Miracle-dz/UniFAR-FTUs huggingface dataset:
Methylation-Independent Chemotaxis Systems Are the Norm for Gastric-Colonizing Helicobacter Species

Chemotaxis signal transduction systems are common in the archaeal and bacterial world, but not all systems contain the same components. The rationale for this system variation remains unknown. ABSTRACT Many bacteria and archaea rely on chemotaxis signal transduction systems for optimal fitness. These complex, multiprotein signaling systems have core components found in all chemotactic microbes, as well as variable proteins found in only some species. We do not yet understand why these variations exist or whether there are specific niches that favor particular chemotaxis signaling organization. One variation is in the presence/absence of the chemotaxis methylation adaptation enzymes CheB and CheR. Genes for CheB and CheR are missing in the gastric pathogen Helicobacter pylori but present in related Helicobacter that colonize the liver or intestine. In this work, we asked whether there was a general pattern of CheB/CheR across multiple Helicobacter species. Helicobacter spp. all possess chemotactic behavior, based on the presence of genes for core signaling proteins CheA, CheW, and chemoreceptors. Genes for the CheB and CheR proteins, in contrast, were variably present. Niche mapping supported the idea that these genes were present in enterohepatic Helicobacter species and absent in gastric ones. We then analyzed whether there were differences between gastric and enterohepatic species in the CheB/CheR chemoreceptor target methylation sites. Indeed, these sites were less conserved in gastric species that lack CheB/CheR. Lastly, we determined that cheB and cheR could serve as markers to indicate whether an unknown Helicobacter species was of enterohepatic or gastric origin. Overall, these findings suggest the interesting idea that methylation-based adaptation is not required in specific environments, particularly the stomach. IMPORTANCE Chemotaxis signal transduction systems are common in the archaeal and bacterial world, but not all systems contain the same components. The rationale for this system variation remains unknown. In this report, comparative genomics analysis showed that the presence/absence of CheR and CheB is one main variation within the Helicobacter genus, and it is strongly associated with the niche of Helicobacter species: gastric Helicobacter species, which infect animal stomachs, have lost their CheB and CheR, while enterohepatic Helicobacter species, which infect the liver and intestine, retain them. This study not only provides an example that a chemotaxis system variant is associated with particular niches but also proposes that CheB and CheR are new markers distinguishing gastric from enterohepatic Helicobacter species.
Quantitative Resilience Assessment under a Tri-Stage Framework for Power Systems

The frequent occurrence of natural disasters and malicious attacks has exerted unprecedented disturbances on power systems, accounting for the extensive attention paid to power system resilience. Combined with the evolving nature of general disasters, this paper proposes resilience assessment approaches for power systems under a tri-stage framework. The pre-disaster toughness is proposed to quantify the robustness of power systems against potential disasters, where the thinking of area division and partitioned multi-objective risk method (PMRM) is introduced. In the case of information deficiency caused by disasters, the during-disaster resistance to disturbance is calculated to reflect the real-time system running state by state estimation (SE). The post-disaster restoration ability consists of response ability, restoration efficiency and restoration economy, which is evaluated by Sequential Monte-Carlo Simulation to simulate the system restoration process. Further, a synthetic metric system is presented to quantify the resilience performance of power systems from the above three aspects. The proposed approaches and framework are validated on the IEEE RTS 79 system, and helpful conclusions are drawn from extensive case studies.
The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue

The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global ‐OMICS analysis. To address some of these barriers, our group has recently focused on using mass‐spectrometry workflows compatible with small amounts of formalin‐fixed paraffin‐embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, “The Brain Protein Atlas (BPA)” (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer‐reviewed mass‐spectrometry‐based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry‐based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built‐in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.
‘I want every minute to be worthwhile now’: The views and experiences of people living with dementia and their care partners about returning to in-person group meetings after COVID-19 lockdown restrictions

COVID-19 and the resulting limitations on freedom of movement has been difficult for many, including individuals living with dementia and those who provide support and care. In the summer of 2021, England’s national lockdown measures eased, and regulations were amended to allow indoor social gatherings. With this enabling a return to in-person meetings, this study explored the experiences of people living with dementia and current and former care partners who had previously attended groups at Salford Institute for Dementia (UK). Two phases of research were conducted. In the first phase, during the summer of 2020, telephone interviews were utilised to ask participants (n = 13) about their views of re-engagement and how the in-person groups might be best reintroduced. Phase two began in the summer of 2021, where mood questionnaires (n = 10) were administered and observations conducted to explore how participants experienced the return to in-person meetings. Thematic analysis resulted in the construction of three overarching themes: planning for and the reality of transitioning; safety versus autonomy; and tensions and complexities of life in the ‘new normal’. Despite initial concerns about their reintegration into the community, participants all enjoyed resuming in-person meetings. An inclusive and consultative approach to re-engagement allowed all participants to feel valued, safe, and informed about their return to campus. However, individuals living with dementia and care partners experienced the transition to re-engagement in different ways and their perceptions shifted over time. We therefore highlight the complexities of responding to different perceptions of risk and safety, while also promoting engagement and inclusivity after a period of social isolation. In this paper, we consider implications for the re-integration of individuals with dementia and their care partners into in-person social groups and propose further avenues for research.
The key role of propane in a sustainable cooling sector

Split air conditioners (ACs) are the most used appliance for space cooling worldwide. The phase-down of refrigerants with high global warming potential (GWP) prescribed by the Kigali Amendment to the Montreal Protocol has triggered a major effort to find less harmful alternative refrigerants. HFC-32 is currently the most common refrigerant to replace HFC-410A in split ACs. The GWP of HFC-32 is about one-third that of HFC-410A but still considerably higher than that of a growing number of nonfluorinated alternatives like propane with a GWP of <1, which have recently become commercially available for split ACs. Here, we show that a switch to propane as an energy-efficient and commercially available low-GWP alternative in split ACs could avoid 0.09 (0.06 to 0.12) °C increase in global temperature by the end of the century. This is significantly more than the 0.03 (0.02 to 0.05) °C avoided warming from a complete switch to HFC-32 in split ACs.
Label noise and self-learning label correction in cardiac abnormalities classification

Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise due to inter- and intra-observer variability and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals. Approach. A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets. Main results. Under high levels of noise, the cross-validation results of self-learning label correction show an improvement of approximately 3% in the challenge score for the PhysioNet/CinC 2021 Challenge dataset and an improvement in accuracy of 5% and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.
Correlations between alignment gaps and nucleotide substitution or amino acid replacement

Significance We introduce a test of the null hypothesis that nucleotide substitution or amino acid replacement processes are independent of gap locations within sequence alignments. When applying this test to alignments that are informed by protein structure, the null is rejected about 2/3 of the time. This indicates that modifications are needed to the usual approach of ignoring gap locations when making evolutionary inferences. Additionally, we demonstrate that optimal alignments introduce spurious correlations between gap locations and nucleotide substitution patterns. Because these spurious correlations will not be eliminated by employing genomic-scale datasets, we emphasize the need for modifying the conventional approach of basing evolutionary inferences upon single optimal alignments.
System for Logging Acoustic Emission Signals from FRP Reinforcing Rods that is Suitable for Field and Long Term Laboratory Testing

Fibre reinforced polymer (FRP) rods have seen increasing use in civil infrastructure applications. FRP rods are corrosion resistant and are easier to handle than steel. They have been used in over 190 projects including many bridge decks and traffic barriers. However, at sufficiently high loads (more than ~25% of the ultimate load) FRP rods can become susceptible to creep failure. In addition, FRP rods have linear stress strain relationships up to failure and hence mechanical property changes to do provide a reliable indicator of failure. To study and monitor failure in FRP rods acoustic emission has been used as a diagnostic tool. Acoustic emission signals characteristics can be correlated with the level of stress within the FRP rod. For example, signal entropy has proven to be a good indicator of the stress level in glass and carbon FRP rods. However, due to the high sampling rates in AE signal acquisition, signal analysis has required post processing of signals. For longer term field and long-term laboratory studies, it is not practical to record and post process the data from the AE signals, which is produced at several megabytes per second. In this work we describe a system that uses triggered recording of the AE signals so that only the signal for a few milliseconds are logged in the vicinity of each AE event. The system is microcontroller based and allows for sampling at ~700k samples per second, with logging to SD cards. The system is suitable for deployment at a large number of nodes. Examples of the system being applied to AE monitoring of glass FRP rods will be shown. The system will be used for longterm laboratory studies of FRP rods at low load levels (less than 50% of ultimate).
Convolutional Neural Network for predicting thyroid cancer based on ultrasound elastography image of perinodular region.

We aimed to develop deep learning models based on perinodular regions' SWE images and US images of thyroid nodules (TNs) and determine their performances in predicting thyroid cancer. A total of 1747 ACR-TIRADS 4 (TR4) TNs in 1582 patients were included in this retrospective study. US images, SWE images, and two quantitative SWE parameters (maximum elasticity of TNs; 5-point average maximum elasticity of TNs) were obtained. Based on US and SWE images of TNs and perinodular tissue, respectively, seven single-image CNN models (US, internal SWE, 0.5 mm SWE, 1.0 mm SWE, 1.5 mm SWE, 2.0 mm SWE of perinodular tissue, and whole SWE ROI image) and another six fusional-image CNN models (US + internal SWE, US + 0.5 mm SWE, US + 1.0 mm SWE, US + 1.5 mm SWE, US + 2.0 mm SWE, US + ROI SWE) were established using RestNet18. All of the CNN models and quantitative SWE parameters were built on a training cohort (1247 TNs) and evaluated on a validation cohort (500 TNs). In predicting thyroid cancer, US + 2.0 mm SWE image CNN model obtained the highest area under the curves (AUCs) in 10 mm < TNs ≤ 20 mm (0.95 for training; 0.92 for validation) and TNs > 20 mm (0.95 for training; 0.92 for validation), while US + 1.0 mm SWE image CNN model obtained the highest AUC in TNs ≤ 10 mm (0.95 for training; 0.92 for validation). The CNN models based on the fusion of SWE segmentation images and US image improve radiological diagnostic accuracy of thyroid cancer.
Feasibility and Preliminary Safety of Smartphone-Based Automated Insulin Delivery in Adolescents and Children With Type 1 Diabetes.

BACKGROUND A smartphone-based automated insulin delivery (AID) controller device can facilitate use of interoperable components and acceptance in adolescents and children. METHODS Pediatric participants (N = 20, 8F) with type 1 diabetes were enrolled in three sequential age-based cohorts: adolescents (12-<18 years, n = 8, 5F), school-age (8-<12 years, n = 7, 2F), and young children (2-<8 years, n = 5, 1F). Participants used the interoperable artificial pancreas system (iAPS) and zone model predictive control (MPC) on an unlocked smartphone for 48 hours, consumed unrestricted meals of their choice, and engaged in various unannounced exercises. Primary outcomes and stopping criteria were defined using fingerstick blood glucose (BG) data; secondary outcomes compared continuous glucose monitoring (CGM) data with preceding sensor augmented pump (SAP) therapy. RESULTS During AID, there was no more than one BG <50 mg/dL except in one young child participant; no instance of more than two episodes of BG ≥300 mg/dL lasting longer than 2 hours; and no adverse events. Despite large meals (total of 404.9 grams of carbs) and unannounced exercise (total of 182 minutes), overall CGM percent time in range (TIR) of 70 to 180 mg/dL during AID was statistically similar to SAP (63.5% vs 57.3%, respectively, P = .145). Overnight glucose standard deviation was 43 mg/dL (vs SAP 57.9 mg/dL, P = .009) and coefficient of variation was 25.7% (vs SAP 34.9%, P < .001). The percent time in closed-loop mode and connected to the CGM was 92.7% and 99.6%, respectively. Surveys indicated that participants and parents/guardians were satisfied with the system. CONCLUSIONS The smartphone-based AID was feasible and safe in sequentially younger cohorts of adolescents and children. CLINICALTRIALS.GOV NCT04255381 (https://clinicaltrials.gov/ct2/show/NCT04255381).
Mechanism of Action of NB2001 and NB2030, Novel Antibacterial Agents Activated by (cid:2) -Lactamases

Two potent antibacterial agents designed to undergo enzyme-catalyzed therapeutic activation were evaluated for their mechanisms of action. The compounds, NB2001 and NB2030, contain a cephalosporin with a thienyl (NB2001) or a tetrazole (NB2030) ring at the C-7 position and are linked to the antibacterial triclosan at the C-3 position. The compounds exploit (cid:1) -lactamases to release triclosan through hydrolysis of the (cid:1) -lactam ring. Like cephalothin, NB2001 and NB2030 were hydrolyzed by class A (cid:1) -lactamases ( Escherichia coli TEM-1 and, to a lesser degree, Staphylococcus aureus PC1) and class C (cid:1) -lactamases ( Enterobacter cloacae P99 and E. coli AmpC) with comparable catalytic efﬁciencies ( k cat / K m ). They also bound to the penicillin-binding proteins of S. aureus and E. coli , but with reduced afﬁnities relative to that of cephalothin. Accordingly, they produced a cell morphology in E. coli consistent with the toxophore rather than the (cid:1) -lactam being responsible for antibacterial activity. In biochemical assays, they inhibited the triclosan target enoyl reductase (FabI), with 50% inhibitory concentrations being markedly reduced relative to that of free triclosan. The transport of NB2001, NB2030, and triclosan was rapid, with signiﬁcant accumulation of triclosan in both S. aureus and E. coli . Taken together, the results suggest that NB2001 and NB2030 act primarily as triclosan prodrugs in S. aureus and E. coli . NB2001 triclosan, of enoyl reductase on triclosan NB2001
A Simulink Model of Photovoltaic Modules under Varying Environmental Conditions

In this paper, a model for predicting the effects of environmental conditions on the performance of photovoltaic panels is developed and analysed. A one-diode model taking into account effects of environmental conditions such as solar irradiance, ambient temperature and wind was developed and simulated in Simulink/Matlab environment. The accuracy of the developed model was confirmed via a comparison of the simulated results with the output characteristics of two polycrystalline photovoltaic (PV) modules from different manufacturers. Effects of various environmental parameters on the cell temperature and output characteristics of PV modules were analysed using the developed model. The results revealed that environmental condition variations have significant effects on the electrical performances of PV modules and should be taken into consideration in PV system design. The developed model could also be useful in the design of optimum cooling systems for photovoltaic/thermal (PV/T) systems since it takes into consideration the cooling effects of the wind.
Trial Randomised controlled trial

Rationale: Low uptake of low-dose CT (LDCT) lung cancer screening, particularly by current smokers of a low socioeconomic position, compromises effectiveness and equity. Objectives: To compare the effect of a ‘targeted, low burden and stepped’ invitation strategy versus control, on uptake of hospital- based ‘Lung Health Check’ appointments offering (LDCT) screening. Methods: A two-arm, blinded, between-subjects, randomised controlled trial. 2012 participants were selected from 16 primary care practices using these criteria: i) aged 60-75, ii) recorded as a current smoker within the last seven years, iii) no pre-specified exclusion criteria contraindicating LDCT screening. Both groups received a stepped sequence of pre-invitation, invitation and reminder letters from their Primary Care Practitioner offering pre-scheduled appointments. The key manipulation was the accompanying leaflet. The intervention group’s leaflet targeted psychological barriers and provided low burden information , mimicking the concept of the UK Ministry of Transport’s annual vehicle test (‘MOT for your lungs’). Measurements and Main Results: Uptake was 52.6%, with no difference between intervention (52.3%) and control (52.9%) groups in unadjusted (OR: 0.98, 0.82-1.16) or adjusted (aOR: 0.98, 0.82-1.17) analyses. Current smokers were less likely to attend (aOR: 0.70, 0.56-0.86) than former smokers. Socioeconomic deprivation was significantly associated with lower uptake for the control group only (p<.01). Conclusions: The intervention did not improve uptake. Regardless of trial arm, uptake was considerably higher than previous clinical and real world studies, particularly given the sample were predominantly lower socioeconomic position smokers. Strategies common to both groups, including a Lung Health Check approach, could represent a minimum standard.
Eﬃcacy and safety of 30 mg/d and 45 mg/d nemiﬁtide compared to placebo in major depressive disorder

Nemiﬁtide is a novel pentapeptide antidepressant, which appears to be eﬀective in the treatment of major depressive disorder (MDD). In the present study 81 patients with MDD, DSM-IV criteria were random-ized following a 1-wk screening period to receive 30 mg/d nemiﬁtide, 45 mg/d nemiﬁtide or placebo in a 6-wk double-blind, multicentre, outpatient eﬃcacy study. Nemiﬁtide or placebo was delivered by subcutaneous injection for 2 wk daily for 5 days (Monday to Friday) in the ﬁrst 2 wk and patients were followed up for a further 4 wk. The primary eﬃcacy measure was the change from baseline on the Montgomery–Asberg Depression Rating Scale. Secondary measures included the 17-item Hamilton Psychiatric Rating Scale for Depression (HAMD), the CGI severity and improvement scale and the Carroll Self-Rating Scale for Depression. This proof-of-principle study demonstrated a statistically signiﬁcant superiority of the 45-mg/d dose vs. placebo at the time-point of peak eﬀect (1 wk after the end of treatment). There appeared to be a greater eﬀect with the 45 mg/d nemiﬁtide dose than with 30 mg/d. An additional exploratory analysis by stratiﬁcation of all patients by severity above and below or equal to the median baseline HAMD score of 22 showed a higher percentage of responders for both doses of nemiﬁtide with statistical separation from placebo for patients with baseline HAMD score of > 22 (above the median). There was no signiﬁcant diﬀerence among treatment groups for patients with baseline HAMD score of f 22. Nemiﬁtide showed a good tolerability and safety proﬁle. There were no dropouts due to adverse events, and the incidence of side-eﬀects with nemiﬁtide was comparable with that of placebo.