Miracle-dz/UniFAR-FTUs huggingface dataset:
Motivational impairment predicts functional remission in first-episode psychosis: 3-Year follow-up of the randomized controlled trial on extended early intervention

Objective: Functional remission represents an intermediate functional milestone toward recovery. Differential relationships of negative symptom sub-domains with functional remission in first-episode psychosis are understudied. We aimed to examine rate and predictors of functional remission in people with first-episode psychosis in the context of a 3-year follow-up of a randomized controlled trial comparing 1-year extension of early intervention (i.e. 3-year early intervention) with step-down psychiatric care (i.e. 2-year early intervention). Method: A total of 160 participants were recruited upon completion of a 2-year specialized early intervention program for first-episode psychosis in Hong Kong and underwent a 1-year randomized controlled trial comparing 1-year extended early intervention with step-down care. Participants were followed up and reassessed 3 years after inclusion to the trial (i.e. 3-year follow-up). Functional remission was operationalized as simultaneous fulfillment of attaining adequate functioning (measured by Social and Occupational Functioning Scale and Role Functioning Scale) at 3-year follow-up and sustained employment in the last 6 months of 3-year study period. Negative symptom measure was delineated into amotivation (i.e. motivational impairment) and diminished expression (i.e. reduced affect and speech output). Data analysis was based on 143 participants who completed follow-up functional assessments. Results: A total of 31 (21.7%) participants achieved functional remission status at 3-year follow-up. Multivariate regression analysis showed that lower levels of amotivation (p = 0.010) and better functioning at study intake (p = 0.004) independently predicted functional remission (Final model: Nagelkerke R2 = 0.40, χ2 = 42.9, p < 0.001). Extended early intervention, duration of untreated psychosis and diminished expression did not predict functional remission. Conclusion: Only approximately one-fifths of early psychosis patients were found to achieve functional remission. Functional impairment remains an unmet treatment need in the early stage of psychotic illness. Our results further suggest that amotivation may represent a critical therapeutic target for functional remission attainment in early psychosis.
Electrical Properties of Indium-Doped Zinc Oxide Nanostructures Doped at Different Dopant Concentrations

This project has been focused on the electrical and optical properties on the effect of Indium doped zinc oxide (ZnO) thin films at different dopant concentrations. These thin films were doped with different In dopant concentrations at 1 at%, 1.5 at%, 2 at%, 3 at%, 4 at% and 5 at% was selected as the parameter to optimize the thin films quality while the annealing temperature is fixed 500 oC. In doped ZnO solutions were deposited onto the glass substrates using sol-gel spin coating method. This project was involved with three phases, which are thin films preparation, deposition and characterization. The thin films were characterized using Current Voltage (I-V) measurement and UV-Vis-NIR spectrophotometer for electrical properties and optical properties. The electrical properties show that the resistivity is the lowest at 4 at% In doping concentration which is 8.27× 103 Ωcm-1 The absorption coefficient spectrum obtained from UV-Vis-NIR spectrophotometer measurement shows all films exhibit very low absorption in the visible (400-800nm) and near infrared (NIR) (>800nm) range but exhibit high absorption in the UV range.
Integrating novel drugs to chemoimmunotherapy in diffuse large B-cell lymphoma

ABSTRACT Introduction: Diffuse Large B-cell Lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma (NHL), with an incidence in Europe of 3.8/100.000/year. A multi-drugs chemoimmunotherapy regimen, containing rituximab, cyclophosphamide, vincristine, doxorubicin and prednisone (R-CHOP) administrated every 21 days, is the standard therapy for DLBCL patients. The discovery of several biological features of DLBCL has encouraged the introduction of novel drugs in the treatment. Areas covered: In this article, the use of standard therapies will be reviewed and will be investigated adoption of novel drugs such as Bortezomib, Bruton’s tyrosine kinase, IMiDs, Venetoclax, mTOR inhibitors and other biological agents. Expert commentary: A better knowledge of the biology of DLBCL is mandatory to tailor treatment and to ameliorate the poor prognosis of DLBCL. The addition of novel drugs to standard RCHOP should represent a modern approach in the treatment of DLBCL. Ibrutinib and lenalidomide showed important results in DLBCL and the integration of these drugs in first line treatment is under investigation. Despite encouraging results using novel drugs in the setting of relapsed/refractory DLBCL, the rate of failures still remains at 40%; for these reason, continued participation in clinical trials should be encouraged.
Quantitative comparison of in situ soil CO 2 flux measurement methods

Development of reliable regional or global carbon budgets requires accurate measurement of soil CO2 flux. We conducted laboratory and field studies to determine the accuracy and comparability of methods commonly used to measure in situ soil CO2 fluxes. Methods compared included two static techniques, sodium hydroxide (NaOH) and soda lime as CO2 traps; a multichamber open dynamic system (OC) with an infrared gas analyzer; and a headspace analysis system, with gas chromatograph with circulating fan (GCF) and gas chromatograph without circulating fan (GC), measuring changes in headspace CO2 concentration using a gas chromatograph. In the laboratory, we compared NaOH, OC, GCF, and GC using sand-filled cylinders to simulate a soil system. Three concentrations of standard CO2 gas (representing low, medium, and high soil-CO2 flux rates) entered the cylinders through individually monitored flow meters. Flux rates measured using the OC method equaled the actual CO2 flux at all three CO2 concentrations. Flux rates determined with GC and GCF were < 55 percent of the actual flux rate. The percent of actual CO2 flux measured increased with increasing CO2 concentrations. At all concentrations, NaOH collected between 40 and 47 percent of CO2 entering the system. A field study was conducted to verify laboratory results and allow comparison with the soda lime trap (SODA) method. In laboratory and field studies, all methods detected significant differences in flux rates among the standard CO2 concentrations and field sites. Regression analyses showed good relationships between NaOH, SODA, and GC methods with flux rates measured using the OC methods (r2 ≥ 0.78). Slope values for these regression equations ranged from 0.34 for NaOH to 0.54 for GC and SODA. These results suggest that data collected using the other methods could be standardized to OC flux rates. However, because methodological differences significantly affect CO2 flux measurements, care should be used in applying these relationships.
Normal Variability of Weekly Musculoskeletal Screening Scores and the Influence of Training Load across an Australian Football League Season

Aim: The sit and reach test (S&R), dorsiflexion lunge test (DLT), and adductor squeeze test (AST) are commonly used in weekly musculoskeletal screening for athlete monitoring and injury prevention purposes. The aim of this study was to determine the normal week to week variability of the test scores, individual differences in variability, and the effects of training load on the scores. Methods: Forty-four elite Australian rules footballers from one club completed the weekly screening tests on day 2 or 3 post-main training (pre-season) or post-match (in-season) over a 10 month season. Ratings of perceived exertion and session duration for all training sessions were used to derive various measures of training load via both simple summations and exponentially weighted moving averages. Data were analyzed via linear and quadratic mixed modeling and interpreted using magnitude-based inference. Results: Substantial small to moderate variability was found for the tests at both season phases; for example over the in-season, the normal variability ±90% confidence limits were as follows: S&R ±1.01 cm, ±0.12; DLT ±0.48 cm, ±0.06; AST ±7.4%, ±0.6%. Small individual differences in variability existed for the S&R and AST (factor standard deviations between 1.31 and 1.66). All measures of training load had trivial effects on the screening scores. Conclusion: A change in a test score larger than the normal variability is required to be considered a true change. Athlete monitoring and flagging systems need to account for the individual differences in variability. The tests are not sensitive to internal training load when conducted 2 or 3 days post-training or post-match, and the scores should be interpreted cautiously when used as measures of recovery.
Assessing the impact of vertical land motion on twentieth century global mean sea level estimates

Near-global and continuous measurements from satellite altimetry have provided accurate estimates of global mean sea level in the past two decades. Extending these estimates further into the past is a challenge using the historical tide gauge records. Not only is sampling nonuniform in both space and time, but tide gauges are also affected by vertical land motion (VLM) that creates a relative sea level change not representative of ocean variability. To allow for comparisons to the satellite altimetry estimated global mean sea level (GMSL), typically the tide gauges are corrected using glacial isostatic adjustment (GIA) models. This approach, however, does not correct other sources of VLM that remain in the tide gauge record. Here we compare Global Positioning System (GPS) VLM estimates at the tide gauge locations to VLM estimates from GIA models, and assess the influence of non-GIA-related VLM on GMSL estimates. We find that the tide gauges, on average, are experiencing positive VLM (i.e., uplift) after removing the known effect of GIA, resulting in an increase of 0.24 ± 0.08 mm yr−1 in GMSL trend estimates from 1900 to present when using GPS-based corrections. While this result is likely dependent on the subset of tide gauges used and the actual corrections used, it does suggest that non-GIA VLM plays a significant role in twentieth century estimates of GMSL. Given the relatively short GPS records used to obtain these VLM estimates, we also estimate the uncertainty in the GMSL trend that results from limited knowledge of non-GIA-related VLM.
On Maximum Eigenmode Beamforming and Multi-User Gain

This paper is concerned with the advantages of multi-user concurrent transmission in multiple-input multiple-output (MIMO) systems with rate constraints. We first study a maximum eigenmode beamforming (MEB) strategy for fading multiple access channels (MACs). This strategy allows each user to transmit only in its maximum eigenmode direction and applies a suboptimal matched-filter receiver with successive interference cancellation at the base station (BS). We derive a closed-form expression for the average minimum transmitted sum power required by MEB. Based on this, we show that: a) the MEB strategy is asymptotically optimal when the number of simultaneous users is sufficiently large; b) multi-user concurrent transmission has a power advantage, referred to as multi-user gain, over orthogonal transmission approaches such as time-division multiple-access; c) the number of antennas at the BS has a far stronger impact on the system performance than that at each user side. These properties are verified by simulation. Both numerical analyses and simulation results show that a major part of multi-user gain can be achieved in practical environments even with a quite small number of simultaneous users. We also study the MEB strategy for MIMO broadcast channels (BCs). The dirty paper coding (DPC) technique is necessary in this case. It is analytically shown that most observations made for MIMO MACs are extendable to MIMO BCs.
Long-term Water Absorption Behaviour of Polypropylene/Wood Flour/Organoclay Hybrid Nanocomposite

Recently, to improve the properties of wood plastic composite (WPC), incorporation of nanofiller has been considered. In this study, hybrid composite of polypropylene/wood flour/nanoclay with different concentrations of nanoclay and maleated polypropylene (MAPP) as compatibilizer were fabricated in an internal mixer. The long-term water absorption of the composites was evaluated by their immersion in water at room temperature for several weeks (up to 3000 h). Water diffusion coefficients were evaluated. The results indicate that water absorption decreases with increase in clay loading. The morphology of nanoclay was determined by X-ray diffraction (XRD) and transmission electron microscope and the effect of morphology on water absorption is discussed. Due to inadequate compatibilizer, exfoliated morphology of nanoclay is not obtained but the intercalation has occurred. The order of intercalation for samples containing 3 phc is higher than that of 6 phc at the same MAPP content due to some agglomerations of organoclay. The effect of compatibilizer has been positive in terms of enhancing the dimensional stability of the composites and mechanical properties. The maximum elastic modulus may be related to 3 phc of nanoclay and 4 phc of MAPP. The maximum water absorption and diffusion coefficients are decreased by increasing the clay and MAPP contents. The mechanism of water absorption of the composites under study followed the kinetics of a Fickian diffusion process.
The Chloroplastic Protein THF1 Interacts with the Coiled-Coil Domain of the Disease Resistance Protein N′ and Regulates Light-Dependent Cell Death1[OPEN]

A chloroplastic protein inhibits defense-induced cell death and is destabilized by activation of a disease resistance protein. One branch of plant immunity is mediated through nucleotide-binding/Leu-rich repeat (NB-LRR) family proteins that recognize specific effectors encoded by pathogens. Members of the I2-like family constitute a well-conserved subgroup of NB-LRRs from Solanaceae possessing a coiled-coil (CC) domain at their N termini. We show here that the CC domains of several I2-like proteins are able to induce a hypersensitive response (HR), a form of programmed cell death associated with disease resistance. Using yeast two-hybrid screens, we identified the chloroplastic protein Thylakoid Formation1 (THF1) as an interacting partner for several I2-like CC domains. Co-immunoprecipitations and bimolecular fluorescence complementation assays confirmed that THF1 and I2-like CC domains interact in planta and that these interactions take place in the cytosol. Several HR-inducing I2-like CC domains have a negative effect on the accumulation of THF1, suggesting that the latter is destabilized by active CC domains. To confirm this model, we investigated N′, which recognizes the coat protein of most Tobamoviruses, as a prototypical member of the I2-like family. Transient expression and gene silencing data indicated that THF1 functions as a negative regulator of cell death and that activation of full-length N′ results in the destabilization of THF1. Consistent with the known function of THF1 in maintaining chloroplast homeostasis, we show that the HR induced by N′ is light-dependent. Together, our results define, to our knowledge, novel molecular mechanisms linking light and chloroplasts to the induction of cell death by a subgroup of NB-LRR proteins.
Mutational analysis of the yeast RNA helicase Sub2p reveals conserved domains required for growth, mRNA export, and genomic stability

Sub2p/UAP56 is a highly conserved DEAD-box RNA helicase involved in the packaging and nuclear export of mRNA. Here we analyze S. cerevisiae Sub2p functions in vivo and in vitro and find that a conserved N-terminal motif is required for mRNA export without affecting Sub2p helicase activity. Mutants in the helicase domain are trans-dominant negative and cause hyper-recombination.
Machine learning based hyperspectral image analysis: A survey

Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense. Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis. Therefore, a solid understanding of machine learning techniques have become essential for remote sensing researchers and practitioners. This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature. We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms that can be applied to them. The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms. The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation. The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning. We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.
Hierarchical Investigation of Genetic Influences on Response Inhibition in Healthy Young Adults

Poor inhibitory control is a known risk factor for substance use disorders, making it a priority to identify the determinants of these deficits. The aim of the current study was to identify genetic associations with inhibitory control using the stop signal task in a large sample (n = 934) of healthy young adults of European ancestry. We genotyped the subjects genome-wide and then used a hierarchical approach in which we tested seven a priori single nucleotide polymorphisms (SNPs) previously associated with stop signal task performance, approximately 9,000 SNPs designated as high-value addiction (HVA) markers by the SmokeScreen array, and approximately five million genotyped and imputed SNPs, followed by a gene-based association analysis using the resultant p values. A priori SNP analyses revealed nominally significant associations between response inhibition and one locus in HTR2A (rs6313; p = .04, dominance model, uncorrected) in the same direction as prior findings. A nominally significant association was also found in one locus in ANKK1 (rs1800497; p = .03, uncorrected), although in the opposite direction of previous reports. After accounting for multiple comparisons, the HVA, genome-wide, and gene-based analyses yielded no significant findings. This study implicates variation in serotonergic and dopaminergic genes while underscoring the difficulty of detecting the influence of individual SNPs, even when biological information is used to prioritize testing. Although such small effect sizes suggest limited utility of individual SNPs in predicting risk for addiction or other impulse control disorders, they may nonetheless shed light on complex biological processes underlying poor inhibitory control.
Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization

We study stochastic algorithms for solving nonconvex optimization problems with a convex yet possibly nonsmooth regularizer, which find wide applications in many practical machine learning applications. However, compared to asynchronous parallel stochastic gradient descent (AsynSGD), an algorithm targeting smooth optimization, the understanding of the behavior of stochastic algorithms for nonsmooth regularized optimization problems is limited, especially when the objective function is nonconvex. To fill this theoretical gap, in this paper, we propose and analyze asynchronous parallel stochastic proximal gradient (Asyn-ProxSGD) methods for nonconvex problems. We establish an ergodic convergence rate of $O(1/\sqrt{K})$ for the proposed Asyn-ProxSGD, where $K$ is the number of updates made on the model, matching the convergence rate currently known for AsynSGD (for smooth problems). To our knowledge, this is the first work that provides convergence rates of asynchronous parallel ProxSGD algorithms for nonconvex problems. Furthermore, our results are also the first to show the convergence of any stochastic proximal methods without assuming an increasing batch size or the use of additional variance reduction techniques. We implement the proposed algorithms on Parameter Server and demonstrate its convergence behavior and near-linear speedup, as the number of workers increases, on two real-world datasets.
Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks

We analyze algorithms for approximating a function f(x)=Φx mapping ℜd to ℜd using deep linear neural networks, that is, that learn a function h parameterized by matrices Θ1,…,ΘL and defined by h(x)=ΘLΘL-1…Θ1x. We focus on algorithms that learn through gradient descent on the population quadratic loss in the case that the distribution over the inputs is isotropic. We provide polynomial bounds on the number of iterations for gradient descent to approximate the least-squares matrix Φ, in the case where the initial hypothesis Θ1=…=ΘL=I has excess loss bounded by a small enough constant. We also show that gradient descent fails to converge for Φ whose distance from the identity is a larger constant, and we show that some forms of regularization toward the identity in each layer do not help. If Φ is symmetric positive definite, we show that an algorithm that initializes Θi=I learns an ε-approximation of f using a number of updates polynomial in L, the condition number of Φ, and log(d/ε). In contrast, we show that if the least-squares matrix Φ is symmetric and has a negative eigenvalue, then all members of a class of algorithms that perform gradient descent with identity initialization, and optionally regularize toward the identity in each layer, fail to converge. We analyze an algorithm for the case that Φ satisfies u⊤Φu>0 for all u but may not be symmetric. This algorithm uses two regularizers: one that maintains the invariant u⊤ΘLΘL-1…Θ1u>0 for all u and the other that “balances” Θ1,…,ΘL so that they have the same singular values.
Comparison of cyclic fatigue resistance and bending properties of two reciprocating nickel‐titanium glide path files

AIM To compare the cyclic fatigue resistance and bending properties of R-Pilot and WaveOne Gold (WOG) Glider files, at intracanal temperature (35°C). METHODOLOGY Forty R-Pilot and 40 WOG Glider files were subjected to a cyclic fatigue resistance test (n = 20), calculating the time to fracture (TTF) in an artificial stainless steel canal. The length of the fractured file tips (FL) was also measured. The fracture surface of fragments was examined with a scanning electron microscope, and the cross-sectional area of the fractured surfaces was measured. Flexibility of the tested files (n = 20) was determined using 45° bending test. Data were analysed statistically using the Mann-Whitney U-test at 5% significance level. RESULTS Time to fracture was significantly higher in the R-Pilot group compared to the WOG Glider (P < 0.05). There was no significant difference between groups for fracture length. The bending resistance of R-Pilot files was significantly greater than WOG Glider files (P < 0.05). CONCLUSIONS A significant greater cyclic fatigue resistance was observed for R-Pilot files compared to WOG Glider instruments, although the bending resistance of WOG Glider files was lower.