Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain-inspired computing with fluidic iontronic nanochannels

The unparalleled energy efficiency of the brain is driving researchers to seek out new brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting new platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the fluidic ion transport found in the brain. However, despite recent interest, a tangible demonstration of neuromorphic computing remains a challenge. Here we successfully perform neuromorphic reservoir computing using easy to fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between colloids, which we show to be a novel memristor (memory resistor). Remarkably, a wide range of typical conductance memory timescales can easily be achieved by constructing channels of different length, a unique and highly desirable feature. This work is inspired and supported by a new theoretical model, which stems directly from traditional diffusion-conduction equations and shows excellent agreement with the experiments, predicting the features and relevant parameters presented here. Our results represent a fundamental step in realising the promise of ion channels as a new platform to emulate the rich aqueous dynamics of the brain.
Sedation Quality and Cardiorespiratory, Echocardiographic, Radiographic and Electrocardiographic Effects of Intramuscular Alfaxalone and Butorphanol in Spanish Greyhound Dogs

Simple Summary Sedation is sometimes necessary in veterinary practice to perform different medical procedures, such as echocardiography or radiography, due to uncooperative or aggressive patients. In patients with cardiovascular disease, anxiety needs to be reduced; however, hemodynamic conditions have to be preserved, so it is advisable to avoid sedatives such as alpha-2 agonists or phenothiazine, due to their cardiovascular side effects. Many studies support the cardiovascular stability obtained by the administration of alfaxalone, alone or in combination, in dogs. Butorphanol is a synthetic opioid characterized by minimal cardiovascular effects, making it an ideal drug for sedation protocols. We aimed to evaluate the hemodynamic impact of the administration of intramuscular alfaxalone and butorphanol in healthy dogs. The evaluation involved assessing the degree of sedation and the effects of the sedation on cardiorespiratory, echocardiographic, radiographic and electrocardiographic parameters at baseline and 30 min after the administration of sedation. Although statistically significant changes were observed in some of the studied parameters, no clinical alterations were found 30 min after drug administration. The results showed that alfaxalone and butorphanol at the doses used in this study provided moderate sedation for performing diagnostic procedures, and they caused mild hemodynamic and respiratory changes. Abstract The quality of sedation and changes in cardiorespiratory variables after the intramuscular administration of alfaxalone and butorphanol in Spanish greyhound dogs were evaluated. Twenty-one adult dogs were included. The dogs received alfaxalone (2 mg/kg) and butorphanol (0.2 mg/kg) intramuscularly. Sedation scoring, cardiorespiratory parameters (including blood gas analysis), echocardiography, thoracic radiography and electrocardiography were performed before sedation and 30 min after drug administration. Moderate sedation was observed, and side effects, such as tremors, nystagmus and auditory hyperesthesia, were noticed. Statistically significant changes in heart rate, invasive blood pressure, pH, arterial saturation of O2 and partial pressure of O2 and CO2 were found. Echocardiographic variables, including end-diastolic volume, left ventricular diameter in diastole, aortic and pulmonic flow, diastolic transmitral flow and left atrial/aortic ratio, and electrocardiography parameters, including PQ interval and QT interval, showed statistically significant changes. In conclusion, the intramuscular administration of alfaxalone and butorphanol to healthy dogs produced moderate sedation with mild cardiorespiratory, echocardiographic and electrocardiographic changes, without alterations in cardiac size on radiographic images.
A symmetric monoidal Comparison Lemma

In this note we study symmetric monoidal functors from a symmetric monoidal 1-category to a cartesian symmetric monoidal $\infty$-category, which are in addition hypersheaves for a certain topology. We prove a symmetric monoidal version of the Comparison Lemma, for lax as well as strong symmetric monoidal hypersheaves. For a strong symmetric monoidal functor between symmetric monoidal 1-categories with topologies generated by suitable cd-structures, we show that if the conditions of the Comparison Lemma are satisfied, then there is also an equivalence between categories of lax and strong symmetric monoidal hypersheaves respectively, taking values in a complete cartesian symmetric monoidal $\infty$-category. As an application of this result, we prove a lax symmetric monoidal version of our previous result about hypersheaves that encode compactly supported cohomology theories.
Self-reported Health-related Quality of Life in Adolescents With Cerebral Palsy

Background: Happiness, comfort, and motor function contribute to satisfaction with life for individuals with cerebral palsy (CP). Evidence-based medical care can improve motor function and physical health of youth with CP. Less is known about medical care and its relationship to health-related quality of life (HRQOL) in adolescents and young adults with CP. This study aimed to describe HRQOL among adolescents with CP to examine differences between adolescent (self) and parent (proxy) reports of HRQOL and to explore associations of pain, age, and gross motor function with HRQOL. Methods: This is a retrospective study including adolescents with CP classified as Gross Motor Function Classification System levels I to V, ages 11 to 20 years, reading ≥ a fourth-grade level, and who completed the self-reported Pediatric Outcomes Data Collection Instrument (PODCI). Parents completed the PODCI concurrently or within 12 months and scores were compared. In addition, self-reported scores were compared between age bands, across Gross Motor Function Classification System levels, with typically developing youth (TDY), and between youth with/without pain. Results: PODCI scores from 102 adolescents [59 males; 15.0 (SD: 2.6) years old] were examined. Scores from 50 adolescents and parents were matched. Mean self-reported scores were significantly higher than mean parent-reported scores in 4 domains: upper extremity and physical function (P=0.018), sports and physical function (P=0.005), happiness (P=0.023), and global functioning (P=0.018). All domains, except Happiness, were significantly < TDY (P<0.01). The presence of pain was associated with lower scores in all domains (P<0.05). Conclusion: Examining HRQOL with the PODCI revealed significant limitations in physical function and higher pain in adolescents with CP compared with TDY. Self- and parent-reported PODCI results should be considered separately. Adolescents report higher HRQOL compared with parent proxy. Recognizing and validating the perspectives of youth and their parents presents an opportunity for providers to discuss different points of view with families. Such engagement can help promote self-efficacy in youth with CP as they transition to the responsibility of guiding their own care in adulthood. Level of Evidence: III, Retrospective comparative study.
Keras/TensorFlow in Drug Design for Immunity Disorders

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3 and one CXC receptor CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier, but as an efficient multi-class classifier that can discard not only inactive compounds but also low or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 were among the top predicted compounds and thus benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors available at: http://db-gpcr-chem.uw. edu.pl. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.
Prognostic significance of psoas muscle index in male hepatocellular carcinoma patients treated with immune checkpoint inhibitors and tyrosine kinase inhibitors

ABSTRACT Currently, the relationship between nutritional indices and the prognosis of hepatocellular carcinoma (HCC) patients treated with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) remains unclear. This study aims to investigate the prognostic value of psoas muscle index (PMI), prognostic nutritional index (PNI), body mass index (BMI), and geriatric nutritional risk index (GNRI) in HCC patients treated with ICIs combined with TKIs. A total of 124 male patients with HCC were included in this study. PNI, PMI, BMI, and GNRI were calculated at the beginning of treatment. The Cox proportional hazards model was used to analyze the effect of various variables. In the univariate analysis, PMI, PNI, GNRI, and ALB were found to impact the outcomes of the patients at different follow-up times. However, the predictive value of these nutritional indices was eliminated when established risk factors were considered. In the multivariate analysis that only included nutrition-related indicators, PMI emerged as an independent prognostic factor for 1-year treatment outcomes. The group with low PMI (≤5.5409 cm2/m2) was found to have a higher risk of death at one year and at the end of the follow-up period.
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute

The Languini Kitchen serves as both a research collective and codebase designed to empower researchers with limited computational resources to contribute meaningfully to the field of language modelling. We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. The number of tokens on which a model is trained is defined by the model's throughput and the chosen compute class. Notably, this approach avoids constraints on critical hyperparameters which affect total parameters or floating-point operations. For evaluation, we pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. On it, we compare methods based on their empirical scaling trends which are estimated through experiments at various levels of compute. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput. While the GPT baseline achieves better perplexity throughout all our levels of compute, our LSTM baseline exhibits a predictable and more favourable scaling law. This is due to the improved throughput and the need for fewer training tokens to achieve the same decrease in test perplexity. Extrapolating the scaling laws leads of both models results in an intersection at roughly 50,000 accelerator hours. We hope this work can serve as the foundation for meaningful and reproducible language modelling research.
Synaptic interactions between stellate cells and parvalbumin interneurons in layer 2 of the medial entorhinal cortex are organized at the scale of grid cell clusters

Interactions between excitatory and inhibitory neurons are critical to computations in cortical circuits but their organization is difficult to assess with standard electrophysiological approaches. Within the medial entorhinal cortex, representation of location by grid and other spatial cells involves circuits in layer 2 in which excitatory stellate cells interact with each other via inhibitory parvalbumin expressing interneurons. Whether this connectivity is structured to support local circuit computations is unclear. Here, we introduce strategies to address the functional organization of excitatory-inhibitory interactions using crossed Cre- and Flp-driver lines to direct targeted presynaptic optogenetic activation and postsynaptic cell identification. We then use simultaneous patch-clamp recordings from postsynaptic neurons to assess their shared input from optically activated presynaptic populations. We find that extensive axonal projections support dense but incomplete connectivity between stellate cells and parvalbumin interneurons. Analysis of the spatial organization of synaptic interactions is consistent with local modules defined by excitatory-inhibitory connectivity. We suggest that dense, but specific, direct excitatory-inhibitory synaptic interactions may operate at the scale of grid cell clusters, while indirect interactions may coordinate activity at the scale of grid cell modules.
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning

In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aims to rank relevant text/video higher than irrelevant ones. The core of this task is to precisely measure the cross-modal similarity between texts and videos. Recently, contrastive learning methods have shown promising results for text-video retrieval, most of which focus on the construction of positive and negative pairs to learn text and video representations. Nevertheless, they do not pay enough attention to hard negative pairs and lack the ability to model different levels of semantic similarity. To address these two issues, this paper improves contrastive learning using two novel techniques. First, to exploit hard examples for robust discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module (DMAE) to mine hard negative pairs from textual and visual clues. By further introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively identify all these hard negatives and explicitly highlight their impacts in the training loss. Second, our work argues that triplet samples can better model fine-grained semantic similarity compared to pairwise samples. We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL designs an adaptive token masking strategy with cross-modal interaction to model subtle semantic differences. Extensive experiments demonstrate that the proposed approach outperforms existing methods on four widely-used text-video retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.
Weak Supervision for Label Efficient Visual Bug Detection

As video games evolve into expansive, detailed worlds, visual quality becomes essential, yet increasingly challenging. Traditional testing methods, limited by resources, face difficulties in addressing the plethora of potential bugs. Machine learning offers scalable solutions; however, heavy reliance on large labeled datasets remains a constraint. Addressing this challenge, we propose a novel method, utilizing unlabeled gameplay and domain-specific augmentations to generate datasets&self-supervised objectives used during pre-training or multi-task settings for downstream visual bug detection. Our methodology uses weak-supervision to scale datasets for the crafted objectives and facilitates both autonomous and interactive weak-supervision, incorporating unsupervised clustering and/or an interactive approach based on text and geometric prompts. We demonstrate on first-person player clipping/collision bugs (FPPC) within the expansive Giantmap game world, that our approach is very effective, improving over a strong supervised baseline in a practical, very low-prevalence, low data regime (0.336 $\rightarrow$ 0.550 F1 score). With just 5 labeled"good"exemplars (i.e., 0 bugs), our self-supervised objective alone captures enough signal to outperform the low-labeled supervised settings. Building on large-pretrained vision models, our approach is adaptable across various visual bugs. Our results suggest applicability in curating datasets for broader image and video tasks within video games beyond visual bugs.
Budget-Aware Pruning: Handling Multiple Domains with Less Parameters

Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant amount of parameters. Such requirements hinder the use in resource-limited environments and demand both software and hardware optimization. Another limitation is that deep models are usually specialized into a single domain or task, requiring them to learn and store new parameters for each new one. Multi-Domain Learning (MDL) attempts to solve this problem by learning a single model that is capable of performing well in multiple domains. Nevertheless, the models are usually larger than the baseline for a single domain. This work tackles both of these problems: our objective is to prune models capable of handling multiple domains according to a user-defined budget, making them more computationally affordable while keeping a similar classification performance. We achieve this by encouraging all domains to use a similar subset of filters from the baseline model, up to the amount defined by the user's budget. Then, filters that are not used by any domain are pruned from the network. The proposed approach innovates by better adapting to resource-limited devices while, to our knowledge, being the only work that handles multiple domains at test time with fewer parameters and lower computational complexity than the baseline model for a single domain.
Research on the correlation between vocabulary depth knowledge and reading proficiency of English majors

: Since vocabulary knowledge has been regarded as a key predictor of second language learners’ reading comprehension, vocabulary acquisition and related works of lexical pedagogy have always been heatedly discussed within the fields of applied linguistics and second language learning and teaching. This study seeks to show build a better understanding of the relationship between second language learners’ vocabulary depth and their reading proficiency. Through the descriptive and correlation analysis of experimental tests, the study demonstrated that vocabulary depth influences learners’ reading comprehension and linguistic ability, which highlights the significance of lexical pedagogy for enhancing learners’ reading proficiency.
Placental insufficiency and heavier placentas in sheep after suppressing CXCL12/CXCR4 signaling during implantation

Abstract During implantation, trophoblast cell invasion and differentiation is predominantly important to achieving proper placental formation and embryonic development. The chemokine, C-X-C motif chemokine ligand 12 (CXCL12) working through its receptor C-X-C motif chemokine receptor 4 (CXCR4) is implicated in implantation and placentation but precise roles of this axis are unclear. Suppressing CXCL12/CXCR4 signaling at the fetal-maternal interface in sheep reduces trophoblast invasion, disrupts uterine remodeling, and diminishes placental vascularization. We hypothesize these negative impacts during implantation will manifest as compromised fetal and placental growth at midgestation. To test, on day 12 postbreeding, osmotic pumps were surgically installed in 30 ewes and delivered intrauterine CXCR4 inhibitor or saline for 7 or 14 days. On day 90, fetal/maternal tissues were collected, measured, weighed, and maternal (caruncle) and fetal (cotyledon) placenta components separated and analyzed. The objectives were to determine if (i) suppressing CXCL12/CXCR4 during implantation results in reduced fetal and placental growth and development and (ii) if varying the amount of time CXCL12/CXCR4 is suppressed impacts fetal/placental development. Fetal weights were similar; however greater placental weight and placentome numbers occurred when CXCL12/CXCR4 was suppressed for 14 days. In caruncles, greater abundance of fibroblast growth factor 2, vascular endothelial growth factor A, vascular endothelial growth factor A receptor 1 (FLT-1), and placental growth factor were observed after suppressing CXCL12/CXCR4. Similar results occurred in cotyledons except less vascular endothelial growth factor in 7 day group and less fibroblast growth factor in 14 day group. Our data underscore the importance of CXCL12/CXCR4 signaling during placentation and provide strong evidence that altering CXCL12-mediated signaling induces enduring placental effects manifesting later in gestation. Summary Sentence Disrupting fetal-maternal CXCR4 signaling during implantation in sheep results in placental insufficiency at midgestation with dams having greater number of placentomes, heavier placentas and altered expression of angiogenic and growth factors. Graphical Abstract Antagonizing CXCL12/CXCR4 at the fetal-maternal interface during implantation for 7 or 14 days impacts placental growth and development at day 90 with longer antagonism resulting in placental insufficiency, heavier placentas and greater placentome numbers compared to control
Adsorption of sulfur-containing contaminant gases by pristine, Cr and Mo doped NbS2 monolayers based on density functional theory

The adsorption and sensor performance of hazardous gases containing sulfur (SO2, H2S and SO3) on pristine, Cr and Mo doped NbS2 monolayers (Cr-NbS2 and Mo-NbS2) were investigated in detail based on density functional theory. The comparative analysis of the parameters such as density of states, adsorption energy, charge transfer, recovery time and work function of the systems showed that the pristine NbS2 monolayer have poor sensor performance for sulfur-containing hazardous gases due to weak adsorption capacity, insignificant charge transfer and insignificant changes in electronic properties after gas adsorption on the surface. After doping with Cr atoms, the adsorption performance of Cr-NbS2 was significantly improved, and it can be used as a sensor for SO2 and H2S gases and as an adsorbent for SO3 gas. The adsorption performance of Mo-NbS2 is also significantly improved by doping with Mo atoms, and it can be used as a sensor for H2S gas and as an adsorbent for SO2 and SO3 gas. Therefore, Cr-NbS2 and Mo-NbS2 are revealed to be sensing or elimination materials for the harmful gases containing sulfur (SO2, H2S and SO3) in the atmosphere.