Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic biology, genetic circuits and machine learning: a new age of cancer therapy

Synthetic biology has made it possible to rewire natural cellular responses to treat disease, notably demonstrated by chimeric antigen receptor (CAR) T cells as cancer immunotherapy. Building on the success of T‐cell activation using synthetic receptors, the field is now investigating how induction of noncanonical signalling pathways and sophisticated synthetic gene circuitry can enhance the antitumour phenotype of engineered T cells. This commentary explores two recently published studies that provide proof of concept for how new technologies achieve this. The first demonstrated that non‐naturally occurring combinations of signalling motifs derived from various immune receptors and arranged as a CAR drove unique signal transduction pathways in T cells and improved their tumour killing ability. Here, machine learning complemented the screening process and successfully predicted CAR T‐cell phenotype dependent on signalling motif choice. The second explored how synthetic zinc fingers can be engineered into controllable transcriptional regulators, where their activity was dependent on the presence or absence of FDA‐approved small‐molecule drugs. These studies are pivotal in expanding the design choices available for gene circuits of the future and highlight how a single cellular therapy could respond to multiple environmental cues including target cell antigen expression, the tumour microenvironment composition and small molecule drugs.
Palliative Care Communication Among Home Health Interprofessional Staff: A Randomized Controlled Trial of Feasibility, Acceptability, and Preliminary Effectiveness

Skilled home health care (HH) is the largest and fastest growing long-term care setting in the United States. Patients in HH are served by an interprofessional team, and may have little direct contact with physicians, when discussing their progress, prognosis, and goals of care. Such conversations are part of primary palliative care communication. Evidence on primary palliative care communication training in the non-physician HH interprofessional team is lacking. The objectives of this study were to assess the feasibility, acceptability, and preliminary effectiveness of using a palliative care communication model known as COMFORT© to provide palliative care communication training to HH staff. A randomized controlled trial was conducted at a regional health system in the southeastern U.S. to test online training modules (n = 10) (Group 1) and online training modules plus face-to-face training (n = 8) (Group 2). Measures included training completion rates, staff acceptability ratings, comfort with palliative and end-of-life communication (C-COPE) and moral distress (MMD-HP). Results showed that COMFORT© training was feasible (92%), highly acceptable (>4 on a 6-point scale), and positively correlated with improved C-COPE scores (P = .037). There was no significant difference in moral distress scores pre- and post-intervention or in effectiveness between the groups. However, acceptability of COMFORT© was positively correlated with history of leaving or considering leaving a job due to moral distress ( χ 2 = 7.6, P = .02). Preliminary findings from this pilot study suggest that administration of COMFORT© training was feasible, and it was correlated with increased HH staff comfort with palliative care communication.
3D printed hydrogel scaffold promotes the formation of hormone-active engineered parathyroid tissue

The parathyroid glands are localized at the back of the thyroid glands in the cervical region and are responsible for regulation of the calcium level in the blood, through specialized cells that sense Ca2+ and secrete parathyroid hormone (PTH) in response to a decline in its serum level. PTH stimulates the skeleton, kidneys and intestines and controls the level of Ca2+ through specialized activities. Iatrogenic removal of the parathyroid gland, as well as damage to its vascular integrity during cauterization are some of the common complications of thyroid surgery. Therefore, regeneration and/or replacement of malfunctioning parathyroid tissue is required. Tissue engineering is an emerging and promising field for patients with organ failure with recent pioneering clinical applications. The success of tissue engineering strategy depends on the use of proper cells, bioactive factors that stimulate the activities of these cells and scaffolds that are produced to recapitulate the tissue structure and support the function of the engineered tissues. 3D printing is a developing strategy for the production of these scaffolds by providing a delicate control over their structure and properties. In this study, human primary parathyroid cells were successfully isolated and their viability and ability to secrete PTH upon stimulation with different levels of Ca2+ were shown in vitro. These cells were then seeded onto 3D printed alginate scaffolds and 3D bioprinted within alginate bioink, and cell viability as well as the ability to secrete PTH upon stimulation were also demonstrated. Therefore, functional hormone-active parathyroid tissue substitute was engineered in vitro through 3D printed hydrogels and autologous cells.
Prime Graph of a Ring

In this paper we consider associative rings R (need not be commutative) and defined a new concept ‘Prime Graph of R’ (denoted by PG(R)). We presented some examples. We obtained few fundamental important results related to PG(R); proved that if R is a semiprime ring, then R is a prime ring if and only if the PG(R) is a tree. Further, we have observed several properties of PG(R) with respect to the properties like: zero divisors, nilpotent elements in R.
Peptides for trans‐blood–brain barrier delivery

Trans‐blood–brain barrier (BBB) delivery of therapeutic and diagnostic agents is a major challenge in the development of central nervous system (CNS) targeted radiopharmaceuticals. This review is an introduction to the use of peptides as delivery agents to transport cargos into the CNS. The most widely used BBB‐penetrating peptides are reviewed here, with a particular emphasis on the broad range of cargos delivered into the CNS using these. Cell‐penetrating peptides (CPPs) have been deployed as trans‐BBB delivery agents for some time; new developments in the CPP field offer exciting opportunities for the design of next generation trans‐BBB complexes. Many of the peptides highlighted here are ready to be combined with diagnostic and therapeutic radiopharmaceuticals to develop highly effective CNS‐targeted agents.
RANGE SEARCHING DATA STRUCTURES WITH CACHE LOCALITY

xi List of Abbreviations and Symbols Used . xii Acknowledgements . xvi Chapter
Genetic and environmental contributions to ancestry differences in gene expression in the human brain

Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson’s disease, and 30% of heritability for Alzhemier’s disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness. Summary We examine the impact of genetic ancestry on gene expression and DNA methylation of admixed African/Black Americans, highlighting how genetic and environmental background affect risk for brain illness.
Population-level transposable element expression dynamics influence trait evolution in a fungal crop pathogen

Rapid adaptive evolution is driven by strong selection pressure acting on standing genetic variation within populations. How adaptive genetic variation is generated within species and how such variation influences phenotypic trait expression is often not well understood though. Here, we focused on recent activity of transposable elements (TEs) using deep population genomics and transcriptomics analyses of a fungal plant pathogen with a highly active content of TEs in the genome. Zymoseptoria tritici causes one of the most damaging diseases on wheat, with recent adaptation to the host and environment being facilitated by TE-associated mutations. We obtained genomic and RNA-sequencing data from 146 isolates collected from a single wheat field. We established a genome-wide map of TE insertion polymorphisms in the population by analyzing recent TE insertions among individuals. We quantified the locus-specific transcription of individual TEs within the population and revealed considerable variation in transcription across individual TE loci. About 20% of all TE copies show activity in the genome implying that genomic defenses such as repressive epigenetic marks and repeat-induced polymorphisms are ineffective at preventing the proliferation of TEs in the genome. A quarter of recent TE insertions are associated with expression variation of neighboring genes providing broad potential to influence trait expression. We indeed found that TE insertions are likely responsible for variation in virulence on the host and secondary metabolite production. Our study emphasizes how TE-derived polymorphisms segregate even in individual populations and broadly underpin transcription and adaptive trait variation in a species.
CsrA Shows Selective Regulation of sRNA-mRNA Networks

Post-transcriptional regulation, by small RNAs (sRNAs) as well as the global Carbon Storage Regulator A (CsrA) protein, play critical roles in bacterial metabolic control and stress responses. The CsrA protein affects selective sRNA-mRNA networks, in addition to regulating transcription factors and sigma factors, providing additional avenues of cross talk between other stress-response regulators. Here, we expand the known set of sRNA-CsrA interactions and study their regulatory effects. In vitro binding assays confirm novel CsrA interactions with ten sRNAs, many of which are previously recognized as key regulatory nodes. Of those 10 sRNA, we identify that McaS, FnrS, SgrS, MicL, and Spot42 interact with CsrA in vivo. We find that the presence of CsrA impacts the downstream regulation of mRNA targets of the respective sRNA. In vivo evidence supports enhanced CsrA McaS-csgD mRNA repression and showcase CsrA-dependent repression of the fucP mRNA via the Spot42 sRNA. We additionally identify SgrS and FnrS as potential new sRNA sponges of CsrA. Overall, our results further support the expanding impact of the Csr system on cellular physiology via CsrA impact on the regulatory roles of these sRNAs.
Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been exploring and expanding the potential of dRNA-seq, the sequencing accuracy and error patterns remain understudied. We present the first comprehensive evaluation of accuracy and systematic errors in dRNA-seq data from diverse species, as well as synthetic RNA. Deletions significantly outnumbered mismatches/insertions, while the median read accuracy exhibited species-level variation. In addition to homopolymer errors, we observed systematic biases across nucleotides and heteropolymeric motifs in all species. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines/adenines. Moreover, the systematic errors were strongly dependent on local sequence contexts. By examining raw signal data, we identified underlying signal-level features potentially associated with the error patterns. While read quality scores approximated error rates at base and read levels, failure to detect DNA adapters may lead to data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (base-calling) artefacts. Lastly, we discuss the implications of such error patterns for downstream applications of dRNA-seq data.
Episodic mass loss and narrow lines in gamma Cassiopeiae and in other early-type stars

High-resolution ultraviolet spectra obtained with the IUE of the B0.5 IVe star . gamma. Cas have revealed conspicuous changes in the profiles of the resonance doublet lines of C IV, N V, and Si IV. In 18 out of the 28 available spectra, narrow absorption components (FWHM< or =160 km s/sup -1/) of varying strengths (up to almost saturated profiles) are found at the blueshifted side of the rest wavelength. Sometimes the components are multiple. In the range covered by the short-wavelength camera on board IUE, no other ions show such behavior. The central velocities of the narrow lines are between -650 and -1500 km s/sup -1/. Within one spectrum the velocities of narrow lines in the three ions do not differ significantly. In the different episodes a larger column density is correlated with a lower velocity. For the development of such narrow high-velocity UV absorptions an upper limit of one week is derived from the data. The decay takes place typically within one month. The mean interval between the different episodes might vary between a week and a month. No periodicity has been found.
Multiple Objective Linear Programming with Interval Criterion Weights

The purpose of this research is to develop a computer applicable methodology for analyzing multiple objective linear programming problems when interval, rather than fixed, weights are assigned to each of the objectives. Rather than obtaining a single efficient extreme point as one would normally expect with fixed weights, a cluster of efficient extreme points is typically generated when interval weights are specified. Then, from the subset of solutions generated, it is contemplated, that the decision-maker will be able to qualitatively identify his efficient extreme point of greatest utility (which should either be the decision-maker's optimal point or be close enough to it to terminate the decision process). The procedure presented in this paper involves converting the multiple objective linear programming problem with interval criterion weights into an equivalent vector-maximum problem. Once in such form, an algorithm for the vector-maximum problem can then be used to determine the subset of “efficient .
Role of Transformational Leadership in Enhancing Followers' Psychological Empowerment

According to recent literature on organizational leadership, the ability of leaders to implement a transformational process appropriately may lead to enhanced followers? psychological empowerment in organizations. Although this relationship is significant, the role of transformational leadership as an important predicting variable has not been given sufficient emphasis in the organizational leadership model. Therefore, this study was conducted to investigate the relationship between transformational leadership and followers? psychological empowerment using 86 usable questionnaires gathered from employees at a multinational company in Sarawak, Malaysia. The results of the SmartPLS path model analysis confirm that idealized influence, individualized consideration, and intellectual stimulation act as important predictors of followers? psychological empowerment. Additionally, discussion, implications and conclusion are presented in detail.
Joint-task learning to improve perceptually-aware super-resolution of aerial images

ABSTRACT Deep neural networks have become very popular for solving many problems in computer vision. Super-resolution (SR) is a particularly challenging task since new information must be created at increased resolution, possibly leading to visual artefacts or incoherent texture. In the context of remote sensing, this image restoration technique has great potential for synthesizing high-resolution (HR) data from low-resolution (LR) images. While there are multiple methods that enhance the perceptual quality of SR images, most of them fail to recover detailed information from aerial imagery. One of the main reasons for that is the difficulty in defining a ‘good-looking’ image from the perspective of the machine. In this work, we propose an end-to-end training procedure that unifies networks related to different tasks: an SR module based on generative adversarial networks (GANs) and a semantic segmentation module. Our claim is that by including a classification loss when estimating the HR image, the GAN generator produces more coherent structures and textural information, synthesizing, therefore, more realistic images according to perception-based scores. Our experimental results show that the proposed method is capable of improving perceptual outputs of deep-learning oriented networks with a small training overhead, surpassing multiple state-of-the-art super-resolution methods. Our code is available at http://github.com/elitonfilho/SegSR.
Anti-Saturation Coordination Control of Permanent Magnet Synchronous Wind Power System

This paper is concerned with the anti-saturation control problem of a permanent magnet synchronous wind power system. By virtue of adaptive sliding mode control method and port-controlled Hamiltonian (PCH) control method, a new coordination controller is designed, which removes the input saturation effects and compensates the uncertainties of system model parameters. The controller takes the exponential function with parameters as the coordination function. By adjusting the parameters of coordination function, the designed coordination controller can respond to input saturation more quickly and improve the system’s dynamic performance. The effectiveness of the proposed strategy is demonstrated through simulations. The comparison results with the traditional control approach are also presented. It is shown that the proposed strategy can realize the speed tracking control of the generator and improve the maximum wind energy capture of wind turbine, so as to further improve the utilization efficiency of wind energy.
Thermoelastic stress mitigation and weight reduction of functionally graded multilayer nonuniform thickness disc

In this paper, the particle swarm optimization method is used to reduce the weight of a multilayer rotating nonuniform thickness disc along with alleviation of the maximum tangential stress and the maximum tangential stress-jump at the interfaces. The proposed disc is made of functionally graded material and is subjected to both mechanical pressure and thermal loads. It is divided into several layers with each one having its unique volume fraction. These volume fractions are considered the design variables of the optimization problem along with two geometrical parameters related to the disc thickness. The equilibrium equation in polar coordinates are solved using the finite difference method. A punch of optimization results is calculated and discussed. It is concluded that the range of design variables’ variation widens by considering more layers. Finally, there is no potential disc configuration or geometry is found dominant to enhance the design parameters concurrently. Therefore, performing similar optimization analyses is compulsory to obtain an efficient and durable structure.
Optical probe using differential confocal technique for surface profile

A non-contact optical probe for surface profiling with up to 2nm position resolution over 100micrometers measurement range has been developed, which includes a confocal light path for non-contact position and a capacitance sensor for Z axis displacement measurement. The principle of the optical probe is based on differential confocal technique. The differential light- intensity distribution depends on the confocal axial response (or depth discrimination) properties. Using the diffraction theory, the mathematical analysis of the method has been performed. Validity of the mathematical theory analysis of the differential confocal technique is experimentally verified.
Classification of Migraine Using Static Functional Connectivity Strength and Dynamic Functional Connectome Patterns: A Resting-State fMRI Study

Migraine is a common, chronic dysfunctional disease with recurrent headaches. Its etiology and pathogenesis have not been fully understood and there is a lack of objective diagnostic criteria and biomarkers. Meanwhile, resting-state functional magnetic resonance imaging (RS-fMRI) is increasingly being used in migraine research to classify and diagnose brain disorders. However, the RS-fMRI data is characterized by a large amount of data information and the difficulty of extracting high-dimensional features, which brings great challenges to relevant studies. In this paper, we proposed an automatic recognition framework based on static functional connectivity (sFC) strength features and dynamic functional connectome pattern (DFCP) features of migraine sufferers and normal control subjects, in which we firstly extracted sFC strength and DFCP features and then selected the optimal features using the recursive feature elimination based on the support vector machine (SVM−RFE) algorithm and, finally, trained and tested a classifier with the support vector machine (SVM) algorithm. In addition, we compared the classification performance of only using sFC strength features and DFCP features, respectively. The results showed that the DFCP features significantly outperformed sFC strength features in performance, which indicated that DFCP features had a significant advantage over sFC strength features in classification. In addition, the combination of sFC strength and DFCP features had the optimal performance, which demonstrated that the combination of both features could make full use of their advantage. The experimental results suggested the method had good performance in differentiating migraineurs and our proposed classification framework might be applicable for other mental disorders.
A new approach to increase the attainable rice yield in intensive irrigated rice systems of Zhejiang province, China

A new site-specific nutrient management approach was developed to break the apparent attainable yield barrier of 6 t·ha−1 in the double rice cropping system of Zhejiang. On-farm experiments involving 21 rice-growing farmer families and NPK long-term experiments commenced in 1997 in the central part of Zhejiang Province to assess the status of soil fertility and productivity under intensive rice-rice cropping. A new site-specific nutrient management (SSNM) approach has been developed for this studied area. Field-specific fertilizer recommendations are calculated considering indigenous nutrient supply, reasonable grain yield targets and corresponding nutrient demand, nutrient balance and nutrient use efficiency, as well as socio-economic factors. The agronomic performance of SSNM was tested against the farmer's fertilizer practice (FFP) in four 1998–1999 cropping seasons. Across seasons and years, SSNM consistently increased plant nutrient uptake, grain yield and profit by about 10%–15% compared to the FFP. Yield levels of 7.5 t·ha−1 or more seem achievable and sustainable through introduction of SSNM and improved extension services in Zhejiang Province.