Specific Modulation Research Articles

Effect of heat exposure on histology, transcriptomics and co-expression network: A synthetic study in Japanese flounder (Paralichthys olivaceus)

Water temperature has continued to increase as part of global warming. The heart of fish is the center that participates in the physiological limitations of thermal performance. However, only a few studies have focused on the response mechanisms of fish hearts to heat stress. Therefore, the economically important cold-water aquaculture fish, Japanese flounder (Paralichthys olivaceus) was chosen to explore the mechanisms of the heart in response to heat stress. Histological observations indicated that heat stress could widen the sarcoplasmic distance and deepen chromatin staining. Further transcriptome analysis identified 958 differentially expressed genes (DEGs), and significantly enriched “Protein processing in endoplasmic reticulum” pathway and related terms in functional enrichment analysis. Weighted gene co-expression network analysis (WGCNA) showed that different tissues had different response modules and hub genes, and calcium ions (Ca2+) related genes and pathways were noteworthy in the heart specific module. Functional identification of key Ca2+ regulatory hub genes illuminated that caveolin-3 (cav3) was an important gene that participates in controlling the Ca2+ into and out of cells. Meanwhile, overexpression of cav3 could reduce heat stress induced apoptosis. This study could not only be a crucial resource for the response mechanisms underlying heat stress in Japanese flounder, but also provide valuable insights into genetic improvement for heat-resistance.

DNA damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease.

Cardiovascular disease (CVD) is associated with both genetic variants and environmental factors. One unifying consequence of the molecular risk factors in CVD is DNA damage, which must be repaired by DNA damage response proteins. However, the impact of DNA damage on global cardiomyocyte protein abundance, and its relationship to CVD risk remains unclear. We therefore treated induced pluripotent stem cell-derived cardiomyocytes with the DNA-damaging agent Doxorubicin (DOX) and a vehicle control, and identified 4,178 proteins that contribute to a network comprising 12 co-expressed modules and 403 hub proteins with high intramodular connectivity. Five modules correlate with DOX and represent distinct biological processes including RNA processing, chromatin regulation and metabolism. DOX-correlated hub proteins are depleted for proteins that vary in expression across individuals due to genetic variation but are enriched for proteins encoded by loss-of-function intolerant genes. While proteins associated with genetic risk for CVD, such as arrhythmia are enriched in specific DOX-correlated modules, DOX-correlated hub proteins are not enriched for known CVD risk proteins. Instead, they are enriched among proteins that physically interact with CVD risk proteins. Our data demonstrate that DNA damage in cardiomyocytes induces diverse effects on biological processes through protein co-expression modules that are relevant for CVD, and that the level of protein connectivity in DNA damage-associated modules influences the tolerance to genetic variation.

Development and Prospect of ChatGpt in the Medical Field

In the current era, there exists an uneven distribution of global medical resources, making it a crucial focal point for medical digital transformation. This paper aims to explore the application status, potential challenges, and future development prospects of ChatGPT, a large language model based on OpenAI, in the field of medicine. Given the current scarcity of global medical resources, this study analyzes existing medical use cases and examines the ethical and regulatory environment surrounding ChatGPT under the Transformer model within specialties like orthopedics and ophthalmology. The findings propose that its present advantages lie in its ability to support multiple languages while enhancing efficiency and quality of medical services. Furthermore, measures should be taken to reinforce privacy and security protocols for ChatGPT data usage while ensuring ethical considerations are met. Additionally, it is recommended that future advancements in ChatGPT focus on deepening its medical knowledge base and developing more specialized modules.

Dysphagia, Voice Problems and Health Related Quality of Life Among Head and Neck Cancer Survivors

Introduction: Head and neck cancer (HNC) and its treatment can cause significant side effects like dysphagia, voice problems which can affect health related quality of life (HRQOL). Examining quality of life among these patients are helpful in streamlining cancer treatment protocols. The present study aimed to find out the relationship between dysphagia, voice problems, and HRQOL among HNC survivors. Methods: A convenient sampling technique was adopted to recruit 110 HNC survivors. Data was collected using Eating Assessment Tool (EAT), Voice Handicap Index (VHI), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core Version 3 (EORTC QLQ-C30) and head and neck specific module of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-H &N 35). Results: Majority of the HNC survivors (85.5%) had dysphagia, and 50% of them reported severe voice problems. The overall QOL mean (SD) score was high in cognitive functioning 80.76 (22.19) and role functioning 80.30 (25.54) of the functional domain. While considering symptom scale highest mean score was reported for pain 42.42 (25.01), fatigue 42.22 (23.82) and financial difficulties 41.21 (28.56). There was a mild positive correlation between dysphagia and voice problem (r=0.202), dysphagia and health related global health (r=0.248) and voice problem and global health (r=0.280). Conclusion: Dysphagia and voice problems were common among HNC survivors and it has great impact on their HRQOL. Therefore, it is important to initiate various measures to increase awareness for prevention and early management of these symptoms and improvement of HRQOL of HNC survivors.

Dual-branch collaborative Siamese network for visual tracking

This paper introduces a dual-branch collaborative Siamese network architecture, CoSiNet, for visual object tracking. The network has a shallow branch for precise target localization and a deep branch for extracting rich semantic information. It integrates two specialized modules - Channel Attention and Spatial Channel Attention Feature Enhancement - for improved feature extraction and background noise reduction. An Adaptive Fusion Module combines response maps from both branches to create an enriched final response map. Experimental results show our algorithm outperforms several contemporary techniques on four different public datasets (OTB100, OTB50, DTB70, TColor128), demonstrating the competitiveness of CoSiNet.

CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data.

In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.

Urban-rural differences in the desired number of children in Austria 1986–2021

This paper focuses on the development of urban-rural differences in the desired number of children in Austria over a 35-year period. Guided by the theory of the Second Demographic Transition, we analyse representative data from a special module added to the Austrian Microcensus/Labour Force Survey approximately every five years between 1986 and 2021. Our empirical analysis confirms urban-rural differences in the desired number of children. In addition, we find evidence of convergence, primarily driven by a decline in the desired number of children in rural regions. Our results further suggest that postponement of parenthood may have led to adaptive processes decisive for this decline. We show that changes in living arrangements, female education and employment have contributed to both developments of the desired number of children and urban-rural differences. Thus, our paper not only contributes to the understanding of how social change affects demographic behaviours, but also adds to the growing body of research demonstrating the continuing relevance of urban-rural differences for current debates about family change.

Deciphering adiabatic rotating frame relaxometry in biological tissues.

This work aims to unravel the intricacies of adiabatic rotating frame relaxometry in biological tissues. The classical formalisms of dipolar relaxation and were systematically analyzed for water molecules reorienting on "fast" and "slow" timescales. These two timescales are, respectively, responsible for the absence and presence of dispersion. A time-averaged or over an adiabatic pulse duration was recast into a sum of and , but with different weightings. These weightings depend on the specific modulations of adiabatic pulse waveforms. In this context, stretched hyperbolic secant ( ) pulses were characterized. Previously published , continuous-wave (CW) , and measures from 12 agarose phantoms were used to validate the theoretical predictions. A similar validation was also performed on previously published ( =1, 4, 8) and from bovine cartilage specimens. Longitudinal relaxation weighting decreases for pulses as increases. Predicted CW values from agarose phantoms align well with the measured CW values, as indicated by a linear regression function: . The predicted adiabatic and from cartilage specimens are consistent with those previously measured, as quantified by: . This work has theoretically and experimentally demonstrated that adiabatic and can be recast into a sum of and , with varying weightings. Therefore, any suggestions that adiabatic rotating frame relaxometry in biological tissues could provide more information than the standard and warrant closer scrutiny.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

Benchmarking analysis of computer vision algorithms on edge devices for the real-time detection of digital dermatitis in dairy cows

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the coronary band of the foot. It causes significant animal welfare and economic losses to the cattle industry. Early detection of DD can lead to prompt treatment and decrease lameness. Current detection and staging methods require a trained individual to evaluate the interdigital space on each foot for clinical signs of DD. Computer vision (CV), a type of artificial intelligence for image analysis, has demonstrated promising results on object detection tasks. However, farms require robust solutions that can be deployed in harsh conditions including dust, debris, humidity, precipitation, other equipment issues. The study aims to train, deploy, and benchmark DD detection models on edge devices. Images were collected from commercial dairy farms with the camera facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system. Models were trained to detect and score DD lesions and embedded on an edge device. The Tiny YOLOv4 model deployed on a CV specific integrated camera module connected to a single board computer achieved a mean average precision (mAP) of 0.895, an overall prediction accuracy of 0.873, and a Cohen’s kappa of 0.830 for agreement between the computer vision model and the trained investigator. The model reached a final inference speed of 40 frames per second (FPS) and ran stably without any interruptions. The CV model was able to detect DD lesions on an edge device with high performance and speed. The CV tool can be used for early detection and prompt treatment of DD in dairy cows. Real-time detection of DD on edge device will improve health outcomes, while simultaneously decreasing labor costs. We demonstrate that the deployed model can be a low-power and portable solution for real-time detection of DD on dairy farms. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time detection of health outcomes in precision farming.

Parabrachial nucleus Vglut2 expressing neurons projection to the extended amygdala involved in the regulation of wakefulness during sevoflurane anesthesia in mice.

The parabrachial nucleus (PBN) promotes wakefulness states under general anesthesia. Recent studies have shown that glutamatergic neurons within the PBN play a crucial role in facilitating emergence from anesthesia. Our previous study indicates that vesicular glutamate transporter 2 (vglut2) expression neurons of the PBN extend into the extended amygdala (EA). However, the modulation of PBNvglut2-EA in general anesthesia remains poorly understood. This study aims to investigate the role of PBNvglut2-EA in alterations of consciousness during sevoflurane anesthesia. We first validated vglut2-expressing neuron projections from the PBN to the EA using anterograde tracing. Then, we conducted immunofluorescence staining of c-Fos to investigate the role of the EA involved in the regulation of consciousness during sevoflurane anesthesia. After, we performed calcium fiber photometry recordings to determine the changes in PBNvglut2-EA activity. Lastly, we modulated PBNvglut2-EA activity under sevoflurane anesthesia using optogenetics, and electroencephalogram (EEG) was recorded during specific optogenetic modulation. The expression of vglut2 in PBN neurons projected to the EA, and c-Fos expression in the EA was significantly reduced during sevoflurane anesthesia. Fiber photometry revealed that activity in the PBNvglut2-EA pathway was suppressed during anesthesia induction but restored upon awakening. Optogenetic activation of the PBNvglut2-EA delayed the induction of anesthesia. Meanwhile, EEG recordings showed significantly decreased δ oscillations and increased β and γ oscillations compared to the EYFP group. Furthermore, optogenetic activation of the PBNvglut2-EA resulted in an acceleration of awakening from anesthesia, accompanied by decreased δ oscillations on EEG recordings. Optogenetic inhibition of PBNvglut2-EA accelerated anesthesia induction. Surprisingly, we found a sex-specific regulation of PBNvglut2-EA in this study. The activity of PBNvglut2-EA was lower in males during the induction of anesthesia and decreased more rapidly during sevoflurane anesthesia compared to females. Photoactivation of the PBNvglut2-EA reduced the sensitivity of males to sevoflurane, showing more pronounced wakefulness behavior and EEG changes than females. PBNvglut2-EA is involved in the promotion of wakefulness under sevoflurane anesthesia. Furthermore, PBNvglut2-EA shows sex differences in the changes of consciousness induced by sevoflurane anesthesia.

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses.

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

Immune modulation and cecal microbiome changes in broilers fed with fenugreek seeds and Bacillus-based probiotics

Intensive broiler production systems face challenges like enteric diseases, impacting global food security. Strategies to enhance broiler immunity and gut health, particularly amidst antibiotic growth promoter restrictions, are crucial. The present study investigated the combined effects of fenugreek seeds (FS) and Bacillus-based direct-fed microbials (DFM) on immune-related gene expression in the ileum and alteration of microbial population in the cecum of broiler. The study involved 160 Ross 308 broiler chicks, which were divided into four groups consisting of 5 replicates, each containing eight birds. The chicks were grown for a period of 42 d, during which they had ad libitum access to feed and water. Dietary treatments were: Control (basal diet), FS5 (basal + 5g/kg fenugreek seeds), FS5DFM (basal + 5g/kg fenugreek seeds + 0.1g/kg Bacillus-based DFM), and DFM (basal + 0.1g/kg Bacillus-based DFM). Ileum tissue and cecal contents were collected on d 42 for gene expression and gut microbiome analysis. Ileal gene expression analysis revealed the downregulation of IL-6, IL-8L2, CASP6, PTGS2, and IRF7 in both FSs and DFMs groups compared to the control, suggesting individual immunomodulatory effects. However, avian β-defensin genes exhibited complex regulation, highlighting the need for further investigation. Cecal microbiome diversity remained stable, with subtle shifts in specific taxa influenced by FSs and DFMs. Interestingly, the combination of the FSs and DFMs uniquely impacted specific taxa, including Clostridiales vadin BB60. These findings suggest that both FSs and DFMs demonstrated potential for improving broiler immunity through inflammation reduction. The combination of FSs and DFMs offers a synergistic effect in immune modulation and specific microbial modulation, warranting further investigation with pathogen challenge models for comprehensive understanding.

Chronic cannabis use associated with subcortical topological reorganization of structural connectivity in adults

BackgroundCannabis is commonly used in the United States. However, chronic cannabis use has been linked to alterations in white matter (WM) integrity. Studies investigating WM in people who use cannabis (PWC) have produced varying results, which may be due to a variety of factors, including a focus on individual WM tracts. Here, we examined WM connectivity using a module-based approach to help clarify whether cannabis use is associated with differences in WM organization. MethodsConnectomics is used to map complex networks of inter and intra-connected cortical and subcortical regions. A key concept of brain organization is the presence of groups of densely interconnected regions, referred to as modules. Here, we used WM structural connectivity estimates to compare connectome organization between adults who used cannabis regularly (n=53), and adults who did not use cannabis (n=60). We quantified aspects of network organization both across the whole brain and within specific modules. ResultsThere were no significant results between groups after correcting for multiple comparisons for whole-brain metrics. When considering group differences in network organization metrics for 10 identified modules, we observed that adult PWC showed higher within-module degree, local efficiency, and network strength in a right subcortical module relative to adults that did not use cannabis. ConclusionsThese results suggest that cannabis use in adults is associated with alterations of subcortical WM network organization. The observed differences in WM organization may be due to the involvement of the endocannabinoid system in the alteration of WM growth processes.

Efficacy of GluN2B-Containing NMDA receptor antagonist for antitumor and antidepressant therapy in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the predominant subtype of lung cancer. Evidence suggests that the ionotropic glutamate receptor N-methyl-D-aspartate (NMDA) receptor, a critical molecule in the central nervous system, is expressed in NSCLC. However, the specific expression patterns, subcellular localization, functional modulation, and pathological implications of NMDA receptor subtypes in NSCLC have not been fully elucidated. In this study, we employed a multi-disciplinary approach, combining biochemical and molecular biology with electrophysiological recordings and behavioral assays, to investigate these aspects. We reveal the expression of GluN2B-containing NMDA receptors in A549 and H460 NSCLC cell lines and the induction of NMDA receptor-mediated currents by glutamate in A549 cells. Furthermore, the GluN2B-specific inhibitors ifenprodil and Ro 25–6981 significantly reduced cell viability and migration, while promoting apoptosis. Importantly, intraperitoneal administration of ifenprodil in nude mice inhibited the growth of subcutaneous tumors derived from A549 and H460 cells and ameliorated depression-like behaviors. These findings underscore the potential antiproliferative effects of ifenprodil and Ro 25–6981 and suggest that GluN2B-containing NMDA receptors may represent novel therapeutic targets for NSCLC, with the added benefit of potential antidepressant action.

Causality-inspired Domain Expansion network for single domain generalization

Most existing single domain generalization (SDG) methods focus on extending the distribution of the training data to achieve good generalizability in the target domain by synthesizing out-of-domain data. However, in practice, the generated data are insufficient to cover all possible shifts. When the target distributions are not covered, the spurious associations between irrelevant features and labels captured by a model will deteriorate the generalization performance. To alleviate this problem, in this paper, we first analyze that augmenting the source domain with samples of new styles (e.g., different backgrounds) can mitigate spurious associations between irrelevant features and labels from a causal perspective, which can reduce the interference of non-causal features on the classification labels then propose a novel Causality-inspired Domain Expansion Network (CDEN) for SDG. Specifically, CDEN contains a domain augmentation subnetwork and a representation learning subnetwork. The former uses a special style generator module AdaIN∗ to generate out-of-domain data with the same semantic information but different styles from the source domain data to help the model capture true associations between discriminative features and labels. The latter introduces a novel class-level contrastive learning, instance-level contrastive learning, and invariant risk minimization to help the model further eliminate spurious associations. CDEN progressively generates diverse style data and learns domain invariant representations containing few spurious associations by optimizing the two subnetworks in an adversarial learning manner. We conduct extensive experiments on three benchmark datasets to evaluate the superiority of CDEN, comparing it to fifteen state-of-the-art methods.

Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation

Accurate polyp segmentation from colonoscopy images is important for the immediate diagnosis and effective treatment of colon cancer. While significant progress has been made in the polyps segmentation task, there are various challenges that need to be addressed. Polyps can vary greatly in size and shape, and often has no clear boundary between surrounding tissues. Furthermore, surgical instrument segmentation can aid surgeons with the precise positioning and orientation of the instruments, helping them to plan the next steps in the robot-assisted surgery. The proposed colorectal polyp segmentation Transformer (CPS-Former) uses innovative attention blocks in a network that encodes-decodes features like classic Semantic Segmentation Network (SegNet). However, it has special self-attention modules with small convolutional kernels that efficiently extract information from different feature-channels. Moreover, it is equipped with an effective positional embedding to capture information from a large area of context for long distance interactions. Additionally, a fusion block is embedded for scaling-attention that combines the outputs from the encoder-decoder blocks to enhance the semantic features and reduce the non-semantic ones. Transformer encoder blocks also modified by adding a local feedforward layer and skips connections, and adjust the channel sizes to reduce the model trainable parameters. We evaluate our colorectal polyp segmentation network (CPS-Former) on four colorectal polyp public datasets and one surgical instrument segmentation dataset, which show its superiority over other state-of-the-art polyp segmentation models. Our implementation source code and network weights are available at GitHub: https://github.com/ahmedeqbal/CPS-Former.

Workshop on hastened death as “space for an appreciative discussion”: A qualitative study

AimTo evaluate the suitability of a drama-based workshop as a method for ethical deliberation. BackgroundNurses worldwide are inadequately prepared to care for people who desire hastened death, which can lead to ethical and moral dilemmas. To address this problem, we developed a drama-based ethical deliberation workshop to assist nurses in these situations. Senghor and Racine’s three moments of ethical deliberation and their quality criteria served as basis for evaluation. DesignQualitative descriptive study design. MethodsNon-participant observations and a survey with incomplete sentences to be completed by nursing students were used. Data analysis followed Mayring’s deductive content analysis approach. ResultsAll three moments of ethical deliberation according to Senghor and Racine were identified. Participants recognised hastened death as morally problematic, shared their experiences and gained a deeper understanding of the problem. They reported feeling better prepared for encounters with persons seeking assisted suicide. In terms of its quality, the drama-based workshop achieved good and partial ethical deliberation. ConclusionsThe workshop has demonstrated its suitability as a method for ethical deliberation. The workshop should be complemented by specific modules, such as communication skills and be tailored to other professional groups. A validated instrument is needed to ensure a more comprehensive assessment of the quality.

An insight into carcinogenic activity and molecular mechanisms of Bis(2-ethylhexyl) phthalate.

Di(ethylhexyl) phthalate (DEHP) is a ubiquitous environmental contaminant to which humans are exposed via multiple routes. Human health risk assessments for this substance have recently been updated, focusing on reproductive toxicity, including DEHP, in the list of chemicals classified as carcinogenic, mutagenic, or toxic to reproduction (CMR). Moreover, DEHP has also been defined as probably and possibly carcinogenic to humans based on its carcinogenicity in rodents. However, the mechanism of action of DEHP and its relevance in humans remain unclear. Rodent data suggests that DEHP induces cancer through non-genotoxic mechanisms related to multiple molecular signals, including PPARα activation, perturbation of fatty acid metabolism, induction of cell proliferation, decreased apoptosis, production of reactive oxygen species, and oxidative stress. According to the DEHP toxicological dataset, several in vitro cell transformation assays have been performed using different protocols and cellular models to produce different results. This study aimed to evaluate the carcinogenic potential of DEHP by using the A31-1-1 BALB/c-3T3 cell line in a standard cell transformation assay. Additionally, transcriptomic analysis was performed to explore the molecular responses and identify the affected toxicological pathways. Although DEHP treatment did not induce transformation in BALB/c-3T3 cells, the transcriptomic results revealed significant modulation of several pathways associated with DEHP metabolism, tissue-specific functions related to systemic metabolism, and basal cellular signaling with pleiotropic outcomes. Among these signaling pathways, modulation of cell-regulating signaling pathways, such as Notch, Wnt, and TGF-β, can be highlighted. More specific modulation of such genes and pathways with double functions in metabolism and neurophysiology underlies the well-known crosstalk that may be crucial for the mechanism of action of DEHP. Our findings offer evidence to support the notion that these models are effective in minimizing the use of animal testing for toxicity assessment.

A novel binary quantizer for variational autoencoder-based image compressor

Neural network-based encoder and decoder are one of the emerging techniques for image compression. To improve the compression rate, these models use a special module called the quantizer that improves the entropy of the generated representation. Here, we propose a novel binary quantizer and an auxiliary support module known as the remapper that co-ordinate for better compression and decompression of an image. The proposed quantizer generates the compressed binary representation whereas the remapper aids in minimizing the reconstruction error during decompression. The combined optimization of the model consisting of the encoder, quantizer, remapper, and the decoder as a single entity is not feasible due to the non-differentiable nature of the quantizer. To mitigate this, we propose a step-by-step training process wherein the encoder and decoder are trained first, then keeping the encoder parameters fixed, the remapper is trained along with the decoder to obtain a fully optimized model. The training is performed with different types of images and the results are compared to understand the best training procedures. Experimental results reveal that the proposed model can compress a 256 × 256 × 3 image to a mere 256 bits, thereby achieving a compression ratio of 6144:1; with excellent perceptual reconstruction during decompression.