Relevant Regions Research Articles

On the Concept Trustworthiness in Concept Bottleneck Models

Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck. However, despite the transparency of the concept-to-label prediction, the mapping from the input to the intermediate concept remains a black box, giving rise to concerns about the trustworthiness of the learned concepts (i.e., these concepts may be predicted based on spurious cues). The issue of concept untrustworthiness greatly hampers the interpretability of CBMs, thereby hindering their further advancement. To conduct a comprehensive analysis on this issue, in this study we establish a benchmark to assess the trustworthiness of concepts in CBMs. A pioneering metric, referred to as concept trustworthiness score, is proposed to gauge whether the concepts are derived from relevant regions. Additionally, an enhanced CBM is introduced, enabling concept predictions to be made specifically from distinct parts of the feature map, thereby facilitating the exploration of their related regions. Besides, we introduce three modules, namely the cross-layer alignment (CLA) module, the cross-image alignment (CIA) module, and the prediction alignment (PA) module, to further enhance the concept trustworthiness within the elaborated CBM. The experiments on five datasets across ten architectures demonstrate that without using any concept localization annotations during training, our model improves the concept trustworthiness by a large margin, meanwhile achieving superior accuracy to the state-of-the-arts. Our code is available at https://github.com/hqhQAQ/ProtoCBM.

Quantification of Adaptive Immune Responses Against Protein-Binding Interfaces in the Streptococcal M1 Protein

Bacterial or viral antigens can contain subdominant protein regions that elicit weak antibody responses upon vaccination or infection although there is accumulating evidence that antibody responses against subdominant regions can enhance the protective immune response. One proposed mechanism for subdominant protein regions is the binding of host proteins that prevent antibody production against epitopes hidden within the protein binding interfaces. Here, we used affinity purification combined with quantitative mass spectrometry (AP-MS) to examine the level of competition between antigen-specific antibodies and host–pathogen protein interaction networks using the M1 protein from Streptococcus pyogenes as a model system. As most humans have circulating antibodies against the M1 protein, we first used AP-MS to show that the M1 protein interspecies protein network formed with human plasma proteins is largely conserved in naïve mice. Immunizing mice with the M1 protein generated a time-dependent increase of anti-M1 antibodies. AP-MS analysis comparing the composition of the M1-plasma protein network from naïve and immunized mice showed significant enrichment of 292 IgG peptides associated with 56 IgG chains in the immune mice. Despite the significant increase of bound IgGs, the levels of interacting plasma proteins were not significantly reduced in the immune mice. The results indicate that the antigen-specific polyclonal IgG against the M1 protein primarily targets epitopes outside the other plasma protein binding interfaces. In conclusion, this study demonstrates that AP-MS is a promising strategy to determine the relationship between antigen-specific antibodies and host-pathogen interaction networks that could be used to define subdominant protein regions of relevance for vaccine development.

Transcranial alternating current stimulation improves quality of life in Parkinson’s disease: study protocol for a randomized, double-blind, controlled trial

BackgroundThe neural cells in the brains of patients with Parkinson’s disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson’s disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson’s disease based on the central-peripheral-central theory.MethodsThe present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson’s disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes.DiscussionThe objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP.Trial registrationChinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.

Detecting bone lesions in X-ray under diverse acquisition conditions.

The diagnosis of primary bone tumors is challenging as the initial complaints are often non-specific. The early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. We propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging. First, the prevalence of bone cancer is very low; any method must show high precision to avoid a prohibitive number of false alarms. Second, radiographs taken in health maintenance organizations (HMOs) or emergency departments (EDs) suffer from inherent diversity due to different X-ray machines, technicians, and imaging protocols. This diversity poses a major challenge to any automatic analysis method. We propose training an off-the-shelf object detection algorithm to detect lesions in radiographs. The novelty of our approach stems from a dedicated preprocessing stage that directly addresses the diversity of the data. The preprocessing consists of self-supervised region-of-interest detection using vision transformer (ViT), and a foreground-based histogram equalization for contrast enhancement to relevant regions only. We evaluate our method via a retrospective study that analyzes bone tumors on radiographs acquired from January 2003 to December 2018 under diverse acquisition protocols. Our method obtains 82.43% sensitivity at a 1.5% false-positive rate and surpasses existing preprocessing methods. For lesion detection, our method achieves 82.5% accuracy and an IoU of 0.69. The proposed preprocessing method enables effectively coping with the inherent diversity of radiographs acquired in HMOs and EDs.

Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images

Lung cancer is identified by the uncontrolled proliferation of cells in lung tissues. The timely detection of malignant cells in the lungs, crucial for processes such as oxygen provision and carbon dioxide elimination in the human body, is imperative. The application of deep learning for discerning lymph node involvement in CT scans and histopathological images has garnered widespread attention due to its potential impact on patient diagnosis and treatment. This paper suggests employing DenseNet for lung cancer detection, leveraging its ability to transmit learned features backward through each layer continuously. This characteristic not only reduces model parameters but also enhances the learning of local features, facilitating a better comprehension of the structural complexity and uneven distribution in CT scans and histopathological cancer images. Furthermore, DenseNet accompanied by an attention mechanism (ATT-DenseNet) allows the model to focus on specific parts of an image, giving more weight to relevant regions. Compared to existing algorithms, the ATT-DenseNet demonstrates a remarkable enhancement in accuracy, precision, recall, and the F1-Score. It achieves an average improvement of 20% in accuracy, 19.66% in precision, 24.33% in recall, and 22.33% in the F1-Score across these metrics. The motivation behind the research is to leverage deep learning technologies to enhance the precision and reliability of lung cancer diagnostics, thus addressing the gap in early detection and treatment. This pursuit is driven by the potential of deep learning models, like DenseNet, to provide significant improvements in analyzing complex medical images for better clinical outcomes.

Melatonin attenuates fentanyl - induced behavioral sensitization and circadian rhythm disorders in mice

Melatonin is a neurohormone synthesized by the pineal gland to regulate the circadian rhythms and has proven to be effective in treating drug addiction and dependence. However, the effects of melatonin to modulate the drug-seeking behavior of fentanyl and its underlying molecular mechanism is elusive. This study was designed to investigate the effects of melatonin on fentanyl - induced behavioral sensitization and circadian rhythm disorders in mice. The accompanying changes in the expression of Brain and Muscle Arnt-Like (BMAL1), tyrosine hydroxylase (TH), and monoamine oxidase A (MAO-A) in relevant brain regions including the suprachiasmatic nucleus (SCN), nucleus accumbens (NAc), prefrontal cortex (PFC), and hippocampus (Hip) were investigated by western blot assays to dissect the mechanism by which melatonin modulates fentanyl - induced behavioral sensitization and circadian rhythm disorders. The present study suggest that fentanyl (0.05, 0.1 and 0.2 mg/kg) could induce behavioral sensitization and melatonin (30.0 mg/kg) could attenuate the behavioral sensitization and circadian rhythm disorders in mice. Fentanyl treatment reduced the expression of BMAL1 and MAO-A and increased that of TH in relevant brain regions. Furthermore, melatonin treatment could reverse the expression levels of BMAL1, MAO-A, and TH. In conclusion, our study demonstrate for the first time that melatonin has therapeutic potential for fentanyl addiction.

Anomaly-guided weakly supervised lesion segmentation on retinal OCT images

The availability of big data can transform the studies in biomedical research to generate greater scientific insights if expert labeling is available to facilitate supervised learning. However, data annotation can be labor-intensive and cost-prohibitive if pixel-level precision is required. Weakly supervised semantic segmentation (WSSS) with image-level labeling has emerged as a promising solution in medical imaging. However, most existing WSSS methods in the medical domain are designed for single-class segmentation per image, overlooking the complexities arising from the co-existence of multiple classes in a single image. Additionally, the multi-class WSSS methods from the natural image domain cannot produce comparable accuracy for medical images, given the challenge of substantial variation in lesion scales and occurrences. To address this issue, we propose a novel anomaly-guided mechanism (AGM) for multi-class segmentation in a single image on retinal optical coherence tomography (OCT) using only image-level labels. AGM leverages the anomaly detection and self-attention approach to integrate weak abnormal signals with global contextual information into the training process. Furthermore, we include an iterative refinement stage to guide the model to focus more on the potential lesions while suppressing less relevant regions. We validate the performance of our model with two public datasets and one challenging private dataset. Experimental results show that our approach achieves a new state-of-the-art performance in WSSS for lesion segmentation on OCT images.

Competitive and synergic evolution of the water-food-ecology system: A case study of the Beijing-Tianjin-Hebei region, China

A vital approach to attaining sustainable development lies in the in-depth examination of both competition and synergy between these subsystems from a water-food-ecology (WFE) system perspective, while previous or existing studies have limitations in to quantitative characterize and evaluation the cooperative and competitive relationships between different systems. In this study, an evaluation indicator system is constructed from the two dimensions of resources and efficiency, and the WFE synergic development capacity (WFE-SDC) is proposed by integrating the order degree of the coupled system, enables a multidimensional and comprehensive quantitative assessment of the sustainable development of the WFE system. Then a synergic evolution model is constructed to explore the competitive and synergic evolution of the WFE system in the Beijing-Tianjin-Hebei region. The following key insights were obtained: (1) The WFE-SDC (range of 0–1) shows a fluctuating increase, indicating a shift from mild dysfunctional recession to intermediate synergic development (0.24 to 0.72). (2) Principal factors impeding WFE-SDC encompass diversion water, ecology water consumption, grain demand, reclaimed water consumption, and outbound water, both come from resource dimension, with a combined impediment degree of over 46 %, and the improvement of efficiency dimension may improve the WFE-SDC. (3) The water subsystem acts as a driving force for synergic development, fostering cooperation within the food and ecology subsystems, although they mainly operate in a competitive state. (4) Within the WFE system, Beijing, Tianjin, and Hebei exhibited mutual cooperation and significantly contributed to one another's development. Beijing has played a pivotal role in the progress of both Tianjin and Hebei. This study offers valuable insights for the formulation of policies and the application of technical approaches for the sustainable development of the WFE system in relevant regions.

Induction of Neuroinflammation and Brain Oxidative Stress by Brain-Derived Extracellular Vesicles from Hypertensive Rats.

Neuroinflammation and brain oxidative stress are recognized as significant contributors to hypertension including salt sensitive hypertension. Extracellular vesicles (EVs) play an essential role in intercellular communication in various situations, including physiological and pathological ones. Based on this evidence, we hypothesized that EVs derived from the brains of hypertensive rats with salt sensitivity could trigger neuroinflammation and oxidative stress during hypertension development. To test this hypothesis, we compared the impact of EVs isolated from the brains of hypertensive Dahl Salt-Sensitive rats (DSS) and normotensive Sprague Dawley (SD) rats on inflammatory factors and mitochondrial reactive oxygen species (mtROS) production in primary neuronal cultures and brain cardiovascular relevant regions, including the hypothalamic paraventricular nucleus (PVN) and lamina terminalis (LT). We found that brain-derived DSS-EVs significantly increased the mRNA levels of proinflammatory cytokines (PICs) and chemokines, including TNFα, IL1β, CCL2, CCL5, and CCL12, as well as the transcriptional factor NF-κB in neuronal cultures. DSS-EVs also induced oxidative stress in neuronal cultures, as evidenced by elevated NADPH oxidase subunit CYBA coding gene mRNA levels and persistent mtROS elevation. When DSS-EVs were injected into the brains of normal SD rats, the mRNA levels of PICs, chemokines, and the chronic neuronal activity marker FOSL1 were significantly increased in the PVN and LT. Furthermore, DSS-EVs caused mtROS elevation in brain PVN and LT, particularly in neurons. Our study reveals a novel role for brain-derived EVs from hypertensive rats in triggering neuroinflammation, upregulating chemokine expression, and inducing excessive ROS production. These findings provide insight into the complex interactions between EVs and hypertension-associated processes, offering potential therapeutic targets for hypertension-linked neurological complications.

Research and implementation of multi-disease diagnosis on chest X-ray based on vision transformer.

Disease diagnosis in chest X-ray images has predominantly relied on convolutional neural networks (CNNs). However, Vision Transformer (ViT) offers several advantages over CNNs, as it excels at capturing long-term dependencies, exploring correlations, and extracting features with richer semantic information. We adapted ViT for chest X-ray image analysis by making the following three key improvements: (I) employing a sliding window approach in the image sequence feature extraction module to divide the input image into blocks to identify small and difficult-to-detect lesion areas; (II) introducing an attention region selection module in the encoder layer of the ViT model to enhance the model's ability to focus on relevant regions; and (III) constructing a parallel patient metadata feature extraction network on top of the image feature extraction network to integrate multi-modal input data, enabling the model to synergistically learn and expand image-semantic information. The experimental results showed the effectiveness of our proposed model, which had an average area under the curve value of 0.831 in diagnosing 14 common chest diseases. The metadata feature network module effectively integrated patient metadata, further enhancing the model's accuracy in diagnosis. Our ViT-based model had a sensitivity of 0.863, a specificity of 0.821, and an accuracy of 0.834 in diagnosing these common chest diseases. Our model has good general applicability and shows promise in chest X-ray image analysis, effectively integrating patient metadata and enhancing diagnostic capabilities.

Attention mechanism-based model for cardiomegaly recognition in chest X-Ray images

<span>Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. </span><span>Besides, cardiomegaly is a sign of c</span><span>ardiovascular diseases </span><span>that refers to various conditions; it is associated with </span><span>the heart enlargement that can be either transient or permanent depending on certain conditions.</span><span>Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases</span><span>.</span><span> In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental</span><span> results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.</span>

Association of abnormal explicit sense of agency with cerebellar impairment in myoclonus-dystonia.

Non-motor aspects in dystonia are now well recognized. The sense of agency, which refers to the experience of controlling one's own actions, has been scarcely studied in dystonia, even though its disturbances can contribute to movement disorders. Among various brain structures, the cerebral cortex, the cerebellum, and the basal ganglia are involved in shaping the sense of agency. In myoclonus dystonia, resulting from a dysfunction of the motor network, an altered sense of agency may contribute to the clinical phenotype of the condition. In this study, we compared the explicit and implicit sense of agency in patients with myoclonus dystonia caused by a pathogenic variant of SGCE (DYT-SGCE) and control participants. We utilized behavioural tasks to assess the sense of agency and performed neuroimaging analyses, including structural, resting-state functional connectivity, and dynamic causal modelling, to explore the relevant brain regions involved in the sense of agency. Additionally, we examined the relationship between behavioural performance, symptom severity, and neuroimaging findings. We compared 19 patients with DYT-SGCE and 24 healthy volunteers. Our findings revealed that patients with myoclonus-dystonia exhibited a specific impairment in explicit sense of agency, particularly when implicit motor learning was involved. However, their implicit sense of agency remained intact. These patients also displayed grey-matter abnormalities in the motor cerebellum, as well as increased functional connectivity between the cerebellum and pre-supplementary motor area. Dynamic causal modelling analysis further identified reduced inhibitory effects of the cerebellum on the pre-supplementary motor area, decreased excitatory effects of the pre-supplementary motor area on the cerebellum, and increased self-inhibition within the pre-supplementary motor area. Importantly, both cerebellar grey-matter alterations and functional connectivity abnormalities between the cerebellum and pre-supplementary motor area were found to correlate with explicit sense of agency impairment. Increased self-inhibition within the pre-supplementary motor area was associated with less severe myoclonus symptoms. These findings highlight the disruption of higher-level cognitive processes in patients with myoclonus-dystonia, further expanding the spectrum of neurological and psychiatric dysfunction already identified in this disorder.

Introgressed eggplant lines with the wild Solanum incanum evaluated under drought stress conditions1

As access to irrigation water becomes increasingly limited, introgression of relevant genomic regions from drought-tolerant wild genotypes is a promising breeding strategy for crop plants. In this study, nine eggplant (Solanum melongena) introgression lines (ILs) covering altogether 71.6% of the genome of the donor wild relative parent S. incanum were evaluated for drought tolerance under water stress conditions. Plants at the five true leaves stage were irrigated at either 100% (control) or 30% (water stress) field capacity for 14 days, and growth and biochemical traits were measured. Reduced irrigation resulted in decreased growth and increased levels of stress markers such as proline and malondialdehyde. Most ILs had lower growth and biomass production than the cultivated parent under both conditions. However, the wild alleles for two genomic regions related to stem and root dry weight (dwt8 and dwr6%) conferred improved tolerance to water stress. In addition, several S. incanum alleles had a positive effect on important traits that may improve yield under drought conditions, such as leaf water content (lwc12%), water use efficiency (wue1%) and chlorophyll content (chl2 and chl8%). Fine-mapping of the genomic regions for tolerance and reducing linkage drag with regions affecting growth will be crucial for significantly improving eggplant drought tolerance through introgression breeding.

Image Captioning: A Comprehensive Review

Abstract: The Image Caption Generator is an intriguing project that bridges the domains of computer vision and natural language processing, aiming to automatically generate descriptive text for images. In the last decade, considerable progress has been made, yet key challenges in optimising Convolutional Neural Networks (CNNs) for precise image feature extraction and refining Long Short-Term Memory (LSTM) networks for coherent text generation persist. Challenges include overfitting, limited context understanding, and difficulties in modelling long-range dependencies. Solutions have emerged in the form of attention mechanisms, which help focus on relevant image regions, and advanced LSTM architectures like the use of Gated Recurrent Units (GRUs) to improve sequential modelling. These innovations have significantly enhanced the overall performance, leading to more accurate and contextually relevant image captions, ultimately advancing the synergy between computer vision and natural language processing in the realm of image captioning

RIDGE, a tool tailored to detect gene flow barriers across species pairs.

Characterizing the processes underlying reproductive isolation between diverging lineages is central to understanding speciation. Here, we present RIDGE-Reproductive Isolation Detection using Genomic polymorphisms-a tool tailored for quantifying gene flow barrier proportion and identifying the relevant genomic regions. RIDGE relies on an Approximate Bayesian Computation with a model-averaging approach to accommodate diverse scenarios of lineage divergence. It captures heterogeneity in effective migration rate along the genome while accounting for variation in linked selection and recombination. The barrier detection test relies on numerous summary statistics to compute a Bayes factor, offering a robust statistical framework that facilitates cross-species comparisons. Simulations revealed RIDGE's efficiency in capturing signals of ongoing migration. Model averaging proved particularly valuable in scenarios of high model uncertainty where no migration or migration homogeneity can be wrongly assumed, typically for recent divergence times <0.1 2Ne generations. Applying RIDGE to four published crow data sets, we first validated our tool by identifying a well-known large genomic region associated with mate choice patterns. Second, while we identified a significant overlap of outlier loci using RIDGE and traditional genomic scans, our results suggest that a substantial portion of previously identified outliers are likely false positives. Outlier detection relies on allele differentiation, relative measures of divergence and the count of shared polymorphisms and fixed differences. Our analyses also highlight the value of incorporating multiple summary statistics including our newly developed outlier ones that can be useful in challenging detection conditions.

Salmonellosis outbreak archive in China: data collection and assembly

Infectious disease outbreaks transcend the medical and public health realms, triggering widespread panic and impeding socio-economic development. Considering that self-limiting diarrhoea of sporadic cases is usually underreported, the Salmonella outbreak (SO) study offers a unique opportunity for source tracing, spatiotemporal correlation, and outbreak prediction. To summarize the pattern of SO and estimate observational epidemiological indicators, 1,134 qualitative reports screened from 1949 to 2023 were included in the systematic review dataset, which contained a 506-study meta-analysis dataset. In addition to the dataset comprising over 50 columns with a total of 46,494 entries eligible for inclusion in systematic reviews or input into prediction models, we also provide initial literature collection datasets and datasets containing socio-economic and climate information for relevant regions. This study has a broad impact on advancing knowledge regarding epidemic trends and prevention priorities in diverse salmonellosis outbreaks and guiding rational policy-making or predictive modeling to mitigate the infringement upon the right to life imposed by significant epidemics.

Transcriptomic effects of paternal cocaine-seeking on the reward circuitry of male offspring

It has been previously established that paternal development of a strong incentive motivation for cocaine can predispose offspring to develop high cocaine-seeking behavior, as opposed to sole exposure to the drug that results in drug resistance in offspring. However, the adaptive changes of the reward circuitry have not been fully elucidated. To infer the key nuclei and possible hub genes that determine susceptibility to addiction in offspring, rats were randomly assigned to three groups, cocaine self-administration (CSA), yoked administration (Yoke), and saline self-administration (SSA), and used to generate F1. We conducted a comprehensive transcriptomic analysis of the male F1 offspring across seven relevant brain regions, both under drug-naïve conditions and after cocaine self-administration. Pairwise differentially expressed gene analysis revealed that the orbitofrontal cortex (OFC) exhibited more pronounced transcriptomic changes in response to cocaine exposure, while the dorsal hippocampus (dHip), dorsal striatum (dStr), and ventral tegmental area (VTA) exhibited changes that were more closely associated with the paternal voluntary cocaine-seeking behavior. Consistently, these nuclei showed decreased dopamine levels, elevated neuronal activation, and elevated between-nuclei correlations, indicating dopamine-centered rewiring of the midbrain circuit in the CSA offspring. To determine if possible regulatory cascades exist that drive the expression changes, we constructed co-expression networks induced by paternal drug addiction and identified three key clusters, primarily driven by transcriptional factors such as MYT1L, POU3F4, and NEUROD6, leading to changes of genes regulating axonogenesis, synapse organization, and membrane potential, respectively. Collectively, our data highlight vulnerable neurocircuitry and novel regulatory candidates with therapeutic potential for disrupting the transgenerational inheritance of vulnerability to cocaine addiction.

Validating a Novel 2D to 3D Knee Reconstruction Method on Preoperative Total Knee Arthroplasty Patient Anatomies.

As advanced technology continues to evolve, incorporating robotics into surgical procedures has become imperative for precision and accuracy in preoperative planning. Nevertheless, the integration of three-dimensional (3D) imaging into these processes presents both financial considerations and potential patient safety concerns. This study aims to assess the accuracy of a novel 2D-to-3D knee reconstruction solution, RSIP XPlan.ai™ (RSIP Vision, Jerusalem, Israel), on preoperative total knee arthroplasty (TKA) patient anatomies. Accuracy was calculated by measuring the Root Mean Square Error (RMSE) between X-ray-based 3D bone models generated by the algorithm and corresponding CT bone segmentations (distances of each mesh vertex to the closest vertex in the second mesh). The RMSE was computed globally for each bone, locally for eight clinically relevant bony landmark regions, and along simulated bone cut contours. In addition, the accuracies of three anatomical axes were assessed by comparing angular deviations to inter- and intra-observer baseline values. The global RMSE was 0.93 ± 0.25 mm for the femur and 0.88 ± 0.14 mm for the tibia. Local RMSE values for bony landmark regions were 0.51 ± 0.33 mm for the five femoral landmarks and 0.47 ± 0.17 mm for the three tibial landmarks. The RMSE along simulated cut contours was 0.75 ± 0.35 mm for the distal femur cut and 0.63 ± 0.27 mm for the proximal tibial cut. Anatomical axial average angular deviations were 1.89° for the trans epicondylar axis (with an inter- and intra-observer baseline of 1.43°), 1.78° for the posterior condylar axis (with a baseline of 1.71°), and 2.82° (with a baseline of 2.56°) for the medial-lateral transverse axis. The study findings demonstrate promising results regarding the accuracy of XPlan.ai™ in reconstructing 3D bone models from plain-film X-rays. The observed accuracy on real-world TKA patient anatomies in anatomically relevant regions, including bony landmarks, cut contours, and axes, suggests the potential utility of this method in various clinical scenarios. Further validation studies on larger cohorts are warranted to fully assess the reliability and generalizability of our results. Nonetheless, our findings lay the groundwork for potential advancements in future robotic arthroplasty technologies, with XPlan.ai™ offering a promising alternative to conventional CT scans in certain clinical contexts.

Comparison and analysis research on occupational exposure limits of coal dust between China and foreign countries

Objective: To study and compare the occupational exposure limits (OELs) of coal dust between China and foreign countries, understand the OEL of coal dust in China, and provide data and basis for revising the OEL of coal dust in China. Methods: In August 2023, by searching the official websites of limits setting institutions in relevant countries and regions at home and abroad, collecting and sorting out the OELs of coal dust issued by 10 limit setting institutions in 6 countries and the background information of the formulation, and conducting specific analysis on the classification, limit level and formulation principles of coal dust OEL in each country/institution. Results: In China and Japan, the total dust and respirable dust of coal dust OEL were established respectively, while in other countries, only the time-weighted average concentration (TWA) of respirable coal dust exposure was established. The TWA prescribed by China's Notional Health Commission, the United States Occupational Safety and Health Administration (OSHA) , the United States Mining Safety and Health Administration (MSHA) and the Australian Safety Work Bureau when the SiO(2) content was less than 5% were 5, 2.4, 2 and 3 mg/m(3) respectively. China GBZ 2.1-2019 sets the limit of 2.5 mg/m(3) for respirable coal dust with SiO(2) content less than 10%. The TWA set by the American Conference of Government Industrial Hygienists (ACGIH) and the South African Department of Mines and Energy (DME) for anthracite coal were 0.4 and 0.8 mg/m(3), respectively, and bituminous coal or lignite were 0.9 and 1.8 mg/m(3), respectively. The respirable coal dust TWA set by the National Institute for Occupational Safety and Health (NIOSH) in the United States was 1 mg/m(3), and the TWA set by the New Zealand Work Safety Authority was 3 mg/m(3). Conclusion: At present, the OEL of coal dust in China is at a relatively loose level, and it is suggested to further explore the possibility of revising coal dust OEL.

International evidence of the forecasting ability of option‐implied distributions

AbstractThis paper analyzes the forecasting ability of option‐implied distributions of 12 stock indexes representative of the most relevant economic regions for a long period ranging from 1996 to 2021. After performing alternative tests, the rejection of the forecasting ability of the risk‐neutral densi (RNDs) is not evident, since results are mixed depending on the test performed and market studied: The forecasting ability of the RNDs of East Asian indexes as well as other smaller European economies cannot be discarded. In addition, subjective (actual) probability densit (SPDs) resulting from the risk adjustments of the RNDs using constanCRRA) preferences improve substantially the test results, leading to a general failure to reject their forecasting ability.