Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher fully charmed tetraquarks: Radial excitations and 
P
-wave states

We systematically calculate the mass spectrum of the higher excited fully-charmed tetraquark $cc\bar c\bar c$ states including the S-wave radial excitations and the P-wave states with a nonrelativistic quark model. The quark model is composed of a vector one-gluon-exchange (OGE) and a scalar linear confinement interaction with the parameters determined by the charmonium spectrum. In the calculation, we consider both the $3_c-\bar 3_c$ and the $6_c-\bar{6}_c$ color representations. For the $cc\bar c\bar c $ state, the $6_c-\bar 6_c$ component is located lower than the $\bar 3_c-3_c$ one because of the stronger attractive interactions between the diquark and antidiquark. We focus on two excited modes and their properties for the P-wave tetraquarks. The mass splitting for the $\rho$-mode excitations with different color configurations is large. The low-lying $6_c-\bar 6_c$ $\rho$-mode component helps to explain the small mass gap between the ground S-wave and the P-wave tetraquark states. The recently observed $X(6900)$ state may be the candidate of the first radially excited tetraquarks with $J^{PC}=0^{++}$ or $2^{++}$, or the $1^{+-}$ or $2^{-+}$ P-wave states based on the mass spectrum. Moreover, the lowest $T_c$ states with the exotic $J^{PC}$ quantum numbers $0^{--}$ and $1^{-+}$ may decay into the P-wave $\eta_c J/\psi$ and di-$J/\psi$ modes, respectively. The future experimental search of these $T_c$ states will enrich the hadronic spectrum.
Extremely low-resource machine translation for closely related languages

An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish geographical regions. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair for which we have data. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results. We collected new parallel data for Võro, North and South Saami and present first results of neural machine translation for these languages.
Exploiting Multi-modal Contextual Sensing for City-bus’s Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction

Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.
Ideal Bayesian Spatial Adaptation

Many real-life applications involve estimation of curves that exhibit complicated shapes including jumps or varying-frequency oscillations. Practical methods have been devised that can adapt to a locally varying complexity of an unknown function (e.g. variable-knot splines, sparse wavelet reconstructions, kernel methods or trees/forests). However, the overwhelming majority of existing asymptotic minimaxity theory is predicated on homogeneous smoothness assumptions. Focusing on locally Holderian functions, we provide new locally adaptive posterior concentration rate results under the supremum loss for widely used Bayesian machine learning techniques in white noise and non-parametric regression. In particular, we show that popular spike-and-slab priors and Bayesian CART are uniformly locally adaptive. In addition, we propose a new class of repulsive partitioning priors which relate to variable knot splines and which are exact-rate adaptive. For uncertainty quantification, we construct locally adaptive confidence bands whose width depends on the local smoothness and which achieve uniform asymptotic coverage under local self-similarity. To illustrate that spatial adaptation is not at all automatic, we provide lower-bound results showing that popular hierarchical Gaussian process priors fall short of spatial adaptation.
Hybrid Exchange–Measurement-Based Qubit Operations in Semiconductor Double-Quantum-Dot Qubits

Measurement-based quantum computing (MBQC) promises natural compatibility with quantum error correcting codes at the cost of a polynomial increase in physical qubits. MBQC proposals have largely focused on photonic systems, where 2-qubit gates are difficult. Semiconductor spin qubits in quantum dots, on the other hand, offer fast 2-qubit gates via the exchange interaction. In exchange-based quantum computing, as with other solid-state qubits, leakage to higher states is a serious problem that must be mitigated. Here, two hybrid measurement-exchange schemes are proposed which quantify the benefits of MBQC on quantum dot-based quantum computing. Measurement of double quantum dot encoded qubits in the singlet-triplet basis, along with inter- and intra-qubit exchange interaction, are used to perform one and two qubit operations. Both schemes suppress individual qubit spin-state leakage errors, offer fast gate times, and require only controllable exchange couplings, up to known phase and Pauli corrections.
Microsatellite instability and Epstein-Barr virus combined with PD-L1 could serve as a potential strategy for predicting the prognosis and efficacy of postoperative chemotherapy in gastric cancer

Background Microsatellite instability (MSI) and Epstein-Barr virus (EBV)-positive molecular subtypes exhibit complex immune responses in gastric cancer (GC), and PD-L1 has emerged as a prognostic biomarker associated with the cancer immune microenvironment. This study aimed to determine the prognostic value of molecular subtypes and whether the addition of PD-L1 would accurately predict the prognosis and guide postoperative chemotherapy for GC patients. Methods We performed molecular subtyping of tissue microarray slides from 226 GC patients who were treated with radical gastrectomy. The MSI status and PD-L1 expression were evaluated through immunohistochemistry (IHC) and EBV status through situ hybridization. Multiplex polymerase chain reaction (PCR) was also performed on 50 cases to validate the accuracy of IHC in defining MSI status. Differences in overall survival (OS) were assessed using the Kaplan-Meier method, log-rank test and Cox proportional hazards regression model. Results Among the 226 GC patients, 52 (23.2%) patients were classified as the MSI subtype, 11 (4.9%) were EBV+ subtype, and 161 (71.9%) were MSS (Microsatellite stable) /EBV subtype according to TCGA analysis. Two patients were both positive for MSI and EBV infection. EBV+ cases showed higher PD-L1 positivity than MSI cases and MSS/EBV cases (81.8% vs. 50.0% vs. 35.4%, P = 0.003). Compared with the non-MSS/EBV (MSI or EBV+ cases) subgroup, GC patients with MSS/EBV were associated with the worst outcomes (HR = 1.610, 95% CI [1.0462.479], P = 0.031). MSS/EBV GCs alone could benefit from postoperative chemotherapy (HR = 0.452, 95% CI [0.2990.682], P<0.001), and PD-L1-positive expression could also predict a better prognosis (HR = 0.612, 95% CI [0.3890.962], P = 0.033) in this subgroup. Considering both chemotherapy efficacy and PD-L1 expression in the MSS/EBV subgroup, chemotherapy could improve the prognosis for PD-L1-negative MSS/EBV GCs (HR = 0.357, 95% CI [0.2170.587], P <0.001) but not PD-L1-positive MSS/EBV GCs. Conclusions Molecular subtyping combined with PD-L1 expression could serve as a potential strategy to better predict prognosis and guide postoperative chemotherapy of GC patients.
Activation of Nrf2/HO-1 by Peptide YD1 Attenuates Inflammatory Symptoms through Suppression of TLR4/MYyD88/NF-κB Signaling Cascade

In this study, we investigate the immunomodulatory effects of a novel antimicrobial peptide, YD1, isolated from Kimchi, in both in vitro and in vivo models. We establish that YD1 exerts its anti-inflammatory effects via up-regulation of the Nrf2 pathway, resulting in the production of HO-1, which suppresses activation of the NF-κB pathway, including the subsequent proinflammatory cytokines IL-1β, IL-6, and TNF-α. We also found that YD1 robustly suppresses nitric oxide (NO) and prostaglandin E2 (PGE2) production by down-regulating the expression of the upstream genes, iNOS and COX-2, acting as a strong antioxidant. Collectively, YD1 exhibits vigorous anti-inflammatory and antioxidant activity, presenting it as an interesting potential therapeutic agent.
Immunoproteasome Activity and Content Determine Hematopoietic Cell Sensitivity to ONX-0914 and to the Infection of Cells with Lentiviruses

Proteasomes are intracellular structures responsible for protein degradation. The 20S proteasome is a core catalytic element of the proteasome assembly. Variations of catalytic subunits generate different forms of 20S proteasomes including immunoproteasomes (iPs), which are present mostly in the immune cells. Certain cells of the immune system are primary targets of retroviruses. It has been shown that several viral proteins directly affect proteasome functionality, while inhibition of proteasome activity with broad specificity proteasome inhibitors stimulates viral transduction. Here we specifically addressed the role of the immunoproteasomes during early stages of viral transduction and investigated the effects of specific immunoproteasome inhibition and activation prior to infection using a panel of cell lines. Inhibition of iPs in hematopoietic cells with immunoproteasome-specific inhibitor ONX-0914 resulted in increased infection by VSV-G pseudotyped lentiviruses. Moreover, a tendency for increased infection of cloned cells with endogenously decreased proteasome activity was revealed. Conversely, activation of iPs by IFN-γ markedly reduced the viral infectivity, which was rescued upon simultaneous immunoproteasome inhibition. Our results indicate that immunoproteasome activity might be determinative for the cellular antiretroviral resistance at least for the cells with high iP content. Finally, therapeutic application of immunoproteasome inhibitors might promote retroviral infection of cells in vivo.
Influence of Personal Protective Equipment on Wildland Firefighters’ Physiological Response and Performance during the Pack Test

The Pack Test (PT) is a widely used test to establish readiness for work in wildland firefighting. It is common to perform this test dressed in regular exercise clothing. However, wildland firefighters (WFF) have to wear personal protective equipment (PPE) during their deployments, which increases the physiological strain and reduces their work capacity. This study aimed to analyse the impact of full PPE on PT performance. Nine male professional WFF performed in random order a PT walking at the fastest possible self-pace wearing two different clothing configurations: (i) traditional short sports gear (SG) and (ii) the PPE currently used by Spanish WFF. Heart rate (HR), rating of perceived exertion and lap time were recorded during the PT. In addition, oxygen uptake (VO2) was estimated through the individual VO2–HR relationship previously obtained during a graded exercise test. There was a significant decrease in the PT performance (i.e., completion time) (~12%, p < 0.05) in PPE. The physiological demands with this configuration were significantly higher (~10%, p < 0.05). WFF spent ~13 min above the anaerobic threshold in PPE vs. ~4 min in SG. A multiple stepwise regression analysis revealed that PT performance variation might be explained by the maximal aerobic velocity (84.5%) in PPE and the VO2max (75.9%) in SG. In conclusion, wearing complete PPE increases WFF’s physiological strain, which translates into a significant PT performance reduction. Performing the test walking at the fastest possible self-pace wearing the PPE would better reflect the high-intensity effort periods reported in real scenarios.
Microbial Hotspots in Lithic Microhabitats Inferred from DNA Fractionation and Metagenomics in the Atacama Desert

The existence of microbial activity hotspots in temperate regions of Earth is driven by soil heterogeneities, especially the temporal and spatial availability of nutrients. Here we investigate whether microbial activity hotspots also exist in lithic microhabitats in one of the most arid regions of the world, the Atacama Desert in Chile. While previous studies evaluated the total DNA fraction to elucidate the microbial communities, we here for the first time use a DNA separation approach on lithic microhabitats, together with metagenomics and other analysis methods (i.e., ATP, PLFA, and metabolite analysis) to specifically gain insights on the living and potentially active microbial community. Our results show that hypolith colonized rocks are microbial hotspots in the desert environment. In contrast, our data do not support such a conclusion for gypsum crust and salt rock environments, because only limited microbial activity could be observed. The hypolith community is dominated by phototrophs, mostly Cyanobacteria and Chloroflexi, at both study sites. The gypsum crusts are dominated by methylotrophs and heterotrophic phototrophs, mostly Chloroflexi, and the salt rocks (halite nodules) by phototrophic and halotolerant endoliths, mostly Cyanobacteria and Archaea. The major environmental constraints in the organic-poor arid and hyperarid Atacama Desert are water availability and UV irradiation, allowing phototrophs and other extremophiles to play a key role in desert ecology.
Prevalence of Psychological Distress and Its Risk Factors in Patients with Primary Bone and Soft Tissue Tumors

Psychological distress is common in patients with soft tissue and bone tumors. We first investigated its frequency and the associated risk factors in patients with pre-operative bone and soft tissue tumors. Participants included 298 patients with bone and soft tissue tumors who underwent surgery in our institution between 2015 and 2020. Psychological distress was evaluated by the Distress and Impact Thermometer (DIT) that consists of two types of questions (questions about the severity of the patient’s distress (DIT-D) and its impact (DIT-I)). We used a cut-off point of 4 on the DIT-D and 3 on the DIT-I for screening patients with psychological distress. We therefore investigated: (1) the prevalence of psychological distress as assessed with DIT or distress thermometer (DT), which can be decided by DIT-D ≥ 4, (2) what are the risk factors for the prevalence of psychological distress, and (3) what is the number of patients who consulted a psychiatrist for psychological distress in patients with pre-operative bone and soft tissue tumors. With DIT and DT, we identified 64 patients (21%) and 95 patients (32%), respectively, with psychological distress. Multivariate logistic regression revealed that older age, sex (female), malignancy (malignant or intermediate tumor), a lower Barthel Index, and higher numeric rating scale were risk factors for psychological distress. Two patients (3%) consulted a psychiatrist after surgery. In conclusion, careful attention to psychological distress is needed, especially for female patients, older patients, and those with malignant soft or bone tissue tumors who have more than moderate pain.
Investigations on solid lipid nanoparticles prepared by precipitation in o/w emulsions

Nanoparticles of tripalmitate were prepared by dissolving the triglyceride in chloroform and emulsifying this solution into an aqueous phase by high pressure homogenization. Lecithin and different co-surfactants were used as emulsifiers. The organic solvent was removed from the emulsion by evaporation under reduced pressure resulting in the precipitation of tripalmitate nanoparticles. Photon correlation spectroscopy suggests that the tripalmitate dispersions display narrow monomodal size distributions. The mean particle size by number ranges from approximately 30 to 100 nm depending on the lecithin/co-surfactant blend. The use of bile salts as co-surfactants is needed to obtain particles with average diameters as small as 30 nm. Comparably small particle size distributions are not achievable by melt emulsification of similar compositions. The size of phospholipid/bile salt stabilized lipid nanoparticles is significantly lower than the initial droplet size of the organic solvent-in-water emulsion. According to transmission electron microscopy, X-ray diffraction and differential scanning calorimetry data the precipitated tripalmitate nanoparticles display platelet-like shapes and crystalline order. The recrystallization tendency and various physicochemical properties of the dispersed precipitated tripalmitate are comparable to those of solid lipid nanoparticles prepared by melt-homogenization indicating that the way of production has only minor influence on the properties of the tripalmitate particles.
Does Risk Awareness of COVID-19 Affect Visits to National Parks? Analyzing the Tourist Decision-Making Process Using the Theory of Planned Behavior

This study aimed to determine whether risk awareness of coronavirus disease (COVID-19) affects visits to national parks. We analyzed the tourist decision-making process during the current pandemic using the theory of planned behavior as a framework, adding variables relevant to the pandemic, such as risk perception and risk reduction behavior, to the model. Based on a literature review, we developed a research model describing the impact relationship between risk perception, the theory of planned behavior, and risk reduction behavior and tested nine hypotheses. Results of a survey of 555 visitors to two national parks supported eight of the nine hypotheses. Although the results are limited, they reaffirm the usefulness of the theory of planned behavior in explaining tourism behavior. This work is significant in that we would be able to extend the scope of subsequent research beyond a discussion of the direct effects on optimistic perceptions (bias) and risk reduction behavior as well as visit intention, by explaining the probability even in unprecedented crises such as COVID-19. Humans may be negotiating the constraints (COVID-19) or embodied tourism need through the personal bias. Furthermore, we discuss the theoretical implications of the results for tourism behavior research.
Performance Limits of Direct Wideband Coherent 3D Localization in Distributed Massive MIMO Systems

We address the accuracy of wideband direct position estimation of a radio transmitter via a distributed antenna array in 5G cellular systems. Our derivations are based only on the presence of spatially coherent line-of-sight (LoS) signal components, which is a realistic assumption in small cells, especially in the mmWave range. The system model considers collocated time and phase synchronized receiving front-ends with antennas distributed in 3D space at known locations and connected to the front-ends via calibrated coaxial cables or analog radio-frequency-over-fiber links. Furthermore, the signal model assumes spherical wavefronts. We derive the Cramér-Rao bounds (CRBs) for two implementations of the system: with (a) known signals and (b) random Gaussian signals. The results show how the bounds depend on the carrier frequency, number of samples used for estimation, and signal-to-noise ratios. They also show that increasing the number of antennas (such as in massive MIMO systems) considerably improves the accuracy and lowers the signal-to-noise threshold for localization even for non-cooperative transmitters. Finally, our derivations show that the square roots of the bounds are two to three orders of magnitude below the carrier wavelength for realistic system parameters.
Changes in Smoking, Alcohol Consumption, and the Risk of Thyroid Cancer: A Population-Based Korean Cohort Study

Simple Summary The inverse association between smoking, alcohol intake, and thyroid cancer has been suggested by observational studies. From the representative data in Korea, we identified the epidemiologic evidence to elucidate the true effect between smoking, alcohol intake, and thyroid cancer incidence by exploring the effect of changes in smoking and alcohol consumption habits. Abstract To elucidate potential causality between smoking and alcohol intake on thyroid cancer incidence, we explored the effect of changes in smoking and alcohol consumption habits. From the Korean National Health Insurance database, we identified 4,430,070 individuals who participated in the national health screening program in 2009 and 2011. The level of smoking and alcohol consumption was measured twice, once in 2009 and again in 2011. The risk of thyroid cancer according to their changes was estimated using the Cox proportional hazard model. During the mean follow-up period of 6.32 ± 0.72 years, 29,447 individuals were diagnosed with thyroid cancer. Compared to those who sustained not smoking, non-smokers who initiated smoking to light (adjusted hazard ratio (aHR) 0.96, 95% confidence interval (CI) 0.81–1.15), moderate (aHR 0.90, 95% CI 0.78–1.04), and heavy level (aHR 0.81, 95% CI 0.69–0.96) had a decreased risk of thyroid cancer. Heavy smokers who quit smoking had an increased risk of thyroid cancer (aHR 1.23, 95% CI 1.06–1.42) compared to those who sustained heavy smoking. Change in drinking status was not significantly associated with thyroid cancer risk compared to drinking at the same level, although a non-significant trend of increased risk was noted in quitters. Participants who initiated both smoking and drinking (HR 0.80, 95% CI 0.69–0.93) had a lower risk of thyroid cancer compared with those who continued not to smoke and drink. Our findings provide further evidence that smoking, and possibly alcohol consumption, would have true protective effects on the development of thyroid cancer.