Miracle-dz/UniFAR-FTUs huggingface dataset:
A human osteoarthritis mimicking goat cartilage explant-based disease model for drug screening.

Osteoarthritis (OA) is the most prevalent human joint disease with a massive global disease burden. Despite having a large socioeconomic burden, OA remains a neglected disease with no clinically approved disease modifying therapies. One of the key reasons for this is that the available disease models poorly recapitulate human OA-like traits possibly because of the challenge of mimicking the disease in an ECM rich cartilage tissue. Therefore, in this study, we report the establishment and validation of a clinically relevant ex vivo OA model using IL1β treated goat articular cartilage explants. Treatment with IL1β induced OA-like traits in goat cartilage explants and caused a shift in cartilage homeostasis towards enhanced catabolism, resulting in higher matrix degradation, overexpression of degradative and inflammatory mediators and chondrocyte hypertrophy. We then validated the developed disease model for drug response using three different drugs viz. BMP7, rapamycin and celecoxib, all of which demonstrated concentration-dependent disease amelioration in the explant model. Finally, we evaluated the translational relevance of the developed ex vivo OA model by comparing it with late-stage OA patient samples and observed a striking resemblance in terms of matrix degradation, expression of degradative enzymes, chondrocyte hypertrophy and inflammation. Overall, the goat ex vivo OA model elicited a biological response to cytokine treatment which mirrors human OA-like traits that may reduce discordance between preclinical and clinical studies in OA drug development.
Transcription factor StABI5-like 1 binding to the FLOWERING LOCUS T homologs promotes early maturity in potato

Abstract Potato (Solanum tuberosum L.) maturity involves several important traits, including the onset of tuberization, flowering, leaf senescence, and the length of the plant life cycle. The timing of flowering and tuberization in potato is mediated by seasonal fluctuations in photoperiod and is thought to be separately controlled by the FLOWERING LOCUS T-like (FT-like) genes SELF-PRUNING 3D (StSP3D) and SELF-PRUNING 6A (StSP6A). However, the biological relationship between these morphological transitions that occur almost synchronously remains unknown. Here, we show that StABI5-like 1 (StABL1), a transcription factor central to abscisic acid (ABA) signaling, is a binding partner of StSP3D and StSP6A, forming an alternative florigen activation complex and alternative tuberigen activation complex in a 14-3-3-dependent manner. Overexpression of StABL1 results in the early initiation of flowering and tuberization as well as a short life cycle. Using genome-wide chromatin immunoprecipitation sequencing and RNA-sequencing, we demonstrate that AGAMOUS-like and GA 2-oxidase 1 genes are regulated by StABL1. Phytohormone profiling indicates an altered gibberellic acid (GA) metabolism and that StABL1-overexpressing plants are insensitive to the inhibitory effect of GA with respect to tuberization. Collectively, our results suggest that StABL1 functions with FT-like genes to promote flowering and tuberization and consequently life cycle length in potato, providing insight into the pleiotropic functioning of the FT gene.
CMS2-Net: Semi-Supervised Sleep Staging for Diverse Obstructive Sleep Apnea Severity

Although the development of computer-aided algorithms for sleep staging is integrated into automatic detection of sleep disorders, most supervised deep learning-based models might suffer from insufficient labeled data. While the adoption of semi-supervised learning (SSL) can mitigate the issue, the SSL models are still limited to the lack of discriminative feature extraction for diverse obstructive sleep apnea (OSA) severity. This model deterioration might be exacerbated during the domain adaptation. Such exploration on the alleviation of domain-shift of SSL model between different OSA conditions has attracted more and more attentions from the clinic. In this work, a co-attention meta sleep staging network (CMS2-net) is proposed to simultaneously deal with two issues: the inter-class disparity problem and the intra-class selection problem. Within CMS2-net, a co-attention module and a triple-classifier are designed to explicitly refine the coarse feature representations by identifying the class boundary inconsistency. Moreover, the mutual information with meta contrastive variance is introduced to supervise the gradient stream from a multi-scale view. The performance of the proposed framework is demonstrated on both public and local datasets. Furthermore, our approach achieves the state-of-the-art SSL results on both datasets.
Ca2+ inactivation of the mammalian ryanodine receptor type 1 in a lipidic environment revealed by cryo-EM

Activation of the intracellular Ca2+ channel ryanodine receptor (RyR) triggers a cytosolic Ca2+ surge, while elevated cytosolic Ca2+ inhibits the channel in a negative feedback mechanism. Cryogenic electron microscopy of rabbit RyR1 embedded in nanodiscs under partially inactivating Ca2+ conditions revealed an open and a closed-inactivated conformation. Ca2+ binding to the high-affinity site engages the central and C-terminal domains into a block, which pries the S6 four-helix bundle open. Further rotation of this block pushes S6 toward the central axis, closing (inactivating) the channel. Main characteristics of the Ca2+-inactivated conformation are downward conformation of the cytoplasmic assembly and tightly knit subunit interface contributed by a fully occupied Ca2+ activation site, two inter-subunit resolved lipids, and two salt bridges between the EF hand domain and the S2–S3 loop validated by disease-causing mutations. The structural insight illustrates the prior Ca2+ activation prerequisite for Ca2+ inactivation and provides for a seamless transition from inactivated to closed conformations.
Quantifying the qualitative: exploring epistemic network analysis as a method to study work system interactions

Abstract Studying interactions faces methodological challenges and existing methods, such as configural diagramming, have limitations. This work demonstrates Epistemic Network Analysis (ENA) as an analytical method to construct configural diagrams. We demonstrated ENA as an analytical tool by applying this method to study dementia caregiver work systems. We conducted 20 semi-structured interviews with caregivers to collect caregiving experiences. Guided by the Patient Work System model, we conducted a directed content analysis to identify work system components and used ENA to study interactions between components. By using ENA to create configural diagrams, we identified five frequently occurring interactions, compared work system configurations of caregivers providing care at home and away from home. Although we were underpowered to determine statistically significant differences, we identified visual and qualitative differences. Our results demonstrate the capability of ENA as an analytical method for studying work system interactions through configural diagramming. Practitioner summary: A new methodology, Epistemic Network Analysis (ENA), was presented to better support the study of work system interactions through configural diagramming. ENA was applied to qualitative data to demonstrate the capabilities of this method to construct configural diagrams of the work system. This study successfully demonstrated that ENA can visually represent and describe work system configurations.
A Practical Approach to Identifying Pediatric Disease-Associated Undernutrition: A Position Statement from the ESPGHAN Special Interest Group on Clinical Malnutrition

ABSTRACT Disease-associated undernutrition (DAU) is still common in hospitalized children and is generally accepted to be associated with adverse effects on disease outcomes; hence making proper identification and assessment essential in the management of the sick child. There are however several barriers to routine screening, assessment, and treatment of sick children with poor nutritional status or DAU, including limited resources, lack of nutritional awareness, and lack of agreed nutrition policies. We recommend all pediatric facilities to 1) implement procedures for identification of children with (risk of) DAU, including nutritional screening, criteria for further assessment to establish diagnosis of DAU, and follow-up, 2) assess weight and height in all children asa minimum, and 3) have the opportunity for children at risk to be assessed by a hospital dietitian. An updated descriptive definition of pediatric DAU is proposed as “Undernutrition is a condition resulting from imbalanced nutrition or abnormal utilization of nutrients which causes clinically meaningful adverse effects on tissue function and/or body size/composition with subsequent impact on health outcomes. ” To facilitate comparison of undernutrition data, it is advised that in addition to commonly used criteria for undernutrition such as z score < −2 for weight-for-age, weight-for-length, or body mass index <−2, an unintentional decline of >1inthese z scores over time should be considered as an indicator requiring further assessment to establish DAU diagnosis. Since the etiology of DAU is multifactorial, clinical evaluation and anthropometry should ideally be complemented by measurements of body composition, assessment of nutritional intake, requirements, and losses, and considering disease specific factors.
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method

The growth of videos in our digital age and the users’ limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information. Despite the remarkable progress that summarization methods have made, most of them can only select a few frames or skims, creating visual gaps and breaking the video context. This paper presents a novel weakly-supervised methodology based on a reinforcement learning formulation to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the input video to a target length without creating gaps in the final video. We also propose the Extended Visually-guided Document Attention Network (VDAN+), which can generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in Precision, Recall, and F1 Score against the baselines while effectively controlling the video’s output length.
Flowchart for Implementing Advanced Imaging and Electrophysiology in Patients With Disorders of Consciousness

In the Contemporary Issues in Practice, Education, & Research article “Flowchart for Implementing Advanced Imaging and Electrophysiology in Patients With Disorders of Consciousness: To fMRI or Not to fMRI? ” by Monti and Schnakers,1 first published online January 20, 2022, pervasive errors were introduced into Figure 1 during the figure editing process. The corrected Figure 1 has been resupplied online. The editorial staff regrets the errors.
Using Plan Decomposition for Continuing Plan Optimisation and Macro Generation

This thesis addresses three problems in the field of classical AI planning: decomposing a plan into meaningful subplans, continuing plan quality optimisation, and macro generation for efficient planning. The importance and difficulty of each of these problems is outlined below. (1) Decomposing a plan into meaningful subplans can facilitate a number of postplan generation tasks, including plan quality optimisation and macro generation – the two key concerns of this thesis. However, conventional plan decomposition techniques are often unable to decompose plans because they consider dependencies among steps, rather than subplans. (2) Finding high quality plans for large planning problems is hard. Planners that guarantee optimal, or bounded suboptimal, plan quality often cannot solve them In one experiment with the Genome Edit Distance domain optimal planners solved only 11.5% of problems. Anytime planners promise a way to successively produce better plans over time. However, current anytime planners tend to reach a limit where they stop finding any further improvement, and the plans produced are still very far from the best possible. In the same experiment, the LAMA anytime planner solved all problems but found plans whose average quality is 1.57 times worse than the best known. (3) Finding solutions quickly or even finding any solution for large problems within some resource constraint is also difficult. The best-performing planner in the 2014 international planning competition still failed to solve 29.3% of problems. Re-engineering a domain model by capturing and exploiting structural knowledge in the form of macros has been found very useful in speeding up planners. However, existing planner independent macro generation techniques often fail to capture some promising macro candidates because the constituent actions are not found in sequence in the totally ordered training plans. This thesis contributes to plan decomposition by developing a new plan deordering technique, named block deordering, that allows two subplans to be unordered even when their constituent steps cannot. Based on the block-deordered plan, this thesis further contributes to plan optimisation and macro generation, and their implementations in two systems, named BDPO2 and BloMa. Key to BDPO2 is a decomposition into subproblems of improving parts of the current best plan, rather than the plan as a whole. BDPO2 can be seen as an application of the large neighbourhood search strategy to planning. We use several windowing strategies to extract subplans from the block deordering of the current plan, and on-line learning for applying the most promising subplanners to the most promising subplans. We demonstrate empirically that even starting with the best plans found by other means, BDPO2 is still able to continue improving plan quality, and often produces
Spectroscopic Studies of BA Class Liquid Crystals in the 6–15 THz Range Using the Fourier Transform Infrared (FT-IR) Method

Fourier transform infrared spectroscopy was performed for two structurally similar liquid crystals: 4-octyloxybenzoic acid and 4-decyloxybenzoic acid in 6–15 THz range. Density functional theory modeling at PBE0/def2-TZVPP level was used to assign the vibrational modes to each observed absorption peak. We observed six peaks that are common for both liquid crystals, assigned to the same vibrational modes and appearing at similar energies. Each of the samples also had additional peaks unique to itself. A strong absorption peak appears at about 280 cm–1 for both samples; however, it corresponds to different vibrational modes for the two samples. This work shows that the spectroscopy in this often-neglected frequency range can easily distinguish structurally similar compounds. Graphical Abstract
Application of higher density wavelet transform to composite fault diagnosis of rolling bearing

Aiming at the difficulties in accurate reorganization of several weak faults currently,a composite fault diagnosis method based on higher density discrete wavelet transform and envelope spectrum is proposed.Firstly,the higher density discrete wavelet transform is used to decompose acquired vibration signals of rolling bearings.Then,the single-subband reconstruction is performed on the wavelet coefficients and scaling coefficients at each scale in order to solve frequency aliasing.Finally,the envelope spectra of all subband signals are calculated,and all faults can be recognized according to the characteristic frequencies of the typical faults.The proposed method is applied to the diagnosis of the rolling bearings with composite faults,and is compared with other common fault diagnosis method.The results show that the proposed method can be effectively used for the early composite fault diagnosis of rolling bearings.
The relationship between vessel traffic and noise levels received by killer whales and an evaluation of compliance with vessel regulations

Cetaceans that rely on their acoustic environment for key life history strategies are susceptible to noise effects from anthropogenic use such as ecotourism. Endangered Southern Resident killer whales (SRKW) are the primary target for vessel-based whale-watching in the Salish Sea. Vessel interactions and associated noise have been identified as potential stressors for SRKW. Previous research has indicated that both stressors negatively impact SRKW; however, there is a missing link between vessel characteristics/behavior and noise levels actually received by individual whales. To investigate this relationship, data were collected concurrently using mobile remote sensing survey equipment packages and digital acoustic recording tags. This allowed us to obtain precise geo-referenced vessel data and noise levels received by the whales. We used linear regression to summarize patterns in vessel characteristics and relate them to received noise levels. Received noise levels (RNL) were correlated with the number o.
An Input-Powered High-Efficiency Interface Circuit with Zero Standby Power in Energy Harvesting Systems

This study presents an input-powered high-efficiency interface circuit for energy harvesting systems, and introduces a zero standby power design to reduce power consumption significantly while removing the external power supply. This interface circuit is composed of two stages. The first stage voltage doubler uses a positive feedback control loop to improve considerably the conversion speed and efficiency, and boost the output voltage. The second stage active diode adopts a common-grid operational amplifier (op-amp) to remove the influence of offset voltage in the traditional comparator, which eliminates leakage current and broadens bandwidth with low power consumption. The system supplies itself with the harvested energy, which enables it to enter the zero standby mode near the zero crossing points of the input current. Thereafter, high system efficiency and stability are achieved, which saves power consumption. The validity and feasibility of this design is verified by the simulation results based on the 65 nm CMOS process. The minimum input voltage is down to 0.3 V, the maximum voltage efficiency is 99.6% with a DC output current of 75.6 μA, the maximum power efficiency is 98.2% with a DC output current of 40.4 μA, and the maximum output power is 60.48 μW. The power loss of the entire interface circuit is only 18.65 μW, among which, the op-amp consumes only 2.65 μW.
Impact of high‐intensity interval training with or without l‐citrulline on physical performance, skeletal muscle, and adipose tissue in obese older adults

Aging is associated with a progressive decline in skeletal muscle mass and strength as well as an increase in adiposity. These changes may have devastating impact on the quality of life of older adults. Mitochondrial dysfunctions have been implicated in aging‐related and obesity‐related deterioration of muscle function. Impairments in mitochondrial quality control processes (biogenesis, fusion, fission, and mitophagy) may underlie this accumulation of mitochondrial dysfunction. High‐intensity interval training (HIIT) was shown to improve muscle and mitochondrial function in healthy young and old adults and to improve body composition in obese older adults. Recent studies also positioned citrulline (CIT) supplementation as a promising intervention to counter obesity‐related and aging‐related muscle dysfunction. In the present study, our objectives were to assess whether HIIT, alone or with CIT, improves muscle function, functional capacities, adipose tissue gene expression, and mitochondrial quality control processes in obese older adults.
Antimicrobial peptides: A plausible approach for COVID-19 treatment

ABSTRACT Introduction Coronavirus disease 2019 (COVID-19), which emerged as a major public health threat, has affected >400 million people globally leading to >5 million mortalities to date. Treatments of COVID-19 are still to be developed as the available therapeutic approaches are not able to combat the virus causing the disease (severe acute respiratory syndrome coronavirus-2; SARS-CoV-2) satisfactorily. However, antiviral peptides (AVPs) have demonstrated prophylactic and therapeutic effects against many coronaviruses (CoVs). Areas covered This review critically discusses various types of AVPs evaluated for the treatment of COVID-19 along with their mechanisms of action. Furthermore, the peptides inhibiting the entry of the virus by targeting its binding to angiotensin-converting enzyme 2 (ACE2) or integrins, fusion mechanism as well as activation of proteolytic enzymes (cathepsin L, transmembrane serine protease 2 (TMPRSS2), or furin) are also discussed. Expert opinion Although extensively investigated, successful treatment of COVID-19 is still a challenge due to emergence of virus mutants. Antiviral peptides are anticipated to be blockbuster drugs for the management of this serious infection because of their formulation and therapeutic advantages. Although they may act on different pathways, AVPs having a multi-targeted approach are considered to have the upper hand in the management of this infection.
Technology-Based Interventions to Promote the HIV Preexposure Prophylaxis (PrEP) Care Continuum: Protocol for a Systematic Review

Background Preexposure prophylaxis (PrEP) is a promising biomedical intervention for HIV prevention. Researchers have proposed the PrEP care continuum to guide and evaluate PrEP implementation programs. Technology-based interventions (TBIs) have been widely used in HIV prevention and treatment programs, including for the promotion of the PrEP care continuum. The rapid development of new interventions using technology and electronic health methods emphasizes the need for a review of the effectiveness of these TBIs. Objective The aim of this systematic review is to summarize the effectiveness and acceptability of TBIs used to promote the HIV PrEP care continuum. Methods We will conduct a systematic literature search in PubMed, Embase, MEDLINE, PsycINFO, Web of Science, CINAHL, and the Cochrane Central Register of Controlled Trials following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Only intervention studies (ie, studies meeting the criteria of randomized controlled trials or quasi-experimental studies) evaluating the effectiveness of TBIs will be included. We will search the National Institutes of Health Research Portfolio Online Reporting Tools (NIH RePORT) for interventions involving PrEP. At least 2 reviewers will independently screen and select the studies, extract the data, and evaluate the quality of the studies, and discrepancies will be resolved by a senior author. We will provide a narrative synthesis of the included studies and present details about the study populations, interventions, and PrEP-related outcomes of significance. Results The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021249562). As of August 2021, we have completed the initial search and identified 1213 records. Study screening and data extracting are in progress. We expect the results to be ready by summer 2022. Conclusions The findings of this review will summarize successful experiences and lessons learned from the existing literature and therefore inform the design and implementation of intervention studies for PrEP care promotion. Trial Registration PROSPERO CRD42021249562; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=249562 International Registered Report Identifier (IRRID) DERR1-10.2196/33045