Miracle-dz/UniFAR-FTUs huggingface dataset:
Heuristic Barycenter Modeling of Fully Absorbing Receivers in Diffusive Molecular Communication Channels

In a recent paper it has been shown that to model a diffusive molecular communication (MC) channel with multiple fully absorbing (FA) receivers, these can be interpreted as sources of negative particles from the other receivers’ perspective. The barycenter point is introduced as the best position where to place the negative sources. The barycenter is obtained from the spatial mean of the molecules impinging on the surface of each FA receiver. This paper derives an expression that captures the position of the barycenter in a diffusive MC channel with multiple FA receivers. In this work, a heuristic model inspired by Newton’s law of gravitation is found to describe the barycenter, and the result is compared with particle-based simulation (PBS) data. Since the barycenter depends on the distance between the transmitter and receiver and the observation time, the condition that the barycenter can be assumed to be at the center of the receiver is discussed. This assumption simplifies further modeling of any diffusive MC system containing multiple FA receivers. The resulting position of the barycenter is used in channel models to calculate the cumulative number of absorbed molecules and it has been verified with PBS.
Online learning techniques for prediction of temporal tabular datasets with regime changes

The application of deep learning to non-stationary temporal datasets can lead to overfitted models that underperform under regime changes. In this work, we propose a modular machine learning pipeline for ranking predictions on temporal panel datasets which is robust under regime changes. The modularity of the pipeline allows the use of different models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks, with and without feature engineering. We evaluate our framework on financial data for stock portfolio prediction, and find that GBDT models with dropout display high performance, robustness and generalisability with reduced complexity and computational cost. We then demonstrate how online learning techniques, which require no retraining of models, can be used post-prediction to enhance the results. First, we show that dynamic feature projection improves robustness by reducing drawdown in regime changes. Second, we demonstrate that dynamical model ensembling based on selection of models with good recent performance leads to improved Sharpe and Calmar ratios of out-of-sample predictions. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility.
Battling the unknown: Using composite vignettes to portray lived experiences of COVID-19 and long-COVID

Understanding the day-to-day lived experiences of individuals who have had or are still recovering from Coronavirus Disease-19 (COVID-19), whilst a complex challenge, presents the opportunity to listen and learn. Composite vignettes provide a novel approach to explore and present descriptive portrayals of the most commonly derived experiences and recovery journeys. The thematic analysis of 47 shared accounts (semi-structured interviews with adults aged ≥18 years; 40 females; 6–11 months post-COVID-19 infection) produced a series of four intricate character stories written through the lens of a single individual. Each vignette gives a voice to and captures a different experience trajectory. From the point of initial symptom development onwards, the vignettes depict how COVID-19 has affected everyday lives, focusing on the secondary non-biological socio-psychological effects and implications. The vignettes highlight in participants’ own words: i) the potential negative implications of not addressing the psychological effects of COVID-19; ii) the lack of symptom and recovery linearity; iii) the ongoing ‘lottery’ of access to healthcare services; and iv) the highly variable, yet generally devastating, impacts that COVID-19 and consequent long-COVID has had across multiple facets of daily living.
Facilitators and Barriers to Care Coordination Between Medicaid Accountable Care Organizations and Community Partners: Early Lessons From Massachusetts

Care coordination is central to health care delivery system reform efforts to control costs, improve quality, and enhance patient outcomes, especially for individuals with complex medical and social needs. The potential impact of addressing health-related social needs further illustrates the importance of coordinating health care services with community-based organizations that provide social services and support. This study offers early findings from a unique approach to care coordination delivered by 17 Medicaid Accountable Care Organizations and 27 partnering community-based organizations for individuals with behavioral health conditions and/or those needing long-term services and supports. Interview data from 54 key informants were qualitatively analyzed to understand factors affecting cross-sector integrated care. Key themes emerged, essential to implementing the new model statewide: clarifying roles and responsibilities; promoting communication; facilitating information exchange; developing workforce capacity; building essential relationships; and responsive, supportive program management through real-time feedback, financial incentives, technical assistance, and flexibility from the state Medicaid program.
Prediction of Heat Transfer Coefficient in Flow Boiling over Tube Bundles Using ANFIS

The applicability of the artificial intelligence technique called ANFIS (for adaptive neuro fuzzy inference system) to model the flow boiling heat transfer over a tube bundle is studied in this paper. The ANFIS model is trained and validated with the experimental data from literature. The heat flux, mass flux, and row height are taken as input and the flow boiling heat transfer coefficient as output. The developed model performance is evaluated in terms of performance parameters such as root mean square error, mean square error, correlation coefficient, variance accounted for, and computational time. The preceding parameters of the model are then determined for different combinations of type and number of membership functions. The model is found to predict experimental heat transfer coefficient within an error of ±5%. The developed model is also compared with the artificial neural network model and is found to be better in predicting the flow boiling heat transfer coefficient. The developed model is further used to observe the variation of heat transfer coefficient of the individual rows and bundle for intermediate value of parameters such as heat flux and mass flux that are not included in the analysis of experimental data. The analysis is able to provide complete information about variation of heat transfer coefficient of individual rows and the bundle with respect to heat flux and mass flux.
A qualitative case study of community experiences with Tungiasis in high prevalence villages of Bungoma County, Kenya: “The whole body aches and the jiggers are torturing me!”

Introduction Tungiasis is a painful skin infection caused by a flea called Tunga Penetrans/jiggers, which enters the epidermis of humans and animals. If untreated it may result in bacterial infection, sepsis, necrosis, and disability. In Kenya, it is estimated that 4% of the population suffer from jigger infestation. The aim of this study was to contribute with knowledge about the experiences of those affected, perceived causes and local coping strategies, to improve the control and elimination of this neglected condition. Method A qualitative case study research design was applied involving fieldwork in Bungoma County, a high-prevalence rural area in Western Kenya. Multiple data collection methods were combined: participant observation, home visits, semi- structured in-depth interviews, and group discussions. In total, 48 informants participated, including infected children and adults, teachers and pupils, public health officers, community health workers and NGO volunteers. Results Those infected suffered with multiple penetrating wounds on hands and feet that cause disability, resulting in an incapacity to work and school drop-out. People described feeling stigmatized, and at school pupils preferred not to play with infected classmates. People perceived that the sand flea infestation was caused by poverty and that those affected were not even able to cover their basic needs. They were often living in sandy huts that they shared with their animals, without access to soap and clean water. Moreover, those infected were often viewed as ignorant by the rest of the community. Informants perceived recurrence after treatment as inevitable, resulting in creation of hopelessness. Those infected felt that they were left alone with an irremediable plague. There was confusion about effective approaches regarding prevention and treatment at all levels. Conclusion Tungiasis is a debilitating and neglected ailment, inflicting severe suffering and increasing the circle of poverty. To address fatalist attitudes among those affected, national guidelines need to be implemented, and coordination of public health measures regarding prevention and treatment need to be strengthened. Further research is recommended to enable the control and elimination of this neglected tropical disease
Where boundaries become bridges: Mosquito community composition, key vectors, and environmental associations at forest edges in the central Brazilian Amazon

Risk of spillover and spillback of mosquito-borne viruses in the neotropics, including yellow fever, dengue, Zika (Flaviviridae: Flavivirus), chikungunya, and Mayaro (Togaviridae: Alphavirus) viruses, is highest at ecotones where humans, monkeys, and mosquitoes coexist. With a view to identifying potential bridge vectors, we investigated changes in mosquito community composition and environmental variables at ground level at distances of 0, 500, 1000, and 2000 m from the edge of a rainforest reserve bordering the city of Manaus in the central Brazilian Amazon. During two rainy seasons in 2019 and 2020, we sampled 9,467 mosquitoes at 244 unique sites using BG-Sentinel traps, hand-nets, and Prokopack aspirators. Species richness and diversity were generally higher at 0 m and 500 m than at 1000 m and 2000 m, while mosquito community composition changed considerably between the forest edge and 500 m before stabilizing by 1000 m. Shifts in environmental variables mainly occurred between the edge and 500 m, and the occurrence of key taxa (Aedes albopictus, Ae. scapularis, Limatus durhamii, Psorophora amazonica, Haemagogus, and Sabethes) was associated with one or more of these variables. Sites where Ae. aegypti and Ae. albopictus were detected had significantly higher surrounding mean NDBI (Normalized Difference Built-up Index) values than sites where they were not detected, while the opposite was true for Sabethes mosquitoes. Our findings suggest that major changes in mosquito communities and environmental variables occur within 500 m of the forest edge, where there is high risk for contact with both urban and sylvatic vectors. By 1000 m, conditions stabilize, species diversity decreases, and forest mosquitoes predominate. Environmental variables associated with the occurrence of key taxa may be leveraged to characterize suitable habitat and refine risk models for pathogen spillover and spillback.
Statistical analysis of beta decays and the effective value of g_A in the pnQRPA framework

We perform a Markov Chain Monte Carlo (MCMC) statistical analysis of a number of measured ground-state-to-ground-state single $\beta^+$/electron-capture and $\beta^-$ decays in the nuclear mass range A = 62 - 142. The corresponding experimental comparative half-lives (log ft values) are compared with the theoretical ones obtained by the use of the proton-neutron quasiparticle random-phase approximation (pnQRPA) with G-matrix based effective interactions. The MCMC analysis is performed separately for 47 isobaric triplets and 28 more extended isobaric chains of nuclei to extract values and uncertainties for the effective axial-vector coupling constant g_A in nuclear-structure calculations performed in the pnQRPA framework. As far as available, measured half-lives for two-neutrino double beta-minus decays occurring in the studied isobaric chains are analyzed as well.
Clinical applications of artificial intelligence in radiology

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI’s adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists’ liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.
Molecular mechanisms of action and chemosensitization of tumor cells in ovarian cancer by phytochemicals: A narrative review on pre‐clinical and clinical studies

Ovarian cancer is the second‐leading cause of death among women with cancer of the genital tract. Currently, drugs derived from platinum and taxanes constitute the majority of ovarian cancer treatments. Patients undergoing this chemotherapy are susceptible to cumulative toxic effects and resistance to chemotherapy. Therefore, it is crucial to identify treatment options that are both more effective and better tolerated by patients. Phytochemicals in this context are plant‐derived chemicals with antitumor activity that can be used as therapeutic or adjuvant agents in the treatment of ovarian cancer. Consequently, the purpose of this literature review is to demonstrate through existing pre‐clinical and clinical trials the potential of phytochemicals in the treatment of ovarian cancer, the mechanisms of action involved, and to contribute to the development of new therapeutic options for ovarian cancer. For this review, the databases PubMed, Scopus, Science Direct, and ClinicalTrials.gov were queried between 2010 and 2022 using terms such as “ovarian cancer,” “phytochemicals,” “phenolic compounds,” “terpenes,” and “alkaloids. ” The present review summarized the possible molecular mechanisms of action by which phytochemicals, such as phenolic acids, flavonoids, diterpenes, triterpenes, saponins, and alkaloids, inhibit this type of cancer, specifically the ability of phytochemicals to induce cell growth regulation, apoptosis, oxidative stress reduction, anti‐angiogenesis, and chemosensitization of tumors in ovarian cancer. As their action and cellular mechanism have already been demonstrated in several pre‐clinical trials, the phytochemicals identified in our study have the potential to be investigated for the treatment of ovarian cancer. Through pre‐clinical and clinical trials, our study demonstrates the potential of phytochemicals in the treatment of ovarian cancer, contributing to the development of novel therapeutic options for ovarian cancer.
The role of O‐GlcNAcylation mediated by OGT during tooth development

To understand the mechanisms underlying tooth morphogenesis, we examined the developmental roles of important posttranslational modification, O‐GlcNAcylation, which regulates protein stability and activity by the addition and removal of a single sugar (O‐GlcNAc) to the serine or threonine residue of the intracellular proteins. Tissue and developmental stage‐specific immunostaining results against O‐GlcNAc and O‐GlcNAc transferase (OGT) in developing tooth germs would suggest that O‐GlcNAcylation is involved in tooth morphogenesis, particularly in the cap and secretory stage. To evaluate the developmental function of OGT‐mediated O‐GlcNAcylation, we employed an in vitro tooth germ culture method at E14.5, cap stage before secretory stage, for 1 and 2 days, with or without OSMI‐1, a small molecule OGT inhibitor. To examine the mineralization levels and morphological changes, we performed renal capsule transplantation for one and three weeks after 2 days of in vitro culture at E14.5 with OSMI‐1 treatment. After OGT inhibition, morphological and molecular alterations were examined using histology, immunohistochemistry, real‐time quantitative polymerase chain reaction, in situ hybridization, scanning electron microscopy, and ground sectioning. Overall, inhibition of OGT resulted in altered cellular physiology, including proliferation, apoptosis, and epithelial rearrangements, with significant changes in the expression patterns of β‐catenin, fibroblast growth factor 4 (fgf4), and sonic hedgehog (Shh). Moreover, renal capsule transplantation and immunolocalizations of Amelogenin and Nestin results revealed that OGT‐inhibited tooth germs at cap stage exhibited with structural changes in cuspal morphogenesis, amelogenesis, and dentinogenesis of the mineralized tooth. Overall, we suggest that OGT‐mediated O‐GlcNAcylation regulates cell signaling and physiology in primary enamel knot during tooth development, thus playing an important role in mouse molar morphogenesis.
Exploring Gender Bias in Six Key Domains of Academic Science: An Adversarial Collaboration

We synthesized the vast, contradictory scholarly literature on gender bias in academic science from 2000 to 2020. In the most prestigious journals and media outlets, which influence many people’s opinions about sexism, bias is frequently portrayed as an omnipresent factor limiting women’s progress in the tenure-track academy. Claims and counterclaims regarding the presence or absence of sexism span a range of evaluation contexts. Our approach relied on a combination of meta-analysis and analytic dissection. We evaluated the empirical evidence for gender bias in six key contexts in the tenure-track academy: (a) tenure-track hiring, (b) grant funding, (c) teaching ratings, (d) journal acceptances, (e) salaries, and (f) recommendation letters. We also explored the gender gap in a seventh area, journal productivity, because it can moderate bias in other contexts. We focused on these specific domains, in which sexism has most often been alleged to be pervasive, because they represent important types of evaluation, and the extensive research corpus within these domains provides sufficient quantitative data for comprehensive analysis. Contrary to the omnipresent claims of sexism in these domains appearing in top journals and the media, our findings show that tenure-track women are at parity with tenure-track men in three domains (grant funding, journal acceptances, and recommendation letters) and are advantaged over men in a fourth domain (hiring). For teaching ratings and salaries, we found evidence of bias against women; although gender gaps in salary were much smaller than often claimed, they were nevertheless concerning. Even in the four domains in which we failed to find evidence of sexism disadvantaging women, we nevertheless acknowledge that broad societal structural factors may still impede women’s advancement in academic science. Given the substantial resources directed toward reducing gender bias in academic science, it is imperative to develop a clear understanding of when and where such efforts are justified and of how resources can best be directed to mitigate sexism when and where it exists.
Role of instrumental factors in Meibomian gland contrast assessment

Meibomian gland contrast has been suggested as a potential biomarker in Meibomian gland dysfunction. This study analysed the instrumental factors related to contrast. The objectives were to determine whether the mathematical equations used to compute gland contrast (e.g., Michelson or Yeh and Lin), impact the ability to identify abnormal individuals, to ascertain whether contrast between the gland and the background could be an effective biomarker and to assess whether using contrast‐enhancement on the gland image improves its diagnostic efficacy.
Cooperation and partner choice among Agta hunter-gatherer children: An evolutionary developmental perspective

Examining development is essential for a full understanding of behaviour, including how individuals acquire traits and how adaptive evolutionary forces shape these processes. The present study explores the development of cooperative behaviour among the Agta, a Filipino hunter-gatherer population. A simple resource allocation game assessing both levels of cooperation (how much children shared) and patterns of partner choice (who they shared with) was played with 179 children between the ages of 3 and 18. Children were given five resources (candies) and for each was asked whether to keep it for themselves or share with someone else, and if so, who this was. Between-camp variation in children’s cooperative behaviour was substantial, and the only strong predictor of children’s cooperation was the average level of cooperation among adults in camp; that is, children were more cooperative in camps where adults were more cooperative. Neither age, sex, relatedness or parental levels of cooperation were strongly associated with the amount children shared. Children preferentially shared with close kin (especially siblings), although older children increasingly shared with less-related individuals. Findings are discussed in terms of their implications for understanding cross-cultural patterns of children’s cooperation, and broader links with human cooperative childcare and life history evolution.
The Clinicogenomic Landscape of Induction Failure in Childhood and Young Adult T-Cell Acute Lymphoblastic Leukemia

PURPOSE Failure to respond to induction chemotherapy portends a poor outcome in childhood acute lymphoblastic leukemia (ALL) and is more frequent in T-cell ALL (T-ALL) than B-cell ALL. We aimed to address the limited understanding of clinical and genetic factors that influence outcome in a cohort of patients with T-ALL induction failure (IF). METHODS We studied all cases of T-ALL IF on two consecutive multinational randomized trials, UKALL2003 and UKALL2011, to define risk factors, treatment, and outcomes. We performed multiomic profiling to characterize the genomic landscape. RESULTS IF occurred in 10.3% of cases and was significantly associated with increasing age, occurring in 20% of patients age 16 years and older. Five-year overall survival (OS) rates were 52.1% in IF and 90.2% in responsive patients (P < .001). Despite increased use of nelarabine-based chemotherapy consolidated by hematopoietic stem-cell transplant in UKALL2011, there was no improvement in outcome. Persistent end-of-consolidation molecular residual disease resulted in a significantly worse outcome (5-year OS, 14.3% v 68.5%; HR, 4.10; 95% CI, 1.35 to 12.45; P = .0071). Genomic profiling revealed a heterogeneous picture with 25 different initiating lesions converging on 10 subtype-defining genes. There was a remarkable abundance of TAL1 noncoding lesions, associated with a dismal outcome (5-year OS, 12.5%). Combining TAL1 lesions with mutations in the MYC and RAS pathways produces a genetic stratifier that identifies patients highly likely to fail conventional therapy (5-year OS, 23.1% v 86.4%; HR, 6.84; 95% CI, 2.78 to 16.78; P < .0001) and who should therefore be considered for experimental agents. CONCLUSION The outcome of IF in T-ALL remains poor with current therapy. The lack of a unifying genetic driver suggests alternative approaches, particularly using immunotherapy, are urgently needed.