Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping fetal myeloid differentiation in airway samples from premature neonates with single-cell profiling

Single-cell genomic technologies hold great potential to advance our understanding of development and disease. A major limitation lies in isolating intact cells from primary tissues for profiling. Sampling methods compatible with current clinical interventions could enable longitudinal studies, the enrollment of large cohorts, and even the development of novel diagnostics. To explore single-cell RNA-seq (scRNA-seq) profiling of the cell types present at birth in the airway lumen of extremely premature (<28 weeks gestation) neonates, we isolated cells from endotracheal aspirates collected from intubated neonates within the first hour after birth. We generated data on 10 subjects, providing a rich view of airway luminal biology at a critical developmental period. Our results show that cells present in the airways of premature neonates primarily represent a continuum of myeloid differentiation, including fetal monocytes (25% of all cells), intermediate myeloid populations (48% of cells), and macrophages (2.6% of cells). To our knowledge, this is the first single-cell transcriptomic characterization of human monocytes in the neonatal airway isolated within an hour of birth. Applying trajectory analysis to the premature neonate myeloid populations, we identified two trajectories consistent with the developmental stages of interstitial and alveolar macrophages, as well as a third trajectory presenting a potential alternative pathway bridging these terminal macrophage states. While the three trajectories share many dynamic genes (5,451), they also have distinct transcriptional changes (259 alveolar-specific genes, 666 interstitial-specific genes, and 285 bridging-specific genes). Overall, our results define high quality single-cell data from cells isolated within the so-called “golden hour of birth” in extremely premature neonate airways representing complex lung biology and can be utilized in studies of human development and disease.
Murine Gammaherpesvirus 68 ORF45 Stimulates B2 Retrotransposon and Pre-tRNA Activation in a Manner Dependent on Mitogen-Activated Protein Kinase (MAPK) Signaling

Gammaherpesviral infection alters the gene expression landscape of a host cell, including through the induction of noncoding RNAs transcribed by RNA polymerase III (RNAPIII). Among these are a class of repetitive genes known as retrotransposons, which are normally silenced elements and can copy and spread throughout the genome, and transfer RNAs (tRNAs), which are fundamental components of protein translation machinery. ABSTRACT RNA polymerase III (RNAPIII) transcribes a variety of noncoding RNAs, including tRNA (tRNA) and the B2 family of short interspersed nuclear elements (SINEs). B2 SINEs are noncoding retrotransposons that possess tRNA-like promoters and are normally silenced in healthy somatic tissue. Infection with the murine gammaherpesvirus MHV68 induces transcription of both SINEs and tRNAs, in part through the activity of the viral protein kinase ORF36. Here, we identify the conserved MHV68 tegument protein ORF45 as an additional activator of these RNAPIII loci. MHV68 ORF45 and ORF36 form a complex, resulting in an additive induction RNAPIII and increased ORF45 expression. ORF45-induced RNAPIII transcription is dependent on its activation of the extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) signaling pathway, which in turn increases the abundance of the RNAPIII transcription factor Brf1. Other viral and nonviral activators of MAPK/ERK signaling also increase the levels of Brf1 protein, B2 SINE RNA, and tRNA, suggesting that this is a common strategy to increase RNAPIII activity. IMPORTANCE Gammaherpesviral infection alters the gene expression landscape of a host cell, including through the induction of noncoding RNAs transcribed by RNA polymerase III (RNAPIII). Among these are a class of repetitive genes known as retrotransposons, which are normally silenced elements and can copy and spread throughout the genome, and transfer RNAs (tRNAs), which are fundamental components of protein translation machinery. How these loci are activated during infection is not well understood. Here, we identify ORF45 from the model murine gammaherpesvirus MHV68 as a novel activator of RNAPIII transcription. To do so, it engages the MAPK/ERK signaling pathway, which is a central regulator of cellular response to environmental stimuli. Activation of this pathway leads to the upregulation of a key factor required for RNAPIII activity, Brf1. These findings expand our understanding of the regulation and dysregulation of RNAPIII transcription and highlight how viral cooption of key signaling pathways can impact host gene expression.
Longitudinal Urine Metabolic Profiling and Gestational Age Prediction in Pregnancy

Pregnancy is a critical time that has long-term impacts on both maternal and fetal health. During pregnancy, the maternal metabolome undergoes dramatic systemic changes, although correlating longitudinal changes in maternal urine remain largely unexplored. We applied an LCMS-based untargeted metabolomics profiling approach to analyze 346 longitudinal maternal urine samples collected throughout pregnancy for 36 women from diverse ethnic backgrounds with differing clinical characteristics. We detected 20,314 metabolic peaks and annotated 875 metabolites. Altered metabolites include a broad panel of glucocorticoids, lipids, and amino acid derivatives, which revealed systematic pathway alterations during pregnancy. We also developed a machine-learning model to precisely predict gestational age (GA) at time of sampling using urine metabolites that provides a non-invasive method for pregnancy dating. This longitudinal maternal urine study demonstrates the clinical utility of using untargeted metabolomics in obstetric settings. One Sentence Summary Machine-learning based gestational age and due date using longitudinal urine samples of pregnancy.
Space Time Block Codes: An Overview

— The demand for mobile communication systems with high data rates has dramatically increased in recent years. New methods are necessary in order to satisfy this huge communications demand, exploiting the limited resources such as bandwidth and power as efficient as possible. In wireless communication systems, multiple antennas when used with appropriate space-time coding (STC) techniques can achieve huge performance in multipath fading wireless links and to provide diversity gain and coding gain, respectively. The use of more than one antenna at transmitting end, improves the receive diversity. Data is encoded using a space–time block code, and then split into n streams which are simultaneously transmitted using n transmit antennas. Maximum likelihood (ML) decoding is achieved by a simple way through decoupling of the signals transmitted from different antennas rather than a joint detection. When the channel is not constant during the period of codeword transmission, the conventional linear maximum likelihood receiver suffers from performance degradation. The fundamentals of space-time coding were established in the context of space-time Trellis coding by Tarokh, Seshadri and Calderbank. Then Alamouti proposed a simple transmit diversity coding scheme and based on this scheme. Our main focus is upon signal detection and channel estimation for wireless communication systems. In this paper, the receiver performance for Alamouti code is analyzed. Here, we evaluate the performances of the two channel estimation schemes when employing 2 transmit and one receive antennas employing BPSK modulation methods.
Directed Shortest Walk on Temporal Graphs

Background The use of graphs as a way of abstracting and representing biological systems has provided a powerful analysis paradigm. Specifically, graph optimization algorithms are routinely used to address various connectivity queries, such as finding paths between proteins in a protein-protein interaction network, while maximizing objectives such as parsimony. While present studies in this field mostly concern static graphs, new types of data now motivate the need to account for changes that might occur to the elements (nodes) that are represented by the graph on the relationships (edges) between them. Results and Discussion We define the notion of Directed Temporal Graphs as a series of directed subgraphs of an underlying graph, ordered by time, where only a subset of vertices and edges are present. We then build up towards the Time Conditioned Shortest Walk problem on Directed Temporal Graphs: given a series of time ordered directed graphs, find the shortest walk from any given source node at time point 1 to a target node at time T ≥ 1, such that the walk is consistent (monotonically increasing) with the timing of nodes and edges. We show, contrary to the Directed Shortest Walk problem which can be solved in polynomial time, that the Time Conditioned Shortest Walk (TCSW) problem is NP-Hard, and is hard to approximate to factor for T ≥ 3 and ε > 0. Lastly, we develop an integer linear program to solve a generalized version of TCSW, and demonstrate its ability to reach optimality with instances of the human protein interaction network. Conclusion We demonstrate that when extending the shortest walk problem in computational biology to account for multiple ordered conditions, the problem not only becomes hard to solve, but hard to approximate, a limitation which we address via a new solver. From this narrow definition of TCSW, we relax the constraint of time consistency within the shortest walk, deriving a direct relationship between hardness of approximation and the allowable step size in our walk between time conditioned networks. Lastly we briefly explore a variety of alternative formulations for this problem, providing insight into both tractable and intractable variants. Availability Our solver for the general k-Time Condition Shortest Walk problem is available at https://github.com/YosefLab/temporal_condition_shortest_walk
Deep attention based variational autoencoder for antimicrobial peptide discovery

Antimicrobial peptides (AMPs) have been proposed as a potential solution against multiresistant pathogens. Designing novel AMPs requires exploration of a vast chemical space which makes it a challenging problem. Recently natural language processing and generative deep learning have shown great promise in exploring the vast chemical space and generating new chemicals with desired properties. In this study we leverage a variational attention mechanism in the generative variational autoencoder where attention vector is also modeled as a latent vector. Variational attention helps with the diversity and quality of the generated AMPs. The generated AMPs from this model are novel, have high statistical fidelity and have similar physicochemical properties such as charge, hydrophobicity and hydrophobic moment to the real to the real antimicrobial peptides.
Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Mechanistic toxicology has emerged as a powerful framework to inform on the safety of chemicals and guide the development of new safe-by-design compounds. Although toxicogenomics provides support towards mechanistic evaluation of chemical exposures, the implementation of toxicogenomics-based evidence in the regulatory setting is still hindered by uncertainties related to the analysis and interpretation of such data. Adverse Outcome Pathways (AOPs) are multi-scale models that link chemical exposures to adverse outcomes through causal cascades of key events (KEs). The use of mechanistic evidence through the AOP framework is actively promoted for the development of new approach methods (NAMs) and to reduce animal experimentation. However, in order to unleash the full potential of AOPs and build confidence into toxicogenomics, robust and unified associations between KEs and patterns of molecular alteration need to be established. Here, we hypothesised that systematic curation of molecular events associated with KEs would enable the modelling of AOPs through gene-level data, creating the much-needed link between toxicogenomics and the systemic mechanisms depicted by the AOPs. This, in turn, introduces novel ways of benefitting from the AOP concept, including predictive models, read-across, and targeted assays, while also reducing the need for multiple testing strategies. Hence, we developed a multi-step strategy to annotate the AOPs relevant to human health risk assessment. We show that our framework successfully highlights relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, we defined and experimentally validated a panel of robust AOP-derived in vitro biomarkers for pulmonary fibrosis.
Adaptive optimal waveform design algorithm based on frequency‐stepped chirp signal

In this study, the authors propose an adaptive optimal waveform design algorithm, based on a frequency-stepped chirp signal with arbitrary sub-pulse bandwidths and chirp slopes. The waveform is designed to maximise the signal-to-interference-plus-noise ratio at the receiver. The integral sidelobe level of an autocorrelation function is constrained at a low level, thereby increasing the detection probability of the weak echoes reflected from dim targets. An energy constraint and a low peak-to-average power ratio constraint are also taken into account owing to the flexible power allocation for interference suppression. By doing so, the radar system improves the efficiency of the transmitter power resources and avoids the non-linear effects of the power amplifier. What is more, a cyclic iterative algorithm is employed to solve the optimisation problem. At last, numerical simulation results evaluate the effectiveness of the designed waveform compared with a linear frequency modulation signal, resulting in a great improvement in the target detection performance.
DETECTION OF TILMICOSIN AND ENROFLOXACIN RESIDUES IN BROILER CHICKEN LIVER AND MEAT SOLED IN MARKETS AT BENHA CITY

Fourty samples of broiler chicken liver and muscles (twenty samples for each) were collected from popular markets of Benha city, Kaluobia governorate, Egypt to detect and analyze of Tilmicosin and Enrofloxacin residues using of High Performance Liquid Chromatography (HPLC), and also to detect their influence by heat processes. Tilmicosin residues were detected in all examined liver samples with a mean concentration of 481.88 ±54.81 ppb while Enrofloxacin residues were detected in only 66.66% of liver samples with a mean concentration of 8.288 ± 1.47 ppb while all examined muscle samples were free from any detectable levels. After boiling and using of autoclave, all residues of liver samples were undetectable. This study emphasized on the importance of strict analyzing of antimicrobial residues prior to marketing and also usefulness of heat cooking with or without pressure, for complete or partial elemination of antibiotic residues.
Recommended performance-based criteria for the design of manufactured home foundation systems to resist wind and seismic loads

This report addresses the issue of tornadoes as a basis for the design of manufactured homes and compares base shears due to wind loading with base shears due to earthquake excitation for various wind and seismic zones. Only for annual exceedance probabilities less than about 2.5 x 104 will tomadic wind speeds govern the design for wind loading. In view of the accepted probabilities of attaining or exceeding design limit states for ordinary buildings, it is concluded that tornadoes should not be a part of the wind load design criteria for manufactured homes. Also, it is concluded that transverse base shear due to wind loading will always exceed the base shear due to earthquake loading, regardless of the wind or seismic zone in which the manufactured home is sited. In the longitudinal direction the ranges of base shear for wind and earthquake are approximately equal, These comparisons are based on the assumption that the structural system is properly detailed to resist earthquake forces and is capable of accommodating inelastic deformations several times greater than the deformation at first yield. In view of the uncertainty regarding the dynamic properties of manufactured homes and their support systems, a simplified equation for the determination of seismic base shear is proposed. Traditional anchor/tie/pier systems and current installation practice do not provide adequate windstorm protection for manufactured homes. Even with preloading, the effectiveness of traditional shallow, helix-plate soil anchors is limited to basic wind speeds of approximately 44.7 m/s (100 mph). However, there are several alternative anchoring and support systems on the market or under development that can provide the required resistance to wind and earthquake loads. Finally, a set of performance-based criteria for anchoring manufactured homes against wind and earthquake loads is proposed.
Master Thesis: ”Advanced bioresin development for 3D printing of hydrogel-based soft tissues with applications in regenerative medicine”

The synergy of hydrogel processing and 3D printing technologies has paved the way for the development of advanced tissue engineering scaffolds. Hydrogels constitute a promising class of materials for the creation of scaffolds due to their permeable environment, suitable for encapsulation, as well as their tunable chemical and mechanical properties [1]. 3D printing, on the other hand, has immersed as a novel manufacturing technology that allows the creation of three dimensional constructs with pre-determined architecture. Current advancements have enabled also the printing of biomaterials incorporating cells and supporting components creating complex structures that can mimic living tissue characteristics with high accuracy [2]. Stereolithography and particularly, the Digital Light Processing (DLP) technology appears as one of the most auspicious printing techniques for the fast creation of mechanically stable hollow structures. However, the difficulty in developing hydrogel bioinks, which could be used for the fabrication of high resolution complicated architectures with structural integrity, remains a challenge. In the present study, efforts were focused on finding the components that could be used for the creation of an optimal bioink suitable for DLP printing. After deciding on the most promising components, the bioink’s optimization in terms of printing resolution was performed taking under consideration the fundamental principles of stereolithography. To achieve this goal, the working curves and all the key parameters, which define a photopolymer’s behavior were studied. After the optimization of the resolution, the prospective bioinks were characterized in terms of their crosslinking efficiency and mechanical properties. Consequently, complex porous scaffolds were fabricated from the selected bioinks and mechanically tested. Finally, the encapsulation of cells into the bioinks, as well as, the seeding method were performed for the creation of cell-laden scaffolds, which were also studied through the performance of cell viability and proliferation tests.
Chapter 8 Postoperative metyrapone test in the early assessment of outcome of pituitary

Objective: The prediction of relapse during the early months after transsphenoidal surgery for Cushing(cid:144)s disease remains dif(cid:147) cult. W e have evaluated the usefulness of the postoperative metyrapone test in this situation. Patients: F rom a retrospective series of 7 7 consecutive primary pituitary operations for Cushing(cid:144)s disease 2 9 patients, w ho also had a metyrapone test at 1 4 days postoperatively, w ere selected. M edian follow -up w as 3 5 months (range: 8 -1 1 8 months). M ain outcome measures: E arly postoperative: fasting morning serum cortisol, 2 4 hour urinary cortisol ex cretion, serum 1 1 -deox ycortisol after 6 x 7 5 0 mg metyrapone. R emission w as de(cid:147) ned as a fasting morning serum cortisol < 1 4 0 nmol/l and/or 2 4 -hour urinary cortisol ex cretion < 2 5 0 nmol. D uring follow -up: serum cortisol, as w ell as serum cortisol in the 1 mg overnight dex amethasone-suppression test w as measured in order to detect relapse of Cushing(cid:144)s disease. R esults: Tw elve of 2 9 patients w ere not in remission after surgery. These patients all had serum 1 1 -deox ycortisol levels > 3 5 0 nmol/l after metyrapone. S eventeen patients met the criteria for early remission. F our of these patients had serum 1 1 deox ycortisol levels betw een 1 5 0 nmol/l and 3 5 0 nmol/l after metyrapone. Three of these 4 patients ex perienced a relapse of Cushing(cid:144)s disease during follow -up. In the 1 3 patients w ith a serum 1 1 -deox ycortisol < 1 5 0 nmol/l after metyrapone, no relapse occurred. Conclusions: The metyrapone test is a useful test in the assessment of outcome of pituitary surgery for Cushing(cid:144)s disease, w ith a sensitivity of 1 0 0 % and a speci(cid:147) city of 7 5 % for the early detection of patients at risk of a relapse. Patients in w hom a serum 1 1 -deox ycortisol > 1 5 0 nmol/l is found after metyrapone are at a high risk for relapse of Cushing(cid:144)s disease.
Effects of 3,4-Methylenedioxymethamphetamine on Conditioned Fear Extinction and Retention in a Crossover Study in Healthy Subjects

Background: 3,4-Methylenedioxymethamphetamine (MDMA) has shown initial promise as an adjunct in psychotherapy to treat posttraumatic stress disorder (PTSD). Its efficacy and safety have been demonstrated across phase I–III studies. However, the mechanism underlying the potential utility of MDMA to treat PTSD in humans has not yet been thoroughly investigated. Preliminary evidence suggests that MDMA may facilitate fear extinction recall, which may be through the release of oxytocin. To test this hypothesis, we examined the efficacy of acute MDMA treatment to enhance fear extinction learning and recall. Methods: We used a two-period, double-blind, randomized, placebo-controlled crossover design in 30 healthy male subjects who received a placebo and a single dose of MDMA (125 mg). Fear extinction was tested using two separate Pavlovian fear conditioning paradigms, one using skin conductance response (SCR), and the other fear-potentiated startle (FPS) to conditioned cues. MDMA treatment occurred after fear conditioning and 2 h before extinction learning. Extinction recall was tested 23 h after MDMA intake. Additional outcome measures included subjective effects, emotion recognition tasks, plasma levels of oxytocin, and pharmacokinetics. Results: Fear conditioning and extinction learning were successful in both fear extinction paradigms (generalized eta–squared [ges] for SCR: 0.08; FPS: 0.07). Compared to placebo treatment, MDMA treatment significantly reduced SCRs to the reinforced conditioned stimulus (CS+) during extinction learning (ges = 0.03) and recall (ges = 0.06). Intensity of the subjective effects of MDMA (good effect, trust, and openness) during extinction learning negatively correlated with the discrimination between CS+ and the safety stimulus (CS−) during recall. MDMA did not influence FPS to conditioned cues. Oxytocin concentration was increased fourfold on average by MDMA during acute effects but was not associated with fear extinction outcomes. Conclusions: MDMA treatment facilitated rapid fear extinction and retention of extinction as measured by SCR to fear cues, in line with animal studies of MDMA facilitation of extinction. However, this effect may be limited to certain forms of learned fear responses, as it was not observed in the extinction model using startle reactivity as the outcome. This study provides further evidence for the facilitation of extinction with MDMA treatment and suggests this may be a component of its efficacy when paired with psychotherapy. Clinical Trial registration: clinicaltrials.gov identifier: NCT03527316