UMBC Student Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/33

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Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold
(IEEE, 2024-09-05) Ale, Tolulope; Janeja, Vandana; Schlegel, Nicole-Jeanne
We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages Variational Autoencoder (VAE) integrated with dynamic thresholding and correlationbased feature clustering. This framework enhances the VAE’s ability to identify localized dependencies and learn the temporal relationships in climate data, thereby improving the detection of anomalies as demonstrated by its higher F1-score on benchmark datasets. The study’s main contributions include the development of a robust anomaly detection method, improving feature representation within VAEs through clustering, and creating a dynamic threshold algorithm for localized anomaly detection. This method offers explainability of climate anomalies across different regions.
Deep Learning for Antarctic Sea Ice Anomaly Detection and Prediction: A Two-Module Framework
(ACM, 2024-11-06) Devnath, Maloy Kumar; Chakraborty, Sudip; Janeja, Vandana
The Antarctic sea ice cover plays a crucial role in regulating global climate and sea level rise. The recent retreat of the Antarctic Sea Ice Extent and the accelerated melting of ice sheets (which causes sea level rise) raise concerns about the impact of climate change. Understanding the spatial patterns of anomalous melting events in sea ice is crucial for improving climate models and predicting future sea level rise, as sea ice serves as a protective barrier for ice sheets. This paper proposes a two-module framework based on Deep Learning that utilizes satellite imagery to identify and predict non-anomalous and anomalous melting regions in Antarctic sea ice. The first module focuses on identifying non-anomalous and anomalous melting regions in the current day by analyzing the difference between consecutive satellite images over time. The second module then leverages the current day's information and predicts the next day's non-anomalous and anomalous melting regions. This approach aims to improve our ability to monitor and predict critical changes in the Antarctic sea ice cover.
The effect of different feature selection methods on models created with XGBoost
(2024-11-08) Neyra, Jorge; Siramshetty, Vishal B.; Ashqar, Huthaifa
This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality of features produces no statistically significant change in the prediction accuracy of the model. This suggests that the traditional idea of removing the noisy training data to make sure models do not overfit may not apply to XGBoost. But it may still be viable in order to reduce computational complexity.
In Context Learning and Reasoning for Symbolic Regression with Large Language Models
(2024-10-22) Sharlin, Samiha; Josephson, Tyler R.
Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets. We prompt GPT-4 to suggest expressions from data, which are then optimized and evaluated using external Python tools. These results are fed back to GPT-4, which proposes improved expressions while optimizing for complexity and loss. Using chain-of-thought prompting, we instruct GPT-4 to analyze the data, prior expressions, and the scientific context (expressed in natural language) for each problem before generating new expressions. We evaluated the workflow in rediscovery of five well-known scientific equations from experimental data, and on an additional dataset without a known equation. GPT-4 successfully rediscovered all five equations, and in general, performed better when prompted to use a scratchpad and consider scientific context. We also demonstrate how strategic prompting improves the model's performance and how the natural language interface simplifies integrating theory with data. Although this approach does not outperform established SR programs where target equations are more complex, LLMs can nonetheless iterate toward improved solutions while following instructions and incorporating scientific context in natural language.
The Dual Role of Student and Creator: Exploring the TikTok Experience
(ACM, 2024-11-13) Bulley, Bharadwaj Kuruba; Tirumala, Shravika; Mahamkali, Bhavani Shankar; Sakib, Md Nazmus; Ahmed, Saquib; Dey, Sanorita
TikTok is one of the most common content-creating social media platforms for youth in the USA. In recent years, its engaging content has significantly influenced people, shaping trends, behaviors, and communication styles among its predominantly young user base. This study evaluates TikTok's impact on college and university students as they invest a lot of time creating content and engaging on TikTok besides their studies. While existing research highlights TikTok's educational benefits and adverse societal and psychological effects, our mixed-method approach provides a focused analysis of student content creators. Survey data quantifies usage patterns and their correlation with academic and mental health indicators, while interviews offer qualitative insights into personal experiences. Findings reveal that TikTok affects students' time management, mental health, academic performance, and self-perception. Although TikTok facilitates creativity and social connections, it also induces stress and distraction. This study aims to fill research gaps and propose new directions, offering practical recommendations for balancing TikTok's benefits and drawbacks for student content creators.
When to Commute During the COVID-19 Pandemic and Beyond: Analysis of Traffic Crashes in Washington, D.C
(2024-11-08) Choi, Joanne; Clark, Sam; Jaiswal, Ranjan; Kirk, Peter; Jayaraman, Sachin; Ashqar, Huthaifa
Many workers in cities across the world, who have been teleworking because of the COVID-19 pandemic, are expected to be back to their commutes. As this process is believed to be gradual and telecommuting is likely to remain an option for many workers, hybrid model and flexible schedules might become the norm in the future. This variable work schedules allows employees to commute outside of traditional rush hours. Moreover, many studies showed that commuters might be skeptical of using trains, buses, and carpools and could turn to personal vehicles to get to work, which might increase congestion and crashes in the roads. This study attempts to provide information on the safest time to commute to Washington, DC area analyzing historical traffic crash data before the COVID-19 pandemic. It also aims to advance our understanding of traffic crashes and other relating factors such as weather in the Washington, DC area. We created a model to predict crashes by time of the day, using a negative binomial regression after rejecting a Poisson regression, and additionally explored the validity of a Random Forest regression. Our main consideration for an eventual application of this study is to reduce crashes in Washington DC, using this tool that provides people with better options on when to commute and when to telework, if available. The study also provides policymakers and researchers with real-world insights that decrease the number of traffic crashes to help achieve the goals of The Vision Zero Initiative adopted by the district.
Post-Roe Public Discourse: A Temporal Analysis of Discussion on US Abortion Law Changes
(ACM, 2024-11-13) Venkata, Harisahan Nookala; Palakurthi, Varshitha; Devalam, Sree Sai Bindu; Sakib, Md Nazmus; Ahmed, Saquib; Dey, Sanorita
The "Post-Roe" refers to the period following the June 2022 Supreme Court decision to overrule Roe v. Wade, the 1973 abortion right law. Since this overturn of this law, substantial public discourse was noticed across the US and this controversial issue has trended on all news media agencies and social media platforms. Several studies have analyzed public opinion and the impact of this legal change on healthcare, economic challenges, and society. However, little work has been done to identify the shift in discussion over time. Our research analyzes YouTube and Reddit comments to perceive insights into the evolving spectrum of public opinion on abortion legislation by utilizing NLP techniques and opinion mining. By systematically categorizing comments to extract themes and employing a temporal analysis approach, we identify shifts in public sentiment across different phases of time, such as immediate reactions, peak debate, and long-term responses. Our preliminary findings show that different themes prevail at different phases and primary concerns shift over time. This study might help policymakers, activists, and social commentators understand these shifts to effectively address the evolving concerns of the public and take measures accordingly.
Morphology and Luminescence Properties of Transition Metal Doped Zinc Selenide Crystals
(Springer Nature, 2024-11-11) Bowman, Eric; Scheurer, Leslie; Arnold, Bradley; Su, Ching Hua; Choa, Fow-Sen; Cullum, Brian; Singh, Narsingh
Zinc selenide is an excellent matrix material to dope with rare-earth and transition metal to achieve mid-infrared luminescence to develop high power lasers. The luminescence, morphology and refractive index is significantly affected by the doping and defects generated due to size and valency of dopants, concentration, growth process and convection during the growth. The aim of the study is to investigate effect of point and line defects generated due to low doping of iron and chromium on the emission and morphology of the zinc selenide. Luminescence and morphological properties of large iron and chromium doped zinc selenide single crystals were studied to evaluate the effect of extremely low residual impurities and defects associated with the doping process. The emission properties following both short wavelength (i.e., ultraviolet; 350–370 nm) excitation and longer wavelength (i.e., near infrared; 850–870 nm) excitation were characterized. Luminescence emission bands were identified in both doped crystals. In addition to the primary emission bands, satellite peaks and intra-center transitions were also observed. Due to local population defects associated with the residual impurities (ppm to ppb) in the Fe-ZnSe and Cr-ZnSe crystals, peak emission wavelengths were observed to shift. The emission bands were found to decrease in intensity due to recombination of residual impurity co-dopants and complex defects generated during growth and fabrication. Cryogenic temperature analyses revealed a very clean emission band due to freezing of some of the point and line defects. An emission band observed at 980 nm for both crystals at room temperature as well as cryogenic temperatures indicates a vibronic peak in ZnSe. The scanning electron microscopy (SEM) images of the local morphology support the conclusion that small crystallites in doped crystals are also present.
Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment
(2024-10-23) Ghosh, Indrajeet; Chugh, Garvit; Faridee, Abu Zaher Md; Roy, Nirmalya
Recent advancements in deep learning-based wearable human action recognition (wHAR) have improved the capture and classification of complex motions, but adoption remains limited due to the lack of expert annotations and domain discrepancies from user variations. Limited annotations hinder the model's ability to generalize to out-of-distribution samples. While data augmentation can improve generalizability, unsupervised augmentation techniques must be applied carefully to avoid introducing noise. Unsupervised domain adaptation (UDA) addresses domain discrepancies by aligning conditional distributions with labeled target samples, but vanilla pseudo-labeling can lead to error propagation. To address these challenges, we propose μDAR, a novel joint optimization architecture comprised of three functions: (i) consistency regularizer between augmented samples to improve model classification generalizability, (ii) temporal ensemble for robust pseudo-label generation and (iii) conditional distribution alignment to improve domain generalizability. The temporal ensemble works by aggregating predictions from past epochs to smooth out noisy pseudo-label predictions, which are then used in the conditional distribution alignment module to minimize kernel-based class-wise conditional maximum mean discrepancy (kCMMD) between the source and target feature space to learn a domain invariant embedding. The consistency-regularized augmentations ensure that multiple augmentations of the same sample share the same labels; this results in (a) strong generalization with limited source domain samples and (b) consistent pseudo-label generation in target samples. The novel integration of these three modules in μDAR results in a range of ≈4-12% average macro-F1 score improvement over six state-of-the-art UDA methods in four benchmark wHAR datasets
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
(Association for Computational Linguistics, 2024-11) Joshi, Abhinav; Saha, Shaswati; Shukla, Divyaksh; Vema, Sriram; Jhamtani, Harsh; Gaur, Manas; Modi, Ashutosh
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick some undesirable information such as personally identifiable information (PII). Consequently, in recent times research in the area of Machine Unlearning (MUL) has become active, the main idea is to force LLMs to forget (unlearn) certain information (e.g., PII) without suffering from performance loss on regular tasks. In this work, we examine the robustness of the existing MUL techniques for their ability to enable leakage-proof forgetting in LLMs. In particular, we examine the effect of data transformation on forgetting, i.e., is an unlearned LLM able to recall forgotten information if there is a change in the format of the input? Our findings on the TOFU dataset highlight the necessity of using diverse data formats to quantify unlearning in LLMs more reliably.
Sustainability of a dual language program during and beyond COVID-19 challenges
(Taylor & Francis, 2024-10-28) Mata-McMahon, Jennifer; Williams, Sabrina; Daramola, Adebola; Kruse, Lance; Hossain, Shahin
This study evaluates the Dual Language Program (DLP) implemented at a Title I public school in Baltimore City during the 2019–2020 and 2020–2021 school years. Building on previous research, the DLP's implementation, sustainability, and effects on students’ learning outcomes were examined. Utilizing a mixed-methods approach, the study’s second phase included participants from the school’s mainstream, English-only program (MP), enriching the overall understanding of the school community’s perception of the program. Data were collected through surveys, classroom observations, and standardized assessments—the DIBELS and the iReady Diagnostic assessments. Findings showed challenges with curriculum standardization and prevalent misconceptions about bilingualism. Nevertheless, the program's successes were evident in DLP students’ enhanced engagement, parental involvement, and community support. Despite the challenges of the COVID-19 pandemic, the DLP demonstrated its capacity for scaleability and sustainability. During the 2020–2021 school year, regardless of virtual learning, DLP students not only maintained but, in the case of Cohort 1, showed greater growth in reading skills compared to MP students, with Cohort 3 DLP students improving to reach statistically similar performance levels to their MP peers. Findings indicate that the DLP has the potential to serve as a sustainable educational program, fostering both dual language proficiency and academic outcomes.
SERN: Simulation-Enhanced Realistic Navigation for Multi-Agent Robotic Systems in Contested Environments
(2024-10-22) Hossain, Jumman; Dey, Emon; Chugh, Snehalraj; Ahmed, Masud; Anwar,Mohammad Saeid; Faridee, Abu Zaher Md; Hoppes, Jason; Trout, Theron; Basak, Anjon; Chowdhury, Rafidh; Mistry, Rishabh; Kim, Hyun; Freeman, Jade; Suri, Niranjan; Raglin, Adrienne; Busart, Carl; Gregory, Timothy; Ravi, Anuradha; Roy, Nirmalya
The increasing deployment of autonomous systems in complex environments necessitates efficient communication and task completion among multiple agents. This paper presents SERN (Simulation-Enhanced Realistic Navigation), a novel framework integrating virtual and physical environments for real-time collaborative decision-making in multi-robot systems. SERN addresses key challenges in asset deployment and coordination through a bi-directional communication framework using the AuroraXR ROS Bridge. Our approach advances the SOTA through accurate real-world representation in virtual environments using Unity high-fidelity simulator; synchronization of physical and virtual robot movements; efficient ROS data distribution between remote locations; and integration of SOTA semantic segmentation for enhanced environmental perception. Our evaluations show a 15% to 24% improvement in latency and up to a 15% increase in processing efficiency compared to traditional ROS setups. Real-world and virtual simulation experiments with multiple robots demonstrate synchronization accuracy, achieving less than 5 cm positional error and under 2-degree rotational error. These results highlight SERN's potential to enhance situational awareness and multi-agent coordination in diverse, contested environments.
Functional analysis of regA paralog rlsD in Volvox carteri
(Wiley, 2024-10-22) Jiménez-Marín, Berenice; Ortega-Escalante, José A.; Tyagi, Antariksh; Seah, Jundhi; Olson, Bradley J. S. C.; Miller, Stephen M.
Volvox carteri is an excellent system for investigating the origins of cell differentiation because it possesses just two cell types, reproductive gonidia and motile somatic cells, which evolved relatively recently. The somatic phenotype depends on the regA gene, which represses cell growth and reproduction, preventing cells expressing it from growing large enough to become gonidia. regA encodes a putative transcription factor and was generated in an undifferentiated ancestor of V. carteri through duplication of a progenitor gene whose ortholog in V. carteri is named rlsD. Here we analyze the function of rlsD through knockdown, overexpression, and RNA-seq experiments, to gain clues into the function of a member of an understudied putative transcription factor family and to obtain insight into the origins of cell differentiation in the volvocine algae. rlsD knockdown was lethal, while rlsD overexpression dramatically reduced gonidial growth. rlsD overexpression led to differential expression of approximately one-fourth of the genome, with repressed genes biased for those typically overexpressed in gonidia relative to somatic cells, and upregulated genes biased toward expression in soma, where regA expression is high. Notably, rlsD overexpression affects accumulation of transcripts for genes/Pfam domains involved in ribosome biogenesis, photosynthetic light harvesting, and sulfate generation, functions related to organismal growth, and responses to resource availability. We also found that in the wild type, rlsD expression is induced by light deprivation. These findings are consistent with the idea that cell differentiation in V. carteri evolved when a resource-responsive, growth-regulating gene was amplified, and a resulting gene duplicate was co-opted to repress growth in a constitutive, spatial context.
Neural Normalized Compression Distance and the Disconnect Between Compression and Classification
(2024-10-20) Hurwitz, John; Nicholas, Charles; Raff, Edward
It is generally well understood that predictive classification and compression are intrinsically related concepts in information theory. Indeed, many deep learning methods are explained as learning a kind of compression, and that better compression leads to better performance. We interrogate this hypothesis via the Normalized Compression Distance (NCD), which explicitly relies on compression as the means of measuring similarity between sequences and thus enables nearest-neighbor classification. By turning popular large language models (LLMs) into lossless compressors, we develop a Neural NCD and compare LLMs to classic general-purpose algorithms like gzip. In doing so, we find that classification accuracy is not predictable by compression rate alone, among other empirical aberrations not predicted by current understanding. Our results imply that our intuition on what it means for a neural network to ``compress'' and what is needed for effective classification are not yet well understood.
Week 2: Linear Classifiers, Logistic Regression, Bias-Variance Trade-off, and Regularization
(2024) Rahman, Mohammad Saidur; Rahman, Mohammad Ishtiaque
In this week, we will explore fundamental machine learning techniques that are widely used for classification tasks: Linear Classifiers and Logistic Regression. Additionally, we will cover core concepts like the Bias-Variance Trade-off and Regularization, which help in understanding the performance and generalization of machine learning models. These concepts are essential for building accurate and interpretable models that can classify data and predict outcomes in various fields. Understanding when and why to use these techniques is key to solving different types of problems in machine learning
Identifying Economic Factors Affecting Unemployment Rates in the United States
(2024-11-04) Green, Alrick; Nasim, Ayesha; Radadia, Jaydeep; Kallam, Devi Manaswi; Kalyanam, Viswas; Owenga, Samfred; Ashqar, Huthaifa
In this study, we seek to understand how macroeconomic factors such as GDP, inflation, Unemployment Insurance, and S&P 500 index; as well as microeconomic factors such as health, race, and educational attainment impacted the unemployment rate for about 20 years in the United States. Our research question is to identify which factor(s) contributed the most to the unemployment rate surge using linear regression. Results from our studies showed that GDP (negative), inflation (positive), Unemployment Insurance (contrary to popular opinion; negative), and S&P 500 index (negative) were all significant factors, with inflation being the most important one. As for health issue factors, our model produced resultant correlation scores for occurrences of Cardiovascular Disease, Neurological Disease, and Interpersonal Violence with unemployment. Race as a factor showed a huge discrepancies in the unemployment rate between Black Americans compared to their counterparts. Asians had the lowest unemployment rate throughout the years. As for education attainment, results showed that having a higher education attainment significantly reduced one chance of unemployment. People with higher degrees had the lowest unemployment rate. Results of this study will be beneficial for policymakers and researchers in understanding the unemployment rate during the pandemic.
QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning
(2024-10-22) Hossain, Jumman; Faridee, Abu Zaher Md; Asher, Derrik; Freeman, Jade; Trout, Theron; Gregory, Timothy; Roy, Nirmalya
Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement learning methods often assume symmetric costs, which can lead to suboptimal navigation paths and increased safety risks in real-world scenarios. In this paper, we introduce QuasiNav, a novel reinforcement learning framework that integrates quasimetric embeddings to explicitly model asymmetric costs and guide efficient, safe navigation. QuasiNav formulates the navigation problem as a constrained Markov decision process (CMDP) and employs quasimetric embeddings to capture directionally dependent costs, allowing for a more accurate representation of the terrain. This approach is combined with adaptive constraint tightening within a constrained policy optimization framework to dynamically enforce safety constraints during learning. We validate QuasiNav across three challenging navigation scenarios-undulating terrains, asymmetric hill traversal, and directionally dependent terrain traversal-demonstrating its effectiveness in both simulated and real-world environments. Experimental results show that QuasiNav significantly outperforms conventional methods, achieving higher success rates, improved energy efficiency, and better adherence to safety constraints.
The Impact of Medicaid Expansion on Medicare Quality Measures
(2024-11-05) Algrain, Hala; Cardosa, Elizabeth; Desai, Shekha; Fong, Eugene; Ringoir, Tanguy; Ashqar, Huthaifa
The Affordable Care Act was signed into law in 2010, expanding Medicaid and improving access to care for millions of low-income Americans. Fewer uninsured individuals reduced the cost of uncompensated care, consequently improving the financial health of hospitals. We hypothesize that this amelioration in hospital finances resulted in a marked improvement of quality measures in states that chose to expand Medicaid. To our knowledge, the impact of Medicaid expansion on the Medicare population has not been investigated. Using a difference-in-difference analysis, we compare readmission rates for four measures from the Hospital Readmission Reduction Program: acute myocardial infarction, pneumonia, heart failure, and coronary artery bypass graft surgery. Our analysis provides evidence that between 2013 and 2021 expansion states improved hospital quality relative to non-expansion states as it relates to acute myocardial infarction readmissions (p = 0.015) and coronary artery bypass graft surgery readmissions (p = 0.039). Our analysis provides some evidence that expanding Medicaid improved hospital quality, as measured by a reduction in readmission rates. Using visualizations, we provide some evidence that hospital quality improved for the other two measures as well. We believe that a refinement of our estimation method and an improved dataset will increase our chances of finding significant results for these two other measures.
Full-field Modal Analysis of a Tensegrity Column Using a Three-dimensional Scanning Laser Doppler Vibrometer with a Mirror
(ASME, 2024-11-05) Yuan, Ke; Yuan, Sichen; Zhu, Weidong
Tensegrity structures become important components of various engineering structures due to their high stiffness, light weight, and deployable capability. Existing studies on their dynamic analyses mainly focus on responses of their nodal points while overlook deformations of their cable and strut members. This study proposes a non-contact approach for experimental modal analysis of a tensegrity structure to identify its three-dimensional (3D) natural frequencies and full-field mode shapes, which include modes with deformations of its cable and strut members. A 3D scanning laser Doppler vibrometer is used with a mirror for extending its field of view to measure full-field vibration of a novel three-strut metal tensegrity column with free boundaries. Tensions and axial stiffnesses of its cable members are determined using natural frequencies of their transverse and longitudinal modes, respectively, to build its theoretical model for dynamic analysis and model validation purposes. Modal assurance criterion (MAC) values between experimental and theoretical mode shapes are used to identify their paired modes. Modal parameters of the first 15 elastic modes of the tensegrity column identified from the experiment, including those of the overall structure and its cable members, can be classified into five mode groups depending on their types. Modes paired between experimental and theoretical results have MAC values larger than 78%. Differences between natural frequencies of paired modes of the tensegrity column are less than 15%. The proposed non-contact 3D vibration measurement approach allows accurate estimation of 3D full-field modal parameters of the tensegrity column.
GnRH—Gonadotropes Interactions Revealed by Pituitary Single-cell Transcriptomics in Zebrafish
(Oxford University Press, 2024-11-06) Tanaka, Sakura; Yu, Yang; Levavi-Sivan, Berta; Zmora, Nilli; Zohar, Yonathan
Gonadotropin-releasing hormone (GnRH) governs reproduction by regulating pituitary gonadotropins. Unlike most vertebrates, gnrh⁻/⁻ zebrafish are fertile. To elucidate the role of the hypophysiotropic-Gnrh3 and other mechanisms regulating pituitary gonadotropes, we profiled the gene expression of all individual pituitary cells of wild-type and gnrh3⁻/⁻ adult female zebrafish. The single-cell RNA-Seq showed that Lh and Fsh gonadotropes express the two gonadotropin beta subunits with a ratio of 140:1 (lhb:fshb) and 4:1 (fshb:lhb), respectively. Lh gonadotropes predominantly express genes encoding receptors for Gnrh (gnrhr2), thyroid hormone, estrogen, and steroidogenic factor 1 (SF1). No Gnrh receptor transcript was enriched in Fsh gonadotropes. Instead, cholecystokinin receptor-b and galanin receptor-1b transcripts were enriched in these cells. The loss of Gnrh3 gene in gnrh3⁻/⁻ zebrafish resulted in downregulation of fshb in Lh gonadotropes and upregulation of pituitary hormones like thyroid-stimulating hormone, growth hormone, prolactin and proopiomelanocortin-a. Likewise, targeted chemogenetic ablation of Gnrh3 neurons led to a decrease in the number of fshb+, lhb+ and fshb+/lhb+ cells. Our studies suggest that Gnrh3 directly acts on Lh gonadotropes through Gnrhr2, but the outcome of this interaction is still unknown. Gnrh3 also regulates fshb expression in both gonadotropes, most likely via a non-Gnrh receptor route. Altogether, while Lh secretion and synthesis are likely regulated in a Gnrh-independent manner, Gnrh3 seems to play a role in the cellular organization of the pituitary. Moreover, the co-expression of lhb and fshb in both gonadotropes provides a possible explanation as to why gnrh3⁻/⁻ zebrafish are fertile.

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