Miracle-dz/UniFAR-FTUs huggingface dataset:
AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very popular due to its adaptive momentum, which tackles the dying gradient problem of SGD. Nevertheless, existing optimizers are still unable to exploit the optimization curvature information efficiently. This paper proposes a new AngularGrad optimizer that considers the behavior of the direction/angle of consecutive gradients. This is the first attempt in the literature to exploit the gradient angular information apart from its magnitude. The proposed AngularGrad generates a score to control the step size based on the gradient angular information of previous iterations. Thus, the optimization steps become smoother as a more accurate step size of immediate past gradients is captured through the angular information. Two variants of AngularGrad are developed based on the use of Tangent or Cosine functions for computing the gradient angular information. Theoretically, AngularGrad exhibits the same regret bound as Adam for convergence purposes. Nevertheless, extensive experiments conducted on benchmark data sets against state-of-the-art methods reveal a superior performance of AngularGrad. The source code will be made publicly available at: https://github.com/mhaut/AngularGrad.
Dynamic Training of Liquid State Machines

Spiking Neural Networks (SNNs) emerged as a promising solution in the field of Artificial Neural Networks (ANNs), attracting the attention of researchers due to their ability to mimic the human brain and process complex information with remarkable speed and accuracy. This research aimed to optimise the training process of Liquid State Machines (LSMs), a recurrent architecture of SNNs, by identifying the most effective weight range to be assigned in SNN to achieve the least difference between desired and actual output. The experimental results showed that by using spike metrics and a range of weights, the desired output and the actual output of spiking neurons could be effectively optimised, leading to improved performance of SNNs. The results were tested and confirmed using three different weight initialisation approaches, with the best results obtained using the Barabasi-Albert random graph method.
Performance of the caries diagnosis feature of intraoral scanners and near-infrared imaging (NIR) technology - A narrative review.

PURPOSE To describe and discuss the benefits and drawbacks of various dental caries diagnostic techniques, including the use of intraoral scanners for caries diagnosis based on near-infrared imaging (NIR) technology. MATERIAL AND METHODS A MEDLINE search from 1980-2023 focused on dental caries diagnostic techniques, emphasizing intraoral scanners using NIR technology. Alternative caries detection methods were also evaluated for their advantages and limitations, enabling a comparison with NIR. The review included traditional caries tools, the latest detection methods, and NIR's role in intraoral scanners, drawing from case reports and both in vivo and in vitro studies. Keywords like "caries detection", "intraoral scanners", and "Near Infrared Imaging (NIRI)" guided the search. After screening titles and abstracts for relevance, full texts with valuable insights were thoroughly analyzed. The data was grouped into three: traditional diagnostics, advanced digital methods, and intraoral scanner-based detection. RESULTS This comprehensive narrative review described and discussed the current state of dental caries diagnostic methods, given the insufficient number of clinical investigations suitable for a systematic review. Traditional caries diagnosis techniques have shown variable accuracy dependent on a dentist's experience and the potential over-removal of healthy tooth structures. Intraoral scanners have emerged as a novel caries detection method, because of their integration of NIR technology. Various studies have confirmed the efficacy of NIR in detecting interproximal caries and in the early diagnosis of non-cavitated caries. Specifically, intraoral scanners have demonstrated promising results, proving comparable to established diagnostic methods like bitewing radiography. Nevertheless, while the integration of NIR into intraoral scanners seems promising, the technology still faces challenges, notably its accuracy in detecting secondary and subgingival cavities. However, with anticipated integrations of AI, NIR in intraoral scanners could revolutionize early caries detection. CONCLUSIONS Intraoral scanners with NIR technology offer non-destructive imaging, real-time lesion visuals, and enhanced patient communication. Although comparable to bitewing radiography in some studies, a universally accepted diagnostic tool is lacking. Future research should compare them with existing methods, focusing on clinical outcomes, cost-effectiveness, and patient acceptance. This article is protected by copyright. All rights reserved.
Microbiome analysis of bile from apparently healthy cats and cats with suspected hepatobiliary disease

Abstract Background Bacterial infection of bile is a common cause of hepatobiliary disease in cats. Whether bile harbors a core microbiota in health or in cases of suspected hepatobiliary disease in cats is unknown. Objectives Establish if gallbladder bile in apparently healthy cats harbors a core microbiota composed of bacterial taxa common to many individuals. Compare results of bile cytology, bile culture, and 16S rRNA gene amplicon sequencing in apparently healthy cats and cats with suspected hepatobiliary disease. Animals Forty‐three client‐owned cats with suspected hepatobiliary disease and 17 control cats. Methods Bile was collected by ultrasound guided cholecystocentesis (cats with suspected hepatobiliary disease) or laparotomy after euthanasia (controls). Bile samples underwent cytologic examination, aerobic and anaerobic culture, and DNA was extracted for 16S rRNA gene amplification and sequencing. Results Microbiome sequencing did not identify a core microbiota in control cats or cats having bile sampled because of clinical suspicion for hepatobiliary disease. Microbiome profiles from control cats were indistinguishable from profiles obtained from sampling instruments and reagents that were not exposed to bile (technical controls). Bacterial taxa that could not be explained by contamination or off‐target amplification were identified only in samples from cats with bactibilia and positive bile culture results for Escherichia coli. In several E. coli positive samples, microbiome sequencing also identified a small number of potentially co‐infecting bacterial genera not identified by culture. Conclusions and Clinical Importance Cat bile does not harbor a core microbiota. Uncultured bacteria may contribute to pathogenesis of hepatobiliary disease in cats with bile E. coli infection.
Comparison of statistical methods for extrapolating survival in previously untreated diffuse large B-cell lymphoma: results based on the POLARIX study

Abstract Objective The ongoing Phase III randomized POLARIX study (GO39942; NCT03274492) demonstrated significantly improved progression-free survival (PFS) with polatuzumab vedotin plus rituximab, cyclophosphamide, doxorubicin and prednisone (Pola-R-CHP) versus rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with previously untreated diffuse large B-cell lymphoma (DLBCL). We compared statistical methodologies to extrapolate long-term PFS data from POLARIX. Materials and methods This analysis explored four different approaches to extrapolate the POLARIX data: standard parametric survival, mixture-cure, landmark, and spline models. The resulting extrapolation curves were validated via comparison with the corresponding Kaplan–Meier (KM) curves from POLARIX and the POLARIX-like population of the Phase III GOYA study (NCT01287741; R-CHOP arm). Results The R-CHOP PFS KM curve from the GOYA validation set was well aligned with the POLARIX KM curve. As we anticipated that PFS in POLARIX would evolve similarly to that of GOYA, the data from GOYA were used to externally validate the extrapolated modelling results. While all four statistical methods were able to fit the data to the POLARIX KM curve, the mixture-cure model was the most accurate in predicting long-term PFS in the GOYA external validation set. In the mixture-cure model, generalized gamma distribution estimated 64% (95% confidence intervals [CI]: 56–71%) of patients to have long-term remission in the R-CHOP arm of POLARIX and GOYA, and 75% (95% CI: 70–79%) in the Pola-R-CHP arm of POLARIX. A limitation of this study was the comparison of the statistical models only in the PFS KM curves, since it was not possible to determine which statistical method was more appropriate to extrapolate the overall survival KM curves. Conclusions Within this analysis, the mixture-cure model provided the best prediction of long-term outcomes from the primary PFS analysis of the POLARIX study.
When will RNA get its AlphaFold moment?

Abstract The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.
Vanquishing the computational cost of passive gamma emission tomography simulations leveraging physics-aware reduced order modeling

Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and determine potential diversion. The analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive and limited, so Monte Carlo simulations are used to augment them. However, Monte Carlo simulations have a high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science. With the aim to create a large dataset of PGET simulated scenarios, we addressed the computational cost of Monte Carlo simulations by developing a physics-aware reduced order modeling approach. This approach combines a small subset of the 360 angular views (limited views approach) with a computationally inexpensive proxy solution (real-time forward model) that brings the essence of the physics to obtain a real-time high-fidelity solution at all angular views but at a fraction of the computational cost. The method’s ability to reconstruct 360 views with accuracy from a limited set of angular views is demonstrated by testing its performance for different types of reactor fuel assemblies.
Sugar-sweetened beverage intake and convenience store shopping as mediators of the food insecurity–Tooth decay relationship among low-income children in Washington state

Introduction There are oral health disparities in the U.S. and children in food-insecure households have a higher burden of tooth decay. Identifying the mechanisms underlying the food insecurity–tooth decay relationship could inform public health interventions. This study examined how sugar-sweetened beverage (SSB) intake and frequent convenience store shopping mediated the food insecurity–tooth decay relationship for lower-income children. Materials and methods Cross-sectional study data included a household survey, beverage questionnaire, and dental examination. The sample included 452 lower-income, racially-diverse, child-caregiver dyads in 2018 from King County in Washington state. The exposure was household food insecurity, the outcome was untreated decayed tooth surfaces, and the proposed mediators were SSB intake and frequent convenience store shopping (≥2 times/week). Causal mediation analyses via the potential outcomes framework was used to estimate natural indirect and direct effects. Results Fifty-five percent of participants were in food-insecure households, the mean number of decayed tooth surfaces among children was 0.87 (standard deviation [SD] = 1.99), the mean SSB intake was 17 fluid ounces (fl/oz)/day (SD = 35), and 18% of households frequently shopped at a convenience store. After adjusting for confounders, household food insecurity and log-transformed SSB intake (fluid ounces/day) were positively associated with decayed tooth surfaces, but not at the a α = 0.05 level (mean ratio [MR] 1.60; 95% confidence interval [CI] 0.89, 2.88; p = .12 and MR 1.16; 95% CI 0.93, 1.46; p = .19, respectively). Frequent convenience store shopping was associated with 2.75 times more decayed tooth surfaces (95% CI 1.61, 4.67; p < .001). SSB intake mediated 10% of the food insecurity–tooth decay relationship (p = .35) and frequent convenience store shopping mediated 22% (p = .33). Conclusions Interventions aimed at addressing oral health disparities in children in food-insecure households could potentially focus on reducing intake of SSBs and improving access to healthful foods in lower-income communities.
The combination of quercetin and leucine synergistically improves grip strength by attenuating muscle atrophy by multiple mechanisms in mice exposed to cisplatin

Both quercetin and leucine have been shown to exert moderately beneficial effects in preventing muscle atrophy induced by cancers or chemotherapy. However, the combined effects of quercetin and leucine, as well as the possible underlying mechanisms against cisplatin (CDDP)-induced muscle atrophy and cancer-related fatigue (CRF) remain unclear. To investigate the issues, male BALB/c mice were randomly assigned to the following groups for 9 weeks: Control, CDDP (3 mg/kg/week), CDDP+Q (quercetin 200 mg/kg/day administrated by gavage), CDDP+LL (a diet containing 0.8% leucine), CDDP+Q+LL, CDDP+HL (a diet containing 1.6% leucine), and CDDP+Q+HL. The results showed that quercetin in combination with LL or HL synergistically or additively attenuated CDDP-induced decreases in maximum grip strength, fat and muscle mass, muscle fiber size and MyHC level in muscle tissues. However, the combined effects on locomotor activity were less than additive. The combined treatments decreased the activation of the Akt/FoxO1/atrogin-1/MuRF1 signaling pathway (associated with muscle protein degradation), increased the activation of the mTOR and E2F-1 signaling pathways (associated with muscle protein synthesis and cell cycle/growth, respectively). The combined effects on signaling molecules present in muscle tissues were only additive or less. In addition, only Q+HL significantly increased glycogen levels compared to the CDDP group, while the combined treatments considerably decreased CDDP-induced proinflammatory cytokine and MCP-1 levels in the triceps muscle. Using tumor-bearing mice, we demonstrated that the combined treatments did not decrease the anticancer effect of CDDP. In conclusion, this study suggests that the combination of quercetin and leucine enhanced the suppressed effects on CDDP-induced muscle weakness and CRF through downregulating muscle atrophy and upregulating the glycogen level in muscle tissues without compromising the anticancer effect of CDDP. Multiple mechanisms, including regulation of several signaling pathways and decrease in proinflammatory mediator levels in muscles may contributed to the enhanced protective effect of the combined treatments on muscle atrophy.
Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACT Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.
Ingroup Empathy, Help, and Blame After Anti-LGBT+ Hate Crime

Crimes motivated by hatred toward a person’s sexual orientation or gender identity typically cause greater physical and emotional harm than comparative crimes not motivated by hate. Compounding these impacts, hate crime victims receive less empathy, less support, and are blamed more for their victimization both by society in general and by criminal justice agencies. However, as hate crimes are the epitome of intergroup hostility, the crimes are also likely to engender an ingroup empathy bias in which fellow LGBT+ people provide greater empathy to hate crime victims, potentially motivating greater support and reducing victim blaming for these particularly marginalized victims. Across three studies, we examined LGBT+ participants’ empathic reactions to hate crime victims, along with their willingness to help victims and blame victims. In the Pilot Study (N = 131) and Study 1 (N = 600), we cross-sectionally showed that indirect experiences of hate crimes predicted a stronger LGBT+ identity which, in turn, was associated with greater empathy that predicted greater willingness to help victims and blame the victim less. In Study 2 (N = 657), we experimentally manipulated the motivation of a crime (hate vs. non-hate) and the group membership of the victim (ingroup-LGBT+ vs. outgroup-heterosexual) and found that crimes that had one or more group elements (i.e., involved an ingroup member and/or was motivated by hate) elicited greater empathy that, in turn, increased the willingness to help the victim and reduced victim blaming. Together, the findings provide cogent evidence that LGBT+ communities respond to anti-LGBT+ hate crimes with overwhelming empathy, and this ingroup empathy bias motivates helping behaviors and reduces victim blame, thereby buffering the marginalizing consequences of hate crimes. Policy implications include acknowledging and harnessing the importance of shared identities when practitioners and criminal justice agencies respond to anti-LGBT+ hate crimes.
Ground-Level Ozone Across Kentucky: Modeling and a Synoptic Analysis of High Concentrations

Rural areas are often more susceptible to high concentrations of ground-level ozone (O3) than urban areas. However, rural populations are, for the most part, unaware of this problem. Currently the rural areas of Kentucky have no daily forecast for O3. This research addresses the issue by using methodologies from previous Kentucky O3 modeling research to develop a daily forecast model within Geographic Information Systems. The rural O3 model developed by Kendrick (2005) will be used in this research, as a Standard model, along with an application of the model introduced by Cobourn and Hubbard (1999), as the Hi model, to be used on days that O3 concentrations are expected. When the forecasted maximum temperature is less (greater) than 87°F, the diurnal temperature range is less (greater) than 27, and the probability for precipitation is less (greater) than 50 percent then the Hybrid will choose the Standard (Hi) model. Data for the both models came from the Model Output Statistic by the National Weather Service. The Standard model proves to be successful in forecasting O3 while the Hi model is less accurate. Synoptic meteorology conditions were analyzed to find patterns that are associated with high O3 concentrations for rural areas. Data collected at Mammoth Cave National
The genetic basis of plants’ battle against witchweeds: linking immune responses to distinct resistance mechanisms

This article comments on: Mutinda S, Mobegi FM, Hale B, Dayou O, Ateka E, Wijeratne A, Wicke S, Bellis ES, Runo S. 2023. Resolving intergenotypic Striga resistance in sorghum. Journal of Experimental Botany 74, 5294–5306.
A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study

Abstract Background Previous studies demonstrated limited accuracy of existing guidelines for predicting choledocholithiasis, leading to overutilization of endoscopic retrograde cholangiopancreatography (ERCP). More accurate stratification may improve patient selection for ERCP and allow use of lower-risk modalities. Methods A machine learning model was developed using patient information from two published cohort studies that evaluated performance of guidelines in predicting choledocholithiasis. Prediction models were developed using the gradient boosting model (GBM) machine learning method. GBM performance was evaluated using 10-fold cross-validation and area under the receiver operating characteristic curve (AUC). Important predictors of choledocholithiasis were identified based on relative importance in the GBM. Results 1378 patients (mean age 43.3 years; 61.2% female) were included in the GBM and 59.4% had choledocholithiasis. Eight variables were identified as predictors of choledocholithiasis. The GBM had accuracy of 71.5% (SD 2.5%) (AUC 0.79 [SD 0.06]) and performed better than the 2019 American Society for Gastrointestinal Endoscopy (ASGE) guidelines (accuracy 62.4% [SD 2.6%]; AUC 0.63 [SD 0.03]) and European Society of Gastrointestinal Endoscopy (ESGE) guidelines (accuracy 62.8% [SD 2.6%]; AUC 0.67 [SD 0.02]). The GBM correctly categorized 22% of patients directed to unnecessary ERCP by ASGE guidelines, and appropriately recommended as the next management step 48% of ERCPs incorrectly rejected by ESGE guidelines. Conclusions A machine learning-based tool was created, providing real-time, personalized, objective probability of choledocholithiasis and ERCP recommendations. This more accurately directed ERCP use than existing ASGE and ESGE guidelines, and has the potential to reduce morbidity associated with ERCP or missed choledocholithiasis.