CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion.

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

Expression-Guided Deep Joint Learning for Facial Expression Recognition.

In recent years, convolutional neural networks (CNNs) have played a dominant role in facial expression recognition. While CNN-based methods have achieved remarkable success, they are notorious for having an excessive number of parameters, and they rely on a large amount of manually annotated data. To address this challenge, we expand the number of training samples by learning expressions from a face recognition dataset to reduce the impact of a small number of samples on the network training. In the proposed deep joint learning framework, the deep features of the face recognition dataset are clustered, and simultaneously, the parameters of an efficient CNN are learned, thereby marking the data for network training automatically and efficiently. Specifically, first, we develop a new efficient CNN based on the proposed affinity convolution module with much lower computational overhead for deep feature learning and expression classification. Then, we develop an expression-guided deep facial clustering approach to cluster the deep features and generate abundant expression labels from the face recognition dataset. Finally, the AC-based CNN is fine-tuned using an updated training set and a combined loss function. Our framework is evaluated on several challenging facial expression recognition datasets as well as a self-collected dataset. In the context of facial expression recognition applied to the field of education, our proposed method achieved an impressive accuracy of 95.87% on the self-collected dataset, surpassing other existing methods.

Pharmacoeconomic evaluation of isavuconazole, posaconazole, and voriconazole for the treatment of invasive mold diseases in hematological patients: initial therapy prior to pathogen differential diagnosis in China.

Background: Invasive mold diseases (IMD) is associated with high mortality and a substantial economic burden. For high-risk patients, fever drive or diagnostic drive therapy is usually initiated prior to the differential diagnosis of the pathogen. This study evaluated the cost-effectiveness of isavuconazole, posaconazole, vs. voriconazole in the treatment of IMD from the perspective of the Chinese healthcare system, informing healthcare decision-making and resource allocation. Methods: A decision analytic model was constructed using TreeAge Pro 2011 software to evaluate the cost-effectiveness of the entire disease course. We assumed that the prevalence of mucormycosis in the patients entering the model was 7.8%. Efficacy, cost, adverse events, and other data included in the model were mainly derived from clinical studies, published literature, and publicly available databases. The primary outcomes of the model output were total cost, quality-adjusted life years (QALYs), life years (Lys), and incremental cost-effectiveness ratio (ICER). The willing-to-pay (WTP) threshold was defined as one to three times China's GDP per capita in 2022. One-way sensitivity analysis and probability sensitivity analysis were used to determine the robustness of the model. At the same time, the cost-effectiveness of three triazole antifungal agents under a broader range of mucormycosis prevalence, when voriconazole was covered by medical insurance reimbursement, and after the price reduction of posaconazole was discussed. Results: Compared with voriconazole, isavuconazole provided an additional 0.38 Lys (9.29 vs. 8.91 LYs) and 0.31 QALYs (7.62 vs. 7.31 QALYs); ICER was $15,702.46/QALY, well-below the WTP threshold ($38,223/QALY). However, posaconazole did not provide a significant economic advantage over voriconazole (9.40 vs. 9.36 Lys; 7.71 vs. 7.68 QALYs; ICER $64,466.57/QALY). One-way sensitivity analysis found that ICER was highly sensitive to the mortality of patients with invasive aspergillus infection. In the probabilistic sensitivity analysis, when the WTP threshold was $38,223/QALY, the probability of isavuconazole being cost-effective was 72.9%. The scenario analysis results indicated that posaconazole would become cost-effective when the price was reduced by 15% or the prevalence of mucormycosis was 14%. Conclusions: Isavuconazole represents a cost-effective initial option for treating IMD in high-risk hematological patients prior to the differential diagnosis of pathogens. It will also be economical when a 15% reduction in posaconazole cost is achieved.

Insulin degludec/liraglutide versus its monotherapy on T2D patients: A lifetime cost-utility analysis in China.

Introduction: IDegLira (brand name Xultophy) is a novel fixed ratio combination of insulin degludec and liraglutide for type 2 diabetes (T2D) patients. This study aimed to investigate the lifetime cost-effective value of IDegLira compared with its single component (Degludec or Liraglutide) and to explore the suitable annual cost of IDegLira if necessary. Methods: UKPDS OM2 was applied to determine the long-term quality-adjusted life years (QALYs) and total costs. The efficacy data that were inputted into the model were synthesized from 6 randomized clinical trials (RCTs) that directly assessed the clinical benefit of IDegLira and its components in the treatment of uncontrolled T2D patients. The economic results were examined by one-way sensitivity analysis (OSA) and probabilistic sensitivity analysis (PSA). Further price reduction of IDegLira was investigated by binary search. Results: The IDegLira, IDeg, and Lira yielded 11.79 QALYs, 11.62 QALYs, and 11.73 QALYs and total cost of $20281.61, $3726.76, and $11941.26, respectively. The incremental cost-utility ratio (ICUR) of IDegLira versus IDeg was $99464.12/QALYs, and the ICUR of IDegLira versus Lira was $143348.26/QALYs, which indicated that IDegLira was not a cost-effective therapy for T2D patients compared with its components at the current price from a Chinese national healthcare system perspective. Base case results were robust to OSA and PSA. A further binary search showed that IDegLira appears to only be cost-effective if the annual cost of IDegLira is decreased by 58% when IDeg is considered as a reference, or by 30.57% when Lira is considered as a reference. Conclusion: In conclusion, IDegLira appears to not be cost-effective when compared with the current prices of IDeg or Lira for T2D patients in China. However, after the binary search, IDegLira appears to only be cost-effective if the annual cost of IDegLira is decreased 58% when IDeg is considered as a reference, or by 30.57% when Lira is considered as a reference.

Identification of off-flavor compounds and deodorizing of cattle by-products.

An unnatural flavor in a food or drink product caused by the presence of undesirable compounds due to contamination or deterioration is called off-flavor. This study determined the characteristics of cattle by-products off-flavor (heart, liver, lung, rumen, and intestine). We identified 25, 34, 26, 22, and 26 volatile compounds from the heart, liver, lung, rumen, and intestine, respectively, in the bovine via headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). Based on the relative odor activity value (ROAV ≥ 1), 16 volatile compounds were labeled as characteristic off-flavor by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The compounds involved in the characteristic off-flavor in bovine heart were E,E-2,4-nonadienal, E,E-2,4-decadien-1-al, hexanal, (E)-2-octenal, and decyl aldehyde. In the bovine liver, the off-flavor compounds were 1-nonanol, ethyl hexanoate, 2-octanone, and dodecyl aldehyde and in bovine lung 3-heptylacrolein was the off-flavor compound. In bovine rumen, heptaldehyde, octanal, p-cresol, and 1-nonanal were off-flavor compounds, and lastly, 1-octen-3-ol and E-2-nonenal were off-flavor compounds with bovine intestine. The cattle by-products were deodorized by shallot-ginger extract masking, baker's yeast fermentation, active dry yeast + ß-cyclodextrin (ß-CD) composite, and ultrasound + chitosan composite. The above 16 labeled characteristic compounds decreased in concentration. The ultrasound + chitosan composite method showed a significantly better effect than the other methods (p < .05). The aim of this study was to determine the characteristic flavor information of cattle by-products and provide idea on how to improve the flavor by various deodorization methods. PRACTICAL APPLICATIONS: This study investigated the volatile flavor compounds of cattle by-products from five organs (heart, liver, lung, rumen, and intestine) by headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). The 16 volatile compounds were labeled as the major characteristic off-flavor compounds by relative odor activity values and principal component analysis. Four different deodorization methods were adopted, and among them, ultrasound + chitosan composite method showed best results. This study has provided useful information about the characteristic off-flavor compounds and suggests how to improve the flavor of cattle by-products through various deodorization methods.

Benzoyl-xanthone derivative induces apoptosis in MCF-7 cells by binding TRAF6.

TNF receptor-associated factor 6 (TRAF6) has been reported to be associated with the development of cancer. Nevertheless, the exact role of TRAF6 in cancer remains unclear. The purpose of the present study was to explore the mechanism of 2-benzoyl-3-hydroxy-4-methyl-9H-xanthen-9-one leading to the inhibition of the activation of AKT and TGF-ß-activated kinase 1 (TAK1), and to the apoptosis of MCF-7 cells. Using a computational docking program and examination of AKT and TAK1 level changes, a new small molecule was identified, 2-benzoyl-3-hydroxy-4-methyl-9H-xanthen-9-one, which competitively bound to TRAF6. Next, the effect of this new compound on MCF-7 cells' biological behavior was studied in vitro. MTT assays were used to investigate cell viability; flow cytometry and invasion assays were performed to detect early apoptosis and invasion in MCF-7 cells, respectively. Immunoprecipitation, western blotting and caspase-3/9 activity assays were carried out to explore changes in protein expression. Briefly, the present data indicated that 2-benzoyl-3-hydroxy-4-methyl-9H-xanthen-9-one could suppress proliferation, induce early apoptosis and inhibit invasion in MCF-7 cells by suppressing the expression of Bcl-2 and promoting the expression of Bax, caspase-9, and caspase-3. These findings indicated that 2-benzoyl-3-hydroxy-4-methyl-9H-xanthen-9-one could induce apoptosis by inhibiting the activation of AKT and TAK1, and affecting the Bcl-2/Bax-caspase-9-caspase-3 pathway by competitively binding with TRAF6.