Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic fields exposure on fetal and childhood abnormalities: Systematic review and meta-analysis

Abstract Today, in the modern world, people are often exposed to electromagnetic waves, which can have undesirable effects on cell components that lead to differentiation and abnormalities in cell proliferation, deoxyribonucleic acid (DNA) damage, chromosomal abnormalities, cancers, and birth defects. This study aimed to investigate the effect of electromagnetic waves on fetal and childhood abnormalities. PubMed, Scopus, Web of Science, ProQuest, Cochrane Library, and Google Scholar were searched on 1 January 2023. The Cochran’s Q-test and I 2 statistics were applied to assess heterogeneity, a random-effects model was used to estimate the pooled odds ratio (OR), standardized mean difference (SMD), and mean difference for different outcomes, and a meta-regression method was utilized to investigate the factors affecting heterogeneity between studies. A total of 14 studies were included in the analysis, and the outcomes investigated were: change in gene expression, oxidant parameters, antioxidant parameters, and DNA damage parameters in the umbilical cord blood of the fetus and fetal developmental disorders, cancers, and childhood development disorders. Totally, the events of fetal and childhood abnormalities were more common in parents who have been exposed to EMFs compared to those who have not (SMD and 95% confidence interval [CI], 0.25 [0.15–0.35]; I 2, 91%). Moreover, fetal developmental disorders (OR, 1.34; CI, 1.17–1.52; I 2, 0%); cancer (OR, 1.14; CI, 1.05–1.23; I 2, 60.1%); childhood development disorders (OR, 2.10; CI, 1.00–3.21; I 2, 0%); changes in gene expression (mean difference [MD], 1.02; CI, 0.67–1.37; I 2, 93%); oxidant parameters (MD, 0.94; CI, 0.70–1.18; I 2, 61.3%); and DNA damage parameters (MD, 1.01; CI, 0.17–1.86; I 2, 91.6%) in parents who have been exposed to EMFs were more than those in parents who have not. According to meta-regression, publication year has a significant effect on heterogeneity (coefficient: 0.033; 0.009–0.057). Maternal exposure to electromagnetic fields, especially in the first trimester of pregnancy, due to the high level of stem cells and their high sensitivity to this radiation, the biochemical parameters of the umbilical cord blood examined was shown increased oxidative stress reactions, changes in protein gene expression, DNA damage, and increased embryonic abnormalities. In addition, parental exposure to ionizing and non-ionizing radiation can lead to the enhancement of different cell-based cancers and developmental disorders such as speech problems in childhood.
Fingerprinting-based Minimal Perfect Hashing Revisited

In this paper we study a fingerprint-based minimal perfect hash function (FMPH for short). While FMPH is not as space-efficient as some other minimal perfect hash functions (for example RecSplit, CHD, or PTHash), it has a number of practical advantages that make it worthy of consideration. FMPH is simple and quite fast to evaluate. Its construction requires very little auxiliary memory, takes a short time and, in addition, can be parallelized or carried out without holding keys in memory. In this paper, we propose an effective method (called FMPHGO) that reduces the size of FMPH, as well as a number of implementation improvements. In addition, we experimentally study FMPHGO performance and find the best values for its parameters. Our benchmarks show that with our method and an efficient structure to support the rank queries on a bit vector, the FMPH size can be reduced to about 2.1 bits/key, which is close to the size achieved by state-of-the-art methods and noticeably larger only compared to RecSplit. FMPHGO preserves most of the FMPH advantages mentioned above, but significantly reduces its construction speed. However, FMPHGO’s construction speed is still competitive with methods of similar space efficiency (like CHD or PTHash), and seems to be good enough for practical applications.
Split and Knit: 3D Fingerprint Capture with a Single Camera

3D fingerprint capture is less sensitive to skin moisture levels and avoids skin deformation, which is common in contact-based sensors, in addition to capturing depth information. Unfortunately, its adoption is limited due to high cost and system complexity. Photometric stereo provides an opportunity to build low-cost, simple sensors capable of high-quality 3D capture. However, it assumes that the surface being imaged is lambertian (unlike our fingers). We introduce the Split and Knit algorithm (SnK), a 3D reconstruction pipeline based on the photometric stereo for finger surfaces. It introduces an efficient way of estimating the direct illumination component, thus allowing us to do a higher-quality reconstruction of the entire finger surface. The algorithm also introduces a novel method to obtain the overall finger shape under NIR illumination, all using a single camera. Finally, we combine the overall finger shape and the ridge-valley point cloud to obtain a 3D finger phalange. The high-quality 3D reconstruction also results in better matching accuracy of the captured fingerprints. 1
REF-SHARP: REFined face and geometry reconstruction of people in loose clothing✱

In this paper, we address the problem of monocular 3D human reconstruction with an acute focus on the challenge of recovering person-specific facial geometry as well as suppressing surface noise, specifically addressing the issue of false geometrical variations caused by textural edges. Most of the existing state-of-the-art methods in this domain fail to address these challenges. More specifically, we propose to integrate facial and wrinkle map priors in a learning-based framework to improve the quality of full-body 3D reconstruction from monocular images. By incorporating facial prior, we recover person-specific identity unlike many of the existing methods which rely on parametric shape models. Similarly, the wrinkle map prior enables our network to alleviate the challenge of false geometrical variations caused by high-frequency textural details present in the input image. We evaluate our method on publicly available datasets & in-the-wild internet images with loose clothing and report superior performance both qualitatively and quantitatively when compared with SOTA methods.
Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray Images✱

The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists’ views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal" classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score).
Causal structure search and modeling of precision dairy farm data for automated prediction of ketosis risk, and the effect of potential interventions

Causal search techniques enable inference from observational data, such as that produced in Precision Livestock Farming. The Peter-Clark algorithm was used to produce four causal models, for the risk of ketosis in individual cows. The data set covered 1542 Holstein-Friesian cows on a commercial dairy farm in Slovakia, over a period of 18 months and had 483 variables, split into four samples for four-way cross validation. The cow data was sorted into quartiles by predicted postpartum blood ketone value. The observed incidences of ketosis by quartile were 3.14%, 6.35%, 6.77%, 15.1%. To test the effect of intervention on the reduction of ketosis cases on the farm, we predicted the expected effect of 20% lower dry matter in the total mixed ration over the 6 months pre-partum. Predicted reductions in incidence of ketosis for the highest risk (4th) quartile were -4.96%, -7.4%, -11.21%, and -11.07% of animals in the herd, respectively for the four models. The different predictions were due to the different causal structures estimated from the four data samples by the Peter-Clark causal model search algorithm. To accurately predict the effect of intervention for automatic optimization of herd performance it is necessary to determine the correct causal structure of the model. Collinearity of inputs due to e.g. grouping by pens, reduced the conditional independence of their effects, and therefore the ability of the Peter-Clark algorithm to determine the correct causal structure. To reduce the collinearity of variables, we recommend causal search on datasets from multiple farms or multiple years.
Risk prediction score for clinical outcome in atrial fibrillation and stable coronary artery disease

Objective Antithrombotic therapy is essential for patients with atrial fibrillation (AF) and stable coronary artery disease (CAD) because of the high risk of thrombosis, whereas a combination of antiplatelets and anticoagulants is associated with a high risk of bleeding. We sought to develop and validate a machine-learning-based model to predict future adverse events. Methods Data from 2215 patients with AF and stable CAD enrolled in the Atrial Fibrillation and Ischaemic Events With Rivaroxaban in Patients With Stable Coronary Artery Disease trial were randomly assigned to the development and validation cohorts. Using the random survival forest (RSF) and Cox regression models, risk scores were developed for net adverse clinical events (NACE) defined as all-cause death, myocardial infarction, stroke or major bleeding. Results Using variables selected by the Boruta algorithm, RSF and Cox models demonstrated acceptable discrimination and calibration in the validation cohort. Using the variables weighted by HR (age, sex, body mass index, systolic blood pressure, alcohol consumption, creatinine clearance, heart failure, diabetes, antiplatelet use and AF type), an integer-based risk score for NACE was developed and classified patients into three risk groups: low (0–4 points), intermediate (5–8) and high (≥9). In both cohorts, the integer-based risk score performed well, with acceptable discrimination (area under the curve 0.70 and 0.66, respectively) and calibration (p>0.40 for both). Decision curve analysis showed the superior net benefits of the risk score. Conclusions This risk score can predict the risk of NACE in patients with AF and stable CAD. Trial registration numbers UMIN000016612, NCT02642419.
Novel Dinitrophenylhydrazones Containing 1,2,3-Triazole Nucleus as Disperse Dyes for Polyester Fabric Dyeing and Functional Finishing and Antibacterial Activities

Abstract In this study, three novel 4-(1-(2-(2,4-dinitrophenyl)hydrazineylidene)ethyl)-5-methyl-1-aryl-1H-1,2,3-triazoles were synthesized by the reaction of 1-(5-methyl-1-(4-subst. phenyl)-1H-1,2,3-triazol-4-yl)ethan-1-one with (2,4-dinitrophenyl)hydrazine in absolute ethanol containing traces Conc. HCl under reflux condition. NMR spectroscopy was used to identify the novel products, which were then investigated as disperse dyes for dyeing and functional finishing of polyester fabric. The results indicated that the dyed fabric samples showed very good fastness to washing, perspiration and light as well as remarkable UV protection and antibacterial properties. Moreover, incorporating of Ag-NPs with a concentration of 1% in the dyeing bath, upgraded the UV protection as well as antibacterial properties of the dyed samples.
De Novo Synthesis of a Conjugative System from Human Gut Metagenomic Data for Targeted Delivery of Cas9 Antimicrobials

Metagenomic sequence represents an untapped source of genetic novelty, particularly for conjugative systems that could be used for plasmid-based delivery of Cas9-derived antimicrobial agents. However, unlocking the functional potential of conjugative systems purely from metagenomic sequence requires the identification of suitable candidate systems as starting scaffolds for de novo DNA synthesis. Here, we developed a bioinformatics approach that searches through the metagenomic ‘trash bin’ for conjugative systems present on contigs that are typically excluded from common metagenomic analysis pipelines. Using a human metagenomic gut dataset representing 2805 taxonomically distinct units, we identified 1598 contigs containing conjugative systems with a differential distribution in human cohorts. We synthesized de novo an entire Citrobacter spp. conjugative system of 54 kb and containing at least 47 genes, pCitro, and found that pCitro conjugates from Escherichia coli to Citrobacter rodentium with a 30-fold higher frequency than to E. coli, and is compatible with Citrobacter resident plasmids. Mutations in the traV and traY conjugation components of pCitro inhibited conjugation. We showed that pCitro can be re-purposed as an antimicrobial delivery agent by programming it with the TevCas9 nuclease and Citrobacter -specific sgRNAs to kill C. rodentium. Our study reveals a trove of uncharacterized conjugative systems in metagenomic data and describes an experimental framework to animate these large genetic systems as novel target-adapted delivery vectors for Cas9-based editing of bacterial genomes.
Neuronal mechanism of the encoding of socially familiar faces in the striatum tail

Although we can quickly locate a familiar person even in a crowd, the underlying neuronal mechanism remains unclear. Recently, we found that the striatum tail (STRt), which is part of the basal ganglia, is sensitive to long-term reward history. Here, we show that long-term value-coding neurons are involved in the detection of socially familiar faces. Many STRt neurons respond to facial images, especially to those of socially familiar persons. Additionally, we found that these face-responsive neurons also encode the stable values of many objects based on long-term reward experiences. Interestingly, the strength of neuronal modulation of social familiarity bias (familiar or unfamiliar) and object value bias (high-valued or low-valued) were positively correlated. These results suggest that both social familiarity and stable object-value information are mediated by a common neuronal mechanism. This mechanism may contribute to the rapid detection of familiar faces in real-world contexts. Teaser The common mechanism mediating social familiarity and stable object-value information may contribute to rapid detection of familiar faces.
PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning

One of the challenges in a viral pandemic is the emergence of novel variants with different phenotypical characteristics. An ability to forecast future viral individuals at the sequence level enables advance preparation by characterizing the sequences and closing vulnerabilities in current preventative and therapeutic methods. In this article, we explore, in the context of a viral pandemic, the problem of generating complete instances of undiscovered viral protein sequences, which have a high likelihood of being discovered in the future using protein language models. Current approaches to training these models fit model parameters to a known sequence set, which does not suit pandemic forecasting as future sequences differ from known sequences in some respects. To address this, we develop a novel method, called PandoGen, to train protein language models towards the pandemic protein forecasting task. PandoGen combines techniques such as synthetic data generation, conditional sequence generation, and reward-based learning, enabling the model to forecast future sequences, with a high propensity to spread. Applying our method to modeling the SARS-CoV-2 Spike protein sequence, we find empirically that our model forecasts twice as many novel sequences with five times the case counts compared to a model that is thirty times larger. Our method forecasts unseen lineages months in advance, whereas models 4× and 30× larger forecast almost no new lineages. When trained on data available up to a month before the onset of important Variants of Concern, our method consistently forecasts sequences belonging to those variants within tight sequence budgets. PandoGen is available at: https://github.com/UIUC-ChenLab/PandoGen
Enhanced mitochondrial fusion during a critical period of synaptic plasticity in adult-born neurons

Integration of new neurons into adult hippocampal circuits is a process coordinated by local and long-range synaptic inputs. To achieve stable integration and uniquely contribute to hippocampal function, immature neurons are endowed with a critical period of heightened synaptic plasticity, yet it remains unclear which mechanisms sustain this form of plasticity during neuronal maturation. We found that, as new neurons enter their critical period, a transient surge in fusion dynamics stabilizes elongated mitochondrial morphologies in dendrites to fuel synaptic plasticity. Conditional ablation of fusion dynamics to prevent mitochondrial elongation selectively impaired spine plasticity and synaptic potentiation, disrupting neuronal competition for stable circuit integration, ultimately leading to decreased survival. Despite profuse mitochondrial fragmentation, manipulation of competition dynamics was sufficient to restore neuronal survival, but left neurons poorly responsive to experiences at the circuit level. Thus, by enabling synaptic plasticity during the critical period, mitochondrial fusion facilitates circuit remodeling by adult-born neurons.
Passive diffusion accounts for the majority of intracellular nanovesicle transport

In contrast to large vesicles and organelles that are moved by motors inside cells, a detailed analysis of intracellular vesicle mobility reveals that passive diffusion is the main mode of small vesicle transport. During membrane trafficking, a vesicle formed at the donor compartment must travel to the acceptor membrane before fusing. For large carriers, it is established that this transport is motor-driven; however, the mode by which small vesicles, which outnumber larger carriers, are transported is poorly characterized. Here, we show that intracellular nanovesicles (INVs), a substantial class of small vesicles, are highly mobile within cells and that this mobility depends almost entirely on passive diffusion (0.1–0.3 μm2 s−1). Using single particle tracking, we describe how other small trafficking vesicles have a similar diffusive mode of transport that contrasts with the motor-dependent movement of larger endolysosomal carriers. We also demonstrate that a subset of INVs is involved in exocytosis and that delivery of cargo to the plasma membrane during exocytosis is decreased when diffusion of INVs is specifically restricted. Our results suggest that passive diffusion is sufficient to explain the majority of small vesicle transport.
Phosphoribulokinase abundance is not limiting the Calvin-Benson-Bassham cycle in Chlamydomonas reinhardtii

Improving photosynthetic efficiency in plants and microalgae is of utmost importance to support the growing world population and to enable the bioproduction of energy and chemicals. Limitations in photosynthetic light conversion efficiency can be directly attributed to kinetic bottlenecks within the Calvin-Benson-Bassham cycle (CBBC) responsible for carbon fixation. A better understanding of these bottlenecks in vivo is crucial to overcome these limiting factors through bio-engineering. The present study is focused on the analysis of phosphoribulokinase (PRK) in the unicellular green alga Chlamydomonas reinhardtii. We have characterized a PRK knock-out mutant strain and showed that in the absence of PRK, Chlamydomonas cannot grow photoautotrophically while functional complementation with a synthetic construct allowed restoration of photoautotrophy. Nevertheless, using standard genetic elements, the expression of PRK was limited to 40% of the reference level in complemented strains and could not restore normal growth in photoautotrophic conditions suggesting that the CBBC is limited. We were subsequently able to overcome this initial limitation by improving the design of the transcriptional unit expressing PRK using diverse combinations of DNA parts including PRK endogenous promoter and introns. This enabled us to obtain strains with PRK levels comparable to the reference strain and even overexpressing strains. A collection of strains with PRK levels between 16% and 250% of WT PRK levels was generated and characterized. Immunoblot and growth assays revealed that a PRK content of ≈86% is sufficient to fully restore photoautotrophic growth. This result suggests that PRK is present in moderate excess in Chlamydomonas. Consistently, the overexpression of PRK did not increase photosynthetic growth indicating that that the endogenous level of PRK in Chlamydomonas is not limiting the Calvin-Benson-Bassham cycle under optimal conditions.
High-throughput bactericidal assays for monoclonal antibody screening against antimicrobial resistant Neisseria gonorrhoeae

Neisseria gonorrhoeae (gonococcus) is an obligate human pathogen and the etiological agent of the sexually transmitted disease gonorrhea. The rapid rise in gonococcal resistance to all currently available antimicrobials has become a significant public health burden and the need to develop novel therapeutic and prophylactic tools is now a global priority. While high-throughput screening methods allowed rapid discovery of extremely potent monoclonal antibodies (mAbs) against viral pathogens, the field of bacteriology suffers from the lack of assays that allow efficient screening of large panels of samples. To address this point, we developed luminescence-based (L-ABA) and resazurin-based (R-ABA) antibody bactericidal assays that measure N. gonorrhoeae metabolic activity as a proxy of bacterial viability. Both L-ABA and R-ABA are applicable on the large scale for the rapid identification of bactericidal antibodies and were validated by conventional methods. Implementation of these approaches will be instrumental to the development of new medications and vaccines against N. gonorrhoeae and other bacterial pathogens to support the fight against antimicrobial resistance.