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IntroductionTraumatic brain injury (TBI) even in the mild form may result in long-lasting post-concussion symptoms. TBI is also a known risk to late-life neurodegeneration. Recent studies suggest that dysfunction in the glymphatic system, responsible for clearing protein waste from the brain, may play a pivotal role in the development of dementia following TBI. Given the diverse nature of TBI, longitudinal investigations are essential to comprehending the dynamic changes in the glymphatic system and its implications for recovery.MethodsIn this prospective study, we evaluated two promising glymphatic imaging markers, namely the enlarged perivascular space (ePVS) burden and Diffusion Tensor Imaging-based ALPS index, in 44 patients with mTBI at two early post-injury time points: approximately 14 days (14Day) and 6–12 months (6–12Mon) post-injury, while also examining their associations with post-concussion symptoms. Additionally, 37 controls, comprising both orthopedic patients and healthy individuals, were included for comparative analysis.ResultsOur key findings include: (1) White matter ePVS burden (WM-ePVS) and ALPS index exhibit significant correlations with age. (2) Elevated WM-ePVS burden in acute mTBI (14Day) is significantly linked to a higher number of post-concussion symptoms, particularly memory problems. (3) The increase in the ALPS index from acute (14Day) to the chronic (6–12Mon) phases in mTBI patients correlates with improvement in sleep measures. Furthermore, incorporating WM-ePVS burden and the ALPS index from acute phase enhances the prediction of chronic memory problems beyond socio-demographic and basic clinical information.ConclusionePVS burden and ALPS index offers distinct values in assessing glymphatic structure and activity. Early evaluation of glymphatic function could be crucial for understanding TBI recovery and developing targeted interventions to improve patient outcomes.

Blast-related traumatic brain injury (bTBI) is a major cause of neurological disorders in the U.S. military that can adversely impact some civilian populations as well and can lead to lifelong deficits and diminished quality of life. Among these types of injuries, the long-term sequelae are poorly understood because of variability in intensity and number of the blast exposure, as well as the range of subsequent symptoms that can overlap with those resulting from other traumatic events (e.g., post-traumatic stress disorder). Despite the valuable insights that rodent models have provided, there is a growing interest in using injury models using species with neuroanatomical features that more closely resemble the human brain. With this purpose, we established a gyrencephalic model of blast injury in ferrets, which underwent blast exposure applying conditions that closely mimic those associated with primary blast injuries to warfighters. In this study, we evaluated brain biochemical, microstructural, and behavioral profiles after blast exposure using in vivo longitudinal magnetic resonance imaging, histology, and behavioral assessments. In ferrets subjected to blast, the following alterations were found: 1) heightened impulsivity in decision making associated with pre-frontal cortex/amygdalar axis dysfunction; 2) transiently increased glutamate levels that are consistent with earlier findings during subacute stages post-TBI and may be involved in concomitant behavioral deficits; 3) abnormally high brain N-acetylaspartate levels that potentially reveal disrupted lipid synthesis and/or energy metabolism; and 4) dysfunction of pre-frontal cortex/auditory cortex signaling cascades that may reflect similar perturbations underlying secondary psychiatric disorders observed in warfighters after blast exposure.

Abstract Background During arthroscopic rotator cuff repair (ARCR), clear surgical field visibility (SFV) is the basis of successful surgery, but the choice of anesthesia maintenance drugs may have different effects on SFV. In this study, we aimed to compare the effects of propofol- and sevoflurane-based general anesthesia on SFV in patients undergoing ARCR. Methods Patients (n = 130) undergoing elective ARCR in the lateral decubitus position were randomized into either the propofol group or sevoflurane group (65 per group). The duration of surgery and increased pressure irrigation (IPI), Boezaart score, rocuronium consumption and usage of remifentanil were recorded. The time of both spontaneous respiration recovery and extubation and the incidences of postoperative nausea and vomiting and agitation were also recorded. Results The Boezaart score, duration of IPI and ratio of the duration of IPI to the duration of surgery (IPI/S ratio) were similar between the groups (P > 0.05). Rocuronium consumption, number of patients requiring remifentanil infusion and total remifentanil consumption were significantly lower in the sevoflurane group (P 0.05). Conclusions Compared with propofol, sevoflurane provides equally clear SFV while improving the convenience of anesthesia maintenance in ARCR patients with interscalene plexus (ISB) combined with general anesthesia. Trial registration This single-center, prospective, RCT was retrospective registered at Chinese Clinical Trial Registry with the registration number ChiCTR2300072110 (02/06/2023).

The study of dynamics of diabetic population infected by COVID-19 is of pressing concern as people with diabetes are considered to be at higher risk of severe illness from COVID-19. A three-compartment mathematical model to describe the interactions of diabetic population and non-diabetic population both infected by COVID-19 with a susceptible population is considered. Time delays in incubation periods of COVID-19 in diabetic and non-diabetic populations are introduced. Besides the basic properties of such a dynamical system, both local and global stability of endemic equilibrium, are studied. The lengths of time delays are estimated for which the stability of the system is preserved locally, while sufficient conditions on system parameters are obtained for global stability. Numerical examples are provided to establish the theory, and simulations are provided to visualize the examples. It is noted that an increase in length of time delay in either of infected populations leads to oscillations in susceptible population but has no impact on infected populations.

ABSTRACT: Objectives: The presence of red-complex bacteria (RCB) has been associated with the occurrence of not only periodontal disease but also other systemic diseases, thereby potentiating an oral–systemic relationship. This study aimed to explore the levels of periodontal RCB in pregnant preeclamptic women with periodontal disease, potentiating the pathophysiology of a bacterial association in both periodontitis and preeclampsia. Methods: Six hundred pregnant women (aged 20 to 35 years) in their third trimester were selected and divided into 3 groups comprising a control group (systemically and periodontally healthy pregnant women), group A (preeclamptic women with chronic periodontitis), and group B (preeclamptic women without chronic periodontitis). Demographic variables and periodontal parameters were recorded. Other variables such as systolic blood pressure (SBP) and diastolic blood pressure (DBP) and the urine protein content were also estimated. RCB, namely Porphyromonas gingivalis (Pg), Treponema denticola (Td), and Tenerella forsythia (Tf), were assessed in both subgingival plaque samples and placental samples of all the pregnant participants in the study. Results: The mean weight, mean body mass index, mean SBP, mean DBP, and mean urine protein content were significantly elevated in group A and group B when compared to the control group. Preeclamptic women with chronic periodontitis (group A) had higher levels of plaque index, probing pocket depth, bleeding on probing, and clinical attachment level as compared with the other groups. The numbers of the RCB were found to be elevated in group A, out of which Pg was found to be more prevalent. Conclusions: Our findings indicate that the periodontal indices, blood pressure, and urine protein content were found to be higher in preeclamptic women with periodontitis. Hence, reducing the periodontal bacterial load in pregnant women via good oral hygiene maintenance would further reduce the putative risk of adverse pregnancy outcomes including preeclampsia in these women.

BackgroundMultiple Myeloma (MM) patients exhibit dysregulated immune system, which is further weakened by chemotherapeutic agents. While cereblon-modulating agents, such as pomalidomide and lenalidomide, have been found to improve the immune profile, the efficacy of their impact in combination with other treatments is yet unknown.MethodsWe conducted an immune-profiling of a longitudinal cohort of 366 peripheral blood samples from the CC4047-MM-007 (OPTIMISMM, NCT01734928) study. This study followed relapsed/refractory Multiple Myeloma (RRMM) patients who were treated with Velcade + dexamethasone (Vd), or Vd with pomalidomide (PVd). 366 blood samples from 186 patients were evaluated using multi-color flow cytometry at 3 timepoints: screening, day 8 of cycle 1, and cycle 3.ResultsAmong NK and NKT cell populations, adding pomalidomide showed no inhibition in the frequency of NK cells. When expression of double positivity for activation markers like, p46/NKG2D, on NK cells was higher than the median, PVd treated patients showed significantly better (p=0.05) progression-free survival (PFS) (additional 15 months) than patients with lower than the median expression of p46/NKG2D on NK cells. PVd treated patients who expressed CD158a/b below the median at cycle 1 demonstrated a significantly better PFS (more than 18months). Among B cell subtypes, PVd treatment significantly increased the abundance of B1b cells (p

Momordica cochinchinensis (Lour.) Spreng. is an important medicinal plant that is used to treat various diseases in South and Southeast Asia. In this study, the complete plastome of M. cochinchinensis was sequenced and found to exhibit a total length of 158,955 bp, with a large single copy (LSC) region of 87,924 bp and a small single copy (SSC) region of 18,479 bp, as well as with two inverted repeats (IRs) that were both 26,726 bp in length. In total, 129 genes were detected, comprising 86 protein-encoding genes, 8 ribosomal RNA (rRNA) genes, and 35 transfer RNA (tRNA) genes. Furthermore, the inferred phylogenetic tree confirmed that M. cochinchinensis belongs to the genus Momordica in the Cucurbitaceae family. The research results will be used for authenticating M. cochinchinensis plant materials and for analyzing the genetic diversity and phylogenetic relationships in Momordica.

The aim of this study was the investigation of how adsorbents made from Leucaena leucocephala (L. Leu) leaf and bark materials were effective in treating wastewater containing methyl red dye (MeReD). The functional groups in the phytochemical elements from the bark and leaves of L.Leu offer active surface sites and enable the adsorption mechanism. The investigations were done in batch mode. Contact time, adsorbent dose concentration, temperature, pH, and initial MeReD concentration were all examined as significant parameters impacting the sorption process. The study found that significant MeReD removal efficiency was achieved after 90 min of contact time, pH 4.0 units, 1.2 gm/L adsorbent dose concentration, 100 ppm initial MeReD quantity, and 30 oC temperature. The developed adsorbents were applied to samples gathered from dye companies in Hyderabad and Guntur. The extraction of MeReD (81.2% to 90.0%) from untreated wastewater contaminated with MeReD using developed adsorbents was very efficient.

Synthetic dyes that escape into the environment haphazardly endanger both human health and ecosystems. Because of its toxicity and endurance, methylene blue (MB) stands out amongst these dyes as a significant contaminant. Conventional dye removal techniques can entail expensive and energy-intensive procedures. However, there has been a rise in interest recently in investigating sustainable and environmentally suitable substitutes for dye degradation. Because of their availability, affordability, and innate capacity to interact with contaminants, natural materials like leaves have become increasingly attractive options for restoration of the environment. Eucalyptus Globulus blended with titanium and magnesium nanoparticles to yield a novel environmentally friendly composite, 'EuTiMg'. The degradative capacity of the composite against methylene blue, a prevalent pollutant in water, was assessed by a range of analytical methods, such as SEM, TGA, FTIR, and BET analysis. To increase the composite’s photocatalytic activity, we also used Response Surface Methodology (RSM) to optimize the synthesis process. According to our research, 'EuTiMg' composite exhibits better pollutant degrading efficiency and is environmentally benign, making it a viable sustainable option for water treatment applications.

Every day, advancements in manufacturing technology research and innovation yield a highly finished surface with lower labour and machining costs. The conventional materials that were frequently employed in the chemical, automotive, and aerospace industries have been replaced by materials like ceramics, metal matrix composites, polymers, and super alloys. The AFM technology is capable of maintaining precision, reliability, and adaptability across adverse range of applications. Internal parts, through holes, intersecting holes, and freeform surfaces that are challenging to finish with other conventional finishing techniques can all be polished and deburred with AFM. The current study aims to determine the process variables that have a major impact on the change in surface roughness of the inner, outer, and side surfaces as well as the amount of material removal. These parameters include abrasive mesh size, abrasive concentration in media, and the number of passes. A review of published technical articles on the AFM process is attempted in this article, and the studies are divided into four categories: experimental setups, abrasive media, modelling and optimisation, and applications of the AFM process. The review article provides fresh directions for future investigation. Page layout

Ensuring device security is a significant obstacle to effectively implementing the Internet of Things (IoT) and fog computing in today's Information Technology (IT) landscape. Researchers and IT firms have investigated many strategies to safeguard systems against unauthorized device assaults, often known as outside device assaults. Cyber-attacks and data thefts have significantly risen in many corporations, organizations, and sectors due to exploiting vulnerabilities in safeguarding IoT gadgets. The rise in the variety of IoT gadgets and their diverse protocols has increased zero-day assaults. Deep Learning (DL) is very effective in big data and cyber-security. Implementing a DL-based Gated Recurrent Unit (GRU) on IoT devices with constrained resources is unfeasible due to the need for substantial computational power and robust storage capacities. This study introduces an IoT-based Malicious Device Detection (IoT-MDD) that is dispersed, resilient, and has a high detecting rate for identifying various IoT cyber-attacks using deep learning. The suggested design incorporates an Intrusion Detection System (IDS) on fog nodes because of its decentralized structure, substantial processing capabilities, and proximity to edge gadgets. Tests demonstrate that the IoT-MDD model surpasses the performance of the other models. The study found that the cybersecurity architecture effectively detects malicious gadgets and decreases the percentage of false IDS alarms.

The emerging smart grid technology known as Electric Spring (ES) has been employed previously to maintain voltage and power stability in renewable energy-based grids that are minimally regulated or self-contained. This study thoroughly investigated the foundational theories, modeling approaches, practical uses, and restrictions associated with Electric Springs by analyzing existing Scholarly works in this domain Electric Springs implement a direct power control approach. solution for the next-generation single-phase Systems, addressing the limitations of current ES control methods. This solution aims to improve power management in microgrids, where integrating renewable energy sources (RESs) poses challenges due to the unpredictable power output. The challenge involves managing two aspects: the variable Power consumed by ES and the power fed into the grid. The suggested straightforward and accurate signal manipulation method, namely, ANN-based Electric Spring (ES), is set to be deployed. This technique can adeptly manage these aspects in both stable conditions and during transitions in Renewable Energy Sources (RES).

Now a days the research work is going on to improve the performance of electric springs (ESs) by integrating a current source inverter (CSI) and a neural network controller. A new ES configuration that includes CSIs is introduced to enhance ES functionality. This configuration is accompanied by a control strategy that involves direct current control and mitigation of harmonic distortion, like the methods used in active power filters (APFs). By transitioning from voltage source inverters (VSIs) to CSIs and integrating a neural network controller, a significant reduction in total harmonic distortion (THD) can be achieved. The paper presents two distinct control loops, each equipped with proportional-integral (PI) controllers. One loop is focused on regulating the Critical load voltage (CL voltage), while the other is specifically designed for reactive power compensation.

This paper presents a vector relation-centric algorithm for solving arithmetic word problems (AWPs), which uses vector relation acquisition and scene knowledge to ensure the performances of problem understanding and symbolic solver correspondingly. The vector relation acquisition procedure builds on the synergy of the vector syntax-semantics method and the deep neural miner. Compared with the syntax-semantics method, the vector syntax-semantics method decreases not only the number of models but also semantic ambiguities and computational costs. For the scene knowledge, this paper proposes a scene-aware symbolic solver which infers relations obeying scene rules to decrease the occurrences of unwanted operations. Experimental results show that the proposed algorithm is superior to the high-performance baseline algorithm in both accuracy and computational cost. In accuracy, the proposed algorithm increased the accuracy by 3.9% on the sum of the three scene subsets due to the use of the scene knowledge and vector computing; as a result, it increased the accuracy by 0.5% on the sum of six authoritative datasets. In computational cost, the proposed algorithm decreased the computing cost by more than 50%. Thus, this paper makes a significant contribution to developing instruments for solving AWPs.

Compared with traditional diesel locomotive and electric locomotive, hydrogen energy dual source locomotive uses hydrogen fuel cell for power supply, which is not only safer and more environmentally friendly, but also has low operating noise, low cost and more convenient maintenance. As a key component of hydrogen energy system, bidirectional DC/DC converter converts fuel cell voltage into working voltage required by the system, but the existing bidirectional DC/DC converter generally adopts non isolated chopper circuit, which has the disadvantages of not realizing online switching of power flow direction, not redundant operation, etc., thus limiting its application in hydrogen energy dual source locomotive. Therefore, this paper designs a bi-directional isolated DC/DC converter, which uses dual active bridge (DAB) circuit, has wide soft switching range, high conversion efficiency, and can realize bidirectional flow of energy and online switching of transmission direction, with electrical isolation and thermal redundancy functions. This paper describes the main circuit design and control strategy of a 440 kW bi-directional isolated DC/DC converter, innovatively proposes a design method of energy storage inductor, which takes the minimum of the peak current of inductor as the optimization objective.The feasibility of the design is verified through principle simulation and test. The converter efficiency can reach 96%, which successfully realizes the loading application of high-power bi-directional isolated converter in hydrogen locomotive.

Temesgen Sidamo,1 Prakruti S Rao,2 Eleni Aklillu,3 Workineh Shibeshi,1 Yumi Park,4,5 Yong-soon Cho,4,5 Jae-Gook Shin,4,5 Scott K Heysell,2 Stellah G Mpagama,6 Ephrem Engidawork1 1Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia; 2Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA; 3Department of Laboratory of Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden; 4Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; 5Center for Personalized Precision Medicine of Tuberculosis (cPMTb), Inje University College of Medicine, Busan, Republic of Korea; 6Kibong’oto Infectious Diseases Hospital, Sanya Juu, TanzaniaCorrespondence: Ephrem Engidawork, Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia, Email ephrem.engidawork@aau.edu.etObjective: This study aimed to explore the population pharmacokinetic modeling (PopPK) of levofloxacin (LFX) and moxifloxacin (MXF), as well as the percent probability of target attainment (PTA) as defined by the ratio of the area under the plasma concentration-time curve over 24 h and the in vitro minimum inhibitory concentration (AUC0-24/MIC) in Ethiopian multidrug resistant tuberculosis (MDR-TB) patients.Methods: Steady state-plasma concentration of the drugs in MDR-TB patients were determined using optimized liquid chromatography-tandem mass spectrometry. PopPK and simulations were run at various doses, and pharmacokinetic parameters were estimated. The effect of covariates on the PK parameters and PTA for maximum mycobacterial kill and resistance prevention was also investigated.Results: LFX and MXF both fit in a one-compartment model with adjustments. Serum-creatinine (Cr) influenced (p = 0.01) the clearance of LFX, whereas body mass index (BMI) influenced (p = 0.01) the apparent volume of distribution (V) of MXF. The PTA for LFX maximal mycobacterial kill at the critical MIC of 0.5 mg/L with the simulated 750 mg, 1000 mg, and 1500 mg doses were 29%, 62%, and 95%, respectively, whereas the PTA for resistance prevention at 1500 mg was only 4.8%, with none of the lower doses achieving this target. At the critical MIC of 0.25 mg/L, there was no change in the PTA for maximum bacterial kill when the MXF dose was increased (600 mg, 800 mg, and 1000 mg), but the PTA for resistance prevention was improved.Conclusion: The standard doses of LFX and MXF may not provide adequate drug exposure. PopPK of LFX is more predictable for maximum mycobacterial kill, whereas MXF’s resistance prevention target increases with dose. Cr and BMI are likely important covariates for dose optimization in Ethiopian patients.Keywords: population pharmacokinetics, probability of target attainment, moxifloxacin, levofloxacin, MDR-TB patients, Ethiopia

Mild (mTBI) traumatic brain injury (TBI) accounts for the majority of all TBI cases. Evidence has suggested that patients with mTBI can suffer from long-lasting cognitive deficits, persistent symptoms, and decreased quality of life. Sleep disorders are commonly observed after TBI, with the prevalence rate of sleep disturbances in persons with TBI being much higher than that in the general population. Poor sleep quality can impair cognitive functions in the general population. This effect of sleep disturbances may impede the recovery processes in the population with TBI. The objective of this study is to add to our understanding of the relationship between self-reported sleep problems and other post-concussion symptoms and look at the association between early sleep problems and long-term outcomes in mTBI. Post-concussion symptoms, neurocognitive functions, level of global outcomes, and rating of satisfaction of life were assessed in 64 patients with mTBI. The results revealed that the presence of sleep disturbances co-occur with an increased level of overall post-concussion symptoms at the subacute stage of mTBI, particularly with symptoms including poor concentration, memory problems, and irritability. In addition, sleep disturbance at the subacute stage is associated with persistent poor concentration and memory problems, as well as worse neurocognitive function, slower overall recovery, and lower satisfactory of life at the long term. Our findings suggest that sleep disturbance can be a prognostic factor of long-term outcomes after mTBI. Early interventions to improve sleep quality can have potential benefits to facilitate the recovery process from mTBI.

Abstract This paper presents a relation-centric algorithm for solving arithmetic word problems (AWPs) by synergizing a syntax-semantics extractor for extracting explicit relations, and a neural network miner for mining implicit relations. This is the first algorithm that has a specific component to acquire implicit knowledge items for solving AWPs. This paper proposes a three-phase scheme to decompose the challenging task of designing an algorithm for solving AWPs into three smaller tasks. The first phase proposes a state-action paradigm; the second phase instantiates the paradigm into a relation-centric approach; and the third phase implements a relation-centric algorithm for solving AWPs. There are two main steps in the proposed algorithm: problem understanding and symbolic solver. By adopting the relation-centric approach, problem understanding becomes a task of relation acquisition. For conducting the task of relation acquisition, a relaxed syntax-semantics method first extracts a group of explicit relation candidates. In parallel, a neural network miner acquires implicit relation candidates. The miner computes the vectors encoded by BERT to determine which implicit relations should be added. Thus, problem understanding can acquire both explicit relations and implicit relations, which addresses the challenge of building a problem understanding method that can acquire all the knowledge items to find the solution. In the subsequent step of symbolic solver, a fusion procedure forms a distilled set of relations from all the candidates by discarding unnecessary relations. Experimentation on nine benchmark datasets validates the superiority of the proposed algorithm that outperforms the state-of-the-art algorithms.

As institutes of higher education (HE) strive to meaningfully address diversity, equity, and inclusion in practice, Universal Design for Learning (UDL) provides a relevant and timely framework for course design to support all learners. Using UDL as an instructional design framework, educators can proactively address learner variability and reduce barriers for students in HE environments. This self-study describes how UDL experts applied a process of UDL design to their courses. The best practices were identified across three phases: (a) a literature review to identify UDL-aligned practices used by HE instructors, (b) individual and collective reflection on UDL-based practices by the UDL experts, and (c) application of UDL to three HE courses delivered in different formats. The practice brief presents a comprehensive overview of various strategies that HE instructors can use in their courses, in alignment with the three UDL principles.

Work-integrated learning (WIL) is crucial for the development of employability skills and has an influence on employment outcomes. Given the significance of WIL pedagogies for graduate preparedness and transitions into work, concerns have been raised on the barriers to access and participation in WIL for some cohorts of learners. Equity and inclusion in WIL, that is, considerations for diverse learners once enrolled into a subject or course with varying WIL components, is not a new concept with some guidelines purported over a decade ago. Designing WIL to accommodate for equity and inclusion, however, has presented challenges with few studies offering navigation for curriculum design. This paper draws on empirically sound curriculum design principles for inclusion for learner variability through Universal Design for Learning (UDL), to offer the UDL for WIL design framework. The paper presents conceptual and practical contributions for educators of WIL experiences to reduce barriers and integrate student voice to support all graduates' career transitions.

The National Education Policy (NEP) 2020 of India reframed the present structure of higher education such as syllabi, examinations, and assessments. Due to the pandemic, many educational and research institutions are building a knowledge and skill-based curriculum and designing new credit-based courses, while encouraging students to attend online courses as a way to meet the recommendations of NEP. Any e-learning platform or online course has four basic components: Videos, Activities, Additional Reading Material, and Discussions, with Additional Reading Material being a more important component. Online courses that are open to everyone (MOOCs) can be adopted irrespective of the students' specialization or area. The instructor provides the reading material to the learners as a fixed, recommended list irrespective of the learner's interest or specialization. Instructors are finding it difficult to cater to the diversity of learners' interests and learning styles. In this paper, I have focused and analyzed the additional reading material and digital reading patterns of learners in various MOOC platforms by conducting online surveys. I concluded that there is a need to use learning eXtension Trajectories (LxT) in the learner-centric MOOC model where an instructor can identify various supportive domains and provide a shortened list of resources comprising various media to the learners.

Research indicates that reading strategy instruction improves comprehension. Conceptualizing strategy training as mediating reading strategy use through collaborative and reflective practices, the present study examined the combined effect of peer-collaborative strategic reading and reflective journaling on strategy use and comprehension. Data were collected from 72 ninth-grade participants, employing a pretest-posttest comparison group design, through Reflective Journals, Reading Comprehension Test, and the Survey of Reading Strategies. The experimental group (n=36) participated in strategy training, while the comparison group (n=36) had regular study hours. Reflective journals show that the experimental group improved in rationalizing the conditional knowledge of strategies, using strategy clusters, employing responsive actions, specifying the details of strategy use and verbalizing the reading process. Findings also indicate that the experimental group outperformed the comparison group in post-test in comprehension and frequency of strategy use. The findings imply that reading strategy instruction models and teachers can increase the collaborative and reflective nature of strategy training to develop students' strategic reading and comprehension.

Sesbania aculeata is a multipurpose legume crop grown primarily for green manuring in the rice-based cropping system. Besides this, it is an industrial crop and is also used as food in many parts of the world. The present work reports for optimization of various parameters (growth medium, plant growth regulators, pre-conditioning, orientation of explant, and presence of thiol compounds) affecting in vitro regeneration using mature cotyledon explants. The 5-day-old mature cotyledon explants excised from seedlings grown on Murashige and Skoog (MS) salts and Gamborg (B5) vitamins medium containing 15 μM 6-benzylaminopurine were cultured with its adaxial side facing on medium containing 2.5 μM 6-benzylaminopurine and 50 mg/L thiourea and produced multiple shoots (7 ‒ 8) in 100% cultures within 28 days. Healthy shoots were rooted on half-strength Murashige and Skoog (MS) salts and full-strength vitamins medium augmented with 2.5 μM indole-3-butyric acid.

IntroductionLower socioeconomic status (SES) is associated with poorer executive function, but the neural mechanisms of this association remain unclear. As healthy brain communication is essential to our cognitive abilities, white matter integrity may be key to understanding socioeconomic disparities.MethodsParticipants were 201 African American and White adults (ages 33–72) from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) SCAN study. Diffusion tensor imaging was used to estimate regional fractional anisotropy as a measure of white matter integrity. Adjusting for age, analyses examined if integrity of the anterior limb of the internal capsule (ALIC), external capsule (EC), superior longitudinal fasciculus (SLF), and cingulum mediated SES-executive function relations.ResultsLower SES was related to poorer cognitive performance and white matter integrity. Lower Trails B performance was related to poorer integrity of the ALIC, EC, and SLF, and lower Stroop performance was associated with poorer integrity of the ALIC and EC. ALIC mediated the SES-Trails B relation, and EC mediated the SES-Trails B and SES-Stroop relations. Sensitivity analyses revealed that (1) adjustment for race rendered the EC mediations non-significant, (2) when using poverty status and continuous education as predictors, results were largely the same, (3) at least some of the study’s findings may generalize to processing speed, (4) mediations are not age-dependent in our sample, and (5) more research is needed to understand the role of cardiovascular risk factors in these models.DiscussionFindings demonstrate that poorer white matter integrity helps explain SES disparities in executive function and highlight the need for further clarification of the biopsychosocial mechanisms of the SES-cognition association.

Objectives: To study the maternal and foetal outcomes in pregnancies complicated by bone marrow disorders. Material and methods: A retrospective descriptive study of women with bone marrow disorders in pregnancy, between January 2011 and January 2016 was carried out. Their antenatal and labour records were reviewed and demographic details noted. The primary outcome measures studied were – severity of anaemia, additional drugs required during pregnancy, gestational age at delivery, requirement of transfusion of blood and blood products and mode of delivery. Obstetric and medical complications and neonatal outcomes were the secondary outcome measures noted. Blood counts during pregnancy and after delivery were noted. Data are presented as descriptive statistics including means and percentage. Results: Prevalence of bone marrow disorder complicating pregnancy was (10/12420) 0.08%. 6 women had aplastic anaemia, 2 had CML, 1 had AML and 1 had myelodysplastic syndrome. 50% were in remission; 60% received drugs for bone marrow disorder. Antenatal complications like preeclampsia (40%), oligohydramnios (40%), infections, IUGR and IUD (30% each), preterm labour, previous caesarean delivery (20% each) and PNH (10%) were noted. 50% had moderate anaemia, 30% severe anaemia, 90% had thrombocytopenia. Transfusion of blood products was required in 30% antepartum, 70% intrapartum and 60% postpartum. 50% had vaginal delivery. There was no PPH or maternal mortality. 70% took home healthy babies. Conclusion: Multidisciplinary approach, preconceptional counseling, good antenatal care, delivery at a tertiary care centre, prompt treatment of infections and sepsis, prophylactic transfusions and optimal use of chemotherapeutic drugs can ensure successful pregnancy.