Miracle-dz/UniFAR-FTUs huggingface dataset:
Review Base editing therapy forges ahead

CRISPR/Cas-based gene editing is an innovative biotechnology that has revolutionized genetic engineering in recent years. The process involves induction of a double-strand break (DSB) at the desired DNA site and subsequent cellular repair. Two primary mechanisms drive DSB repair: non-homologous end joining and homologous recombination-mediated repair. Non-homologous end joining, the primary mode of DSB repair, is a simple high-efficiency process that is susceptible to errors, and unpredictable nucleotide insertion or deletion mutations. In contrast, point mutations account for more than 50% of human genetic disorders and are the most frequent type of genetic variation in nature. Base editing is a precise gene editing approach in which a single DNA base is substituted without introduction of DSBs or use of a repair template. This technique has promising therapeutic potential in gene therapy, owing to its high efficiency and controllable editing results. Since the invention of the first base editing tools, the technique has rapidly developed and undergone clinical trials. This review summarizes progress in gene therapy through base editing, including DNA and RNA base editing, with particular emphasis on recent clinical trial and preclinical research advancements, current limitations and remaining challenges, and prospects for further research and applications.
Citicoline May Prevent Cognitive Decline in Patients with Cerebrovascular Disease

Introduction Neuroprotective drugs such as citicoline could improve cognitive performance and quality of life. We studied the effect of citicoline treatment and its association with Vascular Risk Factors (VRF) and APOE on cognition in patients with Subjective Cognitive Complaints (SCC) and Mild Cognitive Impairment (MCI). Methods This is an observational and prospective study with citicoline during 12 months follow-up. Eighty-one subjects who met criteria for SCC/MCI, aged 50–75 years with VRF were included and prescribed citicoline 1g/day. Subjects with previous cognitive impairment and any other central nervous system affection were excluded. Wilcoxon Signed Ranks test and paired samples t-test were used to analyze the change in neuropsychological performance. Results Mean age of the sample was 68.2 (SD 6.8) years and 26 (32.09%) were females. Fifteen subjects (24.6%) were APOE-ε4 carriers, fifty-six (76.7%) had hypertension, fifty-eight (79.5%) had dyslipidemia, twenty-one (28.8%) had diabetes mellitus and twenty-six (35.6%) had cardiopathy. Thirty-two (43.8%) subjects were diagnosed as SCC and forty-one (56.16%) as MCI. During the follow-up, Tweny-six patients (81.25%) in the group of SCC remained stable, six subjects (18.8%) converted to MCI. Twelve patients (29.9%) with MCI reverted to SCC and twenty-nine patients (70.7%) remained stable. At follow-up, SCC subjects had an improvement in the global language domain (p=0.03), naming (p<0.001), attention (p=0.01) and visuospatial abilities (p<0.01). MCI group showed an improvement in the screening test (p=0.03), delayed memory (p<0.01), global cognition (p=0.04) and in cognitive flexibility (p=0.03). Presence of APOE-ε4 had no impact on the above findings. Discussion SCC subjects showed an improvement in language and attention domains, while those with MCI performed better after 12 months in total scores of MoCA and RBANS domains, some converting back to SCC. This supports the idea that citicoline may prevent cognitive decline in patients with cognitive deficits.
Refining Remote Sensing precipitation Datasets in the South Pacific: An Adaptive Multi-Method Approach for Calibrating the TRMM Product

Calibration techniques are gaining popularity in climate research for refining numerical model outputs, favored for their relative simplicity and fitness-for-purpose in many climate impact applications. Their range of applicability goes beyond numerical model outputs and can be applied to calibrate remote sensing datasets that can exhibit important biases as compared to in situ meteorological observations. This study presents an adaptive calibration approach specifically designed for calibrat-5 ing the Tropical Rainfall Measuring Mission (TRMM) precipitation product across multiple stations in the South Pacific. The methodology involves the daily classification of the target series into five distinct Weather Types (WTs) capturing the diverse spatio-temporal precipitation patterns in the region. Various quantile mapping (QM) techniques, including empirical (eQM), parametric (pQM), and Generalized Pareto Distribution (gpQM), as well as an ordinary scaling, are applied for each WT. We perform a comprehensive validation by evaluating 10 specific precipitation-related indices that hold significance in impact 10 studies, which are then combined into a single Ranking Framework (RF) score, which offers a comprehensive evaluation of the performance of each calibration method for every Weather Type (WT). These indices are assigned user-defined weights, allowing for a customized assessment of their relative importance to the overall RF score. Our ‘adaptive’ approach selects the best performing method for each WT based on the RF score, yielding an optimally calibrated series. Our findings indicate that the adaptive calibration methodology surpasses standard and weather-type conditioned methods 15 based on a single technique, yielding more accurate calibrated series in terms of mean a extreme precipitation indices consistently across locations. Moreover, this methodology provides the flexibility to customize the calibration process based on user preferences, thereby allowing for specific indices, such as extreme rainfall indicators, to be assigned higher weights. This ability enables the calibration to effectively address the influence of intense rainfall events on the overall distribution. Furthermore, the proposed adaptive method is highly versatile and can be applied to different scenarios, datasets, and regions, provided that 20 a prior weather typing exists to capture the pertinent processes related to regional precipitation patterns. Open-source code and illustrative examples are freely accessible to facilitate the application of the method.
Control of triple-shock configurations in high-speed flows over a cylindrically blunted plate in gases for different Mach numbers

In this article, triple-shock configurations in the problem of the influence of an energy source on a supersonic flow past a plate blunted by a cylinder are studied. The changes of angles in triple-shock configurations depending on an external energy source for different gas mixtures have been investigated for freestream Mach numbers from 3 to 4.2. The dependences of angles in triple-type configurations on the characteristics of energy release have been presented. Complicated shock-vortex structures with multiple reflections of simple waves accompanying these configurations have been obtained. Generation of the Richtmyer–Meshkov instability has been established and mechanism for its generation has been suggested. Conclusion about the possibility of the control of unsteady triple-type shock configurations and accompanying slip streams with the use of an external energy source has been made.
Testing the expanding‐contracting polar cap paradigm

The expanding‐contracting polar cap (ECPC) paradigm is tested. Under the ECPC paradigm ionospheric convection in the polar cap is driven by the combined effects of magnetic field dayside merging and nightside reconnection, as opposed to being mapped down from higher altitudes. The ECPC paradigm is tested by separately examining the cross polar cap potential when the polar cap is expanding versus contracting. The open magnetic flux is estimated from Super Dual Auroral Radar Network (SuperDARN) observations of the convection reversal boundary (CRB) made simultaneously at different local times. Sotirelis et al. (2005) established the CRB as a proxy for the open‐closed boundary. The correlation of the ionospheric convection potential, determined from SuperDARN, with solar wind/interplanetary magnetic field (IMF) driving is indeed found to depend on whether the polar cap is expanding or contracting. Specifically, when the polar cap is expanding, ionospheric convection potential correlates best (0.86) with the most recent 10 min of solar wind/IMF driving (versus 0.57 for contracting). When contracting, convection potential correlates best (0.87) with 90 min averages of solar wind/IMF driving (versus 0.51 for expanding). This result is consistent with the expectations of the ECPC paradigm.
Convergence of Relativistic GANs With Zero-Centered Gradient Penalties

Generative adversarial networks (GANs) are prone to mode collapse and non-convergence issues. Relativistic GANs (RpGANs) have been proposed as an alternative to combat mode collapse, but their convergence behavior remains unexplored. In this paper, we use a toy example to demonstrate that unregularized RpGANs do not converge in general. We then introduce zero-centered gradient penalties to address this issue and establish that regularized RpGANs enjoy the same guarantee of local convergence as the original GANs with zero-centered gradient penalties. We further evaluate the effectiveness of gradient-penalized RpGANs on StackedMNIST, where the model achieves full mode coverage and the smallest KL divergence, while maintaining superior stability throughout the course of training. Overall, our re-sults demonstrate that gradient-penalized RpGANs offer a solution to both mode collapse and non-convergence issues in GANs.
Surface ozone over High-Mountain Asia controlled by stratospheric intrusion

: 31 High-Mountain Asia (HMA) is a global hotspot of stratospheric intrusion, and elevated surface ozone were observed at 32 ground monitoring sites. Still, links between the variability of surface ozone and stratospheric intrusion at regional scale remain 33 unclear. This study synthesized ground measurements of surface ozone over the HMA and analyzed their seasonal variations. 34 The monthly mean surface ozone concentrations peaked earlier in the south in April and later in the north in July over the 35 HMA. The migration of monthly surface ozone peaks was coupled with the synchronous movement of tropopause folding and 36 westerly jet that created a conducive conditions for stratospheric ozone intrusion. Such intrusion contributed ~65% of surface 37 ozone at three typical sites across the HMA. We demonstrated that surface ozone over the HMA is mainly controlled by 38 stratospheric intrusion, which warrants a proper consideration in understanding atmospheric chemistry and impacts of ozone 39 over this highland region and beyond. 40
Quantum Money from Abelian Group Actions

We give a construction of public key quantum money, and even a strengthened version called quantum lightning, from abelian group actions, which can in turn be constructed from suitable isogenies over elliptic curves. We prove security in the generic group model for group actions under a plausible computational assumption, and develop a general toolkit for proving quantum security in this model. Along the way, we explore knowledge assumptions and algebraic group actions in the quantum setting, finding significant limitations of these assumptions/models compared to generic group actions.
Termite diversity and abundance based on altitude in Mount Ungaran, Central Java, Indonesia

Abstract. Subekti N, Milanio RR. 2023. Termite diversity and abundance based on altitude in Mount Ungaran, Central Java, Indonesia. Biodiversitas 24: 3332-3337. Termites are social insects whose main diet is the cellulose and lignocellulose found in rotting wood and in some fungi. As the Mount Ungaran area in Central Java, Indonesia, has been converted to agriculture and other purposes, termite habitat has become increasingly narrow, with a reduction in the insects’ food sources. The purpose of this study was to analyze the species of termites found on Mount Ungaran and the factors that influence their lives. In addition, the differences in species according to slope (north, south, east, and west) and altitude were assessed. This research was conducted in three stages: termite collection, termite identification, and data analysis. The main species found in this study were Coptotermes curvignathus Holmgren, 1913, Odontotermes javanicus Holmgren, 1912, Macrotermes gilvus Hagen, 1858, Nasutitermes matangensis Haviland, 1898, and Bulbitermes constrictiformis Holmgren, 1914. Based on a Shannon-Wiener analysis, the west slope had the highest diversity index, which was primarily influenced by four environmental factors (soil pH, soil temperature, soil moisture, and rainfall). In the canonical relationship correspondence analysis, soil pH, light intensity, and canopy cover were the environmental factors that most affected the diversity and abundance of termites on Mount Ungaran. The conclusions that can be drawn from this study are that termite richness varied between slopes and the environmental factors that were the most associated with the variation were soil pH, light intensity, and canopy cover.
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis

Novel Class Discovery (NCD) aims at inferring novel classes in an unlabeled set by leveraging prior knowledge from a labeled set with known classes. Despite its importance, there is a lack of theoretical foundations for NCD. This paper bridges the gap by providing an analytical framework to formalize and investigate when and how known classes can help discover novel classes. Tailored to the NCD problem, we introduce a graph-theoretic representation that can be learned by a novel NCD Spectral Contrastive Loss (NSCL). Minimizing this objective is equivalent to factorizing the graph's adjacency matrix, which allows us to derive a provable error bound and provide the sufficient and necessary condition for NCD. Empirically, NSCL can match or outperform several strong baselines on common benchmark datasets, which is appealing for practical usage while enjoying theoretical guarantees.
An examination of teacher collaboration in professional learning communities and collaborative teaching practices

The present study aims to examine the influence of teacher collaboration within professional learning communities (PLCs) and collaborative teaching practices on both the professional growth and academic achievement of students. The study employed a mixed-methods research design that incorporated both qualitative and quantitative research methodologies. This research comprises both survey responses and interview transcripts obtained from K-12 educators who are currently engaged in Professional Learning Communities (PLCs) or collaborative teaching methodologies. The findings of this study have the potential to enhance the existing literature on teacher collaboration and furnish educators, policymakers, and researchers with valuable perspectives to steer optimal strategies for promoting effective teacher collaboration in Professional Learning Communities (PLCs) and collaborative teaching approaches. The objective of the research is to investigate the influence of teacher collaboration within Professional Learning Communities (PLCs) and collaborative teaching practices on the development of professional skills and academic achievements of students. The results of this study underscore the importance of recognizing the advantageous effects that collaborative teaching methodologies can have on the academic achievements of students. This necessitates the provision of adequate time for collaborative activities, encouraging supportive leadership within educational institutions, and allocating sufficient resources for collaboration.
Supplementary Material for Cross-modality Feature Fusion Network for Few-Shot 3D Point Cloud Classification

We further studied the effectiveness of the sqMA module by using it in a different network than our proposed one. More specifically, we added our proposed sqMA module to MetaOptNet [1]. The results and the improvement obtained are shown in Table 1. As can be seen, on both the ModelNet40-C and ScanObjectNN datasets, MetaOptNet with the sqMA module achieved higher accuracy in both 5-way 1-shot and 5-way 5-shot settings.
Supervised machine learning for estimating the institutional sectors of legal entities on a large scale 1

The Register of Institutions and Affiliates Data (RIAD) is the European System of Central Banks’ (ESCB) shared register providing master data for more than 10 million legal entities. One of the key RIAD features is the provision of institutional sector classification according to the ESA 2010 methodology. The distinction between different types of financial institutions, non-financial corporations and private versus public sector is of high importance for several ESCB tasks. In fact, information on the institutional sectors is mandatory for all entities in RIAD and is maintained on an on-going basis by experts at National Central Banks and at the European Central Bank. Though, the process of classifying entities by institutional sector is currently manual and time consuming – as necessary to ensure the requested accuracy – and therefore hardly applicable on a large scale, e.g. when a high number of entities need to be imported from external registers. To address this use case, we present an automated, high-quality approach for the bulk classification of entities according to their (ESA) institutional sector. The estimates produced serve as good preliminary information supporting the expert assessment and the final entity classification. The approach is based on supervised machine learning with a two-level-approach. It makes use of publicly available information on legal entities, e.g. their name, residence, registration authority or legal form. We use a hierarchical setup of ensemble methods tailored to suit the business needs for the hierarchy of the ESA institutional sectors. Furthermore, we use deep neural networks to create semantic embeddings for company names which have shown to improve classification performance. The approach has been tested and evaluated on a dataset of approximately 550,000 known entities and it was applied to estimate the institutional sector for nearly 1 million entities not yet in RIAD.
Opinion Forming in Binomial Random Graph and Expanders

Assume for a graph $G=(V,E)$ and an initial configuration, where each node is blue or red, in each discrete-time round all nodes simultaneously update their color to the most frequent color in their neighborhood and a node keeps its color in case of a tie. We study the behavior of this basic process, which is called majority model, on the binomial random graph $\mathcal{G}_{n,p}$ and regular expanders. First we consider the behavior of the majority model in $\mathcal{G}_{n,p}$ with an initial random configuration, where each node is blue independently with probability $p_b$ and red otherwise. It is shown that in this setting the process goes through a phase transition at the connectivity threshold, namely $\frac{\log n}{n}$. Furthermore, we discuss the majority model is a `good' and `fast' density classifier on regular expanders. More precisely, we prove if the second-largest absolute eigenvalue of the adjacency matrix of an $n$-node $\Delta$-regular graph is sufficiently smaller than $\Delta$ then the majority model by starting from $(\frac{1}{2}-\delta)n$ blue nodes (for an arbitrarily small constant $\delta>0$) results in fully red configuration in sub-logarithmically many rounds. As a by-product of our results, we show Ramanujan graphs are asymptotically optimally immune, that is for an $n$-node $\Delta$-regular Ramanujan graph if the initial number of blue nodes is $s\leq \beta n$, the number of blue nodes in the next round is at most $\frac{cs}{\Delta}$ for some constants $c,\beta>0$. This settles an open problem by Peleg.
Reply to Review/comment #3 by Luca Lelli

Quantifying the role of clouds in the Earth (cid:58) ’s radiation budget is essential for improving our understanding of the drivers and feedbacks of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of 5 reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations based on state-of-the-art ground-based remote sensing of clouds are contrasted to surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro-and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic 10 representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, likely erroneous, representation of these low-level clouds in the radiative transfer simulations let (cid:58)(cid:58)(cid:58) led to errors in the cloud radiative effect of 43 W m − 2 . The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving
Contemporary biodiversity pattern is affected by climate change at multiple temporal scales in steppes on the Mongolian Plateau

Abstract. Present and historical climate conditions jointly determine contemporary biodiversity patterns and ecosystem functions. However, it remains unclear how contemporary climate and paleoclimate changes together affect the three dimensions of biodiversity (i.e., taxonomic diversity, functional diversity and phylogenetic diversity) and their relationship with ecosystem functions. Here, we assess the impact of current climate, paleoclimate and its anomalies on contemporary biodiversity and ecosystem functions. We estimated the taxonomic, functional and phylogenetic diversity of grassland on the Mongolian Plateau using vegetation survey data and trait information. We then used random forest and structural equation models to assess the relative importance of the present, the Mid-Holocene and the Last Glacial Maximum climate as well as paleoclimate changes as determinants of diversity and aboveground biomass. Our results showed that paleoclimate changes and modern climate jointly determined contemporary biodiversity patterns, while community biomass was mainly affected by modern climate, namely the aridity index. Modern aridity and temperature were two major influences on all three dimensions of biodiversity. Mid-Holocene climate anomalies had a strong effect on species richness and phylogenetic diversity, while functional diversity had mainly been affected by temperature anomalies since the Last Glacial Maximum. These findings suggest that contemporary biodiversity patterns may be affected by processes at divergent temporal scales. Our results show that simultaneously exploring the response of the three dimensions of biodiversity in different periods of climate change and the theoretical framework for its impact on community biomass is helpful to provide a more comprehensive understanding of patterns of biodiversity and its relationship with ecosystem functions.