Miracle-dz/UniFAR-FTUs huggingface dataset:
Exercise promotes growth and rescues volume deficits in the hippocampus after cranial radiation in young mice

Human and animal studies suggest that exercise promotes healthy brain development and function, including promoting hippocampal growth. Childhood cancer survivors that have received cranial radiotherapy exhibit hippocampal volume deficits and are at risk of impaired cognitive function, thus they may benefit from regular exercise. While morphological changes induced by exercise have been characterized using magnetic resonance imaging (MRI) in humans and animal models, evaluation of changes across the brain through development and following cranial radiation is lacking. In this study, we used high‐resolution longitudinal MRI through development to evaluate the effects of exercise in a pediatric mouse model of cranial radiation. Female mice received whole‐brain radiation (7 Gy) or sham radiation (0 Gy) at an infant equivalent age (P16). One week after irradiation, mice were housed in either a regular cage or a cage equipped with a running wheel. In vivo MRI was performed prior to irradiation, and at three subsequent timepoints to evaluate the effects of radiation and exercise. We used a linear mixed‐effects model to assess volumetric and cortical thickness changes. Exercise caused substantial increases in the volumes of certain brain regions, notably the hippocampus in both irradiated and nonirradiated mice. Volume increases exceeded the deficits induced by cranial irradiation. The effect of exercise and irradiation on subregional hippocampal volumes was also characterized. In addition, we characterized cortical thickness changes across development and found that it peaked between P23 and P43, depending on the region. Exercise also induced regional alterations in cortical thickness after 3 weeks of voluntary exercise, while irradiation did not substantially alter cortical thickness. Our results show that exercise has the potential to alter neuroanatomical outcomes in both irradiated and nonirradiated mice. This supports ongoing research exploring exercise as a strategy for improving neurocognitive development for children, particularly those treated with cranial radiotherapy.
Tidal forcing of basal seismicity of ice stream C

Hydrocarbons are converted by contacting them at hydrocarbon conversion conditions with a novel superactive multimetallic catalytic composite comprising a combination of a catalytically effective amount of a pyrolyzed rhenium carbonyl component with a porous carrier material containing a uniform dispersion of a catalytically effective amount of a platinum group component which is maintained in the elemental metallic state. In a highly preferred embodiment, this novel catalytic composite also contains a catalytically effective amount of a halogen component. The platinum group component, pyrolyzed rhenium carbonyl component and optional halogen component are preferably present in the multimetallic catalytic composite in amounts, calculated on an elemental basis, corresponding to about 0.01 to about 2 wt. % platinum group metal, about 0.01 to abour 5 wt. % rhenium and about 0.1 to about 3,5 wt. % halogen. A key feature associated with the preparation of the subject catalytic composite is reaction of a rhenium carbonyl complex with a porous carrier material containing a uniform dispersion of a platnium group metal maintained in the elemental state, whereby the interaction of the rhenium moiety with the platinum group moiety is maximized due to the platinophilic (i.e. platinum-seeking) propensities of the carbon monoxide ligand used in the rhenium reagent. A specific example of the type of hydrocarbon conversion process disclosed herein is a process for the catalytic reforming of a low octane gasoline fraction wherein the gasoline fraction and a hydrogen stream are contacted with the subject superactive multimetallic catalytic composite art reforming conditions.
Gradient Coding through Iterative Block Leverage Score Sampling

We generalize the leverage score sampling sketch for $\ell_2$-subspace embeddings, to accommodate sampling subsets of the transformed data, so that the sketching approach is appropriate for distributed settings. This is then used to derive an approximate coded computing approach for first-order methods; known as gradient coding, to accelerate linear regression in the presence of failures in distributed computational networks, \textit{i.e.} stragglers. We replicate the data across the distributed network, to attain the approximation guarantees through the induced sampling distribution. The significance and main contribution of this work, is that it unifies randomized numerical linear algebra with approximate coded computing, while attaining an induced $\ell_2$-subspace embedding through uniform sampling. The transition to uniform sampling is done without applying a random projection, as in the case of the subsampled randomized Hadamard transform. Furthermore, by incorporating this technique to coded computing, our scheme is an iterative sketching approach to approximately solving linear regression. We also propose weighting when sketching takes place through sampling with replacement, for further compression.
Short‐term outcomes after sodium‐glucose cotransporter‐2 inhibitor initiation in a cohort of heart failure patients

Sodium‐glucose cotransporter‐2 inhibitors (SGLT2i) decrease mortality and risk of hospitalization in patients with heart failure with reduced ejection fraction (HFrEF). SGLT2i have a natriuretic effect shortly after initiation, followed by a lasting osmotic diuretic effect. We sought to evaluate rates of acute kidney injury (AKI) and therapy discontinuation with SGLT2i initiation in a real‐world cohort of HFrEF patients.
High-temperature superconductivity in A15-type La4H23 below 100 GPa

High-temperature superconductivity has been observed in binary hydrides such as LaH10 at pressures above 150 GPa. Hydrogen cage structures have been identified as a common motif beneficial for high critical temperatures Tc. Efforts are now focused on finding hydride high-temperature superconductors at lower pressures. We present evidence for high-temperature superconductivity in binary La4H23 with A15-type structure featuring hydrogen cages at a pressure of P = 95 GPa. We synthesise La4H23 from a lanthanum film capped with a palladium catalyst promoting the dissociation of hydrogen. In resistance measurements, we observe superconductivity with a transition temperature Tc = 90 K. In X-ray diffraction on the same sample, we identify the A15-type cubic body centred structure of the lanthanum sublattice. From comparison with earlier XRD and structure prediction studies, we identify this phase with La4H23. Our study reinforces the concept of hydrogen cages for high-temperature superconductivity.
Carbon-based single photon emitters in hexagonal boron nitride with triplet ground state

Most single photon emitters in hexagonal boron nitride has been identiﬁed as carbon-based defects. These defects, when forming a donor-acceptor pair have spin S ≤ 12 . By means of density functional calculations, we show that two non-adjacent carbon substitutional defects of the same type ( i.e. C B -C B , and C N -C N ), can have a triplet ground state. In particular, one of such defects has a zero phonon line energy of 2.5 eV, and its triplet state is nearly 0.5 eV more stable than its singlet.
Buds of new bone formation within the Femoral Head of Hip Fracture Patients Coincide with Zones of Low Osteocyte Sclerostin

Romosozumab treatment reduces the rate of hip fractures and increases hip bone density, increasing bone formation by inhibiting sclerostin protein. We studied the normal pattern of bone formation and osteocyte expression in the human proximal femur because it is relevant to both antisclerostin treatment effects and fracture. Having visualized and quantified buds of new bone formation in trabeculae, we hypothesized that they would coincide with areas of (a) higher mechanical stress and (b) low sclerostin expression by osteocytes. In patients with hip fracture, we visualized each bud of active modeling‐based formation (forming minimodeling structure [FMiS]) in trabecular cores taken from different parts of the femoral head. Trabecular bone structure was also measured with high‐resolution imaging. More buds of new bone formation (by volume) were present in the higher stress superomedial zone (FMiS density, N.FMiS/T.Ar) than lower stress superolateral (p < 0.05), and inferomedial (p < 0.001) regions. There were fewer sclerostin expressing osteocytes close to or within FMiS. FMiS density correlated with greater amount, thickness, number, and connectivity of trabeculae (bone volume BV/TV, r = 0.65, p < 0.0001; bone surface BS/TV, r = 0.47, p < 0.01; trabecular thickness Tb.Th, r = 0.55, p < 0.001; trabecular number Tb.N, r = 0.47, p < 0.01; and connectivity density Conn.D, r = 0.40, p < 0.05) and lower trabecular separation (Tb.Sp, r = −0.56, p < 0.001). These results demonstrate modeling‐based bone formation in femoral trabeculae from patients with hip fracture as a potential therapeutic target to enhance bone structure. © 2023 American Society for Bone and Mineral Research (ASBMR).
Treatment Effects in Staggered Adoption Designs with Non-Parallel Trends

This paper considers identifying and estimating causal effect parameters in a staggered treatment adoption setting -- that is, where a researcher has access to panel data and treatment timing varies across units. We consider the case where untreated potential outcomes may follow non-parallel trends over time across groups. This implies that the identifying assumptions of leading approaches such as difference-in-differences do not hold. We mainly focus on the case where untreated potential outcomes are generated by an interactive fixed effects model and show that variation in treatment timing provides additional moment conditions that can be used to recover a large class of target causal effect parameters. Our approach exploits the variation in treatment timing without requiring either (i) a large number of time periods or (ii) requiring any extra exclusion restrictions. This is in contrast to essentially all of the literature on interactive fixed effects models which requires at least one of these extra conditions. Rather, our approach directly applies in settings where there is variation in treatment timing. Although our main focus is on a model with interactive fixed effects, our idea of using variation in treatment timing to recover causal effect parameters is quite general and could be adapted to other settings with non-parallel trends across groups such as dynamic panel data models.
Multiple approaches revealed MGc80‐3 as a somatic hybrid with HeLa cells rather than a gastric cancer cell line

The short‐tandem‐repeats (STR) profiles of MGc80‐3 and HeLa partially overlap, raising suspicion of contamination in the MGc80‐3 cell line. However, there has not been any relevant study demonstrating whether MGc80‐3 was fully replaced by HeLa cells, just mixed with HeLa cells (co‐existing), or was a somatic hybrid with HeLa cells. In addition to STR profiling, various approaches, including single nucleotide polymorphisms genotyping, polymerase chain reaction, screening for human papillomaviruses type 18 (HPV‐18) fragment, chromosome karyotyping, pathological examination of xenografts, tissue‐specific‐90‐gene expression signature and high‐throughput RNA sequencing were used to determine the nature of MGc80‐3. Our study found that the abnormal STR profile, partially overlapping with that of HeLa cells (64.62% to 71.64%), could not verify MGc80‐3 as a HeLa cell line. However, the STR 13.3 repeat allele in the D13S317 locus that seemed to be unique to HeLa cells was detected in MGc80‐3. Almost all the MGc80‐3 cells exhibited HPV‐18 fragments in the genome as well as certain HeLa marker chromosomes, such as M7 and M12. The molecular assay of the 90‐gene expression signature still considered MGc80‐3 as a stomach cancer using an algorithmic analysis. The expression pattern of multiple genes in MGc80‐3 was quite different from that in HeLa cells, which showed that certain characteristics belonged to gastric cancer cell lines. High throughput RNA sequencing showed the distinct patterns of gene expression in MGc80‐3. In conclusion, MGc80‐3 cell line is a somatic hybrid with HeLa cells rather than a pure gastric cancer cell line.
European Cities, Planning Systems and Property Markets

Preface. Introduction. European Cities: the interaction of planning systems, property markets and real estate. Part One: European Heartland. London. Amsterdam. Brussels. Dusseldorf. Paris. Part Two: Northern Europe. Helsinki. Stockholm. Copenhagen. Edinburgh. Dublin. Part Three: Southern Europe. Barcelona. Rome. Athens. Istanbul. Part Four: Eastern Europe. Budapest. Prague. Warsaw. Berlin. Property, planning and European progress. Index.
Generative Adversarial Networks for Stain Normalisation in Histopathology

The rapid growth of digital pathology in recent years has provided an ideal opportunity for the development of artificial intelligence-based tools to improve the accuracy and efficiency of clinical diagnoses. One of the significant roadblocks to current research is the high level of visual variability across digital pathology images, causing models to generalise poorly to unseen data. Stain normalisation aims to standardise the visual profile of digital pathology images without changing the structural content of the images. In this chapter, we explore different techniques which have been used for stain normalisation in digital pathology, with a focus on approaches which utilise generative adversarial networks (GANs). Typically, GAN-based methods outperform non-generative approaches but at the cost of much greater computational requirements. However, it is not clear which method is best for stain normalisation in general, with different GAN and non-GAN approaches outperforming each other in different scenarios and according to different performance metrics. This is an ongoing field of study as researchers aim to identify a method which efficiently and effectively normalises pathology images to make AI models more robust and generalisable.
SOCIAL CONTEXT AND THE EVOLUTION OF DELAYED REPRODUCTION IN BIRDS

Classic life history theory makes generalized predictions about phenotypic correlations across large clades. Modern comparative tests of these correlations account for the underlying structure of phylogenetic trees. Yet neither life history theory nor phylogenetic comparative methods automatically specify how biological mechanisms generate correlations. This problem is evident in comparative analyses of birds. Birds show a correlation between body size and age at first reproduction, but do not actually grow larger if they delay reproduction. Instead, field studies raise the hypothesis that social contexts—especially cooperative breeding, coloniality, and lekking—generate unique demands for behavioral development, which in turn result in delayed reproduction. Here, we support that hypothesis with a comparative dataset spanning 961 species in 155 avian families. Continuous (Ornstein-Uhlenbeck), discrete (hidden state Markov), and phylogenetic regression models revealed delayed reproduction in colonial birds, a weaker signal in cooperative birds, and the consistent evolution of sexual bimaturism in polygynous, lekking birds. These results show an association between diverse social contexts, sex-specific developmental demands, and life history evolution in birds. Considering this diversity, we discuss how even statistically powerful phylogenetic correlations—whether focused on mass, lifespan, or broad social categories—can ultimately fail to model the history of life history evolution.
Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

This report provides an introduction to the Bandicoot C++ library for linear algebra and scientific computing on GPUs, overviewing its user interface and performance characteristics, as well as the technical details of its internal design. Bandicoot is the GPU-enabled counterpart to the well-known Armadillo C++ linear algebra library, aiming to allow users to take advantage of GPU-accelerated computation for their existing codebases without significant changes. Adapting the same internal template meta-programming techniques that Armadillo uses, Bandicoot is able to provide compile-time optimisation of mathematical expressions within user code. The library is ready for production use and is available at https://coot.sourceforge. io. Bandicoot is distributed under the Apache 2.0 License.
Marchiafava-Bignami Disease: CT,MRI and 99m Tc HMPAO-SPECT Findings

We report neuroimaging studies of Marchiafave-Bignami disease (MBD) diagnosed on a 39-year-old alcoholic man with acute clinical deterioration. Initially, cranial CT and MRI demonstrated diffusely edematous change from the genu to the splenium of the corpus callosum (CC) characteristic for acute stage of MBD. Decreased blood perfusion in bilat-eral parietal and occipital lobes was also noted in SPECT study. Prompt treatment with thiamine and vitamin B comples resulted in clinical improvement. 8 months later, follow-up MR imaging showed typ-ical features of chronic MBD—atrophy with cystic change in the CC. To our knowledge, this is the first case of MBD reported in Taiwan literature.
Grid homology for spatial graphs and a K\"{u}nneth formula of connected sum

In this paper, we research the grid homology for spatial graphs with cut edges. We show that the grid homology for spatial graph $f$ is trivial if $f$ has sinks, sources, or cut edges. As an application, we give purely combinatorial proofs of some formulas including a K\"{u}nneth formula for the knot Floer homology of connected sums in the framework of the grid homology.
Maleic acid is a biomarker for maleylacetoacetate isomerase deficiency; implications for newborn screening of tyrosinemia type 1

Dried blood spot succinylacetone (SA) is often used as a biomarker for newborn screening (NBS) for tyrosinemia type 1 (TT1). However, false‐positive SA results are often observed. Elevated SA may also be due to maleylacetoacetate isomerase deficiency (MAAI‐D), which appears to be clinically insignificant. This study investigated whether urine organic acid (uOA) and quantitative urine maleic acid (Q‐uMA) analyses can distinguish between TT1 and MAAI‐D. We reevaluated/measured uOA (GC–MS) and/or Q‐uMA (LC–MS/MS) in available urine samples of nine referred newborns (2 TT1, 7 false‐positive), eight genetically confirmed MAAI‐D children, and 66 controls. Maleic acid was elevated in uOA of 5/7 false‐positive newborns and in the three available samples of confirmed MAAI‐D children, but not in TT1 patients. Q‐uMA ranged from not detectable to 1.16 mmol/mol creatinine in controls (n = 66) and from 0.95 to 192.06 mmol/mol creatinine in false‐positive newborns and MAAI‐D children (n = 10). MAAI‐D was genetically confirmed in 4/7 false‐positive newborns, all with elevated Q‐uMA, and rejected in the two newborns with normal Q‐uMA. No sample was available for genetic analysis of the last false‐positive infant with elevated Q‐uMA. Our study shows that MAAI‐D is a recognizable cause of false‐positive TT1 NBS results. Elevated urine maleic acid excretion seems highly effective in discriminating MAAI‐D from TT1.
Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review

Artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has not been systematically assessed. This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment, as well as to comprehensively evaluate its prediction accuracy. Six electronic databases (PubMed, EBSCOhost, Web of Science, Embase, Cochrane Library, and Scopus) were searched up to March 14, 2023. Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Joanna Briggs Institute (JBI) appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias, while the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) assessment was conducted to evaluate the certainty of outcomes. After screening 2500 studies, four non-randomized clinical trials were finally included for full-text evaluation. We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction, whereas the lower lip and chin seemed to be the least predictable regions. Furthermore, the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true. Since all of the included studies that were not randomized clinical trials (non-RCTs) showed a moderate to high risk of bias, more well-designed clinical trials with sufficient sample size are needed in future work.
Exploiting Code Symmetries for Learning Program Semantics

This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations, where forming a code symmetry group enables precise and efficient reasoning of code semantics. Our solution, SymC, develops a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. SymC obtains superior performance on five program analysis tasks, outperforming state-of-the-art code models, including GPT-4, without any pre-training. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.
Business Tendency Surveys and Macroeconomic Fluctuations

We investigate the information content of business tendency surveys for key macroeconomic variables in Switzerland. To summarise the information of a large data set of sectoral business tendency surveys we extract a small number of common factors by a principal components estimator. The estimator is able to deal with mixed-frequency data and missing observations at the beginning and end of the sample period. We show that these survey-based factors explain a relevant share of the movements of key macroeconomic variables, i.e., CPI inflation, GDP, employment, and an output gap. In particular, questions about the current and future expected situation are informative. However, backward-looking questions, for example questions about the situation compared to the previous year, do not contain additional information. We then examine the economic dimension of the data set. Questions about prices, real activity and capacity constraints contain important information for the corresponding macroeconomic variables. Finally, we estimate a dynamic relationship to produce forecasts for our factors and these key macroeconomic variables. It turns out that the predictive ability of our survey-based factor approach is quite encouraging. In a pseudo out-of-sample exercise, our approach beats relevant benchmarks for forecasting CPI inflation and an output gap and adds information to the benchmark forecasts for GDP and employment.