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Abstract Developing programmable bacterial cell-cell adhesion is of significant interest due to its versatile applications. Current methods that rely on presenting cell adhesion molecules (CAMs) on bacterial surfaces are limited by the lack of a generalizable strategy to identify such molecules targeting bacterial membrane proteins in their natural states. Here, we introduce a whole-cell screening platform designed to discover CAMs targeting bacterial membrane proteins within a synthetic bacteria-displayed nanobody library. Leveraging the potency of the bacterial type IV secretion system—a contact-dependent DNA delivery nanomachine—we have established a positive feedback mechanism to selectively enrich for bacteria displaying nanobodies that target antigen-expressing cells. Our platform successfully identified functional CAMs capable of recognizing three distinct outer membrane proteins (TraN, OmpA, OmpC), demonstrating its efficacy in CAM discovery. This approach holds promise for engineering bacterial cell-cell adhesion, such as directing the antibacterial activity of programmed inhibitor cells toward target bacteria in mixed populations.

Abstract In this study, we passively analyzed the near-field characteristics of thermally excited evanescent waves, which are radiation waves generated by the local dynamics of materials, including electron motions and lattice vibrations. The thermally excited evanescent waves on aluminium nitride (AlN) and gallium nitride (GaN) were measured using passive spectroscopic scattering-type scanning near-field optical microscopy (s-SNOM) in the wavelength ranges of 10.5–12.2 μm and 14.0–15.0 μm, which include the surface phonon-polariton (SPhP) wavelength of the studied dielectrics. We determined the unique decay characteristics of AlN and GaN, indicating a ten-fold increase in the probe area contributing to the scattering of waves near the SPhP wavelength compared to that in other wavelength ranges. The extended probe area correlated with the polariton decay lengths, indicating that the non-enhanced polaritons around K ~ ω/c were dominant in the scattered waves near the SPhP wavelength. In addition to the conventional passive detection mechanisms for metals, the proposed detection scheme will be a versatile passive detection model in the near future.

Ecosystem services, such as crop provision, carbon storage, water retention, and nutrient retention, are crucial for sustainable agriculture and local participation. Assessing and mapping these services is crucial for promoting sustainable agriculture and ensuring the active involvement of local communities. However, accurately estimating the values of agricultural ecosystem services is challenging due to the lack of accurate data and an effective economic decision-making system. To address these issues, this study combines field data and modeling approaches to quantify the physical and monetary values of cropland ecosystem services in Yunlin County, Taiwan. The results indicated that the service of crop provision (rice) ranges from 7.96 to 9.70 tonnes ha−1 yr−1, valued at approximately US$7,710 to US$10,650 ha−1 yr−1. The service of soil carbon storage exhibits a carbon stock ranging from 38.2 to 51.9 tonnes of carbon ha−1 yr−1, valued at approximately US$13,150 to US$17,890 ha−1 yr−1. Water retention services retain around 5,380 m3 ha−1 yr−1, valued at approximately US$5,530 ha−1 yr−1. Nutrient retention services retain 1.65 kg ha−1 yr−1 of phosphorus and 30.5 kg ha−1 yr−1 of nitrogen per year, valued at approximately US$60 ha−1 yr−1. We also found that employing the field data approach unveils a higher total ecosystem service value of US$34,105 per hectare per year, which is 28.5 % greater than the value derived from the modeling approach. The results provide insights into enhancing local participation in sustainable soil and agricultural resource management. The quantification of physical and monetary values of ecosystem services can support informed decision-making and promote local participation in sustainable soil and agricultural resource management.

Abstract Background Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a clinically critical pathogen that causes severe infection. Due to improper antibiotic administration, the prevalence of CRKP infection has been increasing considerably. In recent years, the utilization of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has enabled the identification of bacterial isolates at the families and species level. Moreover, machine learning (ML) classifiers based on MALDI-TOF MS have been recently considered a novel method to detect clinical antimicrobial-resistant pathogens. Methods A total of 2683 isolates (369 CRKP cases and 2314 carbapenem-susceptible Klebsiella pneumoniae [CSKP]) collected in the clinical laboratories of Taipei Medical University Hospital (TMUH) were included in this study, and 80% of data was split into the training data set that were submitted for the ML model. The remaining 20% of data was used as the independent data set for external validation. In this study, we established an artificial neural network (ANN) model to analyze all potential peaks on mass spectrum simultaneously. Results Our artificial neural network model for detecting CRKP isolates showed the best performance of area under the receiver operating characteristic curve (AUROC = 0.91) and of area under precision–recall curve (AUPRC = 0.90). Furthermore, we proposed the top 15 potential biomarkers in probable CRKP isolates at 2480, 4967, 12,362, 12,506, 12,855, 14,790, 15,730, 16,176, 16,218, 16,758, 16,919, 17,091, 18,142, 18,998, and 19,095 Da. Conclusions Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients’ outcomes and lower the prevalence of antimicrobial resistance.

BackgroundRisk factors associated with a suboptimal response to Gonadotropin-releasing hormone (GnRH) agonists include a high or low body mass index (BMI), prolonged use of oral contraceptive pills, and low luteinizing hormone (LH) levels on either the start or trigger days of controlled ovarian stimulation (COS). However, this approach may increase the need for a dual trigger and may also result in a higher incidence of ovarian hyperstimulation syndrome (OHSS) in hyper-responders. We aimed to investigate whether the maximum LH level during stimulation can serve as a predictive factor for achieving an optimal oocyte yield using the GnRH agonist trigger alone.MethodsWe retrospectively reviewed all antagonist protocols or progestin-primed ovarian stimulation (PPOS) protocols triggered with GnRH agonist only between May 2012 and December 2022. Subjects were divided into three groups, depending on basal LH level and LH maximum level. The freeze-all strategy was implemented in all cycles: Group 1, consistently low LH levels throughout COS; Group 2, low basal LH level with high LH max level during COS; Group 3, consistently high LH levels throughout COS. The primary outcome was the oocyte yield rate. The secondary outcome includes the number of collected oocytes, suboptimal response to GnRH agonist trigger, oocyte maturity rate, fertilized rate, clinical pregnancy rate, ongoing pregnancy rate, and live birth rate. The pregnancy outcomes were calculated for the first FET cycle.ResultsFollowing confounder adjustment, multivariable regression analysis showed that Group 1 (cycles with consistently low LH levels throughout COS) remains an independent predictor of suboptimal response (OR: 6.99; 95% CI 1.035–47.274). Group 1 (b = −12.72; 95% CI −20.9 to −4.55) and BMI (b = −0.25; 95% CI −0.5 to −0.004) were negatively associated with oocyte yield rate. Patients with low basal LH but high LH max levels had similar clinical outcomes compared to those with high LH max levels through COS.ConclusionsThe maximum LH level during COS may serve as an indicator of LH reserve and could be a more reliable predictor of achieving an optimal oocyte yield when compared to relying solely on the basal LH level. In the case of hyper-responders where trigger agents (agonist-only or dual trigger) are being considered, we propose a novel strategy that incorporates the maximum LH level, rather than just the basal or trigger-day LH level, as a reference for assessing LH reserve. This approach aims to minimize the risk of obtaining suboptimal oocyte yield and improve overall treatment outcomes.

We investigate the spectral shift known as the collective Lamb shift in forward scattering for a cold dense atomic cloud. The shift results from resonant dipole–dipole interaction mediated by real and virtual photon exchange, forming many-body states displaying various super- and subradiant behaviour. However, the scattering spectrum reflects the overall contributions from these states but also averages out the radiative details associated with the underlying spin orders, causing ambiguity in determination and raising controversy on the scaling property of this shift. We employ a Monte–Carlo simulation to study how the collective states contribute to emission. We thus distinguish two kinds of collective shift that follow different scaling laws. One results from dominant occupation of the near-resonant collective states. This shift is usually small and insensitive to the density or the number of participating atoms. The other comes from large spatial correlation of dipoles, associated with the states of higher degree of emission. This corresponds to larger collective shift that is approximately linearly dependent on the optical depth. We further demonstrate that the spatial spin order plays an essential role in superradiant emission. Our analysis provides a novel perspective for understanding collective scattering and cooperative effects.

Abstract The use of nitrogen fertilizers in agriculture produces significant quantities of nitrogenous gases including ammonia, nitric oxide, and nitrous oxide. Through better farmland management practices, the emission of nitrogenous gases can be reduced while realizing clean water environment and climate-smart agriculture. In this article, we first provided an overview of the international movements on reducing nitrogenous gas emissions from farmlands. Then, we summarized the effect of agricultural management practices on nitrogen use efficiency for various crops, and evaluated their effect on nitrogenous gas emissions. The results indicated the importance of implementing site-specific sustainable management practices to enhance nitrogen use efficiency, and thus mitigate nitrogenous gas emissions. We also addressed the impact of agricultural activities on cropland nitrogen cycles, and highlighted the need to perform systematic trade-off evaluations with a well-defined scope to maximize environmental benefits and maintain ecosystem services. Lastly, we proposed three priority directions by moving toward a low-emission agriculture.

We propose a tunable phonon laser prototype with a large trapped ion array, where some of the ions are effectively pinned by optical tweezers, thus isolating a subset of ions that mimics an acoustic cavity used as a phonon lasing resonator. The cavity loss can then be controlled by the tweezer strength and the “wall thickness”, the number of pinned ions for isolation. We pump the resonator by applying blue-sideband lasers, and investigate the lasing dynamics of the “cavity” motional modes such as threshold behavior, population distribution, the second-order coherence, and linewidth narrowing. This scheme can be generalized to multimode resonators, for which we report the mode competition phenomenon and lasing mode multistability. Our work provides an excellent platform for acoustic quantum state manipulation, paving the way towards phonon-mediated quantum computing, communications, and metrology.

Abstract Single incision laparoscopic surgery (SILS) has emerged as least invasive interventions for gynecologic disease. However, SILS is slow to gain in popularity due to difficulties in triangulation and instrument crowding. Besides, the costly instruments may influence patients’ will to have this procedure, and limit other medical expense as well. To optimize outcome and reduce cost, the objective of this study is to evaluate the feasibility and safety for patients undergoing adnexal surgeries using conventional laparoscopic instruments with SILS (SILS-C), and to compare with those of patients subject to TP using conventional laparoscopic instruments (TP-C). This is a retrospective case–control study. The data dated from April 2011 to April 2018. Patients who received concomitant multiple surgeries, were diagnosed with suspected advanced stage ovarian malignancy, or required frozen sections for intraoperative pathologic diagnosis were excluded. Demographic data, including the age, body weight, height, previous abdominal surgery were obtained. The surgical outcomes were compared using conventional statistical methods. 259 patients received SILS-C. The operating time was 63.83 ± 25.31 min. Blood loss was 2.38 ± 6.09 c.c. 58 patients (24.38%) needed addition of port to complete surgery. 384 patients received TP-C. Compared with SILS-C, the operating time was shorter (57.32 ± 26.38 min, OR = 0.984, CI = 0.975–0.992). The patients were further divided into unilateral or bilateral adnexectomy, and unilateral or bilateral cystectomy. Other than the operating time in unilateral cystectomy (66.12 ± 19.5 vs. 58.27 ± 23.92 min, p = .002), no statistical differences were observed in the subgroup analysis. Single incision laparoscopic surgery using conventional laparoscopic instruments is feasible and safe as initial approach to adnexal lesions. In complex setting as unilateral cystectomy or pelvic adhesions, two-port access may be considered.

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

Abstract Polyimides (PIs) possess excellent mechanical properties, thermal stability, and chemical resistance and can be converted to carbon materials by thermal carbonization. The preparation of carbon nanomaterials by carbonizing PI‐based nanomaterials, however, has been less studied. In this work, the fabrication of PI nanofibers is investigated using electrospinning and their transformation to carbon nanofibers. Poly(amic acid) carboxylate salts (PAASs) solutions are first electrospun to form PAAS nanofibers. After the imidization and carbonization processes, PI and carbon nanofibers can then be obtained, respectively. The Raman spectra reveal that the carbon nanofibers are partially graphitized by the carbonization process. The diameters of the PI nanofibers are observed to be smaller than those of the PAAS nanofibers because of the formation of the more densely packed structures after the imidization processes; the diameters of the carbon nanofibers remain similar to those of the PI nanofibers after the carbonization process. The thermal dissipation behaviors of the PI and carbon nanofibers are also examined. The infrared images indicate that the transfer rates of thermal energy for the carbon nanofibers are higher than those for the PI nanofibers, due to the better thermal conductivity of carbon caused by the covalent sp2 bonding between carbon atoms.

Summary: Misregulation of alternative splicing is a hallmark of human tumors, yet to what extent and how it contributes to malignancy are only beginning to be unraveled. Here, we define which members of the splicing factor SR and SR-like families contribute to breast cancer and uncover differences and redundancies in their targets and biological functions. We identify splicing factors frequently altered in human breast tumors and assay their oncogenic functions using breast organoid models. We demonstrate that not all splicing factors affect mammary tumorigenesis in MCF-10A cells. Specifically, the upregulation of SRSF4, SRSF6, or TRA2β disrupts acinar morphogenesis and promotes cell proliferation and invasion in MCF-10A cells. By characterizing the targets of these oncogenic splicing factors, we identify shared spliced isoforms associated with well-established cancer hallmarks. Finally, we demonstrate that TRA2β is regulated by the MYC oncogene, plays a role in metastasis maintenance in vivo, and its levels correlate with breast cancer patient survival. : Park et al. demonstrate that >50% of human breast tumors exhibit an alteration in one of the splicing factors from the SR protein family. Using in vitro and in vivo breast cancer models, they identify three splicing factors that promote cell proliferation and invasion by regulating isoforms associated with cancer hallmarks. Keywords: alternative RNA splicing, breast cancer, splicing factor, SR protein, metastasis, MYC, TRA2-beta, triple negative breast cancer

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

Justicidin A (JA), a novel arylnaphthalide lignan, was previously found to reduce the growth of human hepatocellular carcinoma cells (HCC), Hep 3B cells, in NOD-SCID mice via induction of apoptosis. Here, using L1000 microarray profiling obtained from JA-treated HCC cells, the z-score signatures of JA exhibited positive connectivity to known autophagy inducers among 3122 compounds from the Library of Integrated Network-based Cellular Signatures database. JA-induced autophagy in HCC Hep 3B cells was confirmed by formation of acidic vesicular organelles and an increase in the expression of LC3-II and LAMP2a. JA-induced autophagic flux was demonstrated by accumulation of LC3-II and LAMP2a, as well as a decrease in colocalization of LC3 and LAMP2a puncta in the presence of autophagy inhibitor bafilomycin A1. Administration of bafilomycin A1 also demonstrated that JA-induced autophagy suppressed apoptosis. Activation of Ras/MEK/ERK pathway was observed. Following administration of apoptosis inhibitor zVAD, it was found that JA-induced apoptosis did not affect JA-induced autophagy. It is noteworthy that addition of JA promoted chemosensitivity of sorafenib, the only FDA-approved targeted therapy for advanced HCC. The results demonstrate the effectiveness of this novel strategy for rapid discovery of molecular actions of small molecules and suggest the applications of JA in HCC treatment.

Serine protease PRSS23 is a newly discovered protein that has been associated with tumor progression in various types of cancers. Interestingly, PRSS23 is coexpressed with estrogen receptor α (ERα), which is a prominent biomarker and therapeutic target for human breast cancer. Estrogen signaling through ERα is also known to affect cell proliferation, apoptosis, and survival, which promotes tumorigenesis by regulating the production of numerous downstream effector proteins.In the present study, we aimed to clarify the correlation between and functional implication of ERα and PRSS23 in breast cancer. Analysis of published breast cancer microarray datasets revealed that the gene expression correlation between ERα and PRSS23 is highly significant among all ERα-associated proteases in breast cancer. We then assessed PRSS23 expression in 56 primary breast cancer biopsies and 8 cancer cell lines. The results further confirmed the coexpression of PRSS23 and ERα and provided clinicopathological significance. In vitro assays in MCF-7 breast cancer cells demonstrated that PRSS23 expression is induced by 17β-estradiol-activated ERα through an interaction with an upstream promoter region of PRSS23 gene. In addition, PRSS23 knockdown may suppress estrogen-driven cell proliferation of MCF-7 cells.Our findings imply that PRSS23 might be a critical component of estrogen-mediated cell proliferation of ERα-positive breast cancer cells. In conclusion, the present study highlights the potential for PRSS23 to be a novel therapeutic target in breast cancer research.

Hepatocellular carcinoma (HCC) is an aggressive tumor with a poor prognosis. Currently, only sorafenib is approved by the FDA for advanced HCC treatment; therefore, there is an urgent need to discover candidate therapeutic drugs for HCC. We hypothesized that if a drug signature could reverse, at least in part, the gene expression signature of HCC, it might have the potential to inhibit HCC-related pathways and thereby treat HCC. To test this hypothesis, we first built an integrative platform, the "Encyclopedia of Hepatocellular Carcinoma genes Online 2", dubbed EHCO2, to systematically collect, organize and compare the publicly available data from HCC studies. The resulting collection includes a total of 4,020 genes. To systematically query the Connectivity Map (CMap), which includes 6,100 drug-mediated expression profiles, we further designed various gene signature selection and enrichment methods, including a randomization technique, majority vote, and clique analysis. Subsequently, 28 out of 50 prioritized drugs, including tanespimycin, trichostatin A, thioguanosine, and several anti-psychotic drugs with anti-tumor activities, were validated via MTT cell viability assays and clonogenic assays in HCC cell lines. To accelerate their future clinical use, possibly through drug-repurposing, we selected two well-established drugs to test in mice, chlorpromazine and trifluoperazine. Both drugs inhibited orthotopic liver tumor growth. In conclusion, we successfully discovered and validated existing drugs for potential HCC therapeutic use with the pipeline of Connectivity Map analysis and lab verification, thereby suggesting the usefulness of this procedure to accelerate drug repurposing for HCC treatment.

Background: Risk factors associated with a suboptimal response to Gonadotropin-releasing hormone (GnRH) agonists include a high or low body mass index (BMI), prolonged use of oral contraceptive pills, and low luteinizing hormone (LH) levels on either the start or trigger days of controlled ovarian stimulation (COS). However, this approach may increase the need for a dual trigger and may also result in a higher incidence of ovarian hyperstimulation syndrome (OHSS) in hyper-responders. We aimed to investigate whether the maximum LH level during stimulation can serve as a predictive factor for achieving an optimal oocyte yield using the GnRH agonist trigger alone. Methods: We retrospectively reviewed all antagonist protocols or progestin-primed ovarian stimulation (PPOS) protocols triggered with GnRH agonist only between May 2012 and December 2022. Subjects were divided into three groups, depending on basal LH level and LH maximum level. The freeze-all strategy was implemented in all cycles: Group 1, consistently low LH levels throughout COS; Group 2, low basal LH level with high LH max level during COS; Group 3, consistently high LH levels throughout COS. The primary outcome was the oocyte yield rate. The secondary outcome includes the number of collected oocytes, suboptimal response to GnRH agonist trigger, oocyte maturity rate, fertilized rate, clinical pregnancy rate, ongoing pregnancy rate, and live birth rate. The pregnancy outcomes were calculated for the first FET cycle. Results: Following confounder adjustment, multivariable regression analysis showed that Group 1 (cycles with consistently low LH levels throughout COS) remains an independent predictor of suboptimal response (OR: 6.99; 95% CI 1.035-47.274). Group 1 (b = -12.72; 95% CI -20.9 to -4.55) and BMI (b = -0.25; 95% CI -0.5 to -0.004) were negatively associated with oocyte yield rate. Patients with low basal LH but high LH max levels had similar clinical outcomes compared to those with high LH max levels through COS. Conclusions: The maximum LH level during COS may serve as an indicator of LH reserve and could be a more reliable predictor of achieving an optimal oocyte yield when compared to relying solely on the basal LH level. In the case of hyper-responders where trigger agents (agonist-only or dual trigger) are being considered, we propose a novel strategy that incorporates the maximum LH level, rather than just the basal or trigger-day LH level, as a reference for assessing LH reserve. This approach aims to minimize the risk of obtaining suboptimal oocyte yield and improve overall treatment outcomes. [ABSTRACT FROM AUTHOR]

The complex and heterogeneous nature of the lignin macromolecule has presented a lasting barrier to its utilization. To achieve high lignin yield, the technical lignin extraction process usually severely modifies and condenses the native structure of lignin, which is a critical drawback for its utilization in conversion processes. In addition, there is no method capable of separating lignin from plant biomass with controlled structural properties. Here, we developed an N-heterocycle-based deep eutectic solvent formed between lactic acid and pyrazole (La-Py DES) with a binary hydrogen bonding functionality resulting in a high affinity toward lignin. Up to 93.7% of lignin was extracted from wheat straw biomass at varying conditions from 90 °C to 145 °C. Through careful selection of treatment conditions as well as lactic acid to pyrazole ratios, lignin with controlled levels of ether linkage content, hydroxyl group content, and average molecular weight can be generated. Under mild extraction conditions (90 °C to 120 °C), light-colored native-like lignin can be produced with up to 80% yield, whereas ether linkage-free lignin with low polydispersity can be obtained at 145 °C. Overall, this study offers a new strategy for native lignin extraction and generating lignin with controlled structural properties. [ABSTRACT FROM AUTHOR]