1,859 research outputs found
Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow
This paper considers the noisy sparse phase retrieval problem: recovering a
sparse signal from noisy quadratic measurements , , with independent sub-exponential
noise . The goals are to understand the effect of the sparsity of
on the estimation precision and to construct a computationally feasible
estimator to achieve the optimal rates. Inspired by the Wirtinger Flow [12]
proposed for noiseless and non-sparse phase retrieval, a novel thresholded
gradient descent algorithm is proposed and it is shown to adaptively achieve
the minimax optimal rates of convergence over a wide range of sparsity levels
when the 's are independent standard Gaussian random vectors, provided
that the sample size is sufficiently large compared to the sparsity of .Comment: 28 pages, 4 figure
Stimuli-Responsive Prodrug Nanomaterials for Combination Therapy of Cancer
Cancer is a challenging disease to cure. Current treatment methods mainly include chemotherapy and phototherapy. Chemotherapy drugs, due to their high toxicity and systemic distribution, still cause great suffering for cancer patients. Moreover, the clinical efficacy of single-drug treatment is limited due to the complex pathogenesis of malignant tumors and multi-drug resistance often exhibited by tumor cells. To address these challenges, combination therapy with multiple drugs or multiple treatment modalities is widely used to treat various malignancies. In parallel, the rapid development of nanotechnology has promoted the application of nanomedicines in combination chemotherapy. Still, although nanodrugs can increase the drug concentration in the tumor area, the residual nanodrugs in the liver and kidneys still pose a huge threat to human health. Prodrugs are pharmacologically inactive drugs or compounds that are metabolized into pharmacologically active drugs after ingestion by the human body. Prodrug treatment strategies have become an exploratory direction to address the side effects of chemical therapy. Nanomedicine-based prodrugs can be prepared to improve targeting efficiency by using cancer cell targeting ligands and respond to the slightly acidic and reducing microenvironment of the tumor by using different chemical bonds, which can improve the antitumor effect and reduce the toxic side effects on healthy tissues.
In this thesis, we designed prodrug-based nanomaterials and studied the antitumor effects of the prodrugs using different drug delivery systems. First, reduction-sensitive paclitaxel (PTX) prodrugs were synthesized as connecting units to achieve synergistic treatment of cancer chemotherapy and photodynamic therapy using reactive oxygen species to treat cancer cells. The self-assembled nanoparticles of PTX prodrugs were formed by connecting PTX with different chemotherapeutic drugs or photosensitizers. This strategy effectively overcame drug resistance and exhibited enhanced antitumor effects in vivo with low toxicity. Second, using a combination of bionic and prodrug technologies, a cancer cell-targeted drug delivery system based on mesoporous silica nanoparticles (MSNs) encapsulated by cancer cell membranes was designed. This system demonstrated synergistically enhanced anticancer effects in cellular experiments. In summary, this thesis has provided new ideas for improving the shortcomings of traditional combined chemotherapy and photodynamic therapy, realizing the synchronous delivery and controlled release of different antitumor drugs, enhancing the synergy of drugs, and improving the efficacy of inhibiting tumor proliferation and metastasis.Cancer Ă€r en utmanande sjukdom att bota. Nuvarande behandlingsmetoder inkluderar frĂ€mst kemoterapi och fototerapi. KemoterapilĂ€kemedel orsakar fortfarande stort lidande för cancerpatienter pĂ„ grund av sin höga toxicitet och systemiska distribution. Dessutom Ă€r den kliniska effekten av behandling med ett enda lĂ€kemedel begrĂ€nsad pĂ„ grund av den komplexa patogenesen av maligna tumörer samt multilĂ€kemedelsresistens som ofta uppvisas av tumörceller. För att hantera dessa utmaningar anvĂ€nds i stor utstrĂ€ckning kombinationsterapi med flera lĂ€kemedel eller flera behandlingsmetoder för att behandla olika maligniteter. Parallellt har den snabba utvecklingen av nanoteknik frĂ€mjat tillĂ€mpningen av nanolĂ€kemedel i kombinationskemoterapi. Ăven om nanolĂ€kemedel kan öka lĂ€kemedelskoncentrationen i tumöromrĂ„det, utgör lĂ€kemedlsrester i levern och njurarna fortfarande ett hot mot mĂ€nniskors hĂ€lsa. Prodroger Ă€r farmakologiskt inaktiva lĂ€kemedel eller föreningar som metaboliseras till farmakologiskt aktiva lĂ€kemedel efter intag av mĂ€nniskokroppen. Strategier för behandling med prodroger har blivit en explorativ inriktning för att hantera biverkningarna av kemoterapi. Nanomedicin-baserade prodroger kan framstĂ€llas för att förbĂ€ttra mĂ„lsökningseffektiviteten genom att anvĂ€nda cancercellsmĂ„lsökande ligander och endast friĂ€tta den aktiva substansen som en respons pĂ„ tumörens lĂ€tt sura och reducerande mikromiljö genom att anvĂ€nda olika kemiska bindningar, vilket i sin tur kan förbĂ€ttra antitumöreffekten och minska de toxiska biverkningarna pĂ„ friska vĂ€vnader.
I detta examensarbete designade vi prodrog-baserade nanomaterial och studerade antitumöreffekterna av prodrogerna med hjÀlp av olika lÀkemedelsadministrationssystem. Först syntetiserades reduktionskÀnsliga paklitaxel (PTX) prodroger för att uppnÄ synergistisk behandling av cancerkemoterapi och fotodynamisk terapi för att behandla cancerceller. De sjÀlvaggregerande nanopartiklarna av PTX-prodroger bildades genom att koppla PTX med olika kemoterapeutiska lÀkemedel eller fotosensibilisatorer. Denna strategi övervann effektivt lÀkemedelsresistens och uppvisade förbÀttrade antitumöreffekter in vivo med lÄg toxicitet. Sedan designades med hjÀlp av en kombination av bionisk och prodrugteknologi ett cancercellriktat lÀkemedelslevereringssystem baserat pÄ mesoporösa kiseldioxidnanopartiklar inkapslade av cancercellmembran. Detta system visade synergistiskt förbÀttrade anticancereffekter i cellulÀra experiment. Sammanfattningsvis har denna avhandling kommit fram med nya idéer för att förbÀttra bristerna i traditionell kombinationskemoterapi och fotodynamisk terapi, samt förverkliga den samtidiga tillförseln och kontrollerade frisÀttningen av olika antitumörlÀkemedel, och dÀrmed förbÀttra synergin mellan lÀkemedlen och hÀmning av tumörproliferation och metastasering
Interactive Attention Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims at identifying the sentiment
polarity of specific target in its context. Previous approaches have realized
the importance of targets in sentiment classification and developed various
methods with the goal of precisely modeling their contexts via generating
target-specific representations. However, these studies always ignore the
separate modeling of targets. In this paper, we argue that both targets and
contexts deserve special treatment and need to be learned their own
representations via interactive learning. Then, we propose the interactive
attention networks (IAN) to interactively learn attentions in the contexts and
targets, and generate the representations for targets and contexts separately.
With this design, the IAN model can well represent a target and its collocative
context, which is helpful to sentiment classification. Experimental results on
SemEval 2014 Datasets demonstrate the effectiveness of our model.Comment: Accepted by IJCAI 201
Robust Principal Component Analysis?
This paper is about a curious phenomenon. Suppose we have a data matrix,
which is the superposition of a low-rank component and a sparse component. Can
we recover each component individually? We prove that under some suitable
assumptions, it is possible to recover both the low-rank and the sparse
components exactly by solving a very convenient convex program called Principal
Component Pursuit; among all feasible decompositions, simply minimize a
weighted combination of the nuclear norm and of the L1 norm. This suggests the
possibility of a principled approach to robust principal component analysis
since our methodology and results assert that one can recover the principal
components of a data matrix even though a positive fraction of its entries are
arbitrarily corrupted. This extends to the situation where a fraction of the
entries are missing as well. We discuss an algorithm for solving this
optimization problem, and present applications in the area of video
surveillance, where our methodology allows for the detection of objects in a
cluttered background, and in the area of face recognition, where it offers a
principled way of removing shadows and specularities in images of faces
Stable Principal Component Pursuit
In this paper, we study the problem of recovering a low-rank matrix (the
principal components) from a high-dimensional data matrix despite both small
entry-wise noise and gross sparse errors. Recently, it has been shown that a
convex program, named Principal Component Pursuit (PCP), can recover the
low-rank matrix when the data matrix is corrupted by gross sparse errors. We
further prove that the solution to a related convex program (a relaxed PCP)
gives an estimate of the low-rank matrix that is simultaneously stable to small
entrywise noise and robust to gross sparse errors. More precisely, our result
shows that the proposed convex program recovers the low-rank matrix even though
a positive fraction of its entries are arbitrarily corrupted, with an error
bound proportional to the noise level. We present simulation results to support
our result and demonstrate that the new convex program accurately recovers the
principal components (the low-rank matrix) under quite broad conditions. To our
knowledge, this is the first result that shows the classical Principal
Component Analysis (PCA), optimal for small i.i.d. noise, can be made robust to
gross sparse errors; or the first that shows the newly proposed PCP can be made
stable to small entry-wise perturbations.Comment: 5-page paper submitted to ISIT 201
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