Miracle-dz/UniFAR-FTUs huggingface dataset:
Raman spectroscopy–based water measurements identify the origin of MRI T2 signal in human articular cartilage zones and predict histopathologic score

We investigated for the first time zonal‐dependent water distribution in articular cartilage by Raman spectroscopy (RS). We further investigated the association of histopathologic score with RS‐ and magnetic resonance imaging (MRI)‐based water measurements. Cadaveric human cartilage plugs (N = 16) with different osteoarthritis (OA) severity were used. Water content distribution in cartilage zones was probed using RS‐ and MRI‐based techniques. Histopathologic scoring was performed by two independent observers blindly. Moderate associations existed between RS‐ and MRI‐based water measurements across all cartilage zones. RS‐based analysis of different water compartments helped assign the origin of the T2 signal collected from the various cartilage zones. RS‐based water parameters significantly correlated with OA‐severity score, whereas MRI‐based water measurements did not. RS can probe different water compartments in cartilage zones and predict up to 66% of the variation observed in the histopathologic score. RS‐based water measurement could be developed further to assess cartilage quality in the clinic.
Glioblastoma Cell–Derived lncRNA-Containing Exosomes Induce Microglia to Produce Complement C5, Promoting Chemotherapy Resistance

The authors find glioblastoma cells transfer the long noncoding RNA lnc-TALC to microglia via exosomes. Lnc-TALC activates an ENO1/p38 MAPK pathway that results in C5 production, which promotes chemotherapy resistance. The data identify novel therapeutic strategies for glioblastoma. Glioblastoma (GBM), the most common malignant primary brain cancer in adults, nearly always becomes resistant to current treatments, including the chemotherapeutic temozolomide (TMZ). The long noncoding RNA (lncRNA) TMZ-associated lncRNA in GBM recurrence (lnc-TALC) promotes GBM resistance to TMZ. Exosomes can release biochemical cargo into the tumor microenvironment (TME) or transfer their contents, including lncRNAs, to other cells as a form of intercellular communication. In this study, we found that lnc-TALC could be incorporated into exosomes and transmitted to tumor-associated macrophages (TAM) and could promote M2 polarization of the microglia. This M2 polarization correlated with secretion of the complement components C5/C5a, which occurred downstream of lnc-TALC binding to ENO1 to promote the phosphorylation of p38 MAPK. In addition, C5 promoted the repair of TMZ-induced DNA damage, leading to chemotherapy resistance, and C5a-targeted immunotherapy showed improved efficacy that limited lnc-TALC–mediated TMZ resistance. Our results reveal that exosome-transmitted lnc-TALC could remodel the GBM microenvironment and reduce tumor sensitivity to TMZ chemotherapy, indicating that the lnc-TALC–mediated cross-talk between GBM cells and microglia could attenuate chemotherapy efficacy and pointing to potential combination therapy strategies to overcome TMZ resistance in GBM. See related Spotlight by Zhao and Xie, p. 1372.
Psychosocial factors related to Cardiovascular Disease Risk in Young African American Women: a systematic review

ABSTRACT Objective African American women are exposed to multiple adverse psychosocial factors, including racism, discrimination, poverty, neighborhood stress, anxiety, and depression. The impact of these psychosocial factors on cardiovascular disease (CVD) risk in women during early adulthood is limited. This review aims to summarize and synthesize the recent literature on psychosocial factors related to CVD risk in young African American women. Methods We conducted a comprehensive search of the literature in PubMed, APA PsycINFO, and CINAHL. We systematically reviewed the literature for studies examining associations between psychosocial factors (e.g. racism, discrimination, neighborhood stress, anxiety) and CVD risk factors (e.g. body mass index [BMI], blood pressure, diabetes) in African American women age 19–24 years. Eligible studies measured at least one psychosocial factor, a CVD risk factor, and included young adult African American women (age 19–24) or reported sex-stratified analyses. Results We identified nine studies that met our inclusion criteria: six cross-sectional and three longitudinal studies. Of these, eight studies reported that psychosocial factors (i.e. perceived stress, racial discrimination, internalized racism, depression) are related to higher BMI and blood pressure. The majority of studies were conducted among college students or had a small sample size (<200). The quality of six studies was rated as excellent; the remainder were good and fair. Conclusions Findings from this review suggest that exposure to adverse psychosocial factors may be related to increased CVD risk in early adulthood (age 19–24) in African American women. However, larger prospective analyses are necessary to examine these associations.
Barriers to Cancer Care in Northern Tanzania: Patient and Health-System Predictors for Delayed Presentation

PURPOSE Cancer is a growing problem in Africa, and delays in receiving timely cancer care often results in poorer outcomes. The purpose of this study was to identify the patient and health-system factors associated with delayed cancer care in adults living in the Northern Zone of Tanzania. PATIENTS AND METHODS Between July 2018 and July 2019, we surveyed adult patients presenting to an oncology clinic in Northern Tanzania. Delayed presentation was defined as 12 weeks or longer from initial symptoms to presentation for cancer care. Multivariate logistic regression and adjusted relative risk (aRR) were used to identify factors predicting delayed presentation. RESULTS Among 244 adult patients with cancer who completed the survey, 78% (n = 191) had delayed presentation. Patient-related factors associated with delayed presentation included lower educational attainment (P = .03), increased travel time (P = .05), lack of cancer knowledge (P < .05), and fear of cancer and cancer treatments (P < .05) on multivariate analysis. On analysis of aRR, patients without private car and those with health insurance had higher risk of delayed presentation (aRR: 1.27; 95% CI, 1.02 to 1.32 and aRR: 1.15; 95% CI, 1.01 to 1.32). There was a strong association with increased number of visits before presentation at the cancer center and delayed presentation (P = .0009). CONCLUSION Cancer awareness and prevention efforts targeting patients and community-level health care workers are key to reduce delays in cancer care in Northern Tanzania.
Multi-Level Optimization of an Ultra-Low Power BrainWave System for Non-Convulsive Seizure Detection

We present a systematic evaluation and optimization of a complex bio-medical signal processing application on the BrainWave prototype system, targeted towards ambulatory EEG monitoring within a tiny power budget of <inline-formula><tex-math notation="LaTeX">$< $</tex-math></inline-formula>1 mW. The considered BrainWave processor is completely programmable, while maintaining energy-efficiency by means of a Coarse-Grained Reconfigurable Array (CGRA). This is demonstrated through the mapping and evaluation of a state-of-the-art non-convulsive epileptic seizure detection algorithm, while ensuring real-time operation and seizure detection accuracy. Exploiting the CGRA leads to an energy reduction of 73.1%, compared to a highly tuned software implementation (SW-only). A total of 9 complex kernels were benchmarked on the CGRA, resulting in an <italic>average</italic> 4.7 × speedup and <italic>average</italic> 4.4 × energy savings over highly tuned SW-only implementations. The BrainWave processor is implemented in 28-nm FDSOI technology with 80 kB of Foundry-provided SRAM. By exploiting near-threshold computing for the logic and voltage-stacking to minimize on-chip voltage-conversion overhead, additional 15.2% and 19.5% energy savings are obtained, respectively. At the Minimum-Energy-Point (MEP) (223 <inline-formula><tex-math notation="LaTeX">$\mu \rm {W}$</tex-math></inline-formula>, 8 MHz) we report a measured state-of-the-art 90.6% system conversion efficiency, while executing the epileptic seizure detection in real-time.
Positive selection in noncoding genomic regions of vocal learning birds is associated with genes implicated in vocal learning and speech functions in humans

Vocal learning, the ability to imitate sounds from conspecifics and the environment, is a key component of human spoken language and learned song in three independently evolved avian groups—oscine songbirds, parrots, and hummingbirds. Humans and each of these three bird clades exhibit specialized behavioral, neuroanatomical, and brain gene expression convergence related to vocal learning, speech, and song. To understand the evolutionary basis of vocal learning gene specializations and convergence, we searched for and identified accelerated genomic regions (ARs), a marker of positive selection, specific to vocal learning birds. We found avian vocal learner-specific ARs, and they were enriched in noncoding regions near genes with known speech functions or brain gene expression specializations in humans and vocal learning birds, including FOXP2, NEUROD6, ZEB2, and MEF2C, and near genes with major neurodevelopmental functions, including NR2F1, NRP2, and BCL11B. We also found enrichment near the SFARI class S genes associated with syndromic vocal communication forms of autism spectrum disorders. These findings reveal strong candidate noncoding regions near genes for the evolutionary adaptations that distinguish vocal learning species from their close vocal nonlearning relatives and provide further evidence of molecular convergence between birdsong and human spoken language.
Sleep Duration, Sleep Quality, and the Development of Nonalcoholic Fatty Liver Disease: A Cohort Study

INTRODUCTION: The longitudinal relationship between sleep duration, sleep quality, and the risk of nonalcoholic fatty liver disease (NAFLD) is unknown. We aimed to examine the association between sleep duration, sleep quality, and NAFLD development. METHODS: Using the Pittsburgh Sleep Quality Index, sleep duration and quality were evaluated for 143,306 NAFLD-free Korean adults with a mean age of 36.6 years, who were followed for an average of 4.0 years. Hepatic steatosis (HS) was assessed using ultrasonography and liver fibrosis by the fibrosis-4 index (FIB-4) or the NAFLD fibrosis score. Flexible parametric proportional hazard models were used to determine the hazard ratios (HRs) and 95% confidence intervals. RESULTS: There were 27,817 subjects with incident HS, of whom 1,471 had incident HS plus intermediate/high FIB-4. Multivariable-adjusted HRs (95% confidence intervals) for incident HS comparing sleep durations of ≤5, 6, 8, and ≥ 9 hours with 7 hours were 1.19 (1.14–1.23), 1.07 (1.04–1.10), 0.98 (0.94–1.02), and 0.95 (0.87–1.03), respectively. The corresponding HRs for incident HS plus intermediate/high FIB-4 were 1.30 (1.11–1.54), 1.14 (1.01–1.29), 1.11 (0.93–1.33), and 1.08 (0.71–1.63). The association between sleep duration and HS plus intermediate/high FIB-4 was inverse in individuals with good sleep quality but tended to be U-shaped in those with poor sleep quality. The results were similar if FIB-4 was replaced by the NAFLD fibrosis score. DISCUSSION: In young adults, short sleep duration was independently associated with an increased risk of incident NAFLD with or without intermediate/high fibrosis score, suggesting a role for inadequate sleep quantity in NAFLD risk and severity.
Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast‐Enhanced MRI

Imaging‐driven deep learning strategies focus on training from scratch and transfer learning. However, the performance of training from scratch is often impeded by the lack of large‐scale labeled training data. Additionally, owing to the differences between source and target domains, analyzing medical image tasks satisfactorily via transfer learning based on ImageNet is difficult.
Interferon Alpha, but Not Interferon Beta, Acts Early To Control Chronic Chikungunya Virus Pathogenesis

Chikungunya virus (CHIKV) is a reemerging global pathogen with no effective vaccine or antiviral treatment for acute or chronic disease, and the mechanisms underlying chronic disease manifestations remain poorly defined. The significance of our research is in defining IFN-α, but not IFN-β, as an important host regulator of chronic CHIKV pathogenesis that acts within the first 48 h of infection to limit persistent viral RNA and the number of cells that survive CHIKV infection 1 month postinfection. ABSTRACT Chikungunya virus (CHIKV) is an arthritogenic alphavirus that causes both debilitating acute and chronic disease. Previous work has shown that type I interferons (IFNs) play a critical role in limiting CHIKV pathogenesis and that interferon alpha (IFN-α) and interferon beta (IFN-β) control acute CHIKV infection by distinct mechanisms. However, the role of type I IFNs, especially specific subtypes, during chronic CHIKV disease is unclear. To address this gap in knowledge, we evaluated chronic CHIKV pathogenesis in mice lacking IFN-α or IFN-β. We found that IFN-α was the dominant subtype that controls chronic disease. Despite detecting a varying type I IFN response throughout the course of disease, IFN-α acts within the first few days of infection to control the levels of persistent CHIKV RNA. In addition, using a novel CHIKV-3′-Cre tdTomato reporter system that fate maps CHIKV-infected cells, we showed that IFN-α limits the number of cells that survive CHIKV at sites of dissemination, particularly dermal fibroblasts and immune cells. Though myofibers play a significant role in CHIKV disease, they were not impacted by the loss of IFN-α. Our studies highlight that IFN-α and IFN-β play divergent roles during chronic CHIKV disease through events that occur early in infection and that not all cell types are equally dependent on type I IFNs for restricting viral persistence. IMPORTANCE Chikungunya virus (CHIKV) is a reemerging global pathogen with no effective vaccine or antiviral treatment for acute or chronic disease, and the mechanisms underlying chronic disease manifestations remain poorly defined. The significance of our research is in defining IFN-α, but not IFN-β, as an important host regulator of chronic CHIKV pathogenesis that acts within the first 48 hours of infection to limit persistent viral RNA and the number of cells that survive CHIKV infection 1 month post-infection. Loss of IFN-α had a greater impact on immune cells and dermal fibroblasts than myofibers, highlighting the need to delineate cell-specific responses to type I IFNs. Altogether, our work demonstrates that very early events of acute CHIKV infection influence chronic disease. Continued efforts to delineate early host-pathogen interactions may help stratify patients who are at risk for developing chronic CHIKV symptoms and identify therapeutics that may prevent progression to chronic disease altogether.
Brain mechanisms of pain and dysautonomia in diabetic neuropathy: connectivity changes in thalamus and hypothalamus.

CONTEXT About a third of diabetic patients suffer from neuropathic pain, which is poorly responsive to analgesic therapy and associated with greater autonomic dysfunction. Previous research on diabetic neuropathy mainly links pain and autonomic dysfunction to peripheral nerve degeneration due to systemic metabolic disturbances, but maladaptive plasticity in the central pain and autonomic systems following peripheral nerve injury has been relatively ignored. OBJECTIVE This study aimed to investigate how the brain is affected in painful diabetic neuropathy (PDN), in terms of altered structural connectivity (SC) of the thalamus and hypothalamus that are key regions modulating nociceptive and autonomic responses. METHODS We recruited 25 painful (PDN) and 13 painless (PLDN) diabetic neuropathy patients, and 27 healthy adults as controls. The SC of the thalamus and hypothalamus with limbic regions mediating nociceptive and autonomic responses was assessed using diffusion tractography. RESULTS The PDN patients had significantly lower thalamic and hypothalamic SC of the right amygdala compared with the PLDN and control groups. In addition, lower thalamic SC of the insula was associated with more severe peripheral nerve degeneration, and lower hypothalamic SC of the anterior cingulate cortex was associated with greater autonomic dysfunction manifested by decreased heart rate variability. CONCLUSION Our findings indicate that alterations in brain structural connectivity could be a form of maladaptive plasticity after peripheral nerve injury, and also demonstrate a pathophysiological association between disconnection of the limbic circuitry and pain and autonomic dysfunction in diabetes.
Racial/ethnic, social characteristics and geographic disparities of childhood cancer late-stage diagnosis in Texas, 2005 to 2014

ABSTRACT This study investigated childhood cancer disparities in the state of Texas based on data from 1995 to 2014 from the perspective of race/ethnicity, geographic location, and social factors. The enhanced 2-step floating catchment area (E2SFCA) method was used to measure relative spatial access to Children’s Oncology Group (COG) hospitals. This study also examined the effect of spatial access to specialized COG services along with other variables in explaining the variations of late-stage diagnosis of childhood cancer. Multilevel logistic regression was used to analyse how individual- and contextual-level factors affect the occurrence of childhood cancer disparities (i.e. late-stage diagnosis). The study revealed that Hispanic children were more likely to be diagnosed at a late-stage, after adjusting for age, race/ethnicity, socioeconomic status (SES), socio-culture, education, spatial access to COG hospitals, percent African American, and health insurance coverage. The study also identified that the childhood cancer stage at diagnosis is associated with spatial access to COG services as well as levels of urbanization. Moreover, findings indicate that contextual-level factors, such as SES, socio-cultural factors, education level, and percent health insurance coverage partially explained some of the childhood cancer disparities. Results from this study will contribute to developing more effective and targeted childhood cancer intervention programs in socially underprivileged areas, focusing on population with lower socioeconomic status and lower education levels, limited English-speaking households, areas with a higher percentage of Hispanics and African Americans, locations with a low level of spatial access to COG services.
Experiences of meaningful connection in the first weeks of the COVID-19 pandemic

This qualitative study explored the question, “What kinds of interactions did people experience as meaningful connections with others during the first weeks of the COVID-19 pandemic in the Unites States? ” Eighty-eight participants completed an online survey, 3 to 7 weeks following the World Health Organization’s declaration of global pandemic. Participants completed open-ended questions about social interactions they experienced as meaningful connections with people of three levels of familiarity. Thematic analysis of participant responses identified four overarching themes: openness to the other, affirmation of the self, emotional uplift, and meeting of basic needs. A secondary research question explored, “In what ways did people perceive the onset of the COVID-19 pandemic as affecting how meaningful connections were motivated, enacted, and experienced? ” The context of the pandemic enhanced the meaning of social connection, offered a common struggle to connect over, and motivated prosociality. This study offers a descriptive window into the interactions experienced as meaningful connections in the first weeks of the COVID-19 pandemic and may usefully inform future research and applied work promoting social connections in current and future collective crises.
Automated reasoning tools in GeoGebra discovery

We present some current achievements in the software package GeoGebra Discovery that provides several symbolic tools and commands to mechanically discover (and verify symbolically) relationships on planar geometry constructions. Our presentation includes the novel Discover tool and command, the Relation tool and command, and the Compare command. Our proposal successfully makes the cycle 'conjecturing-checking-proving' in elementary geometry even more accessible for general users, focusing not only on educational uses but research as well.
lunule: An Agile and Judicious Metadata Load Balancer for CephFS

For a decade, the Ceph distributed file system (CephFS) has been widely used to serve the ever-growing big data in many key fields ranging from Internet services to AI computing. To scale out the massive metadata access, CephFS adopts a dynamic subtree partitioning method, splitting the hierarchical namespace and distributing subtrees across multiple metadata servers. However, this method suffers from a severe imbalance problem that may result in poor performance due to its inaccurate imbalance prediction, ignorance of workload characteristics, and unnecessary/invalid migration ac-tivities. To eliminate these inefficiencies, we propose Lunule, a novel CephFS metadata load balancer, which employs an imbalance fac-tor model for accurately determining when to trigger re-balance and tolerate benign imbalanced situations. Lunule further adopts a workload-aware migration planner to appropriately select sub-tree migration candidates. Compared to baselines, Lunule achieves better load balance, increases the metadata throughput by up to 315.8%, and shortens the tail job completion time by up to 64.6% for five real-world workloads and their mixture, respectively. Be-sides, Lunule is capable of handling the metadata cluster expansion and the client workload growth, and scales linearly on a cluster of 16 MDSs.
STM-Multifrontal QR: Streaming Task Mapping Multifrontal QR Factorization Empowered by GCN

Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse matrix. In this work, a graph convolutional network (GCN) for adaptively selecting the optimal reordering algorithm is proposed in symbolic analysis. Using our GCN adaptive classifier, the average numerical factorization time is reduced by 20.78% compared with the default approach, and the additional memory overhead is approximately 4% higher than that of prior work. Moreover, for numerical factorization, an optimized tasks stream parallel processing strategy is proposed and a more efficient computing task mapping framework for NUMA architecture is adopted in this paper, which called STM-Multifrontal QR factorization. Numerical experiments on the TaiShan Server show average 1.22x performance gains over the original SuiteSparseQR. Nearly 80% of datasets have achieved better performance compared with the MKL sparse QR on Intel Xeon 6248.