Similar Measurements Research Articles

European water frame directive ecoregions do not adequately represent freshwater fish biogeography

The quest to understand species' distribution patterns and delineate their limits has led to the establishment of the world's ecoregions, large units of land containing a distinct assemblage of species, both for terrestrial and marine ecosystems. However, freshwaters constitute their own special case within terrestrial ecosystems, and have only recently been divided into freshwater ecoregions, primarily based on watershed limits. Freshwater ecoregions used by the European Water Frame Directive (WFD), which guides freshwater management policies in the European Union, have been criticized for not adequately representing freshwater fish biogeography. This study compares the geographic agreement between WFD ecoregions and freshwater fish distribution at the European scale. Using IUCN Red List distribution maps, we determined the composition of freshwater fish communities in major European basins. Then, we calculated a similarity matrix in order to cluster freshwater basins with more related fish assemblages. We compare our findings with WFD ecoregions and global ecoregions from the Freshwater Ecoregions of the World (FEOW) database. Our results reveal that WFD ecoregions do not sufficiently capture freshwater fish assemblage differences across Europe. Mediterranean basins exhibit high freshwater fish endemism and greater species variation, whereas central European basins harbor more similar fauna. Overall, our results align more with those of FEOW. To enhance the relevance of freshwater ecoregions in Europe for management and conservation, we propose several measures to ensure a comprehensive study involving all stakeholders in the process of defining these ecoregions accurately.

Predictive Analysis of COVID-19 Epidemic in Thailand: Evaluating Control Lockdown Measures using LSTM Networks

This study addresses the critical objective of evaluating the effectiveness of non-pharmaceutical lockdown measures implemented during COVID-19 outbreaks in Thailand. Assessing the outcome of these measures provides valuable insight that can inform and guide response to future outbreaks. Utilizing a closed-loop forecasting model built on Long Short-Term Memory (LSTM) networks, the research focuses on achieving precise daily forecasts of COVID-19 cases. The methodology involves optimizing hyperparameters through grid-search and incorporating training data from other countries that implemented similar measures. The LSTM, configured with an optimal number of hidden processing units, utilizes past lagged data of daily infected cases as predictors to generate multi-step-ahead predicted values, which are subsequently used as predictors in a recursive approach. As a result, the predicted cases closely align with measured data, facilitating the estimation of the effective reproduction number (Reff) to assess the performance of lockdown measures. The effectiveness of the lockdown measures is quantified at different time intervals: 51%, 41%, and 23% one day after implementation, increasing to 84%, 98%, and 34% after one week, and reaching 96%, 99%, and 73% at the endpoint of the first, second, and fourth waves of infection, respectively. Throughout these waves, the final Reff remains below 1, indicating ongoing but controllable COVID-19, demonstrating the efficacy of the implemented lockdown measures. It is noted that these results are based on specific LSTM model, as the effectiveness of lockdown measures may vary with alternative modeling approaches. Therefore, the findings should be interpreted in the context of this LSTM-framework.

Time delays between physiological signals in interpreting the body’s responses to intermittent hypoxia in obstructive sleep apnea

Objective. Intermittent hypoxia, the primary pathology of obstructive sleep apnea (OSA), causes cardiovascular responses resulting in changes in hemodynamic parameters such as stroke volume (SV), blood pressure (BP), and heart rate (HR). However, previous studies have produced very different conclusions, such as suggesting that SV increases or decreases during apnea. A key reason for drawing contrary conclusions from similar measurements may be due to ignoring the time delay in acquiring response signals. By analyzing the signals collected during hypoxia, we aim to establish criteria for determining the delay time between the onset of apnea and the onset of physiological parameter response. Approach. We monitored oxygen saturation (SpO2), transcutaneous oxygen pressure (TcPO2), and hemodynamic parameters SV, HR, and BP, during sleep in 66 patients with different OSA severity to observe body’s response to hypoxia and determine the delay time of above parameters. Data were analyzed using the Kruskal–Wallis test, Quade test, and Spearman test. Main results. We found that simultaneous acquisition of various parameters inevitably involved varying degrees of response delay (7.12–25.60 s). The delay time of hemodynamic parameters was significantly shorter than that of SpO2 and TcPO2 (p< 0.01). OSA severity affected the response delay of SpO2, TcPO2, SV, mean BP, and HR (p < 0.05). SV delay time was negatively correlated with the apnea-hypopnea index (r = −0.4831, p< 0.0001). Significance. The real body response should be determined after removing the effect of delay time, which is the key to solve the problem of drawing contradictory conclusions from similar studies. The methods and important findings presented in this study provide key information for revealing the true response of the cardiovascular system during hypoxia, indicating the importance of proper signal analysis for correctly interpreting the cardiovascular hemodynamic response phenomena and exploring their physiological and pathophysiological mechanisms.

QLDTI: A Novel Reinforcement Learning-based Prediction Model for Drug-Target Interaction

Background: Predicting drug-target interaction (DTI) plays a crucial role in drug research and development. More and more researchers pay attention to the problem of developing more powerful prediction methods. Traditional DTI prediction methods are basically realized by biochemical experiments, which are time-consuming, risky, and costly. Nowadays, DTI prediction is often solved by using a single information source and a single model, or by combining some models, but the prediction results are still not accurate enough. Objective: The study aimed to utilize existing data and machine learning models to integrate heterogeneous data sources and different models, further improving the accuracy of DTI prediction. Methods: This paper has proposed a novel prediction method based on reinforcement learning, called QLDTI (predicting drug-target interaction based on Q-learning), which can be mainly divided into two parts: data fusion and model fusion. Firstly, it fuses the drug and target similarity matrices calculated by different calculation methods through Q-learning. Secondly, the new similarity matrices are inputted into five models, NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, for further training. Then, all sub-model weights are continuously optimized again by Q-learning, which can be used to linearly weight all sub-model prediction results to output the final prediction result. Results: QLDTI achieved AUC accuracy of 99.04%, 99.12%, 98.28%, and 98.35% on E, NR, IC, and GPCR datasets, respectively. Compared to the existing five models NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, the QLDTI method has achieved better results on four benchmark datasets of E, NR, IC, and GPCR. Conclusion: Data fusion and model fusion have been proven effective for DTI prediction, further improving the prediction accuracy of DTI.

Shoulder arthroplasty outcomes in a series of patients diagnosed with Parkinson’s disease: a retrospective analysis

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder that causes resting tremor and rigidity. Total shoulder arthroplasty (TSA) in patients with PD has proven to be a challenging clinical scenario and is associated with a high risk of complications. The purpose of this study is to explore the relationship between patients diagnosed with PD and outcomes following shoulder arthroplasty (SA). MethodsInstitutional records were queried for patients with PD who underwent SA from 2009 to 2020. Patient medical documentation was retrospectively reviewed to assess demographic information, comorbidity status, preoperative and postoperative range of motion measurements, and complications. Mean ± standard deviation was reported for continuous variables. ResultsA total of 17 patients were identified that met the inclusion criteria. The mean age of the cohort was 57.6 ± 10.6 years at the time of PD diagnosis and 68.1 ± 6.9 years at the time of surgery. The mean follow-up was 1.6 ± 1.2 years. The most common indications for surgery were degenerative joint disease, osteoarthritis, rotator cuff arthropathy, proximal humerus fracture, and rotator cuff tear (1). Mean forward elevation improved from 126.6 ± 24.8° to 131.2 ± 27.8°; mean external rotation improved from 29.4 ± 17.3° to 38.8 ± 10.5°. DiscussionSA is an effective method of improving pain and function in PD patients. Patients undergoing anatomic TSA exhibited the most robust functional improvement in this cohort; however, anatomic TSA was associated with postoperative complications and similar functional measurements were achieved at final follow-up in patients who underwent reverse TSA. Thus, significant care must be taken to ensure the correct implant is chosen on an individual basis to maximize the potential for functional improvement.

Respiratory rate monitoring in ICU patients and healthy volunteers using electrical impedance tomography: a validation study

Objective. The respiratory rate (RR) is considered one of the most informative vital signals. A well-validated standard for RR measurement in mechanically ventilated patient is capnography; a noninvasive technique for expiratory CO2 measurements. Reliable RR measurements in spontaneously breathing patients remains a challenge as continuous mainstream capnography measurements are not available. This study aimed to assess the accuracy of RR measurement using electrical impedance tomography (EIT) in healthy volunteers and intensive care unit (ICU) patients on mechanical ventilation and spontaneously breathing post-extubation. Comparator methods included RR derived from both capnography and bioimpedance electrocardiogram (ECG) measurements. Approach. Twenty healthy volunteers wore an EIT belt and ECG electrodes while breathing through a capnometer within a 10–40 breaths per minute (BPM) range. Nineteen ICU patients underwent similar measurements during pressure support ventilation and spontaneously breathing after extubation from mechanical ventilation. Stable periods with regular breathing and no artefacts were selected, and agreement between measurement methods was assessed using Bland–Altman analysis for repeated measurements. Main result. Bland–Altman analysis revealed a bias less than 0.2 BPM, with tight limits of agreement (LOA) ±1.5 BPM in healthy volunteers and ventilated ICU patients when comparing EIT to capnography. Spontaneously breathing ICU patients had wider LOA (±2.5 BPM) when comparing EIT to ECG bioimpedance, but gold standard comparison was unavailable. RR measurements were stable for 91% of the time for capnography, 68% for EIT, and 64% of the ECG bioimpedance signals. After extubation, the percentage of stable periods decreased to 48% for EIT signals and to 55% for ECG bioimpedance. Significance. In periods of stable breathing, EIT demonstrated excellent RR measurement accuracy in healthy volunteers and ICU patients. However, stability of both EIT and ECG bioimpedance RR measurements declined in spontaneously breathing patients to approximately 50% of the time.

Two-pronged feature reduction in spectral clustering with optimized landmark selection

Spectral clustering is widely employed for clustering data points, particularly for non-linear and non-convex structures in high-dimensional spaces. However, it faces challenges due to the high computational cost of eigen decomposition operations and the performance limitations with high-dimensional data. In this paper, we introduce BVA_LSC, a novel spectral clustering algorithm designed to address these challenges. Firstly, we incorporate an advanced feature reduction stage utilizing Barnes-Hut t-SNE and a deep Variational Autoencoder (VAE) to efficiently reduce the dimensionality of the data, thereby accelerating eigen decomposition. Secondly, we propose an adaptive landmark selection strategy that combines the Grey Wolf Optimizer (GWO) with a novel objective function and K-harmonic means clustering. This strategy dynamically determines an optimal number of landmarks, enhancing the representativeness of the data and reducing the size of the similarity matrix. We assess the performance of our algorithm on various standard datasets, demonstrating its superiority over state-of-the-art methods in terms of accuracy and efficiency.

Towards Robust Subspace Clustering via Joint Feature Extraction and Cauchy Loss Function

The purpose of the subspace clustering approach is to discover the similarity between samples by learning a self-representation matrix, and it has been widely employed in machine learning and pattern recognition. Most existing subspace clustering techniques discover subspace structures from raw data and simply adopt L2 loss to characterize the reconstruction error. To break through these limitations, a novel robust model named Feature extraction and Cauchy loss function-based Subspace Clustering (FCSC) is proposed. FCSC performs low dimensional and low-rank feature extraction at the same time, as well as processing large noise in the data to generate a more ideal similarity matrix. Furthermore, we provide an efficient iterative strategy to solve the resultant problem. Extensive experiments on benchmark datasets confirm its superiority in the robustness of some advanced subspace clustering algorithms.

Temperature scaling unmixing framework based on convolutional autoencoder

Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usually impose the same strength of sparsity constraint. However, this often does not hold in practice. Because the abundances of purer regions are generally sparse, while the abundances distribution of more mixed regions should be smoother. Temperature scaling is a technique of introducing a temperature parameter T into softmax activation function to adjust the sparsity of the output. Inspired by this, we propose a temperature scaling unmixing (TSU) framework based on convolutional autoencoder (CAE). In this framework, sparse constraints of different intensities are applied to diverse regions by considering spatial similarity of ground objects distribution while preserving the ability of CAE to extract spatial features. What is more, equal-frequency binning is adopted to guide the division of regions by similarity matrix to realize the automatic temperature parameter setting. In addition, a CAE network is designed under the TSU framework in this paper, called TSUCAE. The TSUCAE method exhibits superior accuracy compared to state-of-the-art approaches, as demonstrated through extensive comparative experiments. Furthermore, the TSU framework can be transferred to other CAE-based unmixing methods directly while keeping the network structure of these methods unchanged. Sufficient ablation experiments also prove that the transfer of framework can improve the performance of unmixing. The code is publicly available at https://github.com/UPCGIT/TSUCAE.

A community-scale study on nature-based solutions (NBS) for stormwater management under tropical climate: The case of the Asian Institute of Technology (AIT), Thailand

ABSTRACT Rapid urbanization and population growth are placing more demands on the world's natural water resources. New infrastructures are increasing the degree of surface sealing as well as the tendency for urban flooding and water quality degradation. These problems can be counteracted by nature-based solutions (NBS) for urban drainage in developed countries mostly having a temperate climate. Hence, there is a need to develop similar sustainable measures for tropical regions as currently there are no guidelines available. In this study, the multi-criteria decision analysis (MCDA) approach was utilized to identify the best site for NBS in the Asian Institute of Technology (AIT) in Bangkok, Thailand. Then, Personnel Computer Storm Water Management Model (PCSWMM) software was used to develop a numerical model. It was found that the MCDA approach is appropriate to determine the best site for NBS implementation considering different aspects including economic, environmental, and technical ones. The results strongly suggested that Site-1 is a suitable alternative to implement NBS in the AIT campus. It was found that a bioretention system can reduce runoff volume by at least 14% and pollutants by at least 14–20%. The present study will provide a guideline for site selection and development of the NBS model for urban water management in a tropical climate.

Dysplastic Hips That Are Too Late for Periacetabular Osteotomy Are Not Too Early for Total Hip Arthroplasty

BackgroundTotal hip arthroplasty (THA) is often performed in symptomatic patients who have hip dysplasia and do not qualify for periacetabular osteotomy. The impact of osteoarthritis (OA) severity on postoperative outcomes in dysplasia patients who undergo THA is not well described. We hypothesized that dysplasia patients who have mild OA have slower initial recovery postoperatively but similar one-year patient-reported outcome measures (PROMs) compared to dysplasia patients who have severe OA. MethodsWe performed a retrospective review at a single academic institution over a 6-year period of patients who have dysplasia who underwent THA compared to patients who have primary OA who underwent THA. There were 263 patients who had dysplasia, compared to 1,225 THA patients who did not have dysplasia. Within the dysplasia cohort, we compared PROMs stratified by dysplasia and OA severity. The diagnosis of dysplasia was verified using the radiographic lateral center edge angle. A minimum one-year follow-up was required. The PROMs were collected through one year postoperatively. Logistic and linear regression models were used, adjusting for age, sex, body mass index, and Charlson comorbidity index. ResultsNo significant differences were found in postoperative PROMs or revision rates (P = .58). When stratified by dysplasia severity, patients who had lower lateral center edge angle had more improvement in physical function scores from preoperative to 2 weeks (P < .01) and higher physical function scores at 2 weeks (P = .03). When stratified by OA severity, patients who had a worse Tönnis score had more improvement in physical function scores from preoperative to 2 weeks (P < .01). Recovery curves in dysplasia patients based on dysplasia and OA severity were not significantly different at 6 weeks, 1 year, and 2 years postoperative. ConclusionsPatients who had hip dysplasia and mild OA had similar recovery curves compared to those who had severe OA or who did not have dysplasia. We believe that THA is a reasonable surgical intervention for symptomatic dysplasia patients who have mild arthritis and do not qualify for periacetabular osteotomy.

OA11 Maintained improvement in patient reported outcomes in early axial spondyloarthritis following certolizumab pegol dose reduction

Abstract Background/Aims Certolizumab pegol (CZP), a PEGylated TNFi without an Fc region, has previously shown efficacy and safety in patients with axial spondyloarthritis (axSpA). EULAR guidelines recommend that tapering of a biologic can be considered for patients in sustained remission. The C-OPTIMISE trial (NCT02505542) identified early (&amp;lt;5 years) axSpA patients in sustained remission who may benefit from dose tapering or stopping treatment. Here we report whether a reduced CZP maintenance dose is associated with sustained improvements in patient reported outcomes (PROs) in patients with axSpA at Week (Wk) 96. Methods C-OPTIMISE was a two-part, multicentre, phase 3b study in adult patients. Part A: up to Wk48 (open-label), patients received CZP 200mg Q2W. At Wk48 (Part B), patients who achieved sustained remission at Wk32 or 36 (ASDAS &amp;lt;1.3), were randomised 1:1:1 to double-blind CZP 200 mg Q2W (full maintenance dose), CZP 200 mg Q4W (reduced maintenance dose), or placebo (PBO [drug withdrawal]) for additional 48Wks. We report disease activity and PROs (BASDAI, PtGADA, nocturnal apinal pain, morning stiffness, fatigue, and ASQoL) during Part B (Wks48-96). Flare is defined as ASDAS ≥2.1 at two consecutive visits, or ASDAS &amp;gt;3.5 at any visit. Results At Wk48 (Part A), 43.9% (323/736) of patients achieved sustained remission, of which 313 patients were randomised to Part B (Wks48-96). During Wks48-96, 87/104 (83.7%) patients receiving the full maintenance dose, 83/105 (79.0%) patients receiving the reduced maintenance dose, and 21/104 (20.2%) patients receiving PBO remained flare-free. Patients receiving full maintenance and those on reduced dose have similar disease activity measures (BASDAI; mean [SD], 0.8 [0.8] vs 0.8 [1.0]) at Wk96 (Table). Sustained improvements in PROs were similar for CZP full maintenance dose and CZP reduced maintenance dose, including ASQoL, fatigue and nocturnal spinal pain, while PBO showed increased disease activity (Table). Conclusion In axSpA patients who achieved sustained remission, CZP reduced dosage resulted in sustained improvements in disease activity and PROs similar to those receiving full maintenance dose. This suggests that dose reduction can be a viable strategy without compromising PROs. Disclosure A. Chan: Member of speakers’ bureau; AC has received speaker bureau from Novartis, UCB, Abbvie, Janssen, Amgen. Grants/research support; AC has received grant support for service and educational projects from Novartis and UCB Pharma. Other; AC has received honoraria/advisory boards from Novartis, Abbvie, UCB, Janssen, Amgen, Lilly. J. Wood: Other; JW is an employee of UCB Pharma. L. Burns: Other; LBu is an employee of UCB Pharma. R. Tham: Other; RT is employed by Veramed and a contractor for UCB Pharma. T. Kumke: Other; TK is an employee and shareholder of UCB Pharma. B. Hoepken: Other; BH is an employee and shareholder of UCB Pharma. M. Kim: Other; MK is an employee of UCB Pharma. L. Bauer: Other; LBa is an employee and shareholder of UCB Pharma. K. MacKay: Other; KM received consultancy &amp; speaker meetings for UCB, AbbVie, Novartis, Lilly, AZ, Janssen and Roche. K. Gaffney: Shareholder/stock ownership; KG is a shareholder of Rheumatology Events. Grants/research support; KG has received grants/research support from NASS, Versus Arthritis, AbbVie, Pfizer, UCB Pharma, Norvartis, Eli Lilly, Cellgene, Celltrion, Janssen, Gilead and Biogen. Other; KG has received honoraria/ consultation fees from Novartis, AbbVie, UCB Pharma, Lilly and Pfizer, KG has received speaker’s bureau from Novartis, UCB Pharma, AbbVie and Lilly; meeting expenses from AbbVie, Lilly, Roche, Novartis, Pfizer and UCB Pharma.

From Local Atomic Environments to Molecular Information Entropy.

The similarity of local atomic environments is an important concept in many machine learning techniques, which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures, and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach.

A Comparison of 2 Cage Sizes in Biportal Endoscopic Transforaminal Lumbar Interbody Fusion.

Retrospective study. This study compared the fusion and subsidence rate and clinical outcomes when using different-sized static PEEK cages in BE-TLIF. Biportal endoscopic techniques for transforaminal lumbar interbody fusion (BE-TLIF) have been shown to have similar clinical and fusion outcomes with faster clinical recovery in comparison to tubular surgery. Subsidence of the interbody, however, could be a complication. Patients who underwent 1 or 2 level BE-TLIF for degenerative and isthmic spondylolisthesis between January 2019 and January 2022 were included. A 32×10mm cage (group A) and a 40×15mm cage (group B) were compared. The visual analog scale (VAS) for back and leg symptoms, and Oswestry disability index (ODI) were collected. Plain radiographs and computed tomography assessed fusion and subsidence at a minimum of 12 months. Of the 69 enrolled patients, 39 group A patients (51 levels) and 30 group B patients (32 levels) were compared. The operation time per level was 123 ± 15.8 and 138 ± 10.5 minutes per fusion level in groups A and B, respectively (P < 0.05). ODI improved from 64.8 ± 6.2 to 15.7 ± 7.1 in group A and from 65.3 ± 5.6 to 15.1 ± 6.3 in group B at the final follow-up (P < 0.05). VAS leg and back score improvement between the groups did not differ; however, the 3-month postoperative VAS back improvement was significantly higher in group B. The final fusion rate at the final follow-up did not significantly differ; however, the fusion ratio at 1 year was higher in group B (P < 0.05). Subsidence occurred in 5 cases (9.8%) in group A and none in group B (P < 0.05). BE-TLIF using a larger cage can be performed safely with similar patient-reported outcome measures with a faster fusion rate with less subsidence risk. III.

Clustering analysis for Pythagorean fuzzy sets and its application in multiple attribute decision making

Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered.

Sparse semi-supervised multi-label feature selection based on latent representation

With the rapid development of the Internet, there are a large number of high-dimensional multi-label data to be processed in real life. To save resources and time, semi-supervised multi-label feature selection, as a dimension reduction method, has been widely used in many machine learning and data mining. In this paper, we design a new semi-supervised multi-label feature selection algorithm. First, we construct an initial similarity matrix with supervised information by considering the similarity between labels, so as to learn a more ideal similarity matrix, which can better guide feature selection. By combining latent representation with semi-supervised information, a more ideal pseudo-label matrix is learned. Second, the local manifold structure of the original data space is preserved by the manifold regularization term based on the graph. Finally, an effective alternating iterative updating algorithm is applied to optimize the proposed model, and the experimental results on several datasets prove the effectiveness of the approach.

Similarity-based active learning methods

Active Learning has been a popular method to circumvent the labeling cost in machine learning methods. The majority of active learning approaches can be classified into two categories: representative-based and informative-based methods, with some hybrid methods that combine both. This work presents a naïve query strategy, namely Similarity-Based Active Learning (SBAL), which computes the sum of a row in the similarity matrix at each selection step, and a general optimization framework that can accommodate a broad range of active learning algorithms. The label complexity for different classification metrics is used as a primary criterion for comparing different algorithms. The proposed algorithm’s numerical performance is illustrated using simulated data scenarios and by applying it to the real-world COVID-19 image classification. The results demonstrate that, based on the classification metric, labeling cost and label complexity, SBAL outperforms other hybrid methods, such as Adaptive Active Learning (AAL) and Maximizing Variance for Active Learning (MVAL).

OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks.

In recent years, many excellent computational models have emerged in microbe-drug association prediction, but their performance still has room for improvement. This paper proposed the OGNNMDA framework, which applied an ordered message-passing mechanism to distinguish the different neighbor information in each message propagation layer, and it achieved a better embedding ability through deeper network layers. Firstly, the method calculates four similarity matrices based on microbe functional similarity, drug chemical structure similarity, and their respective Gaussian interaction profile kernel similarity. After integrating these similarity matrices, it concatenates the integrated similarity matrix with the known association matrix to obtain the microbe-drug heterogeneous matrix. Secondly, it uses a multi-layer ordered message-passing graph neural network encoder to encode the heterogeneous network and the known association information adjacency matrix, thereby obtaining the final embedding features of the microbe-drugs. Finally, it inputs the embedding features into the bilinear decoder to get the final prediction results. The OGNNMDA method performed comparative experiments, ablation experiments, and case studies on the aBiofilm, MDAD and DrugVirus datasets using 5-fold cross-validation. The experimental results showed that OGNNMDA showed the strongest prediction performance on aBiofilm and MDAD and obtained sub-optimal results on DrugVirus. In addition, the case studies on well-known drugs and microbes also support the effectiveness of the OGNNMDA method. Source codes and data are available at: https://github.com/yyzg/OGNNMDA.

Brain morphometry and estimation of aging brain in subjects with congenital untreated isolated GH deficiency.

Individuals with isolated GH deficiency (IGHD) due to a mutation in the GHRH receptor gene have a normal life expectancy and above 50years of age, similar total cognitive performance, with better attention and executive function than controls. Our objectives were to evaluate their brain morphometry and brain aging using MRI. Thirteen IGHD and 14 controls matched by age, sex, and education, were enrolled. Quantitative volumetric data and cortical thickness were obtained by automatic segmentation using Freesurfer software. The volume of each brain region was normalized by the intracranial volume. The difference between the predicted brain age estimated by MRI using a trained neuronal network, and the chronological age, was obtained. p < 0.005 was considered significant and 0.005 < p < 0.05 as a suggestive evidence of difference. In IGHD, most absolute values of cortical thickness and regional brain volumes were similar to controls, but normalized volumes were greater in the white matter in the frontal pole and in the insula bilaterally, and in the gray matter, in the right insula and in left Caudate (p < 0.005 for all comparisons) We also noticed suggestive evidence of a larger volume in IGHD in left thalamus (p = 0.006), right thalamus (p = 0.025), right caudate (p = 0.046) and right putamen (p = 0.013). Predicted brain ages were similar between groups. IGHD is primarily associated with similar absolute brain measurements, and a set of larger normalized volumes, and does not appear to alter the process of brain aging.

Experimenter bias: exploring the interaction between participant’s and investigator’s gender/sex in VR

This study explores the effect of the experimenter’s gender/sex and its interaction with the participant’s gender/sex as potential contributors to the replicability crisis, particularly in the man-gendered domain of VR. 75 young men and women from Western France were randomly evaluated by either a man or a woman during a 13-min immersion in a first-person shooter game. Self-administered questionnaires were used to measure variables commonly assessed during VR experiments (sense of presence, cybersickness, video game experience, flow). MANOVAs, ANOVAs and post-hoc comparisons were used. Results indicate that men and women differ in their reports of cybersickness and video game experience when rated by men, whereas they report similar measures when rated by women. These findings are interpreted as consequences of the psychosocial stress triggered by the interaction between the two genders/sexes, as well as the gender conformity effect induced, particularly in women, by the presence of a man in a masculine domain. Corroborating this interpretation, the subjective measure of flow, which is not linked to video games and/or computers, does not seem to be affected by this experimental effect. Methodological precautions are highlighted, notably the brief systematic description of the experimenter, and future exploratory and confirmatory studies are outlined.Graphical abstract