Miracle-dz/UniFAR-FTUs huggingface dataset:
Translational initiation factor eIF5 replaces eIF1 on the 40S ribosomal subunit to promote start-codon recognition

In eukaryotic translation initiation AUG recognition of the mRNA requires accommodation of Met-tRNAi in a “PIN” state, which is antagonized by the factor eIF1. eIF5 is a GTPase activating protein (GAP) of eIF2 that additionally promotes stringent AUG selection, but the molecular basis of its dual function was unknown. We present a cryo-electron microscopy (cryo-EM) reconstruction of a 48S pre-initiation complex (PIC), at an overall resolution of 3.0 Å, featuring the N-terminal domain (NTD) of eIF5 bound to the 40S subunit at the location vacated by eIF1. eIF5 interacts with and allows a more accommodated orientation of Met-tRNAi. Substitutions of eIF5 residues involved in the eIF5-NTD/tRNAi interaction influenced initiation at near-cognate UUG codons in vivo, and the closed/open PIC conformation in vitro, consistent with direct stabilization of the codon:anticodon duplex by the wild-type eIF5-NTD. The present structure reveals the basis for a key role of eIF5 in start-codon selection.
Supervised Clustering with Structural SVMS

Supervised clustering is the problem of training clustering methods to produce desirable clusterings. Given sets of items and complete clusterings over these sets, a supervised clustering algorithm learns how to cluster future sets of items in a similar fashion, typically by changing the underlying similarity measure between item pairs. This work presents a general approach for training clustering methods such as correlation clustering and k-means/spectral clustering able to optimize to task-specific performance criteria using structural SVMs. We empirically and theoretically analyze our supervised clustering approach on a variety of datasets and clustering methods. This analysis also leads to general insights about structural SVMs beyond supervised clustering. Specifically, since clustering is a NP-hard task and the corresponding training problem likewise must make use of approximate inference during training of the parameters, we present a detailed theoretical and empirical analysis of the general use of approximations in structural SVM training.
Dual-Frequency Combline Resonators for Four-Port Bandpass Filters

This paper presents a new dual-frequency coaxial cavity resonator and its potential in the implementation of a new class of four port filters that provide cost and volume efficient solutions for emerging multiband base transceiver stations (BTS). The proposed concept delivers in a single physical structure two virtually independent bandpass filters without significant sacrifices in design or manufacturing complexity as well as performance. This is achieved by introducing within a single cavity two coaxial stubs for two independent frequency bands. Experimental results on a fabricated prototype demonstrate the validity of the concept and the potential of its implementation.
Intersection Intelligence: Supporting Urban Platooning with Virtual Traffic Lights over Virtualized Intersection-Based Routing

The advent of the autonomous car is paving the road to the realization of ideas that will help optimize traffic flows, increase safety and reduce fuel consumption, among other advantages. We present one proposal to bring together Virtual Traffics Lights (VTLs) and platooning in urban scenarios, leaning on vehicle-to-vehicle (V2V) communication protocols that turn intersections into virtual containers of data. Newly-introduced protocols for the combined management of VTLs and platoons are validated by simulation, comparing a range of routing protocols for the vehicular networks with the baseline given by common deployments of traditional traffic lights ruled by state-of-the-art policies. The simulation results show that the combination of VTLs and platoons can achieve significant reductions in travel times and fuel consumption, provided that proper algorithms are used to handle the V2V communications.
Protein-G-based lateral flow assay for rapid serodiagnosis of brucellosis in domesticated animals

ABSTRACT This study was conducted to develop and evaluate protein-G-based lateral flow assay (LFA) for rapid serodiagnosis of brucellosis in various domesticated animal species. The assay diagnostic performance was tested with 144 reference and 356 field sera samples and then compared with other serological assays. Results revealed that LFA showed 89% and 99% sensitivity and specificity, respectively, when compared with competitive ELISA as the gold standard. This study demonstrated LFA alone as a potential serodiagnostic assay for rapid serodiagnosis of brucellosis in various domesticated animal species.
The Contribution of Genetic Variants of the Peroxisome Proliferator-Activated Receptor-Alpha Gene to High-Altitude Hypoxia Adaptation in Sherpa Highlanders

Kinota, Fumiya, Yunden Droma, Nobumitsu Kobayashi, Toshimichi Horiuchi, Yoshiaki Kitaguchi, Masanori Yasuo, Masao Ota, and Masayuki Hanaoka. The contribution of genetic variants of the gene encoding peroxisome proliferator-activated receptor-alpha gene (PPARA) to high-altitude hypoxia adaptation in Sherpa highlanders. High Alt Med Biol. 24:186–192, 2023. —Sherpa highlanders, who play invaluable roles in the exploration of Mount Everest, have exceptional tolerance to hypobaric hypoxia. Sherpa people are well known to possess the traits determined by genetic background for high-altitude adaptation. The metabolic adaptation mechanism is one of the biological ways for Sherpa highlanders in protecting them from hypoxia stress at high altitude. Studies have suggested that the gene encoding PPARA is associated with metabolic adaptation in the Himalayan population of Tibetans. This study attempts to investigate the genetic variants of the PPARA in Sherpa highlanders and the association with high-altitude hypoxia adaptation. Seven single-nucleotide polymorphisms (SNPs; rs135547, rs5769178, rs881740, rs4253712, rs5766741, and rs5767700 in introns and rs1800234 in exon 6) in the PPARA were genotyped in 105 Sherpa highlanders who lived in the Khumbu region (3440 m above sea level) and 111 non-Sherpa lowlanders who resided in Kathmandu (1300 m) in Nepal. By means of analyses of genetic distances, genotype distributions, allele frequencies, linkage disequilibrium, and haplotype constructions of the seven SNPs in the Sherpa highlanders versus the non-Sherpa lowlanders, it was revealed that the frequencies of minor alleles of rs4253712, rs5766741, rs5767700, and rs1800234 SNPs, as well as the frequency of haplotype constructed by the minor alleles of rs5766741–rs5767700–rs1800234, were significantly overrepresented in the Sherpa highlanders in comparison with the non-Sherpa lowlanders. The results strongly suggest that the genetic variants of the PPARA are likely to contribute to the high-altitude adaptation in Sherpa highlanders.
Electronically-Controllable Bandpass Planar Filter with Ultra-Large Bandwidth-Tuning Ratio and Enhanced in-Band Amplitude Flatness

A class of electronically-reconfigurable quasi-elliptic-type compact bandpass filter (BPF) for application in the intermediate-frequency (IF) stages of modern satellite-communications receivers is presented. By means of dynamic transmission-zero (TZ), external-quality-factor, and pole control, it allows to obtain an ultra-large bandwidth-tuning ratio with enhanced inband amplitude-flatness characteristics in its adaptive transfer function. This is achieved while keeping the BPF inter-resonator couplings static, which results in lower in-band insertion-loss levels. The coupling-matrix foundations and theoretical synthesis examples of the engineered tunable BPF concept are presented. Furthermore, for experimental demonstration, a I-GHz three-pole/two-TZ varactor-tuned microstrip BPF prototype is developed and measured. It exhibits a 1-dB-bandwidth-tuning ratio of 13.9:1 that goes from ultra-wideband to narrow-band states with minimum in-band insertion loss in the range 1.2-6.4 dB.
City-Scale Road Audit System using Deep Learning

Road networks in cities are massive and is a critical component of mobility. Fast response to defects, that can occur not only due to regular wear and tear but also because of extreme events like storms, is essential. Hence there is a need for an automated system that is quick, scalable and cost-effective for gathering information about defects. We propose a system for city-scale road audit, using some of the most recent developments in deep learning and semantic segmentation. For building and benchmarking the system, we curated a dataset which has annotations required for road defects. However, many of the labels required for road audit have high ambiguity which we overcome by proposing a label hierarchy. We also propose a multi-step deep learning model that segments the road, subdivide the road further into defects, tags the frame for each defect and finally localizes the defects on a map gathered using GPS. We analyze and evaluate the models on image tagging as well as segmentation at different levels of the label hierarchy.
Genetic and Environmental Influences on the Affective Regulation Network: A Prospective Experience Sampling Analysis

Background: The study of networks of affective mental states that play a role in psychopathology may help model the influence of genetic and environmental risks. The aim of the present paper was to examine networks of affective mental states (AMS: “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”) over time, stratified by genetic liability for psychopathology and exposure to environmental risk, using momentary assessment technology. Methods: Momentary AMS, collected using the experience sampling method (ESM) as well as childhood trauma and genetic liability (based on the level of shared genes and psychopathology in the co-twin) were collected in a population-based sample of female-female twin pairs and sisters (585 individuals). Networks were generated using multilevel time-lagged regression analysis, and regression coefficients were compared across three strata of childhood trauma severity and three strata of genetic liability using permutation testing. Regression coefficients were presented as network connections. Results: Visual inspection of network graphs revealed some suggestive changes in the networks with more exposure to either childhood trauma or genetic liability (i.e., stronger reinforcing loops between the three negative AMS anxious, insecure, and down both under higher early environmental, and under higher genetic liability exposure, stronger negative association between AMS of different valences: i.e., between “anxious” at t-1 and “relaxed” at t, “relaxed” at t-1 and “down” at t, under intermediate genetic liability exposure when compared to both networks under low and high genetic liability). Yet, statistical evaluation of differences across exposure strata was inconclusive. Conclusions: Although suggestive of a difference in the emotional dynamic, there was no conclusive evidence that genetic and environmental factors may impact ESM network models of individual AMS.
A Deep Learning Approach for Classification of Cleanliness in Restrooms

Facilities hire cleaning companies to maintain and manage cleaning operations on their restrooms by deploying cleaners who are responsible for performing frequent checks to ensure the cleanliness of restrooms. Nevertheless, the perception of quality and word, clean is very subjective to the observer. Hence, it is not an easy task to quantify the cleanliness. This paper presents a deep learning approach using deep convolutional neural networks (DCNN) to detect and classify the level of cleanliness in restrooms into three different categories; namely dirty, average, and clean. Our method sheds new lights on data augmentation, feature extraction and knowledge transfer between models. The proposed architecture achieved a precision of 0.98, 0.95, 0.80 and recall of 0.99, 0.83, 0.95 for dirty, average, and clean categories respectively utilizing a dataset collected from an active restroom facility.
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.
Comprehensive Analysis of Electrical Parameters Degradations for SiC Power MOSFETs Under Repetitive Short-Circuit Stress

The degradations of electrical parameters for silicon carbide power MOSFETs under repetitive short-circuit (SC) stress are investigated in detail in this paper. It demonstrates that the generation of negative charges along the gate–oxide interface of the channel region is the dominant degradation mechanism, which results in the increase in the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula>) and the rise of ON-state resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\text {dson}}$ </tex-math></inline-formula>) under low gate voltage bias condition. Furthermore, degradations of dynamic characteristics including gate charge (<inline-formula> <tex-math notation="LaTeX">${Q}_{\text {g}}$ </tex-math></inline-formula>) and switching behaviors of the device after the repetitive SC stress are extracted and analyzed for the first time. It illustrates that the increased <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> contributes to the rise of the Miller plateau voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {gp}}$ </tex-math></inline-formula>), which further leads to the increase in gate–source charge (<inline-formula> <tex-math notation="LaTeX">${Q}_{\text {gs}}$ </tex-math></inline-formula>). Meanwhile, the increase in the turn-ON time and the reduction of turn-OFF time are observed, which are also resulted from the positive shifts of <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {gp}}$ </tex-math></inline-formula>, leading to the rise of turn-ON switching energy (<inline-formula> <tex-math notation="LaTeX">${E}_{{ \text {on}}}$ </tex-math></inline-formula>) and the decline of turn-OFF switching energy (<inline-formula> <tex-math notation="LaTeX">${E}_{\text {off}}$ </tex-math></inline-formula>), respectively.
369 Acute effects of occupational noise exposure on 24-hour ambulatory blood pressure in workers with hypertension

Objectives Hypertension is the prevalent disease in the workplace. Although the elevation of blood pressure from exposure to occupational noise has been recognised, research on susceptibility to occupational noise exposure in adults with hypertension is not reported. This repeated-measure study investigated the effects of occupational noise exposure on 24-hour ambulatory blood pressure in a cohort with hypertensive and normotensive workers. Methods We enrolled 117 volunteers in an aircraft-manufacturing industrial cohort followed from 1998 to 2008. Individual noise exposure and personal blood pressure were determined simultaneously over 24 hours in 19 hypertensive and 98 normotensive workers during the working and non-working days. Linear mixed-effects regressions were used to investigate the effects of noise exposure on ambulatory systolic blood pressure (SBP) and diastolic blood pressure (DBP) between two groups during different periods by controlling for potential confounders. Results Hypertensive workers had significantly higher mean values of ambulatory SBP (12.6 [95% confidence interval: 10.3–15.0] mmHg; 10.3 [7.8–12.8] mmHg) and DBP (8.0 [6.3–9.7] mmHg; 7.2 [5.3–9.1] mmHg) compared with normotensive workers on both working and non-working days. Such differences between two groups were obviously higher on the working day than on the non-working day. Per one A-weighted decibel (dBA) increase in the 24-hour average noise exposure was significantly associated with transient elevations of SBP (0.25 [0.15–0.36] mmHg) and DBP (0.16 [0.09–0.23] mmHg) among hypertensive workers on the working day. Such effects on SBP and DBP still persisted at the 60-min time-lagged noise exposure and the increases of SBP were more pronounced in the hypertensive group than in the normotensive group. Conclusions Hypertensive workers are more susceptible to noise exposure, especially the effect on ambulatory SBP. These results suggest a need for the more protection to the susceptible population.
An Optimized Architecture of HEVC Core Transform Using Real-Valued DCT Coefficients

Integer discrete cosine transform (DCT) reduces the complexity of the transform kernel in High Efficiency Video Coding (HEVC) by eliminating the need for floating point multiplications. However, the dynamic range of integer DCT is large and therefore hardware cost is high. In this brief, a new transform kernel for HEVC is proposed which uses a new set of real-valued DCT coefficients. The proposed real-valued DCT reduces the hardware cost and the processing time by reducing the complexity as well as intermediate data length. However, it maintains coding performance similar to that of the integer DCT. Further, a hardware efficient data flow model of 2D-DCT architecture is also presented, which shows that a transpose memory of 15-bit data depth is enough to process 9-bit residual data. Field-programmable gate array implementation of the proposed 1-D DCT architecture reduces the area-delay product and power by 37.5% and 43.4%, respectively, as compared to that of the integer DCT. The proposed architecture requires 88.6K logic gates to produce a constant throughput of 32 samples per clock and it operates at 256.4 MHz on CMOS 90-nm ASIC platform.
Massive MIMO Linear Precoding: A Survey

Recently, massive multiple-input multiple-output (MIMO) system has attracted tremendous research interests in wireless communications. Among various performance aspects and design problems regarding massive MIMO, precoding at the base station (BS) is one of the most important function components to ensure reliable downlink transmission. This paper provides a survey of linear precoding techniques for downlink transmission under both single-cell (SC) and multicell (MC) scenarios. In an SC scenario, a series of classical precoding techniques are reviewed and compared. In an MC scenario, massive MIMO suffers from pilot contamination problems, where orthogonal pilot sequences have to be reused among cells, leading to estimation errors of channel state information at the transmitter. Precoding techniques with and without the cooperation among BSs are reviewed and the cooperative precoding techniques are further divided into partial and full cooperations. To the best of our knowledge, this is the first work that comprehensively surveys all the existing linear precoding techniques. The comprehensive review reveals current challenges in designing the precoders, where technical merits are usually bounded by practical implementation issues. We show that a viable precoding technique for massive MIMO systems is still unknown to date, and suggest several research potentials that are worthy of future research efforts.
Generalization of Semantic Roles in Automatic Semantic Role Labeling

FrameNet，PropBank といった意味タグ付きコーパスの出現とともに，機械学習の枠組みを利用した自動意味役割付与システムが数多く研究されてきた．しかし，これらのコーパスは個々のフレームに固有の意味役割を定義するため，コーパス中に低頻度，或いは未出現の意味役割が数多く存在し，効率的な学習を妨げている．本論文は，意味役割付与における意味役割の汎化問題を取り上げ，既存の汎化指標と新たに提案する指標を役割の分類精度を通して比較し，それぞれの特徴を探求する．また，複数の汎化指標を同時に利用する分類モデルが自動意味役割付与の精度を向上させることを示す．実験では，FrameNet において全体の精度で 19.16% のエラー削減，F1 マクロ平均で 7.42% の向上を，PropBank において全体の精度で 24.07% のエラー削減，未知動詞に対するテストで 26.39% のエラー削減を達成した．
Advanced Passive Silicon Photonic Devices With Asymmetric Waveguide Structures

Various passive photonic integrated devices have been developed successfully with silicon-on-insulator (SOI) nanowires in the past decade. It is well known that SOI-nanowire waveguides have ultrahigh index contrast (<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>) and ultrahigh birefringence. As a result, the structures and the design rules of a silicon photonic device are probably very different from the conventional case of using low-<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> optical waveguides. For example, some asymmetric waveguide structures have been used often to realize many silicon photonic devices developed recently. Furthermore, higher order modes have been involved in some of these silicon photonic devices. This paper discusses the special mode properties of silicon nanophotonic waveguides, including birefringence, mode dispersion, and mode hybridness. A review is then given on recent progress of these advanced passive silicon photonic devices with structural asymmetry, including on-chip polarization-handling devices, mode converters/(de)multiplexers, microring-resonator optical filters/switches, and Mach.Zehnder interferometer optical switches. Silicon photonic integrated circuits with these building blocks are also reviewed, including hybrid (de)multiplexers and reconfigurable optical add.drop multiplexers.
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers

We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good classifier of bags based on similarity from a single or finitely many "shapelets" (or patterns), where the similarity of the bag from a shapelet is the maximum similarity of instances in the bag. Classifiers based on a single shapelet are not sufficiently strong for certain applications. Additionally, previous work with multiple shapelets has heuristically chosen some of the instances as shapelets with no theoretical guarantee of its generalization ability. Our formulation provides a richer class of the final classifiers based on infinitely many shapelets. We provide an efficient algorithm for the new formulation, in addition to generalization bound. Our empirical study demonstrates that our approach is effective not only for MIL tasks but also for Shapelet Learning for time-series classification.
Comparison of Performance of AODV Protocol under Black hole Attack on MANET

A mobile ad-hoc network (MANET) is a self-configuring infrastructure less network of mobile nodes. Since the nodes communicate with each other without an infrastructure, they provide the connectivity by forwarding packets over themselves. These nodes which communicate with each other are suspected to have the malicious behaviour. On of such type of attack is Black Hole Attack In this Attack a malicious node advertises itself as it is having the optimal route to the destination and this malicious node absorbs all data packets in itself, similar to a hole which sucks in everything in. As these data packets do not reach the desired destination the large amount of data is dropped hence data loss occurs. In this paper the Black-Hole attack is simulated in the MANET using the AODV routing protocol in terms of throughput, End-to-End delay, packet delivery ratio and the analysis have been done with or without black hole attack .