Selection processes, transportability, and failure time analysis in life history studies.

In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person's prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.

The small molecule ZPD-2 inhibits the aggregation and seeded polymerisation of C-terminally truncated α-Synuclein.

Protein aggregation, particularly the formation of amyloid fibrils, is associated with numerous human disorders, including Parkinson's disease. This neurodegenerative condition is characterised by the accumulation of α-Synuclein amyloid fibrils within intraneuronal deposits known as Lewy bodies or neurites. C-terminally truncated forms of α-Synuclein are frequently observed in these inclusions in the brains of patients, and their increased aggregation propensity suggests a role in the disease's pathogenesis. This study demonstrates that the small molecule ZPD-2 acts as a potent inhibitor of both the spontaneous and seeded amyloid polimerisation of C-terminally truncated α-Synuclein by interfering with early aggregation intermediates. This dual activity positions this molecule as a promising candidate for therapeutic development in treating synucleinopathies.

Semiparametric sensitivity analysis: unmeasured confounding in observational studies.

Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We generalize the sensitivity analysis approach developed by Robins et al., Franks et al., and Zhou and Yao. We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step, split sample, truncated estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has $\sqrt{n}$ asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study.

Optimal survival analyses with prevalent and incident patients.

Period-prevalent cohorts are often used for their cost-saving potential in epidemiological studies of survival outcomes. Under this design, prevalent patients allow for evaluations of long-term survival outcomes without the need for long follow-up, whereas incident patients allow for evaluations of short-term survival outcomes without the issue of left-truncation. In most period-prevalent survival analyses from the existing literature, patients have been recruited to achieve an overall sample size, with little attention given to the relative frequencies of prevalent and incident patients and their statistical implications. Furthermore, there are no existing methods available to rigorously quantify the impact of these relative frequencies on estimation and inference and incorporate this information into study design strategies. To address these gaps, we develop an approach to identify the optimal mix of prevalent and incident patients that maximizes precision over the entire estimated survival curve, subject to a flexible weighting scheme. In addition, we prove that inference based on the weighted log-rank test or Cox proportional hazards model is most powerful with an entirely prevalent or incident cohort, and we derive theoretical formulas to determine the optimal choice. Simulations confirm the validity of the proposed optimization criteria and show that substantial efficiency gains can be achieved by recruiting the optimal mix of prevalent and incident patients. The proposed methods are applied to assess waitlist outcomes among kidney transplant candidates.

Probing Defects in Covalent Organic Frameworks.

Defects in covalent organic frameworks (COFs) play a pivotal role in determining their properties and performance, significantly influencing interactions with adsorbates, guest molecules, and substrates as well as affecting charge carrier dynamics and light absorption characteristics. The present review focuses on the diverse array of techniques employed for characterizing and quantifying defects in COFs, addressing a critical need in the field of materials science. As will be discussed in this review, there are basically two types of defects referring either to missing organic moieties leaving free binding groups in the material or structural imperfections resulting in lower crystallinity, grain boundary defects, and incomplete stacking. The review summarizes an in-depth analysis of state-of-the-art characterization techniques, elucidating their specific strengths and limitations for each defect type. Key techniques examined in this review include powder X-ray diffraction (PXRD), infrared spectroscopy (IR), thermogravimetric analysis (TGA), nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), scanning transmission electron microscopy (STEM), scanning tunneling microscope (STM), high resolution transmission electron microcoe (HRTEM), gas adsorption, acid-base titration, advanced electron microscopy methods, and computational calculations. We critically assess the capability of each technique to provide qualitative and quantitative information about COF defects, offering insights into their complementary nature and potential for synergistic use. The last section summarizes the main concepts of the review and provides perspectives for future development to overcome the existing challenges.

Structural and Electronic Response of Multigap N-Doped In2Se3: A Prototypical Material for Broad Spectral Optical Devices.

The production of controlled doping in two-dimensional semiconductor materials is a challenging issue when introducing these systems into current and future technology. In some compounds, the coexistence of distinct crystallographic phases for a fixed composition introduces an additional degree of complexity for synthesis, chemical stability, and potential applications. In this work, we demonstrate that a multiphase In2Se3 layered semiconductor system, synthesized with three distinct structures─rhombohedral α and ß-In2Se3 and trigonal δ-In2Se3─exhibits chemical stability and well-behaved n-type doping. Scanning tunneling spectroscopy measurements reveal variations in the local electronic density of states among the In2Se3 structures, resulting in a compound system with electronic bandgaps that range from infrared to visible light. These characteristics make the layered In2Se3 system a promising candidate for multigap or broad spectral optical devices, such as detectors and solar cells. The ability to tune the electronic properties of In2Se3 through structural phase manipulation makes it ideal for integration into flexible electronics and the development of heterostructures with other materials.

Identification of the protease inhibitory domain of Trichinella spiralis novel cystatin (TsCstN).

The Trichinella spiralis novel cystatin (TsCstN) inhibits cathepsin L (CatL) activity and inflammation of macrophages during lipopolysaccharide (LPS) induction. To identify the protease inhibitory region, this study applied an in silico modeling approach to simulate truncation sites of TsCstN (Ts01), which created four truncated forms, including TsCstN∆1-39 (Ts02), TsCstN∆1-71 (Ts03), TsCstN∆1-20, ∆73-117 (Ts04), and TsCstN∆1-20, ∆42-117 (Ts05). The superimposition of these truncates modeled with AlphaFold Colab indicated that their structures were more akin to Ts01 than those modeled with I-TASSER. Moreover, Ts04 exhibited the closest resemblance to the structure of Ts01. The recombinant Ts01 (rTs01) and truncated proteins (rTs02, rTs03, and rTs04) were successfully expressed in a prokaryotic expression system while Ts05 was synthesized, with sizes of approximately 14, 12, 8, 10, and 2.5 kDa, respectively. When determining the inhibition of CatL activity, both rTs01 and rTs04 effectively reduced CatL activity in vitro. Thus, the combination of the α1 and L1 regions may be sufficient to inhibit CatL. This study provides comprehensive insights into TsCstN, particularly regarding its protein function and inhibitory domains against CatL.

High fidelity adaptive mirror simulations with reduced order models.

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate's prototype of the adaptive mirror of the Giant Magellan Telescope.

Average treatment effect on the treated, under lack of positivity.

 The use of propensity score methods has become ubiquitous in causal inference. At the heart of these methods is the positivity assumption. Violation of the positivity assumption leads to the presence of extreme propensity scoreweights when estimating average causal effects, which affects statistical inference. To circumvent this issue, trimming or truncating methods have been widely used. Unfortunately, these methods require that we pre-specify a threshold. There are anumber of alternative methods to deal with the lack of positivity when we estimate the average treatment effect (ATE). However, no other methods exist beyond trimming and truncation to deal with the same issue when the goal is to estimate theaverage treatment effect on the treated (ATT). In this article, we propose a propensity score weight-based alternative for the ATT, called overlap weighted average treatment effect on the treated. The appeal of our proposed method lies in its abilityto obtain similar or even better results than trimming and truncation while relaxing the constraint to choose an a priori threshold (or related measures). The performance of the proposed method is illustrated via a series of Monte Carlo simulationsand a data analysis on racial disparities in health care expenditures.

Effects of Paclobutrazol on Reproductive and Vegetative Traits of Phalaenopsis Join Grace 'TH288-4'.

Phalaenopsis is the most popular potted plant worldwide. However, its typically long stalks often lead to increased shipping costs and risks. This study investigates the effectiveness of varying the concentration, timing, and frequency of paclobutrazol (PP333) applications on shortening the stalk of Phalaenopsis Join Grace 'TH288-4'. Concurrently, it also examines the potential for producing visually appealing and single-flower potted phalaenopsis products by means of truncation. Mature phalaenopsis plants were moved to a cool room in the seventh week to induce flowering. Four experimental groups were established based on different PP333 application schedules: the control (CK) group, with reverse osmosis water application in the second week; the T2 group, with a single application in the second week; the T2T3 group, with applications in both the second and third weeks; and the T7T8 group, with applications in the seventh and eighth weeks. The PP333 concentrations used were 250, 500, 750, and 1000 mg·L-1, applied as foliar sprays. The results showed that the shortest stalks, measured from the base to the first flower, were observed in the T2 group treated with PP333 at 750 mg·L-1 and in the T2T3 group with PP333 at 500, 750, and 1000 mg·L-1. These treatments resulted in stalk lengths of 19.18-22.17 cm, which are 67.2-71.6% shorter than the controls. PP333 application had minimal effect on the stalk diameter, pedicel length, flower width, length, and length/width ratio. However, root diameter was thicker in plants treated with PP333 compared with the control plants. For producing single-flower phalaenopsis, a foliar spray of 750 mg·L-1 PP333 is recommended approximately a month before moving the plants to cooler conditions, followed by truncation, retaining only the first flower. As a result, this study establishes a PP333 treatment protocol for phalaenopsis, offering a strategy to effectively shorten the stalks.

Rigidifying Flexible Sites: A Promising Strategy to Improve Thermostability of Lysophospholipase From Pyrococcus abyssi.

High thermostability of the enzymes is one of the distinguishing characteristics that increase their industrial utility. In the current research work, rigidifying the flexible amino acid residues of a lysophospholipase (Pa-LPL) from Pyrococcus abyssi was used as a protein engineering approach to improve its thermostability. A truncated variant of Pa-LPL (t-LPL∆12) was constructed by trimming its 12 amino acid residues (50-61) through overlap extension PCR. The truncated enzyme worked optimally at 65°C and pH 6.5 with remarkable thermostability at 65°C-85°C. In comparison to wild-type Pa-LPL, 5.8 and 1.2-fold increase in half-life (t1/2) of t-LPL∆12 was observed at 65 (optimum temperature) and 95°C, respectively. The activity of t-LPL∆12 was stimulated by 1 mM Cu2+ followed by Ca2+, Ni2+, Co2+, and Mg2+. Both substrate docking and experimental results indicated that the truncated enzyme could hydrolyze a variety of p-nitrophenyl esters. Km, Vmax, and Kcat for enzymatic hydrolysis of p-nitrophenyl butyrate were calculated to be 1 ± 0.087 mM, 1456 ± 36.474 U/mg, and 1.397 × 1011 min-1, respectively. In short, broad substrate specificity and thermostability of t-LPL∆12 are some of the distinctive features that make it an ideal candidate for degumming of vegetable oils.

Bayesian parametric estimation based on left-truncated competing risks data under bivariate Clayton copula models.

In observational/field studies, competing risks and left-truncation may co-exist, yielding 'left-truncated competing risks' settings. Under the assumption of independent competing risks, parametric estimation methods were developed for left-truncated competing risks data. However, competing risks may be dependent in real applications. In this paper, we propose a Bayesian estimator for both independent competing risks and copula-based dependent competing risks models under left-truncation. The simulations show that the Bayesian estimator for the copula-based dependent risks model yields the desired performance when competing risks are dependent. We also comprehensively explore the choice of the prior distributions (Gamma, Inverse-Gamma, Uniform, half Normal and half Cauchy) and hyperparameters via simulations. Finally, two real datasets are analyzed to demonstrate the proposed estimators.

Enhancing the expression of terminal deoxynucleotidyl transferases by N-terminal truncation.

Terminal deoxynucleotidyl transferase (TdT), a unique DNA polymerase that catalyzes the template-free incorporation of nucleotides into single-stranded DNA, has facilitated the development of various oligonucleotide-based tools and methods, especially in the field of template-free enzymatic DNA synthesis. However, expressing vertebrate-derived TdTs in Escherichia coli complicates purification and increases production costs. In this study, N-terminal truncation of TdTs was performed to improve their expression and stability. The results revealed that N-terminal truncation could enhance the expression level of six TdTs. Among the truncated mutants, N-140-ZaTdT and N-140-CpTdT, with 140 amino acids removed, exhibited an increase in protein expression, which was 9.5- and 23-fold higher than their wild-types, respectively. Importantly, the truncation preserves the catalytic function of TdT. Additionally, the Tm values of N-140-ZaTdT increased by 4.9°C. The improved expression of the truncated mutants makes them more suitable for reducing production costs and advancing enzyme engineering.

Identification of nonsense variants in the genomes of 15 Murciano-Granadina bucks and analysis of their segregation in parent-offspring trios.

Nonsense variants can inactivate gene function by causing the synthesis of truncated proteins or by inducing nonsense mediated decay of messenger RNAs. The occurrence of such variants in the genomes of livestock species is modulated by multiple demographic and selective factors. Even though nonsense variants can have causal effects on embryo lethality, abortions, and disease, their genomic distribution and segregation in domestic goats have not been characterized in depth yet. In this work, we have sequenced the genomes of 15 Murciano-Granadina bucks with an average coverage of 32.92 × ± 1.45 × . Bioinformatic analysis revealed 947 nonsense variants consistently detected with SnpEff and Ensembl-VEP. These variants were especially abundant in the 3'end of the protein-coding regions. Genes related to olfactory perception, ATPase activity coupled to transmembrane movement of substances, defense to virus, hormonal response, and sensory perception of taste were particularly enriched in nonsense variants. Seventeen nonsense variants expected to have harmful effects on fitness were genotyped in parent-offspring trios. We observed that several nonsense variants predicted to be lethal based on mouse knockout data did not have such effect, a finding that could be explained by the existence of multiple mechanisms counteracting lethality. These findings demonstrate that predicting the effects of putative nonsense variants on fitness is extremely challenging. As a matter of fact, such a goal could only be achieved by generating a high quality telomere-to-telomere goat reference genome combined with carefully curated annotation and functional testing of promising candidate variants.

Electrochemical aptasensor for sensitive detection of staphylococcal enterotoxin type A in milk and fruit juice.

An aptamer-based electrochemical sensor for the sensitive detection of staphylococcal enterotoxin type A (SEA) is presented. The truncated aptamer AptSEA1.4 used in this work was screened using computational techniques, which reduced the cost of the SELEX screening process. The aptamer-SEA interactions were confirmed by employing circular dichroism (CD) and fluorescence spectroscopy. Afterwards, for developing an electrochemical aptasensor, a fabricated GNR/FTO aptasensor was prepared and characterized using scanning electron microscopy-energy-dispersive X-ray analysis (SEM-EDX), atomic force microscopy (AFM), cyclic voltammetry (CV), and square wave voltammetry (SWV). A detailed investigation of aptamer and SEA interaction in the presence of various experimental conditions was also conducted through SWV and electrochemical impedance spectroscopy (EIS). The aptamer exhibits a strong affinity for SEA, with a dissociation constant (Kd) of 19.93 nM. The aptasensor is sensitive, with a lower limit of detection of 12.44 pg mL-1. It has good stability, repeatability, and specificity and has displayed highly specific and sensitive detection SEA in spiked packaged mixed fruit juice and milk, with a recovery of 95-110%. The aptasensor has high promise for detecting SEA in other food items.

Molecular Modification Enhances Xylose Uptake by the Sugar Transporter KM_SUT5 of Kluyveromyces marxianus.

This research cloned and expressed the sugar transporter gene KM_SUT5 from Kluyveromyces marxianus GX-UN120, which displayed remarkable sugar transportation capabilities, including pentose sugars. To investigate the impact of point mutations on xylose transport capacity, we selected four sites, predicted the suitable amino acid sites by molecular docking, and altered their codons to construct the corresponding mutants, Q74D, Y195K, S460H, and Q464F, respectively. Furthermore, we conducted site-directed truncation on six sites of KM_SUT5p. The molecular modification resulted in significant changes in mutant growth and the D-xylose transport rate. Specifically, the S460H mutant exhibited a higher growth rate and demonstrated excellent performance across 20 g L-1 xylose, achieving the highest xylose accumulation under xylose conditions (49.94 µmol h-1 gDCW-1, DCW mean dry cell weight). Notably, mutant delA554-, in which the transporter protein SUT5 is truncated at position delA554-, significantly increased growth rates in both D-xylose and D-glucose substrates. These findings offer valuable insights into potential modifications of other sugar transporters and contribute to a deeper understanding of the C-terminal function of sugar transporters.

Truncation of a novel C-terminal domain of a ß-glucanase improves its thermal stability and specific activity.

Enzymes that degrade ß-glucan play important roles in various industries, including those related to brewing, animal feed, and health care. Csph16A, an endo-ß-1,3(4)-glucanase encoded by a gene from the halotolerant, xerotolerant, and radiotrophic black fungus Cladosporium sphaerospermum, was cloned and expressed in Pichia pastoris. Two isoforms (Csph16A.1 and Csph16A.2) are produced, arising from differential glycosylation. The proteins were predicted to contain a catalytic Lam16A domain, along with a C-terminal domain (CTD) of unknown function which exhibits minimal secondary structure. Employing PCR-mediated gene truncation, the CTD of Csph16A was excised to assess its functional impact on the enzyme and determine potential alterations in biotechnologically relevant characteristics. The truncated mutant, Csph16A-ΔC, exhibited significantly enhanced thermal stability at 50°C, with D-values 14.8 and 23.5 times greater than those of Csph16A.1 and Csph16A.2, respectively. Moreover, Csph16A-ΔC demonstrated a 20%-25% increase in halotolerance at 1.25 and 1.5 M NaCl, respectively, compared to the full-length enzymes. Notably, specific activity against cereal ß-glucan, lichenan, and curdlan was increased by up to 238%. This study represents the first characterization of a glucanase from the stress-tolerant fungus C. sphaerospermum and the first report of a halotolerant and engineered endo-ß-1,3(4)-glucanase. Additionally, it sheds light on a group of endo-ß-1,3(4)-glucanases from Antarctic rock-inhabiting black fungi harboring a Lam16A catalytic domain and a novel CTD of unknown function.

Pitfalls in time-to-event analysis of registry data: a tutorial based on simulated and real cases.

Survival analysis (also referred to as time-to-event analysis) is the study of the time elapsed from a starting date to some event of interest. In practice, these analyses can be challenging and, if methodological errors are to be avoided, require the application of appropriate techniques. By using simulations and real-life data based on the French national registry of patients with primary immunodeficiencies (CEREDIH), we sought to highlight the basic elements that need to be handled correctly when performing the initial steps in a survival analysis. We focused on non-parametric methods to deal with right censoring, left truncation, competing risks, and recurrent events. Our simulations show that ignoring these aspects induces a bias in the results; we then explain how to analyze the data correctly in these situations using non-parametric methods. Rare disease registries are extremely valuable in medical research. We discuss the application of appropriate methods for the analysis of time-to-event from the CEREDIH registry. The objective of this tutorial article is to provide clinicians and healthcare professionals with better knowledge of the issues facing them when analyzing time-to-event data.

Impact of truncating historical data on prediction ability of dairy sheep selection candidates.

Along the last decades, the genetic evaluation methodology has evolved, improving breeding value estimates. Many breeding programmes have historical phenotypic records and large number of generations, but to make use of them could result in more inconveniences than benefits. In this study, the prediction ability of genotyped young animals was assessed by simultaneously evaluating the removal of historical data, two pedigree deepness and two methodologies (traditional BLUP and single-step genomic BLUP or ssGBLUP), using milk yield records of 40 years of three Latxa dairy sheep populations. The linear regression method was used to compare predictions of young rams before and after progeny testing, with six cut-off points, by intervals of 4 years (from 1992 to 2012), and statistics of ratio of accuracies, bias, and dispersion were calculated. The prediction accuracy of selection candidates, when genomic information was included, was the highest in all Latxa populations (between 0.54 and 0.69 with full data set). Nevertheless, the deletion of historical phenotypic data resulted on moderate accuracy gain in the bigger data size populations (mean gain 2.5%), and the smaller population took advantage of a moderate data deletion (2.7% gain by removing data until 2004), reducing accuracy when more records were removed. The bias of validation individuals was lower when the breeding value was predicted based on genomic information (between 2.1 and 13.9), being lower when the biggest amount of data was deleted in the bigger data size populations (5.2% reduction), and the smaller population was benefited from data deletion between 1996 and 2008 (3.8% bias reduction). Meanwhile, the slope of estimated genetic trend was lower when less data were included, and an overestimation of the unknown parent group estimates was observed. The results indicated that ssGBLUP evaluations were outstanding, compared with traditional BLUP evaluations, while the depth of pedigree had a very small influence, and deletion of historical phenotypic data was beneficial. Thus, Latxa routine genetic evaluations would benefit from truncating phenotypic records between 2000 and 2004, the use of two pedigree generations and the implementation of ssGBLUP methodology.