Miracle-dz/UniFAR-FTUs huggingface dataset:
Berberine induces apoptosis and arrests the cell cycle in multiple cancer cell lines

Introduction To examine the anti-cancer effects of berberine on multiple cancer cell lines, and to clarify the underlying molecular mechanisms. Material and methods The IC50 values for the action of berberine on Tca8113 (oral squamous cell carcinoma), CNE2 (nasopharyngeal carcinoma cell), MCF-7 (breast cancer), Hela (cervical carcinoma), and HT29 (colon cancer) cells were determined by MTT cell viability assay. Early apoptosis and cell cycle arrest were examined by flow cytometry with annexin V and propidium iodide (PI) staining, respectively. For expression of BAX and BCL-2 genes and proteins were detected by real-time PCR and western blotting, respectively. Results Berberine displayed a cytotoxic effect on all the cell lines tested. The IC50 values were determined (Tca8113, 218.52 ±18.71; CNE2, 249.18 ±18.14; MCF-7, 272.15 ±11.06; Hela, 245.18 ±17.33; and HT29, 52.37 ±3.45). PI staining revealed that berberine treatment resulted in cell cycle arrest at G2/M. The treatment also induced early apoptosis as shown by annexin V staining. In addition, berberine significant elevated gene and protein expression of BAX, which was accompanied by substantial decreases in BCL-2 gene and protein levels. The effects of berberine on BAX and BCL-2 were time-dependent. Conclusions Berberine exhibited cytotoxic effects on multiple cancer cell lines by inducing apoptosis and cell cycle arrest. The BCL-2/BAX signaling pathway may be the common pathway underlying the anti-tumor effect of berberine. The findings support the notion that berberine is a dietary compound that can be further developed into a drug candidate for cancer treatment.
Emotional, informational, and decision-making support needs among people seeking abortion in the US: perspectives from All-Options Talkline callers

Abstract People seeking abortion may need or want emotional or informational support before, during, and after their abortion. Feeling supported and affirmed contributes to perceptions of quality care. The All-Options Talkline offers free, telephone-based, peer counselling to callers anywhere in the United States. This study aimed to explore the types of support received through the Talkline and the ways it supplemented other forms of support received by people who obtained an abortion. Between May 2021 and February 2022, we conducted 30 interviews via telephone or Zoom with callers recruited through the Talkline. We coded the interviews and conducted thematic analysis, focusing on themes related to gaps of support from family, friends, and healthcare professionals, as well as types of support received through the Talkline. We identified four key motivations for calling the Talkline, including the need for (1) decision-making support and validation, (2) a neutral perspective, (3) emotional support to discuss negative or complex feelings, and (4) information about the abortion process. Participants indicated that interactions with family, friends, and healthcare professionals ranged from unsupportive and negative, to substantially supportive. Access to the Talkline was particularly useful prior to clinical interactions and in the weeks or months after an abortion. We found that the All-Options Talkline provided person-centred, remote support for callers, filling gaps or supplementing support from one's community or healthcare professionals. Abortion support from non-medically trained support people contributes to high-quality abortion care, especially in a time of increasing abortion restrictions and use of remote abortion services.
Landslide susceptibility mapping at 1:1M scale over France

(1) CNRS University of Strasbourg, School and Observatory of Earth Sciences, Strasbourg, France (jeanphilippe.malet@eost.u-strasbg.fr), (2) CNRS University of Strasbourg, Laboratory Image, Ville et Environnement, Strasbourg, France, (3) Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy, (4) BGR, Federal Institute for Geosciences and Natural Resources, Hannover, Germany, (5) BRGM, Bureau des Recherches Geologiques et Minieres, Orleans, France
A Gigantic Mid-infrared Outburst in an Embedded Class I Young Stellar Object J064722.95+031644.6

We report the serendipitous discovery of a giant mid-infrared outburst from a previously unknown source near a star-forming region in the constellation Monoceros. The source gradually brightened by a factor of 5 from 2014–2016 before an abrupt rise by a factor of more than 100 in 2017. A total amplitude increase of >500 at 4.5 μm has since faded by a factor of about 10. Prior to the outburst, it was only detected at wavelengths longer than 1.8 μm in UKIDSS, Spitzer, and Herschel with a spectral energy distribution of a Class I young stellar object (YSO). It has not been detected in recent optical surveys, suggesting that it is deeply embedded. With a minimum distance of 3.5 kpc, the source has a bolometric luminosity of at least 9 L ☉ in the quiescent state and 400 L ☉ at the peak of the eruption. The maximum accretion rate is estimated to be at least a few 10−5 M ☉ yr−1. It shares several common properties with another eruptive event, WISE J142238.82-611553.7: exceptionally large amplitude, featureless near-infrared spectrum with the exception of H2 lines, intermediate eruption duration, an embedded Class I YSO, and a low radiative temperature (<600–700 K) in outburst. We interpret that the radiation from the inner accretion disk and young star is obscured and reprocessed by either an inflated outer disk or thick dusty outflow on scales >6.5 au during the outburst.
Differential Diagnostic Value of Machine Learning–Based Models for Embolic Stroke

Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.). The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke.
A Multiband Millimeter-Wave Rectangular Dielectric Resonator Antenna with Omnidirectional Radiation Using a Planar Feed

In this study, a millimeter-wave (mmWave) dielectric resonator antenna (DRA) with an omnidirectional pattern is presented for the first time. A key feature of the proposed design is the utilization of a planar feed network to achieve omnidirectional radiation from a rectangular DRA, which has not been reported previously in the open literature. In addition, the proposed antenna offers multiband operation with different types of radiation patterns. The degenerate TE121/TE211 modes were excited at 28.5 GHz with an overall internal electromagnetic field distribution that was similar to that of the HEM21δ mode of a cylindrical DRA. The achieved omnidirectional bandwidth and gain were 1.9% and 4.3 dBi, respectively. Moreover, broadside radiation was achieved by exciting the TE111 fundamental mode at 17.5 GHz together with the resonance of the feeding ring-slot at 23 GHz. The triple-band operation offers a highly versatile antenna that can be utilized in on-body and off-body communications. Furthermore, the proposed design was validated through measurements, demonstrating good agreement with simulations.
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds

Gaussian processes are used in many machine learning applications that rely on uncertainty quantification. Recently, computational tools for working with these models in geometric settings, such as when inputs lie on a Riemannian manifold, have been developed. This raises the question: can these intrinsic models be shown theoretically to lead to better performance, compared to simply embedding all relevant quantities into $\mathbb{R}^d$ and using the restriction of an ordinary Euclidean Gaussian process? To study this, we prove optimal contraction rates for intrinsic Mat\'ern Gaussian processes defined on compact Riemannian manifolds. We also prove analogous rates for extrinsic processes using trace and extension theorems between manifold and ambient Sobolev spaces: somewhat surprisingly, the rates obtained turn out to coincide with those of the intrinsic processes, provided that their smoothness parameters are matched appropriately. We illustrate these rates empirically on a number of examples, which, mirroring prior work, show that intrinsic processes can achieve better performance in practice. Therefore, our work shows that finer-grained analyses are needed to distinguish between different levels of data-efficiency of geometric Gaussian processes, particularly in settings which involve small data set sizes and non-asymptotic behavior.
A Graphical Model Approach for Switched System with Switch Digraphs

This paper presents a graphical model approach for the stability check of switched systems with switch digraphs. To specify the restricted switching, we introduce the concepts of path-dependent average dwell time (PDADT) and path-dependent average switching frequency (PDASF), which identify three classes of graphical models. First, a complete switch digraph depicting the restricted switching is presented based on the PDADT. Together with the PDASF, a fundamental switch digraph is then developed originally, which is a subgraph of the complete switch digraph and rules out the essential switching paths. Finally, a clustering-based switch digraph can be further constructed by considering the periodic property of the fundamental switch digraph. Then, we derive stability criteria for a switched system under a set of time-dependent switching signals that obey the given switch digraph. By contrast to the existing results encapsulating all the possible inequality conditions, we provide a particular set of inequality conditions to guarantee the stability of the constrained switched system. A numerical example is given to illustrate the analysis.
Native ultrastructure of fresh human brain vitrified directly from autopsy revealed by cryo-electron tomography with cryo-plasma focused ion beam milling

Ultrastructure of human brain tissue has traditionally been examined using electron microscopy (EM) following chemical fixation, staining, and mechanical sectioning, which limit attainable resolution and introduce artifacts. Alternatively, cryo-electron tomography (cryo-ET) offers the potential to image unfixed cellular samples at higher resolution while preserving their native structures, but it requires samples to be frozen free from crystalline ice and thin enough to image via transmission EM. Due to these requirements, cryo-ET has yet to be employed to investigate the native ultrastructure of unfixed, never previously frozen human brain tissue. Here we present a method for generating lamellae in human brain tissue obtained at time of autopsy that can be imaged via cryo-ET. We vitrify the tissue directly on cryo-EM grids via plunge-freezing, as opposed to high pressure freezing which is generally used for thick samples. Following vitrification, we use xenon plasma focused ion beam (FIB) milling to generate lamellae directly on-grid. In comparison to gallium FIB, which is commonly used for biological samples, xenon plasma FIB is powerful enough to efficiently mill large volume samples, such as human brain tissue. Additionally, our approach allows for lamellae to be generated at variable depth inside the tissue as opposed to being limited to starting at the surface of the tissue. Lamellae generated in Alzheimer’s disease brain tissue and imaged by cryo-ET reveal intact subcellular structures including components of autophagy and potential tau fibrils. Furthermore, we visualize myelin revealing intact compact myelin and functional cytoplasmic expansions such as cytoplasmic channels and the inner tongue. From these images we also measure the dimensions of myelin membranes, providing insight into how myelin basic protein forces out oligodendrocyte cytoplasm to form compact myelin and tightly links intracellular polar head groups of the oligodendrocyte plasma membrane. This approach provides a first view of unfixed, never previously frozen human brain tissue prepared by cryo-plasma FIB milling and imaged at high resolution by cryo-ET.
On the Growth of Vapour Bubble in Metastable Liquid as Variational Problem

Bubble growing process is considered theoretically when bubble consists saturated vapour of liquid and when we have vapour-gas bubble on the base of generalized Rayleigh-Plesset equation. For this purpose we use variational method with help of which we seek for those cases corresponding Euler-Poisson equations integral curves. Obtained EP- equations allow us to find extremals of our variational task. In conclusion, the Rayleigh-Taylor instability is considered general case of nonspherical perturbances for spherical bubbles and the case of the radial perturbances of bubbles.
DNA‐dependent phase separation by human SSB2 (NABP1/OBFC2A) protein points to adaptations to eukaryotic genome repair processes

Single-stranded DNA binding proteins (SSBs) are ubiquitous across all domains of life and play essential roles via stabilizing and protecting single-stranded (ss) DNA as well as organizing multiprotein complexes during DNA replication, recombination, and repair. Two mammalian SSB paralogs (hSSB1 and hSSB2 in humans) were recently identified and shown to be involved in various genome maintenance processes. Following our recent discovery of the liquid-liquid phase separation (LLPS) propensity of E. coli (Ec) SSB, here we show that hSSB2 also forms LLPS condensates under physiologically relevant ionic conditions. Similar to that seen for EcSSB, we demonstrate the essential contribution of hSSB2’s C-terminal intrinsically disordered region (IDR) to condensate formation, and the selective enrichment of various genome metabolic proteins in hSSB2 condensates. However, in contrast to EcSSB-driven LLPS that is inhibited by ssDNA binding, hSSB2 phase separation requires single-stranded nucleic acid binding, and is especially facilitated by ssDNA. Our results reveal an evolutionarily conserved role for SSB-mediated LLPS in the spatiotemporal organization of genome maintenance complexes. At the same time, differential LLPS features of EcSSB and hSSB2 point to functional adaptations to prokaryotic versus eukaryotic genome metabolic contexts.
Top-k Distance Queries on Large Time-Evolving Graphs

Fast extraction of top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> distances from graph data is a primitive of paramount importance in the fields of data mining, network analytics and machine learning, where ranked distances are exploited for several purposes (e.g., link prediction or network classification). While investigation on computational methods to address this retrieval task for regularly sized, static inputs has been extensive, much less is known when managed graphs are massive, i.e., having millions of vertices/edges, and time-evolving, i.e., when their structure can grow over time, a scenario that introduces a number of scalability and effectiveness issues otherwise not arising. Since, nowadays, most real-world applications exploiting top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> distances have to handle inherently dynamic and rapidly growing graphs, in this paper we present the first dynamic indexing scheme that supports very fast queries on top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> distances when graphs are massive and incrementally time-evolving. We assess the scalability and effectiveness of our method through extensive experimentation on both real-world and artificial graph datasets.
Secondary Breast Angiosarcoma After a Primary Diagnosis of Breast Cancer

Objectives: Angiosarcoma is a rare complication of breast-conserving therapy. This study evaluated the change in incidence between 1992 and 2016 of secondary breast angiosarcoma (SBA) in patients with a history of breast cancer and the impact of management strategies for the original breast carcinoma on angiosarcoma treatment. Methods: Breast cancer and angiosarcoma cases were abstracted from the Surveillance, Epidemiology, and End Result (SEER) database. SBAs were defined as angiosarcomas located in the breast occurring after a prior breast cancer diagnosis. Primary breast angiosarcomas (PBAs) were defined as an angiosarcoma diagnosis listed as “one primary only. ” Incidence rates were estimated using a proportion of the US total population. Survival was analyzed by the Kaplan-Meier method, and Cox proportional hazard models were used to assess the association of clinicopathologic characteristics on overall survival. Results: Between 1992 and 2016, 193 cases of SBA were reported in the SEER dataset in patients with a prior history of breast cancer. The incidence of breast angiosarcoma in patients with a prior diagnosis of breast cancer increased 3-fold from about 10 cases per 100,000 person-years to about 30 cases per 100,000 person-years over this same period (P=0.0037). For treatment of SBA (n=193), almost all (95%) had surgery. Nine percent received radiation (compared with 35% of patients with PBA, P<0.001) and 23% received chemotherapy (vs. 45% for PBA, P=0.11). Conclusions: We demonstrate an increasing incidence of SBA over the study period. These data can help inform shared decision-making for optimal management of locoregional breast cancer and raise awareness of secondary angiosarcoma.
Digital maps go vertical:: TomTom/Tele Atlas and Nolia/NAVTEQ

The Commission recently issued decisions in two vertical merger cases in the satellite navigation industry. In October 2007, TomTom notified the Commission of its acquisition of Tele Atlas. Four months later Nokia notified its acquisition of NAVTEQ. Both of these cases are important from a policy point of view. TomTom/Tele Atlas and Nokia/NAVTEQ are the first purely vertical second phase investigations after the adoption of the non-horizontal merger guidelines. They provide guidance on how the Commission is going to apply the non-horizontal merger guidelines in future cases, in particular in situations where there is a duopoly upstream and where both upstream players integrate vertically in a short period.1
Sequence-to-Sequence Spanish Pre-trained Language Models

In recent years, significant advancements in pre-trained language models have driven the creation of numerous non-English language variants, with a particular emphasis on encoder-only and decoder-only architectures. While Spanish language models based on BERT and GPT have demonstrated proficiency in natural language understanding and generation, there remains a noticeable scarcity of encoder-decoder models explicitly designed for sequence-to-sequence tasks, which aim to map input sequences to generate output sequences conditionally. This paper breaks new ground by introducing the implementation and evaluation of renowned encoder-decoder architectures exclusively pre-trained on Spanish corpora. Specifically, we present Spanish versions of BART, T5, and BERT2BERT-style models and subject them to a comprehensive assessment across various sequence-to-sequence tasks, including summarization, question answering, split-and-rephrase, dialogue, and translation. Our findings underscore the competitive performance of all models, with the BART- and T5-based models emerging as top performers across all tasks. We have made all models publicly available to the research community to foster future explorations and advancements in Spanish NLP: https://github.com/vgaraujov/Seq2Seq-Spanish-PLMs.
Comparison of three spot proteinuria measurements for pediatric nephrotic syndrome: based on the International pediatric Nephrology Association 2022 Guidelines

Abstract Background Pediatric nephrotic syndrome (NS) requires routine proteinuria monitoring, which is costly and affects patients’ quality of life. The gold-standard 24-h urine protein (UP) measurement is challenging in children, and first-morning urine collection requires specific conditions, making it difficult in outpatient settings. Studies have reported comparability of second or random morning urine sample to the first-morning specimen. This study aimed to compare outcomes of random morning proteinuria measurements to 24-h UP and the roles of the urinary protein creatinine ratio (UPCR) and dipstick tests in pediatric NS, based on International Pediatric Nephrology Association (IPNA) 2022 Guidelines. Method Twenty-four-hour and morning urine samples were collected from 92 pediatric NS patients. These were subjected to automated analyses for 24-h UP, UPCR, and semi-automated dipstick analysis. A blinded doctor performed manual dipstick analysis. Results UPCR had a stronger correlation with 24-h UP than with automated and manual urine dipstick tests. UPCR had the highest sensitivity and specificity for predicting no remission/relapse and high sensitivity but low specificity for complete remission. The optimal UPCR cutoff for remission was 0.44 mg/mg and for no remission/relapse was 2.08 mg/mg. Automated and manual dipstick tests demonstrated limited sensitivity but high specificity and similar AUC values for remission/relapse. Conclusion UPCR was sensitive and specific for diagnosing no remission/relapse and sensitive but not specific for detecting remission. Manual and automated urine dipstick tests were comparable for remission and no remission/relapse detection. This study supports the IPNA 2022 Guidelines, as 2 mg/mg was the optimal UPCR cutoff for no remission/relapse, while for remission the optimal cutoff was 0.4 mg/mg.
Distributed Optimal Coordination Control for Nonlinear Multi-Agent Systems Under Dos Attack

This paper is concerned with the distributed optimal coordination control of nonlinear multi-agent systems (NMASs) under the denial of service (DoS) attacks. The purpose of this paper is to design a distributed control rate for an unknown nonlinear multi-agent system with power interval and time-varying packet loss probability under Dos attack that accurately simulates the impact of DoS attack on NMASs. Then adaptive dynamic programming (ADP) algorithm is used to solve the coupled Hamilton-Jacobi-Bellman (HJB) equation. In order to realize the proposed method, the critic neural network(NN) and actor neural network are used to approximate value functions and control policy. Finally the NN actor-critic policy for optimal control of NMASs under DoS attack with power interval is found. It is proved that the weight estimation error and local neighborhood coordination error are uniformly ultimately bounded (UUB). Finally, the simulation results show the effectiveness of the coordinated control.