Simultaneous primary thyroid MALT lymphoma and papillary thyroid cancer

The mucosa-associated lymphoid tissue (MALT) lymphoma subtype, specifically extranodal marginal zone B-cell lymphoma, is a rare variant. Within this subtype, primary thyroid MALT lymphoma is an uncommon occurrence. The literature provides limited documentation on thyroid MALT lymphomas, as their prevalence is comparatively lower than in other organ sites. The coexistence of papillary thyroid carcinoma (PTC) and thyroid MALT lymphomas is exceedingly rare. It presents a rare case of primary thyroid MALT lymphoma accompanied by PTC, thyroid lymphoma not being considered before surgery. A 64-year-old female patient, who had been experiencing symptoms related to a substantial thyroid tumor for a duration of three years, she refused to do a needle biopsy before surgery and expressed a preference for surgical resection. Consequently, the patient underwent a total thyroidectomy along with lymphadenectomy of the central compartment. A histological examination subsequently confirmed the presence of papillary thyroid carcinoma (PTC) and mucosa-associated lymphoid tissue (MALT) lymphoma. Due to the favorable response of the MALT lymphoma to local treatment and the absence of metastasis in other organs, no further treatment was administered for the MALT lymphoma following the surgery. Currently, the patient exhibits no signs of tumor recurrence based on ultrasound and laboratory evaluations. We also provide an overview of the clinical findings on PTC and MALT lymphoma patients already reported and discuss the possible treatment strategy

DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection

In real-world applications, an object detector often encounters object instances from new classes and needs to accommodate them effectively. Previous work formulated this critical problem as incremental object detection (IOD), which assumes the object instances of new classes to be fully annotated in incremental data. However, as supervisory signals are usually rare and expensive, the supervised IOD may not be practical for implementation. In this work, we consider a more realistic setting named semi-supervised IOD (SSIOD), where the object detector needs to learn new classes incrementally from a few labelled data and massive unlabelled data without catastrophic forgetting of old classes. A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them. Observing that learning only the classes of interest tends to preclude detection of other classes, we propose to bridge the coexistence of unlabelled classes by constructing two teacher models respectively for old and new classes, and using the concatenation of their predictions to instruct the student. This approach is referred to as DualTeacher, which can serve as a strong baseline for SSIOD with limited resource overhead and no extra hyperparameters. We build various benchmarks for SSIOD and perform extensive experiments to demonstrate the superiority of our approach (e.g., the performance lead is up to 18.28 AP on MS-COCO). Our code is available at \url{https://github.com/chuxiuhong/DualTeacher}

Effective Few-Shot Named Entity Linking by Meta-Learning

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability.Comment: 14 pages, 4 figures. Accepted at IEEE ICDE 202

Effect of an auxiliary acceptor on D–A–π–A sensitizers for highly efficient and stable dye-sensitized solar cells

As one of the promising photovoltaic technologies, high performance metal-free dye-sensitized solar cells (DSSCs) have been explored due to the fact that they can be potentially produced using low-cost materials, their color can be tuned and they exhibit reasonable stability.</p

A Modeling Study of the Responses of Mesosphere and Lower Thermosphere Winds to Geomagnetic Storms at Middle Latitudes

Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM) simulations are diagnostically analyzed to investigate the causes of mesosphere and lower thermosphere (MLT) wind changes at middle latitudes during the 17 April 2002 storm. In the early phase of the storm, middle‐latitude upper thermospheric wind changes are greater and occur earlier than MLT wind changes. The horizontal wind changes cause downward vertical wind changes, which are transmitted to the MLT region. Adiabatic heating and heat advection associated with downward vertical winds cause MLT temperature increases. The pressure gradient produced by these temperature changes and the Coriolis force then drive strong equatorward meridional wind changes at night, which expand toward lower latitudes. Momentum advection is minor. As the storm evolves, the enhanced MLT temperatures produce upward vertical winds. These upward winds then lead to a decreased temperature, which alters the MLT horizontal wind pattern and causes poleward wind disturbances at higher latitudes

On the Responses of Mesosphere and Lower Thermosphere Temperatures to Geomagnetic Storms at Low and Middle Latitudes

Observations from lidars and satellites have shown that large neutral temperature increases and decreases occur in the middle and low latitudes of the mesosphere and lower thermosphere region during geomagnetic storms. Here we undertake first-principles simulations of mesosphere and lower thermosphere temperature responses to storms using the Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model to elucidate the nature and causes of these changes. Temperature variations were not uniform; instead, nighttime temperatures changed earlier than daytime temperatures, and temperatures changed earlier at high latitudes than at low ones. Furthermore, temperatures increased in some places/times and decreased in others. As the simulation behaves similar to observations, it provides an opportunity to understand physical processes that drive the observed changes. Our analysis has shown that they were produced mainly by adiabatic heating/cooling that was associated with vertical winds resulting from general circulation changes, with additional contributions from vertical heat advection

Midlatitude Plasma Bubbles Over China and Adjacent Areas During a Magnetic Storm on 8 September 2017

This paper presents observations of postsunset super plasma bubbles over China and adjacent areas during the second main phase of a storm on 8 September 2017. The signatures of the plasma bubbles can be seen or deduced from (1) deep field‐aligned total electron content depletions embedded in regional ionospheric maps derived from dense Global Navigation Satellite System networks, (2) significant equatorial and midlatitudinal plasma bite‐outs in electron density measurements on board Swarm satellites, and (3) enhancements of ionosonde virtual height and scintillation in local evening associated with strong southward interplanetary magnetic field. The bubbles/depletions covered a broad area mainly within 20°–45°N and 80°–110°E with bifurcated structures and persisted for nearly 5 hr (∼13–18 UT). One prominent feature is that the bubbles extended remarkably along the magnetic field lines in the form of depleted flux tubes, reaching up to midlatitude of around 50°N (magnetic latitude: 45.5°N) that maps to an altitude of 6,600 km over the magnetic equator. The maximum upward drift speed of the bubbles over the magnetic equator was about 700 m/s and gradually decreased with altitude and time. The possible triggering mechanism of the plasma bubbles was estimated to be storm time eastward prompt penetration electric field, while the traveling ionospheric disturbance could play a role in facilitating the latitudinal extension of the depletions.Key PointsPostsunset midlatitude plasma bubbles were observed over China and adjacent areas using GNSS TEC, Swarm Ne, and ionosonde dataThe plasma bubbles were triggered by PPEF and TID in equatorial regions and extended along the magnetic field lines to 50°N (45.5 MLAT)Plasma bubbles might reach an altitude of 6,600 km over the magnetic equator with the upper limit of upward drift speed being around 700 m/sPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143723/1/swe20573.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143723/2/swe20573_am.pd

Estimates on compressed neural networks regression

When the neural element number nn of neural networks is larger than the sample size mm, the overfitting problem arises since there are more parameters than actual data (more variable than constraints). In order to overcome the overfitting problem, we propose to reduce the number of neural elements by using compressed projection AA which does not need to satisfy the condition of Restricted Isometric Property (RIP). By applying probability inequalities and approximation properties of the feedforward neural networks (FNNs), we prove that solving the FNNs regression learning algorithm in the compressed domain instead of the original domain reduces the sample error at the price of an increased (but controlled) approximation error, where the covering number theory is used to estimate the excess error, and an upper bound of the excess error is given

Vitamin C Enhances the Generation of Mouse and Human Induced Pluripotent Stem Cells

SummarySomatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) by defined factors. However, the low efficiency and slow kinetics of the reprogramming process have hampered progress with this technology. Here we report that a natural compound, vitamin C (Vc), enhances iPSC generation from both mouse and human somatic cells. Vc acts at least in part by alleviating cell senescence, a recently identified roadblock for reprogramming. In addition, Vc accelerates gene expression changes and promotes the transition of pre-iPSC colonies to a fully reprogrammed state. Our results therefore highlight a straightforward method for improving the speed and efficiency of iPSC generation and provide additional insights into the mechanistic basis of the reprogramming process