Readily accessible sp3-rich cyclic hydrazine frameworks exploiting nitrogen fluxionality

Increased molecular complexity correlates with improved chances of success in the drug development process. Here, a strategy for the creation of sp3-rich, non-planar heterocyclic scaffolds suitable for drug discovery is described that obviates the need to generate multiple stereogenic centers with independent control. Asymmetric transfer hydrogenation using a tethered Ru-catalyst is used to efficiently produce a range of enantiopure cyclic hydrazine building blocks (up to 99% ee). Iterative C–N functionalization at the two nitrogen atoms of these compounds produces novel hydrazine and hydrazide based chemical libraries. Wide chemical diversification is possible through variation in the hydrazine structure, use of different functionalization chemistries and coupling partners, and controlled engagement of each nitrogen of the hydrazine in turn. Principal Moment of Inertia (PMI) analysis of this small hydrazine library reveals excellent shape diversity and three-dimensionality. NMR and crystallographic studies confirm these frameworks prefer to orient their substituents in three-dimensional space under the control of a single stereogenic center through exploitation of the fluxional behavior of the two nitrogen atoms

Review of Farmer Training Centers (FTCs) Based Training in The Case of Ethiopia Country

Farmers Training Center (FTC) of the country was established in the year 1980 at Agarfa in Bale, Oromia Region, Ethiopia country. Its main objective was the quick transfer of technology to the rural population so as to raise the quality of agricultural production, the living condition of the rural community and the country as a whole. Effectiveness of FTC based training on agricultural technology adoption and practice change is operationalized as the application of knowledge acquired from the training. It is the transfer of learning. Training is a prerequisite to decrease the complexity of the technology and used to disseminate knowledge and skill to the farming community. Moreover, field visit, tour and demonstration upgrade farmers’ knowledge and skill. Farmer training is a type of education that most often takes place outside formal learning institutions. It differs from education in schools because it is geared towards adult learning. Training has experienced a rapidly changing scenario especially from past decade. The present study was carried out with the aim of assessing review of farmer training centers (FTCs) based training in the case of  Ethiopia country. Analysis of the various aspects of training should be undertaken by organizations, stakeholders, and beneficiaries. Thus, deciding on what and how to evaluate and by who are critical parts of the evaluation process. Keywords: Effectiveness, Evaluation, Farmer Training Center, Relevance, Training. DOI: 10.7176/DCS/11-3-03 Publication date:March 31st 202

Development of outbred CD1 mouse colonies with distinct standardized gut microbiota profiles for use in complex microbiota targeted studies.

Studies indicate that the gut microbiota (GM) can significantly influence both local and systemic host physiologic processes. With rising concern for optimization of experimental reproducibility and translatability, it is essential to consider the GM in study design. However, GM profiles can vary between rodent producers making consistency between models challenging. To circumvent this, we developed outbred CD1 mouse colonies with stable, complex GM profiles that can be used as donors for a variety of GM transfer techniques including rederivation, co-housing, cross-foster, and fecal microbiota transfer (FMT). CD1 embryos were surgically transferred into CD1 or C57BL/6 surrogate dams that varied by GM composition and complexity to establish four separate mouse colonies harboring GM profiles representative of contemporary mouse producers. Using targeted 16S rRNA amplicon sequencing, subsequent female offspring were found to have similar GM profiles to surrogate dams. Furthermore, breeding colonies of CD1 mice with distinct GM profiles were maintained for nine generations, demonstrating GM stability within these colonies. To confirm GM stability, we shipped cohorts of these four colonies to collaborating institutions and found no significant variation in GM composition. These mice are an invaluable experimental resource that can be used to investigate GM effects on mouse model phenotype

Impact of social complexity on outcomes in cystic fibrosis after transfer to adult care

Objective This study evaluates the roles of medical and social complexity in health care use outcomes in cystic fibrosis (CF) after transfer from pediatric to adult care. Methods Retrospective cohort design included patients with CF who were transitioned into adult care at Indiana University from 2005 to 2015. Predictor variables included demographic and comorbidity data, age at transition, treatment complexity score (TCS), and an objective scoring measure of their social complexity (Bob's Level of Social Support, BLSS). Outcome variables included outpatient visit rates and hospitalization rates. Pearson's correlations and linear regression were used to analyze the data. Results The median age of the patients (N = 133) at the time of transition was 20 (IQR 19‐23) years. The mean FEV1 % predicted at transition was 69 ± 24%. TCS correlated with outpatient visit rates (r = 0.3, P = 0.003), as well as hospitalization rates (r = 0.4, P < 0.001); while the BLSS only correlated with hospitalization rates (r = 0.7, P < 0.001). After adjusting for covariates, the strongest predictors of post‐transfer hospitalizations are BLSS (P < 0.0001) and pre‐transfer hospitalization rate (P < 0.0001). Conclusion Greater treatment complexity is associated with greater healthcare utilization overall, while greater social complexity is associated with increased hospitalizations (but not outpatient visits). Screening young adults for social complexity may identify high‐risk subpopulations and allow for patient centered interventions to support them and prevent avoidable health care use

The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }

Channel coded iterative center-shifting K-best sphere detection for rank-deficient systems

Based on an EXtrinsic Information Transfer (EXIT) chart assisted receiver design, a low-complexity near-Maximum A Posteriori (MAP) detector is constructed for high-throughput MIMO systems. A high throughput is achieved by invoking high-order modulation schemes and/or multiple transmit antennas, while employing a novel sphere detector (SD) termed as a center-shifting SD scheme, which updates the SD’s search center during its consecutive iterations with the aid of channel decoder. Two low-complexity iterative center-shifting SD aided receiver architectures are investigated, namely the direct-hard-decision centershifting (DHDC) and the direct-soft-decision center-shifting (DSDC) schemes. Both of them are capable of attaining a considerable memory and complexity reduction over the conventional SD-aided iterative benchmark receiver. For example, the DSDC scheme reduces the candidate-list-generation-related and extrinsic-LLR-calculation related complexity by a factor of 3.5 and 16, respectively. As a further benefit, the associated memory requirements were also reduced by a factor of 16