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Abstract Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure–response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression‐free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.

Sedation in patients with morbid obesity undergoing gastrointestinal endoscopy (GIE) frequently causes respiratory depression. Remimazolam, a gamma-aminobutyric acid receptor agonist, is safer than propofol as it has fewer cardiovascular and respiratory adverse effects. We report a case of successful GIE under sedation and analgesia with a combination of remimazolam and remifentanil, respectively, in a patient with morbid obesity experiencing obstructive sleep apnea (OSA). Remimazolam ensured safe sedation, preventing complications such as respiratory depression and hypotension. Dose adjustment of remifentanil can minimize its adverse effects. Therefore, the combination of remimazolam and remifentanil is a promising choice for performing GIE in patients with morbid obesity and OSA.

Abstract A multistate platform model was developed to describe time‐to‐event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression‐free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well‐described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan‐Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time‐variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations.

Background: Paxlovid is an oral drug composed of nirmatrelvir and ritonavir that has been demonstrated to be effective in decreasing the risk of severe coronavirus disease 2019 (COVID-19). Here, we report the use of paxlovid in pregnant women with COVID-19. Methods: Pregnant women attending a tertiary referral hospital in Taiwan from 29 April to 30 July 2022 were enrolled in the study. We compared baseline characteristics, clinical manifestations, and adverse events between paxlovid-treated women and those without paxlovid use. Maternal and neonatal outcomes were analysed in women who delivered during the study period. Results: A total of 30 paxlovid-treated pregnant women and 55 women without paxlovid use were included in the analysis. The mean duration of COVID-19-associated symptoms in the paxlovid-treated women was shorter than that in the control group (10.10 days versus 15.59 days, p = 0.04). No severe adverse events due to paxlovid use were observed. Dysgeusia and diarrhoea were the most common adverse effects. Thirteen paxlovid-treated and 28 untreated women delivered during the study period. More pregnant women in the paxlovid group who delivered during the study period underwent caesarean delivery compared to the group without antiviral treatment (10 of 13 [76.92%] versus 12 of 28 [42.86%], p = 0.042), and insignificantly more newborns were born small for gestational age in the paxlovid group compared to the control group (3 of 13 [23.08%] versus 1 of 28 [3.57%], p = 0.086). Conclusion: Our study showed that paxlovid was effective and safe for pregnant women during the Omicron wave of the COVID-19 pandemic. A higher proportion of caesarean delivery rates was observed among paxlovid-treated women. Long-term follow-up of pregnant women exposed to paxlovid and their offspring is needed.

Objective: This study aimed to assess the effect of atosiban on in vitro fertilization (IVF) pregnancy outcome among women with both endometriosis and adenomyosis, and compared it to that of patients with endometriosis but without adenomyosis and that of patients with tubal factor only. Materials and methods: 106 infertile women (176 embryo transfers) from a medical center in Taiwan were included in the analysis, where 34 (54), 34 (66), and 38 (56) cases (embryo transfers) were endometriosis without adenomyosis, endometriosis with adenomyosis, and tubal infertility factor only, respectively. Adenomyosis morphologies were classified using an ultrasound-based classification system. The logistic generalized estimating equation model was used to analyze the association between atosiban use and pregnancy outcomes. Results: The crude pregnancy rates for the endometriosis-only group were significantly higher than those for the endometriosis + adenomyosis group (i.e., biochemical pregnancy: 50.0% versus 29.7%, p = 0.041; ongoing pregnancy: 35.2% versus 16.9%, p = 0.038). Significantly higher chances of biochemical pregnancy and ongoing pregnancy among endometriosis patients without adenomyosis versus those with both endometriosis and adenomyosis were found (odds ratios [95% confidence intervals]: 2.981 [1.307, 6.803]; p = 0.009, 2.694 [1.151, 6.304]; p = 0.022). A significant positive association between atosiban use and biochemical pregnancy existed among endometriosis cases without adenomyosis (a 2.43-fold [1.01, 5.89] increase in successful pregnancy; p

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN’s deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.

In cancer treatment, the first-generation, cytotoxic drugs, though effective against cancer cells, also harmed healthy ones. The second-generation targeted cancer cells precisely to inhibit their growth. Enter the third-generation, consisting of immuno-oncology drugs, designed to combat drug resistance and bolster the immune system’s defenses. These advanced therapies operate by obstructing the uncontrolled growth and spread of cancer cells through the body, ultimately eliminating them effectively. Within the arsenal of cancer treatment, monoclonal antibodies offer several advantages, including inducing cancer cell apoptosis, precise targeting, prolonged presence in the body, and minimal side effects. A recent development in cancer therapy is Antibody-Drug Conjugates (ADCs), initially developed in the mid-20th century. The second generation of ADCs addressed this issue through innovative antibody modification techniques, such as DAR regulation, amino acid substitutions, incorporation of non-natural amino acids, and enzymatic drug attachment. Currently, a third generation of ADCs is in development. This study presents an overview of 12 available ADCs, reviews 71 recent research papers, and analyzes 128 clinical trial reports. The overarching objective is to gain insights into the prevailing trends in ADC research and development, with a particular focus on emerging frontiers like potential targets, linkers, and drug payloads within the realm of cancer treatment.

The purpose of this study was to investigate the relationships between English learning motivation, English leisure activities, and the English learning effect on elementary school children by using a linear regression model. The collected data were analyzed with descriptive statistics, multivariate simultaneous linear regression, and mediation tests. The result showed that elementary students lack parental involvement in English learning. Regular parental support leads to better learning outcomes. English leisure activities are crucial for improving motivation and effectiveness. Schools and parents should prioritize English skills through daily learning and leisure activities to incrementally improve English ability and positively impact learning.

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

Abstract Triple-negative breast cancer (TNBC) is a highly diverse group of malignant neoplasms which tend to have poor outcomes, and the development of new targets and strategies to treat these cancers is sorely needed. Antibody–drug conjugate (ADC) therapy has been shown to be a promising targeted therapy for treating many cancers, but has only rarely been tried in patients with TNBC. A major reason the efficacy of ADC therapy in the setting of TNBC has not been more fully investigated is the lack of appropriate target molecules. In this work we were able to identify an effective TNBC target for use in immunotherapy. We were guided by our previous observation that in some breast cancer patients the protein tropomyosin receptor kinase B cell surface protein (TrkB) had become immunogenic, suggesting that it was somehow sufficiently chemically different enough (presumably by mutation) to escaped immune tolerance. We postulated that this difference might well offer a means for selective targeting by antibodies. We engineered site-specific ADCs using a dual variable domain (DVD) format which combines anti-TrkB antibody with the h38C2 catalytic antibody. This format enables rapid, one-step, and homogeneous conjugation of β-lactam-derivatized drugs. Following conjugation to β-lactam-derivatized monomethyl auristatin F, the TrkB-targeting DVD-ADCs showed potency against multiple breast cancer cell lines, including TNBC cell lines. In addition, our isolation of antibody that specifically recognized the breast cancer-associated mutant form of TrkB, but not the wild type TrkB, indicates the possibility of further refining the selectivity of anti-TrkB DVD-ADCs, which should enhance their therapeutic index. These results confirmed our supposition that TrkB is a potential target for immunotherapy for TNBC, as well as for other cancers with mutated cell surface proteins.

It is necessary to detect changes in birds’ behaviors promptly to realize their health and habitat status. Moreover, promptly provide appropriate medical treatment and environmental remediation. Automating bird behavior recognition can solve this problem and assist in breeding and protecting birds. This paper proposes the transposed non-local module based on a time pyramid network to establish a dual feature-rates deep fusion net (DF2-Net) for bird behavior recognition. The time pyramid network uses spatial alignment and time pooling operations to extract features with different rates from features of different depths and then fuse features containing the information. On this basis, the transposed non-local (TNL) module uses features with different rates to calculate the relationship matrix of each time slice and spatial position. Then TNL uses the transpose operation to make the original feature take advantage of the corresponding relationship when it multiplies the original feature by the matrix. The module further deeply integrates the relational information of behaviors at different rates to enhance features of different rates, respectively, which can improve the recognition effect of the model on dynamic behaviors. Our study has three contributions: (1) The TNL module takes features with different rates as inputs and uses a transpose mechanism to get two relationships in different directions that match their corresponding input. (2) Dual feature-rates deep fusion net (DF2-Net) transforms a single rate feature into two rate features and iteratively fuses them to obtain information and behavior relationships at various rates. (3) We collect the unique video dataset of birds’ behaviors to fill in the blank of the video dataset and support the study of birds’ behaviors. The experiments compare the DF2-Net with the well-known video-based recognition model on the self-collected birds’ behavior dataset, containing eight behaviors. DF2-Net has the best classification accuracy and achieves 80.87%, 81.35%, 80.70%, and 81.35% in precision, recall, f1-score, and OA metrics. They are 1.81%, 2.43%, 2.20%, and 2.43% higher than the second-best approach (TPN) with 8 frames, respectively. The experimental results show that DF2-Net outperforms state-of-the-art methods in various frames, in which 16 frames are the most suitable for bird behavior recognition. Moreover, we execute the various ablation experiments to demonstrate the efficiency of the TNL module, the optimal location and internal operations of TNL, and the most suitable parameters of DF2-Net. The ablation experiments demonstrate that the TNL module dramatically improves the recognition accuracy of the dynamic behavior and proves the validity and rationality of the TNL module. Therefore, the proposed model is practical and feasible for automatically recognizing bird behavior.

Preeclampsia (PE) occurs in women pregnant for more than 20 weeks with de novo hypertension and proteinuria, and is a devastating disease in maternal–fetal medicine. Cytokine tumor necrosis factor (TNF)-α may play a key role in the pathogenesis of PE. We conducted this study to investigate the regulatory regions of the TNF genes, by investigating two promoter polymorphisms, TNFA-308G/A (rs1800629) and -238G/A (rs361525), known to influence TNF expression, and their relationship to PE. An observational, monocentric, case–control study was conducted. We retrospectively collected 74 cases of severe PE and 119 pregnant women without PE as control. Polymerase chain reaction (PCR) was carried out for allele analysis. Higher A allele in women with PE was found in rs1800629 but not rs361525. In this study, we first found that polymorphism at the position -308, but not -238, in the promoter region of the TNF-α gene can contribute to severe PE in Taiwanese Han populations. The results of our study are totally different to previous Iranian studies, but have some similarity to a previous UK study. Further studies are required to confirm the roles of rs1800629 and rs361525 in PE with circulating TNF-α in PE.

Objective We assessed the quality of life (QoL) and pregnancy outcomes of in vitro fertilization (IVF) treatment among infertile women with endometriosis, as compared to infertile women without endometriosis. Study design Eighty-one (81) endometriosis women (with 142 embryo transfer [ET] cycles) and 605 non-endometriosis women (with 1063 ET cycles) were included. QoL was measured by FertiQoL at the date before ET. Pregnancy outcomes included biochemical pregnancy, ongoing pregnancy and live birth. Generalized estimating equation analyses were performed to assess the association between QoL and IVF pregnancy. Results Endometriosis-affected women had significantly lower QoL, as indicated by mind/body, treatment environment and total treatment scores, and total scores of FertiQoL (p

Metastatic tumours to the ovary comprise 10–25% of ovarian malignancies and may originate from various primary sites. Here, the case of a 49-year-old female patient who presented with periumbilical nodules and abdominal bloating is reported. She was found to have bilateral ovarian tumours with peritoneal carcinomatosis and ascites. Primary ovarian cancer was suspected while no contributory gastrointestinal lesion was detected by imaging studies and endoscopic examinations. Three cycles of neoadjuvant chemotherapy were administered, followed by interval debulking surgery. Appendiceal cancer was highly suspected based on analysis of a frozen section obtained during surgical debulking. Following the pathology investigation, the patient was finally diagnosed with primary appendiceal adenocarcinoma. She underwent chemotherapy comprising irinotecan and fluorouracil. Due to disease progression despite several chemotherapy regimens, the patient declined further treatment and was lost to follow-up 1 year after the debulking surgery. Metastatic tumours to the ovary may mimic primary ovarian cancers and often present with nonspecific manifestations. Therefore, meticulous exploration of the primary site is warranted if the diagnosis is clinically suspicious.

Surveillance cameras have been widely used in urban environments and are increasingly used in rural ones. Such cameras have mostly been used for security, but they can be applied to the problem of furnishing fine-grained measurements and predictions of precipitation intensity. In this study, we formulated a stacked order-preserving (OP) learning framework to train a network using time-series data. We constructed an OP module, which uses a three-dimensional (3D) convolution operation to extract features with spatial and temporal information and that are associated with ConvLSTM; this feature extraction is used to learn the short-term and OP time-series relationships between features. Furthermore, the OP modules are stacked to form a stacked OP network (SOPNet), which strengthens the relationship between features in long-term time-series image sequences. This SOPNet can be use to obtain fine-grained measurements and predictions of precipitation intensity from images captured by outdoor surveillance cameras. Our main contributions are threefold. First, the SOPNet strengthens the short-term and long-term time-series relationship between features. Second, the SOPNet simultaneously examines spatial and temporal information to measure and predict precipitation intensity. Third, we constructed a precipitation intensity database based on optical images captured by outdoor surveillance cameras. We experimentally evaluated our proposed architecture using our self-collected data set. We found that SOPNet yields better performance and greater accuracy relative to its well-known state-of-the-art counterparts with respect to various metrics.

Background/purpose: This study assessed the quality of life (QoL) and pregnancy outcomes among infertile women undergoing in vitro fertilization (IVF) treatment to investigate the association between QoL and IVF pregnancy outcomes. Methods: This study included 686 women with 1205 embryo transfers (ETs). QoL was measured using the fertility quality of life (FertiQoL) tool before ET. FertiQoL comprises two modules: a Core module (including mind/body, emotional, relational, and social domains) and a Treatment module (covering treatment environment and tolerability domains). The FertiQol total and subscale scores were computed and scored in the range of 0–100 (higher scores indicate better QoL). Multivariate generalized estimating equation analyses were carried out to assess the association between QoL and IVF pregnancy outcomes, with adjustment for time-varying factors across multiple ETs for a given person. Results: The lowest score in the core module was for the emotional domain (62.0), and that in the Treatment module was for the tolerability domain (59.4). QoL scores were significantly and positively associated with pregnancy outcomes (i.e., ongoing pregnancy, live birth); with a one unit increase in the emotional domain score, the probabilities of ongoing pregnancy and live birth significantly increased by 2.4% and 2.6%, respectively (p

Bird behavior reflects its healthy and the habitat condition in which the bird’s posture recognition is the basis of bird behavior research. This paper proposes an end-to-end bird posture recognition based on target keypoints estimation in the dual-task network. The network composes two sub-networks, keypoint feature extraction network and local feature extraction network. In the keypoint feature extraction network, we take the high-resolution network (HRNet) as the backbone to detect the bird’s keypoints in the high-resolution branch and extract the global features from the low-resolution branch for posture recognition. We design the components generation module in the local feature extraction network, which takes detected keypoints into the clustering algorithm to generate the bird’s components. We then utilize the components into convolutional neural networks (CNNs) to extract the local features for posture recognition. Finally, we fuse the global and local features to execute the bird’s posture recognition. Our main contributions are fourfold: (1) We have been modified the first point of contribution. We take the bird’s keypoints, which have never been used in the bird’s posture recognition, to estimate the bird’s posture and achieved good results. (2) We propose an end-to-end dual-task network for bird’s keypoints detection and posture recognition. (3) The components generation module utilizes the bird’s keypoints to crop the bird components and generates the local feature for bird posture recognition with a shallow network. (4) Creating a bird posture image dataset named IMLab-P8-2020 containing eight bird postures. We experimentally evaluated our proposed net on self-collected data set and verified that the proposed net yields better performance and greater accuracy relative to well-known counterparts concerning various metrics.

Summary: Exosomes are important for cell–cell communication. Deficiencies in the human dihydroceramide desaturase gene, DEGS1, increase the dihydroceramide-to-ceramide ratio and cause hypomyelinating leukodystrophy. However, the disease mechanism remains unknown. Here, we developed an in vivo assay with spatially controlled expression of exosome markers in Drosophila eye imaginal discs and showed that the level and activity of the DEGS1 ortholog, Ifc, correlated with exosome production. Knocking out ifc decreased the density of the exosome precursor intraluminal vesicles (ILVs) in the multivesicular endosomes (MVEs) and reduced the number of exosomes released. While ifc overexpression and autophagy inhibition both enhanced exosome production, combining the two had no additive effect. Moreover, DEGS1 activity was sufficient to drive ILV formation in vitro. Together, DEGS1/Ifc controls the dihydroceramide-to-ceramide ratio and enhances exosome secretion by promoting ILV formation and preventing the autophagic degradation of MVEs. These findings provide a potential cause for the neuropathy associated with DEGS1-deficient mutations.

Ever since the first games launched in the 1970s, the rapid growth of the video games market has successfully attracted more and more purchasers. In this study, we use Nintendo Switch as a case study to examine the extended technology acceptance model (TAM), which integrates the factors of Perceived Playfulness and Compatibility to determine their relevance in the usage behavior. A total of 266 questionnaires were obtained using snowball sampling and analyzed using the Structural Equation Modeling (SEM). The study results revealed that the expanded TAM model can be effectively used in this context and it also showed a good fit for the proposed model from the data analyzed. Attitude explained an R2 of 0.85, which indicates that 85% of the variance in user’s behavior intention is accounted by attitude. Perceived Ease of Use and Perceived Usefulness were significantly positively associated with attitude. Perceived Playfulness was significantly positively associated with Perceived Usefulness. This study also addresses the implications of the findings for researchers and the game marketing strategies practitioners.

Plant classification is a science that is used to assess the quality of forest resources and has been studied extensively. In this paper, we proposed a novel convolutional neural network known as the Fourier Dense Network (FDN) which is a data-driven method to classify the optical aerial images of plants. To efficiently classify plants, the FDN learns and extracts the features of plants in time and frequency domains from the optical images captured using an unmanned aerial vehicle (UAV). In FDN, we designed a fast Fourier dense block (FF-dense block) that describes the features of the plants by using the magnitude and phase information in the frequency domain. Moreover, we used a transition layer to reduce the dimension of feature maps between two FF-dense blocks in the time domain. The primary contributions of this study are as follows: (1) an FF-dense block that considers frequency information and transfers the information into various layers of a block was designed; and (2) the characteristics between the time and frequency domains were repeatedly extracted and combined to more effectively describe the characteristics of tree species. To evaluate our study, we established a novel dataset comprising the UAV-based optical images of plants-vegetational optical aerial image dataset-for conducting plant classification and information retrieval. The dataset contains more than 21863 images of 12 plants. To the best of our knowledge, this is the largest publicly available dataset of the UAV-based optical images of plants. The experimental results demonstrated that the FDN can achieve state-of-the-art performance in terms of plant classification.

Establishing the conservation reserves is an efficient approach to protect various species, in which maintaining the spatial characteristics of the habit is an important issue. A continuous and compact reserve is vital for long-term survival and reproduction of various species. However, the existing methods for nature reserve design have the problem of the selected sites which are sparse in space. The sparseness refers to the isolated regions which are the unselected (selected) sites and surrounded by the selected (unselected) sites in the designed nature reserve and thus making the management of protected areas and exchange of information between species difficult. In this paper, we propose the concept of a dual-flow mechanism which is called the dual-flow structure (DFS) to address the problem of sparseness in sites selected for the reserve network design. The DFS constructs two consecutive arcs: one is for selected sites and the other is for unselected sites, simultaneously. Those two arcs ensure that each site has at least one adjacent site which has the same status (selected/unselected) with the competitive mechanism. The concept of two consecutive arcs can solve the problem of sparseness and achieve spatial continuity. The DFS further utilizes the perimeter in dual-flow structure to achieve the spatial characteristic of compactness. The main contributions of this study are: (1) we design a novel mathematical model which solves the problem of sparseness in the space, including selected and un-selected sites; and (2) the DFS generates the connectivity and compactness of the reserve network with the scientific and reasonable techniques. In the experiments, we take the Daiyun Mountains as a study area to demonstrate the feasibility and effectiveness of the proposed model.

Incessant antigenic evolution enables the persistence and spread of influenza virus in the human population. As the principal target of the immune response, the hemagglutinin (HA) surface antigen on influenza viruses continuously acquires and replaces N-linked glycosylation sites to shield immunogenic protein epitopes using host-derived glycans. Anti-glycan antibodies, such as 2G12, target the HIV-1 envelope protein (Env), which is even more extensively glycosylated and contains under-processed oligomannose-type clusters on its dense glycan shield. Here, we illustrate that 2G12 can also neutralize human seasonal influenza A H3N2 viruses that have evolved to present similar oligomannose-type clusters on their HAs from around 20 years after the 1968 pandemic. Using structural biology and mass spectrometric approaches, we find that two N-glycosylation sites close to the receptor binding site (RBS) on influenza hemagglutinin represent the oligomannose cluster recognized by 2G12. One of these glycan sites is highly conserved in all human H3N2 strains and the other emerged during virus evolution. These two N-glycosylation sites have also become crucial for fitness of recent H3N2 strains. These findings shed light on the evolution of the glycan shield on influenza virus and suggest 2G12-like antibodies can potentially act as broad neutralizers to target human enveloped viruses.

To achieve sustainable business operations, corporate betting on the implementation of social responsibility has become a trend of global concern. Therefore, companies that pay attention to and invest many resources in corporate social responsibility (CSR) have gradually become critical strategies for business operations. This strategy has a substantial effect on business performance, especially regarding the financial impact. This study aims to explore the effect of CSR improvement on financial performance, return on assets (ROA), return on equity (ROE), size, debt ratio, and asset turnover on its interference. A total of 346 items of data from Taiwan companies that have won the “CommonWealth Corporate Citizenship Award” from 2012 to 2018 were analyzed via descriptive statistics and hierarchical regression methods to determine the influence and adjustment of various factors layer by layer. CSR, firm size, debt ratio, and asset turnover have a significant prediction on ROA. CSR, firm size, and turnover have a significant prediction on ROE. Firm size and debt ratio have a significant negative moderation effect on CSR to ROA. The debt ratio has a significant negative moderation effect on CSR to ROE. This study concludes that CSR has a significant impact on business performance. CSR affects ROA moderated by firm size and ROA and ROE moderated by debt ratio. This study puts forward practical and future research suggestions for the relevant units to promote CSR development.

This research is aimed at exploring the influencing factors of users’ behavioral intention toward the Nike Training Club (NTC) app, using the decomposed theory of planned behavior as the main research framework. In this study, users of the NTC app were the research objects. A total of 300 questionnaires were distributed on the Internet by the purposive sampling method. After excluding invalid questionnaires, a total of 280 copies were obtained. The effective questionnaire recovery rate was 93.33%. All data were verified by descriptive statistics and multiple regression analysis. The results of this study were as follows. First, the NTC app users were mostly female, aged between 19 and 25 years, were students, had a monthly income below 20,000, and exercised one to two times a week. Second, the application of the decomposed theory of planned behavior found the following: perceived usefulness and perceived ease of use had positive effects on attitude; interpersonal influence and electronic word of mouth had a positive influence on subjective norms; self-efficacy and technology facilitation had positive effects on perceptual behavior control; and attitude, subjective norms, and perceptual behavior control all had a positive impact on behavioral intention. The NTC app users thought that the program interface was easy to use and that the app could help them effectively achieve the effect of exercise and fitness, which increased the benefits of continuously using the program.

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

The receptor-binding site (RBS) of influenza A viruses evolves to evade immune pressure, while maintaining efficient attachment to the host receptor. Wu et al. here identify the complex epistatic network in RBS of H3N2 viruses that limits reversibility of naturally occurring mutations to retain infectivity.

Plant species, structural combination, and spatial distribution in different regions should be adapted to local conditions, and the reasonable arrangement can bring the best ecological effect. Therefore, it is essential to understand the classification and distribution of plant species. This paper proposed an end-to-end network with Enhancing Nested Downsampling features (END-Net) to solve complex and challenging plant species segmentation tasks. There are two meaningful operations in the proposed network: (1) A compact and complete encoder–decoder structure nests in the down-sampling process; it makes each downsampling block obtain the equal feature size of input and output to get more in-depth plant species information. (2) The downsampling process of the encoder–decoder framework adopts a novel pixel-based enhance module. The enhanced module adaptively enhances each pixel’s features with the designed learnable variable map, which is as large as the corresponding feature map and has n×n variables; it can capture and enhance each pixel’s information flexibly effectively. In the experiments, our END-Net compared with eleven state-of-the-art semantic segmentation architectures on the self-collected dataset, it has the best PA (Pixel Accuracy) score and FWloU (Frequency Weighted Intersection over Union) accuracy and achieves 84.52% and 74.96%, respectively. END-Net is a lightweight model with excellent performance; it is practical in complex vegetation distribution with aerial and optical images. END-Net has the following merits: (1) The proposed enhancing module utilizes the learnable variable map to enhance features of each pixel adaptively. (2) We nest a tiny encoder–decoder module into the downsampling block to obtain the in-depth plant species features with the same scale in- and out-features. (3) We embed the enhancing module into the nested model to enhance and extract distinct plant species features. (4) We construct a specific plant dataset that collects the optical images-based plant picture captured by drone with sixteen species.

Classifying fine-grained categories (e.g., bird species, car, and aircraft types) is a crucial problem in image understanding and is difficult due to intra-class and inter-class variance. Most of the existing fine-grained approaches individually utilize various parts and local information of objects to improve the classification accuracy but neglect the mechanism of the feature fusion between the object (global) and object’s parts (local) to reinforce fine-grained features. In this paper, we present a novel framework, namely object–part registration–fusion Net (OR-Net), which considers the mechanism of registration and fusion between an object (global) and its parts’ (local) features for fine-grained classification. Our model learns the fine-grained features from the object of global and local regions and fuses these features with the registration mechanism to reinforce each region’s characteristics in the feature maps. Precisely, OR-Net consists of: (1) a multi-stream feature extraction net, which generates features with global and various local regions of objects; (2) a registration–fusion feature module calculates the dimension and location relationships between global (object) regions and local (parts) regions to generate the registration information and fuses the local features into the global features with registration information to generate the fine-grained feature. Experiments execute symmetric GPU devices with symmetric mini-batch to verify that OR-Net surpasses the state-of-the-art approaches on CUB-200-2011 (Birds), Stanford-Cars, and Stanford-Aircraft datasets.

Most antibodies currently in use have been selected based on their binding affinity. However, nowadays, antibodies that can not only bind but can also alter the function of cell surface signaling components are increasingly sought after as therapeutic drugs. Therefore, the identification of such functional antibodies from a large antibody library is the subject of intensive research. New methods applied to combinatorial antibody libraries now allow the isolation of functional antibodies in the cellular environment. These selected agonist antibodies have provided new insights into important issues of signal transduction. Notably, when certain antibodies bind to a given receptor, the cell fate induced by them may be the same or different from that induced by natural agonists. In addition, combined with phenotypic screening, this platform allows us to discover unexpected experimental results and explore various phenomena in cell biology, such as those associated with stem cells and cancer cells.

We present data showing that Iodotyrosine Deiodinase (IYD) is a dual-function enzyme acting as a catalyst in metabolism and a receptor for cooperative stem cell differentiation. IYD is present both in thyroid cells where it is critical for scavenging iodine from halogenated by-products of thyroid hormone production and on hematopoietic stem cells. To close the cooperative loop, the mono- and di-Iodotyrosine (MIT and DIT) substrates of IYD in the thyroid are also agonists for IYD now acting as a receptor on bone marrow stem cells. While studying intracellular combinatorial antibody libraries, we discovered an agonist antibody, H3 Ab, of which the target is the enzyme IYD. When agonized by H3 Ab, IYD expressed on stem cells induces differentiation of the cells into brown adipocyte-like cells, which selectively migrate to mouse heart tissue. H3 Ab also binds to IYD expressed on human myocardium. Thus, one has a single enzyme acting in different ways on different cells for the cooperative purpose of enhancing thermogenesis or of regenerating damaged heart tissue.

The purpose of this research was aimed to explore the importance of fitness club employees' management by using multi-dimensional social network point of view and use the method purpose by Preacher and Hayes (2008) to analyze the mediating effect of multi-dimensional social networks in empowerment and group effectiveness. The subjects of this study were employees from a well-known chain fitness club in southern of Taiwan. The results show that: The “meaning” and “competence” of employees' empowerment can significant predict the social networks and group effectiveness; the “network centrality”, “social solidarity” and “social impact” of employees' social networks can significantly predict group effectiveness; and the social network of fitness club employees has significant mediating effect between empowerment and group effectiveness. The “meaning” of empowerment has a low impact on group effectiveness, and has a moderate impact on group effectiveness through by social network; the “competence” of empowerment has no significant direct impact on group effectiveness, but also can influence group effectiveness through by social networks. According to the results of this study, practical strategies and recommendations were purposed. Keywords: Empowerment, Social Networking, Group Effectiveness JEL Classifications: M10, M14, M16

DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential functions can be applied on protein-DNA co-crystallized structures to generate PWMs that are considerably consistent with experimental data. However, this success has not been extended to DNA-binding proteins lacking co-crystallized structures. This study aims at investigating the possibility of predicting the DNA sequences bound by DNA-binding proteins from the proteins' unbound structures (structures of the unbound state). Given an unbound query protein and a template complex, the proposed method first employs structure alignment to generate synthetic protein-DNA complexes for the query protein. Once a complex is available, an atomic-level knowledge-based potential function is employed to predict PWMs characterizing the sequences to which the query protein can bind. The evaluation of the proposed method is based on seven DNA-binding proteins, which have structures of both DNA-bound and unbound forms for prediction as well as annotated PWMs for validation. Since this work is the first attempt to predict target sequences of DNA-binding proteins from their unbound structures, three types of structural variations that presumably influence the prediction accuracy were examined and discussed. Based on the analyses conducted in this study, the conformational change of proteins upon binding DNA was shown to be the key factor. This study sheds light on the challenge of predicting the target DNA sequences of a protein lacking co-crystallized structures, which encourages more efforts on the structure alignment-based approaches in addition to docking- and homology modeling-based approaches for generating synthetic complexes.