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Seafloor methane emissions can affect Earth’s climate and ocean chemistry. Vast quantities of methane formed by microbial decomposition of organic matter are locked within gas hydrate and free gas on continental slopes, particularly in large areas with high sediment accumulations such as deep-sea fans. The release of methane in slope environments has frequently been associated with dissociation of gas hydrates near the edge of the gas hydrate stability zone on the upper slope, with discharges in greater water depths less understood. Here we show, using data from the Rio Grande Cone (western South Atlantic), that the intrinsic, gravity-induced downslope collapse of thick slope sediment accumulations creates structures that serve as pathways for gas migration, unlocking methane and causing seafloor emissions via giant gas flares in the water column. The observed emissions in the study region (up to 310 Mg year−1) are three times greater than estimates for the entire US North Atlantic margin and reveal the importance of collapsing sediment accumulations for ocean carbon cycling. Similar outgassing systems on the Amazon and Niger fans suggest that gravity tectonics on passive margins is a common yet overlooked mechanism driving massive seafloor methane emissions in sediment-laden continental slopes.

Salah satu komoditi hasil tani Gampong Baro adalah kacang tanah. Pemberdayaan ekonomi masyarakat dapat ditingkatkan dengan meningkatkan penjualan hasil panen. Pada saat musim panen tiba hasil panen kacang tanah sangat melimpah yang mengakibatkan penurunan harga. Oleh sebab itu diperlukan diversifikasi produk kacang tanah yang dapat disimpan lebih lama dalam bentuk selai dan permen kacang tanah. Pengabdian ini bertujuan untuk memotivasi warga untuk mengambil bagian dari usaha produksi melalui pengembangan produk pangan, memberikan infomasi dan pelatihan pengolahan pangan terutama pengolahan berbasis kacang tanah berupa selai dan permen yang bernilai lebih untuk meningkatkan perekonomian masyarakat setempat. Adapun metode pelaksanaan pengabdian ini adalah dengan memberikan pelatihan pembuatan dan demo pembuatan produk selai dan permen kacang tanah secara langsung. Hasil kegiatan berupa produk selai dan permen kacang tanah, serta peserta pelatihan yang telah memiliki keterampilan dalam membuat selai dan permen kacang tanah.

Diabetic peripheral neuropathy (DPN) affects half of diabetic patients with complicated etiologies, in their lifetime. Despite glycemic control, these consequences occur. To date, medical interventions are inconsistent in DN treatment. The purpose of our study is to investigate the neuroprotective effects of Salvia spinosa in diabetes-induced mice. After the preparation of the methanolic extract of S. spinosa, the antioxidant properties were evaluated by DPPH free radical scavenger assay and phosphomolybdenum reduction assay (PRA). Our study includes six groups of mice (n = 5): control, diabetic, and treatment groups (received 50 mg/kg, 100 mg/kg, and 200 mg/kg of methanolic extract of S. spinosa). Body weight and blood glucose (FBS) were assessed before the induction of diabetes and subsequently after treatment. DN was evaluated by using hot plate and tail flick tests. Finally, all animals were euthanized and brain tissues were used for the assessment of oxidative stress parameters. Our results showed that FBS levels ameliorated in the S. spinosa-treated groups compared to diabetic mice. Early hyperalgesia and late hypoalgesia were shown in diabetic mice and the treatment with S. spinosa significantly improved them. the antioxidant effect of S. spinosa (DPPH IC50: 83 μg/mL, antioxidant capacity in phosphomolybdenum reduction assay (PRA): 270.79 mg αTE/g extract) amended the oxidative stress damages (lipid peroxidation and glutathione oxidation) induced in diabetic mice. This study emphasized the reliable neuroprotective properties of S. spinosa in DN due to its antioxidative properties.

Copper (Cu) and iron (Fe) are essential micronutrients that are toxic when accumulating in excess in cells. Thus, their uptake by roots is tightly regulated. While plants sense and respond to local Cu availability, the systemic regulation of Cu uptake has not been documented in contrast to local and systemic control of Fe uptake. Fe abundance in the phloem has been suggested to act systemically, regulating the expression of Fe uptake genes in roots. Consistently, shoot-to-root Fe signaling is disrupted in Arabidopsis thaliana mutants lacking the phloem companion cell-localized Fe transporter, OLIGOPEPTIDE TRANSPORTER 3 (AtOPT3). We report that AtOPT3 also transports Cu in heterologous systems and contributes to its delivery from sources to sinks in planta. The opt3 mutant contained less Cu in the phloem, was sensitive to Cu deficiency and mounted a transcriptional Cu deficiency response in roots and young leaves. Feeding the opt3 mutant and Cu- or Fe-deficient wild-type seedlings with Cu or Fe via the phloem in leaves downregulated the expression of both Cu- and Fe-deficiency marker genes in roots. These data suggest the existence of shoot-to-root Cu signaling, highlight the complexity of Cu/Fe interactions, and the role of AtOPT3 in fine-tuning root transcriptional responses to the plant Cu and Fe needs.

Glioblastoma (GBM), also known as grade IV astrocytoma, is the most common and deadly type of central nervous system malignancy in adults. Despite significant breakthroughs in current GBM treatments such as surgery, radiotherapy, and chemotherapy, the prognosis for late-stage glioblastoma remains bleak due to tumor recurrence following surgical resection. The poor prognosis highlights the evident and pressing need for more efficient and targeted treatment. Vaccination has successfully treated patients with advanced colorectal and lung cancer. Therefore, the potential value of using tumor vaccines in treating glioblastoma is increasingly discussed as a monotherapy or in combination with other cellular immunotherapies. Cancer vaccination includes both passive administration of monoclonal antibodies and active vaccination procedures to activate, boost, or bias antitumor immunity against cancer cells. This article focuses on active immunotherapy with peptide, genetic (DNA, mRNA), and cell-based vaccines in treating GBM and reviews the various treatment approaches currently being tested. Although the ease of synthesis, relative safety, and ability to elicit tumor-specific immune responses have made these vaccines an invaluable tool for cancer treatment, more extensive cohort studies and better guidelines are needed to improve the efficacy of these vaccines in anti-GBM therapy.

Abstract: Acute myeloid leukemia (AML) is a malignant disorder characterized by myeloid differentiation arrest and uncontrolled clonal expansion of abnormal myeloid progenitor cells. AML is the most common malignant bone marrow (BM) disease in adults and accounts for approximately 80% of adult leukemia cases. There has been little improvement in the treatment of patients with AML over the past decade. Cytogenetic and morphologic heterogeneity of AML and the difficulty in distinguishing leukemic stem cells (LSCs) from normal hematopoietic stem cells (HSCs) continue to be the major challenges in treating this malignancy. In recent years, intensive efforts have been made to explore novel potential markers for the efficient identification and characterization of leukemic stem cells. Aldehyde dehydrogenase (ALDH) is a potential target molecule that plays crucial roles in leukemic stem cell survival and multidrug resistance, mainly through its involvement in the detoxification of many endogenous and exogenous aldehydes. The selection and isolation of cancer stem cells based on high ALDH activity seem to be a useful approach in many human malignancies, especially leukemia. Moreover, it is worth mentioning that several previous studies have indicated that a high ALDH activity (classified as ALDHbr cells in flow cytometry) can act as an independent prognostic factor in several types of cancer. In the present review, we update and critically discuss the available data regarding the importance of ALDH activity in normal and leukemic stem cells and its potential diagnostic and therapeutic implications.

This study investigated the elicitation effects of several methyl jasmonate (MeJ) concentrations (0, 25, 50, and 100 µM) on various biochemical traits of caraway (Carum carvi L.) callus cultures. The 25 μM MeJ concentration yielded the highest callus growth rate (0.57 mm day−1), total flavonols content (2.58 mg QE g−1 FW) and total carotenoids content (0.04 μg g−1 FW), whereas the highest relative fresh weight (75.72%), total phenolics content (76.90 mg GAE g−1 FW), total flavonoids content (58.49 mg QE g−1 FW) and phenylalanine ammonia lyase activity (3.40 nmol cinnamic acid mg−1 h−1 FW) were obtained with the 50 μM MeJ concentration. The highest antioxidant activity through DPPH assay (8.37%) and malondialdehyde content (7.82 μmol g−1 FW) were observed at 100 μM MeJ. The HPLC conducted 21 days post-elicitation revealed a 5.55-fold increase of carvone accumulation (1.83 μg g−1 DW) at 50 μM MeJ and a 2.7-fold increase (0.62 μg g−1 DW) of limonene at 50 μM MeJ. The optimal dosages applied for carvone and limonene accumulation under MeJ elicitation could be used to scale up the economic production of these elite medicinal compounds through caraway callus cultures.

Multi-camera networks are increasingly becoming pervasive in many monitoring and surveillance applications, and have attracted much attention in distributed systems with collaborative, real-time decision-making capabilities. While in-network data compression brings significant energy savings in camera nodes, signal representation using sparse approximations and overcomplete dictionaries have been shown to outperform traditional compression methods. In this work, an end-to-end and real-time solution is designed and implemented to enable energy-efficient and robust dictionary learning in distributed camera networks by leveraging the spatial correlation of the collected multimedia data. Traditional distributed dictionary learning relies on consensus-building algorithms, which involve communicating with neighboring nodes until convergence is achieved. Existing methods, however, do not exploit spatial correlations in camera networks for improved energy efficiency. In contrast, low-computational-complexity metrics are employed in this work to quantify and exploit the spatial correlation across camera nodes in a wireless network for efficient distributed dictionary learning and in-network image compression. The performance of the proposed approach is validated through extensive simulations on public datasets as well as via real-world experiments on a testbed composed of Raspberry Pi nodes.

Multiple sclerosis (MS) remains a challenging disease that requires timely diagnosis. Therefore, an ultrasensitive optical biosensor based on hybridization chain reaction (HCR) was developed to detect microRNA-145 (miRNA-145) as an MS biomarker. To construct such a sensor, HCR occurred between specific hairpin probes, as MB1 contains a poly-cytosine nucleotide loop and MB2 has a poly-guanine nucleotide sticky end. By introducing miR-145 as a target sequence, long-range dsDNA polymers are formed. Then, positively charged gold nanoparticles (AuNPs) were incubated with the HCR product, which adsorbed onto the dsDNA polymers due to electrostatic adsorption. This resulted in the precipitation of the AuNPs. By incubating different concentrations of miR-145 with AuNPs, the changes in the UV-vis spectrum of the supernatant were analyzed. The proposed biosensor showed a great ability to detect miR-145 in a wide linear range from 1 pM-1 nM with an excellent detection limit (LOD) of 0.519 nM. Furthermore, the developed biosensor indicated considerable selectivity in discriminating between miR-145 and mismatched sequences. It shows high selectivity in differentiating targets. Interestingly, the proposed method was also able to detect miRNA-145 in the diluted serum samples. In conclusion, this sensing platform exhibits high selectivity and specificity for the detection of circulating microRNAs, which holds great promise for translation to routine clinical applications.

Achieving reliable acoustic wireless video transmissions in the extreme and uncertain underwater environment is a challenge due to the limited bandwidth and the error-prone nature of the channel. Aiming at optimizing the received video quality and the user's experience, an adaptive solution for underwater video transmissions is proposed that is specifically designed for Multi-Input Multi-Output (MIMO -based Software-Defined Acoustic Modems (SDAMs . To keep the video distortion under an acceptable threshold and to keep the Physical-Layer Throughput (PLT high, cross-layer techniques utilizing diversity-spatial multiplexing and Unequal Error Protection (UEP are presented along with the scalable video compression at the application layer. Specifically, the scalability of the utilized SDAM with high processing capabilities is exploited in the proposed structure along with the temporal, spatial, and quality scalability of the Scalable Video Coding (SVC H.264/MPEG-4 AVC compression standard. The transmitter broadcasts one video stream and realizes multicasting at different users. Experimental results at the Sonny Werblin Recreation Center, Rutgers University-NJ, are presented. Several scenarios for unknown channels at the transmitter are experimentally considered when the hydrophones are placed in different locations in the pool to achieve the required SVC-based video Quality of Service (QoS and Quality of Experience (QoE given the channel state information and the robustness of different SVC scalability. The video quality level is determined by the best communication link while the transmission scheme is decided based on the worst communication link, which guarantees that each user is able to receive the video with appropriate quality.

Today, cancer is one of the main health-related challenges, and in the meantime, breast cancer (BC) is one of the most common cancers among women, with an alarming number of incidences and deaths every year. For this reason, the discovery of novel and more effective approaches for the diagnosis, treatment, and monitoring of the disease are very important. In this regard, scientists are looking for diagnostic molecules to achieve the above-mentioned goals with higher accuracy and specificity. RNA interference (RNAi) is a posttranslational regulatory process mediated by microRNA intervention and small interfering RNAs. After transcription and edition, these two noncoding RNAs are integrated and activated with the RNA-induced silencing complex (RISC) and AGO2 to connect the target mRNA by their complementary sequence and suppress their translation, thus reducing the expression of their target genes. These two RNAi categories show different patterns in different BC types and stages compared to healthy cells, and hence, these molecules have high diagnostic, monitoring, and therapeutic potentials. This article aims to review the RNAi pathway and diagnostic and therapeutic potentials with a special focus on BC.

In mechanised tunnelling, the filling of the annular gap behind the precast concrete segments is one of the essential operations. The main goal of filling this space is to reduce surface settlements and limit movements due to tunnel excavation in the soil mass. To achieve this aim, a two-component grout is often used to fill the annular gap. Determination of the amounts of ingredients in the two-component grout should be carried out in such a way that the injection materials will be able to fulfil the required performance and technical properties. In this study, laboratory tests were carried out to investigate two-component grout properties, such as flowability, bleeding, gelling time and uniaxial compressive strength. The effect of lateral pressure on grout resistance was studied according to real excavation situations. The influences of the grout ingredients and effective parameters such as bentonite and cement contents, water–cement ratio and the dosage of accelerator (sodium silicate) and accelerator ratio were also investigated.

The eruption of Mount Semeru did not only have a physical impact. Victims can experience serious long-term psychological effects and affect their psychological well-being. The psychological impact that is allowed to continue will be permanent and disrupt the survival of disaster survivors. Therefore, psychosocial support is carried out to support the psychological recovery of the survivors of the Mount Semeru eruption. Methods of self-motivation and play therapy are used to achieve these goals. The stages of implementing this activity include preparation, program implementation, and evaluation. The preparation phase of the team conducts a needs analysis to identify problems, coordinate with various parties and map out infrastructure. The implementation phase includes socialization, preparation of materials and training programs. The evaluation stage is carried out to measure the success of the service program implementation. The results showed that the activities carried out at SDN 1 and SDN 2 Supiturang on 5-6 August 2022 ran smoothly. Most of the participants were children who followed enthusiastically from the beginning to the end of the activity. At the evaluation stage, it was known that in teaching and learning activities the children were very enthusiastic and emotional expression went well after this service activity was held.

This paper revisits the Space-Time Gradient (STG) method which was developed for efficient analysis of unsteady flows due to rotor–stator interaction and presents the method from an alternative time-clocking perspective. The STG method requires reordering of blade passages according to their relative clocking positions with respect to blades of an adjacent blade row. As the space-clocking is linked to an equivalent time-clocking, the passage reordering can be performed according to the alternative time-clocking. With the time-clocking perspective, unsteady flow solutions from different passages of the same blade row are mapped to flow solutions of the same passage at different time instants or phase angles. Accordingly, the time derivative of the unsteady flow equation is discretized in time directly, which is more natural than transforming the time derivative to a spatial one as with the original STG method. To improve the solution accuracy, a ninth order difference scheme has been investigated for discretizing the time derivative. To achieve a stable solution for the high order scheme, the implicit solution method of Lower-Upper Symmetric Gauss-Seidel/Gauss-Seidel (LU-SGS/GS) has been employed. The NASA Stage 35 and its blade-count-reduced variant are used to demonstrate the validity of the time-clocking based passage reordering and the advantages of the high order difference scheme for the STG method. Results from an existing harmonic balance flow solver are also provided to contrast the two methods in terms of solution stability and computational cost.

Objectives To evaluate osteogenic markers and alveolar ridge profile changes in guided bone regeneration (GBR) of chronic non-contained bone defects using a non-resorbable TiO2 block. Material and methods Three buccal bone defects were created in each hemimandible of eight beagle dogs and allowed to heal for 8 weeks before GBR. Treatment was assigned by block randomisation: TiO2 block: TiO2-scaffold and a collagen membrane, DBBM particulates: Deproteinised bovine bone mineral (DBBM) and a collagen membrane, Empty control: Only collagen membrane. Bone regeneration was assessed on two different healing timepoints: early (4 weeks) and late healing (12 weeks) using several immunohistochemistry markers including alpha smooth muscle actin (α-SMA), osteopontin, osteocalcin, tartrate-resistant acid phosphatase and collagen type I. Histomorphometry was performed on Movat Pentachrome and Von Kossa/Van Gieson stained sections. Stereolithographic models were used to compare alveolar profile changes. Results The percentage of α-SMA and osteopontin increased in TiO2 group after 12 weeks of healing at the bone-scaffold interface, while collagen type I increased in the empty control group. In the defect area, α-SMA decreased in the empty control group, while collagen type I increased in the DBBM group. All groups maintained alveolar profile from 4 to 12 weeks, but TiO2 group demonstrated the widest soft tissue contour profile. Conclusions The present findings suggested contact osteogenesis when GBR is performed with a TiO2 block or DBBM particulates. The increase in osteopontin indicated a potential for bone formation beyond 12 weeks. The alveolar profile data indicated a sustained lateral increase in lateral bone augmentation using a TiO2 block and a collagen membrane, as compared to DBBM and a collagen membrane or a collagen membrane alone.

The research aims to find out whether the students reading comprehension can be improved through the use of the Communicative Language Teaching(CLT) method. In order to achieve the objectives, the researcher held action research at SMA N 5 Lhokseumawe, from 16- 19th March 2021. There were 2 cycles in which 2 meetings were held for each cycle. The subject of the study is the second year with the numbers of population 32 students. The instruments used were observation sheets, questionnaires, and tests. The result of the study is the problem of motivating students to read. This can be solved by using Communicative Language Teaching(CLT). Based on the questionnaire, all of the students said that the use of the Communicative Language Teaching(CLT) method was interesting. In line with the questionnaire result, the observation sheet also showed excellent results on students’ study habits. There was an improvement in students’ comprehension, it can be seen in cycle 1 which was 89.06 points, and cycle II which was 95.6 points. In this study, I suggest that the teachers should be able to use Communicative Language Teaching(CLT) as an alternative to motivating students learning.

We aimed to compare the ovarian reserve of rats exposed to oral D-galactose during prenatal and early life with rats exposed to D-galactose only during the prenatal period. Fifteen female pregnant Wistar rats were randomly divided into three groups. The first and second groups were fed a D-galactose enriched diet (35%) from the third day of pregnancy to parturition (PP) and the third day to the end of lactation (PL), respectively. The control group (C group) was fed a standard diet. The study population was the female offspring of three groups (PP', PL', and C'), in which some reproductive factors were examined between 45 and 50 days of age. When compared with the PP' group, the number of primordial follicles was significantly higher in the PL' group at PND 45-50 (40 vs. 30; p = .01); however, the antimullerian hormone level was significantly reduced in the PL' group versus control group (-2.2, 95% confidence interval [CI]: -2.83, -1.53 ng/ml p = .000), and follicle-stimulating hormone level significantly increased in PP' group versus control (4.5 mIU/ml, 95% CI: 1.40-7.62, p = .005). There was no significant difference in leukocyte infiltration or antiovarian antibody among the groups. Continued exposure to D-galactose during the lactation period inhibits the primordial follicle loss in rats in terms of producing fewer atretic follicles.

Generative adversarial networks (GANs) have drawn considerable attention in recent years for their proven capability in generating synthetic data which can be utilized for multiple purposes. While GANs have demonstrated tremendous successes in producing synthetic data samples that replicate the dynamics of the original datasets, the validity of the synthetic data and the underlying privacy concerns represent major challenges which are not sufficiently addressed. In this work, we design a cascaded tabular GAN framework (CasTGAN) for generating realistic tabular data with a specific focus on the validity of the output. In this context, validity refers to the the dependency between features that can be found in the real data, but is typically misrepresented by traditional generative models. Our key idea entails that employing a cascaded architecture in which a dedicated generator samples each feature, the synthetic output becomes more representative of the real data. Our experimental results demonstrate that our model well captures the constraints and the correlations between the features of the real data, especially the high dimensional datasets. Furthermore, we evaluate the risk of white-box privacy attacks on our model and subsequently show that applying some perturbations to the auxiliary learners in CasTGAN increases the overall robustness of our model against targeted attacks.

Infertility affects millions of people of reproductive age. The failure of a blastocyst to implant is a leading cause of psychological distress. It became increasingly evident that an effective immune dialogue occurs at each step in the fluids surrounding the oocyte, the spermatozoa, the embryo, or the endometrium. Exploring and deciphering this dialogue could potentially help understand why 50% of healthy euploid blastocysts fail to implant. Introducing immunology into reproductive medicine requires a change of mindset to bring immune hypothesis to clinical applications. Implantation of an embryo requires a prepared uterus in order to dialogue with the embryo, which is able to express and repair itself. Exploring the uterine immune profile of patients with previous implantation failures (IF) or recurrent miscarriages (RM) has already been developed and is under evaluation as a precision tool to equilibrate the uterine environment before implantation to increase the subsequent live birth rate after the embryo transfer. Immunology may also be fundamental in the future to identify through non-invasive procedure the competence of oocytes or embryos through reliable immune biomarkers quantified in follicular fluids or embryo supernatants during the in vitro fertilization (IVF) process. Non-invasive biomarkers would allow physicians to identify competent oocytes or embryos based on their ability to communicate with the mother and their energetic potential for all the self-repair processes that should occur during the preimplantation and the implantation period. This area of research is only beginning.

Traffic safety is a critical aspect of transportation. Transportation agencies and researchers have been continuously making an effort to ensure minimal error margins in the road system. There is a growing interest in the Southeast Michigan region because of the active role of original equipment manufacturers in spurring research in the new mobility field and autonomous vehicle technology. As new jobs are created and commuting trips increase, traffic safety becomes even more important. Thus, this study presents one of the first efforts to study crash patterns for Southeast Michigan using machine learning models to analyze and predict crash severity in the region based on various contributing factors. We combine a three-step method of correlation analysis, machine learning predictive models, and spatial analysis to develop a rigid schema for crash prediction. Results show that decision tree classifiers provide accurate predictions and rapid computations. Also, spatial plots of predicted injury severities reveal disproportionate errors in model predictions at intersections prompting the need to stratify the crash data for further analyses. This significantly improves severe injury predictions at non-intersection locations and highlights pedestrians as a main important feature. The converse holds for intersections that identify motorcycles as the main feature for severity prediction. Further results are elucidated in this paper.

The deployment of connected, automated vehicles (CAVs) provides the opportunity to enhance the safety and efficiency of transportation systems. However, despite the rapid development of this technology, human-driven vehicles are predicted to predominate the vehicle fleet, compelling CAVs to be able to operate in a mixed traffic environment. The key to achieving a reliable and safe human-CAV collaboration in such environments is to characterize the interactions between the actors and incorporate the underlying decision-making mechanism of human drivers into CAVs' motion planning algorithms. Towards this goal and extending a previously developed game theoretical model, the present study proposes a decision-making dynamic to achieve more realistic models of human behavior when making conflicting maneuvers at intersections. A novel field test is conducted to extract the required modeling data directly from CAVs' perception system, facilitating the incorporation of the model into CAV navigation algorithms. Model validation and sensitivity analysis provided invaluable insights into the nature of human decisions and indicated that the proposed structure is robust to environmental uncertainties and can well capture the real-world behavior of human drivers in unprotected left-turn maneuvers. The derived knowledge can be directly used in CAV motion planning algorithms to provide the vehicle with more accurate predictions of human actions when operating in mixed traffic environments.