Miracle-dz/UniFAR-FTUs huggingface dataset:
Pregnancy outcomes in female multiple sclerosis patients exposed to intramuscular interferon beta-1a or peginterferon beta-1a reported in a German Patient Support Programme – results from the non-interventional post-authorization safety study PRIMA

Background: Based on data from two large cohort studies, a label update became applicable for the class of interferon beta therapies in 9/2019, allowing interferons during pregnancy and breastfeeding. Objective: To assess pregnancy outcomes of women with multiple sclerosis (MS) exposed to peginterferon beta-1a or intramuscular interferon beta-1a therapy (IFN). Design: Non-interventional post-authorization safety study. Methods: PRIMA was conducted from April to October 2021 in Germany. Retrospective pregnancy data were retrieved from adult female patients diagnosed with relapsing-remitting MS or clinically isolated syndrome, exposed to IFN before or during pregnancy and registered in the patient support programme (PSP) of the marketing authorization holder’s MS Service Centre. The primary endpoint was the outcome of pregnancy. Prospective postpartum data were collected from mothers reporting live births. Results: In total, 426 women reporting 542 pregnancies between December 2001 and July 2020 (14 pregnancies after the label update) were enrolled. Among patients with confirmed exposure during pregnancy (N = 362), 306 pregnancies (84.5%) resulted in live births (77.6% without defects, 1.9% with defects and 4.4% preterm). Spontaneous abortion, elective termination and stillbirth were reported in 10.9%, 2.8% and 0.2% of the cases, respectively. Higher rates of spontaneous abortions were reported in women with continuous IFN use. A total of 162 women completed the questionnaire for 192 live births within the prospective study part. Mothers restarted IFN therapy or switched to another disease-modifying therapy postpartum in 51.0% and 14.1% of cases, respectively. 158/192 infants (82.3%) were breastfed [34/158 (21.5%)] during IFN therapy. Postpartum relapse activity was low (mothers of 87.3% of breastfed infants remained relapse-free during lactation). Conclusion: Overall, the prevalence of spontaneous abortions and congenital anomalies of females exposed to IFN exposure before or during pregnancy was within the range reported for the general population. Most mothers paused IFN during pregnancy and breastfeeding. Relapse activity during pregnancy and lactation was observed to be low. These real-world data from a PSP corroborate European and Scandinavian registry data. Trial registration: NCT04655222, EUPAS38347.
Targeted therapies in bladder cancer: signaling pathways, applications, and challenges

Abstract Bladder cancer (BC) is one of the most prevalent malignancies in men. Understanding molecular characteristics via studying signaling pathways has made tremendous breakthroughs in BC therapies. Thus, targeted therapies including immune checkpoint inhibitors (ICIs), antibody–drug conjugates (ADCs), and tyrosine kinase inhibitor (TKI) have markedly improved advanced BC outcomes over the last few years. However, the considerable patients still progress after a period of treatment with current therapeutic regimens. Therefore, it is crucial to guide future drug development to improve BC survival, based on the molecular characteristics of BC and clinical outcomes of existing drugs. In this perspective, we summarize the applications and benefits of these targeted drugs and highlight our understanding of mechanisms of low response rates and immune escape of ICIs, ADCs toxicity, and TKI resistance. We also discuss potential solutions to these problems. In addition, we underscore the future drug development of targeting metabolic reprogramming and cancer stem cells (CSCs) with a deep understanding of their signaling pathways features. We expect that finding biomarkers, developing novo drugs and designing clinical trials with precisely selected patients and rationalized drugs will dramatically improve the quality of life and survival of patients with advanced BC.
Variability of Mars' Seasonal North Polar Cap

Mars’ seasonal north polar cap (hereafter sNPC) underwent year-to-year changes between 2008 and 2015 or Mars years 29–33. The writer monitored changes in the sNPC using images made with the MARCI camera onboard the Mars Reconnaissance Orbiter (MRO). Nine isolated bright spots corresponding to Korolev, Lomonosov, and Louth Craters, the albedo features Ierne, Olympia (or Lemuria), and Cecropia, and three unnamed features were examined. The following was concluded: 1) the sNPC underwent small yearto-year changes between 2008 and 2015 [MY = 29–33], 2) there are no years where all temporary frost features lasted longer than their mean lifespan, and 3) local clouds, dust storms, and winds may affect the location of the sNPC edge.
Dopamine in the prefrontal cortex plays multiple roles in the executive function of patients with Parkinson's disease

Parkinson's disease can affect not only motor functions but also cognitive abilities, leading to cognitive impairment. One common issue in Parkinson's disease with cognitive dysfunction is the difficulty in executive functioning. Executive functions help us plan, organize, and control our actions based on our goals. The brain area responsible for executive functions is called the prefrontal cortex. It acts as the command center for the brain, especially when it comes to regulating executive functions. The role of the prefrontal cortex in cognitive processes is influenced by a chemical messenger called dopamine. However, little is known about how dopamine affects the cognitive functions of patients with Parkinson's disease. In this article, the authors review the latest research on this topic. They start by looking at how the dopaminergic system, is altered in Parkinson's disease with executive dysfunction. Then, they explore how these changes in dopamine impact the synaptic structure, electrical activity, and connection components of the prefrontal cortex. The authors also summarize the relationship between Parkinson's disease and dopamine-related cognitive issues. This information may offer valuable insights and directions for further research and improvement in the clinical treatment of cognitive impairment in Parkinson's disease.
LLM Instruction-Example Adaptive Prompting (LEAP) Framework for Clinical Relation Extraction

Objective: To investigate the demonstration in Large Language Models (LLMs) for clinical relation extraction. We focus on examining two types of adaptive demonstration: instruction adaptive prompting, and example adaptive prompting to understand their impacts and effectiveness. Materials and Methods: The study unfolds in two stages. Initially, we explored a range of demonstration components vital to LLMs clinical data extraction, such as task descriptions and examples, and tested their combinations. Subsequently, we introduced the Instruction-Example Adaptive Prompting (LEAP) Framework, a system that integrates two types of adaptive prompts: one preceding instruction and another before examples. This framework is designed to systematically explore both adaptive task description and adaptive examples within the demonstration. We evaluated the performance of LEAP framework on the DDI and BC5CDR chemical interaction datasets, applying it across LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B. Results: The study revealed that Instruction + Options + Examples and its expanded form substantially raised F1-scores over the standard Instruction + Options mode. LEAP framework excelled, especially with example adaptive prompting that outdid traditional instruction tuning across models. Notably, the MedLLAMA-13b model scored an impressive 95.13 F1 on the BC5CDR dataset with this method. Significant improvements were also seen in the DDI 2013 dataset, confirming the robustness of method in sophisticated data extraction. Conclusion: The LEAP framework presents a promising avenue for refining LLM training strategies, steering away from extensive finetuning towards more contextually rich and dynamic prompting methodologies.
The anomaly of the CMB power with the latest Planck data

The lack of power anomaly is an unexpected feature observed at large angular scales in the CMB maps by the COBE, WMAP and Planck satellites. This signature, which consists in a missing of power with respect to that predicted by the $\Lambda$CDM model, might hint at a new cosmological phase before the standard inflationary era. The main point of this paper is taking the latest Planck polarisation data into account to investigate how CMB polarisation improves the understanding of this feature. With this aim, we apply to the last Planck data, both PR3 (2018) and PR4 (2020) releases, a new class of estimators able to evaluate this anomaly considering temperature and polarisation data both separately and in a jointly way. This is the first time that the PR4 dataset is used to study this anomaly. In order to critically evaluate this feature, taking into account the residuals of known systematic effects present in the Planck datasets, we analyse the cleaned CMB maps using different combinations of sky masks, harmonic range and binning on the CMB multipoles. Our analysis shows that the estimator based only on temperature data confirms the presence of a lack of power with a lower-tail-probability (LTP), depending on the component separation method, $\leq 0.33\%$ and $\leq 1.76\%$, for PR3 and PR4 respectively. To our knowledge the $LTP \leq 0.33\%$ for the PR3 dataset is the lowest one present in the literature obtained from Planck 2018 data considering the Planck confidence mask. We find significant differences between these two datasets when polarisation is taken into account. However, we also show that for the PR3 dataset the inclusion of the subdominant polarisation information provides estimates which are less likely accepted in a $\Lambda$CDM cosmological model than the only-temperature analysis on the whole harmonic-range considered.
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling

In this paper, a rate adaptive geometric constellation shaping (GCS) scheme which is fully backward-compatible with existing state of the art bit-interleaved coded modulation (BICM) systems is proposed and experimentally demonstrated. The system relies on optimization of the positions of the quadrature amplitude modulation (QAM) points on the I/Q plane for maximized achievable information rate, while maintaining quantization and fiber nonlinear noise robustness. Furthermore, `dummy' bits are multiplexed with coded bits before mapping to symbols. Rate adaptivity is achieved by tuning the ratio of coded and `dummy' bits, while maintaining a fixed forward error-correction block and a fixed modulation format size. The points' positions and their labeling are optimized using automatic differentiation. The proposed GCS scheme is compared to a time-sharing hybrid (TH) QAM modulation and the now mainstream probabilistic amplitude shaping (PAS) scheme. The TH without shaping is outperformed for all studied data rates in a simulated linear channel by up to 0.7 dB. In a linear channel, PAS is shown to outperform the proposed GCS scheme, while similar performances are reported for PAS and the proposed GCS in a simulated nonlinear fiber channel. The GCS scheme is experimentally demonstrated in a multi-span recirculating loop coherent optical fiber transmission system with a total distance of up to 3000 km. Near-continuous zero-error flexible throughput is reported as a function of the transmission distance. Up to 1-2 spans of increased reach gains are achieved at the same net data rate w.r. t. conventional QAM. At a given distance, up to 0.79 bits/2D symbol of gain w.r. t. conventional QAM is achieved. In the experiment, similar performance to PAS is demonstrated.
Challenges with unsupervised LLM knowledge discovery

We show that existing unsupervised methods on large language model (LLM) activations do not discover knowledge -- instead they seem to discover whatever feature of the activations is most prominent. The idea behind unsupervised knowledge elicitation is that knowledge satisfies a consistency structure, which can be used to discover knowledge. We first prove theoretically that arbitrary features (not just knowledge) satisfy the consistency structure of a particular leading unsupervised knowledge-elicitation method, contrast-consistent search (Burns et al. - arXiv:2212.03827). We then present a series of experiments showing settings in which unsupervised methods result in classifiers that do not predict knowledge, but instead predict a different prominent feature. We conclude that existing unsupervised methods for discovering latent knowledge are insufficient, and we contribute sanity checks to apply to evaluating future knowledge elicitation methods. Conceptually, we hypothesise that the identification issues explored here, e.g. distinguishing a model's knowledge from that of a simulated character's, will persist for future unsupervised methods.
Risk-Aware Continuous Control with Neural Contextual Bandits

Recent advances in learning techniques have garnered attention for their applicability to a diverse range of real-world sequential decision-making problems. Yet, many practical applications have critical constraints for operation in real environments. Most learning solutions often neglect the risk of failing to meet these constraints, hindering their implementation in real-world contexts. In this paper, we propose a risk-aware decision-making framework for contextual bandit problems, accommodating constraints and continuous action spaces. Our approach employs an actor multi-critic architecture, with each critic characterizing the distribution of performance and constraint metrics. Our framework is designed to cater to various risk levels, effectively balancing constraint satisfaction against performance. To demonstrate the effectiveness of our approach, we first compare it against state-of-the-art baseline methods in a synthetic environment, highlighting the impact of intrinsic environmental noise across different risk configurations. Finally, we evaluate our framework in a real-world use case involving a 5G mobile network where only our approach satisfies consistently the system constraint (a signal processing reliability target) with a small performance toll (8.5% increase in power consumption).
When Contracts Meets Crypto: Exploring Developers' Struggles with Ethereum Cryptographic APIs

To empower smart contracts with the promising capabilities of cryptography, Ethereum officially introduced a set of cryptographic APIs that facilitate basic cryptographic operations within smart contracts, such as elliptic curve operations. However, since developers are not necessarily cryptography experts, requiring them to directly interact with these basic APIs has caused real-world security issues and potential usability challenges. To guide future research and solutions to these challenges, we conduct the first empirical study on Ethereum cryptographic practices. Through the analysis of 91,484,856 Ethereum transactions, 500 crypto-related contracts, and 483 StackExchange posts, we provide the first in-depth look at cryptographic tasks developers need to accomplish and identify five categories of obstacles they encounter. Furthermore, we conduct an online survey with 78 smart contract practitioners to explore their perspectives on these obstacles and elicit the underlying reasons. We find that more than half of practitioners face more challenges in cryptographic tasks compared to general business logic in smart contracts. Their feedback highlights the gap between low-level cryptographic APIs and high-level tasks they need to accomplish, emphasizing the need for improved cryptographic APIs, task-based templates, and effective assistance tools. Based on these findings, we provide practical implications for further improvements and outline future research directions.
DreamTalk: When Expressive Talking Head Generation Meets Diffusion Probabilistic Models

Diffusion models have shown remarkable success in a variety of downstream generative tasks, yet remain under-explored in the important and challenging expressive talking head generation. In this work, we propose a DreamTalk framework to fulfill this gap, which employs meticulous design to unlock the potential of diffusion models in generating expressive talking heads. Specifically, DreamTalk consists of three crucial components: a denoising network, a style-aware lip expert, and a style predictor. The diffusion-based denoising network is able to consistently synthesize high-quality audio-driven face motions across diverse expressions. To enhance the expressiveness and accuracy of lip motions, we introduce a style-aware lip expert that can guide lip-sync while being mindful of the speaking styles. To eliminate the need for expression reference video or text, an extra diffusion-based style predictor is utilized to predict the target expression directly from the audio. By this means, DreamTalk can harness powerful diffusion models to generate expressive faces effectively and reduce the reliance on expensive style references. Experimental results demonstrate that DreamTalk is capable of generating photo-realistic talking faces with diverse speaking styles and achieving accurate lip motions, surpassing existing state-of-the-art counterparts.
Voltammetric detection of chlorite ion using carbon paste sensor modified with N-cetylpyridinium–bentonite

ABSTRACT A voltammetric carbon paste sensor (CPS) was described for the detection of chlorite ion in water analysis. For the construction of the voltammetric CPS, organo-bentonites were synthesised by intercalation of N-cetylpyridinium (NCP) at different amounts into interlayer gap of bentonite. Some features of the obtained organo-bentonites such as structural, thermal and morphological were investigated by using various techniques. Afterwards, the voltammetric behaviour of the solution was investigated with cyclic voltammetry in the potential range of 0.0–1.0 V at a scan rate of 50 mV s−1 based on the obtained organo-bentonites as electroactive material. The characterisation of the prepared organo-bentonites as electroactive material, the effect of the amount of NCP–bentonite (NCP–B) in modified CPS on the 10.0 mM response was tested by chancing the percentage of NCP–B in the range of 2.50–12.50% under identical experimental conditions. The pH of the measurement cell was optimised by using 0.04 M Britton–Robinson buffer in the range of pH 2.0–12.0. Under the optimised conditions, developed sensor exhibited a linear response in the range of 2.0 × 10−5 to 2.0 × 10−4 M chlorite ions concentrations. The detection limit and the limit of quantification were figured out as 6.0 × 10−6 and 2.0 × 10−5 M, respectively. The repeatability and reproducibility of the sensor were calculated as 5.0% and 6.8% (N = 5), respectively. Finally, the sensor was successfully applied for the analysis of bottled water, and the mean recovery was found as 103 ± 3%.
The cost of artificial latency in the PBS context

We present a comprehensive analysis of the implications of artificial latency in the Proposer-Builder Separation framework on the Ethereum network. Focusing on the MEV-Boost auction system, we analyze how strategic latency manipulation affects Maximum Extractable Value yields and network integrity. Our findings reveal both increased profitability for node operators and significant systemic challenges, including heightened network inefficiencies and centralization risks. We empirically validates these insights with a pilot that Chorus One has been operating on Ethereum mainnet. We demonstrate the nuanced effects of latency on bid selection and validator dynamics. Ultimately, this research underscores the need for balanced strategies that optimize Maximum Extractable Value capture while preserving the Ethereum network's decentralization ethos.
Probing the Mott-insulating behavior of Ba$_2$MgReO$_6$ with DFT+DMFT

We investigate the interplay of spin-orbit coupling, electronic correlations, and lattice distortions in the $5d^1$ double perovskite Ba$_2$MgReO$_6$. Combining density-functional theory (DFT) and dynamical mean-field theory (DMFT), we establish the Mott-insulating character of Ba$_2$MgReO$_6$ in both its cubic and tetragonal paramagnetic phases. Despite substantial spin-orbit coupling, its impact on the formation of the insulating state is minimal, consistent with theoretical expectations for $d^1$ systems. We further characterize the electronic properties of the cubic and tetragonal phases by analyzing spectral functions and local occupations in terms of multipole moments centered on the Re sites. Our results confirm the presence of ferroically ordered $z^2$ quadrupoles in addition to the antiferroic $x^2-y^2$-type order. We compare two equivalent but complementary descriptions in terms of either effective Re-\ttg frontier orbitals or more localized atomic-like Re-d and O-p orbitals. The former maps directly on a physically intuitive picture in terms of nominal $d^1$ Re cations, while the latter explicitly demonstrates the role of hybridization with the ligands in the spin-orbit splitting and the formation of the charge quadrupoles around the Re sites. Finally, we compare our DFT+DMFT results with a previous DFT+$U$ study of the tetragonal paramagnetic state. We find good qualitative agreement for the dominant charge quadrupoles, but also notable differences in the corresponding spectral functions, underscoring the need for more comparative studies between these two methods.
On the calibration of neural networks for histological slide-level classification

Deep Neural Networks have shown promising classification performance when predicting certain biomarkers from Whole Slide Images in digital pathology. However, the calibration of the networks' output probabilities is often not evaluated. Communicating uncertainty by providing reliable confidence scores is of high relevance in the medical context. In this work, we compare three neural network architectures that combine feature representations on patch-level to a slide-level prediction with respect to their classification performance and evaluate their calibration. As slide-level classification task, we choose the prediction of Microsatellite Instability from Colorectal Cancer tissue sections. We observe that Transformers lead to good results in terms of classification performance and calibration. When evaluating the classification performance on a separate dataset, we observe that Transformers generalize best. The investigation of reliability diagrams provides additional insights to the Expected Calibration Error metric and we observe that especially Transformers push the output probabilities to extreme values, which results in overconfident predictions.
TMP: Temporal Motion Propagation for Online Video Super-Resolution

Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information, while the strict latency requirement makes accurate and efficient alignment very challenging. Though much progress has been achieved, most of the existing online-VSR methods estimate the motion fields of each frame separately to perform alignment, which is computationally redundant and ignores the fact that the motion fields of adjacent frames are correlated. In this work, we propose an efficient Temporal Motion Propagation (TMP) method, which leverages the continuity of motion field to achieve fast pixel-level alignment among consecutive frames. Specifically, we first propagate the offsets from previous frames to the current frame, and then refine them in the neighborhood, which significantly reduces the matching space and speeds up the offset estimation process. Furthermore, to enhance the robustness of alignment, we perform spatial-wise weighting on the warped features, where the positions with more precise offsets are assigned higher importance. Experiments on benchmark datasets demonstrate that the proposed TMP method achieves leading online-VSR accuracy as well as inference speed. The source code of TMP can be found at https://github.com/xtudbxk/TMP.