Detection and Anti-Detection with Microwave-Infrared Compatible Camouflage Using Asymmetric Composite Metasurface.

Detection and anti-detection with multispectral camouflage are of pivotal importance, while suffer from significant challenges due to the inherent contradiction between detection and anti-detection and conflict microwave and infrared (IR) stealth mechanisms. Here, a strategy is proposed to asymmetrically control transmitted microwave wavefront under radar-IR bi-stealth scheme using composite metasurface. It is engineered composed of infrared stealth layer (IRSL), microwave absorbing layer (MAL), and asymmetric microwave transmissive structure (AMTS) with polarization conversion from top to bottom. Therein, IR emissivity, microwave reflectivity, and transmissivity are simultaneously modulated by elaborately designing the filling ratio of ITO square patches on IRSL, which ensures both efficient microwave transmission and IR camouflage. Furthermore, full-polarized backward microwave stealth is achieved on MAL by transmitting and absorbing microwaves under x- and y- polarization, respectively, while forward wavefront is controlled by precise curvature phase compensation on AMTS according to ray-tracing technology. For verification, a proof-of-concept metadevice is numerically and experimentally characterized. Both results coincide well, demonstrating spiral detective wavefront manipulation under y-polarized forward wave excitation while effective reduction of radar cross section within 8-18 GHz and low IR emissivity (<0.3) for backward detection. This strategy provides a new paradigm for integration of detection and anti-detection with multispectral camouflage.

Enhanced electromagnetic wave absorption of bacterial cellulose/ reduced graphene oxide aerogel by eco-friendly in-situ construction.

Electromagnetic wave absorption materials (EWAMs) have become an effective means to address electromagnetic (EM) radiation and enhance stealth technology, among which aerogels are valued for their lightweight nature and excellent designability. This study utilized environmentally friendly preparation and in-situ reduction techniques to fabricate bacterial cellulose (BC) / reduced graphene oxide (RGO) aerogels, achieving tailored EM wave loss capabilities by controlling the reduction time of ascorbic acid. Benefitting from the effects of freeze-casting, BC winding, hydrogen bond, and RGO layers coupling, the aerogel maintains their original structure after reduction and exhibits satisfactory EM wave absorption. The minimum reflection loss (RLmin) is -38.52 dB, with an effective absorption bandwidth (EAB) of 6.68 GHz and a maximum radar cross section (RCS) reduction of 44.69 dBsm. Additionally, the aerogel's lightweight (a low density of 9.03 mg/cm3) and outstanding thermal insulation properties enable it to adapt to complex conditions. Thus, the study provides a novel approach for the construction of industrialized and sustainable RGO-based EWAMs.

Urinary fatty acid biomarkers for prostate cancer detection.

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models.

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.

Investigating the effects of storage conditions on urinary volatilomes for their reliability in disease diagnosis.

BACKGROUND: Cancer detection presents challenges regarding invasiveness, cost, and reliability. As a result, exploring alternative diagnostic methods holds significant clinical importance. Urinary metabolomic profiling has emerged as a promising avenue; however, its application for cancer diagnosis may be influenced by sample preparation or storage conditions. OBJECTIVE: This study aimed to assess the impact of sample storage and processing conditions on urinary volatile organic compounds (VOCs) profiles and establish a robust standard operating procedure (SOP) for such diagnostic applications. METHODS: Five key variables were investigated: storage temperatures, durations, freeze-thaw cycles, sample collection conditions, and sample amounts. The analysis of VOCs involved stir bar sorptive extraction coupled with thermal desorption-gas chromatography/mass spectrometry (SBSE-TD-GC-MS), with compound identification facilitated by the National Institute of Standards and Technology Library (NIST). Extensive statistical analysis, including combined scatterplot and response surface (CSRS) plots, partial least squares-discriminant analysis (PLS-DA), and probability density function plots (PDFs), were employed to study the effects of the factors. RESULTS: Our findings revealed that urine storage duration, sample amount, temperature, and fasting/non-fasting sample collection did not significantly impact urinary metabolite profiles. This suggests flexibility in urine sample collection conditions, enabling individuals to contribute samples under varying circumstances. However, the influence of freeze-thaw cycles was evident, as VOC profiles exhibited distinct clustering patterns based on the number of cycles. This emphasizes the effect of freeze-thaw cycles on the integrity of urinary profiles. CONCLUSIONS: The developed SOP integrating SBSE-TD-GC-MS and statistical analyses can serve as a valuable tool for analyzing urinary organic compounds with minimal preparation and sensitive detection. The findings also support that urinary VOCs for cancer screening and diagnosis could be a feasible alternative offering a robust, non-invasive, and sensitive approach for cancer screening.

Broadband electromagnetic wave absorption using pure carbon aerogel by synergistically modulating propagation path and carbonization degree.

Carbon materials were widely used as electromagnetic (EM) wave absorption due to their advantages of light weight, environmental resistance and high electrical conductivity. However, conventional means were typically available by combining carbon and other materials to achieve effective absorption. Herein, a novel strategy using pure carbon aerogel with oriented structure was reported to enhance the EM wave absorption by synergistically modulating the wave propagation path and carbonization degree. The aerogel contained proposed modified carbon nanofibers (MCNF) derived from bacterial cellulose (BC), and core-shell carbon nanofibers @ reduced oxide graphene (CNF@RGO). The oriented structure was induced by the temperature field, which manifests anisotropic EM constitutive parameters (εx ≠ Îµz) at different directions of incident wave. The carbonization degree was adjusted by varying the carbonization temperature. At the carbonization temperature of 700 °C, the maximum reflection loss and effective absorption bandwidth reached -53.94 dB and 7.14 GHz, respectively, enabling the aerogel to outperform its previous counterparts. To clarify the EM wave mode-of-action in conjunction, physical models of the aerogel were established in addition to finite element simulation and theoretical analysis. Notably, the aerogel with a density of 3.6 mg/cm3 featured ultra-light weight, superhydrophobicity, superior compressibility, and thermal insulation. Our work offers an efficient strategy for designing broadband and multifunctional EM wave absorption materials (EWAMs), promising great potentials in complex stealth equipment.

Multifunctional microwave absorption materials of multiscale cobalt sulfide/diatoms co-doped carbon aerogel.

Microwave absorption materials (MAMs) have attracted much attention for their potential applications in stealth technology and prevention of electromagnetic pollution problems. Multifunctional MAMs are highly demanded because they can be applied in harsh environments. Hence, based on multiscale manipulation of atomic engineering, nanostructure and microstructure, a multiscale hollow cobalt sulfide/diatoms co-doped carbon aerogel was preparedthrough the physical crosslinking of divalent ions, unidirectional freezing, kirkendall effect, and heteroatomic doping. The aerogel with a low density of 13.1 mg/mm3 has a unique "lamellar-pillar" network structure due to the growth of ice crystals during the preparation process. With the assistance of thiourea, the doping of N, S atoms and the construction of hollow cobalt sulfide are accomplished simultaneously. The ingenious integration facilitates the synergistic effect of conductive loss, defect polarization, interfacial polarization, and multiple scattering. The multiscale hollow cobalt sulfide/diatoms co-doped carbon aerogel had a maximum reflection loss of -51.96 dB and an effective absorption bandwidth of 6.4 GHz, which is higher than that of other reported MAMs. It is further verified through finite element simulation and experiments that the aerogel has an excellent microwave absorption properties. In addition, the aerogel has excellent thermal insulation and flame retardant properties. Therefore, the development of this aerogel can help to use MAMs in complex applications.

Refined moderation analysis with binary outcomes in precision medicine research.

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.

Vibration training reducing falls in community-living older adults: a pilot randomized controlled trial.

BACKGROUND: Although vibration training has been applied in older adults, it remains unclear if it can reduce falls. AIMS: This pilot randomized-controlled trial aimed to test the effects of an 8-week vibration training program on reducing falls among community-dwelling adults. METHODS: Forty-eight older adults were randomized to two groups: training and control. The training group received three weekly training sessions over eight weeks while the control group maintained their normal lifestyle over the 8-week period. Immediately before (or baseline), following (post-training), and three months after (retest) the 8-week training course, a group of fall risk factors were assessed for all participants. Each participant was also exposed to an unexpected gait-slip on a treadmill during post-training and retest sessions. Their daily-living fall incidence was collected for 12 months after the baseline test. The slip fall was the primary outcome, prospective all-cause falls were the secondary outcome, and fall risk factors acted as the tertiary ones. RESULTS: The vibration training program significantly reduced the risk of slip-falls and improved all fall risk factors immediately after the training course. The training effect may be carried over for three months. The 8-week training program could also lower the number of falls between the baseline test and retest and reduce the recurrent faller rate across the 12 months after the baseline test. DISCUSSION: This study indicates that vibration training might have some effects on fall-related measures in older adults. CONCLUSIONS: An 8-week vibration training program could be effective to reduce falls in older adults. GOV REGISTRATION NUMBER: NCT02694666.

Refined moderation analysis with categorical outcomes in precision medicine.

Moderation analysis is an integral part of precision medicine research. Concerning moderation analysis with categorical outcomes, we start with an interesting observation, which shows that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and the treatment variable in logistic regression models. Hence two estimators of moderating effects can be obtained. We then established the joint asymptotic normality for the two estimators, on which basis refined inference can be made for moderation analysis. The improved precision is helpful in addressing the lack-of-power problem that is common in search of moderators. The above-mentioned results hold for both experimental and observational data. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.