Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and Validation of a Pyroptosis-Related Prognostic Model for Gastric Cancer

Pyroptosis is an inflammatory form of programmed cell death triggered by caspase-1/4/5/11 that plays an important role in the occurrence and development of gastric cancer (GC). We investigated the prognostic value of pyroptosis-related genes in GC. The “LIMMA” R package and univariate Cox analysis were used to find pyroptosis-related genes with differential expression and prognostic value in the TCGA cohort and the identified genes were analyzed for GO enrichment and KEGG pathways. The selected genes were then included in a multivariate Cox proportional hazard regression analysis, and a ten genes prognostic model (BIRC2, CD274, IRGM, ANXA2, GBP5, TXNIP, POP1, GBP1, DHX9, and TLR2) was established. To evaluate the predictive value of the risk score on prognosis, patients were divided into high-risk and low-risk groups according to the median risk score, and survival analysis was carried out. Compared with the low-risk group, the OS of GC patients in the high-risk group was significantly worse. Additionally, these results were verified in the GSE84437 and GSE66229 datasets. Finally, through the combination of prognostic gene characteristics and clinicopathological features, a nomogram was established to predict individual survival probability. The results show that the genetic risk characteristics related to clinical features can be used as independent prognostic indicators for patients with GC. In summary, the pyroptosis-related risk signals proposed in this study can potentially predict the prognosis of patients with GC. In addition, we also found significant infiltration of dendritic cells, macrophages, and neutrophils in tissues of high-risk patients.
Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort

Aging is accompanied by structural brain changes that are thought to underlie cognitive decline and dementia. Yet little is known regarding the association between increasing age, structural brain damage, and alterations of functional brain connectivity. The aim of this study was to evaluate whether cortical thickness and white matter damage as markers of age-related structural brain changes are associated with alterations in functional connectivity in non-demented healthy middle-aged to older adults. Therefore, we reconstructed functional connectomes from resting-state functional magnetic resonance imaging (MRI) (rsfMRI) data of 976 subjects from the Hamburg City Health Study, a prospective population-based study including participants aged 45–74 years from the metropolitan region Hamburg, Germany. We performed multiple linear regressions to examine the association of age, cortical thickness, and white matter damage quantified by the peak width of skeletonized mean diffusivity (PSMD) from diffusion tensor imaging on whole-brain network connectivity and four predefined resting state networks (default mode, dorsal, salience, and control network). In a second step, we extracted subnetworks with age-related decreased functional connectivity from these networks and conducted a mediation analysis to test whether the effect of age on these networks is mediated by decreased cortical thickness or PSMD. We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. Mediation analysis identified cortical thickness as a partial mediator between age and default subnetwork connectivity and functional connectivity within the default subnetwork as a partial mediator between age and executive cognitive function. These results indicate that, on a global scale, functional connectivity is not determined by structural damage in healthy middle-aged to older adults. There is a weak association of higher age with decreased functional connectivity which, for specific subnetworks, appears to be mediated by cortical thickness.
Near Real-Time Petrologic Monitoring on Volcanic Glass to Infer Magmatic Processes During the February–April 2021 Paroxysms of the South-East Crater, Etna

The South-East crater of Etna (SEC) is the most active summit crater over the last 20 years, producing lava fountains in 2000, 2007–08, and 2011–14. It has been monitored by the INGV Etna Observatory by instrumental networks, field surveys and petrologic monitoring. The syn-eruptive petrologic monitoring consists of an articulated work chain which is generally carried out within 24 h from the moment the sample was emplaced to detect possible changes of magma composition episode by episode, as well as over a longer period. The findings of petrologic monitoring are integrated with the results provided by geophysical networks and gas geochemistry to check the volcano’s behavior during the eruption and to communicate potentially dangerous variations in eruptive features to the local authorities. This paper presents the variation of volcanic glass compositions during the paroxysmal activity of the SEC, which began in December 2020 and climaxed with 17 episodes from 16 February to 1 April 2021. We infer pre-eruptive magmatic processes (e.g., fractional crystallization and mixing) based on temporal trends of some key compositional parameters (i.e., CaO/Al2O3; FeOtot/MgO). Correlation between magma dynamics and volcanological characteristics of the paroxysms requires future studies. We demonstrated that petrologic monitoring carried out during a volcanic crisis at Etna, as well as in other volcanoes worldwide, may be crucial to acquire preliminary insights into the structure of the plumbing system and the pre-eruptive processes governing the eruptive activity. Interestingly, this goal has been achieved also thanks to the collaboration with local citizens, who kindly contributed to collecting samples.
Associations of Sustainable Development Goals Accelerators With Adolescents’ Well-Being According to Head-of-Household’s Disability Status–A Cross-Sectional Study From Zambia

Objectives: We examined associations between accelerators (interventions impacting ≥2 SDG targets) and SDG-aligned well-being indicators among adolescents 16–24 years old in Zambia. Methods: We surveyed adults from 1,800 randomly sampled households receiving social cash transfers. We examined associations between accelerators (social cash transfers, life-long learning, mobile phone access) and seven well-being indicators among adolescents using multivariate logistic regressions. Results: The sample comprised 1,725 adolescents, 881 (51.1%) girls. Mobile phone access was associated with no poverty (adjusted Odds Ratio [aOR] 2.08, p < 0.001), informal cash transfers (aOR 1.82, p = 0.004), and seeking mental health support (aOR 1.61, p = 0.020). Social cash transfers were associated with no disability-related health restrictions (aOR 2.56, p = 0.004) and lesser odds of seeking mental health support (aOR 0.53, p = 0.029). Life-long learning was associated with informal cash transfers (aOR 3.49, p < 0.001) and lower school enrollment (aOR 0.70, p = 0.004). Adolescents with disabled head-of-household reported worse poverty, good health but less suicidal ideation. Conclusions: Social cash transfers, life-long learning, and mobile phone access were positively associated with well-being indicators. Adolescents living with disabled head-of-household benefited less. Governments should implement policies to correct disability-related inequalities.
Vehicle Speed Detection Using Multi-Branch Networks From Temporal Image Pairs

Vehicle Speed Detection is important for traffic monitoring and surveillance. But very little research has been done on techniques that don't require specialist hardware or hand-crafted image processing method on video streams. This paper presents a novel end-to-end learning-based system that utilizes a multi-branched network architecture to estimate the speed of any vehicle from only two time-varied, unprocessed, images. The system also propose an optimized approach for generating a ground truth dataset by creating an estimation method for a 3D Rendering Engine. And further, propose a very computationally cheap method of extracting license plates by custom training a YOLOv4 tiny architecture in tflite format and Tesseract OCR. The proposed low-cost system is easily implementable in the existing infrastructure and provides a lightweight performance with computation time of ≤ 101.58 ms on average and accuracy of 97.3 % in a wide range of applications.
A Survey on Space-Air-Ground-Sea Integrated Network Security in 6G

Space-air-ground-sea integrated network (SAGSIN), which integrates satellite communication networks, aerial networks, terrestrial networks, and marine communication networks, has been widely envisioned as a promising network architecture for 6G. In consideration of its cooperation characteristics of multi-layer networks, open communication environment, and time-varying topologies, SAGSIN faces many unprecedented security challenges, and there have been a number of researches related to SAGSIN security performed over the past few years. Based on such observation, we provide in this paper a detailed survey of recent progress and ongoing research works on SAGSIN security in the aspects of security threats, attack methodologies, and defense countermeasures. To the best of our knowledge, we are the first to present the state-of-the-art of security for SAGSIN, since existing surveys focused either on a certain segment or on several segments of the integrated network, and little can be found on the full coverage network. In addition to reviewing existing works on SAGSIN security, we also present some discussions on cross-layer attacks and security countermeasure in SAGSIN, and identify new challenges ahead and future research directions.
System design for wireless sensor networks

The advent of wireless electronics and sensing technologies has made the production of versatile low-cost wireless sensor nodes possible. A wireless sensor network typically consists of a large number of wireless sensor nodes. The main use of wireless sensor networks is to collect data from sensing areas where human beings are not able to access. In contrast to conventional sensing systems, sensor networks utilize a huge volume of low-cost wireless sensor nodes to perform close-range sensing. The data collected will undergo in-network processes and then return to the user who is located in a remote site. This high redundancy of sensing power can greatly enhance the sensing resolution and make sensor networks robust to any adverse environmental conditions. However, these large number of wireless sensor nodes have also introduced a lot of challenging problems in system design. Some major problems are network lifetime, data collection efficiency, interference among wireless sensor nodes, and retransmissions due to noise and interference. Among all the problems mentioned above, the network lifetime problem and the data collection efficiency problem have been selected as the focus of this thesis. Since wireless sensor nodes are power-constrained devices, the number of distant transmissions should be minimized in order to reduce energy consumption in wireless sensor nodes and prolong the network lifetime. An effective approach to improve efficiency is to divide the network into several clusters. By raising the sensing power of a sensor network in excess of the necessary level, the overall target tracking capability can be increased. However, a high sensing power also implies a greater interference to the network in addition to higher energy consumption. Such disadvantages can be relieved
Thermodynamic limitations of metabolic strategies for PHB production from formate and fructose in Cupriavidus necator

The chemolithotroph Cupriavidus necator H16 is known as a natural producer of the bioplastic-polymer PHB, as well as for its metabolic versatility to utilize different substrates, including formate as the sole carbon and energy source. Depending on the entry point of the substrate, this versatility requires adjustment of the thermodynamic landscape to maintain sufficiently high driving forces for biological processes. Here we employed a model of the core metabolism of C. necator H16 to analyze the thermodynamic driving forces and PHB yields of different metabolic engineering strategies. For this, we enumerated elementary flux modes (EFMs) of the network and evaluated their PHB yields as well as thermodynamics via Max-min driving force (MDF) analysis and random sampling of driving forces. A heterologous ATP:citrate lyase reaction was predicted to increase driving force for producing acetyl-CoA. A heterologous phosphoketolase reaction was predicted to increase maximal PHB yields as well as driving forces. These enzymes were verified experimentally to enhance PHB titers between 60 and 300% in select conditions. The EFM analysis also revealed that metabolic strategies for PHB production from formate may be limited by low driving forces through citrate lyase and aconitase, as well as cofactor balancing, and identified reactions of the core metabolism associated with low and high PHB yield. The findings of this study aid in understanding metabolic adaptation. Furthemore, the outlined approach will be useful in designing metabolic engineering strategies in other non-model bacteria. Highlights Elementary flux modes of C. necator for PHB synthesis from fructose and formate. Metabolite sampling identified common reactions among EFMs with low driving force. PHB from formate shows low driving forces for aconitase, citrate lyase, NADPH synthesis. Phosphoketolase and ATP citrate lyase increased driving forces and PHB production.
An in-silico study of the mutation-associated effects on the spike protein of SARS-CoV-2, Omicron variant

The emergence of Omicron (B.1.1.529), a new Variant of Concern in the COVID-19 outbreak, while accompanied by the ongoing Delta variant infection, has once again fueled fears of a new infection wave and global health concern. In the Omicron variant, the receptor-binding domain (RBD) of its spike glycoprotein is heavily mutated, a feature critical for the transmission rate of the virus by interacting with hACE2. In this study, we used a combination of conventional and advanced neural network-based in silico approaches to predict how these mutations would affect the spike protein. The results demonstrated a decrease in the electrostatic potentials of residues corresponding to receptor recognition sites, an increase in the alkalinity of the protein, a change in hydrophobicity, variations in functional residues, and an increase in the percentage of alpha-helix structure. Our next step was to predict the structural changes of the spike protein using the AI-based tool Alphafold2 and dock it with hACE2. The results revealed that the RBD of the Omicron variant had a higher affinity than the reference. Moreover, all-atom molecular dynamics simulations concluded that the RBD of the Omicron variant exhibits a more dispersed interaction network since mutations resulted in an increased number of hydrophobic interactions and hydrogen bonds with hACE2 compared to the reference RBD. In summary, our current study highlighted the potential structural basis for the enhanced transmissibility and pathogenicity of the Omicron variant, although further research is needed to investigate its epidemiological and biological implications.
Spatiotemporal growth pattern during plant nutation implies fast dynamics for cell wall mechanics and chemistry: a multiscale study in Averrhoa carambola

Nutation is the most striking and ubiquitous example of the rhythmic nature of plant development. Although there is a consensus that this wide oscillatory motion is driven by growth, its internal mechanisms have not been fully elucidated yet. In this work, we study the specific case of nutation in compound leaves in the archetypal Averrhoa carambola plant. We quantify the macroscopic growth kinematics with time lapse imaging, image analysis and kinematics modeling. We further characterize the mechanical and chemical properties of the cell wall with atomic force microscopy and immunolabelling. Our data first reveal that the differential growth driving nutation is localized and peaks where the average growth drops. We then show this specific spatiotemporal growth profile is compatible with local contraction events. At the cell wall level, differential growth is further colocalized with an asymmetry of the cell wall elastic modulus, and with an asymmetric distribution of homogalacturonans (HG). Our results not only back up the hypothesis of HG being involved in plant growth, but also build up on it by suggesting a dynamic nature for this process. Significance Statement Nutation is an oscillatory motion displayed by many organs of growing plants. Most works on nutation focus on its relation to external stimuli attempting to explain its origins. By contrast, its internal physiological mechanisms remain to be fully explored. Here we propose an experimental and multiscale characterization of undisturbed nutation. We determine the macroscopic growth profile and show it is compatible with cell expansion but also local contractions in the tissues. At the microscopic level, we reveal that both the rigidity and composition of the cell wall are asymmetrically distributed where nutation occurs. The combination of results on both scales brings contributions to the understanding of interplay between global movement, local growth, cell wall mechanics and cell wall biochemistry.
Serological screening in wild ruminants in Germany, 2021/2022: No evidence of SARS‐CoV‐2, bluetongue virus or pestivirus spread but high seroprevalences against Schmallenberg virus

Wildlife animals may be susceptible for multiple infectious agents of public health or veterinary relevance, thereby potentially forming a reservoir that bears the constant risk of re-introduction into the human or livestock population. Here, we serologically investigated 493 wild ruminant samples collected in the 2021/22 hunting season in Germany for the presence of antibodies against the severe acute respiratory coronavirus 2 (SARS-CoV-2) and four viruses pathogenic for domestic ruminants, namely the orthobunyavirus Schmallenberg virus (SBV), the reovirus bluetongue virus (BTV) and ruminant pestiviruses like bovine viral diarrhoea virus or border disease virus. The animal species comprised fallow deer, red deer, roe deer, mouflon and wisent. For coronavirus serology, additional 307 fallow, roe and red deer samples collected between 2017 and 2020 at three military training areas were included. While antibodies against SBV could be detected in about 13.6% of the samples collected in 2021/22, only one fallow deer of unknown age tested positive for anti-BTV antibodies and all samples reacted negative for antibodies against ruminant pestiviruses. In an ELISA based on the receptor-binding domain (RBD) of SARS-CoV-2, 25 out of 493 (5.1%) samples collected in autumn and winter 2021/22 scored positive. This sero-reactivity could not be confirmed by the highly specific virus neutralization test, occurred also in 2017, 2018 and 2019, i.e. prior to the human SARS-CoV-2 pandemic, and was likewise observed against the RBD of the related SARS-CoV-1. Therefore, the SARS-CoV-2-seroreactivity was most likely induced by another, hitherto unknown deer virus belonging to the subgenus Sarbecovirus of betacoronaviruses.
Structural basis for RNA-duplex unwinding by the DEAD-box helicase DbpA

DEAD-box RNA helicases are implicated in most aspects of RNA biology, where these enzymes unwind short RNA duplexes in an ATP-dependent manner. During the central step of the unwinding cycle, the two domains of the helicase core form a distinct closed conformation that destabilizes the RNA duplex, which ultimately leads to duplex melting. Despite the importance of this step for the unwinding process no high resolution structures of this state are available. Here, we employ nuclear magnetic resonance spectroscopy and X-ray crystallography to determine structures of the DEAD- box helicase DbpA in the closed conformation, complexed with substrate duplexes and single- stranded unwinding product. These structures reveal that DbpA initiates duplex unwinding by interacting with up to three base-paired nucleotides and a 5’ single-stranded RNA duplex overhang. These high-resolution snapshots, together with biochemical assays, rationalize the destabilization of the RNA duplex and are integrated into a conclusive model of the unwinding process.
Genome-Wide Association Study Reveals That PvGUX1_1 Is Associated with Pod Stringlessness in Snap Bean (Phaseolus vulgaris L.)

The suture strings is a particularly important pod trait that determines the quality and texture of snap bean (Phaseolus vulgaris L.). The St locus on chromosome 2 has been described as a major locus associated with suture strings. However, the gene and genetic basis underlying this locus remain unknown. Here, we investigated the suture strings of 138 snap bean accessions across two years. A total of 3.66 million single-nucleotide polymorphisms (SNPs) were obtained by deep resequencing. Based on these SNPs, we identified a strong association signal on Chr02 and a promising candidate gene, PvGUX1_1. Further analysis revealed that the 2-bp deletion in exon of PvGUX1_1 was significantly associated with stringlessness. Comparative mapping indicated that PvGUX1_1 was a domesticated locus and diverged from PvGUX1_2 during an early stage. Our study provides important insights into the genetic mechanism of suture string formation and useful information for snap bean improvement.
Confirming Multiplex RT-qPCR Use in COVID-19 with Next-Generation Sequencing: Strategies for Epidemiological Advantage

Rapid classification and tracking of emerging SARS-CoV-2 variants are critical for understanding the transmission dynamics and developing strategies for interrupting the transmission chain. Next-Generation Sequencing (NGS) is an exceptional tool for whole-genome analysis and deciphering new mutations. The technique has been instrumental in identifying the Variants of Concern and tracking this pandemic. However, NGS remains expensive and time-consuming for large-scale monitoring of COVID-19. This study analyzed a total of 78 de-identified samples that screened positive for SARS-CoV-2 from two timeframes, August 2020 and July 2021. All 78 samples were classified into WHO lineages by whole genome sequencing then compared with two commercially available Q-PCR assays for spike protein mutation(s). The data showed good concordance with Q-PCR and NGS analysis for specific SARS-COV-2 lineages and characteristic mutations. Deployment of Q-PCR testing to detect known SARS-COV-2 variants may be extremely beneficial. These assays are quick and cost-effective, thus can be implemented as an alternative to sequencing for screening known mutations of SARS-COV-2 for clinical and epidemiological interest. The findings support the great potential for Q-PCR to be an effective strategy offering several COVID-19 epidemiological advantages.