Miracle-dz/UniFAR-FTUs huggingface dataset:
IL15 Enhances CAR-T Cell Antitumor Activity by Reducing mTORC1 Activity and Preserving Their Stem Cell Memory Phenotype

IL15 promotes metabolically fit CAR-T cells with a lessdifferentiated stem cell memory phenotype. Addition of IL15 is a clinically translatable method for improving the antitumor function of ex vivo expanded CAR-T cells, which could improve outcomes for patients. Improvements in the quality and fitness of chimeric antigen receptor (CAR)-engineered T cells, through CAR design or manufacturing optimizations, could enhance the therapeutic potential of CAR-T cells. One parameter influencing the effectiveness of CAR-T cell therapy is the differentiation status of the final product: CAR-T cells that are less-differentiated and less exhausted are more therapeutically effective. In the current study, we demonstrate that CAR-T cells expanded in IL15 (CAR-T/IL15) preserve a less-differentiated stem cell memory (Tscm) phenotype, defined by expression of CD62L+CD45RA+ CCR7+, as compared with cells cultured in IL2 (CAR-T/IL2). CAR-T/IL15 cells exhibited reduced expression of exhaustion markers, higher antiapoptotic properties, and increased proliferative capacity upon antigen challenge. Furthermore, CAR-T/IL15 cells exhibited decreased mTORC1 activity, reduced expression of glycolytic enzymes and improved mitochondrial fitness. CAR-T/IL2 cells cultured in rapamycin (mTORC1 inhibitor) shared phenotypic features with CAR-T/IL15 cells, suggesting that IL15-mediated reduction of mTORC1 activity is responsible for preserving the Tscm phenotype. CAR-T/IL15 cells promoted superior antitumor responses in vivo in comparison with CAR-T/IL2 cells. Inclusion of cytokines IL7 and/or IL21 in addition to IL15 reduced the beneficial effects of IL15 on CAR-T phenotype and antitumor potency. Our findings show that IL15 preserves the CAR-T cell Tscm phenotype and improves their metabolic fitness, which results in superior in vivo antitumor activity, thus opening an avenue that may improve future adoptive T-cell therapies.
Directed firefly algorithm for multimodal problems

Firefly Algorithm (FFA) is very recently introduced one of the Swarm Intelligence (SI) algorithms, developed by the inspiration from flashing light characteristics of fireflies. This algorithm assumes each firefly as potential solutions and brightness associated with them depends on their performance over optimizing problem. The Swarm of firefly get attracted towards the goal by following the brighter firefly and if no such firefly is found, they will move randomly. The basic FFA algorithm follows classical update strategy of attraction and movement of the swarm. This strategy may not produce good quality of results and seems to converge prematurely. The basic movement strategy of firefly may be modified and searching quality of FFA can be improved. This paper proposes novel fireflys movement update strategy. The basic FFA algorithm move the firefly randomly if brighter firefly is not found, this flaw is attended in this paper and thus proposed Directed Firefly Algorithm (DFA). The DFA directs the randomly moving firefly to search around the brightest firefly in the current iteration. To preserve the diversity and avoid premature convergence, the directing strategy is followed at certain refresh rate. The complete performance comparisons of the proposed algorithm are validated against various SI algorithms over standard test cases. The test suite comprises of complex, multimodal and scalable optimization problems, whose dimensions are varied form 10, 30 and 50. The proposed strategy has remarkably improved the quality of solution and convergence rate of proposed algorithm.
How repeatable is the Environmental Impact Classification of Alien Taxa (EICAT)? Comparing independent global impact assessments of amphibians

Abstract The magnitude of impacts some alien species cause to native environments makes them targets for regulation and management. However, which species to target is not always clear, and comparisons of a wide variety of impacts are necessary. Impact scoring systems can aid management prioritization of alien species. For such tools to be objective, they need to be robust to assessor bias. Here, we assess the newly proposed Environmental Impact Classification for Alien Taxa (EICAT) used for amphibians and test how outcomes differ between assessors. Two independent assessments were made by Kraus (Annual Review of Ecology Evolution and Systematics, 46, 2015, 75‐97) and Kumschick et al. (Neobiota, 33, 2017, 53‐66), including independent literature searches for impact records. Most of the differences between these two classifications can be attributed to different literature search strategies used with only one‐third of the combined number of references shared between both studies. For the commonly assessed species, the classification of maximum impacts for most species is similar between assessors, but there are differences in the more detailed assessments. We clarify one specific issue resulting from different interpretations of EICAT, namely the practical interpretation and assigning of disease impacts in the absence of direct evidence of transmission from alien to native species. The differences between assessments outlined here cannot be attributed to features of the scheme. Reporting bias should be avoided by assessing all alien species rather than only the seemingly high‐impacting ones, which also improves the utility of the data for management and prioritization for future research. Furthermore, assessments of the same taxon by various assessors and a structured review process for assessments, as proposed by Hawkins et al. (Diversity and Distributions, 21, 2015, 1360), can ensure that biases can be avoided and all important literature is included.
Analyzing heart rate variability using a photoplethysmographic signal measuring system

A heart rate variability (HRV) measuring system and its analysis method have been developed in this study. It is composed of a hardware measuring system based on a noninvasive photoplethysmographic (PPG) signal measuring device to acquire oxyhemoglobin saturation using pulse oximetry (SpO2) signals and a further software package including the methods used to filter and analyze the signals for heart rate variability. Firstly, an experiment is designed for measuring heartbeat using the system to observe whether the empirical mode decomposition(EMD) can really inhibit noise or not on one volunteer with 10 minutes repeated for 10 times. Then, the hardware system and analysis method are tested on another 10 volunteers before and after receiving cold face immersion. The results of the first experiment have no significant difference with commercial instrument (p > 0.05), but the results using EMD perform better when signals are contaminated by artifacts. The second part experiment is subdivided into two stages. The results show that HR values at each stage have no significant difference with commercial instrument (p > 0.05). The LF significantly decreases from 0.33 ± 0.03 to 0.31 ± 0.03, while HF significantly increases from 0.41 ± 0.07 to 0.43 ± 0.07 indicating cold face immersion can increase parasympathetic and decrease sympathetic actions. Hence, LF/HF changes significantly from high (0.85 ± 0.17) to low (0.74 ± 0.17) before and after adding stimulation. Due to the reasons above, it confirms that the developed system can measure heartbeat and observe the heart rate variability. So the findings of this research may be useful for developing a home-made system to continuously measure HR and HRV for the purpose of convenience, portability, and operability.
Cascaded Neural Networks with Selective Classifiers and its evaluation using Lung X-ray CT Images

Lung nodule detection is a class imbalanced problem because nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority class. We therefore propose cascaded convolutional neural networks to cope with the class imbalanced problem. In the proposed approach, cascaded convolutional neural networks that perform as selective classifiers filter out obvious non-nodules. Successively, a convolutional neural network trained with a balanced data set calculates nodule probabilities. The proposed method achieved the detection sensitivity of 85.3% and 90.7% at 1 and 4 false positives per scan in FROC curve, respectively.
Multi-label collective classification in multi-attribute multi-relational network data

Classical machine learning techniques assume the data to be i.i. d. , but the real world data is inherently relational and can generally be represented using graphs or some variants of a graph representation. The importance of modeling relational data is evident from its increasing presence in many domains: Telecom networks, WWW, social networks, organizational networks, images, protein sequences, etc. This field has recently been receiving a lot of attention in various communities under different themes depending on the problem addressed and the nature of solution proposed. Collective classification is one such popular approach which involves the use of a local classifier that embeds the node's own attributes and neighbors' information in a feature vector, and classifies the nodes in an iterative procedure. Despite the increasing popularity, there is not much attention paid towards datasets with multiple attributes and multi-relational (MAMR) networks under multi-label scenarios. In MAMR data, nodes can be represented using multiple types of attributes (attribute views) and there are multiple link types between the nodes. For example, in Twitter, users can be represented using their tweets, urls shared, hashtags and list memberships. And different Twitter users can be connected using follower, followed by and re-tweet links. Secondly, in many networks, nodes are associated with more than one label. For instance, Twitter users can be tagged with one or more labels from a set L, where L contains various movie genres that a user might like. Motivated by this, we propose a learning technique for multi-label collective classification using multiple attribute views on multi-relational network data which captures complex label correlations within and across attribute/relationship types. We empirically evaluate our proposed approach on Twitter and MovieLens datasets, and we show that it performs better than the state-of-art approaches.
Data Fusion: Boosting Performance in Keyword Extraction

In a time when volatile data is in constant growth, the importance of keyword extraction becomes particularly evident. Keywords can quickly identify, structure and reveal potentially worthwhile information. The quality of automatically extracted keywords reflects the individual characteristics of the various retrieval approaches that may be used for extraction. A combinatorial approach using multiple heuristic keyword extraction algorithms may enhance the quality of the results significantly, though it may also compound the inherent limitations. In our paper we compare different ranking aggregation and data fusion methods for single documents. Furthermore we apply principal component analysis to determine an optimal selection of retrieval algorithms for combination with respect to the use-case. To validate our approach, we provide a statistical evaluation with real-world examples.
A Metasurface-Based Multilayer Wideband Circularly Polarized Patch Antenna Array With a Parallel Feeding Network for Q-Band

In this letter, a metasurface (MS)-based multilayer wideband circularly polarized (CP) 4 × 4 truncated corner patch antenna array is proposed for Q-band applications. The patch array consists of four 2 × 2 patch subarrays, an MS structure, and an H-shaped substrate integrated waveguide power divider. Each 2 × 2 patch subarray acts as the CP radiating element, which is fed by a compact parallel-fed microstrip power divider using a sequential rotation technology. The MS structure has 4 × 4 square plates covering over each patch to improve |S11|, axial ratio (AR), and gain performances. The antenna was fabricated and measured to verify the practical feasibility. −10 dB |S11| bandwidth of 32% (37.4–52.2 GHz), 3 dB AR bandwidth of 25.8% (39.2–51.5 GHz), 3 dB gain bandwidth of 27.6%, and the peak gain of 18.2 dBic are achieved in the vicinity of 45 GHz.
Optical Properties Measurements of Rat Muscle and Myocardium at 980 and 1860 nm Using Single Integrating Sphere Technique

of a thesis at the University of Miami. Thesis supervised by Suhrud M. Rajguru, Ph.D., and Nelson Salas, Ph.D. No. of pages in text. (50) The optical properties of a rat muscle and myocardium were measured at two wavelengths 980 and 1860 nm. The tissues used in this experiment were fresh and sliced to a specific thickness of 600 μm. A single integrating sphere system was used to determine the diffuse reflectance, diffuse transmittance, and collimated transmittance of these tissue samples. An inverse Monte Carlo algorithm was then used to calculate the optical properties from the measurements obtained from the single integrating sphere system. The system was calibrated and validated by using distilled water. The absorption coefficient of the rat’s muscles was found to be greater at 1860 nm than at 980 nm. At 980 and 1860 nm, the mean absorption coefficient over 6 samples was 0.182 mm-1 and 1.9785 mm-1 respectively. At 980 and 1860 nm the mean absorption coefficient over 6 samples of the rat’s myocardium was 0.6263 mm-1 and 1.6370 mm-1 respectively. The results showed that there is an increase in absorption in 1860 nm, which follows the general trend of water absorption spectra. Identifying the optical properties is a key step to understanding the mechanism behind photostimulation.
Interaction Effects of the Leu162Val PPARα and Pro12Ala PPARγ2 Gene Variants with Renal Function in Metabolic Syndrome Population

Leu162Val PPARα and Pro12Ala PPARγ2 were investigated for their individual and their interactive impact on MS and renal functionality (RF). 522 subjects were investigated for biochemical and anthropometric measurements. The diagnosis of MS was based on the IDF definition (2009). The HOMA 2 was used to determine HOMA-β, HOMA-S and HOMA-IR from FPG and FPI concentrations. RF was assessed by estimating the GFR. PCR-RFLP was performed for DNA genotyping. Allele frequencies were 0.845 for Pro and 0.155 for Ala, and were 0.915 for Leu and 0.085 for Val. We showed that carriers of the PPARα Val 162 allele had lower urea, UA and higher GFR compared to those homozygous for the Leu162 allele. Subjects carried by PPARγ2Ala allele had similar results. They also had reduced FPG, FPI and HOMA-IR, and elevated HOMA-β and HOMA-S compared to those homozygous for the Pro allele. Subjects were divided into 4 groups according to the combinations of genetic alleles of the 2 polymorphisms. Subjects carrying the Leu/Val with an Ala allele had lower FPG, PPI, HOMA-IR, urea, UA levels, higher HOMA-β, HOMA-S and GFR than different genotype combinations. Leu162Val PPARα and Pro12Ala PPARγ2 can interact with each other to modulate glucose and insulin homeostasis and expand their association with overall better RF.
Efficient Search in Very Large Databases

We consider the poblean of performin efficient seer& in a large database system. We wt a novel data slructurhig technique and show how a branch and bound search algorithm canusethepmposeddataorganktiontopnmetheseaxch space. Simulation results confirm that, using these techniquq a search can be expedited signikantly without incuring a large storage penalty. As a side benefik it is possible to organize the search to obtain suwessiveappro~tothe desired solution with considerable reduction in total search
Packet‐based Hierarchal Soft Shadow Mapping

Recent soft shadow mapping techniques based on back‐projection can render high quality soft shadows in real time. However, real time high quality rendering of large penumbrae is still challenging, especially when multilayer shadow maps are used to reduce single light sample silhouette artifact. In this paper, we present an efficient algorithm to attack this problem. We first present a GPU‐friendly packet‐based approach rendering a packet of neighboring pixels together to amortize the cost of computing visibility factors. Then, we propose a hierarchical technique to quickly locate the contour edges, further reducing the computation cost. At last, we suggest a multi‐view shadow map approach to reduce the single light sample artifact. We also demonstrate its higher image quality and higher efficiency compared to the existing depth peeling approaches.
Chiral primaries in the Leigh-Strassler deformed Script N = 4 SYM?a perturbative study

We look for chiral primaries in the general Leigh-Strassler deformed = 4 super Yang-Mills theory by systematically computing the planar one-loop anomalous dimension for single trace operators up to dimension six. The operators are organised into representations of the trihedral group, Δ(27), which is a symmetry of the Lagrangian. We find an interesting relationship between the U(1)R-charge of chiral primaries and the representation of Δ(27) to which the operator belongs. Up to scaling dimension Δ0 = 6 (and conjecturally to all dimensions) the following holds: The planar one-loop anomalous dimension vanishes only for operators that are in the singlet or three dimensional representations of Δ(27). For other operators, the vanishing of the one-loop anomalous dimension occurs only in a sub-locus in the space of couplings.
Deriving the PCC iterative decoder from the SCC iterative decoder

In this paper, we show that the iterative decoder for parallel concatenated codes can be derived from the iterative decoder for serially concatenated codes. In order to show this, we use the observation that any parallel concatenated code (PCC) may be regarded as a serially concatenated code (SCC) with certain code constraints satisfied. By applying these code constraints to the SCC iterative decoder we obtain the PCC iterative decoder, and hence show that the latter can be seen as a special case of the former.
SMaSH: a benchmarking toolkit for human genome variant calling

MOTIVATION Computational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call variants from human sequencing data disagree on many of their predictions, and current methods to evaluate accuracy and computational performance are ad hoc and incomplete. Agreement on benchmarking variant calling methods would stimulate development of genomic processing tools and facilitate communication among researchers. RESULTS We propose SMaSH, a benchmarking methodology for evaluating germline variant calling algorithms. We generate synthetic datasets, organize and interpret a wide range of existing benchmarking data for real genomes and propose a set of accuracy and computational performance metrics for evaluating variant calling methods on these benchmarking data. Moreover, we illustrate the utility of SMaSH to evaluate the performance of some leading single-nucleotide polymorphism, indel and structural variant calling algorithms. AVAILABILITY AND IMPLEMENTATION We provide free and open access online to the SMaSH tool kit, along with detailed documentation, at smash.cs. berkeley.edu
Biochemical and Functional Analysis of Two Plasmodium falciparum Blood-Stage 6-Cys Proteins: P12 and P41

The genomes of Plasmodium parasites that cause malaria in humans, other primates, birds, and rodents all encode multiple 6-cys proteins. Distinct 6-cys protein family members reside on the surface at each extracellular life cycle stage and those on the surface of liver infective and sexual stages have been shown to play important roles in hepatocyte growth and fertilization respectively. However, 6-cys proteins associated with the blood-stage forms of the parasite have no known function. Here we investigate the biochemical nature and function of two blood-stage 6-cys proteins in Plasmodium falciparum, the most pathogenic species to afflict humans. We show that native P12 and P41 form a stable heterodimer on the infective merozoite surface and are secreted following invasion, but could find no evidence that this complex mediates erythrocyte-receptor binding. That P12 and P41 do not appear to have a major role as adhesins to erythrocyte receptors was supported by the observation that antisera to these proteins did not substantially inhibit erythrocyte invasion. To investigate other functional roles for these proteins their genes were successfully disrupted in P. falciparum, however P12 and P41 knockout parasites grew at normal rates in vitro and displayed no other obvious phenotypic changes. It now appears likely that these blood-stage 6-cys proteins operate as a pair and play redundant roles either in erythrocyte invasion or in host-immune interactions.
Energy-efficient caching strategies in ad hoc wireless networks

In this paper, we address the problem of energy-conscious cache placement in wireless ad hoc networks. We consider a network comprising a server with an interface to the wired network, and some nodes requiring access to the information stored at the server. In order to reduce access latency in such a communication environment, an effective strategy is caching the server information at some nodes distributed across the network. Caching, however, can considerably impact the system energy expenditure; for instance, disseminating information incurs additional energy burden. Since wireless devices have limited amounts of available energy, we need to design caching strategies that optimally trade-off between energy consumption and access latency. We pose our problem as an integer linear program. We show that this problem is the same as a special case of the connected facility location problem, which is known to be NP-hard. We devise a polynomial time algorithm which provides a sub-optimal solution. The proposed algorithm applies to any arbitrary network topology and can be implemented in a distributed and asynchronous manner. In the case of a tree topology, our algorithm gives the optimal solution. In the case of an arbitrary topology, it finds a feasible solution with an objective function value within a factor of 6 of the optimal value. This performance is very close to the best approximate solution known today, which is obtained in a centralized manner. We compare the performance of our algorithm against three candidate caching schemes, and show via extensive simulation that our algorithm consistently outperforms these alternative schemes.
Complementarity and Institutional Change: How Useful a Concept?

The concept of institutional complementarity – i.e. the idea that the co-existence of two or more institutions enhances the functioning of each – is increasingly used to explain why institutions are resistant to change and why introducing new institutions into a system often leads to unintended consequences or failure to achieve the intended objective. While the concept is appealing and intuitive, in reality its utility for explaining change is less than straightforward. This paper utilizes examples from comparative political economy to, first, unpack and delineate the concept and address the issue of how to measure the strength or ‘binding force’ of complementarities. Second, it assesses the utility of the concept for explaining institutional change. It is suggested that one’s view of the methods and utility of measuring complementarity will hinge importantly on one’s general theory of institutions and institutional change. In the end, while institutional complementarities are significant, assessing their causal effect on institutional change is difficult and ambiguous in most instances. A better understanding requires that we embed complementarities within a more general theory of institutional change which takes a broader view of the ways in which institutions interconnect and change.