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In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables.

Reactive oxygen species (ROS) have been recognized as prevalent contributors to the development of inner retinal injuries including optic neuropathies such as glaucoma, non‐arteritic anterior ischemic optic neuropathy, traumatic optic neuropathy, and Leber hereditary optic neuropathy, among others. This underscores the pivotal significance of oxidative stress in the damage inflicted upon retinal tissue. To combat ROS‐related challenges, this study focuses on creating an injectable and tissue‐adhesive hydrogel with tailored antioxidant properties for retinal applications. GelCA, a gelatin‐modified hydrogel with photo‐crosslinkable and injectable properties, is developed. To enhance its antioxidant capabilities, curcumin‐loaded polydopamine nanoparticles (Cur@PDA NPs) are incorporated into the GelCA matrix, resulting in a multifunctional nanocomposite hydrogel referred to as Cur@PDA@GelCA. This hydrogel exhibits excellent biocompatibility in both in vitro and in vivo assessments, along with enhanced tissue adhesion facilitated by NPs in an in vivo model. Importantly, Cur@PDA@GelCA demonstrates the potential to mitigate oxidative stress when administered via intravitreal injection in retinal injury models such as the optic nerve crush model. These findings underscore its promise in advancing retinal tissue engineering and providing an innovative strategy for acute neuroprotection in the context of inner retinal injuries. This study addresses inner retinal injuries, emphasizing the role of reactive oxygen species (ROS). GelCA, a photo‐crosslinkable and injectable hydrogel synthesized by grafting cinnamic acid onto gelatin is developed. Incorporating curcumin‐loaded polydopamine nanoparticles results in Cur@PDA@GelCA, a multifunctional nanocomposite hydrogel. This innovative hydrogel demonstrates excellent biocompatibility, enhanced tissue adhesion, and the potential to mitigate oxidative stress in retinal injury models.

The coronavirus disease 2019 (COVID-19) pandemic is an exceptional public health crisis that demands the timely creation of new therapeutics and viral detection. Owing to their high specificity and reliability, monoclonal antibodies (mAbs) have emerged as powerful tools to treat and detect numerous diseases. Hence, many researchers have begun to urgently develop Ab-based kits for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Ab drugs for use as COVID-19 therapeutic agents. The detailed structure of the SARS-CoV-2 spike protein is known, and since this protein is key for viral infection, its receptor-binding domain (RBD) has become a major target for therapeutic Ab development. Because SARS-CoV-2 is an RNA virus with a high mutation rate, especially under the selective pressure of aggressively deployed prophylactic vaccines and neutralizing Abs, the use of Ab cocktails is expected to be an important strategy for effective COVID-19 treatment. Moreover, SARS-CoV-2 infection may stimulate an overactive immune response, resulting in a cytokine storm that drives severe disease progression. Abs to combat cytokine storms have also been under intense development as treatments for COVID-19. In addition to their use as drugs, Abs are currently being utilized in SARS-CoV-2 detection tests, including antigen and immunoglobulin tests. Such Ab-based detection tests are crucial surveillance tools that can be used to prevent the spread of COVID-19. Herein, we highlight some key points regarding mAb-based detection tests and treatments for the COVID-19 pandemic.

The novel coronavirus disease (COVID-19) pandemic remains a global public health crisis, presenting a broad range of challenges. To help address some of the main problems, the scientific community has designed vaccines, diagnostic tools and therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The rapid pace of technology development, especially with regard to vaccines, represents a stunning and historic scientific achievement. Nevertheless, many challenges remain to be overcome, such as improving vaccine and drug treatment efficacies for emergent mutant strains of SARS-CoV-2. Outbreaks of more infectious variants continue to diminish the utility of available vaccines and drugs. Thus, the effectiveness of vaccines and drugs against the most current variants is a primary consideration in the continual analyses of clinical data that supports updated regulatory decisions. The first two vaccines granted Emergency Use Authorizations (EUAs), BNT162b2 and mRNA-1273, still show more than 60% protection efficacy against the most widespread current SARS-CoV-2 variant, Omicron. This variant carries more than 30 mutations in the spike protein, which has largely abrogated the neutralizing effects of therapeutic antibodies. Fortunately, some neutralizing antibodies and antiviral COVID-19 drugs treatments have shown continued clinical benefits. In this review, we provide a framework for understanding the ongoing development efforts for different types of vaccines and therapeutics, including small molecule and antibody drugs. The ripple effects of newly emergent variants, including updates to vaccines and drug repurposing efforts, are summarized. In addition, we summarize the clinical trials supporting the development and distribution of vaccines, small molecule drugs, and therapeutic antibodies with broad-spectrum activity against SARS-CoV-2 strains.

Background The triglyceride-glucose (TyG) index and estimated glucose disposal rate (eGDR), which are calculated using different parameters, are widely used as markers of insulin resistance and are associated with cardiovascular diseases and prognosis. However, whether they have an additive effect on the risk of mortality remains unclear. This study aimed to explore whether the combined assessment of the TyG index and eGDR improved the prediction of long-term mortality in individuals with and without diabetes. Methods In this cross-sectional and cohort study, data were derived from the National Health and Nutrition Examination Survey (NHANES) 2001–2018, and death record information was obtained from the National Death Index. The associations of the TyG index and eGDR with all-cause and cardiovascular mortality were determined by multivariate Cox regression analysis and restricted cubic splines. Results Among the 17,787 individuals included in the analysis, there were 1946 (10.9%) all-cause deaths and 649 (3.6%) cardiovascular deaths during a median follow-up of 8.92 years. In individuals with diabetes, the restricted cubic spline curves for the associations of the TyG index and eGDR with mortality followed a J-shape and an L-shape, respectively. The risk of mortality significantly increased after the TyG index was > 9.04 (all-cause mortality) or > 9.30 (cardiovascular mortality), and after eGDR was < 4 mg/kg/min (both all-cause and cardiovascular mortality). In individuals without diabetes, the association between eGDR and mortality followed a negative linear relationship. However, there was no association between the TyG index and mortality. Compared with individuals in the low TyG and high eGDR group, those in the high TyG and low eGDR group (TyG > 9.04 and eGDR < 4) showed the highest risk for all-cause mortality (hazard ratio [HR] = 1.592, 95% confidence interval [CI] 1.284–1.975) and cardiovascular mortality (HR = 1.683, 95% CI 1.179-2.400) in the overall population. Similar results were observed in individuals with and without diabetes. Conclusions There was a potential additive effect of the TyG index and eGDR on the risk of long-term mortality in individuals with and without diabetes, which provided additional information for prognostic prediction and contributed to improving risk stratification.

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.

Abundance and visitation of pollinator assemblages tend to decrease with altitude, leading to an increase in pollen limitation. Thus increased competition for pollinators may generate stronger selection on attractive traits of flowers at high elevations and cause floral adaptive evolution. Few studies have related geographically variable selection from pollinators and intraspecific floral differentiation. We investigated the variation of Trollius ranunculoides flowers and its pollinators along an altitudinal gradient on the eastern Qinghai-Tibet Plateau, and measured phenotypic selection by pollinators on floral traits across populations. The results showed significant decline of visitation rate of bees along altitudinal gradients, while flies was unchanged. When fitness is estimated by the visitation rate rather than the seed number per plant, phenotypic selection on the sepal length and width shows a significant correlation between the selection strength and the altitude, with stronger selection at higher altitudes. However, significant decreases in the sepal length and width of T. ranunculoides along the altitudinal gradient did not correspond to stronger selection of pollinators. In contrast to the pollinator visitation, mean annual precipitation negatively affected the sepal length and width, and contributed more to geographical variation in measured floral traits than the visitation rate of pollinators. Therefore, the sepal size may have been influenced by conflicting selection pressures from biotic and abiotic selective agents. This study supports the hypothesis that lower pollinator availability at high altitude can intensify selection on flower attractive traits, but abiotic selection is preventing a response to selection from pollinators.

Oral squamous cell carcinoma (OSCC), with high potential for metastasis, is the most common malignant tumor of the head and neck. Cancer-associated fibroblasts (CAFs) are the main stromal cells in the microenvironment and aggravate tumor progression. However, whether CAFs are associated with the progression of OSCC remains unknown and the underlying mechanism remains unclear. In the present study, the role of CAFs in mediating OSCC cell migration and invasion was investigated, and the participation of exosomal miR-382-5p in this process was elucidated. In this study, according to the α-SMA staining with immunohistochemistry, 47 OSCC patients were divided into CAFs-rich and CAFs poor groups, and association of CAF density and clinicopathologic features of the OSCC patients were analyzed with Pearson χ2 test. Transwell assay was used for evaluating cell migration and invasion ability of OSCC cells after being co-cultured with NFs or CAFs, or after added exosomes. qPCR was used to detect the expression of miR-382-5p. Western blot analysis was used to measure the expression of migration and invasion-associated proteins. In the present study, the CAF density in tumor tissues was found to be relevant to OSCC lymph node metastasis and TNM stage. Furthermore, we revealed that miR-382-5p was overexpressed in CAFs compared with that in fibroblasts of adjacent normal tissue and miR-382-5p overexpression was responsible for OSCC cell migration and invasion. Finally, we demonstrated that CAF-derived exosomes transported miR-382-5p to OSCC cells. The present study confirmed a new mechanism of CAF-facilitated OSCC progression and may be beneficial for identifying new cancer therapeutic targets.

Thouless pumping enables topological transport and the direct measurement of topological invariants. So far, realizations of Thouless pumping rely on the adiabatic evolution of a physical system following a non-degenerate band, but it has been predicted that pumping can become non-Abelian in nature when degenerate bands exist. The resulting non-Abelian gauge fields and associated non-commutative operations would be promising for applications related to unitary matrices, such as photonic quantum logic. Here we propose the experimental realization of non-Abelian Thouless pumping in an on-chip photonic platform. By modulating the coupling coefficients within photonic waveguides with degenerate flat bands, we observe non-Abelian Thouless pumping in a three-step pumping device where the outcomes depend on the sequence of the pumping operations. We anticipate our versatile platform to reveal more complex non-Abelian topological physics and realize on-chip non-Abelian photonic devices in the future. Non-Abelian Thouless pumping, whose outcome depends on the order of pumping operations, has been observed in photonic waveguides with degenerate flat bands.

Functional group interconversion, a pivotal synthetic technique for precise editing of molecular building blocks, is particularly rare when facilitated by energy transfer catalysis. Herein, we showcase two instances of photochemical rearrangement of isonitriles, facilitated by energy transfer catalysis under visible light. The di-π-ethane rearrangement and di-π-propane rearrangement proceed through a six-membered transition state, offering a fresh synthetic paradigm for constructing three- and five-membered molecular architectures. Notably, these open-shell rearrangements demonstrate a vast substrate scope, compatibility with diverse functional groups, and applicability to complex drug and natural product derivatives, thereby presenting a complementary strategy for advancing energy transfer-enabled functional group interconversion. Furthermore, the photochemical rearrangements of isonitriles have been supported by computational studies. Functional group interconversion, a pivotal synthetic technique for precise editing of molecular building blocks, is rare when facilitated by energy transfer catalysis. Herein, the authors report two instances of photochemical rearrangement of isonitriles, facilitated by energy transfer catalysis under visible light.