Multiple Areas Research Articles

The Prevalence of Parkinson's Disease in Poland: Regional and Sex-Related Differences.

Given the growing evidence for an environmental contribution to the etiology of Parkinson's disease (PD), searching for local and regional differences in PD prevalence in multiple areas across the world may further clarify the role of environmental toxins. To provide local and regional prevalence estimates of PD in Poland. We analyzed the prevalence of PD and its trend over the last decade (2010 to 2019) based on data from the National Health Fund in Poland. We specifically examined sex differences in PD prevalence, as well as differences across Polish regions. During the above period, the prevalence of PD in Poland increased from 226 per 100,000 to 269 per 100,000 inhabitants. Unexpectedly, we found that PD was 1.2-times more common in women than men. The increase in prevalence over the past decade was different between both sexes: an increase from 250 to 283 per 100,000 for women (13.3% increase), and from 200 to 254 per 100,000 for men (27.1% increase). In addition, we observed differences in prevalence across different Polish regions, with some regions having up to 51% lower prevalence rates than others. The prevalence of PD in Poland is in line with previously reported prevalence rates across Europe. However, unlike the situation in most of the world, PD was more prevalent in women than men. We discuss several possible explanations as well as potential measures that might help to reduce the growth of PD.

My Catastrophizing and Your Catastrophizing: Dyadic Associations of Pain Catastrophizing and the Physical, Psychological, and Relational Well-being of Persons With Endometriosis and Their Partners.

Endometriosis, a painful chronic gynecologic condition, contributes to disruptions in multiple areas of life for both those affected and their partner. Pain catastrophizing has been associated with worse pain outcomes and quality of life for women with endometriosis and with more cognitive load for partners. Examining both partners' pain catastrophizing dyadically with our variables of interest will enhance understanding of its associations with the distressing nature of experiencing and responding to pain during sex for couples with endometriosis. Persons with endometriosis experiencing pain during sex and their partners (n=52 couples; 104 individuals) completed online self-report measures of pain catastrophizing, depressive symptoms, sexual satisfaction, and partner responses to pain. Persons with endometriosis reported on pain during sexual activity. Analyses were guided by the Actor-Partner Interdependence Model. Persons with endometriosis' pain catastrophizing was associated with their higher pain intensity and unpleasantness during sex. When persons with endometriosis reported more pain catastrophizing, they were less sexually satisfied and reported their partners responded more negatively to their pain. When partners reported higher catastrophizing, they were more depressed and responded more negatively to the pain. Consistent with the Communal Coping Model of pain catastrophizing, although meant to elicit support from the environment, the often-deleterious cognitive process of magnifying, ruminating, and feeling helpless about one's pain (or one's partner's pain) is associated with poorer outcomes for the individual with pain and their romantic partner. Implications for pain management include the relevance of involving the partner and attending to the pain cognitions of both members of the couple.

Abstract 4048: Comparative effects of SOS1 inhibitor with IL-6 blockade and their combination on Kras mutant lung cancer

Abstract Despite recent advances in treatment, lung adenocarcinoma (LUAD), particularly harboring Kras mutations, is still the leading cause of cancer mortality. We and others have previously shown that Kras mutant LUAD development and progression is associated with intrinsic inflammatory characteristics driven by epithelial NF-κB activation, increased levels of IL-6 (which is transcriptionally regulated by NF-κB), and activation of the IL-6-responsive transcription factor STAT3. We further demonstrated that IL-6 blockade suppresses lung tumorigenesis by reducing tumor-associated immunosuppressive myeloid populations and induction of a T cell-mediated anti-tumor response. The recent discovery of a novel SOS1-targeting compound (BI-3406) that selectively inhibits the KRAS-SOS1 interaction and leads to anti-proliferative activity has opened up the possibility to more efficiently inhibit Kras mutant lung cancer. Accordingly, in this study we used a mouse model of KRAS mutant LUAD (CC-LR) to compare the therapeutic effects of BI-3406, IL-6 blockade, and MEK inhibitor (trametinib). We also tested whether inhibiting IL-6 driven pro-tumor immune response would provide a synergistic/additive response and increase the tumor inhibitory effect of either BI-3406 or Trametinib. Eight cohorts of 14-week-old female mice were treated with either anti-IL-6 antibody, trametinib, or BI-3406, or their combinations for 4 weeks, and their tumor burden was compared at the end of study. We observed a significant reduction in tumor multiplicity and tumor area in response to all monotherapies, with the highest effect in the trametinib treated cohort. Neither combination therapy led to decreased tumor burden over either of the monotherapy regimens in our model. However, we observed an increase in T helper 1 and CD8 T expressing IFNγ cell populations in the cohort treated with Trametinib and anti-IL-6 Ab combination. In addition, using qPCR analysis of RNA extracted from the whole lung we found a higher expression of IFNγ along with a reduced expression of IL-10 in the cohort treated with BI-3406 and anti-IL-6 Ab combination. This suggests that these combinations may have the potential to improve the anti-tumor immune responses. Hence, we conclude that further adjustment of the treatment regimen (doses, interval, duration) may allow to obtain improved anti-tumor efficacy with these combinations. Overall, this study provides a new research perspective for the improvement of currently available treatment modalities for patients with Kras mutant lung cancer that could be further explored preclinically. Citation Format: Katherine E. Larsen, Maria J. Arredondo Sancristobal, Melody Zarghooni, Avantika Krishna, Maria T. Grimaldo, Nastaran Karimi, Arnav Gaitonde, Michael J. Clowers, Seyed Javad Moghaddam. Comparative effects of SOS1 inhibitor with IL-6 blockade and their combination on Kras mutant lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4048.

Game-theoretic analysis of competition between access service providers utilizing a Nash genetic algorithm

Fiber-to-the-home (FTTH) technology is a promising solution for providing advanced service delivery to end-users, but its implementation requires substantial capital expenditures. To minimize investment risks and aid decision-making for access service providers, we propose a game theoretic framework based on a modified Nash genetic algorithm. We illustrate how this framework can be applied to analyze the competition between access providers offering a flat-rate FTTH service on multiple geographical areas. Each provider determines its price for all areas simultaneously and decides whether to invest on a particular area depending on the anticipated revenues. Two distinct demand models are adopted to describe different types of consumer behavior. A solution engine, based on a modified mixed-variable Nash genetic algorithm is implemented under an open-source license. The significance and practical implications of the equilibrium points obtained for both single and multiple area games are discussed. The proposed framework and the solution engine developed, aid both providers and regulatory bodies to analyze competitive environments. They can also be used to implement decision support tools for similar problems as well. The paper concludes by pointing out further research directions in this context.

Safety and efficacy of dynamic catheter-directed cerebral digital subtraction angiography for diagnosis of bowhunter syndrome spectrum disorders: A systematic review of the literature.

Dynamic catheter-directed cerebral digital subtraction angiography (dcDSA) is the gold standard for diagnosing dynamic vascular occlusion syndromes such as bowhunter syndrome (BHS). Nonetheless, concerns about its safety exist and no standardized protocols have been published to date. We describe our methodology and insights regarding the use of dcDSA in patients with BHS. We also perform a systematic literature review to identify cases of typical and atypical presentations of BHS wherein dcDSA was utilized and report on any procedural complications related to dcDSA. Our study included 104 cases wherein dcDSA was used for the diagnosis of BHS. There were 0 reported complications of dcDSA. DcDSA successfully established diagnosis in 102 of these cases. Thirty-eight cases were deemed atypical presentations of BHS. Fourteen patients endorsed symptoms during neck flexion/extension. In eight cases, there was dynamic occlusion of bilateral vertebral arteries during a single maneuver. Three patients had multiple areas of occlusion along a single vertebral artery (VA). An anomalous entry of the VA above the C6 transverse foramen was observed in four patients. One patient had VA occlusion with neutral head position and recanalization upon contralateral lateral head tilt. Our study highlights the safety and diagnostic benefits of dcDSA in characterizing the broad spectrum of BHS pathology encountered in clinical practice. This technique offers a powerful means to evaluate changes in cerebral blood flow and cervical arterial morphology in real time, overcoming the constraints of static imaging methods. Our findings pave the way for further studies on dcDSA to enhance cross-sectional imaging methods for the characterization of BHS and other dynamic vascular occlusion syndromes.

Algorithms for the Reconstruction of Genomic Structures with Proofs of Their Low Polynomial Complexity and High Exactness

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called “structures”, meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity.

Tracing histoplasmosis genomic epidemiology and species occurrence across the USA

ABSTRACT Histoplasmosis is an endemic mycosis in North America frequently reported along the Ohio and Mississippi River Valleys, although autochthonous cases occur in non-endemic areas. In the United States, the disease is provoked by two genetically distinct clades of Histoplasma capsulatum sensu lato, Histoplasma mississippiense (Nam1) and H. ohiense (Nam2). To bridge the molecular epidemiological gap, we genotyped 93 Histoplasma isolates (62 novel genomes) including clinical, environmental, and veterinarian samples from a broader geographical range by whole-genome sequencing, followed by evolutionary and species niche modelling analyses. We show that histoplasmosis is caused by two major lineages, H. ohiense and H. mississippiense; with sporadic cases caused by H. suramericanum in California and Texas. While H. ohiense is prevalent in eastern states, H. mississipiense was found to be prevalent in the central and western portions of the United States, but also geographically overlapping in some areas suggesting that these species might co-occur. Species Niche Modelling revealed that H. ohiense thrives in places with warmer and drier conditions, while H. mississippiense is endemic to areas with cooler temperatures and more precipitation. In addition, we predicted multiple areas of secondary contact zones where the two species co-occur, potentially facilitating gene exchange and hybridization. This study provides the most comprehensive understanding of the genomic epidemiology of histoplasmosis in the USA and lays a blueprint for the study of invasive fungal diseases.

Quality and Quantity of Hydroponic Rice Forage Ciherang and IR64 Varieties at Different Harvest Ages

The purpose of this study is to determine how different harvest ages and varieties affect the quantity and quality of fodder produced by hydroponically cultivated rice plants. The study design was a 2 x 3 factorial, completely randomized design (CRD) experiment with five replications. Tiller count, plant height, plant weight, and graph power were among the characteristics that were observed. The data were analyzed using analysis of variance (ANOVA), and any differences were found using Duncan’s multiple area test. The plant height, number of tillers, plant weight, and dry matter production were all significantly impacted by the harvest age factor, according to the data. The variety factor is significantly influenced by the observation variables. The interaction between the parameters at 60 days of yield after planting (DYAP) has a significant impact on the variables plant height, plant weight, number of tillers, and dry matter production. The IR64 variety outperformed the Ciherang variety, according to the results of the proximate analysis, and the best harvest age in terms of crude protein, crude fiber, and crude fat content was at 40 days after planting, while the best TDN was at 60 DYAP. The IR64 cultivar produced the most biomass at a harvest age of 60 DYAP. In summary, the study’s findings suggest that hydroponically grown paddy forage has great promise in terms of the nutrients it can produce, yet biomass output is still lower than that of cutter grass, while dry matter production is higher than that of fodder.

Light-Responsive Hydrogel Microcrawlers, Powered and Steered with Spatially Homogeneous Illumination.

Sub-millimeter untethered locomoting robots hold promise to radically change multiple areas of human activity such as microfabrication/assembly or health care. To overcome the associated hurdles of such a degree of robot miniaturization, radically new approaches are being adopted, often relying on soft actuating polymeric materials. Here, we present light-driven, crawling microrobots that locomote by a single degree of freedom actuation of their light-responsive tail section. The direction of locomotion is dictated by the robot body design and independent of the spatial modulation of the light stimuli, allowing simultaneous multidirectional motion of multiple robots. Moreover, we present a method for steering such robots by reversibly deforming their front section, using ultraviolet (UV) light as a trigger. The deformation dictates the robot locomotion, performing right- or left-hand turning when the UV is turned on or off respectively. The robots' motion and navigation are not coupled to the position of the light sources, which enables simultaneous locomotion of multiple robots, steering of robots and brings about flexibility with the methods to deliver the light to the place of robot operation.

A self-supervised learning framework based on masked autoencoder for complex wafer bin map classification

Wafer bin map (WBM) automatic classification is one of the critical challenges for semiconductor intelligent manufacturing. Many deep learning-based classification models have performed well in WBM classification, but all require a large amount of labeled data for training. Since real-world WBMs are highly complex and can be labeled correctly only by seasoned engineers, such requirements undermine the practical value of those methods. Several self-supervised learning methods have recently been proposed for WBM to improve classification performance. However, they still require much labeled data for fine-tuning and are only adapted for binary WBM with a single gross failure area. To address these limitations, this study introduces a self-supervised framework based on masked autoencoder (MAE) for complex WBMs with mixed bin signatures and multiple gross failure area patterns. A patchMC encoder is proposed to improve MAE’s representation ability for complex WBMs with mixed bin signatures. Moreover, the pre-trained MAE encoder with a multi-label classifier fine-tuned by labeled WBMs enables a few-shot classification of complex WBMs with multiple gross failure areas. Experimental validation of the proposed method is performed on a real-world complex WBM dataset from Intel Corporation. The results demonstrate that the proposed method can make good use of unlabeled WBMs and reduce the demand for labeled data to a few-shot level and, at the same time, guarantees a classification accuracy of more than 90%. By comparing MAE with other self-supervised learning methods, MAE outperforms other existing self-supervised methods for WBM data.

Littoral cell angioma: An incidental finding in a traumatic spleen

Littoral cell angioma (LCA) of the spleen, a rare vascular tumor, is usually asymptomatic, often discovered incidentally and a majority of them are benign in nature. A 20-year-old boy with a history of blunt abdominal trauma, who underwent splenectomy showed the presence of a capsular tear and variegated outer surface on gross examination of the spleen. On the cut section, multiple nodular areas with areas of hemorrhage were noted. Microscopical examination revealed the proliferation of anastomosing, irregular, tortuous blood-filled channels with plenty of histiocytes in the splenic tissue and extensive areas of hemorrhage, inflammatory cell infiltration, and fibrinoid deposits. Immunohistochemistry revealed positivity for CD31 (endothelial differentiation), CD68, and lysozyme (histiocytic differentiation) but was negative for CD34. The final diagnosis was LCA in a case of ruptured spleen after ruling out angiosarcoma. The possibility of this benign entity to have contributed to the potentially fatal splenic injury cannot be overlooked.

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.

Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.

Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.

Geospatial and multilevel clustering of zero-dose children in Kikwit, Democratic Republic of the Congo in 2022.

Zero-dose children remain highly vulnerable to vaccine-preventable diseases and can sustain transmission even in highly vaccinated populations. The WHO Immunization Agenda 2030 has prioritised reaching out to these children. We assessed the spatial distribution of zero-dose children together with the associated risk factors in a provincial capital in the Democratic Republic of Congo. A cross sectional survey was conducted in the city of Kikwit between September 28 and October 14, 2022. Data were collected both at household and health area level. QGIS and SATscan were used to describe and identify hotspots among zero-dose children, and a mixed effect logistic regression model was used to identify risk factors. Overall, 1,863 children aged 12-23 months were enrolled. Kikwit city had a 16.3% zero-dose prevalence, with significant variation between and within health zones. Two hotspots were identified through geospatial analysis, each spanning multiple health areas. Multilevel analysis revealed significant clustering at health area level and found six associated risk factors. These include the absence of home visits by community health workers (aOR = 1.90), living more than a kilometre from a health centre (aOR = 1.95), the mother's lack of tetanus vaccination (aOR = 3.16), and inability to name a vaccine-preventable disease (aOR = 3.20). However, secondary (aOR = 0.56) or tertiary (aOR = 0.21) education of mothers/guardians and belonging to Bunda (aOR = 0.36) or Mbala (aOR = 0.52) ethnicity reduced the risk of zero-dose. We observed a high prevalence of zero-dose children with a heterogeneous spatial distribution of epidemiological importance. Due to sub-zonal diversity, a health zone approach to reduce zero-dose immunization appears very limited. Zero-dose prevalence was related to the community health workers' home visit, to the distance of residence to a health centre and to household-level factors. Geospatial results could help in targeting priority health areas and communities for vaccination.

Letter from the Editor

Dear Reader, We are excited to share The Columbia University Journal of Global Health Fall 2023 Issue with you. As a journal we strive to uplift diverse perspectives that address the most pressing challenges to health equity faced by communities across the globe. This issue highlights the work of four academics, whose research explores themes of silence and confinement within healthcare systems. Ranging from the impacts of COVID-19 on incarcerated people to the overlooked struggles of obstetric fistulas among Ethiopian women, the articles critically examine the health environments of different marginalized groups. We hope you find their manuscripts to be insightful and thought-provoking. We have continued to host events that promote health and wellbeing in our community, including a food and nutrition event that brought together the expertise of Dr. Jessica Bihuniak, Assistant Professor of Clinical Nutrition in the Department of Nutrition and Food Studies at New York University, and the cooking skills of Columbia Gormand to discuss affordable, nutritious meals for college students. In our journal’s aim of highlighting student voices, we have been increasing our efforts in staff writing and publication. Our staff members have shared their perspectives on public health through our “What is Global Health?” blog and podcast, discussing important topics ranging from community health workers to asbestos in homes. Our most recent podcast features a discussion of careers in scientific research with Dr. Rachel Narehood Austin, Diana T. and P. Roy Vagelos Professor of Chemistry at Barnard College. On our blog, we were able to publish our first bilingual staff article “Obesity in Latine Communities in the U.S: What is at play? / Obesidad en Comunidades Latinas en Estados Unidos: ¿Qué está en juego?”. As we consider how to make our journal more accessible, we hope to continue to share staff perspectives across multiple subject areas and in multiple languages, furthering our journal’s mission of diversity and inclusivity. The past year has been an exciting one for our journal: we published ten new articles that accumulated almost 22,000 downloads and were accessed by over 10,000 site users from 143 countries. We are honored to publish work that has reached such a wide, diverse community, and we hope to continue to expand this impact with the publication of our Fall 2023 Issue. We also recently welcomed two new faculty advisors, Dr. Julianna A. Bol, PhD and Dr. Ana Navas-Acien, MD, PhD, MPH, who will assist our journal in this growth while maintaining the academic rigor that we have always been proud of. Our journal would not be possible without the dedication of our incredible team, which has grown in the past year. We are truly grateful for the support of our faculty advisor Esther Jackson, the resilience of our journal team, the insight of our peer reviewers, and the submissions from our authors. As always, thank you to our readers and listeners for engaging with our work and forming a vital part of The Columbia University Journal of Global Health community. Sincerely, Kairaluchi Oraedu & Ann Phan Co-Editors-in-Chief, The Columbia University Journal of Global Health

STEMM unveiled: Female undergraduates’ journey through gendered stereotypes – A qualitative exploration

Despite significant progress in gender equality, pervasive gender stereotypes and discrimination persist worldwide. These ingrained perceptions, based on gender, contribute to the disadvantage experienced by women in multiple areas of their lives. This is especially evident in female professionals studying and working within male-dominated fields like Science, Technology, Engineering, Mathematics, and Medicine (STEMM), where the representation of women collectively amounts to less than 17% in Australia. Therefore, this study aimed to investigate how female undergraduate students in STEMM fields conceptualised gender-based stereotypes within their academic programs, and how these experiences shaped their outlook on being women in a field that defies traditional gender norms. Employing an exploratory qualitative approach grounded in the social constructionist, and feminist, perspectives, face-to-face semi-structured interviews were carried out with 13 female undergraduates in STEMM disciplines, aged between 19 and 43, from Australian universities. An inductive reflexive thematic analysis of the data led to the construction of four themes that contribute to the comprehension of how female undergraduates recognise and manage prevalent gender-based stereotypes during the early stages of their professional journeys. Participants recognised their gender and its related traits as a drawback to their presence in STEMM, and felt that these attributes did not align, leading to a sense of academic disadvantage. The prevalence of male supremacy within STEMM was acknowledged as originating from the embedded patriarchal system within these fields, granting undeserved advantages to male undergraduates, enabling them to perpetuate a narrative that solely favours them.

Lorazepam Therapy for Canine Status Epilepticus

Canine status epilepticus is naturally occurring epilepsy in dogs, a life-threatening emergency needs to be treated immediately. Prolonged episodes are known to cause damage in multiple areas of brain. For instantly stopping the seizure activity benzodiazepines are chosen as first-line therapy in dogs. The effectiveness of intravenous lorazepam has not yet been well established in veterinary patients. The purpose of the study was to find the seizure free interval in status epilepticus dogs when compared to diazepam. Twelve actively seizing dogs were randomly divided into two groups and administered with intravenous lorazepam @ 0.2mg.kg and diazepam @ 0.5 mg/kg. The parameters like physical parameters, seizure free period, level of consciousness and motor activity were analysed in both the groups. On evaluating the physical parameters there was a significant post ictal temperature when compare to control animals. There was a significant increase in seizure free period when compare to diazepam group. Lorazepam didn’t alter the level of consciousness and motor activity in treated dogs. To conclude, lorazepam significantly increased the seizure free period when compared to diazepam in status epilepticus dogs.

Rodents' visual gamma as a biomarker of pathological neural conditions.

Neural gamma oscillations (indicatively 30-100Hz) are ubiquitous: they are associated with a broad range of functions in multiple cortical areas and across many animal species. Experimental and computational works established gamma rhythms as a global emergent property of neuronal networks generated by the balanced and coordinated interaction of excitation and inhibition. Coherently, gamma activity is strongly influenced by the alterations of synaptic dynamics which are often associated with pathological neural dysfunctions. We argue therefore that these oscillations are an optimal biomarker for probing the mechanism of cortical dysfunctions. Gamma oscillations are also highly sensitive to external stimuli in sensory cortices, especially the primary visual cortex (V1), where the stimulus dependence of gamma oscillations has been thoroughly investigated. Gamma manipulation by visual stimuli tuning is particularly easy in rodents, which have become a standard animal model for investigating the effects of network alterations on gamma oscillations. Overall, gamma in the rodents' visual cortex offers an accessible probe on dysfunctional information processing in pathological conditions. Beyond vision-related dysfunctions, alterations of gamma oscillations in rodents were indeed also reported in neural deficits such as migraine, epilepsy and neurodegenerative or neuropsychiatric conditions such as Alzheimer's, schizophrenia and autism spectrum disorders. Altogether, the connections between visual cortical gamma activity and physio-pathological conditions in rodent models underscore the potential of gamma oscillations as markers of neuronal (dys)functioning.

VIBRANT: spectral profiling for single-cell drug responses.

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.