BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling

Like masked language modeling (MLM) in natural language processing, masked image modeling (MIM) aims to extract valuable insights from image patches to enhance the feature extraction capabilities of the underlying deep neural network (DNN). Contrasted with other training paradigms like supervised learning and unsupervised contrastive learning, masked image modeling (MIM) pretraining typically demands significant computational resources in order to manage large training data batches (e.g., 4096). The significant memory and computation requirements pose a considerable challenge to its broad adoption. To mitigate this, we introduce a novel learning framework, termed~\textit{Block-Wise Masked Image Modeling} (BIM). This framework involves decomposing the MIM tasks into several sub-tasks with independent computation patterns, resulting in block-wise back-propagation operations instead of the traditional end-to-end approach. Our proposed BIM maintains superior performance compared to conventional MIM while greatly reducing peak memory consumption. Moreover, BIM naturally enables the concurrent training of numerous DNN backbones of varying depths. This leads to the creation of multiple trained DNN backbones, each tailored to different hardware platforms with distinct computing capabilities. This approach significantly reduces computational costs in comparison with training each DNN backbone individually. Our framework offers a promising solution for resource constrained training of MIM

Evaluating the Information Usefulness of Online Health Information for Third-party Patients

Online health interactions (OHIs) can benefit patients, physicians, and society. However, little research has been conducted that studies the social value of OHIs for third-party patients who view previous OHIs concerning similar health issues to theirs. Drawing on the literature on social support and information uncertainty, this study established a theoretical model to explore the roles of treatment information, prevention information, and emotional support in determining information usefulness perceived by third-party patients, and whether such relationships are contingent on information uncertainty. The model was tested using “health questions and answers” textual data from 1,848 OHIs. The results indicate that prevention information and emotional support significantly improve information usefulness perceived by third-party patients. When the level of information uncertainty regarding physicians’ replies is high, the effect of treatment information is strengthened and the effect of emotional support is weakened, indicating both positive and negative contingent roles of information uncertainty. This study has implications for practitioners and also contributes to the literature on online health information, social support, information science, and information uncertainty

Research progress in deep learning in single-photon computed tomography

The research of artificial intelligence deep learning technology has become a hot topic in the field of medical imaging in recent years. As an important branch of nuclear medicine imaging diagnosis， single-photon emission computer tomography （SPECT） has unique advantages in providing reliable image information for clinicians， especially in functional imaging. In this article， the main research directions， values and application status of deep learning in SPECT in recent years were reviewed， the main problems existing in current research were summarized， and the future research was predicted

Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection

The utilization of computer technology to solve problems in medical scenarios has attracted considerable attention in recent years, which still has great potential and space for exploration. Among them, machine learning has been widely used in the prediction, diagnosis and even treatment of Sepsis. However, state-of-the-art methods require large amounts of labeled medical data for supervised learning. In real-world applications, the lack of labeled data will cause enormous obstacles if one hospital wants to deploy a new Sepsis detection system. Different from the supervised learning setting, we need to use known information (e.g., from another hospital with rich labeled data) to help build a model with acceptable performance, i.e., transfer learning. In this paper, we propose a semi-supervised optimal transport with self-paced ensemble framework for Sepsis early detection, called SPSSOT, to transfer knowledge from the other that has rich labeled data. In SPSSOT, we first extract the same clinical indicators from the source domain (e.g., hospital with rich labeled data) and the target domain (e.g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance. On the whole, SPSSOT is an end-to-end transfer learning method for Sepsis early detection which can automatically select suitable samples from two domains respectively according to the number of iterations and align feature space of two domains. Extensive experiments on two open clinical datasets demonstrate that comparing with other methods, our proposed SPSSOT, can significantly improve the AUC values with only 1% labeled data in the target domain in two transfer learning scenarios, MIMIC $rightarrow$ Challenge and Challenge $rightarrow$ MIMIC.Comment: 14 pages, 9 figure

Problematika Penyelesaian Sengketa Kewenangan Lembaga Negara Oleh Mahkamah Konstitusi

According to Article 24C verse (1) of the 1945 Constitution, Constitutional court has an authority to examine the dispute among the state institution in which its authority is given by the constitution directly. But there is a certain problem in practice which is related to definition of “state institution” and “authorities are granted the Constitution” in the 1945 Constitution. This condition opens a debate the interpretation in executing the settlement on authority dispute among the institutions. In addition, should be considered the settlement of disputes the authority of institutions, whose authority derived from regulation other than the Constitution Menurut ketentuan Pasal 24C ayat (1) UUD NRI Tahun 1945, penyelesaian sengketa kewenangan lembaga negara yang kewenangannya diberikan oleh UUD merupakan kewenangan Mahkamah Konstitusi. Namun dalam praktiknya, proses penyelesaian sengketa kewenangan lembaga negara menghadapi problem tersendiri seiring tidak adanya batasan ruang lingkup dan definisi “lembaga negara” dan frasa “kewenangannya diberikan UUD” secara pasti dalam UUD NRI Tahun 1945. Situasi ini pada akhirnya menimbulkan multitafsir yang berpotensi mengakibatkan tidak efektifnya penyelesaian sengketa kewenangan lembaga negara di Indonesia. Selain itu, perlu dipikirkan mekanisme penyelesaian sengketa kewenangan lembaga yang kewenangannya bersumber dari peraturan selain UUD

Identification of the C-Reactive Protein Interaction Network Using a Bioinformatics Approach Provides Insights into the Molecular Pathogenesis of Hepatocellular Carcinoma

Background/Aims: C reactive protein (CRP) levels are elevated in many diseases, including malignant tumors and cardiovascular disorders. In this study, the protein interaction network for CRP was evaluated to determine the importance of CRP and its interacting proteins in the molecular pathogenesis of hepatocellular carcinoma (HCC). Methods: Isobaric tags for relative and absolute quantitation (iTRAQ) and mass spectrometry were used to identify CRP interacting proteins in SMMC7721 cells. Moreover, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to evaluate enriched genes and pathways for differentially expressed genes using DAVID and WebGestalt. Co-immunoprecipitation and western blot analyses were employed to assess interactions between CRP and KRT8, ANXA2, ENO2, and HSP90B1. Results: In total, 52 proteins that interact with CRP were identified. A GO analysis suggested that most of the interacting proteins were involved in CRP complexes and regulated metabolic processes. A KEGG pathway analysis suggested that most CRP-interacting proteins contribute to the TRAIL signaling pathway, Class I PI3K/Akt signaling pathway, plasma membrane estrogen receptor signaling, Nectin adhesion pathway, and S1P1 pathway. Immunoprecipitation and western blot analyses revealed interactions between CRP and KRT8, ANXA2, ENO2, and HSP90B1. Conclusions: iTRAQ based proteomic profiling revealed the network of CRP interacting proteins. This network may activate the PI3K/Akt signaling pathway, thereby contributing to the pathogenesis of HCC

Manipulation of ionized impurity scattering for achieving high thermoelectric performance in n-type Mg

Achieving higher carrier mobility plays a pivotal role for obtaining potentially high thermoelectric performance. In principle, the carrier mobility is governed by the band structure as well as by the carrier scattering mechanism. Here, we demonstrate that by manipulating the carrier scattering mechanism in n-type Mg[subscript 3]Sb[subscript 2 ]-based materials, a substantial improvement in carrier mobility, and hence the power factor, can be achieved. In this work, Fe, Co, Hf, and Ta are doped on the Mg site of Mg[subscript 3.2]Sb[subscript 1.5]Bi[subscript 0.49]Te [subscript 0.01], where the ionized impurity scattering crosses over to mixed ionized impurity and acoustic phonon scattering. A significant improvement in Hall mobility from ∼16 to ∼81 cm 2 ·V[superscript −1]·s[superscript − 1] is obtained, thus leading to a notably enhanced power factor of ∼13 μW·cm [superscript −1]·K [superscript −2] from ∼5 μW·cm[superscript −1]·K[superscript −2]. A simultaneous reduction in thermal conductivity is also achieved. Collectively, a figure of merit (ZT) of ∼1.7 is obtained at 773 K in Mg[subscript 3.1]Co[subscript 0.1]Sb[subscript 1.5]Bi[subscript 0.49]Te [subscript 0.01]. The concept of manipulating the carrier scattering mechanism to improve the mobility should also be applicable to other material systems. Keywords: thermoelectric; carrier scattering mechanism; ionized impurity scattering; n-type; Mg[subscript 3]Sb[subscript 2]; defect

Serum Activity of Platelet-Activating Factor Acetylhydrolase Is a Potential Clinical Marker for Leptospirosis Pulmonary Hemorrhage

Pulmonary hemorrhage has been recognized as a major, often lethal, manifestation of severe leptospirosis albeit the pathogenesis remains unclear. The Leptospira interrogans virulent serogroup Icterohaemorrhagiae serovar Lai encodes a protein (LA2144), which exhibited the platelet-activating factor acetylhydrolase (PAF-AH) activity in vitro similar to that of human serum with respect to its substrate affinity and specificity and thus designated L-PAF-AH. On the other hand, the primary amino acid sequence of L-PAF-AH is homologous to the α1-subunit of the bovine brain PAF-AH isoform I. The L-PAF-AH was proven to be an intracellular protein, which was encoded unanimously and expressed similarly in either pathogenic or saprophytic leptospires. Mongolian gerbil is an appropriate experimental model to study the PAF-AH level in serum with its basal activity level comparable to that of human while elevated directly associated with the course of pulmonary hemorrhage during severe leptospirosis. Mortality occurred around the peak of pulmonary hemorrhage, along with the transition of the PAF-AH activity level in serum, from the increasing phase to the final decreasing phase. Limited clinical data indicated that the serum activity of PAF-AH was likely to be elevated in the patients infected by L. interrogans serogroup Icterohaemorrhagiae, but not in those infected by other less severe serogroups. Although L-PAF-AH might be released into the micro-environment via cell lysis, its PAF-AH activity apparently contributed little to this elevation. Therefore, the change of PAF-AH in serum not only may be influential for pulmonary hemorrhage, but also seems suitable for disease monitoring to ensure prompt clinical treatment, which is critical for reducing the mortality of severe leptospirosis