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The formation of passivation films by interfacial reactions, though critical for applications ranging from advanced alloys to electrochemical energy storage, is often poorly understood. In this work, we explore the formation of an exemplar passivation film, the solid-electrolyte interphase (SEI), which is responsible for stabilizing lithium-ion batteries. Using stochastic simulations based on quantum chemical calculations and data-driven chemical reaction networks, we directly model competition between SEI products at a mechanistic level for the first time. Our results recover the Peled-like separation of the SEI into inorganic and organic domains resulting from rich reactive competition without fitting parameters to experimental inputs. By conducting accelerated simulations at elevated temperature, we track SEI evolution, confirming the postulated reduction of lithium ethylene monocarbonate to dilithium ethylene monocarbonate and H2. These findings furnish fundamental insights into the dynamics of SEI formation and illustrate a path forward toward a predictive understanding of electrochemical passivation.

The conductive polyelectrolyte complex poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is ubiquitous in research dealing with organic electronic devices (e.g., solar cells, wearable and implantable sensors, and electrochemical transistors). In many bioelectronic applications, the applicability of commercially available formulations of PEDOT:PSS (e.g., Clevios) is limited by its poor mechanical properties. Additives can be used to increase the compliance but pose a risk of leaching, which can result in device failure and increased toxicity (in biological settings). Thus, to increase the mechanical compliance of PEDOT:PSS without additives, we synthesized a library of intrinsically stretchable block copolymers. In particular, controlled radical polymerization using a reversible addition-fragmentation transfer process was used to generate block copolymers consisting of a block of PSS (of fixed length) appended to varying blocks of poly(poly(ethylene glycol) methyl ether acrylate) (PPEGMEA). These block copolymers (PSS(1)-b-PPEGMEA(x), where x ranges from 1 to 6) were used as scaffolds for oxidative polymerization of PEDOT. By increasing the lengths of the PPEGMEA segments on the PEDOT:[PSS(1)-b-PPEGMEA(1-6)] block copolymers, ("Block-1" to "Block-6"), or by blending these copolymers with PEDOT:PSS, the mechanical and electronic properties of the polymer can be tuned. Our results indicate that the polymer with the longest block of PPEGMEA, Block-6, had the highest fracture strain (75%) and lowest elastic modulus (9.7 MPa), though at the expense of conductivity (0.01 S cm-1). However, blending Block-6 with PEDOT:PSS to compensate for the insulating nature of the PPEGMEA resulted in increased conductivity [2.14 S cm-1 for Blend-6 (2:1)]. Finally, we showed that Block-6 outperforms a commercial formulation of PEDOT:PSS as a dry electrode for surface electromyography due to its favorable mechanical properties and better adhesion to skin.

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem-classifying reactions into distinct families-and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets, as well as those based on reaction fingerprints derived from masked language modelling. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels.

Riboswitches are structured non-coding RNAs often located upstream of essential genes in bacterial messenger RNAs. Such RNAs regulate expression of downstream genes by recognizing a specific cellular effector. Although nearly 50 riboswitch classes are known, only a handful recognize multiple effectors. Here, we report the 2.60-Å resolution co-crystal structure of a class I type I preQ1-sensing riboswitch that reveals two effectors stacked atop one another in a single binding pocket. These effectors bind with positive cooperativity in vitro and both molecules are necessary for gene regulation in bacterial cells. Stacked effector recognition appears to be a hallmark of the largest subgroup of preQ1 riboswitches, including those from pathogens such as Neisseria gonorrhoeae. We postulate that binding to stacked effectors arose in the RNA World to closely position two substrates for RNA-mediated catalysis. These findings expand known effector recognition capabilities of riboswitches and have implications for antimicrobial development.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Excessive calorie intake leads to mitochondrial overload and triggers metabolic inflexibility and insulin resistance. In this study, we examined how attenuated p38α activity affects glucose and fat metabolism in the skeletal muscles of mice on a high-fat diet (HFD). Mice exhibiting diminished p38α activity (referred to as p38αAF) gained more weight and displayed elevated serum insulin levels, as well as a compromised response in the insulin tolerance test, compared to the control mice. Additionally, their skeletal muscle tissue manifested impaired insulin signaling, leading to resistance in insulin-mediated glucose uptake. Examination of muscle metabolites in p38αAF mice revealed lower levels of glycolytic intermediates and decreased levels of acyl-carnitine metabolites, suggesting reduced glycolysis and β-oxidation compared to the controls. Additionally, muscles of p38αAF mice exhibited severe abnormalities in their mitochondria. Analysis of myotubes derived from p38αAF mice revealed reduced mitochondrial respiratory capacity relative to the myotubes of the control mice. Furthermore, these myotubes showed decreased expression of Acetyl CoA Carboxylase 2 (ACC2), leading to increased fatty acid oxidation and diminished inhibitory phosphorylation of pyruvate dehydrogenase (PDH), which resulted in elevated mitochondrial pyruvate oxidation. The expected consequence of reduced mitochondrial respiratory function and uncontrolled nutrient oxidation observed in p38αAF myotubes mitochondrial overload and metabolic inflexibility. This scenario explains the increased likelihood of insulin resistance development in the muscles of p38αAF mice compared to the control mice on a high-fat diet. In summary, within skeletal muscles, p38α assumes a crucial role in orchestrating the mitochondrial adaptation to caloric surplus by promoting mitochondrial biogenesis and regulating the selective oxidation of nutrients, thereby preventing mitochondrial overload, metabolic inflexibility, and insulin resistance.

Based on the lack of differences in progression-free and overall survival after a median follow-up of 93 months in our HOVON-65/GMMG-HD4 trial (German part; n = 395) randomizing VAD induction (vincristin/adriamycin/dexamthasone)/tandem-transplantation/thalidomide-maintenance vs. PAD induction (bortezomib/adriamycin/dexamethasone)/tandem transplantation/bortezomib maintenance, we discern how chromosomal aberrations determine long-term prognosis by different patterns of association with proliferation and treatment-dependent response, whether responses achieved by different regimens are equal regarding prognosis, and whether subpopulations of patients could be defined as treatable without upfront “novel agents” in cases of limited resources, e.g., in low- or middle-income countries. Serum parameters and risk factors were assessed in 395 patients. CD138-purified plasma cells were subjected to fluorescence in situ hybridization (n = 354) and gene expression profiling (n = 204). We found chromosomal aberrations to be associated in four patterns with survival, proliferation, and response: deletion (del) del17p13, del8p21, del13q14, (gain) 1q21+, and translocation t(4;14) (all adverse) associate with higher proliferation. Of these, del17p is associated with an adverse response (pattern 1), and 1q21+, t(4;14), and del13q14 with a treatment-dependent better response (pattern 2). Hyperdiploidy associates with lower proliferation without impacting response or survival (pattern 3). Translocation t(11;14) has no association with survival but a treatment-dependent adverse response (pattern 4). Significantly fewer patients reach a near-complete response or better with “conventional” (VAD) vs. bortezomib-based treatment after induction or high-dose melphalan. These patients, however, show significantly better median progression-free and overall survival. Molecularly, patients responding to the two regimens differ in gene expression, indicating distinct biological properties of the responding myeloma cells. Patients with normal renal function (89.4%), low cytogenetic risk (72.5%), or low proliferation rate (37.9%) neither benefit in progression-free nor overall survival from bortezomib-based upfront treatment. We conclude that response level, the treatment by which it is achieved, and molecular background determine long-term prognosis. Chromosomal aberrations are associated in four patterns with proliferation and treatment-dependent responses. Associations with faster and deeper responses can be deceptive in the case of prognostically adverse aberrations 1q21+ and t(4;14). Far from advocating a return to “outdated” treatments, if resources do not permit state-of-the-art-treatment, normal renal function and/or molecular profiling identifies patient subpopulations doing well without upfront “novel agents”.

Markers that allow for the selection of tailored treatments for individual patients with inflammatory bowel diseases (IBD) are yet to be identified. Our aim was to describe trends in real-life treatment usage. For this purpose, patients from the ENEIDA registry who received their first targeted IBD treatment (biologics or tofacitinib) between 2015 and 2021 were included. A subsequent analysis with Machine Learning models was performed. The study included 10,009 patients [71% with Crohn’s disease (CD) and 29% with ulcerative colitis (UC)]. In CD, anti-TNF (predominantly adalimumab) were the main agents in the 1st line of treatment (LoT), although their use declined over time. In UC, anti-TNF (mainly infliximab) use was predominant in 1st LoT, remaining stable over time. Ustekinumab and vedolizumab were the most prescribed drugs in 2nd and 3rd LoT in CD and UC, respectively. Overall, the use of biosimilars increased over time. Machine Learning failed to identify a model capable of predicting treatment patterns. In conclusion, drug positioning is different in CD and UC. Anti-TNF were the most used drugs in IBD 1st LoT, being adalimumab predominant in CD and infliximab in UC. Ustekinumab and vedolizumab have gained importance in CD and UC, respectively. The approval of biosimilars had a significant impact on treatment.

One-to-one laptop initiatives have become prevalent in schools aiming to enhance active learning and assist students in developing twenty-first-century skills. This paper reports on a qualitative investigation of teachers in a junior high school in Northern Israel, who were gradually implementing one-to-one computing. The research reported in this paper is based on longitudinal data collected over a five-year period and included all of the teachers who participated in the project. We triangulated teachers' perspectives and actual behavior by employing classroom observations, followed up with semi-structured interviews. This study aims to map teachers' perspectives on changes in teaching in one-to-one computing settings, and the pedagogical strategies that they use in one-to-one classrooms. To explore the depth of change in teaching, teachers' perspectives were mapped based on the SAMR framework, while the TEUCT-TEUIT approach was employed to categorize pedagogical strategies. The findings pointed to several benefits of using a laptop for teaching, as well as classroom management problems and technological problems while teaching. Analysis of the interviews revealed four categories which appeared in the original TEUCT/ TEUCT scales, alongside three additional categories which did not appear in the original scales, and emerged bottom-up from our data. Furthermore, findings indicated that some of the categories originally included in TEUCT, should rather be included in the TEUIT scale. The findings point to a change in teaching strategies, from a more teacher-centered teaching approach to a more student-centered learning approach.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Duchenne muscular dystrophy (DMD) is a rare X-linked recessive disease that is associated with severe progressive muscle degeneration culminating in death due to cardiorespiratory failure. We previously observed an unexpected proliferation-independent telomere shortening in cardiomyocytes of a DMD mouse model. Here, we provide mechanistic insights using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Using traction force microscopy, we show that DMD hiPSC-CMs exhibit deficits in force generation on fibrotic-like bioengineered hydrogels, aberrant calcium handling, and increased reactive oxygen species levels. Furthermore, we observed a progressive post-mitotic telomere shortening in DMD hiPSC-CMs coincident with downregulation of shelterin complex, telomere capping proteins, and activation of the p53 DNA damage response. This telomere shortening is blocked by blebbistatin, which inhibits contraction in DMD cardiomyocytes. Our studies underscore the role of fibrotic stiffening in the etiology of DMD cardiomyopathy. In addition, our data indicate that telomere shortening is progressive, contraction dependent, and mechanosensitive, and suggest points of therapeutic intervention.

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

PurposeStandard-of-care treatment for metastatic hormone receptor-positive (HR+), HER2-negative (HER2-) breast cancer includes endocrine therapy (ET) combined with a cyclin-dependent kinase 4/6 inhibitor (CDK4/6i). Optimal treatment after progression on CDK4/6i is unknown. The TRINITI-1 trial investigated ribociclib, a CDK4/6i that has recently demonstrated significant overall survival benefit in two phase III trials, in combination with everolimus and exemestane in patients with HR+, HER2- advanced breast cancer (ABC) after progression on a CDK4/6i.Patients and methodsThis multicenter, open-label, single-arm, phase I/II study included patients with locally advanced/metastatic HR+/HER2- breast cancer. The primary endpoint was clinical benefit rate (CBR) at week 24 among patients with ET-refractory disease with progression on a CDK4/6i. Other endpoints included safety and biomarker analysis.ResultsOf 104 patients enrolled (phases I and II), 96 had prior CDK4/6i. Recommended phase II doses (all once daily days 1-28 of 28-day cycle) were ribociclib 300 mg, everolimus 2.5 mg, and exemestane 25 mg (group 1) and ribociclib 200 mg, everolimus 5 mg, and exemestane 25 mg (group 2). CBR among 95 efficacy-evaluable patients (phases I and II) at week 24 was 41.1% (95% confidence interval, 31.1-51.6), which met the primary endpoint (predetermined threshold: 10%). Common adverse events included neutropenia (69.2%) and stomatitis (40.4%). No new safety signals were observed; no grade 3/4 QTc prolongation was reported.ConclusionsPreliminary TRINITI-1 safety and efficacy results support further investigation of CDK4/6 blockade and targeting of the PI3K/AKT/mTOR signaling pathway in patients with ET-refractory HR+/HER2- ABC after progression on a CDK4/6i.

Business students are increasingly taking online courses to supplement their more traditional face-to-face (F2F) course-delivered education. This study explored the relationship of demographic, curricula, and motivation variables on business school transfer intent for a mixed course delivery sample of undergraduate business students taking online classes. Two separate samples of students taking both online and F2F courses (i.e., mixed course delivery format) filled out an online survey in the fall 2016 and spring 2017 semesters. Intent to transfer business schools was lower for both samples. Results showed that being male, perceived favorability of online courses, and lower institutional commitment were significant correlates of intent to transfer across both samples. This outcome variable, intent to transfer, should be added to the research agenda for ongoing efforts across all universities and colleges when testing the impact of online education.

Prior research has not investigated perceived added education value in courses. Using a sample of 165 graduating business students, two business administration (BA) scales were created from six required BA core courses as part of students' Bachelor of Business Administration (BBA) degree. Students were asked if each required course "added value to their education". The two core scales (number of items) were labeled: BA Unique (4 items) and BA Generic (2 items). Analysis showed that the BA Unique scale had higher perceived added education value than the BA Generic scale. The BA Unique scale had stronger relationships to program degree satisfaction and Business School reputation than the BA Generic scale. These results supported the development of more unique required core courses based on business school stakeholder needs. Other schools should consider their stakeholders' needs to see if more unique required core courses, beyond generic, are needed. Although only six of 21 required courses could be tested due to sample size limitations, these initial results suggest it is important to evaluate the perceived added education value of required courses in a curriculum.