Optimal Pricing Strategy for Multichannel Healthcare Services

As a combination of online and offline channel services, multichannel healthcare services currently play important roles in helping consumers solve their health problems. In this study, we establish a stylized model to investigate how healthcare service providers should price in multi-channels and when consumers should choose online service, taking misdiagnosis rate and the severity of disease problems into account. Our results show that the prices of the online channel and offline channel can increase when the misdiagnosis rate is low and minor problem inspection rate online is high. Moreover, when the diagnosis rate is high, the profit of online channel would increase, and then improve the profit of multichannel services. These findings provide insights for the theoretical research of online healthcare services and practice management on pricing strategies in multichannel healthcare services

ROAM: memory-efficient large DNN training via optimized operator ordering and memory layout

As deep learning models continue to increase in size, the memory requirements for training have surged. While high-level techniques like offloading, recomputation, and compression can alleviate memory pressure, they also introduce overheads. However, a memory-efficient execution plan that includes a reasonable operator execution order and tensor memory layout can significantly increase the models' memory efficiency and reduce overheads from high-level techniques. In this paper, we propose ROAM which operates on computation graph level to derive memory-efficient execution plan with optimized operator order and tensor memory layout for models. We first propose sophisticated theories that carefully consider model structure and training memory load to support optimization for large complex graphs that have not been well supported in the past. An efficient tree-based algorithm is further proposed to search task divisions automatically, along with delivering high performance and effectiveness to solve the problem. Experiments show that ROAM achieves a substantial memory reduction of 35.7%, 13.3%, and 27.2% compared to Pytorch and two state-of-the-art methods and offers a remarkable 53.7x speedup. The evaluation conducted on the expansive GPT2-XL further validates ROAM's scalability

A Novel Perspective Linkage Between Kidney Function and Alzheimer’s Disease

It has long been believed that kidney function is linked to brain activity. Clinical studies demonstrate that patients with chronic kidney disease (CKD) are more prone to cognitive impairment and Alzheimer’s disease (AD), and the degree of cognitive impairment is closely related to CKD progression and renal failure. Moreover, the fact that cognitive function in CKD patients is significantly improved after successful kidney transplantation reveals a linkage between CKD and AD. However, the mechanisms behind this linkage are unclear. The physiological function of the kidney is to maintain the stability of the internal environment, including the cerebrovascular circulation, whereas abnormal kidney function often leads to ischemia and hypoxia. Many CKD patients experience chronic hypoxia, and many urinary toxins accumulate after renal function is impaired. In this mini review, we will propose a novel perspective on the association between AD and CKD and the connection between the kidney and brain

Anomalous optical and electronic properties of dense sodium

Based on ab initio density-functional-theory using generalized gradient approximation, we systematically study the optical and electronic properties of the insulating dense sodium phase (Na-hp4) reported recently [Ma \textit{et al.}, Nature \textbf{458}, 182 (2009)]. The structure is found optically anisotropic and transparent to visible light, which can be well interpreted using its electronic band structure and angular moment decomposed density of states. Through the bader analysis of Na-hp4 at different pressures, we conclude that ionicity exists in the structure and becomes stronger with increasing pressure. In addition, the absorption spectra in the energy range from 1.4 to 2.4 eV are compared with recent experimental results and found good agreement. It is found that the deep-lying valence electrons participate in the interband transition.Comment: 7 pages, 7 figure

The associations between gut microbiota and chronic respiratory diseases: a Mendelian randomization study

IntroductionGrowing evidence indicates that variations in the composition of the gut microbiota are linked to the onset and progression of chronic respiratory diseases (CRDs), albeit the causal relationship between the two remains unclear.MethodsWe conducted a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the relationship between gut microbiota and five main CRDs, including chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), sarcoidosis, and pneumoconiosis. For MR analysis, the inverse variance weighted (IVW) method was utilized as the primary method. The MR–Egger, weighted median, and MR-PRESSO statistical methods were used as a supplement. To detect heterogeneity and pleiotropy, the Cochrane and Rucker Q test, MR–Egger intercept test, and MR-PRESSO global test were then implemented. The leave-one-out strategy was also applied to assess the consistency of the MR results.ResultsBased on substantial genetic data obtained from genome-wide association studies (GWAS) comprising 3,504,473 European participants, our study offers evidence that several gut microbial taxa, including 14 probable microbial taxa (specifically, 5, 3, 2, 3 and 1 for COPD, asthma, IPF, sarcoidosis, and pneumoconiosis, respectively) and 33 possible microbial taxa (specifically, 6, 7, 8, 7 and 5 for COPD, asthma, IPF, sarcoidosis, and pneumoconiosis, respectively) play significant roles in the formation of CRDs.DiscussionThis work implies causal relationships between the gut microbiota and CRDs, thereby shedding new light on the gut microbiota-mediated prevention of CRDs

Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases

In Mandarin Chinese, when the noun head appears in the context, a quantity noun phrase can be reduced to a quantity phrase with the noun head omitted. This phrase structure is called elliptical quantity noun phrase. The automatic recovery of elliptical quantity noun phrase is crucial in syntactic parsing, semantic representation and other downstream tasks. In this paper, we propose a hybrid neural network model to identify the semantic category for elliptical quantity noun phrases and realize the recovery of omitted semantics by supplementing concept categories. Firstly, we use BERT to generate character-level vectors. Secondly, Bi-LSTM is applied to capture the context information of each character and compress the input into the context memory history. Then CNN is utilized to capture the local semantics of n-gramswith various granularities. Based on the ChineseAbstractMeaning Representation (CAMR) corpus and Xinhua News Agency corpus, we construct a hand-labeled elliptical quantity noun phrase dataset and carry out the semantic recovery of elliptical quantity noun phrase on this dataset. The experimental results show that our hybrid neural network model can effectively improve the performance of the semantic complement for the elliptical quantity noun phrases

Janus monolayers of transition metal dichalcogenides.

Structural symmetry-breaking plays a crucial role in determining the electronic band structures of two-dimensional materials. Tremendous efforts have been devoted to breaking the in-plane symmetry of graphene with electric fields on AB-stacked bilayers or stacked van der Waals heterostructures. In contrast, transition metal dichalcogenide monolayers are semiconductors with intrinsic in-plane asymmetry, leading to direct electronic bandgaps, distinctive optical properties and great potential in optoelectronics. Apart from their in-plane inversion asymmetry, an additional degree of freedom allowing spin manipulation can be induced by breaking the out-of-plane mirror symmetry with external electric fields or, as theoretically proposed, with an asymmetric out-of-plane structural configuration. Here, we report a synthetic strategy to grow Janus monolayers of transition metal dichalcogenides breaking the out-of-plane structural symmetry. In particular, based on a MoS2 monolayer, we fully replace the top-layer S with Se atoms. We confirm the Janus structure of MoSSe directly by means of scanning transmission electron microscopy and energy-dependent X-ray photoelectron spectroscopy, and prove the existence of vertical dipoles by second harmonic generation and piezoresponse force microscopy measurements

Bone morphogenetic proteins in tissue engineering: the road from laboratory to the clinic: part I (basic concepts)

Discovered in 1965, bone morphogenetic proteins (BMPs) are a group of cytokines from the transforming growth factor-β (TGFβ) superfamily with significant roles in bone and cartilage formation. BMPs are used as powerful osteoinductive components of diverse tissue-engineering products for the healing of bone. Several BMPs with different physiological roles have been identified in humans. The purpose of this review is to cover the biological function of the main members of BMP family, the latest research on BMPs signalling pathways and advances in the production of recombinant BMPs for tissue engineering purposes