Miracle-dz/UniFAR-FTUs huggingface dataset:
Lacking Invariant Chain and DM Functions MHC Class II Expression in Double Mutant Mice

Invariant (Ii) chain and DM functions are required at distinct stages during class II maturation to promote occupancy by diverse peptide ligands. The class II molecules expressed by mutant mouse strains lacking Ii chain or DM activities display discrete structural and functional abnormalities. The present report describes the cellular and biochemical characteristics of Ii 2 DM 2 doubly deﬁcient mice. As for Ii chain mutants, their mature A a b A b b dimers similarly exhibit reduced mobilities in SDS-PAGE, and in functional assays these molecules behave as if empty or occupied by an easily displaced peptide. Additionally, the present experiments demonstrate that the production of ﬂoppy A a b A b b dimers is TAP independent. In comparison with Ii chain mutants, Ii 2 DM 2 doubly deﬁcient cell populations exhibit increased peptide binding activities and consistently greater presentation abilities in T cell stimulation assays. These functional differences appear to reﬂect higher class II surface expression associated with their increased representation of B lymphocytes. We also observe defective B cell maturation in mice lacking Ii chain or DM expression, and interestingly, B cell development appears more severely compromised in Ii 2 DM 2 double mutants. These mutant mice lacking both Ii chain and DM activities should prove useful for analyzing nonconventional class II Ag presentation under normal physiological conditions in the intact animal. TheJournalofImmunology,1998, 160: 606–614.
Flexible silicon-based alpha-particle detector

The detection of alpha particles in the field can be challenging due to their short range in air of often only a few centimeters or less. This short range is a particular issue for measuring radiation inside contaminated pipework in the nuclear industry, for which there is currently no simple method available without cutting the pipes open. Here, we propose an approach for low cost, rapid, and safe identification of internally contaminated pipework based on a flexible 30 × 10 mm2 sheet of 50 μm thin crystalline silicon. Following established fabrication steps of pn-junction diodes, we have constructed a device with a signal-to-noise ratio of >20 in response to 5.5 MeV alpha-particles using a bespoke amplifier circuit. As flexible detectors may readily conform to a curved surface and are able to adapt to the curvature of a given pipeline, our prototype device stands out as a viable solution for nuclear decommissioning and related applications.
Amblyopia: Review of Prevalence, Natural History, Treatment and Outcomes

Amblyopia, defined as poor vision due to abnormal visual experience early in life, affects approximately three percent of the population and carries a projected lifetime risk of vision loss of at least 1.2%. The presence of amblyopia or its risk factors, mainly strabismus or refractive error, have been primary conditions targeted in childhood vision screenings. The continued support for such screenings requires evidence-based understanding of the prevalence and natural history of amblyopia and its predisposing conditions, and proof that treatment is effective in the long term with minimal negative impact on the patient and their family. This review summarises recent research relevant to the clinical understanding of amblyopia, including prevalence data, risk factors, the functional impact of amblyopia and optimum treatment regimes and their justification from a visual and life skills perspective. Collectively, these studies indicate that treatment for amblyopia is effective as it reduces overall prevalence and severity of vision loss. Correction of refractive error alone has been shown to significantly reduce amblyopia and lessor amounts of occlusion can be just as effective as greater amounts. Occlusion or penalisation in amblyopia treatment can create negative changes in behaviour in children impact on family life and these factors should be considered in prescribing treatment, particularly because of their influence on compliance with prescribed treatment. Treatment trials regarding the maximum age at which amblyopia treatment can still be effective and the importance of near activities during occlusion are ongoing. This review highlights the expansion of current knowledge regarding amblyopia and current treatment of amblyopic patients and aims to keep clinicians abreast of this knowledge.
Bioinformatics for Studying Environmental Microorganisms

The term “bioinformatics” was first introduced in 1970 by Ben Hesper and Paulien Hogeweg to indicate the study of information processing in biotic systems (Hesper and Hogeweg 1970). Since the late 1980s, bioinformatics has mostly been used to refer to computational analysis of biological data (Hogeweg 2011), and has brought a revolution to biology. Bioinformatics is an interdisciplinary field that combines computer science, statistics, mathematics, and biology. Current-day biological research is highly data driven, generated by microarray gene expression measurements, high-throughput sequencing technologies, and proteomic mass spectrometry, to name a few examples. Bioinformaticists develop critical computational tools for organizing, visualizing, storing, and analyzing the data obtained (Kesh 2004). As these large-scale data generation methods become more accessible to researchers around the world, the biological questions that may be addressed become more advanced, and the amount and complexity of biological data increase. For example, the speed and throughput of DNA sequencing platforms have increased faster than the computational power required to analyze the resulting data (Carlson 2003), making research projects increasingly dependent on innovative bioinformatics solutions. This is known as the “bioinformatics bottleneck. ” Bioinformatics has become an intrinsic component in molecular biological research, in which it plays an important role in the large-scale analysis of sequence data. These sequences may represent proteins (amino acids), or genes and genomes (DNA), and are stored in large databases, maintained CONTENTS
An Overview on the Factors Affecting Water-soluble Carbohydrates Concentration during Ensiling of Silage

Water-soluble carbohydrates (WSC) concentration during the ensiling process is influenced by both controllable and uncontrollable factors such as temperature, moisture level, fertilizers, additives nutrients, and time of ensiling of silage. The WSC contents may vary among the different fodder and forage species. The ensiling temperature has a limiting impact on water-soluble carbohydrates and their concentration decrease with increasing temperature. Crops should be harvested at optimum moisture and dry matter level to reach the required concentration of water-soluble carbohydrates to produce organic acids. Water-soluble carbohydrate concentration decreased with the crop's maturity due to the accumulation of carbohydrates in the grains. The evening cut has more concentration of WSC than that of the morning cut due to the photosynthesis process. The contents of WSC can be increased by using different kinds of additives during the ensiling process. Ensiling time has not much influence on the water-soluble carbohydrates. To understand these factors, we have a detailed review of the factors affecting the WSC of silage.
Game Design Proposal through Verification of Game “Locomo de Balamingo” Supporting Exercise for Elderly: Standing on One Leg with Eyes Open

We developed a game called " Locomo de Balamingo " to improve assertiveness and sustainability of participants at the “Standing Exercise with Eyes Open” training, which is used for the prevention of locomotive syndrome. This training requires participants to balance on a single foot, keeping their eyes open for 60 s, three times for each foot with the aim of keeping their bones, joints, and muscles healthy. The subjects comprise healthy elderly persons. A Windows PC and Microsoft Kinect were used for human movement sensing to conduct this game. In this paper, we discuss the interface and game design of “ Locomo de Balamingo ” and describe its application in the training of elderly people. Moreover, in the case of healthcare games targeting elderly persons who do not have enough experience in using digital games, we aim to identify those aspects of game design, including failure experiences, graphics, and music, that are effective in enhancing the participants’ assertiveness and sustainability. Verification shows that although graphics with relatively bright and easy-to-understand scenes are preferred, some participants reported that they do not remember the graphics, or they do not particularly care about them. Thus, it is concluded that any scene can be invoked provided that the pattern does not give a sense of discomfort. The game design presented an intuitive interface using simple body movements, which functioned effectively. The addition of failure experience can also be considered an effective means of improving assertiveness. In summary, many instances of positive feedback were received from elderly participants, indicating that the game is useful in promoting the health of elderly people.
VISUAL 2016 Proceedings

In this paper, we describe an ontology-based system to inventory and model installed sensor and respective data processing resources on-board airborne surveillance aircrafts. The algorithms are packaged and described in form of discrete processing modules, each representing a low level image processing step. While the implementation of the algorithms is kept detached in a separate library, the description of modules and its parameters are stored in an ontology, representing a knowledge database using Web Ontology Language as a knowledge representation language. Based on the module description stored in the knowledge database, it is possible to identify and manage processing chains capable of solving complex image processing tasks. Keywords-Ontology; Web Ontology Language (OWL); Image Processing Management; Knowledge Management; sensor and data ressources.
EXOSOMAL NON-CODING RNAS FOR MONITORING BREAST CANCER PATIENTS

Exosomal non-coding RNAs were explored as a basis for monitoring breastcancer patients. Specifically, exosomal microRNAs (miRNAs), and especially miR-21, were the focus of this project. Consistent with our hypothesis, we found that miR-21 was highly expressed in exosomes of cancer patients compared to ‘healthy’ individuals. However, circulating miR-21 levels, when compared in postversus presurgery serum, did not decline for all five experimental patients. This unexpected observation could be the result of small sample size and lack of both endogenous and exogenous miRNA controls. All five patients had early stage breast cancer. We found that our methods were possibly not sensitive enough to detect very small differences between preand post-surgery serum. Future experiments might involve samples at a later stage of cancer. High miR-21 levels in post-tumor resection serum raised an issue concerning how long cancerous exosomes can stay in the blood after tumor removal. As well, recovery time from surgery might vary from patient to patient. Consequently, additional serum samples from patients might be obtained at a later time after surgery. This research signals an advance over current modes of cancer detection and involves a more patient-centric approach. Customized-miRNA panels might someday be the standard for each breast-cancer patient. The release of breast-tumor exosomes and their miRNA cargo
Effects of debonding on Lamb wave propagation in a bonded composite structure under variable temperature conditions

Bonded composite structures are a special type of sandwich-like structures in which multiple carbon-fibre laminates are bonded with adhesives, and debonding can appear at the bond layer due to variable loading and uncertain operating conditions. This study aims to investigate the debonding effects on Lamb wave propagation in a bonded composite structure under variable ambient temperature conditions. In the process, a combined theoretical analysis, time-domain spectral element simulation and experimental analysis of elastic wave propagation in a carbon-fibre reinforced adhesively-bonded composite structure have been carried out. It is shown that theoretical analysis and spectral formulation are effectively able to capture the behaviour of Lamb modes in the healthy structure due to variations in ambient temperature. It is found that the primary anti-symmetric Lamb wave mode amplitude and velocity decrease with an increase in ambient temperature. Furthermore, the spectral formulation accurately captures the effects of debonding on the Lamb wave signals under variable temperature conditions that are consistent with the experimental results. Finally, temperature correction factors are proposed for the primary anti-symmetric mode velocity and amplitude difference calculations and the effectiveness of the factors is successfully verified for selected study cases.
Epidemiology of In uenza Virus Reinfection: A Retrospective Analysis of a Nine-year In uenza Surveillance Data

Jing Wang Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention Lina Jiang Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention Yunan Xu Duke University Weitao He Guangxi Center for Disease Control and Prevention Chao Zhang Guangxi Center for Disease Control and Prevention Fuyin Bi Guangxi Center for Disease Control and Prevention Yi Tan Guangxi Center for Disease Control and Prevention Chuanyi Ning (  ningchuanyi@126.com ) Guangxi Medical University https://orcid.org/0000-0002-8748-2780
Testing history matching algorithms

1 Introduction 51.1 General . 51.2 Background . 51.3 Bayesian history matching . 51.4 Inverse modelling tool . 51.5 Test example, Veslefrikk . 62 Bayesian history matching 72.1 Theory . 72.2 Prior . 72.3 Likelihood . 72.4 Algorithms . 82.4. 1 Combination of ﬂuvial realisations . 92.5 Norms . 92.5. 1 The objective function . 92.5. 2 Distance in the state space . 102.5. 3 Fitness sharing . 113 Veslefrikk 133.1 The Prior Model . 133.1. 1 Layers . 133.1. 2 The modelling . 143.1. 3 The Data . 143.1. 4 History matching parameters . 143.2 Synthetic data . 154 Genetic algorithm 174.1 Theory . 174.2 Implemented algorithm . 184.3 Results . 185 MCMC Algorithm 215.1 Theory . 215.2 Results . 215.2. 1 Proposing from the prior . 215.2. 2 Proposing using combination of ﬂuvial reservoirs . 225.2. 3 Conclusions MCMC runs . 246 Conclusion 27Bibliography 293
Local rainbow colorings for various graphs

Motivated by a problem in theoretical computer science suggested by Wigderson, Alon and Ben-Eliezer studied the following extremal problem systematically one decade ago. Given a graph $H$, let $C(n,H)$ be the minimum number $k$ such that the following holds. There are $n$ colorings of $E(K_{n})$ with $k$ colors, each associated with one of the vertices of $K_{n}$, such that for every copy $T$ of $H$ in $K_{n}$, at least one of the colorings that are associated with $V(T)$ assigns distinct colors to all the edges of $E(T)$. In this paper, we obtain several new results in this problem including: \begin{itemize} \item For paths of short length, we show that $C(n,P_{4})=\Omega(n^{1/5})$ and $C(n,P_{t})=\Omega(n^{1/3})$ with $t\in\{5,6\}$, which significantly improve the previously known lower bounds $(\log{n})^{\Omega(1)}$. \item We make progress on the problem of Alon and Ben-Eliezer about complete graphs, more precisely, we show that $C(n,K_{r})=\Omega(n^{2/3})$ when $r\geqslant 8$. This provides the first instance of graph for which the lower bound goes beyond the natural barrier $\Omega(n^{1/2})$. Moreover, we prove that $C(n,K_{s,t})=\Omega(n^{2/3})$ for $t\geqslant s\geqslant 7$. \item When $H$ is a star with at least $4$ leaves, a matching of size at least $4$, or a path of length at least $7$, we give the new lower bound for $C(n,H)$. We also show that for any graph $H$ with at least $6$ edges, $C(n,H)$ is polynomial in $n$. All of these improve the corresponding results obtained by Alon and Ben-Eliezer.
LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech

Existing Text-to-Speech (TTS) systems need to read messages from the email which may have Personal Identifiable Information (PII) to text messages that can have a streak of emojis and punctuation. 92% of the world’s online population use emoji1 with more than 10 billion emojis sent everyday2. Lack of preprocessor leads to messages being read as-is including punctuation and infographics like emoticons. This problem worsens if there is a continuous sequence of punctuation/emojis that are quite common in real-world communications like messaging, Social Networking Site (SNS) interactions etc. [1]. In this work, we aim to introduce a lightweight intelligent preprocessor (LIP) that can enhance the readability of a message before being passed downstream to existing TTS systems. We propose multiple sub-modules including: expanding contraction, censoring swear words, and masking of PII, as part of our preprocessor to enhance the readability of text. With a memory footprint of only 3.55 MB and inference time of 4 ms for up to 50-character text, our solution is suitable for real-time deployment. This work being the first of its kind, we try to benchmark with an open independent survey, the result of which shows 76.5% preference towards LIP enabled TTS engine as compared to standard TTS.
GALVIN’S PROPERTY AT LARGE CARDINALS AND THE AXIOM OF DETERMINACY

In the ﬁrst part of this paper we explore the possibility for a very large cardinal κ to carry a κ -complete ultraﬁlter without Galvin’s property. In this context we prove the consistency of every ground model κ -complete ultraﬁlter extends to a non-Galvin one. Oppositely, it is also consistent that every ground model κ -complete ultraﬁlter extends to a P -point ultraﬁlter, hence to another one satisfying Galvin’s property. We also study Galvin’s property at large cardinals in the choiceless context, especially under AD . Finally, we apply this property to a classical problem in partition calculus by proving the relation λ → ( λ, ω + 1) 2 under “ AD + V = L ( R )” for unboundedly many λ > cf( λ ) > ω below Θ.
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multi-modality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated cross-attention block (MCCA) is designed to extract the multi-modality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the proposed model with six CNN-based models and six transformer-based models on the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the proposed model achieves state-of-the-art brain tumor segmentation performance compared with all the competitors.
Random projections for high-dimensional curves

Modern time series analysis requires the ability to handle datasets that are inherently high-dimensional; examples include applications in climatology, where measurements from numerous sensors must be taken into account, or inventory tracking of large shops, where the dimension is deﬁned by the number of tracked items. The standard way to mitigate computational issues arising from the high-dimensionality of the data is by applying some dimension reduction technique that preserves the structural properties of the ambient space. The dissimilarity between two time series is often measured by “discrete” notions of distance, e.g. the dynamic time warping, or the discrete Fréchet distance, or simply the Euclidean distance. Since all these distance functions are computed directly on the points of a time series, they are sensitive to diﬀerent sampling rates or gaps. The continuous Fréchet distance oﬀers a popular alternative which aims to alleviate this by taking into account all points on the polygonal curve obtained by linearly interpolating between any two consecutive points in a sequence. We study the ability of random projections à la Johnson and Lindenstrauss to preserve the continuous Fréchet distance of polygonal curves by eﬀectively reducing the dimension. In particular, we show that one can reduce the dimension to O ( ε − 2 log N ) , where N is the total number of input points while preserving the continuous Fréchet distance between any two determined polygonal curves within a factor of 1 ± ε . We conclude with applications on clustering.
Rectification of polymer translocation through nanopores by chiral and nonchiral active particles

We study unbiased translocation of a ﬂexible polymer chain through a membrane pore under the inﬂuence of active noise and steric exclusion using Langevin dynamics simulations. The active noise is incorporated by introducing nonchiral and chiral active particles on one or both sides of the membrane. Translocation of the polymer into either side of the pore is assisted by an eﬀective pulling due to particle activity and is hindered by an eﬀective pushing due to steric repulsions between the polymer and active particles. As a result of the competition between these eﬀective forces, we ﬁnd a transition between two rectiﬁed ( cis -to- trans and trans -to- cis ) states. This transition is identiﬁed by a sharp increase of translocation time. It varies depending on the system parameters such as particle activity, area fraction and chirality whose eﬀects are explored in this work.
FRAS: Federated Reinforcement Learning empowered Adaptive Point Cloud Video Streaming

Point cloud video transmission is challenging due to high encoding/decoding complexity, high video bitrate, and low latency requirement. Consequently, conventional adaptive streaming methodologies often find themselves unsatisfactory to meet the requirements in threefold: 1) current algorithms reuse existing quality of experience (QoE) definitions while overlooking the unique features of point cloud video thus failing to provide optimal user experience, 2) most deep learning approaches require long-span data collections to learn sufficiently varied network conditions and result in long training periods and capacity occupation, 3) cloud training approaches pose privacy risks caused by leakage of user reported service usage and networking conditions. To overcome the limitations, we present FRAS, the first federated reinforcement learning framework, to the best of our knowledge, for adaptive point cloud video streaming. We define a new QoE model which takes the unique features of point cloud video into account. Each client uses reinforcement learning (RL) to train video quality selection with the objective of optimizing the user's QoE under multiple constraints. Then, a federated learning framework is integrated with the RL algorithm to enhance training performance with privacy preservation. Extensive simulations using real point cloud videos and network traces reveal the superiority of the proposed scheme over baseline schemes. We also implement a prototype that demonstrates the performance of FRAS via real-world tests.