Background: This prospective study evaluated the impact of anatomical and tomographic biomarkers on clinical outcomes of intravitreal dexamethasone implants in patients with macular edema secondary to retinal vein occlusion (RVO). Methods: The study included 46 patients (28 with branch RVO (BRVO) and 18 with central RVO (CRVO)). Best corrected visual acuity (BCVA) significantly improved from a mean baseline of 0.817 ± 0.220 logMAR to 0.663 ± 0.267 logMAR at six months and 0.639 ± 0.321 logMAR at twelve months (p < 0.05). Central retinal thickness (CRT) showed a significant reduction from 666.2 ± 212.2 µm to 471.1 ± 215.6 µm at six months and 467 ± 175.7 µm at twelve months (p < 0.05). No significant differences were found in OCT biomarkers between baseline and follow-ups. Results: The study analysed improvements in visual acuity relative to baseline biomarkers. At six months, ellipsoid zone disruption (EZD) was significant for all subgroups. Disorganization of retinal inner layers (DRIL), external limiting membrane (ELM) disruption, macular ischemia (MI), CRT, and BRVO showed significance for any improvement, while DRIL and ELM were significant for changes greater than 0.3 logMAR (p < 0.05). At twelve months, EZD remained significant for all subgroups. ELM, MI, CRT, and BRVO were significant for any improvement, while MI and BRVO were significant for changes greater than 0.3 logMAR (p < 0.05). Hyperreflective foci were not statistically significant at either time point (p > 0.05). Conclusions: The regression model suggested that MI and CRVO could be negative predictive factors for visual outcomes, while ELM and EZD were associated with BCVA improvement one-year post-treatment.

In this study, we propose a self-limiting growth model forBacillus subtilisspores confined within porous polyacrylamide (PA) hydrogels. We observed thatB. subtilisspores germinate into vegetative cells within the hydrogel matrix, forming spherical colonies. These colonies expand until the mechanical stress they exert on their environment surpasses the yield stress of the hydrogel, leading to formation of a nonpermeable layer that halts nutrient diffusion and forces the bacteria to resporulate. These novel observations suggest a model to explain why bacterial growth in confined environments and material interfaces may be limited, providing insight for natural phenomena and biotechnological applications involving bacterial encapsulation.

Emulsion liquid membrane (ELM) stands out as an extraction process that has drawn much attention due to its promising prospects in industrial wastewater treatment technology. Nevertheless, the pivotal challenge is to reach high membrane stability to overcome the obstacle of applying ELM at the industrial scale. In this study, ELM was boosted by using nanoparticles (superparamagnetic iron oxide (Fe2O3)) in the stripping phase (W1) and ionic liquid (1-methyl-3-octyl-imidazolium-hexafluorophosphate [OMIM][PF6) in the oil phase (O) for recovering/extracting vanadium from synthetic wastewater to near completion and at the same time enhancing emulsion stability to be appropriate for industrial application. The vanadium recovery/extraction percentage has been raised significantly in 3 min to 99.6% when adding 0.01% (w/w) Fe2O3 NPs (20 to 50 nm in size) in the internal phase (W1) and 5% (v/v) [OMIM]PF6 ionic liquid in the oil phase (O). Also, the emulsion stability was considerably improved, and the leakage percentage was reduced to 16% after 3 days. The results of this study could be used in the future to remove additional heavy metal ions from industrial effluents.

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state-space systems. It is shown that this class is very rich and possesses useful features and universal approximation properties. The reservoir architectures used for the approximation and estimation of elements in the new class are randomly generated echo state networks with either linear or ReLU activation functions. Their readouts are built using randomly generated neural networks in which only the output layer is trained (extreme learning machines or random feature neural networks). The results in the paper yield a recurrent neural network-based learning algorithm with provable convergence guarantees that do not suffer from the curse of dimensionality when learning input/output systems in the class of generalized Barron functionals and measuring the error in a mean-squared sense.

BACKGROUND: Plants can retain atmospheric particulate matter (PM) through their unique foliar microstructures, which has a profound impact on the phyllosphere microbial communities. Yet, the underlying mechanisms linking atmospheric particulate matter (PM) retention by foliar microstructures to variations in the phyllosphere microbial communities remain a mystery. In this study, we conducted a field experiment with ten Ulmus lines. A series of analytical techniques, including scanning electron microscopy, atomic force microscopy, and high-throughput amplicon sequencing, were applied to examine the relationship between foliar surface microstructures, PM retention, and phyllosphere microbial diversity of Ulmus L.
RESULTS: We characterized the leaf microstructures across the ten Ulmus lines. Chun exhibited a highly undulated abaxial surface and dense stomatal distribution. Langya and Xingshan possessed dense abaxial trichomes, while Lieye, Zuiweng, and Daguo had sparsely distributed, short abaxial trichomes. Duomai, Qingyun, and Lang were characterized by sparse stomata and flat abaxial surfaces, whereas Jinye had sparsely distributed but extensive stomata. The mean leaf retention values for total suspended particulate (TSP), PM2.5, PM2.5-10, PM10-100, and PM> 100 were 135.76, 6.60, 20.10, 90.98, and 13.08 µg·cm- 2, respectively. Trichomes substantially contributed to PM2.5 retention, while larger undulations enhanced PM2.5-10 retention, as evidenced by positive correlations between PM2.5 and abaxial trichome density and between PM2.5-10 and the adaxial raw microroughness values. Phyllosphere microbial diversity patterns varied among lines, with bacteria dominated by Sediminibacterium and fungi by Mycosphaerella, Alternaria, and Cladosporium. Redundancy analysis confirmed that dense leaf trichomes facilitated the capture of PM2.5-associated fungi, while bacteria were less impacted by PM and struggled to adhere to leaf microstructures. Long and dense trichomes provided ideal microhabitats for retaining PM-borne microbes, as evidenced by positive feedback loops between PM2.5, trichome characteristics, and the relative abundances of microorganisms like Trichoderma and Aspergillus.
CONCLUSIONS: Based on our findings, a three-factor network profile was constructed, which provides a foundation for further exploration into how different plants retain PM through foliar microstructures, thereby impacting phyllosphere microbial communities.

Urinary tract infection (UTI) is one of the most common bacterial infections worldwide. The main causative agent of UTI is uropathogenic Escherichia coli (UPEC). There is an immediate need for novel prophylactic and treatment strategies against UTI because of the increasing incidence of antimicrobial resistance among uropathogens. ABU 83972, an asymptomatic bacteriuria-causing E. coli strain, prevents UTI by suppressing the colonization of UPEC. However, the nature of competition and growth repression of UPEC by ABU 83972 is unclear and is the subject of our investigation. Here, we characterized the growth kinetics of ABU 83972 and uropathogens in human urine and laboratory media. Next, we performed a series of competitive co-culture experiments where ABU 83972 and uropathogens were inoculated at a 1:1 ratio in human urine and in various media, and their relative abundance was determined. In human urine, ABU 83972 outcompeted UPEC and additional uropathogens, reaching up to 90% of the total population after 24 hours of incubation. In contrast, UPEC outcompeted ABU 83972 in LB and M9 minimal media and exhibited superior colonization than ABU 83972 in the mouse urinary bladder. Since engineered living materials (ELMs) can be used to retain an organism of interest in a particular location, we developed ABU 83972-containing ELMs that effectively outcompeted UPEC in human urine. In summary, our work establishes that ABU 83972 outcompetes UPEC in a milieu- and cell-density-dependent manner, highlighting the importance of the metabolites and nutrients found in the human urine as determinants of the competitive fitness of ABU 83972.

It has always been the goal of many researchers to gain a thorough understanding of the patterns in the stock market and forecast the trends it will follow. The use of an advanced forecasting model can assist with accurately forecasting the future price of stocks, their fluctuations in the markets, as well as make profits in trading. With this motivation, in this study, a novel stock index trend predictor model is designed by integrating Multiple Criteria Decision-Making (MCDM) with an optimized Online Sequential Extreme Learning Machine (OSELM). Forecasting the future stock index prices and analyzing the upward or downward trends of these price forecasts are the two objectives of the proposed model. As the performance of OSELM is heavily dependent on the activation functions used in it, suitable selection of the activation function for OSELM is addressed as a MCDM problem. According to this approach, the trend prediction performance of six popular activation functions is assessed based on five regression-based and five classification-based criteria. In this investigation, three MCDM approaches are used to assess the performance matrix and determine which activation function is the best for OSELM based on six alternative models and ten criteria. To further optimize OSELM\'s performance, a hybrid crow search algorithm (hCSA) is incorporated in its training phase. By introducing the chaotic map and mutation operator in position update scheme and catfish behavior in the search process of original CSA, the proposed hCSA is able to achieve the right balance between exploration and exploitation improving the convergence. The proposed trend predictor model is empirically evaluated over historical data of three stock indices such as BSE SENSEX, S&P 500 and DJIA collected during pre-COVID and COVID time frame. In most of the test cases, the hCSA-OSELM model outperforms the state-of-the-art baseline models in terms of all evaluation criteria. When compared to the second-best baseline model, the suggested model is able to achieve the MSE improvements of 4-6%, 25-31%, and accuracy improvements of 0.4-0.8%, 0.9-1.3% over the pre-COVID and COVID time-frames, respectively. The statistical test also reveals the better performance of the proposed model. The robust and reliable MCDM-based model selection, superior prediction and classification outcomes clearly reveal that the proposed model can be used for financial time-series forecasting amid daily volatility as well as highly volatile markets.

In this study, we examine China\'s cognitive warfare coordinated with military air operations during the COVID pandemic in Taiwan. In May 2021, Taiwan experienced its first novel coronavirus outbreak with up to 500 daily cases. The Chinese government launched a series of coordinated \"cognitive warfare\" campaigns targeting Taiwan in addition to the People\'s Liberation Army (PLA) frequent air force incursions into Taiwan\'s air zone. Meanwhile, through manipulation of the vaccine supply, China turned COVID vaccine into a political issue in Taiwan involving multiple players including pharmaceutical developers, tech giants, and local politicians. Combining multiple sources of data, we analyze the Chinese Government\'s orchestrated cognitive and information warfare (IW) efforts targeted at influencing the Taiwan public\'s trust in the Democratic Progressive Party (DPP) government as well as its home-developed vaccine. Identifying the patterns of influencing using cognitive and IW, we found China\'s ultimate goal was to instill skepticism and confusion in Taiwan\'s public about the President Tsai Ing-wen\'s health policy and more generally undermine the creditability of the DPP government.

To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background. To begin, the CEEMDAN method is applied to disentangle the measured sea clutter data into a set of Intrinsic Mode Functions (IMFs). Afterwords, the Refined Composite Multiscale Dispersion Entropy (RCMDE) is computed for each individual IMF. This process categorizes the IMFs into three distinct components: noise-dominant, signal-noise mixture, and signal-dominant segments. The noise-dominate of IMF component is subjected to denoising through AWT, the signal-noise mixture of IMF components are processed using SG filtering, while the signal-dominant of IMF remains unaltered. The denoised sea clutter signal is reconstructed by concatenating the denoised and unprocessed IMFs. Based on the chaotic nature of sea clutter signals, first-order sea clutter data is transformed into high-dimensional data through phase space reconstruction. The initial weights and thresholds of the ELM are optimized through the IZOA to establish an optimal prediction model. This model is then used to detect small, low-altitude targets by analyzing the prediction error. The algorithm\'s effectiveness in noise removal is validated using IPIX and SPRR measured sea clutter data, demonstrating a significant improvement in the root mean square of prediction error (RMSE) by one order of magnitude after denoising compared to the pre-denoising state. Furthermore, we observed that the IZOA-ELM method can be effectively applied to detect small targets at low altitudes across various sea conditions. However, when the sea state is complex and greatly affected by the surrounding noise, an effective approach is to first employ CEEMDAN-AWT-SG to denoise the original signal, and then utilize IZOA-ELM for target detection.

UNASSIGNED: To evaluate the efficacy and safety of faricimab injections for treatment-naïve neovascular age-related macular degeneration (nvAMD) patients, including subtypes and pachychoroid phenotypes, and identify predictive factors for visual outcomes.
UNASSIGNED: nvAMD patients were prospectively recruited, receiving three monthly faricimab (6 mg) injections. Best-corrected visual acuity (BCVA) two months after the last injection (month 4) was compared between subtypes, and between pachychoroid neovasculopathy (PNV) and non-PNV eyes. Regression analysis determined factors influencing month 4 BCVA.
UNASSIGNED: The study involved 23 patients (12 typical AMD [tAMD], 10 polypoidal choroidal vasculopathy [PCV], 1 retinal angiomatous proliferation [RAP]). Eleven exhibited PNV phenotype. Significant BCVA (P = 4.9 × 10-4) and central retinal thickness (CRT) (P = 1.3 × 10-5) improvements were observed post-faricimab treatment. The therapy demonstrated favourable results for both tAMD and PCV eyes, and non-PNV and PNV eyes. Faricimab achieved dry macula in 77.3% of eyes, with subretinal fluid resolution in most cases, although intraretinal fluid (IRF) often persisted. Multivariable analysis identified external limiting membrane (ELM) presence and IRF as BCVA contributors at month 4.
UNASSIGNED: Faricimab demonstrated significant effectiveness and safety in treatment-naïve nvAMD patients, particularly for PCV and PNV eyes. ELM presence and IRF is predictive of visual outcomes.