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Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm
Get PDFBackground: Differential expression analysis is pivotal in single-cell transcriptomics for unraveling cell-type–specific responses to stimuli. While numerous methods are available to identify differentially expressed genes in single-cell data, recent evaluations of both single-cell–specific methods and methods adapted from bulk studies have revealed significant shortcomings in performance. In this paper, we dissect the four major challenges in single-cell differential expression analysis: excessive zeros, normalization, donor effects, and cumulative biases. These “curses” underscore the limitations and conceptual pitfalls in existing workflows. Results: To address the limitations of current single-cell differential expression analysis methods, we propose GLIMES, a statistical framework that leverages UMI counts and zero proportions within a generalized Poisson/Binomial mixed-effects model to account for batch effects and within-sample variation. We rigorously benchmarked GLIMES against six existing differential expression methods using three case studies and simulations across different experimental scenarios, including comparisons across cell types, tissue regions, and cell states. Our results demonstrate that GLIMES is more adaptable to diverse experimental designs in single-cell studies and effectively mitigates key shortcomings of current approaches, particularly those related to normalization procedures. By preserving biologically meaningful signals, GLIMES offers improved performance in detecting differentially expressed genes. Conclusions: By using absolute RNA expression rather than relative abundance, GLIMES improves sensitivity, reduces false discoveries, and enhances biological interpretability. This paradigm shift challenges existing workflows and highlights the need for careful consideration of normalization strategies, ultimately paving the way for more accurate and robust single-cell transcriptomic analyses.</p
Addressing Two Major Complications in Studying Natural Selection: Linked Selection and Gene Flow
No full textStudying natural selection is of great importance to understand how different organisms adapt to different environmental conditions. However, natural selection does not act in isolation; it interacts with other evolutionary mechanisms such as mutation, genetic drift, migration, and genetic linkage, all of which collectively shape the genetic and phenotypic composition of populations. In this thesis, we explore the interplay between natural selection and two key evolutionary mechanisms: genetic linkage and migration. Because sites that are closer together in the genome are more likely to be inherited together, elimination of deleterious variants can reduce variation at linked neutral sites. This process is known as background selection. In Chapter 2, we investigate how background selection influences the frequency trajectory of a single selected locus under both additive and underdominant selection. We then extend this analysis to explore its impact on the genetic architecture and evolution of complex traits, including a complex disease under directional selection and a quantitative trait under stabilizing selection. Our findings demonstrate that background selection can have a profound effect on complex traits that cannot be inferred by assuming independent selection on individual variants. For example, in some cases, background selection can increase disease prevalence. Another important factor that affects the allele frequency of a selected locus is gene flow. Allele frequency changes that cannot be explained by genetic drift can signal natural selection; however, these changes may also result from migration. Failing to properly account for allele frequency shifts caused by gene flow can lead to false signals of selection. This issue becomes particularly relevant in our case study, where we aim to detect selection using ancient DNA (aDNA) samples from the island of Sardinia, as described in Chapter 3. To address this challenge, we introduce a novel method for detecting selection in ancient DNA while accounting for gene flow in Chapter 4. We validate our method through extensive simulations and apply it to empirical aDNA data from the Carpathian Basins. Finally, based on insights gained from these projects, Chapter 5 discusses future directions for refining selection inference approaches and advancing our understanding of trait evolution while accounting for genetic linkage and gene flow.<p
Associations of Serum Inflammatory Biomarkers During Pregnancy With Placental Pathology and Placental Gene Expression at Delivery
Get PDFProblem: We sought to investigate whether maternal inflammatory cytokines during pregnancy are associated with histologic inflammatory or vascular lesions in the placenta and/or correlated with gene expression patterns in the placenta. Method of Study: We leveraged data from a large randomized controlled trial (RCT) at a single site. Maternal serum was collected in the second and third trimesters, and a composite inflammatory score was created using five measured biomarkers (CRP, IL-6, IL-1ra, IL-10, and TNF-α). Placentas were collected at delivery for histological analysis and four major patterns of placental injury were characterized. Fresh small chorionic villous biopsies were collected for placental genome-wide mRNA profiling. Transcripts showing >2-fold differential expression over the 4-SD range of circulating inflammatory biomarkers were reported, adjusting for potential confounders. Results: The primary analysis included 601 participants. A one standard deviation increase in the third-trimester inflammatory composite was associated with increased odds of chronic inflammation in the placenta (OR: 1.23, 95% CI 1.01, 1.51;). This was driven primarily by elevations in IL-10 (OR: 1.37; 99% CI: 1.06, 1.77). Higher maternal IL-10 in circulation was associated with bioinformatic indications of reduced pro-inflammatory gene regulation pathways in the placenta (AP1 decreased 25%, p = 0.003; NF-kB decreased 53%, p = 0.003) and indications of increased STAT family signaling pathways which mediate signaling through the IL-10 receptor (increased 73%, p = 0.002). Conclusions: Our results indicate that elevated maternal circulating IL-10 during pregnancy is associated with chronic inflammatory lesions in the placenta at delivery. Additionally, higher levels of circulating IL-10 are associated with upregulated STAT signaling pathways in placental tissues.</p
Forecasting of infection prevalence of <i>Helicobacter pylori</i> (H. pylori) using regression analysis
Get PDFGlobal warming may have a significant impact on human health because of the growth of the population of harmful bacteria such as Helicobacter pylori infection. It is crucial to predict the prevalence of a pathogen in a society in a faster and more cost-effective way in order to manage caused disease. In this research, we have done predictive analysis of H. pylori infection spread behavior with respect to weather parameters (e.g., humidity, dew point, temperature, pressure, and wind speed) of Istanbul based on a database from Istanbul Samatya Hospital. We developed a forecasting model to predict H. pylori infection prevalence. The goal is to develop a machine learning model to predict H. pylori (Hp) related infection diseases (e.g., gastric ulcer diseases, gastritis) based on climate variables. The dataset for this study covered years from 1999 to 2003 and contained a total of 7014 rows from the Samatya Hospital in Istanbul. The weather information related to those years and location, including humidity (H), dew point (D), temperature (T), pressure (P) and wind speed (W), were collected from the following website: https://www.wunderground.com. In this paper we analyzed the forecasting model, which was used to predict H. pylori infection prevalence, by non-linear multivariate linear regression model (MLRM). We applied the non-linear least square method of minimization for the sum of squares to find optimal parameters of MLRM. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the Hp infection disease is most influenced by humidity. Hp prevalence is modelled using the Multiple Regression Method equation, the average H, D, T, P, and W were the most important parameters to deviation of the datasets (testing dataset was 17% and 18% for training dataset). This showed that the statistical model predicts the Hp prevalence with about 83% accuracy of the testing data set (11 months) and 87% accuracy of the training data set (42 months). Based on the proposed model, monthly infection can be predicted early for medical services to take preventative measures and for government to prepare against the bacteria. In addition, drug producers can adjust their drug production rates based on forecasting results. Pemanasan global mungkin mempunyai kesan langsung terhadap kesihatan manusia kerana pertambahan populasi bakteria merbahaya seperti infeksi H. pylori. Adalah penting bagi mengesan kehadiran patogen dalam masyarakat bagi mengawal penularan penyakit dengan cepat, dan melalui kaedah kurang mahal. Kajian ini berkaitan analisis ramalan penularan infeksi H. pylori secara langsung terhadap parameter cuaca (cth: kelembapan, titik embun, suhu, tekanan, kelajuan angin) di Istanbul berdasarkan data dari Hospital Samatya Istanbul. Kajian ini membentuk model ramalan bagi menjangka penyebaran infeksi H. pylori. Matlamat adalah bagi mencipta model pembelajaran mesin bagi mengjangka penyakit berkaitan infeksi H. pylori (Hp) (cth: penyakit ulser gastrik, gastrik) berdasarkan pembolehubah cuaca. Dari tahun 1999 ke 2003, set data telah digunakan bagi mempelajari di mana sejumlah 7014 baris dari Hospital Samatya di Istanbul. Informasi berkaitan tahun-tahun tersebut dan lokasi mengenai kelembapan (H), titik embun (D), suhu (T), tekanan (P) dan kelajuan angin (W) dikumpul dari laman sesawang https://www.wunderground.com. Kajian ini mengguna pakai model ramalan bagi meramal kelaziman infeksi H. pylori, melalui model regresi berkadaran multivariat tidak-berkadaran (MLRM). Kaedah Kuasa Dua Terkecil tidak linear digunakan bagi pengurangan jumlah ganda dua bagi mencapai parameter optimum MLRM. Kaedah Regresi Gandaan digunakan bagi mencari persamaan antara kriteria pembolehubah dan gabungan pembolehubah ramalan. Dapatan menunjukkan infeksi penyakit Hp adalah disebabkan oleh faktor kelembapan. Penyebaran Hp dimodel menggunakan persamaan Kaedah Regresi Gandaan, purata H, D, T, P dan W adalah parameter terpenting bagi sisihan data latihan iaitu sebanyak 17% dan 18% bagi set data latihan. Ini menunjukkan model statistik menjangkakan penyebaran Hp adalah sebanyak 83% adalah tepat pada set data yang diuji (selama 11 bulan) dan 87% tepat pada set data latihan (selama 42 bulan). Berdasarkan model yang dicadangkan ini, infeksi bulanan dapat di jangka lebih awal bagi membendung servis kepada perubatan dan kerajaan bersiap-sedia memerangi bakteria ini. Tambahan, prosedur jumlah ubatan dapat dihasilkan lebih atau kurang daripada jumlah ubatan berdasarkan dapatan ramalan.</p
Tracing the Social Construction of Abortion Attitudes in Online Discourse
No full textBuilding on research that highlights how elite actors have progressively constructed abortion as a polarized moral issue (Cassese et al., 2025; Ziegler, 2020), this study examines abortion attitudes through the lens of a social constructionist theory (Berger and Luckmann, 1966). It analyzes how polarization and social construction surrounding abortion materialize within bottom-up digital dis- course, and it evaluates the effectiveness of computational methods in capturing these patterns. While previous work has examined the presence of factors like elite polarization and social construc- tion on abortion attitudes, fewer have investigated how these dynamics manifest in digital spaces. This work offers insight into the digital reproduction of ideological structures and the potential and limitations of computational approaches for studying social construction processes. Using a dataset of Reddit comments comprising 2.5 million comments, later refined to a subset of 27,451, this study applies dictionary-based keyword classification and Support Vector Classifi- cation to classify the comments into abortion attitudinal stances (‘Pro-Choice’, ‘Pro-Life’ and ‘No Stance/Neutral’) followed by topic modeling (BERTTopic) to analyze large-scale digital text. Results show that, when informed by domain knowledge and iterative qualitative refinements, computational models can assist in identifying theoretical constructs such as polarization and so- cial meaning-making within digital discourse. Additionally, findings show that Pro-Life discourse has a more rigid, elite-driven structure, evidenced through the tight semantic structure and dis- proportionate topic distribution, with many of the dominant themes closely identifying with elite narratives outlined in previous research. Conversely, Pro-Choice discourse is much more decentral- ized, suggesting that the Pro-Life ideology has been carefully socially constructed over the past century
Drug Trafficking Organizations and United States Drug Policy: A Comparative Case Analysis of Mexico and Colombia
No full textThis essay explores how the end of the Cold War and the fall of the Communist Party have affected U.S. foreign policies that target anti-drug and control narcotics strategies in Latin America. This paper will specifically be looking at Mexico and Colombia through a comparative case study analysis. The end of the Cold War left some Western countries, specifically the United States, with many uncertainties and fears of communist influence spreading globally. Through a case study analysis on Mexico and Colombia, it is revealed that the War on Drugs was a direct result of the Cold War; it was a tactic created by the United States government to target drug production, consumption, and smuggling from entering its borders, but more importantly and covertly, was a way to stop the spread of communist ideology and influence spreading to the west. In doing so, the War on Drugs has established ways and incentives for the United States government to create and implement a variety of policies targeted harshly towards certain Latin American countries that they deemed a threat to their national security; not so much because of the threat drug production caused, but due to the impending threat of communist influence in our southern borders
Challenging Dual-Coding Theory: Picture Superiority Effect Persists in Aphantasia
No full textDual-coding theory proposes that superior memory for pictures compared to words (the ‘picture superiority effect’) and concrete words compared to abstract words (the ‘concrete- ness effect’) both stem from the ability to store information as verbal and image codes in memory. According to the theory, having two distinct memory codes increases recall prob- ability, as either code can independently lead to successful retrieval, and one code can serve to cue the other. The current study tests this theoretical explanation by examining recall performance in individuals with aphantasia—the inability to voluntarily generate mental imagery. Based on dual-coding theory, aphantasic individuals should show neither picture superiority nor concreteness effects due to these individuals’ inability to generate mental images and retrieve image-based memory codes. We recruited 62 aphantasic individuals and 69 typical imagers who completed a memory task with free recall of studied stimuli. Data were analyzed using mixed ANOVA and planned comparison t-tests. We compared recall performance between aphantasic and typical imagers across four stimulus types: pictures, symbols, concrete words, and abstract words. Despite their mental imagery deficit, aphan- tasic individuals demonstrated a robust picture superiority effect. Moreover, while typical populations show similar recall for pictures and symbols, aphantasic individuals displayed superior recall for symbols compared to pictures, perhaps because symbols’ simpler visual properties enable more efficient encoding through alternative strategies like motor imagery. In addition, both aphantasics and controls showed better performance with abstract relative to concrete words—contrary to typical concreteness effects—possibly because our abstract words had corresponding everyday symbols that made them more readily processed despite imagery limitations. These recall patterns across different stimuli in aphantasics challenge dual-coding theory’s explanation that superior recall for visual stimuli stems primarily from the formation and retrieval of supplementary image codes, indicating that either dual-coding is incorrect and some other non-dual-coding mechanism is responsible, or that, at the very least, dual-coding theory requires substantial modification to account for our present find- ings. This study provides novel insights into the relation between mental imagery and memory, challenging traditional theoretical frameworks and suggesting the need for alter- native explanations of these well-established memory phenomena
Delay of Game: Visa Restrictions, Tourism, and the 2026 World Cup
No full textIn the lead up to the World Cup, visa delays have exposed the deficiencies of the United States’ travel system. This research paper explores political and economic communications from the White House, travel industry representatives, and U.S. representatives to evaluate the causes of visa delays and how it may affect travel to the World Cup in 2026. By examining the current the visa process and its relation to immigration policy, I argue that prioritizing national security concerns can negatively affect visa access, and ultimately hurts tourism. Given the Trump administration’s aspirations to increase the global standing of the United States, this paper reveals how its ability to manage visa applications may affect the legacy of the World Cup
Assessing Burnout Dynamics Through Weibo: The Spatial-temporal Heterogeneity of Environmental and Economic Factors in Shanghai Pre and Post-Lockdown
No full textThis study explores the influence of integrating macro-built environment data, micro-level subjective and objective street environment perceptions, socioeconomic demographics of residents' burnout emotions and their spatial-temporal heterogeneity, with a particular focus on the changing patterns across three phases of Shanghai's pandemic lockdown. The research establishes a pipeline based on large language models to extract burnout emotions from social media content, and constructs a multi-dimensional spatial-temporal analytical framework integrating OLS, geographically weighted regression, and nonlinear interpretable machine learning modeling techniques. By collecting social media data, the study first validates the effectiveness of large language models in identifying burnout emotions from social media (with a consistency coefficient of 0.61 with human ratings), providing a scalable method for real-time psychological monitoring during crises. Analysis using spatial analysis reveals significant spatial clustering patterns of burnout emotions among Shanghai residents. Geographically weighted regression identifies five typical spatial patterns of influencing factors. Walkability consistently protected against burnout citywide, while green spaces lost restorative function in commercial centers. High-density housing areas with complex visual environments amplified psychological distress, and central districts formed "youth burnout peaks" where young, educated populations concentrated. Longitudinal results reveals significant nonlinear relationships and critical threshold effects between influencing factors and residents' burnout emotions, with these relationship patterns exhibiting various forms of transformation across the three phases. For example, population density's impact on burnout shifted from a negative correlation and "Reverse Excitement" pattern before lockdown to a "Basic Need" pattern during lockdown. Similarly, the young population ratio evolved from a single-threshold model with positive correlation ("Semi-Performance") to negative correlation during and after lockdown ("Performance" to "Excitement") with complex multi-threshold interval models. Green space transformed from weak positive correlation as an "Excitement Factor" before lockdown to strong positive correlation as a "Performance Factor" during lockdown, indicating that visually accessible but functionally inaccessible green spaces became sources of stress. Visual complexity reversed from positive correlation ("Basic Need") before lockdown to negative correlation ("Excitement Factor") highlighting how environmental monotony enhanced the value of perceptual stimulation. These findings challenge static urban health standards, suggesting environmental influences operate through dynamic systems that transform during crises, providing frameworks for resilient urban planning integrating spatial structure with psychological wellbeing
Perspectives of healthcare providers in family planning centers on increasing pre-exposure prophylaxis uptake among women who have migrated from sub-Saharan Africa to France
Get PDFPre-exposure prophylaxis (PrEP) for HIV remains largely underused among women who have migrated from sub-Saharan Africa (WMSSA), despite their accounting for a significant proportion of new HIV diagnoses in France and Western European countries. To expand PrEP reach, we explored healthcare providers’ perspectives on PrEP implementation within family planning centers (FPCs) in the Paris region through focus groups. The focus group discussion guide and rapid content analysis were informed by the Consolidated Framework for Implementation Research (CFIR) 2.0, which uses five domains to capture implementation determinants (Innovation, Outer Setting, Inner Setting, Individuals, and Implementation Process). Twenty providers participated across five focus groups and one key informant interview (median age 45; 80% women, 70% physicians). Oral PrEP was seen as easy to prescribe, but providers advocated for choices beyond the daily pill for better acceptability. While providers recognized increased HIV prevention needs among WMSSA, they found low PrEP demand among women stemming from a lack of knowledge. Although providers acknowledged that PrEP aligned with FPC missions, they cited significant implementation barriers, including limited resources, staff shortages, insufficient on-site capacity, competing priorities, and physicians being the sole prescribers. Provider-level implementation challenges included insufficient training and discomfort in discussing HIV risk and PrEP with WMSSA. Recommendations for implementing PrEP within FPCs included provider training and mentorship, tailored information campaigns for WMSSA, flexible delivery processes, support groups for women, and authorizing midwives and nurses to prescribe PrEP. These results support the need for tailored and multi-level implementation strategies to increase PrEP uptake among WMSSA attending FPCs in France