Miracle-dz/UniFAR-FTUs huggingface dataset:
Duplication and sub-functionalisation characterise diversification of opsin genes in the Lepidoptera

Gene duplication is a vital process for evolutionary innovation. Functional diversification of duplicated genes is best explored in multicopy gene families such as histones, hemoglobin, and opsins. Rhodopsins are photo-sensitive proteins that respond to different wavelengths of light and contribute to diverse visual adaptations across insects. While there are several instances of gene duplications in opsin lineages, the functional diversification of duplicated copies and their ecological significance is properly characterised only in a few insect groups. We examined molecular and structural evolution that underlies diversification and sub-functionalisation of four opsin genes and their duplicated copies across 132 species of the diverse insect order—Lepidoptera. Opsins have largely evolved under purifying selection with few residues showing signs of episodic and pervasive diversifying selection. Although these do not affect overall protein structures of opsins, substitutions in key amino acids in the chromophore-binding pocket of duplicated copies might cause spectral sensitivity shifts leading to sub-functionalisation or neofunctionalisation. Duplicated copies of opsins also exhibit developmental stage-specific expression in Papilio polytes, suggesting functional partitioning during development. Together, altered spectral sensitivities owing to key substitutions and differential expression of duplicated copies across developmental stages might enable enhanced colour perception and improved discrimination across wavelengths in this highly visual insect group.
Prospective isolation according to melanin pigment content of melanoma cells with heterogeneous potentials for disease propagation

Functional variation between cancer cells (intra-tumoral heterogeneity) poses a major challenge to treating and managing cancer patients. Melanomas are typically composed of cancer cells with heterogeneous content of melanin pigment. Pigment production is a hallmark of normal melanocytic differentiation, however the functional consequences of melanin production in melanoma cells remains poorly understood owing to a lack of experimental approaches for detection of pigment in unfixed cells. Here, we describe a novel flow cytometric method for high purity separation of viable melanoma cells based on their content of melanin pigment, exploiting the infrared light scattering properties of melanin. By fluorescence-activated cell sorting, we show that melanoma cells with low-pigment content (LPCs) in culture and in patient tumors are far more abundant than high-pigment cells (HPCs), and have substantially increased potentials for colony formation in vitro and for tumor formation in vivo. RNAseq analysis revealed activation of P53 in HPCs associated with perturbed cell cycling, whereas LPCs displayed upregulation of MYC-associated transcription and activated ribosome biogenesis. The latter was reduced by topoisomerase 2 beta targeting with CX-5461, which also induced senescent HPC phenotypes and irreversible loss of clonogenic activity. These data illuminate an ‘inverted pyramid’ hierarchical model of melanoma cell propagation wherein abundant LPCs frequently renew their own malignant potential to propagate disease, but also rarely generate HPCs that lose this ability in a manner that may be promoted as an anti-melanoma strategy.
Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

Mechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be trained directly on the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with the same temporal and geometrical properties as those observed. This is called dynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes a surrogate for the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.
The role of representational competencies for students’ learning from an educational video game for astronomy

Educational video games can engage students in authentic STEM practices, which often involve visual representations. In particular, because most interactions within video games are mediated through visual representations, video games provide opportunities for students to experience disciplinary practices with visual representations. Prior research on learning with visual representations in non-game contexts suggests that visual representations may confuse students if they lack prerequisite representational-competencies. However, it is unclear how this research applies to game environments. To address this gap, we investigated the role of representational-competencies for students’ learning from video games. We first conducted a single-case study of a high-performing undergraduate student playing an astronomy game as an assignment in an astronomy course. We found that this student had difficulties making sense of the visual representations in the game. We interpret these difficulties as indicating a lack of representational-competencies. Further, these difficulties seemed to lead to the student’s inability to relate the game experiences to the content covered in his astronomy course. A second study investigated whether interventions that have proven successful in structured learning environments to support representational-competencies would enhance students’ learning from visual representations in the video game. We randomly assigned 45 students enrolled in an undergraduate course to two conditions. Students either received representational-competency support while playing the astronomy game or they did not receive this support. Results showed no effects of representational-competency supports. This suggests that instructional designs that are effective for representational-competency supports in structured learning environments may not be effective for educational video games. We discuss implications for future research, for designers of educational games, and for educators.
Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.
An economic analysis of high milk somatic cell counts in dairy cattle in Chiang Mai, Thailand

Mastitis in dairy cattle can lead to significant financial losses due to a reduction in milk yield, the withdrawal period after treatment when milk cannot be sold, and an increase in somatic cell count (SCC) which can reduce the milk's per liter commercial value. Dairy cooperatives point at high-SCC problems as an important factor leading to suboptimal levels of milk quantity and quality. This study aims at describing farm characteristics and milking practices associated with high SCC, identifying risk factors, and assessing the economic loss due to high SCC in three dairy cooperatives in Chiang Mai, Thailand. A cross-sectional study was conducted on 208 dairy cattle farms from July to September 2018. Structured interviews were conducted to collect the data. Univariate and multivariate analyses were performed to determine the degree of association between factors and high SCC. A retrospective cost assessment of high SCC was conducted to estimate the losses in affected farms, and two potential coping strategies were assessed: (1) culling and (2) treating the cow. More than 12% of farms had high SCC (SCC > 500,000 cells/ml). Inappropriate vacuum pressure and inappropriate pulsation rate of milking machines were identified as significant risk factors according to the multiple logistic regression (P < 0.01). Both factors can decrease the natural protection of teat tissue, increasing the likelihood of bacterial infection. The average economic loss of high SCC in affected farms was 557 USD for a three-month period. When comparing response strategies (i.e., treatment vs. culling), treating the affected cow was found to be more cost-effective. With a probability of successful treatment of 54%, treating an affected cow leads to 1,158.7 USD in gains over 3 years (vs. doing nothing). The results of this economic analysis can be used to advocate to cooperatives the value of veterinarians and for investigating and treating cases of mastitis.
A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
Characteristics of lung metastasis in testicular cancer: A large-scale population analysis based on propensity score matching

Background This study aims to systematically evaluate predictive factors for lung metastasis (LM) in patients with testicular cancer (TC) and to investigate cancer-specific survival (CSS) and overall survival (OS) of LM in TC patients based on a large population-cohort. Methods A total of 10,414 patients diagnosed with TC during 2010–2015 were adopted from the Surveillance, Epidemiology, and End Results (SEER). After propensity score matching (PSM), 493 patients with LM were included for subsequent analysis. Univariate and multivariate logistic regression analyses were employed to identify risk factors, a nomogram was developed, and the receiver operating characteristic (ROC) curve was utilized to confirm the validation of the nomogram. Prognostic factors for OS and CSS among TC patients with LM were estimated via Cox proportional hazards models. Results Postmatching indicated that 11 parameters were successfully balanced between both groups (P > 0.05). After PSM, TC patients with LM presented an undesirable prognosis in both CSS and OS than those without LM (P < 0.001). The logistic regression model showed that tumor size; T stage; N stage; liver, brain, and bone metastases; and histology were positively associated with LM (P < 0.05). A nomogram was developed to predict diagnostic possibilities based on the independent risk variables, and the ROC curve verified the predictive capacity of the logistic regression model [area under the curve (AUC) = 0.910]. Conclusion The selected variates in the nomogram can be predictive criteria for TC patients with LM. Brain metastasis, liver metastasis, and larger tumor size were prognostic factors for CCS and OS among TC patients with LM.
A case series report on successful management of patients with COVID-19-associated lymphopenia and potential application of PG2

Background Lymphopenia and the resultant high neutrophil-to-lymphocyte ratio (NLR) are hallmark signs of severe COVID-19, and effective treatment remains unavailable. We retrospectively reviewed the outcomes of COVID-19 in a cohort of 26 patients admitted to Chung Shan Medical University Hospital (Taichung City, Taiwan). Twenty-five of the 26 patients recovered, including 9 patients with mild/moderate illness and 16 patients with severe/critical illness recovered. One patient died after refusing treatment. Case presentation We report the cases of four patients with high NLRs and marked lymphopenia, despite receiving standard care. A novel injectable botanical drug, PG2, containing Astragalus polysaccharides, was administered to them as an immune modulator. The decrease in the NLR in these four patients was faster than that of other patients in the cohort (0.80 vs. 0.34 per day). Conclusion All patients recovered from severe COVID-19 showed decreased NLR and normalized lymphocyte counts before discharge. Administration of PG2 may be of benefit to patients with moderate to severe COVID-19 and lymphopenia.
Transient Ca2+ entry by plasmalogen-mediated activation of receptor potential cation channel promotes AMPK activity

Ethanolamine-containing alkenyl ether glycerophospholipids, plasmalogens, are major cell membrane components of mammalian cells that activate membrane protein receptors such as ion transporters and G-protein coupled receptors. However, the mechanism by which plasmalogens modulate receptor function is unknown. Here, we found that exogenously added plasmalogens activate transient receptor potential cation channel subfamily C member 4 (TRPC4) to increase Ca2+ influx, followed by calcium/calmodulin-dependent protein kinase 2-mediated phosphorylation of AMP-activated protein kinase (AMPK). Upon topical application of plasmalogens to the skin of mice, AMPK activation was observed in TRPC4-expressing hair bulbs and hair follicles. Here, TRPC4 was co-localized with the leucine-rich repeat containing G protein-coupled receptor 5, a marker of hair-follicle stem cells, leading to hair growth. Collectively, this study indicates that plasmalogens could function as gate openers for TRPC4, followed by activating AMPK, which likely accelerates hair growth in mice.
Robot-assisted Ivor Lewis Esophagectomy (RAILE): A review of surgical techniques and clinical outcomes

In the past 20 years, robotic system has gradually found a place in esophagectomy which is a demanding procedure in the deep and narrow thoracic cavity containing crucial functional structures. Ivor Lewis esophagectomy (ILE) is a mainstream surgery type for esophagectomy and is widely accepted for its capability in lymphadenectomy and relatively mitigated trauma. As a minimally invasive technique, robot-assisted Ivor Lewis esophagectomy (RAILE) has been frequently compared with the video-assisted procedure and the traditional open procedure. However, high-quality evidence elucidating the advantages and drawbacks of RAILE is still lacking. In this article, we will review the surgical techniques, both short and long-term outcomes, the learning curve, and explicate the current progress and clinical efficacy of RAILE.
Linear Cosmological Structure Limits on Warm Dark Matter

I consider constraints from observations on a cutoff scale in clustering due to free streaming of the dark matter in a warm dark matter cosmological model with a cosmological constant. The limits are derived in the framework of a sterile neutrino warm dark matter universe, but can be applied to gravitinos and other models with small scale suppression in the linear matter power spectrum. With freedom in all cosmological parameters including the free streaming scale of the sterile neutrino dark matter, limits are derived using observations of the fluctuations in the cosmic microwave background, the 3D clustering of galaxies and 1D clustering of gas in the Lyman-alpha (Ly-alpha) forest in the Sloan Digital Sky Survey (SDSS), as well as the Ly-alpha forest in high-resolution spectroscopic observations. In the most conservative case, using only the SDSS main-galaxy 3D power-spectrum shape, the limit is m_s > 0.11 keV; including the SDSS Ly-alpha forest, this limit improves to m_s > 1.7 keV. More stringent constraints may be placed from the inferred matter power spectrum from high-resolution Ly-alpha forest observations, which has significant systematic uncertainties; in this case, the limit improves to m_s > 3.0 keV (all at 95% CL).
Barriers to mental health help-seeking in veterinary professionals working in Australia and New Zealand: A preliminary cross-sectional analysis

Despite higher reported rates of mental ill-health than the general population, professionals working in the animal care industry have low reported rates of help-seeking behavior. Potential factors involved in veterinary professionals' reluctance to seek help include stigma toward mental ill-health, practical barriers to accessing supports, and a cultural normalization of symptoms in the industry. This preliminary study sought to explore these factors in a sample of veterinarians, veterinary nurses, and veterinary technicians and examine effects of gender, years' experience, and practice location. A total of 408 veterinary professionals working in Australia and New Zealand completed an online survey between June and December 2021 measuring perceived stigma, practical barriers to mental health help-seeking, perceptions of normalized psychopathology and sickness presenteeism. Results indicated moderate levels of both perceived stigma and barriers to mental health help-seeking. Interestingly, psycho/pathology (e.g., burnout, fatigue, and sickness presenteeism) was perceived to be a normalized aspect of the profession by majority of respondents. Although no effect of gender or geographic location were observed, stage of career did have an effect on findings. Early career veterinary professionals were identified as more vulnerable to perceived stigma and barriers to care. The practical and research implications of the findings are discussed and include the need for mental health to be more centrally incorporated into the veterinary curriculum and professional development. Also discussed is an agenda for future research aimed at improving the mental health and wellbeing of professionals working in the animal care industry.
Transvaginal natural orifice endoscopic surgery for myomectomy: Can it be a conventional surgery?

Introduction As a new minimally invasive surgery, transvaginal natural orifice transluminal endoscopic surgery (vNOTES) has been proved to be suitable for the treatment of a variety of gynecological benign diseases. However, compared with other minimally invasive surgeries that have been widely used, such as conventional multiport laparoscopy and transumbilical laparoendoscopic single-site surgery (LESS), their advantages and disadvantages and how to choose are still unknown. The purpose of our study is to compare the advantages and disadvantages of the three minimally invasive surgeries in myomectomy and to provide theoretical basis for the wider development of vNOTES surgery. Material and methods This retrospective study included 282 patients at our hospital who underwent laparoscopic myomectomy from May 2021 to March 2022. Based on the surgical approach, patients were classified into multiport, transumbilical LESS, and vNOTES groups. The patients’ demographic characteristics and follow-up data were collected during the perioperative period and at 1 month postoperatively. Results Among the three procedures, vNOTES had the shortest anal exhaust time but also the highest postoperative infection rate. Multiple linear regression analysis showed that the operative time increased by 3.5 min for each 1 cm increase in myoma, and intraoperative bleeding increased by approximately 12 ml. The average duration of single pores increased by 25 min compared to that of multiports, and the operative duration increased by 10.48 min for each degree of adhesion. Conclusions For gynecologists who have mastered vNOTES, this procedure has the same efficacy and safety as the two existing minimally invasive surgeries in myomectomy, but it shows obvious advantages in postoperative recovery.