (n = 54). The classification performances were measured using metrics including precision, recall, F1 score, accuracy, and multi-class area under the receiver operating characteristic curve. The proposed model achieved a precision of 0.522 ± 0.028, sensitivity of 0.643 ± 0.017, specificity of 0.847 ± 0.009, F1 score of 0.559 ± 0.018, accuracy of 64.325 ± 1.694%, and area under the curve of 0.745 ± 0.030; thus, the method was found to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. The prediction results are interpretable using visualization methods to highlight the important calcification nodes in graphs. Conclusions: The proposed deep neural network framework is an AI solution that automatically detects and classifies calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.