Miracle-dz/UniFAR-FTUs huggingface dataset:
Hidden Markov model analysis for fluorescent time series of quantum dots

We present a hidden Markov model analysis for ﬂuorescent time series of quantum dots. A fundamental quantity to measure optical performance of the quantum dots is a distribution function for the light-emission duration. So far, to estimate it, a threshold value for the ﬂuorescent intensity was introduced, and the light-emission state was evaluated as a state above the threshold. With this deﬁnition, the light-emission duration was estimated, and its distribution function was derived as a blinking plot. Due to the noise in the ﬂuorescent data, however, this treatment generates a large number of artiﬁcially short-lived emission states, thus leading to an erroneous blinking plot. In the present paper, we propose a hidden Markov model to eliminate these artifacts. The hidden Markov model introduces a hidden variable specifying the light-emission and quenching states behind the observed ﬂuorescence. We found that it is possible to avoid the above artifacts by identifying the state from the hidden-variable time series. We found that, from the analysis of experimental and theoretical benchmark data, the accuracy of our hidden Markov model is beyond human cognitive ability.
LDA-2IoT : A Level Dependent Authentication using Two Factor for IoT Paradigm

The widespread expansion of the IoT based services are changing peoples living habits. With the vast data generation and intelligent decision support system, an IoT is supporting many industries to improve their products and services. The major challenge for IoT developers is to design a secure data transmission system and a trustworthy inter device and user device communication system. The data starts its journey from the sensing devices and reaches the user dashboard through a different medium. Authentication between two IoT devices provides a reliable and lightweight key generation system. In this paper, we put forward a novel authentication approach for the IoT paradigm. We postulate an ECC based two factor Level Dependent Authentication for Generic IoT (LDA 2IoT) in which users at a particular level in the hierarchy can access the sensors deployed at below or the equal level of the hierarchy. We impart the security analysis for the proposed LDA 2IoT based on the Dolev Yao channel and widely accepted random oracle based ROR model. We provide the implementation of the proposed scheme using the MQTT protocol. Finally, we set forth a performance analysis for the proposed LDA 2IoT system by comparing it with the other existing scheme.
MetroGAN: Simulating Urban Morphology with Generative Adversarial Network

Simulating urban morphology with location attributes is a challenging task in urban science. Recent studies have shown that Generative Adversarial Networks (GANs) have the potential to shed light on this task. However, existing GAN-based models are limited by the sparsity of urban data and instability in model training, hampering their applications. Here, we propose a GAN framework with geographical knowledge, namely Metropolitan GAN (MetroGAN), for urban morphology simulation. We incorporate a progressive growing structure to learn hierarchical features and design a geographical loss to impose the constraints of water areas. Besides, we propose a comprehensive evaluation framework for the complex structure of urban systems. Results show that MetroGAN outperforms the state-of-the-art urban simulation methods by over 20% in all metrics. Inspiringly, using physical geography features singly, MetroGAN can still generate shapes of the cities. These results demonstrate that MetroGAN solves the instability problem of previous urban simulation GANs and is generalizable to deal with various urban attributes.
Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer

We introduce Astroconformer, a Transformer-based model to analyze stellar light curves from the Kepler mission. We demonstrate that Astrconformer can robustly infer the stellar surface gravity as a supervised task. Importantly, as Transformer captures long-range information in the time series, it outperforms the state-of-the-art data-driven method in the field, and the critical role of self-attention is proved through ablation experiments. Furthermore, the attention map from Astroconformer exemplifies the long-range correlation information learned by the model, leading to a more interpretable deep learning approach for asteroseismology. Besides data from Kepler, we also show that the method can generalize to sparse cadence light curves from the Rubin Observatory, paving the way for the new era of asteroseismology, harnessing information from long-cadence ground-based observations.
Heterogeneous adsorbent based on CeZrO2 nanoparticles doped magnetic graphene oxide used for vortex assisted magnetic dispersive solid phase extraction of erythromycin in chicken

Abstract A simple, fast, and efficient method of vortex assisted magnetic dispersive solid phase extraction for separation and pre-concentration of erythromycin in chicken samples prior to high LC-UV determination has been developed. The novel heterogeneous CeZrO2 nanoparticles doped magnetic graphene oxide, for use as an efficient nanosorbent, was synthetised and applied for the adsorption of erythromycin. The synthetised nanosorbent was characterised using both Fourier-transform infra-red (FT-IR) and energy dispersive X-Ray (EDX) spectroscopy together with field emission scanning electron microscopy-EDX. To obtain the best extraction condition and maximum extraction efficiency of erythromycin, the effect of important parameters including pH, amount of sorbent, vortexing time, ionic strength, sample volume, and desorption conditions were investigated. At optimum conditions, a linear range of 0.25–300 µg kg−1, LOD (S/N = 3) of 0.079 µg kg−1, and LOQ (S/N = 10) of 0.270 µg kg−1 were obtained. The precision of the method was established as having an RSD (%) at 100 µg kg−1 of erythromycin for seven replicates of 2.6% and 3.2% for the intra-day and the inter-day, respectively. Recoveries over 94.0% confirmed a high capability of the proposed method for separation and determination of erythromycin residues in chicken being one of the most important animal products.
Joint Impact of Key Air Pollutants on COVID-19 Severity: Prediction based on Toxicogenomic Data Analysis

Abstract Considering that some researchers point to a possible influence of air pollution on COVID-19 transmission, severity, and death rate, the aim of our in silico study was to determine the relationship between the key air pollutants [sulphur dioxide (SO), carbon monoxide (CO), 2particulate matter (PMx), nitrogen dioxide (NO2), and ozone (O3)] and COVID-19 complications using the publicly available toxicogenomic analytical and prediction tools: (i) Comparative Toxicogenomic Database (CTD) to identify genes common to air pollutants and COVID-19 complications; (ii) GeneMANIA to construct a network of these common and related genes; (iii) ToppGene Suite to extract the most important biological processes and molecular pathways; and (iv) DisGeNET to search for the top gene-disease pairs. SO2, CO, PMx, NO2, and O3 interacted with 6, 6, 18, 9, and 12 COVID-19-related genes, respectively. Four of these are common for all pollutants (IL10, IL6, IL1B, and TNF) and participate in most (77.64 %) physical interactions. Further analysis pointed to cytokine binding and cytokine-mediated signalling pathway as the most important molecular function and biological process, respectively. Other molecular functions and biological processes are mostly related to cytokine activity and inflammation, which might be connected to the cytokine storm and resulting COVID-19 complications. The final step singled out the link between the CEBPA gene and acute myelocytic leukaemia and between TNFRSF1A and TNF receptor-associated periodic fever syndrome. This indicates possible complications in COVID-19 patients suffering from these diseases, especially those living in urban areas with poor air quality.
Rb Tumor Suppressor in Small Cell Lung Cancer: Combined Genomic and IHC Analysis with a Description of a Distinct Rb-Proficient Subset

Abstract Purpose: RB1 mutations and loss of retinoblastoma (Rb) expression represent consistent but not entirely invariable hallmarks of small cell lung cancer (SCLC). The prevalence and characteristics of SCLC retaining wild-type Rb are not well-established. Furthermore, the performance of targeted next-generation sequencing (NGS) versus immunohistochemistry for Rb assessment is not well-defined. Experimental Design: A total of 208 clinical SCLC samples were analyzed by comprehensive targeted NGS, covering all exons of RB1, and Rb IHC. On the basis of established coordination of Rb/p16/cyclinD1 expression, p16-high/cyclinD1-low profile was used as a marker of constitutive Rb deficiency. Results: Fourteen of 208 (6%) SCLC expressed wild-type Rb, accompanied by a unique p16-low/cyclinD1-high profile supporting Rb proficiency. Rb-proficient SCLC was associated with neuroendocrine-low phenotype, combined SCLC with non-SCLC (NSCLC) histology and aggressive behavior. These tumors exclusively harbored CCND1 amplification (29%), and were markedly enriched in CDKN2A mutations (50%) and NSCLC-type alterations (KEAP1, STK11, FGFR1). The remaining 194 of 208 SCLC were Rb-deficient (p16-high/cyclinD1-low), including 184 cases with Rb loss (of which 29% lacked detectable RB1 alterations by clinical NGS pipeline), and 10 cases with mutated but expressed Rb. Conclusions: This is the largest study to date to concurrently analyze Rb by NGS and IHC in SCLC, identifying a 6% rate of Rb proficiency. Pathologic-genomic data implicate NSCLC-related progenitors as a putative source of Rb-proficient SCLC. Consistent upstream Rb inactivation via CDKN2A/p16↓ and CCND1/cyclinD1↑ suggests the potential utility of CDK4/6 inhibitors in this aggressive SCLC subset. The study also clarifies technical aspects of Rb status determination in clinical practice, highlighting the limitations of exon-only sequencing for RB1 interrogation. See related commentary by Mahadevan and Sholl, p. 4603
Sympathetic activation and heart rate thresholds for cardiovascular risk in chronic kidney disease

Aim: The current study was designed at assessing whether the sympathetic cardiovascular drive (SNS) is differently activated in chronic kidney disease (CKD) patients displaying less or more elevated resting heart rate (HR) values. It was also designed at determining at which HR cutoff value the SNS displays a greater activation. Methods: In 95 CKD middle-age patients we evaluated muscle sympathetic nerve activity (MSNA, microneurography) and venous plasma norepinephrine (HPLC assay), subdividing the patients in different groups according to their resting clinic and 24-h HR. Results: In CKD progressively greater values of clinic or 24-h HR were associated with a progressive increase in both MSNA and norepinephrine. HR cutoff values indicated by large-scale clinical trials for determining cardiorenal risk, that is more than 80 bpm, were associated with MSNA values significantly greater than the ones detected in patients with lower HR, this being the case also for norepinephrine. Both MSNA and norepinephrine were significantly related to clinic (r = 0.47, P < 0.0001 and r = 0.26, P < 0.0001, respectively) and 24-h (r = 0.42, P < 0.0001 and r = 0.27, P < 0.0001, respectively) HR. MSNA, norepinephrine, but not HR, were significantly and inversely related to estimated glomerular filtration rate (eGFR) values (r = −0.47, r = −0.23, P < 0.0001 and P < 0.02, respectively). Conclusion: In CKD both clinic and 24-h HR values greater than 80 bpm are associated with an enhanced sympathetic activation, which parallelles for magnitude the HR elevations. The sensitivity of HR as sympathetic marker is limited; however, no significant relationship being detected between HR and eGFR or left ventricular mass index.
Optimized expression of large fragment DNA polymerase I from Geobacillus stearothermophilus in Escherichia coli expression system

Abstract Bst DNA polymerase is a DNA polymerase derived from Geobacillus stearothermophilus, has a strand-displacement activity, and is used in loop-mediated isothermal amplification (LAMP) for rapid detection of COVID-19. Despite its potential to be employed in the detection of COVID-19, using commercially available enzymes is not economically feasible. The use of noncommercial enzyme for routine use is desirable. However, research on Bst DNA polymerase is still limited in Indonesia. For those reasons, a preliminary study of scale-up production of recombinant Bst polymerase was conducted. Therefore, the optimization of expression conditions was performed. The optimum conditions for Bst polymerase expression were as follows: 1 mM of IPTG, post-induction incubation time of 6 h, and induction at OD600 1.1. Employing optimum conditions could result in 2.8 times increase in protein yield compared to the initial conditions. Subsequently, an operation in 1 L working volume by a lab-scale bioreactor had been performed, followed by purification and dialysis. The optimum result for a 1 L lab-scale bioreactor was achieved by applying 100 rpm and 3 vvm, giving 11.7 mg/L of protein yield. Bst polymerase was successfully purified showing 813.56 U/mg of polymerase activity.
The Role of PARP‐1 and NF‐κB in Bile‐Induced DNA Damage and Oncogenic Profile in Hypopharyngeal Cells

We recently documented that acidic bile, a gastroesophageal reflux content, can cause invasive hypopharyngeal squamous cell carcinoma, by inducing widespread DNA damage and promoting nuclear factor kappa B (NF‐κB)‐related oncogenic molecular events. Poly or adenosine diphosphate (ADP)‐ribose polymerase‐1 (PARP‐1), a sensitive sensor of DNA damage, may interact with NF‐κB. We hypothesized that PARP‐1 is activated in hypopharyngeal cells (HCs) with marked DNA damage caused by acidic bile, hence there is an association between PARP‐1 and NF‐κB activation or its related oncogenic profile, in this process.
Dentistry students' opinion toward Biochemistry course and its relevance in clinical practice in Chennai, India

Biochemistry is a subject that is essential to understand the molecular basis of a disease, its diagnosis, and prognosis. However, study of biochemistry in dentistry at the undergraduate level is considered to be much teacher‐oriented and the students show a lackluster attitude toward the subject. This questionnaire study is carried out to understand the perception of dental students in studying biochemistry and its relevance in dental practice. A pre‐set questionnaire was sent to Bachelor of Dental Surgery students from first to final year and the responses were recorded. Although students are positive about liking biochemistry as a theory subject, still many are wary of the detailed study of complex reactions and metabolic pathways, which hampers the extensive study of the subject and its application in clinical practice.
Combining GWAS and population genomic analyses to characterize coevolution in a legume-rhizobia symbiosis.

​​The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two-species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host Medicago truncatula with a community of 88 Ensifer meliloti strains. Multi-strain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single-strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, i.e., genotype-by-genotype, effects on rhizobial fitness and some annotated rhizobial genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation-selection balance. Consistent with the lack of positive selection acting on host genes, we found that among-host variation in growth was similar whether plants were grown with rhizobia or N-fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multi-strain contexts.
E$^{3}$3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection

Existing unsupervised outlier detection (OD) solutions face a grave challenge with surging visual data like images. Although deep neural networks (DNNs) prove successful for visual data, deep OD remains difficult due to OD's unsupervised nature. This paper proposes a novel framework named E 3 Outlier that can perform effective and end-to-end deep outlier removal. Its core idea is to introduce self-supervision into deep OD. Specifically, our major solution is to adopt a discriminative learning paradigm that creates multiple pseudo classes from given unlabeled data by various data operations, which enables us to apply prevalent discriminative DNNs (e.g. ResNet) to the unsupervised OD problem. Then, with theoretical and empirical demonstration, we argue that inlier priority, a property that encourages DNN to prioritize inliers during self-supervised learning, makes it possible to perform end-to-end OD. Meanwhile, unlike frequently-used outlierness measures (e.g. density, proximity) in previous OD methods, we explore network uncertainty and validate it as a highly effective outlierness measure, while two practical score refinement strategies are also designed to improve OD performance. Finally, in addition to the discriminative learning paradigm above, we also explore the solutions that exploit other learning paradigms (i.e. generative learning and contrastive learning) to introduce self-supervision for E 3 Outlier. Such extendibility not only brings further performance gain on relatively difficult datasets, but also enables E 3 Outlier to be applied to other OD applications like video abnormal event detection. Extensive experiments demonstrate that E 3 Outlier can considerably outperform state-of-the-art counterparts by 10%-30% AUROC. Demo codes are available at https://github.com/demonzyj56/E3Outlier.
Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech

People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by considering individual differences in speech auditory–motor synchronization, we demonstrate that recruitment of a specific neural network supports behavioral differences in SL from speech. While independent component analysis (ICA) of fMRI data revealed that a network of auditory and superior pre/motor regions is universally activated in the process of learning, a frontoparietal network is additionally and selectively engaged by only some individuals (high auditory–motor synchronizers). Importantly, activation of this frontoparietal network is related to a boost in learning performance, and interference with this network via articulatory suppression (AS; i.e., producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on SL from speech and reconciles previous contrasting findings. These findings also highlight a more general need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
Neural regeneration therapy after spinal cord injury induces unique brain functional reorganizations in rhesus monkeys

Abstract Purpose Spinal cord injury (SCI) destroys the sensorimotor pathway and induces brain plasticity. However, the effect of treatment-induced spinal cord tissue regeneration on brain functional reorganization remains unclear. This study was designed to investigate the large-scale functional interactions in the brains of adult female Rhesus monkeys with injured and regenerated thoracic spinal cord. Materials and methods Resting-state functional magnetic resonance imaging (fMRI) combined with Granger Causality analysis (GCA) and motor behaviour analysis were used to assess the causal interaction between sensorimotor cortices, and calculate the relationship between causal interaction and hindlimb stepping in nine Rhesus monkeys undergoing lesion-induced spontaneous recovery (injured, n = 4) and neurotrophin-3/chitosan transplantation-induced regeneration (NT3-chitosan, n = 5) after SCI. Results The results showed that the injured and NT3-chitosan-treated animals had distinct spatiotemporal features of brain functional reorganization. The spontaneous recovery followed the model of “early intra-hemispheric reorganization dominant, late inter-hemispheric reorganization dominant”, whereas regenerative therapy animals showed the opposite trend. Although the variation degree of information flow intensity was consistent, the tendency and the relationship between local neuronal activity properties and coupling strength were different between the two groups. In addition, the injured and NT3-chitosan-treated animals had similar motor adjustments but various relationship modes between motor performance and information flow intensity. Conclusions Our findings show that brain functional reorganization induced by regeneration therapy differed from spontaneous recovery after SCI. The influence of unique changes in brain plasticity on the therapeutic effects of future regeneration therapy strategies should be considered. Key messages Neural regeneration elicited a unique spatiotemporal mode of brain functional reorganization in the spinal cord injured monkeys, and that regeneration does not simply reverse the process of brain plasticity induced by spinal cord injury (SCI). Independent “properties of local activity – intensity of information flow” relationships between the injured and treated animals indicating that spontaneous recovery and regenerative therapy exerted different effects on the reorganization of the motor network after SCI. A specific information flow from the left thalamus to the right insular can serve as an indicator to reflect a heterogeneous “information flow – motor performance” relationship between injured and treated animals at similar motor adjustments.