Ottebundgaard7118

Chemical reaction dynamics are studied to monitor and understand the concerted motion of several atoms while they rearrange from reactants to products. When the number of atoms involved increases, the number of pathways, transition states and product channels also increases and rapidly presents a challenge to experiment and theory. Here we disentangle the dynamics of the competition between bimolecular nucleophilic substitution (SN2) and base-induced elimination (E2) in the polyatomic reaction F- + CH3CH2Cl. We find quantitative agreement for the energy- and angle-differential reactive scattering cross-sections between ion-imaging experiments and quasi-classical trajectory simulations on a 21-dimensional potential energy hypersurface. The anti-E2 pathway is most important, but the SN2 pathway becomes more relevant as the collision energy is increased. In both cases the reaction is dominated by direct dynamics. Our study presents atomic-level dynamics of a major benchmark reaction in physical organic chemistry, thereby pushing the number of atoms for detailed reaction dynamics studies to a size that allows applications in many areas of complex chemical networks and environments.Biochemical networks interconnect, grow and evolve to express new properties as different chemical pathways are selected during a continuous cycle of energy consumption and transformation. In contrast, synthetic systems that push away from equilibrium usually return to the same self-assembled state, often generating waste that limits system recyclability and prevents the formation of adaptable networks. Here we show that annealing by slow proton dissipation selects for otherwise inaccessible morphologies of fibres built from DNA and cyanuric acid. Using single-molecule fluorescence microscopy, we observe that proton dissipation influences the growth mechanism of supramolecular polymerization, healing gaps within fibres and converting highly branched, interwoven networks into nanocable superstructures. Just as the growth kinetics of natural fibres determine their structural attributes to modulate function, our system of photoacid-enabled depolymerization and repolymerization selects for healed materials to yield organized, robust fibres. Our method provides a chemical route for error-checking, distinct from thermal annealing, that improves the morphologies and properties of supramolecular materials using out-of-equilibrium systems.The Born-Oppenheimer approximation, assuming separable nuclear and electronic motion, is widely adopted for characterizing chemical reactions in a single electronic state. However, the breakdown of the Born-Oppenheimer approximation is omnipresent in chemistry, and a detailed understanding of the non-adiabatic dynamics is still incomplete. Here we investigate the non-adiabatic quenching of electronically excited OH(A2Σ+) molecules by H2 molecules using full-dimensional quantum dynamics calculations for zero total nuclear angular momentum using a high-quality diabatic-potential-energy matrix. Good agreement with experimental observations is found for the OH(X2Π) ro-vibrational distribution, and the non-adiabatic dynamics are shown to be controlled by stereodynamics, namely the relative orientation of the two reactants. The uncovering of a major (in)elastic channel, neglected in a previous analysis but confirmed by a recent experiment, resolves a long-standing experiment-theory disagreement concerning the branching ratio of the two electronic quenching channels.There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep-learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence-activity predictions. The predicted miniproteins, termed 'Mach', reach an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.Over the past three decades, organocatalysis has emerged as a powerful catalysis platform and has gradually been incorporated into the routine synthetic toolbox to obtain chiral molecules. However, its application in the site- and enantioselective functionalization of inactive aryl C-H bonds remains in its infancy. Here, we present an organocatalyst-controlled para-selective arene C-H functionalization strategy that addresses this issue, which remains an enduring challenge in arene functionalization chemistry. By emulating enzyme catalysis, the chiral phosphoric acid catalyst offers an ideal chiral environment for stereoinduction, and the projecting substituents give control of chemo- and site-selectivity. Various types of nucleophile are compatible with this method, affording more than 100 para-selective adducts with stereodefined carbon centres or axes in viable molecular contexts. This protocol is expected to provide a general strategy for para-selective functionalization of arene C-H bonds in a controlled manner.Linkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, but are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. In addition, we provide standard errors for these moment-based estimators. All methods discussed in this manuscript are implemented in the ldsep package, available on the Comprehensive R Archive Network ( https//cran.r-project.org/package=ldsep ).Using the skin tissue engineering approach is a way to help the body to recover its lost skin in cases that the spontaneous healing process is either impossible or inadequate, such as severe wounds or burns. In the present study, chitosan/gelatin-based scaffolds containing 0.25, 0.5, 0.75, and 1% allantoin were created to improve the wounds' healing process. Chidamide purchase EDC and NHS were used to cross-link the samples, which were further freeze-dried. Different in-vitro methods were utilized to characterize the specimens, including SEM imaging, PBS absorption and degradation tests, mechanical experiments, allantoin release profile assessment, antibacterial assay, and cell viability and adhesion tests. The results indicated that the scaffolds' average pore sizes were approximately in the range of 390-440 µm, and their PBS uptake amounts were about 1000% to 1250% after being soaked in PBS for 24 h. Around 70% of the specimens were degraded in 6 days, but they were not fully degraded after 21 days. Besides, the samples showed antibacterial activity against S. aureus and E. coli bacteria. In general, the MTT cell viability test indicated that the cells' density increased slightly or remained the same during the experiment. SEM images of cells seeded on the scaffolds indicated appropriate properties of the scaffolds for cell adhesion.

Prospective observational cohort study.

First, describe pressure injury (PI) and associated risk factors in individuals with spinal cord injury/disorder (SCI/D) during first rehabilitation. Second, evaluate a prediction model for hospital acquired PI (HAPI) development.

Acute care and rehabilitation clinic specialized in SCI/D.

Patients ≥18 years of age with SCI/D were included during first rehabilitation between 08/2018 and 12/2019. We performed a systematic literature search to identify risk factors for PI development. Patients were classified according to HAPI developed. Between group differences of patients' characteristics and risk factors were analyzed using descriptive statistics. Logistic predictive models were performed to estimate HAPI development and receiver operator characteristic (ROC) curve was used to test the model.

In total, 94 patients were included, 48 (51.1%) developed at least one HAPI and in total 93 were observed, mainly stage I and stage II HAPI according to the European Pressure Ulcer Advisory Panel. We found nine significantly associated risk factors completeness of SCI/D, pneumonia, sedative medications, autonomic dysreflexia, Braden ≤12 points, SCIPUS ≥9 points, lower admission SCIM and lower admission FIM-cognition, longer length of stay (LOS) (p ≤ 0.0005). In a predictive model, none of the risk factors was associated with HAPI development (AUC = 0.5).

HAPIs in patients with SCI/D during first rehabilitation are a frequent and complex condition and associated with several risk factors. No predictive model exists but with the identified risk factors of this study, larger studies can create a tailored and flexible HAPI risk prediction model.

HAPIs in patients with SCI/D during first rehabilitation are a frequent and complex condition and associated with several risk factors. No predictive model exists but with the identified risk factors of this study, larger studies can create a tailored and flexible HAPI risk prediction model.

Systematic review and meta-analysis OBJECTIVES The objective was to summarise prior research regarding the efficacy of active physiotherapy interventions and prevention strategies on shoulder pain, decreased physical function and quality of life in people with a spinal cord injury (SCI).

A systematic literature search was conducted in CENTRAL, EMBASE (via Ovid), CINAHL and MEDLINE (via Ovid). Randomised controlled trials investigating effects of active physiotherapy interventions on shoulder pain, physical function and quality of life were included. Further, prospective cohort studies investigating effects of active physiotherapy interventions in prevention of shoulder pain and reduced physical function were included. Mean difference (MD) for pain (15 items on a 0-10 scale) and standardised mean difference (SMD) for physical function were summarised in a random effects meta-analysis.

Four studies on treatment (totalling 167 participants), and no studies on prevention were included. Significant and clinically meaningful improvements on shoulder pain (MD 19.