Miracle-dz/UniFAR-FTUs huggingface dataset:
Liposome-Encapsulated Eribulin Shows Enhanced Antitumor Activity over Eribulin for Combination Therapy with Anti–PD-1 Antibody

Abstract Eribulin is a microtubule dynamics inhibitor with tumor microenvironment modulation activity such as vascular remodeling activity. Here, we investigated antitumor and immunomodulatory activities of eribulin and its liposomal formulation (eribulin-LF) as monotherapies or in combination with anti–programmed death 1 (PD-1) Ab. The antitumor activity of eribulin or eribulin-LF as monotherapy or in combination with anti–PD-1 Ab was examined in a P-glycoprotein–knockout 4T1 model. Eribulin and eribulin-LF showed stronger antitumor activity in immunocompetent mice compared with immunodeficient mice, indicating that they have immunomodulatory activity that underlies its antitumor activity. Combination therapy of eribulin and eribulin-LF with anti–PD-1 Ab showed antitumor activity, and the combination activity of eribulin-LF with anti–PD-1 Ab was observed at a lower dose and longer interval of administration compared with that using eribulin. To examine the immunomodulatory activity of eribulin and eribulin-LF and its underlying mechanisms, we performed flow cytometry, IHC, and gene expression profiling. IHC and flow cytometry revealed that eribulin-LF increased microvessel density and intratumoral populations of cytotoxic T cells and natural killer cells rather than eribulin. Gene expression profiling demonstrated that eribulin-LF induces IFNγ signaling. Furthermore, IHC also showed that eribulin-LF increased infiltration of CD8-positive cells together with increased CD31-positive cells. Eribulin-LF also increased ICAM-1 expression, which is essential for lymphocyte adhesion to vascular endothelial cells. In conclusion, eribulin showed combination antitumor activity with anti–PD-1 Ab via immunomodulation due to its vascular remodeling activity, and the liposomal formulation showed improved antitumor activity over the standard formulation.
Randomised clinical trial to test the phenolization in sacrococcygeal pilonidal disease

To test the efficacy and safety of phenolization in uncomplicated Sacrococcygeal pilonidal disease (SPD) the phenolization in uncomplicated SPD is feasible and secure in selected patients in observational studies. The greatest benefits are obtained to reduce the length of sick leave (LSL) and complications. Single‐center randomised controlled clinical trial. Patients were recruited at University Hospital of Tarragona Joan XXIII of Spain. Patients were randomised into two treatment groups. All patients with uncomplicated sacrococcygeal disease, localised in the midline and with only 1 fistulous orifice. The patients were randomly assigned to the phenolization group (PhG) or conventional‐surgery group (CsG). Both groups were managed without admission. The main endpoint was the recurrence of sacrococcygeal disease. Secondary endpoints included time of sick leave, complications, and readmission. 124 patients were included in the study. No disease recurrence was observed in either group. Clinical follow‐up was carried out with a mean of 493.8 days (SD 6.59). The LSL was shorter in the PhG (mean 19.63 days, SD 28.15) than in the CSG (43.95 days, SD 38.60). The LSL reduction was −24.31 days (P .002). The phenolization in selected SPD is a safe and feasible procedure in selected patients. This approach could become the standard of care for patients with selected Sacrococcygeal pilonidal.
Visual outcomes and postoperative complications of eyes with dropped lens fragments during cataract surgery: multicenter database study

This multicenter study found worse visual outcomes and higher odds of postoperative macular edema, retinal detachment, and epiretinal membrane surgery with posterior capsule rupture or dropped lens fragments during phacoemulsification. Purpose: To analyze the visual outcomes and postoperative complications of phacoemulsification cataract surgery in eyes with posterior capsule rupture (PCR) and dropped lens fragments (DLFs) in comparison with a reference group with uneventful surgery. Setting: 8 UK National Health Service departments. Design: Retrospective comparative nonrandomized study. Methods: Demographic, medical history, and ocular examination data were automatically extracted from the electronic records. The main outcome variables were postoperative visual acuity (VA), and the development of postoperative cystoid macular edema (CME) as well as rhegmatogenous retinal detachment (RRD) and epiretinal membrane (ERM) requiring surgery. Results: The analysis included 175 589 eyes in the reference group, 2751 eyes in the PCR group, and 519 eyes in the DLF group. During all postoperative intervals, the mean VA in the DLF and PCR groups was significantly worse than the reference group (P < .001). On multivariate analysis, the odds of having a VA ≤0.3 logMAR at 4 to 12 weeks postoperatively among eyes with DLF and PCR were 88% and 73% lower than the reference group (P < .001). More eyes developed CME in the DLF and PCR groups (P < .001). The odds of requiring RRD and ERM surgery were 3.6 and 2.1 times higher in the DLF group, and 1.8 and 1.3 times higher in the PCR group, respectively, as compared with the reference group. Conclusions: Eyes undergoing phacoemulsification complicated by PCR, and more so with DLF, have worse visual outcomes and higher chances of CME, ERM, and RRD when compared with uneventful surgery.
Cross-Sectional Study of Resident-Reported Surgical Experience in Female Pelvic Medicine and Reconstructive Surgery

Importance Comprehensive surgical training is an essential component of obstetrics and gynecology residency, yet subspecialty experience may be declining. Objective The objective of this study was to describe trends in reported obstetrics and gynecology resident experience in female pelvic medicine and reconstructive surgery (FPMRS). Study Design This study was a retrospective analysis of national case log reports from the Accreditation Council for Graduate Medical Education (ACGME) for obstetrics and gynecology residents. Results From 2013 through 2019, ACGME collected data from a median of 241 programs (range, 239–242) with a median of 1,259 residents (range, 1,213–1,286) reporting annually. There was a 26% decrease in resident-reported experience with incontinence and pelvic floor procedures from 74 ± 38 in 2013 to 55 ± 34 in 2019 (P < 0.001). Cystoscopy increased by 19% from 43 ± 25 in 2013 to 51 ± 29 in 2019 (P < 0.001). From 2014 to 2019, vaginal hysterectomy cases decreased by 12% (P < 0.001). Conclusions There was a significant decrease in resident-reported FPMRS surgical experience from 2013 to 2019, while cystoscopy numbers increased. After laparoscopic and vaginal hysterectomies were unbundled in 2013, resident vaginal hysterectomy cases decreased from 2014 to 2019. Given that the ACGME Incontinence and Pelvic Floor designation comprises a wide range of procedures, future investigation is needed to elucidate the gaps in FPMRS-specific surgical training during residency and its implication in comprehensive resident surgical training.
The Flexible Fourier Form and Local Generalised Least Squares De‐Trended Unit Root Tests

In two recent papers Enders and Lee (2009) and Becker, Enders and Lee (2006) provide Lagrange multiplier and ordinary least squares de-trended unit root tests, and stationarity tests, respectively, which incorporate a Fourier approximation element in the deterministic component. Such an approach can prove useful in providing robustness against a variety of breaks in the deterministic trend function of unknown form and number. In this article, we generalize the unit root testing procedure based on local generalized least squares (GLS) de-trending proposed by Elliott, Rothenberg and Stock (1996) to allow for a Fourier approximation to the unknown deterministic component in the same way. We show that the resulting unit root tests possess good finite sample size and power properties and the test statistics have stable non-standard distributions, despite the curious result that their limiting null distributions exhibit asymptotic rank deficiency.
The Informational Value of Pressure-Based Single-Station Proxies for Storm Activity

Air pressure readings and their variations are commonly used to make inferences about storm activity. More precisely, it is assumed that the variation of annual and seasonal statistics of several pressure-based proxies describes changes in the past storm climate qualitatively, an assumption that has yet to be proven. A systematic evaluation of the informational content of five pressure-based proxies for storm activitybased on single-station observations of air pressure is presented. The number of deep lows, lower percentiles of pressure, the frequency of absolute pressure tendencies above certain thresholds, as well as mean values and high percentiles of absolute pressure tendencies is examined. Such an evaluation needs long and homogeneous records ofwind speed, something thatis not availablefrom observations. Consequently,the proxiesare examined by using datasets of ground-level wind speeds and air pressure from the NCEP-driven and spectrally nudged regional model, REMO. The proxiesare gaugedagainst the 95th and 99th percentile time series of ground-level wind speeds to quantify the relation between pressure-based proxies and storminess. These analyses rely on bootstrap and binomial hypothesis testing. The analyses of single-station-based proxies indicate that the proxies are generally linearly linked to storm activity, and that absolute pressure tendencies have the highest informational content. Further, it is investigated as to whether the proxies have the potential for describing storminess over larger areas, also with regard to surface conditions. It is found that absolute pressure tendencies have improved informational value when describing storm activity over larger areas, while low pressure readings do not show improved informational value.
A Deligne-Simpson problem for irregular $G$-connections over $\mathbb{P}^{1}$

We give an algebraic and a geometric criterion for the existence of $G$-connections on $\mathbb{P}^{1}$ with prescribed irregular type with equal slope at $\infty$ (isoclinic) and with regular singularity of prescribed residue at $0$. The algebraic criterion is in terms of an irreducible module of the rational Cherednik algebra, and the geometric criterion is in terms of affine Springer fibers. We use these criteria to give complete solutions to the isoclinic Deligne-Simpson problem for classical groups, and for arbitrary $G$ when the slope at $\infty$ has Coxeter number as the denominator. Among our solutions, we classify the cohomologically rigid connections, and obtain new cases in types $B,C$ and $F_4$.
MusicLM: Generating Music From Text

We introduce MusicLM, a model generating high-fidelity music from text descriptions such as"a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consistent over several minutes. Our experiments show that MusicLM outperforms previous systems both in audio quality and adherence to the text description. Moreover, we demonstrate that MusicLM can be conditioned on both text and a melody in that it can transform whistled and hummed melodies according to the style described in a text caption. To support future research, we publicly release MusicCaps, a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts.
Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data

We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is an expensive task in terms of labor, time, and cost. In contrast to manually annotating all the training samples, separately collecting uni-modal datasets is immensely easier, e.g., a large-scale image dataset and a sentence dataset. We leverage such massive unpaired image and caption data upon standard paired data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples in an adversarial learning fashion, where the joint distribution of image and caption is learned. Our method trains a captioner to learn from a paired data and to progressively associate unpaired data. This approach shows noticeable performance improvement even in challenging scenarios including out-of-task data (i.e., relational captioning, where the target task is different from the unpaired data) and web-crawled data. We also show that our proposed method is theoretically well-motivated and has a favorable global optimal property. Our extensive and comprehensive empirical results both on (1) image-based and (2) dense region-based captioning datasets followed by comprehensive analysis on the scarcely-paired COCO dataset demonstrate the consistent effectiveness of our semisupervised learning method with unpaired data compared to competing methods.
Federated Learning Over Coupled Graphs

Graphs are widely used to represent the relations among entities. When one owns the complete data, an entire graph can be easily built, therefore performing analysis on the graph is straightforward. However, in many scenarios, it is impractical to centralize the data due to data privacy concerns. An organization or party only keeps a part of the whole graph data, i.e., graph data is isolated from different parties. Recently, Federated Learning (FL) has been proposed to solve the data isolation issue, mainly for Euclidean data. It is still a challenge to apply FL on graph data because graphs contain topological information which is notorious for its non-IID nature and is hard to partition. In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers’ communication networks and banks’ transaction networks. We theoretically prove the correctness and security of FedCog. Experimental results demonstrate that our method FedCog significantly outperforms traditional FL methods on graphs. Remarkably, our FedCog improves the accuracy of node classification tasks by up to 14.7%.
QCD’s equation of state from Dyson-Schwinger equations

In this contribution, we summarize a truncation-independent method to compute the equation of state within nonperturbative functional approaches. After demonstrating its viability, the method is applied to solutions obtained from a set of truncated Dyson-Schwinger equations for the quark and gluon propagators of (2+1)-flavor QCD to obtain thermodynamic quantities across the phase diagram of strong-interaction matter.
deepPERFECT: Novel Deep Learning CT Synthesis Method for Expeditious Pancreatic Cancer Radiotherapy

Simple Summary Pancreatic cancer is a devastating disease with more than 60,000 new cases each year and a less than 10 percent 3-year overall survival rate. Radiation therapy is an effective treatment for locally advanced pancreatic cancer. The current clinical RT workflow, however, is lengthy and involves separate image acquisition for diagnostic CT and planning CT, which imposes a huge burden on patients and their caretakers. Moreover, studies have shown a reduction in mortality rate from expeditious radiotherapy treatment courses. Here, we proposed an innovative deep learning solution to adapt the shape of a patient’s body in diagnostic CT to the treatment delivery setup, and consequently, reduce the time to treatment initiation by half. As a result, our method also reduces the time to surgery and greatly decreases the risk of progression for pancreatic cancer. Abstract Major sources of delay in the standard of care RT workflow are the need for multiple appointments and separate image acquisition. In this work, we addressed the question of how we can expedite the workflow by synthesizing planning CT from diagnostic CT. This idea is based on the theory that diagnostic CT can be used for RT planning, but in practice, due to the differences in patient setup and acquisition techniques, separate planning CT is required. We developed a generative deep learning model, deepPERFECT, that is trained to capture these differences and generate deformation vector fields to transform diagnostic CT into preliminary planning CT. We performed detailed analysis both from an image quality and a dosimetric point of view, and showed that deepPERFECT enabled the preliminary RT planning to be used for preliminary and early plan dosimetric assessment and evaluation.
Mobility Analysis of Screw-Based Locomotion and Propulsion in Various Media

Robots “in-the-wild” encounter and must traverse widely varying terrain, ranging from solid ground to granular materials like sand to full liquids. Numerous approaches exist, including wheeled and legged robots, each excelling in specific domains. Screw-based locomotion is a promising approach for multi-domain mobility, leveraged in exploratory robotic designs, including amphibious vehicles and snake robotics. However, unlike other forms of locomotion, there is limited exploration of the models, parameter effects, and efficiency for multi-terrain Archimedes screw locomotion. In this work, we present work towards this missing component in understanding screw-based locomotion: comprehensive experimental results and performance analysis across different media. We designed a mobile test bed for indoor and outdoor experimentation to collect this data. Beyond quantitatively showing the multi-domain mobility of screw-based locomotion, we envision future researchers and engineers using the presented results to design effective screw-based locomotion systems.
A benchmark for toxic comment classification on Civil Comments dataset

Toxic comment detection on social media has proven to be essential for content moderation. This paper compares a wide set of different models on a highly skewed multi-label hate speech dataset. We consider inference time and several metrics to measure performance and bias in our comparison. We show that all BERTs have similar performance regardless of the size, optimizations or language used to pre-train the models. RNNs are much faster at inference than any of the BERT. BiLSTM remains a good compromise between performance and inference time. RoBERTa with Focal Loss offers the best performance on biases and AUROC. However, DistilBERT combines both good AUROC and a low inference time. All models are affected by the bias of associating identities. BERT, RNN, and XLNet are less sensitive than the CNN and Compact Convolutional Transformers.
Convolutional Learning on Simplicial Complexes

We propose a simplicial complex convolutional neural network (SCCNN) to learn data representations on simplicial complexes. It performs convolutions based on the multi-hop simplicial adjacencies via common faces and cofaces independently and captures the inter-simplicial couplings, generalizing state-of-the-art. Upon studying symmetries of the simplicial domain and the data space, it is shown to be permutation and orientation equivariant, thus, incorporating such inductive biases. Based on the Hodge theory, we perform a spectral analysis to understand how SCCNNs regulate data in different frequencies, showing that the convolutions via faces and cofaces operate in two orthogonal data spaces. Lastly, we study the stability of SCCNNs to domain deformations and examine the effects of various factors. Empirical results show the benefits of higher-order convolutions and inter-simplicial couplings in simplex prediction and trajectory prediction.
A Decentralized Policy for Minimization of Age of Incorrect Information in Slotted ALOHA Systems

The Age of Incorrect Information (AoII) is a metric that can combine the freshness of the information available to a gateway in an Internet of Things (IoT) network with the accuracy of that information. As such, minimizing the AoII can allow the operators of IoT systems to have a more precise and up-to-date picture of the environment in which the sensors are deployed. However, most IoT systems do not allow for centralized scheduling or explicit coordination, as sensors need to be extremely simple and consume as little power as possible. Finding a decentralized policy to minimize the AoII can be extremely challenging in this setting. This paper presents a heuristic to optimize AoII for a slotted ALOHA system, starting from a threshold-based policy and using dual methods to converge to a better solution. This method can significantly outperform state-independent policies, finding an efficient balance between frequent updates and a low number of packet collisions.
Multiple stellar populations in Globular Clusters with JWST: A NIRCam view of 47 Tucanae

We use images collected with the near-infrared camera (NIRCam) on board the James Webb Space Telescope and with the Hubble Space Telescope (HST) to investigate multiple populations at the bottom of the main sequence (MS) of 47 Tucanae. The mF115W vs. mF115W − mF322W2 CMD from NIRCam shows that, below the knee, the MS stars span a wide color range, where the majority of M-dwarfs exhibit blue colors, and a tail of stars are distributed toward the red. A similar pattern is observed from the mF160W vs. mF110W − mF160W color-magnitude diagram (CMD) from HST, and multiple populations of M-dwarfs are also visible in the optical mF606W vs. mF606W − mF814W CMD. The NIRCam CMD shows a narrow sequence of faint MS stars with masses smaller than $0.1\, \mathcal {M}_{\odot }$. We introduce a chromosome map of M-dwarfs that reveals an extended first population and three main groups of second-population stars. By combining isochrones and synthetic spectra with appropriate chemical composition, we simulate colors and magnitudes of different stellar populations in the NIRCam filters (at metallicities [Fe/H]=-1.5 and [Fe/H]=-0.75) and identify the photometric bands that provide the most efficient diagrams to investigate the multiple populations in globular clusters. Models are compared with the observed CMDs of 47 Tucanae to constrain M-dwarfs’ chemical composition. Our analysis suggests that the oxygen range needed to reproduce the colors of first- and second-population M-dwarfs is similar to that inferred from spectroscopy of red giants, constraining the proposal that the chemical variations are due to mass transfer phenomena in proto-clusters.