Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum geometry of the null cone

We present a non-perturbative quantization of gravitational null initial data. Our starting point is the characteristic null initial problem for tetradic gravity with a parity-odd Holst term in the bulk. After a basic review about the resulting Carrollian boundary field theory, we introduce a specific class of impulsive radiative data. This class is defined for a specific choice of relational clock. The clock is chosen in such a way that the shear of the null boundary follows the profile of a step function. The angular dependence is arbitrary. Next, we solve the residual constraints, which are the Raychaudhuri equation and a Carrollian transport equation for an $SL(2,\mathbb{R})$ holonomy. We show that the resulting submanifold in phase space is symplectic. Along each null generator, we end up with a simple mechanical system. The quantization of this system is straightforward. Our basic strategy is to start from an auxiliary Hilbert space with constraints. The physical Hilbert space is the kernel of a constraint, which is a combination of ladder operators. The constraint and its hermitian conjugate are second-class. Solving the constraint amounts to imposing a simple recursion relation for physical states. On the resulting physical Hilbert space, the $SL(2,\mathbb{R})$ Casimir is a Dirac observable. This observable determines the spectrum of the two radiative modes. The area at the initial and final cross sections are Dirac observables as well. They have a discrete spectrum, which agrees with earlier results on this topic.
Education Policy and Intergenerational Income Mobility: Evidence from the Finnish Comprehensive School Reform

This paper estimates the effect of a major education reform on the intergenerational income mobility in Finland. The Finnish comprehensive school reform of 1972-1977 replaced the old two-track school system with a uniform nine-year comprehensive school and significantly reduced the degree of heterogeneity in the Finnish primary and secondary education. We estimate the effect of this reform on the intergenerational income elasticity using a representative sample of males born during 1960-1966. The identification strategy relies on a difference-in-differences approach and exploits the fact that the reform was implemented gradually across country during a six-year period. The results indicate that the reform reduced the intergenerational income elasticity by about seven percentage points.
The smallest mono-unstable, homogeneous convex polyhedron has at least 7 vertices

We prove that every homogeneous convex polyhedron with only one unstable equilibrium (known as a mono-unstable convex polyhedron) has at least $7$ vertices. Although it has been long known that no mono-unstable tetrahedra exist, and mono-unstable polyhedra with as few as $18$ vertices and faces have been constructed, this is the first nontrivial lower bound on the number of vertices for a mono-unstable polyhedron. There are two main ingredients in the proof. We first establish two types of relationships, both expressible as (non-convex) quadratic inequalities, that the coordinates of the vertices of a mono-unstable convex polyhedron must satisfy, taking into account the combinatorial structure of the polyhedron. Then we use numerical semidefinite optimization algorithms to compute easily and independently verifiable, rigorous certificates that the resulting systems of quadratic inequalities (5943 in total) are indeed inconsistent in each case.
An attempt to generate new bridge types from latent space of energy-based model

Use energy-based model for bridge-type innovation. The loss function is explained by the game theory, the logic is clear and the formula is simple and clear. Thus avoid the use of maximum likelihood estimation to explain the loss function and eliminate the need for Monte Carlo methods to solve the normalized denominator. Assuming that the bridge-type population follows a Boltzmann distribution, a neural network is constructed to represent the energy function. Use Langevin dynamics technology to generate a new sample with low energy value, thus a generative model of bridge-type based on energy is established. Train energy function on symmetric structured image dataset of three span beam bridge, arch bridge, cable-stayed bridge, and suspension bridge to accurately calculate the energy values of real and fake samples. Sampling from latent space, using gradient descent algorithm, the energy function transforms the sampling points into low energy score samples, thereby generating new bridge types different from the dataset. Due to unstable and slow training in this attempt, the possibility of generating new bridge types is rare and the image definition of generated images is low.
Optimizing Grid Resilience: A Capacity Reserve Market for High Impact Low Probability Events

This paper addresses the challenges of high-impact low-probability (HILP) events by proposing a novel capacity reserve event market for mobile generation assets, aimed at supporting the transmission network during such incidents. Despite the usefulness of portable generators and mobile energy units in restoring power, there are drawbacks such as environmental impact, finite operation, and complex cost recovery. The proposed market integrates these resources into a dispatch framework based on pre-established contracts, ensuring fair compensation and considering factors like capacity, pricing, and travel distance. Resource owners receive advanced notifications for potential events, allowing them to adjust their bids for cost recovery. Simulations on an IEEE 30-bus case have been conducted to demonstrate the model effectiveness in increasing grid resiliency.
Scenario Predict-then-Optimize for Data-Driven Online Inventory Routing

The real-time joint optimization of inventory replenishment and vehicle routing is essential for cost-efficiently operating one-warehouse, multiple-retailer systems. This is complex, as future demand predictions should capture correlation retailer demand, and based upon such predictions, replenishment and routing decisions must be taken. Traditionally, such decisions are made by either making distributional assumptions or using machine-learning-based point forecasts. The former approach ignores nonstationary demand patterns, while the latter approach only provides a point forecast ignoring the inherent forecast error. Consequently, in practice, service levels often do not meet their targets, and truck fill rates fall short, harming the efficiency and sustainability of daily operations. We propose Scenario Predict-then-Optimize. This fully data-driven approach for online inventory routing consists of two subsequent steps at each real-time decision epoch. The scenario-predict step exploits neural networks, specifically multi-horizon quantile recurrent neural networks, to predict future demand quantiles, upon which we design a scenario sampling approach. The subsequent scenario-optimize step then solves a scenario-based stochastic programming approximation. Results show that our approach outperforms the classic Predict-then-Optimize paradigm and empirical sampling methods. We show this both on synthetic data and real-life data. Our approach is appealing to practitioners. It is fast, does not rely on distributional assumptions, and does not face the burden of single-scenario forecasts. We show it is robust for various demand and cost parameters, enhancing the efficiency and sustainability of daily replenishment and routing decisions. Finally, Scenario Predict-then-Optimize is general and can be easily extended to account for other operational constraints, making it a useful tool in practice.
Investigate the causality and genetic association between migraine and Parkinson's disease

Background: The relationship between migraine and Parkinson's disease (PD) remains controversial. We intend to investigate the causal and genetic association between migraine and PD. Methods: Data related to migraine [any migraine (AM), migraine without aura (MO), and migraine with aura (MA)] and PD were respectively obtained from the latest Genome-wide meta-analysis conducted by the International Headache Genetics Consortium and the International Parkinson's Disease Genomics Consortium. Univariate Mendelian Randomization (UMR) and multivariate MR (MVMR) were conducted to estimate their bidirectional causality, and global/local genetic correlation and tissue expression analyses were utilized to assess their genetic associations. Results: The UMR presented that AM (OR: 1.016, 95% CI: 0.908-1.135, p = 0.785), MO (OR: 0.944, 95% CI: 0.836-1.067, p = 0.358) and MA (OR: 1.006, 95% CI: 0.951, 1.064, p = 0.846) were unlikely to be associated with PD risk. Similarly, the reverse UMR analyses demonstrated that PD was unrelated to the risks of migraine and its subtypes. These null associations were supported by the MVMR when adjusted for coronary heart disease and hypertension. The global genetic correlation analyses presented that AM (rg = -0.061, p = 0.127), MA (rg = -0.047, p = 0.516), MO (rg = -0.063, p = 0.492) were generally not related to PD, and the local genetic correlation analyses shown they were also uncorrelated in any region. Additionally, the tissue expression analyses found they had no shared tissues. Conclusion: This study suggested no causality or association between migraine and PD from the genetic perspective.
The nunatak and tabula rasa hypotheses may be compatible: the European phylogeography of a riparian earthworm

The tabula rasa hypothesis of postglacial immigration supports the notion that species now found in northern European areas must have been recently recolonized from historical refugia. Until the 1960s, however, there was almost complete consensus that disjunctions and endemism in the North Atlantic region of Europe could not be explained without in situ survival during glacial periods (the nunatak hypothesis). Although some earthworms can survive in permafrost and tolerate cold conditions, it is generally believed that most earthworms were eradicated from northern latitudes during the Last Glacial Maximum. To test which hypothesis explains the phylogeography of the riparian and parthenogenetic earthworm Eiseniella tetraedra, we collected 1,640 specimens from 19 different countries in Europe. We examined three molecular markers (COI, 16S and 28S) and their morphology. Eleven lineages were found, nested in five clades. Clade I was more prevalent in cold biogeographical regions such as the continental, the Atlantic or even the Arctic, while clade II was prevalent in Mediterranean regions. We investigated their potential niches through Species Distribution Models, which agreed with the distribution trends. The presence of restricted clades in the Iberian and Scandinavian peninsulas, as well as in Eastern Europe, suggests that these three regions served as refugia during the Last Glacial Maximum. Thus, both hypotheses were necessary to explain the actual distribution of this shore-dwelling earthworm.
Inhibition of ADAM17 increases cytotoxic effect of cisplatin in cervical spheroids and organoids

Background Cervical cancer represents one of the main causes of female, cancer-related mortality worldwide. The majority of cancers are caused by human papillomaviruses such as HPV16 and HPV18. As chemotherapeutic resistance to first-line platinum treatment is still a predominant clinical challenge in advanced cervical cancer, novel treatment options including combinatorial therapies are urgently required to overcome chemotherapeutic resistance. Inhibition of A Disintegrin And Metalloproteinase (ADAM)-family members, heavily involved in tumour progression of a vast range of solid tumours, strongly improved response to chemotherapeutic treatment in other tumour entities including ovarian cancer. Methods We established two- and three-dimensional models derived from three traditional cervical cancer cell lines and ectocervical cancer-derived organoids. Following characterisation, these models were used to investigate their response to cisplatin treatment in the absence and presence of ADAM inhibitors using viability assays and automated live cell imaging. Results The pivotal role of the metalloprotease ADAM17 driving chemotherapy resistance was detectable in all ectocervical cultures irrespective of the model system used, whereas ADAM10 inhibition was predominantly effective only in loosely aggregated spheroids. We showed prominent differences regarding treatment responses between 2D monolayers compared to 3D spheroid and 3D organoid model systems. Particularly, the organoid system, regarded as the closest representation of primary tumours, exhibited reliably the combinatorial effect of ADAM17 inhibition and cisplatin in all three individual donors. Conclusions As two- and three-dimensional models of the same cell lines differ in their responses to chemotherapy it is essential to validate treatment strategies in more advanced model systems representing the patient situation more realistically. Ectocervical organoids showed reliable results regarding treatment responses closely mimicking the primary tumours and could therefore serve as an important tool for personalized medicine in cervical cancer. These findings strengthen the role of ADAM17 as a potential novel target for combinatorial treatments to overcome chemoresistance in cervical cancer.
scATAcat: Cell-type annotation for scATAC-seq data

Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of “marker regions” which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat.
Transverse Aortic COnstriction Multi-omics Analysis (TACOMA) uncovers pathophysiological cardiac molecular mechanisms

Time-course multi-omics data of a murine model of progressive heart failure induced by transverse aortic constriction (TAC) provide insights into the molecular mechanisms that are causatively involved in contractile failure and structural cardiac remodelling. We employ Illumina-based transcriptomics, Nanopore sequencing, and mass spectrometry-based proteomics on samples from the left ventricle (LV) and right ventricle (RV, RNA only) of the heart at 1, 7, 21, and 56 days following TAC and Sham surgery. Here, we present TACOMA, as an interactive web-application that integrates and visualizes transcriptomics and proteomics data collected in a TAC time-course experiment. TACOMA enables users to visualize the expression profile of known and novel genes and protein products thereof. Importantly, we capture alternative splicing events by assessing differential transcript and exon usage as well. Co-expression-based clustering algorithms and functional enrichment analysis revealed overrepresented annotations of biological processes and molecular functions at the protein and gene levels. To enhance data integration, TACOMA synchronizes transcriptomics and proteomics profiles, enabling cross-omics comparisons. With TACOMA (https://shiny.dieterichlab.org/app/tacoma), we offer a rich web-based resource to uncover molecular events and biological processes implicated in contractile failure and cardiac hypertrophy. For example, we highlight: (i) changes in metabolic genes and proteins in the time course of hypertrophic growth and contractile impairment; (ii) identification of RNA splicing changes in the expression of Tpm2 isoforms between RV and LV; and (iii) novel transcripts and genes likely contributing to the pathogenesis of heart failure. We plan to extend these data with additional environmental and genetic models of heart failure to decipher common and distinct molecular changes in heart diseases of different aetiologies. Database URL https://shiny.dieterichlab. org/app/tacoma
Using splines for point spread function calibration at non-uniform depths in localization microscopy

Single-molecule localization microscopy methods extensively leverage the microscope point spread function (PSF) for fitting the molecules. Calibrating an accurate PSF model is especially difficult in the presence of depth-dependent aberrations which alter the PSF shape depending on the imaging depth. The aberrations at depths of a few micrometers become substantial enough to considerably impoverish the conventional calibration methods’ performance. In our work, we propose a novel spline model which enables the depth-dependent PSF model calibration by interpolating between the beads at arbitrary depths. We show that diffspline reduces the PSF intensity overestimation by 67.8 percentage points and underestimation by 21.8 percentage points. Moreover, it eliminates the depth-dependent bias and improves the localization precision two-fold compared to previous approaches.
Hexose transporters CsHT3/16 involved in post-phloem transport and affected cucumber fruit development

Hexoses are crucial for plant growth and fruit development. However, the role of hexose transporters in post-phloem sugar transport and maintenance of cellular sugar homeostasis in rapidly growing fruits, such as cucumber, is not yet fully understood. To clarify the impact of hexose transporters in cucumber fruits, we conducted systematic analyses of their tissue expression, localization, transport characteristics and physiological functions. The study revealed that CsHT3, CsHT12 and CsHT16 are the primary hexose transporter genes expressed in cucumber fruit. During the ovary and young fruit stages, CsHT3 and CsHT16 were located in the SE/CC system, but as the cucumber fruit developed and expanded, both transporters shifted to phloem parenchyma cells. The knock-out mutants of CsHT16 display shorter fruits with a larger circumference, likely due to impaired homeostasis of sugars and hormones. Simultaneously reducing the expression of CsHT3, CsHT12 and CsHT16 leads to decreased fruit size. Conversely, overexpression of CsHT3 results in increased fruit size and higher fruit sugar levels. Our data suggest that CsHT16 plays an important role in maintaining sugar homeostasis to shape the fruit, while CsHT3, CsHT12 and CsHT16 together determine the carbohydrates requirement of the enlarged cucumber fruit.
Comparative Analysis of Biomarkers in Type 2 Diabetes Patients with and without Comorbidities: Insights into the Role of Hypertension and Cardiovascular Disease

Background Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycaemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease. Objectives Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications. Design In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively. Methods Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications. Results MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1β (IL-1β), and 8-isoprostane were significant (p<0.05) for the transition from prediabetes to T2DM, highlighting the importance of mitochondrial involvement. Complement component 5a (C5a) is a biomarker associated with disease progression and comorbidities, with GSH/GSSG, monocyte chemoattractant protein-1 (MCP-1), and 8-isoprostane being the most important biomarkers. Conclusions Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.
Single-cell RNA-seq based elucidation of the antidepressant hyperforin biosynthesis de novo in St. John’s wort

Hyperforin is the compound responsible for the effectiveness of St. John’s wort (Hypericum perforatum) as an antidepressant, but its biosynthesis remains unknown. Gene discovery based on co-expression analysis of bulk RNA-sequencing data or genome mining failed to discover the missing steps in hyperforin biosynthesis. Here we sequenced the tetraploid H. perforatum genome. By single-cell RNA-seq, we identified a distinct type of cells, Hyper cells, wherein hyperforin biosynthesis de novo takes place. Through pathway reconstitution in yeast and tobacco, we identify and characterize four transmembrane prenyltransferases to resolve hyperforin biosynthesis. The hyperforin polycyclic scaffold is created by a reaction cascade involving an irregular isoprenoid coupling and a tandem cyclization. Our findings reveal how and where hyperforin is biosynthesized that enables synthetic-biology reconstitution of the complete pathway. These results deepen our comprehension of specialized metabolism at the cellular level, and we anticipate acceleration of pathway elucidation in plant metabolism.
Undoing the ‘nasty: dissecting touch-sensitive stigma movement (thigmonasty) and its loss in self-pollinating monkeyflowers

Rapid touch-sensitive stigma closure is a novel plant reproductive trait found in hundreds of Lamiales species. The origins, mechanisms, and functions of stigma closure remain poorly understood, but its repeated loss in self-fertilizing taxa and direct tests implicate adaptive roles in animal-mediated cross-pollination. Here, we document several additional losses of stigma closure in monkeyflowers (Mimulus), then use quantitative trait locus (QTL) mapping and gene expression analyses to provide a first glimpse into the genetic and molecular basis of stigma mechanosensing and movement. Variation in stigma closure in hybrids between selfer/non-closer Mimulus nasutus and outcrosser/fast-closer M. guttatus has a moderately complex genetic basis, with four QTLs together explaining ∼70% of parental divergence.Loss of stigma closure in M. nasutus appears genetically independent from other aspects of the floral selfing syndrome and from a parallel loss in M. parishii. Analyses of stylar gene expression in closer M. guttatus, M. nasutus, and a rare M. guttatus non-closer genotype identify functional candidates involved in mechanosensing, turgor regulation, and cell wall remodeling. Together, these analyses reveal a polygenic genetic architecture underlying gain and loss of a novel plant movement, illuminate selfer-outcrosser reproductive divergence, and initiate mechanistic investigations of an unusually visible manifestation of plant intelligence.