Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Monte Carlo-drift-diffusion simulation of transport in III-N LEDs

III-N LEDs have started a revolutionary change in the lighting industry, but many problems stand in the way of further improvements. One of the most serious problems is the efficiency droop, which has been studied and discussed for several years without a clear conclusion on its cause [1], [2]. The two most widely accepted possible explanations are a form of electron leakage [3] and direct or indirect Auger recombination [4]. One of the solution methods proposed to avoid droop is to use multi-quantum well (MQW) devices. However, in a typical device this does not help, since only one of the quantum wells (QWs) emits all the light [5], [6]. In spite of numerous theoretical and experimental attempts to explain the origins of the droop, discussion has not advanced for some time. The discussion is further complicated due to difficulties in fitting various theoretical and experimental results together. Therefore advanced physical models are needed for further investigations. We introduce a coupled Monte Carlo–driftdiffusion (MCDD) device simulation method to study the efficiency droop in III-Nitride MQW LEDs. We apply the method to answer two relevant questions: (i) the importance of electron leakage in the efficiency droop of MQW LEDs and (ii) the causes of uneven carrier distribution between the QWs. The novelty of our work lies in applying the Monte Carlo (MC) method [7]: by employing a selfconsistent MC simulation for the electron gas we gain accurate information of the droop mechanisms based on the device physics. This cannot be fully done by using the conventional DD model which relies on the assumption of carrier thermalisation. In this work, we also report the discrepancies between the DD model and the more sophisticated MC method in these specific devices.
Identifying Clusters in Graph Representations of Genomes

In many bioinformatics applications the task is to identify biologically significant locations in an individual genome. In our work, we are interested in finding high-density clusters of such biologically meaningful locations in a graph representation of a pangenome, which is a collection of related genomes. Different formulations of finding such clusters were previously studied for sequences. In this work, we study an extension of this problem for graphs, which we formalize as finding a set of vertex-disjoint paths with a maximum score in a weighted directed graph. We provide a linear-time algorithm for a special class of graphs corresponding to elastic-degenerate strings, one of pangenome representations. We also provide a fixed-parameter tractable algorithm for directed acyclic graphs with a special path decomposition of a limited width.
Sclerotome is compartmentalized by parallel Shh and Bmp signaling downstream of CaMKII

The sclerotome in vertebrates comprises an embryonic population of cellular progenitors that give rise to diverse adult tissues including the axial skeleton, ribs, intervertebral discs, connective tissue, and vascular smooth muscle. In the thorax, this cell population arises in the ventromedial region of each of the segmented tissue blocks known as somites. How and when sclerotome adult tissue fates are specified and how the gene signatures that predate those fates are regulated has not been well studied. We have identified a previously unknown role for Ca2+/calmodulin-dependent protein kinase II (CaMKII) in regulating sclerotome patterning in zebrafish. Mechanistically, CaMKII regulates the activity of parallel signaling inputs that pattern sclerotome gene expression. In one downstream arm, CaMKII regulates distribution of the established sclerotome-inductive morphogen sonic hedgehog (Shh), and thus Shh-dependent sclerotome genes. In the second downstream arm, we show a previously unappreciated inductive requirement for Bmp signaling, where CaMKII activates expression of bmp4 and consequently Bmp activity. Bmp activates expression of a second subset of stereotypical sclerotome genes, while simultaneously repressing Shh-dependent markers. Our work demonstrates that CaMKII promotes parallel Bmp and Shh signaling as a mechanism to first promote global sclerotome specification, and that these pathways subsequently regionally activate and refine discrete compartmental genetic programs. Our work establishes how the earliest unique gene signatures that likely drive distinct cell behaviors and adult fates arise within the sclerotome.
Neuron-specific RNA-sequencing reveals different regeneration mechanisms in peripheral neurons after nerve injury

Peripheral neurons are heterogeneous and functionally diverse, but all share the capability to switch to a pro-regenerative state after nerve injury. Despite the assumption that the injury response is similar among neuronal subtypes, functional recovery may differ. Understanding the distinct intrinsic regenerative properties between neurons may help to improve the quality of regeneration, prioritizing the growth of axon subpopulations to their targets. Here, we present a comparative analysis of regeneration across four key peripheral neuron populations: motoneurons, proprioceptors, cutaneous mechanoreceptors, and nociceptors. Using Cre/Ai9 mice that allow fluorescent labelling of neuronal subtypes, we found that nociceptors showed the greater regeeneration after a sciatic crush, followed by motoneurons, mechanoreceptors and, finally, proprioceptors. By breeding these Cre mice with Ribotag mice, we isolated specific translatomes and defined the regenerative response of these neuronal subtypes after axotomy. Only 20% of the regulated genes were common, revealing a diverse response to injury among neurons, which was also supported by the differential influence of neurotrophins among neuron subtypes. Among differentially regulated genes, we proposed MED12 as a specific regulator of the regeneration of proprioceptors. Altogether, we demonstrate that the intrinsic regenerative capacity differs between peripheral neuron subtypes, opening the door to selectively modulate these responses.
Acetyl-CoA carboxylase 1-dependent lipogenesis drives breast cancer progression

Dysregulation of cellular energetics, including lipid synthesis mediated through de novo lipogenesis, is a feature of many cancers. Here we report that acetyl-coenzyme A carboxylase (ACC) 1, the rate-limiting enzyme of de novo lipogenesis, is a key regulator of breast cancer progression and cancer cell phenotype. Mammary epithelial-specific deletion of ACC1 impaired tumour progression and decreased cancer cell proliferation in the PyMT model of breast cancer in vivo. ACC1 knockout in human breast cancer cell lines resulted in decreased cell number and altered cell and membrane morphology. Lipidomic profiling demonstrated reduced levels of acyl-carnitines (CARs) and several phospholipid (PL) classes, whilst also shifting the lipid profiles to exhibit more elongated and less saturated lipids in ACC1 knockout breast cancer cells. Palmitate rescue of ACC1 deletion phenotypes demonstrated a critical role for ACC1 driven de novo lipogenesis in breast cancer cell function. Analysis of human breast tumour-microarrays identified strong ACC1 expression at all breast cancer stages, grade and metastasis, compared to normal adjacent tissue. Together our data demonstrate a novel role for ACC1 in breast cancer progression and cancer cell function, mediated through its lipogenic role, that together with its expression profile, identify ACC1 as a potential therapeutic target in breast cancer. Statement of significance This study investigates the impact of ACC1 deletion in breast cancer progression, revealing the importance of ACC1-derived lipids in breast cancer cell phenotypes and identifies ACC1 as a potential novel therapeutic target.
Model-based evaluation of critical nursery habitats for juvenile blue crabs through ontogeny: abundance and survival in seagrass, salt marsh, and unstructured bottom

Nursery habitats confer higher food availability and reduced predation risk, thereby contributing disproportionately more individuals per unit area to the population compared with other habitats. Nursery status is inferred through evaluation of four metrics: density, growth, survival, and linkage between juveniles and adults. Moreover, organisms commonly use multiple habitats as nurseries throughout ontogeny to satisfy shifting resource requirements. To this end, we conducted manipulative and mensurative field experiments to evaluate two nursery metrics, abundance and survival, for juvenile blue crabs across multiple size classes and habitats, including structurally complex habitats — seagrass meadows and salt marshes — and unstructured habitat (sand flats) in the York River, Chesapeake Bay. We also considered effects of site-specific spatial orientation within the York River, seasonality, physicochemical variables, and postlarval influx. Our results showed that abundance was higher in both seagrass meadows and salt marshes relative to unstructured sand, and positively associated with turbidity and post-larval abundance. Notably, seagrass habitats harbored the highest abundances of small (≤ 15 mm carapace width) juveniles, whereas salt marsh edge harbored the highest abundance of medium (16–30 mm carapace width) and large (31– 60 mm carapace width) juveniles. Moreover, survival was positively associated with juvenile size and structurally complex habitats relative to unvegetated controls. Seasonally, survival peaked in April, reached a seasonal minimum in August, and increased throughout fall. Finally, habitat-specific survival was dependent on spatial position: survival was elevated at upriver salt marsh and unstructured sand habitats compared to downriver counterparts. In nursery habitats of marine species, evaluation of survival, growth, abundance and ontogenetic habitat shifts has typically focused on relatively broad size ranges through the juvenile phase. Yet, ontogenetic shifts in habitat use may occur within much narrower size ranges, which has not been well studied and which is critical to the conservation and restoration of nursery habitats. We found that habitat-specific utilization rates differed by juvenile size class over a surprisingly narrow range of size, and were related to (1) the structural and biological characteristics of the nominal nursery habitats, (2) spatial gradients of environmental variables within the tributary, and (3) the likely trade-offs between growth and survival through ontogeny. Taken together, abundance and survival results indicate that seagrass meadows are key nurseries primarily for early-stage juveniles, whereas salt marshes are an intermediate nursery habitat for larger individuals to maximize growth-to-mortality ratios. Our results underscore the need to consider both habitats as critical nurseries for juvenile blue crabs throughout ontogeny.
FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array®. The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 seconds per 96-well plate, and achieves this result even in heavily downsampled data (∼30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.
Viscosin synthesis influence Pseudomonas fluorescens SBW25 colonization and microbial assembly at the wheat rhizoplane in response to plant genotype

The importance of microbial functions for plant health and performance is unquestioned. Beneficial rhizosphere bacteria such as Pseudomonas spp. promote plant growth and protect against pathogens by producing a range of bioactive compounds, including cyclic lipopeptides (CLPs) known for their antimicrobial activities. However, the natural roles of CLPs during interactions between bacteria and plants remain underexplored especially in natural systems. Here, Pseudomonas fluorescens SBW25 producing the CLP viscosin was used to unravel the impact of viscosin on bacterial colonization potential and microbiome assembly at the plant root. Two varieties of winter wheat were inoculated with SBW25 or its viscosin-deficient mutant. Viscosin was shown to improve colonization potential and have a significant effect on assembly of both bacterial and protist communities at the root-soil interphase. This effect was shown to be plant cultivar dependent and pinpoints the importance of microbe-microbe interactions in colonization of plant roots. This study provides new insights into the natural role of viscosin and specifically the importance of viscosin for the bacterial colonization of plant roots and in shaping the microbial communities associated with different wheat varieties.
Development of Specific barcodes for identification of Aloe species based on chloroplast DNA barcoding

DNA barcoding is currently an effective and extensively used tool for species identification. The chloroplast matK (maturase K) and rbcL (Ribulose-bisphosphate large subunit) are one of the most variable coding genes of angiosperms and have been recommended as a universal barcode for land plants. Aloe is a large genus of around 500 species, and most species are widely used for traditional medicinal purposes worldwide, viz., Aloe vera, Aloe ferox, Aloe arborescens, and Aloe maculate. This study evaluated the two chloroplast DNA barcodes (matK and rbcL) to develop a theoretical base for species identification and germplasm conservation of Aloe species. The Maximum Parsimony analysis was conducted to study the evolutionary relatedness of Aloe sequences using matK and rbcL sequences retrieved from the NCBI database. The results revealed that 49 Aloe accessions were segregated into four major clades based on the matK sequence. Likewise, the 29 Aloe accessions were distributed into two major clades based on the rbcL sequence. SNP (Single Nucleotide Polymorphism) site analysis was conducted to obtain the specific barcode of Aloe species and generate the corresponding DNA QR code that electronic devices could immediately recognize. This study provides innovative research methods for efficient species identification of the genus Aloe and indicates the possibility of correctly identifying, discriminating, and documenting the Aloe species.
Sequencing 4.3 million mutations in wheat promoters to understand and modify gene expression

Wheat is an important contributor to global food security, and further improvements are required to feed a growing human population. New functional genetics and genomics tools can help us to understand the function of different genes and to engineer beneficial changes. In this study, we used a promoter capture assay to sequence 2-kb regions upstream of all high-confidence annotated genes from 1,513 mutagenized plants from the tetraploid wheat variety Kronos. We identified 4.3 million induced mutations with an accuracy of 99.8%, resulting in a mutation density of 41.9 mutations per kb. We also remapped Kronos exome capture reads to Chinese Spring RefSeq v1.1, identified 4.7 million mutations, and predicted their effects on annotated genes. Using these predictions, we identified 59% more non-synonymous substitutions and 49% more truncation mutations than in the original study. To show the biological value of the new promoter dataset, we selected two mutations within the promoter of the VRN-A1 vernalization gene. Both mutations, located within transcription factor binding sites, significantly altered VRN-A1 expression, and one reduced the number of spikelets per spike. These publicly available sequenced mutant datasets provide rapid and inexpensive access to induced variation in the promoters and coding regions of most wheat genes. These mutations can be used to understand and modulate gene expression and phenotypes for both basic and commercial applications, where limited governmental regulations can facilitate deployment. These mutant collections, together with gene editing, provide valuable tools to accelerate functional genetic studies in this economically important crop. Significance Statement We sequenced 4.3 million induced mutations in the promoters and 4.7 million in the coding regions of most wheat genes. We also show how this public resource can be used to understand gene function, modulate gene expression, and generate changes in valuable wheat agronomic traits.
Basic colour terms for black and white in Chinese: with discussions on models of language development

The basic colour terms for black and white are studied in four archaic and two contemporary linguistic norms of the Chinese language. It is presented that studied Chinese linguistic norms use a common term for white and three different terms for black. It is suggested that the different basic colour terms for black might originate from different source languages. The study supports a panchronic language development instead of a diachronic one, and includes introductions to histories of the Chinese linguistic norms.
Metric Conjoint Segmentation Methods: A Monte Carlo Comparison

The authors compare nine metric conjoint segmentation methods. Four methods concern two-stage procedures in which the estimation of conjoint models and the partitioning of the sample are performed separately; in five, the estimation and segmentation stages are integrated. The methods are compared conceptually and empirically in a Monte Carlo study. The empirical comparison pertains to measures that assess parameter recovery, goodness-of-fit, and predictive accuracy. Most of the integrated conjoint segmentation methods outperform the two-stage clustering procedures under the conditions specified, in which a latent class procedure performs best. However, differences in predictive accuracy were small. The effects of degrees of freedom for error and the number of respondents were considerably smaller than those of number of segments, error variance, and within-segment heterogeneity.
Quantum Information Protocols with Gaussian States of Light

Quantum Information Protocols with Gaussian States of Light Quantum cryptography is widely regarded as the most mature field within the context of quantum information in the sense that its application and development has produced companies that base their products on genuine quantum mechanical principles. Examples include quantum random number generators and hardware for secure quantum key distribution. These technologies directly exploit quantum effects, and indeed this is where they offer advantages to classical products. This thesis deals with the development and implementation of quantum information protocols that utilize the rather inexpensive resource of Gaussian states. A quantum information protocol is essentially a sequence of state exchanges between some number of parties and a certain ordering of quantum mechanical unitary operators performed by these parties. An example of this is the famous BB84 protocol for secret key generation, where photons in different polarization states are sent from one party to the other and subsequently detected. In particular we introduce the idea of measurement device independence for continuous variable states and we present a proof-of-principle implementation of this protocol. Measurement device independence with Gaussian states is a promising avenue for the development of practical quantum key distribution with a relay network structure in environments where the distances are relatively short and there is a high number of users, such as an urban environment. In addition to this we consider various point-to-point configurations that utilize Gaussian states to achieve security. Notably, we also present a novel experiment demonstrating the feasibility of delegated quantum computing on encrypted data, where we show that we can reliably encrypt and decrypt input and output states when a server with quantum computing capabilities performs Gaussian operations.
Orthogonal expansions for VIX options under affine jump diffusions

In this work we derive new closed-form pricing formulas for VIX options in the jump-diffusion SVJJ model proposed by Duffie et al. [Econometrica, 2000, 68, 1343–1376]. Our approach is based on the classic methodology of approximating a density function with an orthogonal expansion of polynomials weighted by a kernel. Orthogonal expansions based on the Gaussian distribution, such as Edgeworth or Gram–Charlier expansions, have been successfully employed by a number of authors in the context of equity options. However, these expansions are not quite suitable for volatility or variance densities as they inherently assign positive mass to the negative real line. Here we approximate option prices via expansions that instead are based on kernels defined on the positive real line. Specifically, we consider a flexible family of distributions, which generalizes the gamma kernel associated with the classic Laguerre expansions. The method can be employed whenever the moments of the underlying variance distribution are known. It provides fast and accurate price computations, and therefore it represents a valid and possibly more robust alternative to pricing techniques based on Fourier transform inversions.
A semiparametric linear transformation model for general biased-sampling and right-censored data

The semiparametric linear transformation (SLT) model is a useful alternative to the proportional hazards ([9]) and proportional odds ([4]) models for studying the dependency of survival time on covariates. In this paper, we consider the SLT model for biased-sampling and right-censored data, a feature commonly encountered in clinical trials. Specifically, we develop an unbiased estimating equations approach based on counting process for the simultaneous estimation of unknown coefficients and handling of sampling bias. We establish the consistency and the asymptotic normality of the proposed estimator, and provide a closed form expression for the estimator’s covariance matrix that can be consistently estimated by a plug-in method. In a simulation study, we compare the finite sample properties of the proposed estimator with those of existing methods that either assumes that the sampling bias is of the length-bias type, or ignores the sampling bias altogether. The proposed method is further illustrated by two real clinical datasets.