Conventional ESD may not ensure complete resection of submucosal tumors, as the dissecting layer behind the tumor is difficult to see. To conquer this dilemma, we present a novel pre-traction strategy using clips and rubber-band, which facilitate ESD removal of rectal NETs.

A new Decision-Based Adaptive Denoising Filter (DBADF) algorithm & hardware architecture are proposed for restoring the digital image that is highly corrupted with impulse noise. The proposed DBADF detects only the corrupted pixels and that pixel is restored by the noise-free median value or previous value based upon the noise density in the image. The proposed DBADF uses a 3 × 3 window initially and adaptively goes up to 7 × 7 window based on the noise corruption more than 50% by impulse noise in the current processing window. The proposed architecture was found to exhibit better visual qualitative and quantitative evaluation based on PSNR, IEF, EKI, SSIM, FOM, and error rate. The DBAMF architecture also preserves the original information of digital image with a high density of salt & pepper noise, when compared to many standard conventional algorithms. The proposed architecture has been simulated using the VIRTEX7 FPGA device and the reported maximum post place and route frequency are 149.995MHz and the dynamic power consumption is 179mW.

Cutaneous polyarteritis nodosa is a rare neutrophilic vasculitis. We present two cases that reflect the gamut of this disorder including one case whose delayed diagnosis led to permanent nerve deficit and scarring.

Sternal fractures are commonly due to blunt force trauma and reduction is an invasive surgical procedure typically indicated for refractory pain sternal instability. Retrospective literature. There were various modalities used for treatment and fixation of the sternal fractures.Sternal displacement fractures are traumatic injuries that may require surgical correction.

A 58-year-old woman had gastric cancer treated with nivolumab. Her serum creatinine (Cr) increased to 5.94 mg/dL post two cycles of nivolumab and co-administered with acemetacin. A kidney biopsy showed acute tubular injury (ATI).

As the first point of contact for patients, General Practitioner (GP) plays an important role in the National Health System. An accurate primary diagnosis from the GP on the patient will relieve specialists’ pressure and save time from confirming the patient’s condition and doing examinations. Because GP has broader but less specialized knowledge, the accuracy of their diagnosis is limited. Therefore, it is imperative to introduce an intelligent system to assist GP to make decisions. This paper proposed a hybrid architecture, which fuses the features of words from different representation spaces. Two data augmentation methods (Complaint-Symptoms Integration method and Symptom Dot Separating Method) have been proposed to integrate essential information into the training data. Experiments demonstrate that this hybrid architecture has good performance in the classification of 4 common neurological diseases 1. Finally, this paper develops an AI diagnosis assistant webapp which leverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.

Translation models tend to ignore the rich semantic information in triads in the process of knowledge graph complementation. To remedy this shortcoming, this paper constructs a knowledge graph complementation method that incorporates adaptively enhanced semantic information. The hidden semantic information inherent in the triad is obtained by fine-tuning the BERT model, and the attention feature embedding method is used to calculate the semantic attention scores between relations and entities in positive and negative triads and incorporate them into the structural information to form a soft constraint rule for semantic information. The rule is added to the original translation model to realize the adaptive enhancement of semantic information. In addition, the method takes into account the effect of high-dimensional vectors on the effect, and uses the BERT-whitening method to reduce the dimensionality and generate a more efficient semantic vector representation. After experimental comparison, the proposed method performs better on both FB15K and WIN18 datasets, with a numerical improvement of about 2.6% compared with the original translation model, which verifies the reasonableness and effectiveness of the method.

Introduction: Congenital diaphragmatic hernia (CDH) is known with high mortality rates and significant pulmonary morbidities. The objective of this study was to describe the histopathological findings of necropsy and clinical manifestations in CDH patients to find the clinicopathological correlations. Methods: We reviewed the postmortem findings with associated clinical characteristics retrospectively in 8 CDH cases from 2017 to2022 July. Results: Of the eight cases, one was bilateral congenital eventration diagnosed by autopsy. Severe pulmonary hypertension with a right to left shunting of large patent ductus arteriosus (PDA) obtained from echocardiogram were most common. And the average time of survive was 46 (8-624) h. According to the autopsy reports, the major pathological lung changes were diffuse alveolar damages (congestion and hemorrhage) and hyaline membrane formation. Notably, although the lung volume was significantly reduced, pulmonary structural dysplasia was not observed at all, presented normal lung development (50%); bilateral (25%) or ipsilateral (25%). Lobulated deformities were accompanied in three (37.5%) cases. All patients exhibited large PDA and a patent foramen ovale with increased right ventricle (RV) volume, and the myocardial fibers were slightly congested and swollen. Pulmonary vessels showed mild to moderate arterial media thickening. Lung hypoplasia and diffuse lung damages reduced gas exchange, meanwhile the PDA and PH caused RV failure, contributed a clinical picture of organ dysfunction, which lead to death. Conclusions: Pulmonary structures have certain heterogeneity in CDH. The arteries’ pathological changes are not consistent with clinical diagnosis. And the adverse outcome may should be due to the cardiopulmonary vicious cycle.

The current system for disclosing financial conflicts of interest (COIs) can be traced back to the “Uniform requirements for manuscripts submitted to medical journals,” published in 1997. Meanwhile, new molecular and genetic therapies are transforming clinical medicine and these therapies have radically altered the financial landscape of drug development. The average price of new drugs has risen sharply, and in niche areas like rare diseases, the cost of new therapies can be stratospheric. Now, even rare or “orphan” diseases are funded by private investment. With high profitability, there is a new pathway for drug development involving unprecedented ties between industry and academia. The potential for COIs has greatly expanded and decisions that were once science-driven have become profit-driven. As a result, the risk that marginally effective, ineffective and even hazardous treatments will make their way to patients has greatly increased. In this editorial, we propose a system built on process-based COIs. This new system would trace the developmental journey of a drug from the lab to patients. All financial ties that institutions and individuals have to the drug would be disclosed at each developmental step. The goal would be to create a healthy skepticism and debate over the studies behind new drugs, restoring a scientific focus to the assessment of drug efficacy. When society’s limited resources are used to pay for drugs whose excessive cost primarily benefits investors and corporations, it derails scientific objectivity, harms patients, and threatens the financial stability of our health care systems.

Addressing the recognised challenges and inequalities in providing high quality health care for rare diseases such as children’s interstitial lung disease (chILD) requires collaboration across institutional, geographical, discipline, and system boundaries. The Children’s Interstitial Lung Disease Respiratory Network of Australia and New Zealand (chILDRANZ) is an example of a clinical network that brings together multidisciplinary health professionals for collaboration, peer learning, and advocacy with the goal of improving the diagnosis and management of this group of rare and ultra-rare conditions. This narrative review explores the multifaceted benefits arising from social learning spaces within rare disease clinical networks by applying the Value Creation Framework. The operation of the chILDRANZ network is used as an example across the framework to highlight how value is generated, realised, and transferred within such collaborative clinical and research networks. The community of clinical practice formed in the chILDRANZ multidisciplinary clinical peer support meetings provides a strong example of social learning that engages with the uncertainty inherent in rare disease diagnosis and management and pays attention to generate new knowledge and best practice to make a difference for children and families living with chILD. This review underscores international calls for further investment in, and support of, collaborative expert clinical networks for rare disease.

Microorganisms are the most biodiverse life forms on our planet, yet we know little about the spatial processes underlying their ecology and evolution. Here, we highlight the importance of two spatial processes that act on individual cells - spatial intermixing of different populations and mechanical cell shoving during growth - to improve our understanding of microbial eco-evolutionary dynamics. Using an individual-based model, we show that the coexistence of slow- and fast-growing populations becomes highly constrained under two conditions: when the slow- and fast-growing populations are highly spatially intermixed and when the ability to shove other cells (both conspecific and heterospecific) is weak. The potential for evolution through plasmid-mediated horizontal gene transfer between slow- and fast- growing populations also becomes restricted in the same scenario. Our modeling highlights that ecological constraints can dampen evolutionary opportunities within microbial communities due to variation in spatial intermixing and mechanical shoving at the cellular scale.

Sea waves represents a very promising energy source. Pioneered by Wiercigroch a pendulum system would be feasible for such an energy harvesting purpose. These devices consist of a pendulum with a vertical motion induced by the sea waves. As it is well known, pendulum's stable rotations generate enough energy able to be extracted by an electrical generator attached to its axis. In this paper, using a brushless dc motor as an input torque u(t) to maintain stable pendulum rotations, a singular optimal control formalism provides a very simple control law using a mechanical model.Besides the main objective in this paper: stable controlled rotations, in order to control every motion possibility: rotation, stability or even chaos, a singular optimal control policy is defined A salient property of the the resulting control law lies on its very simple hardware  implementation bang-bang control: only sign is needed using Arduino and operational amplifiers (see Figure 1). While Matlab/Simulink simulations will be presented, the possibility of being tuned for stable/unstable rotations or even asymptotic stability is also an interesting analysis. Conclusions and future work are also depicted.

Biological sequence data mining is a long-term hot spot in bioinformation. Biological sequence can be regarded as a set of characters composed of a number of letters and contain an evolutionary relationship. Time series is a set of numbers arranged according to time and contains the temporal progressive relationship. Time series is similar to biological sequence in terms of both representation and mechanism. Therefore, in the paper, biological sequence is represented with time series to form biological time sequence (BTS). Based on advanced time series methods, hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are constructed based on self-adaption pre-training one-dimensional convolutional recurrent neural network (CNN-LSTM) and autoregressive fractional integrated moving average (ARFIMA) integrated evolutionary algorithm, respectively. In the DNA sequence experiments of six kinds of viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance, the prediction accuracy and correlation were 1.7073 and 0.9186, respectively. The effectiveness and stability of SaPt-CNN-LSTM-AR-EA were verified through the comparison with other five benchmark models. In addition, compared with other benchmark models, SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. This study opened up a new field of BTS research. The framework proposed in this paper is significant in many disciplines, such as biology, biomedicine, computer science and economics. Especially in sequence splicing, genome, computational biology, bioinformation, theoretical biology, evolutionary biology, signal processing, medicine and health care and other fields, the framework has a wide range of applications.

Background: Thailand’s fourth and fifth waves of coronavirus disease 19 (COVID-19) started in July and December 2021, respectively, and greatly overloaded the nation’s public health system. The massive imbalance between health resources and patient demand for services was one of the most significant challenges hampering the country’s response to the catastrophic COVID-19 pandemic. Hospital-based facilities were overwhelmed with an exponential rise in new patient numbers, with a rapidly growing backlog of patients with delayed care or even acceptance within the healthcare system. In response, “outpatient self-isolation” (SI), “home-based isolation” (HI), and “community-based isolation” (CI) strategies were adopted to stabilize COVID-19 cases with mild to moderate symptoms. We present the lessons learned from the system management of HI by drawing on the experiences gained at a university hospital that provided patient-to-professional remote support during the pandemic. The vast majority of patients were assigned to HI immediately after being diagnosed with COVID-19. This system enabled remote consultation, needed medications, and survival-kit supplies to be initiated and delivered to patients’ homes. Conclusion: Our investigation indicates that the HI teleconsultation system was a productive approach to COVID-19 management. It allowed a prompt response to patients’ needs and provided timely access to medical support, especially for patients with mild to moderate symptoms.

To describe treatment using aligners in an adult female patient with dental class II malocclusion associated with crowding. Treatment objectives were achieved in 10 months with patient satisfaction. Combining aligners with appropriate attachment location, geometry and staging are efficacious means of resolving class II malocclusion.

A necessary-sufficient condition for the existence or nonexistence of global solutions to the following reaction-diffusion equations { u t = Δ u + ψ ( t ) u p , in R N ×( 0 , t ∗ ) , u ( ⋅ , 0 )= u 0 ≥ 0 , in R N , has not been known and remained as an open problem for a few decades. The purpose of this paper is to resolve this problem completely, even for more general source ψ( t) f( u) as follows: There is a global solution to the equation if and only if ∫ 0 ∞ ψ ( t ) f ( ‖ S ( t ) u 0 ‖ ∞ ) ‖ S ( t ) u 0 ‖ ∞ dt < ∞ for some nonnegative and nontrivial u 0 ∈ C 0 ( R N ) . Here, ( S ( t ) ) t ≥ 0 is the heat semigroup on R N .

In this paper, the static output periodic preview control (SOPPC) problem of discrete-time periodic systems (D-TPSs) is proposed. First, an augmented error periodic system (AEPS) is derived by introducing auxiliary variables related to the system state and input. Consequently, the SOPPC problem is transformed into a stabilization problem. Second, sufficient conditions are derived to ensure asymptotic stability, and these conditions are extended to the D-TPSs with disturbance. Moreover, the corresponding solvability condition for the design of the SOPPC laws is proposed using linear matrix inequality (LMI). Finally, a numerical example is used to illustrate the effectiveness of the results.

Lifespan is a key attribute of a species’ life cycle and varies extensively among major lineages of animals. In fish, lifespan varies by several orders of magnitude, with reported values ranging from less than one year to approximately 400 years. Lifespan information is particularly useful for species management, as it can be used to estimate invasion potential, extinction risk and sustainable harvest rates. Despite its utility, lifespan is unknown for most fish species. This is due to the difficulties associated with accurately identifying the oldest individual(s) of a given species, and/or deriving lifespan estimates that are representative for an entire species. Recently it has been shown that CpG density in gene promoter regions can be used to predict lifespan in mammals and other vertebrates, with variable accuracy across taxa. To improve accuracy of lifespan prediction in a non-mammalian vertebrate, here we develop a fish-specific genomic lifespan predictor. Addressing previous issues of low sample size and sequence dissimilarity, we incorporate more than eight times the number of fish species used previously (n = 442) and use fish-specific gene promoters as reference sequences. Our model predicts fish lifespan from genomic CpG density alone (measured as CpG observed/expected ratio), explaining 64 % of the variance between known and predicted lifespans. The results demonstrate the value of promoter CpG density as a universal predictor of fish lifespan that can applied where empirical data are unavailable, or impracticable to obtain.

A document by Sayed A. Dahy. Click on the document to view its contents.

Seaweeds are colonized by a microbial community which can be directly linked to their performance. This community is shaped by an interplay of stochastic and deterministic processes, including mechanisms which the holobiont host deploys to manipulate its associated microbiota. The Anna Karenina Principle predicts that when a holobiont is exposed to suboptimal or stressful conditions, these host mechanisms may be compromised. This leads to a relative increase of stochastic processes that may potentially result in the succession of a microbial community harmful to the host. Based on this principle, we used the variability in microbial communities (i.e., beta diversity) as a proxy for stability within the invasive holobiont Gracilaria vermiculophylla during a simulated invasion in a common garden experiment. At elevated temperature (22 °C), host performance declined and disease incidence and beta diversity increased. At optimal temperature (15 °C), beta diversity did not differ between native and non-native populations. However, under thermally stressful conditions beta diversity increased more in epibiota from native populations. This suggests that epibiota associated with holobionts from non-native populations are under thermal stress more stable than holobionts from native populations. This pattern reflects an increase of deterministic processes acting on epibiota associated with non-native hosts, which in the setting of a common garden can be assumed to originate from the host itself. Therefore, these experimental data suggest that the invasion process may have selected for hosts better able to maintain stable microbiota during stress. Future studies are needed to identify the underlying host mechanisms.

The study of the host-microbiome by the collection of non-invasive samples has the potential to become a powerful tool for conservation monitoring and surveillance of wildlife. However, multiple factors can bias the quality of data recovered from scats, particularly when field-collected samples are used given that the time of defecation is generally unknown and could have been as recent as hours, days, or weeks. Previous studies using scats have shown that exposure to aerobic conditions can compromise the microbial composition and that this rate of exposure differs between species. However, the impact that this aging process has on the relationship between the bacterial and fungal composition has yet to be explored. In this study, we measured the effects of time post-defecation on bacterial and fungal compositions and structures in a controlled experiment using scat sample from the endangered koala (Phascolarctos cinereus). We found that targeting the core of the scat for DNA extraction reduced the impact of oxygen exposure as we did not observe the previously reported reduction in obligate anaerobic bacteria nor an increase in facultative anaerobes even after aging for 10 days. We found that even though bacteria remain stable through the scat aging process, the fungal composition did not. We report a cluster of fungal taxa that colonises scats after defecation which can dilute the genetic material from the autochthonous mycoflora and inhibit recovery. Finally, we propose strategies to combat the effects of time and preserve the integrity of a scat sample collected in the wild.

Soil protists are increasingly studied due to a release from previous methodological constraints and the acknowledgement of their immense diversity and functional importance in ecosystems. However, these studies often lack a sufficient depth in knowledge, which is visible in the form of falsely used terms and false- or over-interpreted data with conclusions that cannot be drawn from the data obtained. As we welcome that also non-experts include protists in their still mostly bacterial and/or fungal focused studies, our aim here is to help avoid some common errors. We provide an overview of current terms to be used when working on soil protists, like protist instead of protozoa, predator instead of grazer, microorganisms rather than microflora and terms to be used to describe the prey spectrum of protists. We then highlight some do’s and don’ts in soil protist ecology including challenges related to interpreting 18S rRNA gene amplicon sequencing data. We caution against the use of standard bioinformatic settings optimized for bacteria and the uncritical reliance on incomplete and partly erroneous reference databases. We also show why causal inferences cannot be drawn from sequence-based correlation analyses or any sampling/monitoring, study in the field without thorough experimental confirmation and sound understanding of the biology of taxa. Together, we envision this work to help non-experts to more easily include protists in their soil ecology analyses, and obtain more reliable interpretations from their protist data and other biodiversity data that, in the end, will help to better understand soil ecology.

The rise of sedimentary ancient DNA (sedaDNA) studies has opened up new possibilities for studying pre-historic ecology. The use of sediments to identify organisms even where macroscopic remains are limited or no longer exist is an exciting and potentially ground-breaking area of genomics. There are special considerations however when managing this substrate in Indigenous Australian contexts. Sediments and soils are often considered as waste by-products during archaeological and paleontological excavations, and as such are not typically considered of high value in ethical considerations in traditional western research. Nevertheless, the product of sedaDNA work – genetic information from past fauna, flora, microbial communities, and human ancestors – is likely to be of cultural value for Indigenous peoples. We argue that the integration of Traditional Knowledges into sedaDNA research would a) allow identification of sensitive, secret, or sacred genomic data, and b) improve research outcomes by providing ecological context for species through multi-millennia oral histories.

Obvious changes of hydrologic regimes occurred in wetland around the world, and it also were expected to arise in the following 100 years. This study examined the response of soil microbial functional structure and metabolic potential to early dry season (EDS) of Dongting Lake wetland, China. We measured the soil properties, microbial functional structure of samples E40, E20 and E0 (dry season arrived early by 40, 20 and 0 days, respectively). The results showed that the EDS caused the changes of functional structure: the functional genes of E40 had significantly higher abundance and diversity than those of E0, but no significant difference was found between those of E20 and E0. Functional genes associated with matter cycling and organic contaminant degradation (OCD) also were changed. It could cause the increase of the degradation of pesticides related compound of E20 and matter cycling and OCD of E40 (except no change in the phosphorus oxidation and the degradation of polycyclic aromatics and terpenoid).