Genome-Scale Analyses Reveal Roadblocks to Monkey Cloning.

Cloning by somatic cell nuclear transfer (SCNT) remained challenging for Rhesus monkeys, mostly due to its low efficiency and neonatal death. Genome-scale analyses revealed that monkey SCNT embryos displayed widespread DNA methylation and transcriptional alterations, thus including loss of genomic imprinting that correlated with placental dysfunction. The transfer of inner cell masses (ICM) from cloned blastocysts into ICM-depleted fertilized embryos rescued placental insufficiency and gave rise to a cloned Rhesus monkey that reached adulthood without noticeable abnormalities.

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments.

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

Synthetic data generation methods in healthcare: A review on open-source tools and methods.

Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving multi-modal synthetic data generation were also explored. The type of method used for the synthetic data generation process was identified in each study and was categorized into statistical, probabilistic, machine learning, and deep learning. Emphasis was given to the programming languages used for the implementation of each method. Our evaluation revealed that the majority of the studies utilize synthetic data generators to: (i) reduce the cost and time required for clinical trials for rare diseases and conditions, (ii) enhance the predictive power of AI models in personalized medicine, (iii) ensure the delivery of fair treatment recommendations across diverse patient populations, and (iv) enable researchers to access high-quality, representative multimodal datasets without exposing sensitive patient information, among others. We underline the wide use of deep learning based synthetic data generators in 72.6 % of the included studies, with 75.3 % of the generators being implemented in Python. A thorough documentation of open-source repositories is finally provided to accelerate research in the field.

Improved clearance of host cell protein impurities at the polishing purification step using multimodal chromatography.

In biotherapeutic protein production, host cell proteins (HCPs) are one of the main process related impurities which must be cleared and controlled through downstream processing. In this paper, we studied a novel therapeutic protein molecule which had a high level of HCP co-purification throughout the downstream process. Here, we focused on the polishing purification step and developed an effective strategy for improving HCP clearance using multimodal chromatography (MMC) resin, Nuvia cPrime. A high throughput process development (HTPD) workflow was used to identify the resin and process conditions which could enable significant HCP clearance while maintaining acceptable product quality and process performance. HCP analysis of gradient elution fractions on multimodal chromatography found that HCPs eluted at the beginning of the gradient, at a lower salt concentration than the therapeutic protein. Based on these findings, a step elution process involving an intermediate low salt wash was developed to clear weak-binding HCPs, while retaining the therapeutic protein on the column. This strategy was highly effective and enabled 80 % reduction in total HCP content, including some problematic and difficult to remove candidates such as Peroxiredoxin-1, Serine protease HTRA1, Clusterin and Lipoprotein lipase.

Management of Severe Pain in a Case of Sensory Guillain-Barre Syndrome.

Guillain-Barre syndrome (GBS) is an acute post-infectious polyradiculoneuropathy characterized by autoantibodies targeting host antigens, resulting in nerve fiber demyelination and axonal degeneration. While symmetric ascending weakness is typical, neuropathic pain is a common yet variable manifestation. We present a case of a 52-year-old man with progressive bilateral leg weakness and severe neuropathic pain following a flu-like illness. Despite conventional analgesics, his pain persisted, necessitating a unique pain management approach. The patient's examination revealed hyporeflexia and sensory deficits consistent with GBS. Diagnostic workup, including lumbar puncture, showed albuminocytologic dissociation. Plasma exchange therapy was initiated, but severe nocturnal neuropathic pain persisted, exacerbating during treatment. Conventional pain medications were ineffective, prompting a multimodal approach. Combining hydromorphone and lorazepam provided significant pain relief, enabling completion of plasmapheresis sessions. This regimen, supplemented with gabapentin, proved effective in managing both GBS-associated and treatment-induced pain. This case underscores the debilitating nature of GBS-related pain and the importance of tailored pain management strategies. While conventional agents may fail, a multimodal approach, including opioids and adjunctive medications, can offer relief, facilitating essential treatments like plasmapheresis. Careful monitoring is imperative to mitigate risks associated with potent analgesics. Our experience contributes to the armamentarium for managing GBS-related pain, emphasizing individualized care to improve patient outcomes.

Toward a semiotics of midwifery: Multimodal communication's effects on accessibility, equity, and power dynamics.

According to semiotics, we live in a world of signs, where almost anything can act as a signifier and convey meaning. But what of the semiotic landscape of midwifery? What signs are present within a client's multi-sensory experience of their midwifery care? How are these signs functioning to increase equity and accessibility? Or worse, how might certain aspects of the client's experience communicate unjust power dynamics? Semiotics allows us to examine a wide communicative and educational environment. By paying particular attention to the multivalent meanings of different signs-be they written, visual, oral, or even physical-we can start to see how multimodal communication plays a vital role in a client's perception of equity and power. One way to improve client experience is by approaching education and semiotic experience from the same place as trauma-informed care. A more health-literate sensitive approach viewed through the lens of semiotics assumes all clients have little previous knowledge or comfort within a care setting. This hyperawareness and criticality of the semiotic environment would allow midwives to acknowledge various sensory and communicative biases and intentionally redesign the entire client experience. The semiotic landscape is then curated to meet the needs of the most important audience-those marginalized and discriminated against whether that is because of education, finances, race, gender, or any other intersectional identity. We must acknowledge the fact that all sign systems can either reinforce abusive power relations or work to improve them. For what is at stake here is not just a client's overall comfort, but their full understanding of the care they are receiving, the options they have, and their autonomy within their entire perinatal experience.

Differential response to preoperative exercise training in patients candidates to cardiac valve replacement.

BACKGROUND: There is lack of evidence regarding safety, effectiveness and applicability of prehabilitation on cardiac surgery population, particularly in patients candidates to cardiac valve replacement. The aim of the study is to assess and compare the effect of a multimodal prehabilitation program on functional capacity in patients with severe aortic stenosis (AoS) and severe mitral regurgitation (MR) proposed for valve replacement surgery. METHODS: Secondary analysis from a randomised controlled trial whose main objective was to analyze the efficacy of a 4-6 weeks multimodal prehabilitation program in cardiac surgery on reducing postoperative complications. For this secondary analysis, only candidates for valve replacement surgery were selected. The primary outcome was the change in endurance time (ET) from baseline to preoperative assessment measured by a cycling constant work-rate cardiopulmonary exercise test. RESULTS: 68 patients were included in this secondary analysis, 34 (20 AoS and 14 MR) were allocated to the prehabilitation group and 34 (20 AoS and 14 MR) to control group. At baseline, patients with AoS had better left systolic ventricular function and lower prevalence of atrial fibrillation compared to MR (p = 0.022 and p = 0.035 respectively). After prehabilitation program, patients with MR showed greater improvement in ET than AoS patients (101% vs. 66% increase from baseline). No adverse events related to the prehabilitation program were observed. CONCLUSIONS: A 4-6 week exercise training program is safe and overall improves functional capacity in patients with severe AoS and MR. However, exercise response is different according to the cardiac valve type disfunction, and further studies are needed to know the factors that predispose some patients to have better training response. TRIAL REGISTRATION: The study has been registered on the Registry of National Institutes of Health ClinicalTrials.gov (NCT03466606) (05/03/2018).

MATI: Multimodal Adaptive Tracking Integrator for Robust Visual Object Tracking.

Visual object tracking, pivotal for applications like earth observation and environmental monitoring, encounters challenges under adverse conditions such as low light and complex backgrounds. Traditional tracking technologies often falter, especially when tracking dynamic objects like aircraft amidst rapid movements and environmental disturbances. This study introduces an innovative adaptive multimodal image object-tracking model that harnesses the capabilities of multispectral image sensors, combining infrared and visible light imagery to significantly enhance tracking accuracy and robustness. By employing the advanced vision transformer architecture and integrating token spatial filtering (TSF) and crossmodal compensation (CMC), our model dynamically adjusts to diverse tracking scenarios. Comprehensive experiments conducted on a private dataset and various public datasets demonstrate the model's superior performance under extreme conditions, affirming its adaptability to rapid environmental changes and sensor limitations. This research not only advances visual tracking technology but also offers extensive insights into multisource image fusion and adaptive tracking strategies, establishing a robust foundation for future enhancements in sensor-based tracking systems.

Rebound pain after arthroscopic cuff repair with interscalene brachial plexus block anesthesia is reduced by surgeon administered multimodal shoulder injections: A prospective randomized controlled trial.

PURPOSE: To compare the effects of additional multimodal shoulder injections on postoperative rebound pain in patients undergoing arthroscopic rotator cuff repair (ASRCR) under interscalene brachial plexus block (ISBPB) anesthesia. METHODS: A single-blind randomized controlled trial was conducted with 67 patients between April 2023 and December 2023. Patients undergoing ASRCR got an ISBPB anesthesia, not general anesthesia, were included with a minimum follow-up period of 48 h. The injection group received 40 mL of 0.75% ropivacaine, 20 mg morphine, 1:200,000 epinephrine, and saline solution, totaling 100 mL. Following surgery, the injection was administered to the subacromial space (50 ml) with blind suprascapular nerve block (25 ml) and blind axillary nerve block (25 ml). Controls received 100 mL of saline solution. Intravenous patient-controlled analgesia (IV-PCA) was used as adjuvant analgesia for all patients. The primary outcome was evaluated using the visual analog scale (VAS) pain score at 12 h after surgery, with secondary outcomes of the incidence of rebound pain and VAS pain scores at 0, 2, 4, 8, 24, 36, and 48 h postoperatively. Fentanyl in IV-PCA and rescue analgesic amounts, complications, and satisfaction were recorded. RESULTS: Sixty-seven patients (32 in the injection group, 35 in the control group) with a mean age of 61.1±9.0 years were included. The primary outcome assessment, VAS pain score at 12 h, significantly favored the injection group (2.7±0.93 vs. 4.1±1.70, p<0.001). The incidence of rebound pain was 18.8% and 65.7% in the injection and control groups, respectively (18.8% vs 65.7%, p<0.001). The injection group reported better VAS pain scores at 24, 36, and 48 h, and lower fentanyl use over the 48 h postoperative period (p=0.014). The use of rescue analgesics was similar between groups and no complications were associated with multimodal shoulder injections. Satisfaction levels were similar in both groups. CONCLUSION: The present study found that multimodal shoulder injections during ASRCR under ISBPB anesthesia significantly lowered VAS pain scores at 12 hours postoperatively and reduced the incidence of rebound pain compared to the control group. Pain levels were consistently lower from 12 to 48 hours postoperatively. Additionally, the injection group had reduced opioid consumption within the first 48 hours postoperatively, with no complications observed. LEVEL OF EVIDENCE: Level I, randomized controlled trial.

Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600 µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80 °C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Extensive macular atrophy with pseudodrusen-like appearance: comprehensive review of the literature.

PURPOSE: This review focuses on extensive macular atrophy with pseudodrusen-like appearance (EMAP), a recently described maculopathy presenting with pseudodrusen-like lesions and chorioretinal atrophy more pronounced in the vertical axis. METHODS: Narrative review of the literature published until May 2024. RESULTS: The early onset age of EMAP (50-55 years) and its distinctive natural history, which includes night blindness followed by severe vision loss, differentiate it from atrophic age-related macular degeneration (AMD). A clear pathogenesis has not been determined, but risk factors include female gender and complement system abnormalities (altered levels of C3 and CH50). Moreover, lifelong exposure to pesticides has been suggested as risk factor for direct neuronal degeneration involving rods and cones. In the early phase of the disease, reticular pseudodrusen-like lesions appear in the superior perifovea and tend to coalescence horizontally into a flat, continuous, reflective material localized between the retinal pigmented epithelium and Bruch's membrane. Over time, EMAP causes profound RPE and outer retinal atrophy in the macular area, with a recent classification reporting a 3-stages evolution pattern. Blue autofluorescence showed rapidly evolving atrophy with either hyperautofluorescent or isoautofluorescent borders. Significant similarities between the diffuse-trickling phenotype of geographic atrophy and EMAP have been reported. Macular neovascularization is a possible complication. CONCLUSION: EMAP is specific form of early-onset atrophic macular degeneration with rapid evolution and no treatment. Further studies are needed to assess the best management.

Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion.

Monitoring suspended sediment concentration (SSC) in rivers is pivotal for water quality management and sustainable river ecosystem development. However, achieving continuous and precise SSC monitoring is fraught with challenges, including low automation, lengthy measurement processes, and high cost. This study proposes an innovative approach for SSC identification in rivers using multimodal data fusion. We developed a robust model by harnessing colour features from video images, motion characteristics from the Lucas-Kanade (LK) optical flow method, and temperature data. By integrating ResNet with a mixed density network (MDN), our method fused the image and optical flow fields, and temperature data to enhance accuracy and reliability. Validated at a hydropower station in the Xinjiang Uygur Autonomous Region, China, the results demonstrated that while the image field alone offers a baseline level of SSC identification, it experiences local errors under specific conditions. The incorporation of optical flow and water temperature information enhanced model robustness, particularly when coupling the image and optical flow fields, yielding a Nash-Sutcliffe efficiency (NSE) of 0.91. Further enhancement was observed with the combined use of all three data types, attaining an NSE of 0.93. This integrated approach offers a more accurate SSC identification solution, enabling non-contact, low-cost measurements, facilitating remote online monitoring, and supporting water resource management and river water-sediment element monitoring.

Auto-delineation of treatment target volume for radiation therapy using large language model-aided multimodal learning.

PURPOSE: Artificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches. METHODS AND MATERIALS: A vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects. RESULTS: Our Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms. CONCLUSIONS: Auto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV.

Multimodal speech biomarkers for remote monitoring of ALS disease progression.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons' speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n = 36) and non-bulbar onset (n = 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care.

A 3D hierarchical cross-modality interaction network using transformers and convolutions for brain glioma segmentation in MR images.

BACKGROUND: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions. PURPOSE: To overcome these problems, in this paper, we propose a 3D hierarchical cross-modality interaction network (HCMINet) using Transformers and convolutions for accurate multi-modal glioma segmentation, which leverages an effective hierarchical cross-modality interaction strategy to sufficiently learn modality-specific and modality-shared knowledge correlated to glioma sub-region segmentation from multi-parametric MR images. METHODS: In the HCMINet, we first design a hierarchical cross-modality interaction Transformer (HCMITrans) encoder to hierarchically encode and fuse heterogeneous multi-modal features by Transformer-based intra-modal embeddings and inter-modal interactions in multiple encoding stages, which effectively captures complex cross-modality correlations while modeling global contexts. Then, we collaborate an HCMITrans encoder with a modality-shared convolutional encoder to construct the dual-encoder architecture in the encoding stage, which can learn the abundant contextual information from global and local perspectives. Finally, in the decoding stage, we present a progressive hybrid context fusion (PHCF) decoder to progressively fuse local and global features extracted by the dual-encoder architecture, which utilizes the local-global context fusion (LGCF) module to efficiently alleviate the contextual discrepancy among the decoding features. RESULTS: Extensive experiments are conducted on two public and competitive glioma benchmark datasets, including the BraTS2020 dataset with 494 patients and the BraTS2021 dataset with 1251 patients. Results show that our proposed method outperforms existing Transformer-based and CNN-based methods using other multi-modal fusion strategies in our experiments. Specifically, the proposed HCMINet achieves state-of-the-art mean DSC values of 85.33% and 91.09% on the BraTS2020 online validation dataset and the BraTS2021 local testing dataset, respectively. CONCLUSIONS: Our proposed method can accurately and automatically segment glioma regions from multi-parametric MR images, which is beneficial for the quantitative analysis of brain gliomas and helpful for reducing the annotation burden of neuroradiologists.

Utilizing multimodal radiomics technology from cervical MRI for diagnosis of cervical spinal cord injury and spinal cord concussion.

The primary aim of this study is to assess the viability of employing multimodal radiomics techniques for distinguishing between cervical spinal cord injury and spinal cord concussion in cervical magnetic resonance imaging. This is a multicenter study involving 288 patients from a major medical center as the training group, and 75 patients from two other medical centers as the testing group. Data regarding the presence of spinal cord injury symptoms and their recovery status within 72 h were documented. These patients underwent sagittal T1-weighted and T2-weighted imaging using cervical magnetic resonance imaging. Radiomics techniques are used to help diagnose whether these patients have cervical spinal cord injury or spinal cord concussion. 1197 radiomics features were extracted for each modality of each patient. The accuracy of T1 modal in testing group is 0.773, AUC is 0.799. The accuracy of T2 modal in testing group is 0.707, AUC is 0.813. The accuracy of T1 + T2 modal in testing group is 0.800, AUC is 0.840. Our research indicates that multimodal radiomics techniques utilizing cervical magnetic resonance imaging can effectively diagnose the presence of cervical spinal cord injury or spinal cord concussion.

Refining pain management in mice by comparing multimodal analgesia and NSAID monotherapy for neurosurgical procedures.

While neurosurgical interventions are frequently used in laboratory mice, refinement efforts to optimize analgesic management based on multimodal approaches appear to be rather limited. Therefore, we compared the efficacy and tolerability of combinations of the non-steroidal anti-inflammatory drug carprofen, a sustained-release formulation of the opioid buprenorphine, and the local anesthetic bupivacaine with carprofen monotherapy. Female and male C57BL/6J mice were subjected to isoflurane anesthesia and an intracranial electrode implant procedure. Given the multidimensional nature of postsurgical pain and distress, various physiological, behavioral, and biochemical parameters were applied for their assessment. The analysis revealed alterations in Neuro scores, home cage locomotion, body weight, nest building, mouse grimace scales, and fecal corticosterone metabolites. A composite measure scheme allowed the allocation of individual mice to severity classes. The comparison between groups failed to indicate the superiority of multimodal regimens over high-dose NSAID monotherapy. In conclusion, our findings confirmed the informative value of various parameters for assessment of pain and distress following neurosurgical procedures in mice. While all drug regimens were well tolerated in control mice, our data suggest that the total drug load should be carefully considered for perioperative management. Future studies would be of interest to assess potential synergies of drug combinations with lower doses of carprofen.

Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data.

The distribution coefficient (Kd) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, Kd depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict Kd values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R2) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed Kd. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict Kd. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict Kd values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil.

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates.

Identifying the aging time of Liu-Pao Tea (LPT) presents a persistent challenge. We utilized an AI-Multimodal fusion method combining FTIR, E-nose, and E-tongue to discern LPT's aging years. Compared to single-source and two-source fusion methods, the three-source fusion significantly enhanced identifying accuracy across all four machine learning algorithms (Decision tree, Random forest, K-nearest neighbor, and Partial least squares Discriminant Analysis), achieving optimal accuracy of 98-100 %. Physicochemical analysis revealed monotonic variations in tea polysaccharide (TPS) conjugates with aging, observed through SEM imaging as a transition from lamellar to granular TPS conjugate structures. These quality changes were reflected in FTIR spectral characteristics. Two-dimensional correlation spectroscopy (2D-COS) identified sensitive wavelength regions of FTIR from LPT and TPS conjugates, indicating a high similarity in spectral changes between TPS conjugates and LPT with aging years, highlighting the significant role of TPS conjugates variation in LPT quality. Additionally, we established an index for evaluating quality of aging, which is sum of three fingerprint peaks (1029 cm-1, 1635 cm-1, 2920 cm-1) intensities. The index could effectively signify the changes in aging years on macro-scale (R2 = 0.94) and micro-scale (R2 = 0.88). These findings demonstrate FTIR's effectiveness in identifying aging time, providing robust evidence for quality assessment.