Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of ...isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dimensionality greater than 1.6*10
were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images.
This paper introduces a wideband millimeter-wave Fabry-Pérot cavity (FPC) antenna with switched-beam radiations for 5G applications. A quasi-curve reflector is used to excite multiresonant modes in ...FPC. This method contributes to a wide operating bandwidth and a high antenna gain. The proposed FPC antenna consists of an SIW-based feeding source, a substrate-integrated quasi-curve reflector, and a partially reflective surface (PRS). For validation, a prototype of the FPC antenna is designed and fabricated with an impedance bandwidth of 24%. The antenna yields a measured gain of 17.6 dBi and a measured directivity of 18.8 dB at 60 GHz. The overall efficiency and aperture efficiency are 76% and 31%, respectively. In addition, a switched-beam antenna based on our proposed PFC antenna is designed and measured. It achieves three switched beams at −18°, 0°, and 18° related to the broadside direction with a realized gain of 16 dBi at 60 GHz. Compared with the other switched-beam FPC antennas, the proposed antenna acquires the characteristics of wideband, high gain, and large beam tilted angle. This antenna technology finds a potential application in millimeter-wave communications such as 5G applications.
Previous methods of designing a bolt supporting network, which depend on engineering experiences, seek optimal bolt supporting schemes in terms of supporting quality. The supporting cost and time, ...however, have not been considered, which restricts their applications in real-world situations. We formulate the problem of designing a bolt supporting network as a three-objective optimization model by simultaneously considering such indicators as quality, economy, and efficiency. Especially, two surrogate models are constructed by support vector regression for roof-to-floor convergence and the two-sided displacement, respectively, so as to rapidly evaluate supporting quality during optimization. To solve the formulated model, a novel interactive preference-based multiobjective evolutionary algorithm is proposed. The highlight of generic methods which interactively articulate preferences is to systematically manage the regions of interest by three steps, that is, "partitioning-updating-tracking" in accordance with the cognition process of human. The preference regions of a decision-maker (DM) are first articulated and employed to narrow down the feasible objective space before the evolution in terms of nadir point, not the commonly used ideal point. Then, the DM's preferences are tracked by dynamically updating these preference regions based on satisfactory candidates during the evolution. Finally, individuals in the population are evaluated based on the preference regions. We apply the proposed model and algorithm to design the bolt supporting network of a practical roadway. The experimental results show that the proposed method can generate an optimal bolt supporting scheme with a good balance between supporting quality and the other demands, besides speeding up its convergence.
Although Short Form (SF)-12 × 2® has been extensively studied and used as a valid measure of health-related quality of life in a variety of population groups, no systematic studies have described the ...reliability of the measure in patients with behavioral conditions or serious mental illness (SMI).
We assessed the internal consistency, split-half reliability and annual test-retest correlations in a sample of 1587 participants with either a combination of physical and behavioral conditions or SMI. The Mosier's alpha was 0.70 for the Physical Composite Scale (PCS) and 0.69 for the Mental Health Composite Scale (MCS), indicating good internal consistency. We observed strong correlations between physical functioning, physical role and body pain scales (r = 0.55-0.56), and between social functioning, emotional role, and mental health (r = 0.53-0.58). We calculated split-half reliabilities to be 0.74 for physical functioning, 0.75 for physical role, 0.73 for emotional role and 0.65 for mental health respectively. We assessed the annual test-retest correlation using intraclass correlation (ICC) and found an ICC of 0.61 for PCS and 0.57 for MCS composite scores, adjusting for age, sex, race/ethnicity, and CRG. We found no decline in the correlations between baseline and the following study years until year 3.
Our results encourage using SF-12v2® to assess health-related quality of life in the Medicaid population with combined physical and behavioral conditions or similar cohorts.
The WIN study was registered with clinicaltrials.gov on April 22, 2015.
NCT02440906 . Retrospectively registered.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Infectious diseases caused by pathogens and food poisoning caused by spoilage microorganisms are threatening human health all over the world. The efficacies of some antimicrobial agents, which are ...currently used to extend shelf-life and increase the safety of food products in food industry and to inhibit disease-causing microorganisms in medicine, have been weakened by microbial resistance. Therefore, new antimicrobial agents that could overcome this resistance need to be discovered. Many spices-such as clove, oregano, thyme, cinnamon, and cumin-possessed significant antibacterial and antifungal activities against food spoilage bacteria like Bacillus subtilis and
, pathogens like Staphylococcus aureus and Vibrio parahaemolyticus, harmful fungi like Aspergillus flavus, even antibiotic resistant microorganisms such as methicillin resistant Staphylococcus aureus. Therefore, spices have a great potential to be developed as new and safe antimicrobial agents. This review summarizes scientific studies on the antibacterial and antifungal activities of several spices and their derivatives.
In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system ...at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables.
An efficient yttrium‐catalyzed intramolecular hydroalkoxylation/Claisen rearrangement sequence has been achieved, thus enabling facile access to a diverse array of valuable medium‐sized lactams. ...Furthermore, a mechanistic rationale for this novel cascade reaction is well supported by a variety of control experiments.
Ring maker: An efficient yttrium‐catalyzed tandem intramolecular hydroxyalkylation/Claisen rearrangement has been achieved, enabling facile access to a diverse array of valuable medium‐sized lactams. A mechanistic rationale for this novel reaction sequence is well supported by a variety of control experiments.
Lithium-sulfur batteries are promising technologies for powering flexible devices due to their high energy density, low cost and environmental friendliness, when the insulating nature, shuttle effect ...and volume expansion of sulfur electrodes are well addressed. Here, we report a strategy of using foldable interpenetrated metal-organic frameworks/carbon nanotubes thin film for binder-free advanced lithium-sulfur batteries through a facile confinement conversion. The carbon nanotubes interpenetrate through the metal-organic frameworks crystal and interweave the electrode into a stratified structure to provide both conductivity and structural integrity, while the highly porous metal-organic frameworks endow the electrode with strong sulfur confinement to achieve good cyclability. These hierarchical porous interpenetrated three-dimensional conductive networks with well confined S
lead to high sulfur loading and utilization, as well as high volumetric energy density.
The surgical procedure in skin‐tumor therapy usually results in cutaneous defects, and multidrug‐resistant bacterial infection could cause chronic wounds. Here, for the first time, an injectable ...self‐healing antibacterial bioactive polypeptide‐based hybrid nanosystem is developed for treating multidrug resistant infection, skin‐tumor therapy, and wound healing. The multifunctional hydrogel is successfully prepared through incorporating monodispersed polydopamine functionalized bioactive glass nanoparticles (BGN@PDA) into an antibacterial F127‐ε‐Poly‐L‐lysine hydrogel. The nanocomposites hydrogel displays excellent self‐healing and injectable ability, as well as robust antibacterial activity, especially against multidrug‐resistant bacteria in vitro and in vivo. The nanocomposites hydrogel also demonstrates outstanding photothermal performance with (near‐infrared laser irradiation) NIR irradiation, which could effectively kill the tumor cell (>90%) and inhibit tumor growth (inhibition rate up to 94%) in a subcutaneous skin‐tumor model. In addition, the nanocomposites hydrogel effectively accelerates wound healing in vivo. These results suggest that the BGN‐based nanocomposite hydrogel is a promising candidate for skin‐tumor therapy, wound healing, and anti‐infection. This work may offer a facile strategy to prepare multifunctional bioactive hydrogels for simultaneous tumor therapy, tissue regeneration, and anti‐infection.
This paper reports an intrinsically multifunctional bioactive hybrid hydrogel for treating multidrug resistant infection, skin‐tumor therapy, and wound healing. The hybrid hydrogels display excellent self‐healing and injectable ability, as well as robust antibacterial activity, especially against multidrug‐resistant bacteria in vitro and in vivo, and also efficiently inhibits tumor growth and enhances wound healing.
The fission of a string connecting two charges is an astounding phenomenon in confining gauge theories. The dynamics of this process have been studied intensively in recent years, with plenty of ...numerical results yielding a dichotomy: the confining string can decay relatively fast or persist up to extremely long times. Here, we put forward a dynamical localization transition as the mechanism underlying this dichotomy. To this end, we derive an effective string breaking description in the light-meson sector of a confined spin chain and show that the problem can be regarded as a dynamical localization transition in Fock space. Fast and suppressed string breaking dynamics are identified with delocalized and localized behavior, respectively. We then provide a further reduction of the dynamical string breaking problem onto a quantum impurity model, where the string is represented as an "impurity" immersed in a meson bath. It is shown that this model features a localization-delocalization transition, giving a general and simple physical basis to understand the qualitatively distinct string breaking regimes. These findings are directly relevant for a wider class of confining lattice models in any dimension and could be realized on present-day Rydberg quantum simulators.
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